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eLife Assessment

This manuscript aims to identify the pacemaker cells in the lymphatic collecting vessels - the cells that initiate the autonomous action potentials and contractions needed to drive lymphatic pumping. Through the exemplary use of existing approaches (genetic deletions and cytosolic calcium detection in multiple cell types), the authors convincingly determine that lymphatic muscle cells are the origin of the action potential that triggers lymphatic contraction. The inclusion of scRNAseq and membrane potential data enhances a tremendous study. This fundamental discovery establishes a new standard for the field of lymphatic physiology.

Reviewer #1 (Public review):

Summary:

This manuscript explores the multiple cell types present in the wall of murine collecting lymphatic vessels with the goal of identifying cells that initiate the autonomous action potentials and contractions needed to drive lymphatic pumping. Through the use of genetic models to delete individual genes or detect cytosolic calcium in specific cell types, the authors convincingly determine that lymphatic muscle cells are the origin of the action potential that triggers lymphatic contraction.

Strengths:

The experiments are rigorously performed, the data justify the conclusions and the limitations of the study are appropriately discussed.

There is a need to identify therapeutic targets to improve lymphatic contraction and this work helps identify lymphatic muscle cells as potential cellular targets for intervention.

Reviewer #2 (Public review):

Summary:

This is a well written manuscript describing studies directed at identifying the cell type responsible for pacemaking in murine collecting lymphatics. Using state-of-the-art approaches, the authors identified a number of different cell types in the wall of these lymphatics and then using targeted expression of Channel Rhodopsin and GCaMP, the authors convincingly demonstrate that only activation of lymphatic muscle cells produces coordinated lymphatic contraction and that only lymphatic muscle cells display pressure-dependent Ca2+ transients as would be expected of a pacemaker in these lymphatics.

Strengths:

The use of targeted expression of channel rhodopsin and GCaMP to test the hypothesis that lymphatic muscle cells serve as the pacemakers in musing lymphatic collecting vessels.

Weaknesses:

The only significant weakness was the lack of quantitative analysis of most of the imaging data shown in Figures 1-11. In particular, the colonization analysis should be extended to show cells not expected to demonstrate colocalization as a negative control for the colocalization analysis that the authors present. These weaknesses have been resolved by revision and addition of new and novel RNAseq data, additional colocalization data and membrane potential measurements.

Reviewer #3 (Public review):

Summary:

Zawieja et al. aimed to identify the pacemaker cells in the lymphatic collecting vessels. Authors have used various Cre-based expression systems and optogentic tools to identify these cells. Their findings suggest these cells are lymphatic muscle cells that drive the pacemaker activity in the lymphatic collecting vessels.

Strengths:

The authors have used multiple approaches to test their hypothesis. Some findings are presented as qualitative images, while some quantitative measurements are provided.

Weaknesses:

- More quantitative measurements.<br /> - Possible mechanisms associated with the pacemaker activity.<br /> - Membrane potential measurements.

Comments on revisions:

The authors have answered my comments with additional experiments, data and manuscript edits.

Author response:

The following is the authors’ response to the original reviews.

Public Reviews: 

Reviewer #1 (Public Review): 

Summary: 

This manuscript explores the multiple cell types present in the wall of murine-collecting lymphatic vessels with the goal of identifying cells that initiate the autonomous action potentials and contractions needed to drive lymphatic pumping. Through the use of genetic models to delete individual genes or detect cytosolic calcium in specific cell types, the authors convincingly determine that lymphatic muscle cells are the origin of the action potential that triggers lymphatic contraction. 

Strengths: 

The experiments are rigorously performed, the data justify the conclusions, and the limitations of the study are appropriately discussed. 

There is a need to identify therapeutic targets to improve lymphatic contraction and this work helps identify lymphatic muscle cells as potential cellular targets for intervention. 

Weaknesses: 

My only major comment would be that the manuscript provides a lot of rich information describing the cellular components of the muscular lymphatic vessel wall and that these data are not well represented by the title. The title (while currently accurate) could be tweaked to better represent all that is in this manuscript. Maybe something like

"Characterization/Interrogation of the cellular components of murine collecting lymphatic vessels reveals that lymphatic muscle cells are the innate pacemaker cells regulating lymphatic contractions" or "Discovery/Confirmation of lymphatic muscle cells as innate pacemaker cells of lymphatic contraction through characterization of the cellular components of murine collecting lymphatic vessels". Potentially a cartoon summary figure of the components that make up the collecting lymphatic vessel wall could also be included. In my opinion, these changes will make this manuscript of more interest to a broader group of scientists. I have a few additional comments for consideration to improve the clarity and enhance the discussion of this work. 

We agree with the reviewer that our original manuscript, and our resubmission even more so with the addition of the scRNAseq data, provides a significant amount of information regarding the composition of the lymphatic collecting vessel wall. We have changed our title to match one suggestion of the reviewer: “Characterization of the cellular components of murine collecting lymphatic vessels reveals that lymphatic muscle cells are the innate pacemaker cells regulating lymphatic contractions".

Reviewer #2 (Public Review): 

Summary: 

This is a well-written manuscript describing studies directed at identifying the cell type responsible for pacemaking in murine-collecting lymphatics. Using state-of-the-art approaches, the authors identified a number of different cell types in the wall of these lymphatics and then using targeted expression of Channel Rhodopsin and GCaMP, the authors convincingly demonstrate that only activation of lymphatic muscle cells produces coordinated lymphatic contraction and that only lymphatic muscle cells display pressure-dependent Ca2+ transients as would be expected of a pacemaker in these lymphatics. 

Strengths: 

The use of a targeted expression of channel rhodopsin and GCaMP to test the hypothesis that lymphatic muscle cells serve as the pacemakers in musing lymphatic collecting vessels. 

Weaknesses: 

The only significant weakness was the lack of quantitative analysis of most of the imaging data shown in Figures 1-11. In particular, the colonization analysis should be extended to show cells not expected to demonstrate colocalization as a negative control for the colocalization analysis that the authors present. 

We understand the reviewer’s concern regarding the lack of a control for the colocalization analysis and that the colocalization analysis was limited to just one set of cell markers. We have now provided a colocalization analysis of Myh11 and PDGFRα, to serve as a co-localization negative control based on our RT-PCR and scRNASeq findings, which is incorporated into the current Supplemental figure 1. In regard to the staining pattern of other various marker combinations, the results were often quite clear with the representative images that two separate cell populations were being stained such as the case with labeling endothelial cells with CD31, macrophage labeling with the MacGreen mice, or hematopoietic cells with CD45. 

During our lengthy rebuttal process we completed a single cell RNA sequence analysis using our isolated and cleaned mouse inguinal axillary lymphatic collecting vessels to aid in our characterization of the vessel wall and to more thoroughly answer these questions regarding colocalization in arguably a robust manner. The generation of our scRNAseq dataset, derived from isolated and cleaned mouse inguinal axillary collecting vessels from 10 mice, 5 male and 5 females, allowed us to profile over 2200 of the adventitial fibroblast like cells (AdvCs) we had identified in our original submission. Using this dataset, we were able to confirm co-expression of Cd34 and Pdgfrα in AdvCs and assess the co-expression of other genes of interest from our RT-PCR experiments and immunofluorescence experiments. This approach will also allow other lymphatic investigators to assess their genes of interest as our dataset is uploaded to the NIH Gene Omnibus and will be uploaded to the Broad Institute Single Cell Portal upon publication.

Here we show that the vast majority of non-muscle fibroblast like cells referred to as AdvCs were double positive for both CD34 and PDGFRα. We also show that the AdvCs that express commonly used pericyte markers Pdgfrb and Cspg4 also co-expressed Pdgfrα. Critically, this data also shows that the AdvCs that express genes linked with lymphatic contractile dysfunction (Ano1, Gjc1 or connexin 45, and Cacna1c “Cav1.2”) co-express Pdgfrα and would render these genes susceptible to Cre-mediated recombination using our Pdgfrα-CreER^TM model.  

Reviewer #3 (Public Review): 

Summary: 

Zawieja et al. aimed to identify the pacemaker cells in the lymphatic collecting vessels. Authors have used various Cre-based expression systems and optogenetic tools to identify these cells. Their findings suggest these cells are lymphatic muscle cells that drive the pacemaker activity in the lymphatic collecting vessels. 

Strengths: 

The authors have used multiple approaches to test their hypothesis. Some findings are presented as qualitative images, while some quantitative measurements are provided.   

Weaknesses: 

-  More quantitative measurements. 

-  Possible mechanisms associated with the pacemaker activity. 

-  Membrane potential measurements. 

We thank the reviewers for their concerns and have addressed them in the following manner. 

- We added novel single cell RNA sequencing of isolated and cleaned inguinal axillary vessels from 10 mice (5 males and 5 females). This allowed us to quantify the number of AdvCs that coexpress CD34 and Pdgfrα as well as the number of cells co-expressing Pdgfrα and other markers.

- We have added a negative control with quantification for the co-localization analysis assessing Myh11 and Pdgfrα. We have added a negative control with quantification for the ChR2-photo stimulated contraction experiments using Myh11CreERT2-ChR2 mice that were not injected with tamoxifen. 

- We also used Biocytin-AF488 in our intracellular Vm electrodes to map the specific cells in which we recorded action potentials and in neighboring cells since Biocytin-AF488 is under 1KDa and can pass through gap junctions. This approach independently labeled lymphatic muscle cells and their direct neighbors for 3 IALVs from 3 separate mice. 

- We performed membrane potential recordings in isolated, pressurized (under isobaric conditions), and spontaneously contracting inguinal axillary lymphatic collecting vessels at different pressures. 

- We also show that the pressure-frequency relationship is dependent on the slope of the diastolic depolarization as no other parameter was significantly altered in our study and the diastolic depolarization slope was highly correlated with contraction frequency. 

We believe the addition of these novel data, controls, experiments, and quantifications have improved the manuscript in line with the reviewers’ suggestions.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors): 

Lines 149-162: The authors rule out the methylene blue staining cells in the cLV wall as pacemakers because they don't form continuous longitudinal connections to drive propagation. Is it possible for a pacemaker cell to only initiate the contraction and then have the LMCs make the axial electrical connections to propagate the electrical wave? I am not trying to suggest the methylene blue cells are pacemakers, but I am not sure the lack of longitudinal (or radial) connectivity is sufficient evidence to rule out the possibility. This comment also is relevant to the 3 criteria for a pacemaker cell listed in the Discussion (Lines 413-417). 

We agree with the reviewer’s broader point that a pacemaker cell may not require direct contact with other ‘pacemaker’ cells within the tissue as long as they are still within the same electrical syncytium. However, we do expect a continuous presence of a pacemaker cell type throughout the vessel wall length to account for the persistence of spontaneous contractile behavior despite vessel length, and the ability for contraction initiation to shift (Akl et al 2011, Castorena et al 2018 and Castorena et al 2022) and the occurrence of spontaneous action potentials. In Dirk van Helden’s seminal work in 1993 on lymphatic pacemaking, a major finding was that “SM of small lymphangions or that of short segments, cut from lymphangions of any length, behaved similarly”. We have adjusted our phrase regarding the requirement of a contiguous network and instead suggest a continuous presence along the vessel network and integrated into the electrical syncytium. 

Methylene blue is an alkaline stain that will stain acidic structures and historically methylene blue is noted to stain Interstitial cells of Cajal in the gastrointestinal tract which typically exist as network of cells(Huizinga et al 1993 and Berezin 1988). No such network was readily apparent in our methylene blue staining nor did the stained cells have a similar morphology to the ICCs of the gastrointestinal tract. Further, methylene blue is staining is not limited to ICCs or pacemaker cells at large as it has been used to kill cancer cells. Within the small intestine methylene blue was noted to also stain macrophage like cells (Mikkelsen et al 1988), and we too draw parallels between the macrophage morphology observed with Macgreen mice and methylene-blue stained cells. The specific structure for the ICC affinity for methylene blue is not well described and while the innate cytotoxicity of methylene blue and light has been used to kill ICCs and impair slow wave generation, the lack of specificity of this method leaves much to be desired. What is clear is that the ICC network highlighted by methylene blue in the gut is absent in lymphatic collecting vessels.

In Figure 15/Video12, is it possible that the cells that are showing intracellular Ca2+ in diastole are the cells that reach a threshold membrane potential that then trigger the rest of the LMCs? As the authors have shown heterogeneity in the LMCs surface markers, is it possible that the cells with Ca2+ activity during diastole are identifiable by a distinct molecular phenotype? Or is the thought that these cells are randomly active in diastole? Some discussion/speculation about this seems appropriate. 

We are in agreement with the reviewer’s conclusion that there is heterogeneity in the LMCs as it pertains to the calcium oscillations in diastole, either under normal buffer conditions or when L-type channels are inhibited with nifedipine. We also note significant heterogeneity in the gene expression noted within the four LMC subclusters (0-3), though we did not see significant differences in either in Ip3R1 or Ano1 expression. However, subcluster “0” had increased expression of Itprid2, also known as KRas-induced actin-interacting protein (KRAP) which is thought to tether, and thus immobilize, IP3 receptors to the actin cortex beneath the cell membrane. KRAP has been recently proposed to be a critical player in IP3 receptor “licensing” which allows IP3 receptors to release calcium (Vorontsova et al., 2022).  However, whether similar requirement of IP3R licensing is necessitated in all cells or specifically in LMCs is unknown it is quite clear there are specific release sites within the cell and this topic is currently under further investigation for a separate manuscript. We would like to note that there is yet to be a clear consensus on whether IP3R licensing is required as much of these studies are performed in cultured cells and this mechanism has only recently been described. 

Healthy lymphatic collecting vessels typically have a single pacemaker driving a coordinated propagated contraction in ex vivo isobaric myograph studies (Castorena-Gonzalez et al., 2018), which is typically at either end of the cannulated vessel. We believe that this is due to the lack of a bordering cell in one direction and allows charge to accumulate and voltage to reach threshold at these sites preferentially. We have tried to image calcium at the pacemaking pole of the vessel to observe the specific Ca²⁺ transients at these sites though invariably the act of imaging GCaMP6f results in the pacemaker activity initiating from the other pole of the vessel. It is our opinion that the fact that LMCs are heterogenous in their Ca²⁺ transients is a feature to the system as it permits a wider range of depolarization signals, and thus allows finer control of the pacing as different physical/pressure or signaling stimuli is encountered. Likely, the cells with the higher propensity of Ca²⁺ transients act as the contraction initiation site in vivo, though it must also be noted that the LMC density decreases around lymphatic valve sites. In fact, in guinea pig collecting vessels there are very few LMCs at the valves which can render them electrically uncoupled or poorly coupled (Van Helden, 1993). Thus, valve sites in which there is greater electrical resistance due to lower LMC-LMC coupling may allow for charge accumulation in the LMCs at the valve site, similar to the artificial condition achieved in our myograph preparations with two cut ends, and allow them to reach threshold first and drive coordination at the valve sties.

An additional description of what the PTCL analysis is meant to represent physiologically would be helpful for readers. 

We have better described the conversion of the calcium signals into “particles” for analysis at first mention in the methods and results section and have included the requisite reference to this specific methodology in Line 429-30. 

A description of how DMAX is experimentally determined is needed. 

We have adjusted our methods section to describe DMAX in line 774-775.

“with Ca²⁺-free Krebs buffer (3mM EGTA) and diameter at each pressure recorded under passive conditions (DMAX).”

I think the vessels referred to as popliteal lymphatic vessels are actually saphenous lymphatic vessels (afferent to the popliteal lymph node). Please clarify. 

Indeed, some of the vessels used in this study are the afferents to the single popliteal node. They travel with the caudal branch of the saphenous vein, but have routinely been described as popliteal vessels, as opposed to saphenous lymphatic vessels, by the lymphatic field at large (Tilney 1971 PMCID: PMC1270981, Liao 2015 PMID: 25512945). To move away from this nomenclature would likely add to confusion although we agree that the lymphatic field may need to improve or correct the vessel naming paradigm to match the vascular pairs they follow.

Reviewer #2 (Recommendations For The Authors): 

Lines 214-215 - can you cite a reference for the observation that rhythmic contractions don't require the presence of valves? 

We have added the reference. In Dr. Van Helden’s seminal work on the topic in 1993, “Vessel segments were then cut from selected small lymphangions (length 300-500 um) by cutting at the valves.” Additionally, work by Dr Anatoliy Gashev utilized sections of lymphatic vessels that lacked valves to study orthograde and retrograde shear sensitivity (Gashev et al., 2002).

Lines 224-230 - It would have been nice to see colocalization analysis for all cell types so that "negative" results could be compared with the "positives" that you report. This would help bolster evidence of your ability to separate cell types. 

We understand the reviewer’s sentiment and agree. We have now added a “negative control” colocalization staining and analysis for PDGFR and Myh11 which has been added to the current SuppFigure 1. We stained 3 IALVs from 3 separate mice with PDGFRα and Myh11 and performed confocal microscopy. We ran the FIJI BIOP-JACOP colocalization plugin as before and observed very little colocalization of the two signals. Additionally, we have also added a coexpression assessment for CD34 and PDGFRα and other genes using our scRNAseq dataset.  

line 293 - Should read "Cx45 in..." 

This has been corrected. 

“The expression of the genes critically involved in cLV function—Cav1.2, Ano1, and Cx45—in the PdgfrαCreER^TM-ROSA26mTmG purified cells and scRNAseq data prompted us to generate PdgfrαCreER^TM-Ano1^fl/fl, PdgfrαCreER^TM-Cx45^fl/fl, and PdgfrαCreER^TM-Cav1.2^fl/fl mice for contractile tests.”

lines 470-473 - A reference for this statement should be cited. 

We have added the reference. In Dr. Van Helden’s seminal work on the topic in 1993, “Vessel segments were then cut from selected small lymphangions (length 300-500 um) by cutting at the valves.” Additionally, work by Dr Anatoliy Gashev utilized sections of lymphatic vessels that lacked valves to study orthograde and retrograde shear sensitivity (Gashev et al., 2002).

Lines 483-487 - References should be cited for these statements. 

We have narrowed and clarified this statement and supported it with the necessary citations. 

“Of course, mesenchymal stromal cells (Andrzejewska et al., 2019) and fibroblasts (Muhl et al., 2020; Buechler et al., 2021; Forte et al., 2022) are present, and it remains controversial to what extent telocytes are distinct from or are components/subtypes of either cell type (Clayton et al., 2022). Telocytes are not monolithic in their expression patterns, displaying both organ directed transcriptional patterns as well as intra-organ heterogeneity (Lendahl et al., 2022) as readily demonstrated by recent single cell RNA sequencing studies that provided immense detail about the subtypes and activation spectrum within these cells and their plasticity (Luo et al., 2022).”

Lines 584-585 - Missing a reference citation. 

Thank you for catching this error, the correct citation was for Boedtkjer et al 2013 and is now properly cited. 

Line 638 - "these this" should read "this" 

Thank you for catching this error. This particular sentence was removed in light of the addition of the scRNAseq data.

Reviewer #3 (Recommendations For The Authors): 

This manuscript from Zawieja et al. explored an interesting hypothesis about the pacemaker cells in lymphatic collecting vessels. Many aspects of lymphatic collecting vessels are still under investigation; hence this work provides timely knowledge about the lymphatic muscle cells as a pacemaker. Although it is an important topic of the investigation, the data provided do not support the overall goal of the manuscript. Many figures (Figure 1-5) provide quantitative estimation and the description provided in the results section might only be useful for a restricted audience, but not to the broader audience. Some of the figures are very condensed with multiple imaging panels and it is hard to follow the differences in qualitative analysis. Overall, this manuscript can be improved by more streamlined description/writing and figure arrangements (some of the figures/panels can be moved to the supplementary figures). 

We disagree with the notion that the original data provided did not support the goal of the manuscript- to identify and test putative pacemaker cell types. Nonetheless we believe we have also added ample novel data to the manuscript, including membrane potential recordings and scRNAseq to highlight and to add further support to our conclusion that the pacemaker cell is an LMC. We believe the scRNAseq data will also greatly enhance the appeal of the manuscript to a broader audience and have renamed the manuscript in line with the wealth of data we have collected on the components of the vessel wall as we tested for putative pacemaker cells.

As requested, we have moved many figures to the supplement to allow readers to focus more on the more critical experiments.

A few other points that need to be addressed: 

(1) Authors used immunofluorescence-based differences in various cell types in the collecting vessels. Initially, they chose ICLC, pericytes, and lymphatic muscle cells. But then they started following adventitial cells and endothelial cells. It is not clear from the description, why these other cells could be possibly involved in the pacemaker activity. It will be easier to follow if authors provide a graphical abstract or summary figure about their hypothesis and what is known from their and others' work. 

We would like to clarify that we used the endothelial cells as controls to ensure what we observed via immunofluorescence and FACs RT-PCR were a separate cell type from either lymphatic muscle or lymphatic endothelial cells on the vessel wall. Staining for the endothelium also allowed us to assess where these PDGFRα+CD34+ cells reside in the vessel wall.  We started with a wide range of markers that are conventionally used for targeting specific cell types, but as expected those markers are not always 100% specific. Specifically, we focused on CD34, Kit, and Vimentin as those were the markers for the non-muscle cells observed in the lymphatic collecting vessel wall previously. What we found was that CD34 and PDGFRα labeled the same cell type. As there was not a CD34Cre mouse available at the time we instead utilized the inducible PDGFRαCreERTM. We are unsure how well an abstract figure will condense the conclusions from the experiments listed here but if absolutely required for publication we can attempt to highlight the representative cell populations identified on the vessel wall.

(2) Authors used many acronyms in the manuscript without defining them (when they appeared for the first time). Please follow the convention. 

We have checked the manuscript and made several corrections regarding the use of abbreviations.

(3) How specific PDGFR-alpha as a marker of the pericytes? It can also label the mesenchymal cells. Why did the author choose PDGFR-alpha over beta for their Cre-based expression approach? 

We tried to assess if there were a pericyte like cell present in or along the wall using PDGFRbeta (Pdgfrβ). Pdgfrβ is commonly used to identify pericytes (Winkler et al., 2010), while in contrast Pdgfrα is a known fibroblast marker (Lendahl et al., 2022). Pdgfrβ CreERT2 resulted in recombination in both LMCs and AdvCs, preventing it from being a discriminating marker for our study where as Myh11CreER^T2 and PDGFRαCreER^TM were specific at least to cell type based on our FACSs-RT-PCR and staining. As you can tell from the scRNAseq data in Figure 5, there was no cell cluster that Pdgfrβ was specific for in contrast to PDGFRα and Myh11.  In Figure 6 we show the expression of another commonly used pericyte marker NG2 (Cspg4) in our scRNAseq dataset which was observed in both LMCs and AdvCs as well. Lastly, MCAM (Figure 6) can also be a marker for pericytes though we see only expression in the LMCs and LECs for this marker. Notably, almost all of the AdvCs express PDGFRα rendering the PDGFRαCreER^TM a powerful tool to study this population of cells on the vessel wall including those that were PDGFRα+Cspg4+ or PDGFRα+ Pdgfrβ+.

We were reliant on PDGFRαCreER^TM as that was the only available PDGFRα Cre model at the time. Note we used PdgfrβCreER^T2 and Ng2Cre in our study but found that both Cre models recombined both LMCs and AdvCs.

(4) Please include appropriate references for all the labeling markers (PDGFR-alpha, beta, and myc11 etc.) that are used in this manuscript. 

We have added multiple references to the manuscript to support the use of these common cell “specific” markers as of course each marker is limited in some capacity to fully or specifically label a single population of cells (Muhl et al., 2020).

(5) One of the criteria for the pacemaker cells is depolarization-induced propagated contractions. Authors have used optogenetics-induced depolarization to test this phenomenon. Please include negative controls for these experiments. 

We have now added negative controls to this experiment which were non-induced (no tamoxifen) Myh11CreER^T2-Chr2 popliteal vessels. This data has been added to the Figure 8.  

(6) What are the resting membrane potentials of Lymphatic muscle cells? The authors should provide some details about this in the manuscript. 

We agree with the reviewer and have added membrane potential recordings (Figure 13) at different pressures and filled our recording electrode with the cell labeling molecule BiocytinAF488 to highlight the action potential exhibiting cells, which were the LMCs. Lymphatic resting membrane potential is dynamic in pressurized vessels, which appears to be a critical difference in this approach as compared to pinned out vessels or those on wire myographs likely due to improper stretch or damage to the vessel wall. In mesenteric lymphatic vessels isolated from rats the minimum membrane potential achieved during repolarization ranges from -45 to 50mV typically while IALVs from mice are typically around -40mV, though IALVs have a notably higher contraction frequency. Critically, we have also added novel membrane potential recordings to this manuscript in IALVs at different pressures and show that the diastolic depolarization rate is the critical factor driving the pressure-dependent frequency.

(7) In the discussion, the authors discussed SR Ca2+ cycling in Pacemaking, but the relevant data are not included in this manuscript, but a manuscript from JGP (in revision) is cross-referenced. 

As discussed above, we have recently published our work where studied IALVs from Myh11CreERT2-Ip3R1fl/fl (Ip3r1ismKO) and Myh1CreERT2-Ip3r1fl/fl-Ip3r2fl/fl-Ip3r3fl/fl mice (Zawieja et al., 2023). Deletion of Ip3r1 from LMCs recapitulated the dramatic reduction in frequency we previously published in Myh11CreERT2-Ano1fl/fl mice and the loss of pressure dependent chronotropy. Furthermore, in this manuscript we also showed that the diastolic calcium transients are nearly completely lost in ILAVs from Myh11CreERT2-Ip3R1fl/fl knockout mice. There was no difference in the contractile function between IALVs from single Ip3r1 knockout and the triple Ip3r1-3 knockout mice suggesting that it is Ip3r1 that is required for the diastolic calcium oscillations. Further, in the presence of 1uM nifedipine there were still no calcium oscillations in the Myh11CreERT2-Ip3r1fl/fl LMCs. These findings provide further support for our interpretation that the pacemaking is of myogenic origin.

Andrzejewska, A., B. Lukomska, and M. Janowski. 2019. Concise Review: Mesenchymal Stem Cells: From Roots to Boost. Stem Cells. 37:855-864.

Buechler, M.B., R.N. Pradhan, A.T. Krishnamurty, C. Cox, A.K. Calviello, A.W. Wang, Y.A. Yang, L.

Tam, R. Caothien, M. Roose-Girma, Z. Modrusan, J.R. Arron, R. Bourgon, S. Muller, and S.J. Turley. 2021. Cross-tissue organization of the fibroblast lineage. Nature. 593:575579.

Castorena-Gonzalez, J.A., S.D. Zawieja, M. Li, R.S. Srinivasan, A.M. Simon, C. de Wit, R. de la Torre, L.A. Martinez-Lemus, G.W. Hennig, and M.J. Davis. 2018. Mechanisms of Connexin-Related Lymphedema. Circ Res. 123:964-985.

Clayton, D.R., W.G. Ruiz, M.G. Dalghi, N. Montalbetti, M.D. Carattino, and G. Apodaca. 2022. Studies of ultrastructure, gene expression, and marker analysis reveal that mouse bladder PDGFRA(+) interstitial cells are fibroblasts. Am J Physiol Renal Physiol. 323:F299F321.

Forte, E., M. Ramialison, H.T. Nim, M. Mara, J.Y. Li, R. Cohn, S.L. Daigle, S. Boyd, E.G. Stanley, A.G. Elefanty, J.T. Hinson, M.W. Costa, N.A. Rosenthal, and M.B. Furtado. 2022. Adult mouse fibroblasts retain organ-specific transcriptomic identity. Elife. 11.

Gashev, A.A., M.J. Davis, and D.C. Zawieja. 2002. Inhibition of the active lymph pump by flow in rat mesenteric lymphatics and thoracic duct. J Physiol. 540:1023-1037.

Lendahl, U., L. Muhl, and C. Betsholtz. 2022. Identification, discrimination and heterogeneity of fibroblasts. Nat Commun. 13:3409.

Luo, H., X. Xia, L.B. Huang, H. An, M. Cao, G.D. Kim, H.N. Chen, W.H. Zhang, Y. Shu, X. Kong, Z.

Ren, P.H. Li, Y. Liu, H. Tang, R. Sun, C. Li, B. Bai, W. Jia, Y. Liu, W. Zhang, L. Yang, Y. Peng, L. Dai, H. Hu, Y. Jiang, Y. Hu, J. Zhu, H. Jiang, Z. Li, C. Caulin, J. Park, and H. Xu. 2022. Pancancer single-cell analysis reveals the heterogeneity and plasticity of cancer-associated fibroblasts in the tumor microenvironment. Nat Commun. 13:6619.

Muhl, L., G. Genove, S. Leptidis, J. Liu, L. He, G. Mocci, Y. Sun, S. Gustafsson, B. Buyandelger, I.V.

Chivukula, A. Segerstolpe, E. Raschperger, E.M. Hansson, J.L.M. Bjorkegren, X.R. Peng, M. Vanlandewijck, U. Lendahl, and C. Betsholtz. 2020. Single-cell analysis uncovers fibroblast heterogeneity and criteria for fibroblast and mural cell identification and discrimination. Nat Commun. 11:3953.

Van Helden, D.F. 1993. Pacemaker potentials in lymphatic smooth muscle of the guinea-pig mesentery. J Physiol. 471:465-479.

Vorontsova, I., J.T. Lock, and I. Parker. 2022. KRAP is required for diffuse and punctate IP(3)mediated Ca(2+) liberation and determines the number of functional IP(3)R channels within clusters. Cell Calcium. 107:102638.

Winkler, E.A., R.D. Bell, and B.V. Zlokovic. 2010. Pericyte-specific expression of PDGF beta receptor in mouse models with normal and deficient PDGF beta receptor signaling. Mol Neurodegener. 5:32.

Zawieja, S.D., G.A. Pea, S.E. Broyhill, A. Patro, K.H. Bromert, M. Li, C.E. Norton, J.A. CastorenaGonzalez, E.J. Hancock, C.D. Bertram, and M.J. Davis. 2023. IP3R1 underlies diastolic ANO1 activation and pressure-dependent chronotropy in lymphatic collecting vessels. J Gen Physiol. 155.

As discussed, I think side notes are distracting and are actually hard to spot. This is basically the type of stuff (explore a concept/definition ) I would put in the tooltip.

Maatregel blijft gelden totdat het verzoek is behandeld.

for - Heart Sutra analysis - from Medium article - Heart Sutra and the nyams of Dzogchen - Aleander Vezhnevets - 2022, Sept 7

Heart Sutra analysis - Form is emptiness - Intellectual analysis - Reductionist analyzes into smaller and smaller parts but - where is the essence to be found?

for - adjacency - Buddhism - Tibetan - Dzogchen practice - Heart Sutra - from Medium article - Heart Sutra and the nyams of Dzogchen - Aleander Vezhnevets - 2022, Sept 7

adjacency - between - Dzogchen practice - Heart Sutra<br /> - Deep Humanity BEing journey - new adjacency - Interesting to connect Dzogchen with the Heart Sutra, as an alternating exploration of emptiness and form - I wonder if elements of this could be used for Deep Humanity BEing journeys - Going from the familiar ground of forms to the unfamiliar ground of emptiness, then - going from emptiness back to form - then finally cultivating the NONDUAL experience of ONE TASTE for both - This maps to Vajrayana practice: - Sutric path - form to emptiness - Tantric path - emptiness to form - Dzogchen - nonduality of both

「速度」と「所要時間」が混在しているようです。

これは後ろのカッコのあとで書くとよさそうです。

これはトルでよさそうです。

これは後ろのカッコのあとで書くとよさそうです。

业主 /资本主 /东主 /所有者

吝啬的，抠门的

直觉：指对某事有一种直觉上的感觉。

远足 /科学考察 /短途旅行

聚集，召集

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eLife Assessment

This valuable study presents new observations on white matter organisation at the micron scale, using a combination of synchrotron imaging and diffusion MRI across two species. Notably, the authors provide solid evidence for the fasciculation of axons within major fibre bundles into laminar structures, though these structures are not consistently observed across modalities or species. The study will be of general interest to neuroanatomists and those interested in white matter imaging.

Reviewer #1 (Public Review):

This study presents valuable observations of white matter organisation from diffusion MRI and two types of synchrotron imaging in both monkeys and mice. Cross-modality comparisons are interesting as the different methods are able to probe anatomical structures at different length scales, from single axons in high-resolution synchrotron (ESRF) imaging, to clusters of axons in lower-resolution synchrotron (DEXY) data, to axon populations at the mm-scale in diffusion MRI. By acquiring all modalities in monkey and mouse ex vivo samples, the authors can observe principles of fibre organisation, and characterise how fibre characteristics, such as tortuosity and micro-dispersion, vary across select brain regions and in healthy tissue versus a demyelination model.

One very interesting result is the observation of apparent laminar organisation of fibres in ex vivo monkey white matter samples. DESY data from the corpus callosum shows fibres with two dominant orientations (one L-R, one slightly inclined), clustered in laminar structures within this major fibre bundle. Thanks to the authors providing open data, I was able to look through the raw DESY volume and observe regions with different "textures" (different orientations) in the described laminar arrangement. That this organisation can be observed by eye, as well as by structure tensor, is fairly convincing.

Reviewer #2 (Public Review):

Summary:

In this work, the authors combine diffusion MRI and high-resolution x-ray synchrotron phase-contrast imaging in monkey and mouse brains to investigate the 3D organization of brain white matter across different scales and species. The work is at the forefront of the anatomical investigation of the human connectome and aligns with several current efforts to bridge the resolution gap between what we can see in vivo at the millimeter scale and the complexity of the human brain at the sub-micron scale. The authors compare the 3D white matter organization across modalities within 2 small regions in one monkey brain (body of the corpus callosum, centrum semiovale) and within one region (splenium of the corpus callosum) in healthy mice and in one murine model of focal demyelination. The study compares measures of tissue anisotropy and fiber orientations across modalities, performs a qualitative comparison of fasciculi trajectories across brain regions and tissue conditions using streamlined tractography based on the structure tensor, and attempts to quantify the shape of fasciculi trajectories by measuring the tortuosity index and the maximum deviation for each reconstructed streamline. Results show measures of anisotropy and fiber orientations largely agree across modalities, especially for larger FOV data. The high-resolution data allows us to explore the fiber trajectories in relation to tissue complexity and pathology. The authors claim the study reveals new common organization principles of white matter fibers across species and scales, for which axonal fasciculi arrange into sheet-like laminar structures.

Strengths:

The aim of the study is of central importance within present efforts to bridge the gap between macroscopic structures observable in vivo in humans using conventional diffusion MRI and the microscopic organization of white matter tissue. Results obtained from this type of study are important to interpret data obtained in vivo, inform the development of novel methodologies, and expand our knowledge of the structural and thus functional organization of brain circuits.

Multi-scale data acquired across modalities within the same sample constitute extremely valuable data that is often hard to acquire and represent a precious resource for validation of both diffusion MRI tractography and microstructure methods.

The inclusion of multi-species data adds value to the study, allowing the exploration of common organization principles across species.

The addition of data from a murine cuprizone model of focal demyelination adds interesting opportunities to study the underlying biological changes that follow demyelination and how these impact tissue anisotropy and fiber trajectories. These data can inform the interpretation and development of diffusion MRI microstructure models.

[Editors' note: The Reviewing Editor considers that the authors addressed the reviewers' questions adequately. The original reviews are here: https://elifesciences.org/reviewed-preprints/94917/reviews]

Author response:

The following is the authors’ response to the original reviews.

Reviewer #1 (Public Review):

This study presents valuable observations of white matter organisation from diffusion MRI and two types of synchrotron imaging in both monkeys and mice. Cross-modality comparisons are interesting as the different methods are able to probe anatomical structures at different length scales, from single axons in high-resolution synchrotron (ESRF) imaging, to clusters of axons in lower-resolution synchrotron (DEXY) data, to axon populations at the mm-scale in diffusion MRI. By acquiring all modalities in monkey and mouse ex vivo samples, the authors can observe principles of fibre organisation, and characterise how fibre characteristics, such as tortuosity and micro-dispersion, vary across select brain regions and in healthy tissue versus a demyelination model. The results are solid, though some statements (in the abstract/discussion) do not appear to be fully supported, and statistical tests would help confirm whether tissue characteristics are similar/different between different conditions.

R1.1: Thank you for the kind feedback. We have included statistical tests in the paper for tissue characteristics where appropriate.

Due to the very high number of sample points (one per voxel) within the 3D synchrotron volumes, testing for statistical significance is challenging for the structure tensor-based tissue fractional anisotropy (FA) metric. This causes any standard statistical test to have sufficient power to evaluate even minute differences between the volumes as statistically significant with high confidence. In other words, the null hypothesis (H0) will always be rejected with p = 0, regardless of the practical significance of the difference. Therefore, we have not added statistical analysis for FA results.

For the tractography based metrics, the number of sample points (one per streamline) is not as high as that for the structure tensor FA, thus making it more reasonable to test for statistical significance. The statistical analyses performed included tests for equality of distributions (Two-sample Kolmogorov-Smirnov tests), equality of medians (Two-sided Wilcoxon rank sum tests), and equality of variance (Brown-Forsythe tests). The results are described in relation to Figure 5(B, D), Figure 8(CF), and detailed in the Methods section.

One very interesting result is the observation of apparent laminar organisation of fibres in ex vivo monkey white matter samples. DESY data from the corpus callosum shows fibres with two dominant orientations (one L-R, one slightly inclined), clustered in laminar structures within this major fibre bundle. Thanks to the authors providing open data, I was able to look through the raw DESY volume and observe regions with different "textures" (different orientations) in the described laminar arrangement. That this organisation can be observed by eye, as well as by structure tensor, is fairly convincing. As not all readers will download the data themselves, the manuscript could benefit from additional figures/videos to demonstrate (1) the quality of the DESY data and (2) a more 3D visualisation of the laminar structures (where the coronal plane shows convincing columnar structure or stripes). Similarly in Figure 5A, though this nicely depicts two populations with different orientations, it is somewhat difficult to see the laminar structure in the current image.

ESRF data of the centrum semiovale (CS) contributes evidence for similar laminar structures in a crossing fibre region, where primarily AP fibres are shown to cluster in 3 laminar structures. As above, further visualisations of the ESRF volume in the CS (as shown in Figure 4E) would be of value (e.g. showing consistency across the 4 volumes, 2D images showing stripey/columnar patterns along different axes, etc).

R1.2: Conveying complex 3D geometry through 2D still images is indeed challenging, and we greatly appreciate the reviewer’s comments and suggestions. To better communicate the understanding of the 3D anatomical environments, we have taken the following actions:

(1) To enhance insights into the tractography results in Figures 5A and 5D, we have rendered and added animations of the tractography scenes as supplemental material.

(2) To visually support 3D insights concerning the consistency of the laminar organisation of the callosal fibres, we have replaced the 2D slice views in Figures 3A and 3B with 3D renderings similar to the one in Figure 4E.

(3) An animation of Figure 4E was created to display the colour-coded structure tensor directions of all four stacked scans. This animation visually supports the complexity of the fibre orientation and the layered structural laminar organisation of the CS sample.

A key limitation of this result is that, though the DESY data from the CC seems convincing, the same structures were not observed in high-resolution synchrotron (ESRF) data of the same tissue sample in the corpus callosum. This seems surprising and the manuscript does not provide a convincing explanation for this inconsistency. The authors argue that this is due to the limited FOV of the ESRF data (~200x200x800 microns). However, the observed laminar structures in DESY are ~40 microns thick, and ERSF data from the CST suggests laminar thicknesses in the range of 5-40 microns with a similar FOV. This suggests the ERSF FOV would be sufficient to capture at least a partial description of the laminar organisation. Further, the DESY data from the CC shows columnar variations along the LR axis, which we might expect to be observed along the long axis of the ESFR volume of the same sample. Additional analyses or explanations to reconcile these apparently conflicting observations would be of value. For example, the authors could consider down-sampling the ESRF data in an appropriate manner to make it more similar to the DESY data, and running the same analysis, to see if the observed differences are related to resolution (i.e. the thinner laminar structures cluster in ways that they look like a thicker laminar structure at lower resolution), or crop the DESY data to the size of the ESRF volume, to test whether the observed differences can be explained by differences in FOV. Laminar structures were not observed in mouse data, though it is unclear if this is due to anatomical differences or somewhat related to differences in data quality across species.

R1.3: We have clarified and expanded upon the results regarding the laminar organisation observed in the monkey CC DESY data. As noted in R1.2, we replaced the 2D images in Figures 3A (DESY) and 3B (ESRF) with 3D renderings to better display the spatial outline of the laminar organisation in the volumes. The reviewer is correct that, although the smaller field of view (FOV) of the ESRF data should allow us to at least partially capture parts of the laminar organisation observed in the larger FOV of the DESY data, this is not guaranteed. It depends on how the smaller FOV is positioned relative to the structural organisation, and since we lack co-registration, we do not know this. It should now be visually evident that the ESRF FOV can be placed such that it does not cover the observed laminae, a point which is now also emphasised in the Discussion. 

Secondly, it is important to emphasise that the voxel colouring using the primary structure tensor direction is just a visualisation technique, which has limitations when it comes to assessing laminar organisation. Mapping 3D directions to RGB colours is inherently difficult and will always have ambiguities. If we had used the standard R-G-B to LR-AP-IS colouring in Figure 3, the laminar organisation would not be evident. Additionally, the laminae will only be visible when there are clear angular differences. There can still be a layered organisation even if we don’t observe it, which is the case for the mouse. The primary direction differences of these layers could be very low (i.e., parallel layers), and consequently not visually evident. This point has been clarified in both the Results and Discussion sections.

Finally, in response to R1.6, we have added analyses regarding the shape of the FOD, specifically estimating the Orientation Dispersion Index (ODI) and Dispersion Anisotropy (DA). This provides further context to the reviewer’s comments about the discrepancies in laminar organisation. We have reflected on the relationship between DA and the visually observed laminar organisation, and this has been integrated into the relevant parts of the Results and Discussion sections.

The changes to manuscript reflecting the statements above are listed here: 

The Discussion section (page 21): “In the monkey CC DESY data, which has a field of view (FOV) comparable to a dMRI voxel, a columnar laminar organisation at a macroscopic level was visually revealed from the structure tensor (ST) direction colouring. However, this laminar organisation was not visible in the higher-resolution ESRF data for the same tissue sample. Although the two samples were not co-registered, the size of a single ESRF FOV within the DESY sample is illustrated in Fig. 3A. This demonstrates the possibility of placing the ESRF sample where the observed laminar structure is absent. Consequently, knowledge of the tissue structural organisation and its orientation is important to fully benefit from the stacked FOV of the ESRF sample and when choosing appropriate minimal FOV sizes in future experiments.

Interestingly, when characterising FODs with measures like ODI and DA as indicators of fibre organisation, rather than relying on visualisation, results from large- and small-FOV data show no discrepancies. This statistical approach discards the spatial context (visually perceived as laminae), highlighting the need to combine both methods.” 

The Results section (page 8): “The mid-level DA values suggest some anisotropic spread of the directions, reflecting the angled laminar organisation observed in the DESY sample. Interestingly, the DA value for the ESRF sample is almost identical, despite the laminar bands being less visually apparent.”

The Results section (page 17): “Nevertheless, visualisation of orientations did not reveal any axonal organisation in the mouse CC due to the lack of local angular contrast, unlike the clear laminar structures seen in the monkey sample (Fig. 3A). Any parallel organisation in tissue remains undetectable because our visual contrast relies on angular differences.”

The Discussion section (page 22): “In the monkey CC (mid-body), we observed laminar organisation indicated by clear spatial angular differences in the ST directions in the sample (Fig. 3A). Quantifications of the FOD shape showed DA indices of 0.55 and 0.59 for the DESY and ESRF samples, respectively. In contrast, the mouse CC (splenium) did not visually reveal a similar angled laminar organisation (Fig. 7C), and the DA indices were lower, at 0.49 and 0.32, respectively. Two possible explanations exist. First, the within-pathway laminar organisation may not be identical across the entire CC. Consequently, more scans from other CC regions would be required to confirm. Second, the different species might account for the differences. Larger brains like the monkey might foster a different level of within-pathway axon organisation compared to the smaller mouse. Although we could not visually detect laminar organisation from the colour coding of the ST direction in the mouse, the non-zero DA values suggest some level of organisation. This is supported by our streamline tractography, which indicates a vertical layered organisation (Fig. 8A, B). It further aligns with studies using histological tracer mapping that shows a stacked parallel organisation of callosal projections in mice, between cortex regions M1 and S1 (Zhou et al. 2013). Nevertheless, we cannot rely solely on voxel-wise ST directions to fully describe axonal organisation, as this method does not contrast almost parallel fasciculi (inclination angles approaching 0 degrees). Analysing patterns in tractography streamlines would be an interesting future direction for this purpose.”

The authors further quantify various other characteristics of the white matter, such as micro-dispersion, tortuosity, and maximum displacement. Notably, the microscopic FA calculated via structure tensor is fairly consistent across regions, though not modalities. When fibre orientations are combined across the sample, they are shown to produce similar FODs to dMRI acquired in the same tissue, which is reassuring. As noted in the text, the estimates of tortuosity and max displacement are dependent on the FOV over which they are calculated. Calculating these metrics over the same FOV, or making them otherwise invariant to FOV, could facilitate more meaningful comparisons across samples and/or modalities.

R1.4: This raises an interesting point about the necessity of normalising the FOV to obtain invariant, tractography-based metrics of tortuosity and maximum deviation across different samples and modalities. In general, achieving this is challenging, and in this study, it is practically not possible. Between species, we encounter significant differences in brain volume ratios, which complicates the establishment of a common reference FOV due to the distinct anatomical organisation of monkey and mouse brains (see our response to R1.8). Within species, we would encounter challenges due to missing contrast—such as issues with staining—and the lack of perfect co-registration.

The Discussion section (page 28) has been extended to reflect this: ”Within the same species, assuming perfect co-registration of samples, it would be possible to perform correlative imaging and analysis. This would allow validation of whether tractography streamlines could be reproduced at different image resolutions within the same normalised FOV. Although this was not possible with the current data and experimental setup, it would be an interesting point to pursue in future work.”

Though the results seem solid, some statements, particularly in the abstract and discussion, do not seem to be fully supported by the data. For example, the abstract states "Our findings revealed common principles of fibre organisation in the two species; small axonal fasciculi and major bundles formed laminar structures with varying angles, according to the characteristics of major pathways.", though the results show "no strong indication within the mouse CC of the axonal laminar organisation observed in the monkey". Similarly, the introduction states: "By these means, we demonstrated a new organisational principle of white matter that persists across anatomical length scales and species, which governs the arrangement of axons and axonal fasciculi into sheet-like laminar structures." Further comments on the text are provided below.

R1.5: We understand that it can be misunderstood that the laminar organisation is identical in monkeys and mice, which is not the case. For example, we show that in the corpus callosum, pathways are parallel in the mouse but not in the monkey. We have clarified that while the principle of layered laminar organisation of pathways is shared between monkeys and mice, species-specific differences do exist.

We have made the following clarifying changes to the manuscript:

The Abstract (page 2): “Our findings revealed common principles of fibre organisation that apply despite the varying patterns observed across species” 

The Introduction (page 4-5): “Through these methods, we demonstrated organisational principles of white matter that persists across anatomical length scales and species. These principles govern the organisation of axonal fasciculi into sheet-like laminar shapes (structures with a predominant planar arrangement). Interestingly, while these principles remain consistent, they result in varied structural organisations in different species.” 

The Discussion (page 21): “despite species differences”.

One observation not notably discussed in the paper is that the spherical histograms of Figure 3E/H appear to have an anisotropic spread of the white points about 0,0. It would be interesting if the authors could comment on whether this could be interpreted as the FOD having asymmetric dispersion and if so, whether the axis of dispersion relates to the fibre orientations of the laminar structures.

R1.6: That is a good point, and to address it, we have fitted spherical Bingham distributions to the FODs, allowing us to quantify their shapes. From each Bingham distribution, we derived two wellknown indices from the diffusion MRI community: the Orientation Dispersion Index (ODI) and Dispersion Anisotropy (DA) index. The ODI explains the dispersion of fibres for a single bundle FOD, whereas DA expresses the shape of the FOD on the unit sphere surface, i.e., the degree of anisotropy. We have integrated the Bingham-based analysis into the Methods, Discussion, and Results sections concerning Figures 3 and 7, but not Figure 4, which contains multiple fibre bundles that we cannot separate on a voxel level. The analysis does not impact the overall message and conclusion but adds interesting context to the discussion around laminar organisation.

A limitation of the study is that it considers only small ex vivo tissue samples from two locations in a single postmortem monkey brain and slightly larger regions of mouse brain tissue. Consequently, further evidence from additional brain regions and subjects would be required to support more generalised statements about white matter organisation across the brain.

R1.7: Collecting more samples from various locations in the brain would provide valuable insights into the consistency of white matter organisation across anatomical length scales, as well as the structuretensor based anisotropy and tortuosity metrics. However, being awarded beamtime at two different synchrotron facilities to scan the same sample with different imaging setups is practically challenging. At the ESRF, we have gathered additional image volumes from other white matter regions of the monkey brain that support all our findings, which will be published separately. X-ray synchrotron imaging technology is advancing rapidly, with faster acquisition times enabling more image volumes to be stitched together. This extends the FOV and allows for a more robust statistical description of the anatomy. Consequently, future studies with an extended FOV and varying image resolutions could utilise a single synchrotron facility to collect additional samples, further supporting our findings.

The Discussion section (page 27) has been extended to reflect this: “Increasing the number of samples across both species and examining laminar organisation at various length scales in more regions would strengthen our findings. However, securing beamtime at two different synchrotron facilities to scan the same sample with varying image resolutions is a limiting factor. Beamline development for multiresolution experimental setups, along with faster acquisition methods, is a rapidly advancing field. For instance, the Hierarchical Phase-Contrast Tomography (HiP-CT) imaging beamline at ID-18 at the ESRF, enables multi-resolution imaging within a single session to address this challenge, though it is currently limited to a resolution of 2.5 μm (Walsh et al. 2021).”

Given the monkey results, the mouse study (section 2.5 onwards) lacks some motivation. In particular, it is unclear why a demyelination model was studied and if/how this would link to the laminar structure observed in the monkey data. Further, it is unclear how comparable tortuosity/max deviation values are across species, considering the differences in data quality and relative resolution, given that the presented results show these values are very modality-dependent.

R1.8: We have clarified the motivation for including the mouse part of the study in both the Introduction and the Results sections.

The Introduction section (page 5): “Furthermore, using a mouse model of focal demyelination induced by cuprizone (CPZ) treatment, we investigate the inflammation-related influence on axonal organisation. This is achieved through the same structure tensor-derived micro-anisotropy and tractography streamline metrics.”

The Results section (page 15): “Finally, we investigated the organisation of fasciculi in both healthy mouse brains and a murine model of focal demyelination induced by five weeks of cuprizone (CPZ) treatment. This allowed for the exploration of the disease-related influence on axonal organisation, particularly under inflammation-like conditions with high glial cell density at the demyelination site (He et al. 2021). The experimental setup for DESY and ESRF is similar to that described for the monkey, with the exception that we did not perform dMRI and synchrotron imaging on the same brains, and only collected MRI data for healthy mouse brains. This approach allowed us to apply the same structure tensor and tractography streamline analysis used previously, but in a healthy versus disease comparison, demonstrating the methodology’s ability to provide insights into pathological conditions.”

Across species, the comparison of tortuosity and maximum deviation must be approached with caution. On one hand, we observe a comparable influence of the extra-axonal environment in both the monkey and mice, as discussed in the section “Sources to the non-straight trajectories of axon fasciculi.” On the other hand, the anatomical scale and relative image resolution are significant factors, as correctly pointed out. In the mouse, for instance, the measures are influenced by white matter pathway macroscopic effects, making cross-species comparison challenging to perform in a normalised way.

The limitations section of the Discussion (page 28) has been updated to reflect this: ”A limiting consequence of having samples imaged at differing anatomical scales is that certain measures become inherently hard to compare in a normalised way. The tractography-based metrics—tortuosity and maximum deviation—serve as good examples of this resolution and FOV dependence. In the ESRF samples, the anatomical scale was at the level of individual axons, and the streamline metrics primarily reflect micro-scale effects from the extra-axonal environment, such as the influence of cells and blood vessels. In comparison, the larger anatomical scale in the DESY samples represents the level of fasciculi and above, with metrics influenced by macroscopic effects, such as the bending of the CC pathway. Both scales are interesting and can provide valuable insights in their own right, but caution is required when comparing the numbers, especially for cross-species studies where there is a significant difference in brain volume ratios.”

The paper introduces a new method of "scale-space" parameters for structure tensors. Since, to my understanding, this is the first description of the method, some simple validation of the method would be welcomed. Further, the same scale parameters are not used across monkeys and mice, with a larger kernel used in mice (Table 2) which is surprising given their smaller brain size. Some explanation would be helpful.

R1.9: We have expanded the description of the scale-space structure tensor approach in the Methods section. Specifically, we have elaborated on the empirical process used to select the scale-space parameters shown in Table 2 and explained why multiple scales were applied only to the monkey samples scanned at ESRF (see Table 2, sample IDs 2 and 3) but not to the other datasets. Additionally, we have added a supplementary figure to assist in illustrating the concept.

Reviewer #2 (Public Review):

Summary:

In this work, the authors combine diffusion MRI and high-resolution x-ray synchrotron phase-contrast imaging in monkey and mouse brains to investigate the 3D organization of brain white matter across different scales and species. The work is at the forefront of the anatomical investigation of the human connectome and aligns with several current efforts to bridge the resolution gap between what we can see in vivo at the millimeter scale and the complexity of the human brain at the sub-micron scale. The authors compare the 3D white matter organization across modalities within 2 small regions in one monkey brain (body of the corpus callosum, centrum semiovale) and within one region (splenium of the corpus callosum) in healthy mice and in one murine model of focal demyelination. The study compares measures of tissue anisotropy and fiber orientations across modalities, performs a qualitative comparison of fasciculi trajectories across brain regions and tissue conditions using streamlined tractography based on the structure tensor, and attempts to quantify the shape of fasciculi trajectories by measuring the tortuosity index and the maximum deviation for each reconstructed streamline. Results show measures of anisotropy and fiber orientations largely agree across modalities, especially for larger FOV data. The high-resolution data allows us to explore the fiber trajectories in relation to tissue complexity and pathology. The authors claim the study reveals new common organization principles of white matter fibers across species and scales, for which axonal fasciculi arrange into sheet-like laminar structures.

Strengths:

The aim of the study is of central importance within present efforts to bridge the gap between macroscopic structures observable in vivo in humans using conventional diffusion MRI and the microscopic organization of white matter tissue. Results obtained from this type of study are important to interpret data obtained in vivo, inform the development of novel methodologies, and expand our knowledge of the structural and thus functional organization of brain circuits.

Multi-scale data acquired across modalities within the same sample constitute extremely valuable data that is often hard to acquire and represent a precious resource for validation of both diffusion MRI tractography and microstructure methods.

The inclusion of multi-species data adds value to the study, allowing the exploration of common organization principles across species.

The addition of data from a murine cuprizone model of focal demyelination adds interesting opportunities to study the underlying biological changes that follow demyelination and how these impact tissue anisotropy and fiber trajectories. These data can inform the interpretation and development of diffusion MRI microstructure models.

Weaknesses:

The main claim of a newly discovered laminar organization principle that is consistent across scales and species is not supported strongly enough by the data. The main evidence in support of the claim comes from the larger FOV data obtained from the body of the corpus callosum in the monkey brain. A laminar organization principle is partially shown in the centrum semiovale in the monkey brain and it is not shown in mice data. Additionally, the methods lack details to help the correct interpretation of these findings (e.g., how were these fasciculi defined?; how well do they represent different axonal populations?; what is the effect of blood vessels on the structure tensor reconstruction?; how was laminar separation quantified?) and the discussion does not provide a biological background for this organization. The corpus callosum sample suggests axons within a bundle of fibers are organized in a sheet-like fashion, while data from the centrum semiovale suggest fibers belonging to different fiber bundles are organized in a sheet-like arrangement. While I acknowledge the challenges in acquiring such high-resolution data, additional samples from different regions in the same animals and from different animals would help strengthen this claim.

R2.1 

-  how were these fasciculi defined?

In the introduction (page 3), we have clarified our definition of an axon fasciculus: “A fasciculus is a bundle of axons that travel together over short or long distances. Its size and shape can vary depending on its internal organisation and its relationship to neighbouring fasciculi.”

Additionally, we emphasise in the Results section (page 12) that the centroid streamlines are not guaranteed to be actual fasciculi, but rather representations of them. The paragraph now states: “To ease visualisation and quantification, we used QuickBundle clustering(Garyfallidis et al. 2012) to group neighbouring streamlines with similar trajectories into a centroid streamline. This centroid streamline serves as an approximation of the actual trajectory of a fasciculus.”

- what is the effect of blood vessels on the structure tensor reconstruction?

Fair point, that was not clear from our description. The clarification contains two parts. First, the estimation of the structure tensor occurs in all voxels, and in that sense, the blood vessels respond very similarly to axons. Second, when it comes to sample statistics derived from the structure tensor analysis (FA histograms and the FODs), they will have an influence, albeit a small one, given the low volume percentage of the blood vessels within the FOVs. In the monkey samples, segmenting the blood vessels was achievable with little effort, allowing us to exclude their contribution from FA statistics and FODs. To make this clear, we have added a paragraph to the Methods section (page 34) titled “Structure tensor-based quantifications,” reflecting this clarification. Additionally, we have restructured the entire structure tensor methods description (starting on page 32) as part of the reviewer comments in R1.6 and R1.9.

- how was laminar separation quantified?

We have added a clarification in Results section (page 7): “The laminar thickness was determined by manual measurements on laminae visually identified in the 3D volume”.

- discussion does not provide a biological background for this organization.

A good point. Including the biological background is relevant as it supports the laminar organisation of white matter pathways observed in our findings and those of others.

We have added a section on this background in the Discussion (page 24): “We believe our observed topological rule of white matter laminar organisation can be explained by a biological principle known from studies of nervous tissue development. The first axons to reach their destination, guided by their growth cones, are known as “pioneering” axons. “Follower” axons use the shaft of the pioneering axon for guidance to efficiently reach the target region (Breau and Trembleau 2023). Axons can form a fasciculus by fasciculating or defasciculating along their trajectory through a zippering or unzipping mechanism, controlled by chemical, mechanical, and geometrical parameters. Zippering “glues” the axons together, while unzipping allows them to defasciculate at a low angle (Šmít et al. 2017). Although speculative, the zippering mechanism may be responsible for forming the laminar topology observed across length scales. The defasciculation effect can explain our results in the corpus callosum (CC) of monkeys, with laminar structures at low angles (~35 degrees) also observed by (Innocenti et al. 2019; Caminiti et al. 2009), as well as in other major pathways (Sarubbo et al. 2019). In contrast, a fasciculation mechanism may be observed in the mouse CC (0 degrees). If the geometrical angle between two axons is high, i.e., toward 90 degrees, the zippering mechanism will not occur, and the two axons (fasciculi) will cross (Šmít et al. 2017). This supports our and other findings that crossing fasciculi or pathways occur at high angles toward 90 degrees in the fully matured brain (Wedeen et al. 2012). Once myelination begins, the zippering mechanism is lost (Šmít et al. 2017), suggesting that laminar topology is established at the earliest stages of brain maturation.”

- additional samples from different regions in the same animals and from different animals would help strengthen this claim

Reviewer #1 also pointed to the inclusion of additional samples, and this is now discussed as part of the study limitations on page 27 (see also R1.7).

The main goal of the study is to bridge the organization of white matter across anatomical length scales and species. However, given the substantial difference in FOVs between the two imaging modalities used, and the absence of intermediate-resolution data, it remains difficult to effectively understand how these results can be used to inform conventional diffusion MRI. In this sense, the introduction does not do a good enough job of building a strong motivation for the scientific questions the authors are trying to answer with these experiments and for the specific methodology used.

R2.2: Indeed, this is an essential point now emphasised in the introduction, page 3, which now states: ”Despite the limited resolution of dMRI, the water diffusion process can reveal microstructural geometrical features, such as axons and cell bodies, though these features are compounded at the voxel level. Consequently, estimating microstructural characteristics depends on biophysical modelling assumptions, which can often be simplistic due to limited knowledge of the 3D morphology of cells and axons and their intermediate-level topological organisation within a voxel. Thus, complementary highresolution imaging techniques that directly capture axon morphology and fasciculi organisation in 3D across different length scales within an MRI voxel are essential for understanding anatomy and improving the accuracy of dMRI-based models(Alexander et al. 2019).”

Additionally, in the introduction, page 4, we have made the following changes to strengthen the link across modalities, such that it now states: “In the x-ray synchrotron data, we applied a scale-space structure tensor analysis, which allowed for the quantification of structure tensor-derived tissue anisotropy and FOD in the same anatomical regime indirectly detected by dMRI.”

The cuprizone data represent a unique opportunity to explore the effect of demyelination on white matter tissue. However, this specific part of the study is not well motivated in the introduction and seems to represent a missed opportunity for further exploration of the qualitative and quantitative relationship between diffusion MRI and sub-micron tissue information (although unfortunately not within the same brain sample). This is especially true considering the diffusion MRI protocol for mice would allow extrapolation of advanced measures from different tissue compartments.

R2.3: A similar point was raised by Reviewer 1 (R1.8), and we have clarified the motivation for including the healthy mice and the demyelination samples.  

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Many thanks to the authors for providing open data. This was very helpful when reviewing the manuscript and is a valuable resource for the community.

R1.10: We are happy to share our data with the community. Understanding anatomy in 3D is hard to achieve through still images and animations, so the ability to explore it on your own is quite important. The link to the data repository has been added in the Methods section in the following paragraph: “Due to the size of the data selected, processed image volumes, masks and results are available at https://zenodo.org/records/10458911. Other datasets can be shared on request.“

One confusing element of the paper is that orientations (or axes) do not seem to be consistent across samples/modalities. For example, the green tensors in Figures 3 C and D are tilted up/down in opposite directions and the streamlines in Figure 5A seem opposite (SL) from what we would expect from Figure 2A (SR). Having consistent orientations across modalities and images would help the reader. When colouring tensors (e.g. in Figure 3), the authors could consider a 3D colour scheme (similar to that used by diffusion MRI) rather than colouring by only inclination, as this would provide useful information on whether different laminae have similar orientations, as implied by the tractography in Figure 4.

R1.11: Thank you for spotting the suboptimal consistency between Figures 2, 3, and 5. Figure 2 has been corrected and updated. The left-right direction in the coronal views was not correctly displayed. Additionally, the glyph directions have been updated in Figures 2 and 3.

By default, we use the “standard” RGB colour scheme used in dMRI. However, for the monkey CC— essentially Figure 3—this did not effectively illustrate our findings. We decided to use a different directional colour encoding scheme, which captures the angular deviation from the L-R axis. This was to assist in the visualisation of the inclination angle between the laminars. We have used the same colour scheme for the tensors in Figure 3 to avoid confusion.

On a general note, the standard colour scheme has uniform “colour contrast” in all directions, but when there is only a single dominant direction in the sample, it can make sense to concentrate the colour contrast in that axis.

Results: "even higher FA anisotropy in the micro-tensor domain of 0.997, i.e., the micro (μ)FA (20, 21)." I understand these references lead to a definition of μFA that is based on multiple diffusion tensor encodings which is quite different from that suggested by Kaden. It may be preferable to reference Kaden directly (since I understand this is the method used) to avoid confusion.

R1.12: Correctly spotted, and we now reference the method from Kaden et al. and use the other references elsewhere when relevant.

"and scanned the mouse brain in a whole." - typo?

R1.13: Thank you for spotting the typo. The mouse brain was kept in the skull during MRI scanning, which has been clarified in the Methods section.

The crossing fibre region appears to be sometimes referred to as the centrum semiovale, and other times as the CST. CS seems the better description and keeping this naming consistent would avoid confusion to the reader.

R1.14: Well spotted, thank you. We have replaced the usage of Corticospinal Tract (CST) with centrum semiovale (CS) where relevant.

Direct comments on the text:

Abstract: "Individual axon fasciculi exhibited tortuous paths .... in a manner independent of fibre complexity and demyelination"

Do statistical comparisons of the various distributions support this? The data shows somewhat increased tortuosity in the CST compared to the CC, and somewhat lower tortuosity in CPZ tissue.

R1.15: The intention of the text was not to point to the comparison of tortuosity, but rather to highlight the maximum deviation. We observe a high probability density of maximum deviations at approximately 5-10 microns in all samples, which corresponds to the size of structures in the extraaxonal environment, such as blood vessels and cells.

Additionally, we understand that the original statement might imply an expectation of a statistical analysis demonstrating independence, which is not the case. To clarify, we have reformulated the sentence in the Abstract (page 2) to address these points: “Fasciculi exhibited non-straight paths around obstacles like blood vessels, comparable across the samples of varying fibre complexity and demyelination.”

Abstract: "A quantitative analysis of tissue anisotropies and fibre orientation distributions gave consistent results for different anatomical length scales and modalities, while being dependent on the field-of-view."

To my understanding, the FODs here from different modalities are calculated over different FOVs (in monkeys at least), and FODs are only presented for a single FOV for each modality, meaning it is difficult to separate the effects of modality from FOV. The microscopic anisotropy is also noticeably different across modalities (DESY < ESRF < dMRI).

R1.16: That is a fair point. Our statement was trying to capture too much condensed content to be correctly interpretable. We have reformulated the sentence to state: “Quantifications of fibre orientation distributions were consistent across anatomical length scales and modalities, whereas tissue anisotropy had a more complex relationship, both dependent on the field-of-view”.

While it is true that we only present the ST-derived quantifications – FOD and FA statistics – for a single FOV per modality and sample, the results shown for the ESRF monkey samples (Figures 3 and 4) are a merge of four individually processed volumes. The quantifications of each individual subFOV have now been added as a supplementary figure (Figure S3) to highlight the consistency of the methodology and the effect of shifting the FOV position. In the case of the mouse, we have two volumes from different mice, which also display similar FOD and FA statistics.

Abstract: "Our study emphasises the need to balance field-of-view and voxel size when characterising white matter features across anatomical length scales."

This point does not seem very well explored in the paper, rather it is an observation of the limitations of the different imaging modalities. For example, there aren't analyses to compare metrics from highresolution data at different FOVs (i.e. by taking neighbourhoods of different sizes), nor are metrics compared from data at different resolutions and the same FOV.

R1.17: The question is related to R1.16, R1.4, and R1.8, and we have addressed this point in our responses to those comments.

Figure 7 - Taking into account the eigenvalues can be helpful when interpreting the secondary and tertiary eigenvectors of tensors (V2 and V3). It would be interesting to know whether the eigenvalues L2 ~= L3 are approximately equal (suggesting isotropic diffusion about V1, where the definition of V2 versus V3 isn't very meaningful), or if L2 is noticeably larger than L3 (suggesting anisotropic diffusion about V1, potentially similar to the anisotropic dispersion discussed above).

R1.18: It would be interesting to explore the eigenvalues of the structure tensor in more detail, as has been done for the diffusion tensor. However, we believe this belongs to future work, as such additional detailed methodological analysis would complicate the already complex story. As mentioned in response to R1.10, most processed data has been made publicly available, and the rest can be requested (due to the storage size of the data sets) to perform such additional analysis.

Discussion: "Importantly, our findings revealed common principles of fibre organisation in both monkeys and mice; small axonal fasciculi and major bundles formed sheet-like laminar structures," See above regarding the lack of evidence for laminar structures in mouse data.

R1.19: We have reformulated the text for clarification as part of R1.3. Additionally, we added FOD quantifications to support why we do not observe an apparent laminar organisation in the mouse CC— please see our response to R1.6.

Discussion: "Interestingly, the dispersion magnitude is indicative of fasciculi that skirt around obstacles in the white matter such as cells and blood vessels, and the results are largely independent of both white matter complexity (straight vs crossing fibre region) and pathology." Again, do statistical tests of the various distributions support this?

R1.20: As part of R1.1, we have added statistical tests of significance for the quantifications of how max deviation changes when bending around objects. Indeed, the distributions are not statistically the same, and we do not wish to convey that sentiment, but they are comparable in the object sizes that they detect. As done in the abstract, we have reformulated the sentence to avoid misunderstanding and have replaced “largely independent” with “observed across.”

Discussion: "Tax et al. have demonstrated the calculation of a sheet probability index from diffusion MRI data, which suggested the presence of sheet-like features in the CC"

My understanding was that this was observed in crossing fibre regions, such as where fibres projecting with the CC cross the CST, but not the main body of the CC itself. Tax defines sheet structure as "composed of two tracts that cross each other on the same surface in certain regions along their trajectories." Is this a different phenomenon to the laminar structures observed here (where we observe fibres within a single tract being locally organised into laminar structures)?

R1.21: Thank you for pointing our attention to this. We have corrected the section in the Discussion (page 23), so it now states: “Additionally, Tax et al. have demonstrated the calculation of a gridcrossing sheet probability index from diffusion MRI data, which suggested the presence of sheet-like features in a crossing fibre region (Tax et al. 2016), which is in line with our findings in the synchrotron data. Note that the method by Tax et al. only detects sheet-like structures crossing on a grid and does not reveal laminar structures with lower inclination angles, as we observed in the monkey CC.”

Discussion: "We found that FODs were consistent across image resolutions and modalities, but only given that the FOV is the same." See above.

R1.22: As part of our response to R1.6, we quantified the FODs using the ODI and DA indices, which should help support our statement. Nevertheless, we have toned down the statement and reformulated the text as follows: “We found that FODs were comparable across image resolutions and modalities. The observed discrepancies can be attributed to the fact that the FOVs are not exactly matched.”

Discussion: "microscopic FA were highly correlated across modalities."

The data shows FA is considerably lower in DESY to ESRF; within modality FA is quite consistent irrespective of tissue region; and differences between the CC and CG shown in ESRF data in mice are not repeated in DESY. It is unclear from the current data if this would lead to a high correlation across modalities. Some evidence would be helpful.

R1.23: This is a fair point; we have not performed a correlation analysis. However, the pattern we observe for the synchrotron samples is as follows: When the anatomical length scale increases (becomes more macroscopic), the FA distribution shifts to lower values. This reflects the scale of information captured with the ST analysis (see also R1.9). Therefore, the most interesting comparison of FA statistics occurs when the resolution and anatomical length scale are approximately the same.  The sentence in question has been reformulated to the following: ”Estimates of structure tensor derived microscopic FA show a clear pattern across modalities.”

Discussion: "If so, the (inclination angle) information might serve to form rules for low-resolution diffusion MRI based tractography about how best to project through bottleneck regions, which is currently a source of false-positives trajectories (6)."

This is an interesting idea but it is unclear to me how this inclination information would help track through bottlenecks where, by definition, fibres are passing through with the same orientation. Some further explanation would be helpful.

R1.24: We have elaborated on the section in the Discussion (page 23), explaining how this can be used to improve tractography tracing through complex regions: “The reason is that standard tractography methods do not "remember" or follow anatomical organisation rules as they trace through complex regions. Our findings on pathway lamination and inclination angles—low for parallel-like trajectories and high for crossing-like trajectories—can help incorporate trajectory memory into these methods, reducing the risk of false trajectories”.

Reviewer #2 (Recommendations For The Authors):

Below I report comments that if addressed I believe would improve the clarity and readability of the manuscript.

-  Figures 1 and 2 would be more meaningful if combined into one figure. This would allow for a direct visual comparison of the two modalities. If space is needed, I believe the second row of Figure 1 (coronal views of CC) does not add much information. It is often hard to navigate the different orientations of the tissue in the images; thus any effort in trying to help the reader visually clarify would improve readability.

R2.4: We considered the reviewer’s suggestion to merge Figures 2 and 3. However, this made both the figures and the main text additionally complex, so we chose to retain the original figure layout. Secondly, Figure 3 utilises a non-standard directional colormap. Keeping the colormap consistent within each figure is a feature we wish to preserve. In response to R1.11, the figures have been updated to have more consistent orientations for the monkey samples.

In Figure 2, the second row, showing a coronal view of the CC, is essential for comparison with human data in Figure S1. It highlights where we observed the columnar laminar organisation and their inclination angle, as also detected by DTI.

-  Figure 4 shows synchrotron data revealing an anterior-posterior component within the centrum semiovale that is not necessarily seen in the dMRI data. Could the authors comment on this?

R2.5: Thank you for pointing this out. We have now addressed this in the Results section (page 10), where we describe the observation in detail: “Interestingly, visual inspection of the colour-coded structure tensor directions in Fig. 4E shows the existence of voxels whose primary direction is along the A-P axis. However, this represents a small enough portion of the volume that it does not appear as a distinct peak on the FOD.“

-  The authors claim they observed several purple axons crossing orthogonally in Figure 5c. However, that is not necessarily clear in the figure.

R2.6: We appreciate the feedback. We have now coloured the streamlines of the crossing fasciculi in Figure 5C in red.

-  Figure 5 would benefit from adding the color encoding scheme for Figure 5d, as sometimes this is not necessarily consistent.

R2.7: We appreciate the feedback. We have added an indication of the standard directional colour coding to Figure 5D.

-  Figure 5d shows interesting data from the complex region. However, it is hard to visualize and it looks like there are not many streamlines traveling entirely I-S? Maybe a different orientation of the sample would help visualization.

R2.8: A similar point was raised by Reviewer 1 (see R1.2). We have added an animation of the scene to assist in the interpretation of the 3D organisation within this complex sample.

-  The concept of axon fasciculi is not necessarily immediately clear. Adding an explanation for what the authors refer to when using this term would improve clarity.

R2.9: In the introduction, we now state our conceptual definition of an axon fasciculus as a number of axons that follow each other (see also R2.1).

-  The methods do not provide details on how structure tensor FA is measured.

R2.10: Thank you for pointing this out. We have restructured and expanded the structure tensor description in the Methods section (see also R1.9 and R2.1), which now includes the definition of FA.

-  Why didn't the authors select the same cc region for both mice and monkeys? It seems this would have increased the strength of the comparison.

R2.11: We agree. The reason lies in the chronology of experiments and the fact that we cannot control where demyelination takes place. We have added a clarifying description in the Methods section (page 31): “Note that several separate beamline experiments were conducted to collect the volumes listed in Table 1. In the first two experiments, samples from the monkey brain were scanned at ESRF and DESY, respectively. The samples from the mouse brain were imaged in two subsequent experiments. Consequently, the location of the identified demyelinating lesion in the cuprizone mice, which cannot be precisely controlled, did not match the location of the CC biopsies in the monkey.”

-  While it is mentioned in the results, the methods do not explain how vessel segmentations or cell segmentation in mice was performed and for which datasets it was performed.

R2.12: For the small ROI shown in Figure 6, the labelling was a manual process using the software ITK-SNAP, which has now been clarified in the corresponding figure caption. The generation of ROI masks and blood vessel segmentations involved a combination of intensity thresholding, morphological operations, and manual labelling in ITK-SNAP. This has been clarified in the restructured and expanded description of structure tensor analysis in the Methods section (starting on page 32).

-  From the methods it is hard to understand (1) how many mice were used; (2) why dMRI was done on a different sample; (3) whether the same selenium region was selected for both healthy and CPZ animals; (4) how the registration across samples was performed.

R2.13: We appreciate the feedback and have inserted clarifying statements in the relevant parts of the Methods section. (1) The total number of mice included was three: one normal, one cuprizone, and one normal for MRI scanning. (2) The quality of the collected dMRI on the mouse was too poor to use, and it could not be redone as the brain had already been sliced and prepared for synchrotron experiments. (3) The same splenium section was selected for both healthy and cuprizone mice. (4) A paragraph on image registration has been added.

-  Diffusion MRI method sections would benefit from additional details on the protocols used.

R2.14: Thank you for pointing this out. We have added more details about the diffusion MRI protocols, including the b-value, gradient strength, and other relevant parameters.

Was genau ist mit "Prozentsatz der 1-Werte" gemeint?

不是静态预设的，而是动态生成的。

eLife Assessment

This valuable study extends the previous interesting work of this group to address the potentially different control of movement and posture. Through experiments in which stroke participants used a robotic manipulandum, the authors provide solid evidence supporting a lack of a relation between the resting force postural bias they measure (closely related to the flexor synergy in stroke) and kinematic deficits during movement. Based on these results, the authors propose a conceptual framework that differentially weights the two main descending pathways (corticospinal tract and reticulospinal tract) for neurologically intact and stroke patients. Discussing the potential impact of differences on muscle/spinal circuit state and their responses between holding a posture and movement, as well as the assumptions of their statistical comparisons, would further improve the paper.

Reviewer #1 (Public review):

This study extends the previous interesting work of this group to address the potentially differential control of movement and posture. Their earlier work explored a broad range of data to make the case for a downstream neural integrator hypothesized to convert descending velocity movement commands into postural holding commands. Included in that data were observations from people with hemiparesis due to stroke. The current study uses similar data, but pushes into a different, but closely related direction, suggesting that these data may address the independence of these two fundamental components of motor control. I find the logic laid out in the second sentence of the abstract ("The paretic arm after stroke is notable for abnormalities both at rest and during movement, thus it provides an opportunity to address the relationships between control of reaching, stopping, and stabilizing") less then compelling, but the study does make some interesting observations. Foremost among them, is the relation between the resting force postural bias and the effect of force perturbations during the target hold periods, but not during movement. While this interesting observation is consistent with the central mechanism the authors suggest, it seems hard to me to rule out other mechanisms, including peripheral ones. These limitations should should be discussed.

Reviewer #2 (Public review):

Summary:

Here the authors address the idea that postural and movement control are differentially impacted with stroke. Specifically, they examined whether resting postural forces influenced several metrics of sensorimotor control (e.g., initial reach angle, maximum lateral hand deviation following a perturbation, etc.) during movement or posture. The authors found that resting postural forces influenced control only following the posture perturbation for the paretic arm of stroke patients, but not during movement. They also found that resting postural forces were greater when the arm was unsupported, which correlated with abnormal synergies (as assessed by the Fugl-Meyer). The authors suggest that these findings can be explained by the idea that the neural circuitry associated with posture is relatively more impacted by stroke than the neural circuitry associated with movement. They also propose a conceptual model that differentially weights the reticulospinal tract (RST) and corticospinal tract (CST) to explain greater relative impairments with posture control relative to movement control, due to abnormal synergies, in those with stroke.

Comments on revisions:

The authors should be commended for being very responsive to comments and providing several further requested analyses, which have improved the paper. However, there is still some outstanding issues that make it difficult to fully support the provided interpretation.

The authors say within the response, "We would also like to stress that these perturbations were not designed so that responses are directly compared to each other ***(though of course there is an *indirect* comparison in the sense that we show influence of biases in one type of perturbation but not the other)***." They then state in the first paragraph of the discussion that "Remarkably, these resting postural force biases did not seem to have a detectable effect upon any component of active reaching but only emerged during the control of holding still after the movement ended. The results suggest a dissociation between the control of movement and posture." The main issue here is relying on indirect comparisons (i.e., significant in one situation but not the other), instead of relying on direct comparisons. Using well-known example, just because one group / condition might display a significant linear relationship (i.e., slope_1 > 0) and another group / condition does not (slope_2 = 0), does not necessarily mean that the two groups / conditions are statistically different from one another [see Figure 1 in Makin, T. R., & Orban de Xivry, J. J. (2019). Ten common statistical mistakes to watch out for when writing or reviewing a manuscript. eLife, 8, e48175.].

The authors have provided reasonable rationale of why they chose certain perturbation waveforms for different. Yet it still holds that these different waveforms would likely yield very different muscular responses making it difficult to interpret the results and this remains a limitation. From the paper it is unknown how these different perturbations would differentially influence a variety of classic neuromuscular responses, including short-range stiffness and stretch reflexes, which would be at play here.

Much of the results can be interpreted when one considers classic neuromuscular physiology. In Experiment 1, differences in resting postural bias in supported versus unsupported conditions can readily be explained since there is greater muscle activity in the unsupported condition that leads to greater muscle stiffness to resist mechanical perturbations (Rack, P. M., & Westbury, D. R. (1974). The short-range stiffness of active mammalian muscle and its effect on mechanical properties. The Journal of physiology, 240(2), 331-350.). Likewise muscle stiffness would scale with changes in muscle contraction with synergies. Importantly for experiment 2, muscle stiffness is reduced during movement (Rack and Westbury, 1974) which may explain why resting postural biases do not seem to be impacting movement. Likewise, muscle spindle activity is shown to scale with extrafusal muscle fiber activity and forces acting through the tendon (Blum, K. P., Campbell, K. S., Horslen, B. C., Nardelli, P., Housley, S. N., Cope, T. C., & Ting, L. H. (2020). Diverse and complex muscle spindle afferent firing properties emerge from multiscale muscle mechanics. eLife, 9, e55177.). The concern here is that the authors have not sufficiently considered muscle neurophysiology, how that might relate to their findings, and how that might impact their interpretation. Given the differences in perturbations and muscle states at different phases, the concern is that it is not possible to disentangle whether the results are due to classic neurophysiology, the hypothesis they propose, or both. Can the authors please comment.

The authors should provide a limitations paragraph. They should address 1) how they used different perturbation force profiles, 2) the muscles were in different states which would change neuromuscular responses between trial phase / condition, 3) discuss a lack of direct statistical comparisons that support their hypothesis, and 4) provide a couple of paragraphs on classic neurophysiology, such as muscle stiffness and stretch reflexes, and how these various factors could influence the findings (i.e., whether they can disentangle whether the reported results are due to classic neurophysiology, the hypothesis they propose, or both).

Author response:

The following is the authors’ response to the original reviews.

Public Reviews:

Reviewer #1 (Public Review):

This study extends the previous interesting work of this group to address the potentially differential control of movement and posture. Their earlier work explored a broad range of data to make the case for a downstream neural integrator hypothesized to convert descending velocity movement commands into postural holding commands. Included in that data were observations from people with hemiparesis due to stroke. The current study uses similar data but pushes into a different, but closely related direction, suggesting that these data may address the independence of these two fundamental components of motor control. I find the logic laid out in the second sentence of the abstract ("The paretic arm after stroke is notable for abnormalities both at rest and during movement, thus it provides an opportunity to address the relationships between control of reaching, stopping, and stabilizing") less than compelling, but the study does make some interesting observations. Foremost among them, is the relation between the resting force postural bias and the effect of force perturbations during the target hold periods, but not during movement. While this interesting observation is consistent with the central mechanism the authors suggest, it seems hard to me to rule out other mechanisms, including peripheral ones. 

Response 1.1. Thank you for your comments, which we address in detail below and in our response to Recommendations to the authors (see pp. 15-19 of this letter). We would first like to clarify the motivation behind our use of a stroke population to understand the interactions between the control of reaching in and holding. We agree that this idea can be laid out in a more compelling way.

The fact that stroke patients usually display issues with their control of both reaching and holding, allows for within-individual comparisons of those two modes of control. Further, the magnitude of abnormalities is relatively large, making it easier to measure, compare and investigate effects. And, importantly, these two modes of control can be differentially affected after stroke (also pointed out by Reviewer 2, point 4 in Comments to the Authors). Finally, this kind of work – examining interactions between positive signs of stroke (such as abnormal posture or synergy) vs. negative signs (such as loss of motor control) – needs to be done in humans, as positive signs are relatively absent even in primates (Tower, 1940).

We have changed our abstract (changes shown below in red), and our intro (expanding the second paragraph, lines 75-76), to lay out our motivation more clearly.

From the abstract:

“The paretic arm after stroke exhibits different abnormalities during rest vs. movement, providing an opportunity to ask whether control of these behaviors is independently affected in stroke. “

On the other hand, the relation between force bias and the well-recognized flexor synergy seems rather self-evident, and I don't see that these results add much to that story.

Response 1.2. While it seems natural that these biases would be the resting expression of abnormal flexor synergies (given their directionality towards the body, as shown in Figures 2-3, and the other similarities we demonstrate in Figure 8), we do not believe it is self-evident. These biases are measured at rest, with the patient passively moved and held still, whereas abnormal synergies emerge when the patient actively tries to move. The lack of relationship we find between these resting force biases and active movement underlines that the relation between force bias and flexor synergy should not be taken as self-evident, making it worthwhile to examine it (as we motivate in lines 589-596 and show in Figure 8).

The paradox here is that, in spite of a relationship between force bias and flexor synergy (itself manifesting during attempted movement), there seems to be no relationship between force bias and direct measures of active movement (Figures 5,6). This is the paradox that inspired our conceptual model (Figure 9) and inspires to further investigate the factors under which these two systems are intermingled or kept separate. We thus find it to be a helpful element in the story.

I am also struck by what seems to be a contradiction between the conclusions of the current and former studies: "These findings in stroke suggest that moving and holding still are functionally separable modes of control" and "the commands that hold the arm and finger at a target location depend on the mathematical integration of the commands that moved the limb to that location." The former study is mentioned here only in passing, in a single phrase in the discussion, with no consideration of the relation between the two studies. This is odd and should be addressed. 

Response 1.3. While these two sets of findings are not contradictory, we understand how they can appear as such without providing context. We now discuss the relationship between our present study and the previous one more directly (lines 66-70 and 663-669 of the revised manuscript).

The previous study examined how the control of movement informs the control of holding after the movement was over; the current study examines whether abnormalities in holding measured at rest with the movement leading to the rest position being passive. There are thus two important distinctions:

First, directionality of potential effects: here we examine the effect of (abnormalities in) holding control upon movement, but the 2020 study (Albert et al., 2020) examines the effects of movement upon holding control. Stroke patient data in the 2020 study showed that, under CST damage, while the reach controller is disrupted, the hold controller can continue to integrate the malformed reach commands faithfully. In line with this, we proposed a model where the postural controller system sits downstream of the moving controller (Figure 7G in the 2020 paper). We thus did not claim, in 2020, that integration of movement commands is the only way to do determine posture control, as we stated explicitly back then, e.g. (emphasis ours):

“Equations (1) and (2) describe how the integration of move activity may relate to changes in hold commands, but does not specify the hold command at the target.”

In short, finding no effect of holding abnormalities upon movement (present finding) does not mean there is no potential effect of movement upon holding (2020 finding). This is something we had alluded to in the Discussion but not clarified, which we do now (see edits at the end of our response to this point).

Second, active vs. passive movement: here, we measure holding control at rest (Experiment 1). The 2020 study shows that endpoint forces reflect the integration of learned dynamics exerted during active movement that led to the endpoint position. However, in Experiment 1, there is no active reaching to integrate, as the robot passively moves the arm to the held position. Thus, resting postural forces measured in Experiment 1 could not reflect the integration of reach commands that led to each rest position.  

Thus, the two sets of findings are not contradictory. Taking our current and 2020 findings together suggests that active holding control would comprise would reflect both the integration of movement control that led to assuming the held position, plus the force biases measured at rest.

Hence our decision to describe these two systems as functionally separable: while these systems can interact, the effects of post-stroke malfunctions in each can be independent depending on the function and conditions at hand. This does not make this a limited finding: being able to dissociate post-stroke impairment based on each of these two modes of control may inform rehabilitation, and also importantly, understanding the conditions in which these two modes of control become separable can substantially advance our understanding of both how different stroke signs interact with each other and how motor control is assembled in the healthy motor system. Figure 9 illustrates our conceptual model behind this and may serve as a blueprint to further dissect these circuits in the future.

We discuss these issues briefly in lines 663-669 in our Discussion section, reproduced below for convenience:

“It should be noted, however, that having distinct neural circuits for reaching and holding does not rule out interactions between them. For example, we recently demonstrated how arm holding control reflects the integration of motor commands driving the preceding active movement that led to the hold position, in both healthy participants and patients with hemiparesis (Albert et al., 2020). However, in that paper, we did not claim that this integration is the only source of holding control. Indeed, in Experiment 1 of the current study, we used passive movement to bring the arm to each probed position, which means that the postural biases could not be the result of integration of motor commands.” 

And, we have adjusted our Introduction to provide pertinent context regarding our 2020 work (first paragraph, lines 66-70 of the updated manuscript).

A minor wording concern I had is that the term "holding still" is frequently hard to parse. A couple of examples: "These findings in stroke suggest that moving and holding still are functionally separable modes of control." This example is easily read, "moving and holding [continue to be] functionally separable". Another: "...active reaching and holding still in the same workspace, " could be "...active reaching and holding [are] still in the same workspace." Simply "holding", "posture" or "posture maintenance" would all be better options.

Response 1.4. Thank you for your suggestion. Following your comment, we have abbreviated this term to simply “holding”, both on the title and throughout the text.

Reviewer #2 (Public Review):

Summary: 

Here the authors address the idea that postural and movement control are differentially impacted with stroke. Specifically, they examined whether resting postural forces influenced several metrics of sensorimotor control (e.g., initial reach angle, maximum lateral hand deviation following a perturbation, etc.) during movement or posture. The authors found that resting postural forces influenced control only following the posture perturbation for the paretic arm of stroke patients, but not during movement. They also found that resting postural forces were greater when the arm was unsupported, which correlated with abnormal synergies (as assessed by the Fugl-Meyer). The authors suggest that these findings can be explained by the idea that the neural circuitry associated with posture is relatively more impacted by stroke than the neural circuitry associated with movement. They also propose a conceptual model that differentially weights the reticulospinal tract (RST) and corticospinal tract (CST) to explain greater relative impairments with posture control relative to movement control, due to abnormal synergies, in those with stroke.

Strengths: 

The strength of the paper is that they clearly demonstrate with the posture task (i.e., active holding against a load) that the resting postural forces influence subsequent control (i.e., the path to stabilize, time to stabilize, max. deviation) following a sudden perturbation (i.e., suddenly removal of the load). Further, they can explain their findings with a conceptual model, which is depicted in Figure 9. 

Weaknesses: 

Current weaknesses and potential concerns relate to i) not displaying or reporting the results of healthy controls and non-paretic arm in Experiment 2 and ii) large differences in force perturbation waveforms between movement (sudden onset) and posture (sudden release), which could potentially influence the results and or interpretation. 

Response 2.0. Thank you for your assessment, and for pointing out ways to improve our paper. We address the weakness and potential concerns in detail below.

Larger concerns

(1) Additional analyses to further support the interpretation. In Experiment 1 the authors present the results for the paretic arm, non-paretic arm, and controls. However, in Experiment 2 for several key analyses, they only report summary statistics for the paretic arm (Figure 5D-I; Figure 6D-E; Figure 7F). It is understood that the controls have much smaller resting postural force biases, but they are still present (Figure 3B). It would strengthen the position of the paper to show that controls and the non-paretic arm are not influenced by resting postural force biases during movement and particularly during posture, while acknowledging the caveat that the resting positional forces are smaller in these groups. It is recommended that the authors report and display the results shown in Figure 5D-I; Figure 6D-E; Figure 7F for the controls and non-paretic arm. If these results are all null, the authors could alternatively place these results in an additional supplementary. 

Response 2.1a. Thank you for your recommendations. We agree both on the value of these analyses and the caveat associated with them: these resting postural force biases are substantially smaller for the non-paretic and control data (for example, the magnitude of resting biases in the supported condition is 2.8±0.4N for the paretic data, but only 1.8±0.4N and 1.3±0.2N for the non-paretic and control data, respectively; the difference is even greater in the unsupported condition, though this is not the one being compared to Experiment 2).

We now conduct a comprehensive series of supplementary analyses, including the examination of non-paretic and control data for all three components of Experiment 2 (unperturbed reaches; pulse perturbations; and active holding control). These are mentioned in the Results (lines 422-424, 512513, and 574-574 of the revised manuscript) and illustrated in the supplementary materials: Supplementary Figures S5-1, S6-1, and S7-1 contain the main analyses (comparisons of instances with the most extreme resting biases for each individual) for the unperturbed reach analysis, pulse perturbation analysis, and active holding control analysis, respectively.

We find that non-paretic and control data do not display effects of resting biases upon unperturbed reaching control (Figure S5-1) or control against a pulse perturbation early during movement (Figure S6-1) – as is the case with the paretic data. Non-paretic and control data do not display evidence of influence of their resting force biases upon active holding control either (Figure S7-1), unlike the paretic data. For the non-paretic data, however, these influences are nominally towards the same direction as in the paretic data. Given that resting biases are substantially weaker for the non-paretic case, it is possible a similar relationship exists but requires increased statistical power to discern. Moreover, it is possible that the effect of resting biases is non-linear, with small biases effectively kept under check so that their impact upon active holding control is even less than a linearly scaled version of the impact of the stronger, paretic-side biases. This can be the subject of future work.

Please also note that, following your recommendation (Recommendations to the Authors, point 2.1), we have conducted secondary analyses which estimate sensitivity to resting bias using all datapoints, validating our main analyses; these analyses were also performed for control and non-paretic data, with similar results (Response 2.A.1).

Further, the results could be further boosted by reporting/displaying additional analyses. In Figure 6D the authors performed a correlation analysis. Can they also display the same analysis for initial deviation and endpoint deviation for the data shown in Figure 5D-F & 5G-I, as well for 7F for the path to stabilization, time to stabilization, and max deviation? This will also create consistency in the analyses performed for each dependent variable across the paper.

Response 2.1b. Here, we set to test whether resting biases affect movement. It is best to do this using a within-individual comparison design, rather than using across-individual correlations: while correlation analyses can in general be informative, they obscure within-individual effects which are the main comparisons of interest in our study. Consider a participant with strong resting bias towards one direction, tested on opposing perturbations; averaging these responses for each individual would mostly cancel out any effects of resting biases. Even if we were to align responses to the direction of the perturbation before averaging, the power of correlation analyses may be diluted by inter-individual differences in other factors, such as overall stiffness.

Thus, our analysis design was instead focused on examining the differential effects of resting posture biases within each individual’s data. We compared the most extreme opposing/aligned or clockwise/counter-clockwise instances within each individual, specifically to assess these differential effects. In our revised version, we have further reinforced these analyses to include all data rather than the most extreme instances (see response 2.A.1.a to the Reviewer’s recommendation to the authors) where we performed correlations of within-individual resting posture vs. the corresponding dependent variables and compared the resulting slopes. 

The across-individual correlation analyses add little to that for the reasons we outlined above. At the same time, it is possible they can be helpful in e.g. illustrating across-individual variability. We thus now include across-individual correlation analyses for all dependent variables, but, given their limited value, only in the supplementary material. This also means that, for consistency, we moved the correlation analysis in Figure 6 to the corresponding supplementary figure as well (Figure S6-3).

In addition, following the Reviewer’s comment about consistency in the analyses performed for each dependent variable across the paper, we added within-individual comparisons for settling time following the pulse perturbations (Figure 6D, right).

(2) Inconsistency in perturbations that would differentially impact muscle and limb states during movement and posture. It is well known that differences in muscle state (activation / preloaded, muscle fiber length and velocity) and limb state (position and velocity) impact sensorimotor control (Pruszynski, J. A., & Scott, S. H. (2012). Experimental brain research, 218, 341-359.). Of course, it is appreciated that it is not possible to completely control all states when comparing movement and posture (i.e., muscle and limb velocity). However, using different perturbations differentially impacts muscle and limb states. Within this paper, the authors used very different force waveforms for movement perturbations (i.e., 12 N peak, bell-shaped, 0.7ms duration -> sudden force onset to push the limb; Figure 6A) and posture perturbations (i.e., 6N, 2s ramp up -> 3s hold -> sudden force release that resulted in limb movement; Figure 4) that would differentially impact muscle (and limb) states. Preloaded muscle (as in the posture perturbation) has a very different response compared to muscle that has little preload (as in the movement perturbations, where muscles that would resist a sudden lateral perturbation would likely be less activated since they are not contributing to the forward movement). Would the results hold if the same perturbation had been used for both posture and movement (e.g., 12 N pulse for both experiments)? It is recommended that the authors comment and discuss in the paper why they chose different perturbations and how that might impact the results. 

Response 2.2a. We agree that it can be impossible to completely control all states when comparing movement and posture. We would also like to stress that these perturbations were not designed so that responses are directly compared to each other (though of course there is an indirect comparison in the sense that we show influence of biases in one type of perturbation but not the other). Instead, Experiment 2 tried to implement a probe optimized for each motor control modality (moving vs. holding). However, the Reviewer has a point that the potential impact of differences between the perturbations is important to discuss in the paper.

The Reviewer points out two potentially interesting differences between the two perturbations. First, the magnitude (6N for the posture perturbation vs. 12N for the pulse perturbation); second, the presence of background load in the posture perturbation, in contrast to the pulse perturbation.

For the movement perturbation, we used a 12-N, 70ms pulse. This perturbation and scaled versions have been tested before in both control and patient populations (Smith et al., 2000; Fine and Thoroughman, 2006). For the holding perturbation, we used a background load to ensure that active holding control is engaged, and the duration of the probe (holding for about 5s) made using a stronger perturbation impractical –maintaining a background load at, say, 12N for that long could lead to increased fatigue.

The question raised by the Reviewer, whether the findings would be the same if the same, 12-N pulse were used to probe both moving and holding control, is interesting to investigate. We would expect the same qualitative findings (i.e. there would still be a connection between resting posture and active holding control when the latter were probed with a 12N pulse). Recent work provides more specific insight into what to expect. Our posture perturbation task is similar to the Unload Task in (Lowrey et al., 2019), whereby a background torque is released, whereas our pulse perturbation is more similar to their Load Task, whereby a torque is imposed against no background load (though it is a step perturbation rather than a pulse). Lowrey et al., 2019 find that their Unload task is harder than the Load task, with 2x the fraction of patient trials classified as failed (with failure defined as task performance being outside of the 95% confidence interval for controls), though there are still clear effects for the Load task. 

This suggests that the potential effects of using a pulse-like perturbation to probe posture control would likely be weaker in magnitude, all other things being equal. At the same time, however, the Load and Unload tasks in Lowrey et al., 2019 were perturbations of the same magnitude; it is thus also likely that the reduction in effect would be mitigated, or reversed, by the fact that we would be using a 12N instead of a 6N perturbation.

A relevant consequence of the Lowrey et al., 2019 findings is that the Unload paradigm is superior in its ability to detect impairment in static, posture perturbations, and thus provides a better signal to detect potential relationships with resting posture biases. This is not surprising, as a background load further engages the control of active holding, which what we were trying to probe in the first place.

But then why not use the same paradigm (preloading and release) for movement? There are two main reasons. First, requiring a background load throughout the experiment is unfeasible due to fatigue. Second, for the holding perturbation, we wanted to ensure that the postural control system is meaningfully engaged when the perturbation hits, hence we picked the background load. Were we to impose the same during moving – i.e. impose a lateral background load on the movement - we could be engaging posture control on top of movement control. This preloading would reduce the degree to which the pulse probe isolates movement control, and lead to intrusion of the posture control system in the movement task by design. This relates to what the Reviewer proposes in the comment below: preloading may result in postural biases i.e. engage posture control; see below where we argue this interpretation is within the scope of our conceptual model rather a counter to it.

We now explain the rationale behind our perturbation design in the Methods section (lines 211-220).

Relatedly, an alternative interpretation of the results is that preloading muscle for stroke patients, whether by supporting the weight of one's arm (experiment 1) or statically resisting a load prior to force release (experiment 2), leads to a greater postural force bias that can subsequently influence control. It is recommended that the authors comment on this. 

Response 2.2b. We find this interpretation valid, but we do not see how it meaningfully differs from the framework we propose. We already state that the RST may be tailored for both posture/holding control and the production of large forces (which would include muscle preloading):

“Thus, the accumulated evidence suggests that the RST could control posture and large force production in the upper limb.“ (lines 698-699 in the current version)

“the RST, in contrast, is weighted more towards slower postural control and generation of large isometric forces” (lines 724-726 in the current version)

And, we discuss other conditions where the RST is involved in large force production, such as power grip, and how these interact with the role of the RST in posture/holding control (lines 758-768 in the current version).

To better explain our model, we now provide the two examples mentioned by the reviewer along with our description of the proposed role for the RST (lines 726-727):

“…the RST, in contrast, is weighted more towards slower postural control and generation of large isometric forces (such as vertical forces for arm support, or horizontal forces for holding the arm still against a background load like in our posture/release perturbation trials).”

We note, however, that we find resting posture abnormalities even in the presence of arm support, suggesting the involvement of the RST in holding control even when the forces involved (and the need to preload the muscle) are small.

Reviewer #3 (Public Review): 

The authors attempt to dissociate differences in resting vs active vs perturbed movement biases in people with motor deficits resulting from stroke. The analysis of movement utilizes techniques that are similar to previous motor control in both humans and non-human primates, to assess impairments related to sensorimotor injuries. In this regard, the authors provide additional support to the extensive literature describing movement abnormalities in patients with hemiparesis both at rest and during active movement. The authors describe their intention to separate out the contribution of holding still at a position vs active movement as a demonstration that these two aspects of motor control are controlled by two separate control regimes.

Strengths: 

(1) The authors utilize a device that is the same or similar to devices previously used to investigate motor control of movement in normal and impaired conditions in humans and non-human primates. This allows comparisons to existing motor control studies. 

(2) Experiment 1 demonstrates resting flexion biases both in supported and unsupported forelimb conditions. These biases show a correlated relationship with FM-UE scores, suggesting that the degree of motor impairment and the degree of resting bias are related.

(3) The stroke patient participant population had a wide range of both levels of impairment and time since stroke, including both sub-acute and chronic cases allowing the results to be compared across impairment levels.

The authors describe several results from their study: 1. Postural biases were systematically toward the body (flexion) and increased with distance from the body (when the arm was more extended) and were stronger when the arm was unsupported. 2. These postural biases were correlated with FM-UE score. 3. They found no evidence of postural biases impacting movement, even when that movement was perturbed. 4. When holding a position at the end of a movement, if the position was perturbed opposite of the direction of bias, movement back to the target was improved compared to the perturbation in the direction of bias. Taken together, the authors suggest that there are at least two separate motor controls for tasks at rest versus with motion. Further, the authors propose that these results indicate that there is an imbalance between cortical control of movement (through the corticospinal tracts) and postural control (through the reticulospinal tract).

Response 3.1. Thank you for pointing out some of the strengths of our work and summarizing our findings. A minor clarification we would like to make, related to (3), is that, while our study did enroll two patients towards the end of the subacute stage (2-3 months), the rest of the population were at the chronic stage, at one year and beyond. We thus find it very unlikely that time after stroke was the primary driver of differences in impairment in the population we studied.

There are several weaknesses related to the interpretation of the results:

In Experiment 1, the participants are instructed to keep their limbs in a passive position after being moved. The authors show that, in the impaired limb, these resting biases are significantly higher when the limb is unsupported and increase when the arm is moved to a more extended position.

When supported by the air sled, the arm is in a purely passive position, not requiring the same antigravity response so will have less RST but also less CST involvement. While the unsupported task invokes more involvement of the reticulospinal tract (RST), it likely also has significantly higher CST involvement due to the increased difficulty and novelty of the task.

If there were an imbalance in CST regulating RST as proposed by the authors, the bias should be higher in the supported condition as there should be relatively less CST activation/involvement/ modulation leading to less moderating input onto the RST and introducing postural biases. In the unsupported condition, there is likely more CST involvement, potentially leading to an increased modulatory effect on RST. If the proportion of CST involvement significantly outweighs the RST activation in the unsupported task, then it isn't obvious that there is a clear differentiation of motor control. As the degree of resting force bias and FM-UE score are correlated, an argument could be made that they are both measuring the impairment of the CST unrelated to any RST output. If it is purely the balance of CST integrity compared to RST, then the degree of bias should have been the same in both conditions. In this idea of controller vs modulator, it is unclear when this switch occurs or how to weigh individual contributions of CST vs. extrapyramidal tracts. Further, it isn't clear why less modulation on the RST would lead only to abnormal flexion.

Response 3.2. Our model posits two mechanisms by which CST impairment would lead to increased RST involvement. The first – which is the one discussed by the Reviewer here - is a direct one, whereby weaker modulation of the RST by the CST leads to increased RST involvement. The second is an indirect one, whereby the incapacity of CST to drive sufficient motor output to deal with tasks eventually leads to increased RST drive.

The reviewer suggests it is likely that the unsupported task demands increased activation through both the CST and the RST. If that were the case, however, it would exaggerate the effects of CST/RST imbalance after stroke compared to healthy motor control: if task conditions (lack of support) required higher CST involvement, then CST damage would have an even larger effect. In turn, this would lead to even higher RST involvement and further diminishing the ability of CST to moderate RST. Thus, RST-driven biases would be higher in the unsupported condition.

And, given that the CST itself is damaged and has to deal with an even-increased RST activation, we would not expect that the proportion of CST involvement would outweigh RST activation, but the opposite. In fact, a series of relatively recent findings suggest just this. For example,

• Zaaimi et al., 2012  showed that unilateral CST lesions in monkeys lead to significant increases in the excitability of the contralesional RST (Zaaimi et al., 2012). Interestingly, this effect was present in flexors but not extensors, potentially explaining why less modulation and/or overactivation of the RST would primarily lead to abnormal flexion. 

• McPherson et al. (further discussed in point 2.A.23, by Reviewer 2 – Recommendations to the Authors) showed that, after stroke, contralesional activity (which would include the ipsilateral RST) increases relative to ipsilesional activity (which would include the contralateral CST)

(McPherson et al., 2018). The same study also provides evidence that FM-UE may primarily reflect RST-driven impairment. The ipsilateral(RST)/contralateral(CST) balance, expressed as a laterality index, correlated with FM-UE, with lower FM-UE for indices indicating higher RST involvement. (Interestingly, the slope of this relationship was steeper when the laterality of brain activation patterns was examined under tasks with less arm support, mirroring the steeper FM-UE vs resting bias slope when arm support is absent, as shown in our Figure 8).

• Wilkins et al., 2020 (Wilkins et al., 2020) found that providing less support (i.e. requiring increased shoulder abduction) increases ipsilateral activation (representing RST) relative to contralateral activation (representing CST).

This resting bias could be explained by an imbalance in the activation of flexors vs extensors which follows the results that this bias is larger as the arm is extended further, and/or in a disconnect in sensory integration that is overcome during active movement. Neither would necessitate separate motor control for holding vs active movement. 

Response 3.3. We do not think that either of these points necessarily argue against our model. First, the resting biases we observe are clearly pointed towards increased flexion, and can thus be seen as the outcome of an imbalance in the activation of flexors vs. extensors at rest. This imbalance between flexors/extensors can also be explained by the CST/RST imbalance posited by our conceptual model: in their study of CST lesions in the monkey, Zaaimi et al., 2012 found increased RST activation for flexors but not extensors, suggesting that RST over-involvement may specifically lead to flexor abnormalities (Zaaimi et al., 2012). Second, overcoming a disconnect in sensory integration may be one way the motor system switches between separate controllers; how this switch happens is not examined by our conceptual model.

In Experiment 2, the participants are actively moving to and holding at targets for all trials while being supported by the air sled. Even with the support, the paretic participants all showed start- and endpoint force biases around the movement despite not showing systematic deviations in force direction during active movement start or stop. There could be several factors that limit systematic deviations in force direction. The most obvious is that the measured biases are significantly higher when the limb is unsupported and by testing with a supported limb the authors are artificially limiting any effect of the bias.

Response 3.4. We do expect, in line with what the reviewer suggests, that any potential effects would be stronger in the unsupported condition. The decision to test active motor control with arm support was done as running the same Experiment 2 would pose challenges, particularly with our most impaired patients, given the duration of Experiment 2 (~2 hours, about 1 hour with each arm) and the expected fatigue that would ensue.

However, a key characteristic of our comparisons is that we are comparing Experiment 2 active control data under arm support, against Experiment 1 resting bias data also under arm support. While Experiment 1 measured biases without arm support as well, these are not used for this comparison. And, while resting biases are weaker with arm support, they are still clear and significant; yet they do not lead to detectable changes in active movement.

At the same time, we do not rule out that, if we were to repeat Experiment 2 without arm support, we could find some systematic deviation in the direction of resting bias in movement control. Our conceptual model, in fact, suggests that this may be the case, as we described in lines 618-620 of our original manuscript. The idea here is that, when arm support is not provided, the increased strength requirements lead to increased drive through the RST, to the point that posture control (and its abnormalities) spills into movement control (Figure 9). We now better clarify this position in our Discussion (lines 744-750):

“The interesting implication of this conceptual model is that synergies are in fact postural abnormalities that spill over into active movement when the CST can no longer modulate the increased RST activation that occurs when weight support is removed (i.e. resting biases may influence active reaching in absence of weight support). Supporting this idea, a study found increased ipsilateral activity (which primarily represents activation via the descending ipsilateral RST (Zaaimi et al., 2012)) when the paretic arm had reduced support compared to full support (McPherson et al., 2018).”

It is also possible that significant adaptation or plasticity with the CST or rubrospinal tracts could give rise to motor output that already accounts for any intrinsic resting bias.  

Response 3.5. This kind of adaptation – regardless of the tracts potentially involved – is an issue we examined in our experiment. As we talk about in our Results (lines 458-460 in the updated manuscript), with most of our patient population in the chronic stage, it could be likely that their motor system adapted to those biases to the point that movement planning took them into account, thereby limiting their effect. This motivated us to examine responses to unpredictable perturbations during movement (Figure 6) where we still find lack of an obvious effect of resting biases upon reaching control. We thus believe that our findings are not explained by this kind of adaptation, though we agree it would be of great interest for future work to compare resting biases and reaching control in acute vs. chronic stroke populations to examine the degree to which stroke patients adapt to these biases as they recover.

In any case, the results from the reaching phase of Experiment 2 do not definitively show that directional biases are not present during active reaching, just that the authors were unable to detect them with their design. The authors do acknowledge the limitations in this design (a 2D constrained task) in explaining motor impairment in 3D unconstrained tasks. 

Response 3.6. It is, of course, an inherent limitation of a negative finding is that it cannot be proven. What we show here is that, there is no hint of intrusion of resting posture abnormalities upon active movement in spite of these resting posture abnormalities being substantial and clearly demonstrated even under arm support. To allow for the maximum bandwidth to detect any such effects, we specifically chose to compare the most extreme instances (resting bias-wise) for each individual, and yet we did not find any relationship between biases and active reaching.

This suggests that, even if these biases could be in some form present during active movement, their effect would be minimal and thus limited in meaningfully explaining post-stroke impairment in active movement under arm support.

Note that, as we already discuss, our conceptual model (Figure 9) suggests that the degree to which directional biases would be present in active reaching may be influenced by arm support (or the specific movements examined – hence our limitation in not examining 3D movement). Thus we do not claim that this independence is absolute. Examples include the last line of the passage quoted right above, and the summary statement of our Discussion quoted below (lines 639-641):

“…which raises the possibility that the observed dissociation of movement and posture control for planar weight-supported movements may break down for unsupported 3D arm movements.”

Finally, we now more explicitly acknowledge that abnormal resting biases may influence active movement in the absence of arm support (see Response 3.4).

It would have been useful, in Experiment 2, to use FM-UE scores (and time from injury) as a factor to determine the relationship between movement and rest biases. Using a GLMM would have allowed a similar comparison to Experiment 1 of how impairment level is related to static perturbation responses. While not a surrogate for imaging tractography data showing a degree of CST involvement in stroke, FM-UE may serve as an appropriate proxy so that this perturbation at hold responses may be put into context relative to impairment.

Response 3.7. Here the Reviewer suggests we use FM-UE scores as a proxy for CST integrity. We do not think this analysis would be particularly helpful in our case for a number of reasons:

First, while FM-UE is a general measure of post-stroke impairment, it was designed to track - among other things - the emergence and resolution of abnormal synergies, a sign assumed to result from abnormally high RST outflow (McPherson et al., 2018; McPherson and Dewald, 2022). In line with this, the FM-UE scales with EMG-based measures of synergy abnormality (Bourbonnais et al., 1989). Impairments in dexterity, a sign associated with damage to the CST (Lawrence and Kuypers, 1968; Porter and Lemon, 1995; Duque et al., 2003), dissociate with synergy abnormalities when compared under arm support as we do here (Levin, 1996; Hadjiosif et al., 2022). This means that FM-UE would be a stronger proxy for RST activity and thus not a direct proxy for CST integrity particularly when one wants to dissociate RST-specific vs. CST-specific abnormalities. In fact, as we discuss in Response 3.2 above, there is a number of studies supporting this idea: for example, Zaaimi et al., 2012 show that relative RST activation – the balance between ipsilateral excitability, primarily reflecting RST, and contralateral excitability, primarily reflecting the CST, scales with FM-UE (Zaaimi et al., 2012).

Second, this kind of analysis would obscure within-individual effects, since FM-UE scores are, of course, assigned to each individual. This is the same issue as doing across-individual correlation analyses in general (see response 2.1b).Strong resting force bias would have opposite effects on opposing perturbations, averaging across subjects would occlude these effects.

Third, while FM-UE is a good measure of synergy abnormality, weakness alone could also give an abnormal FM-UE (Avni et al., 2024).

The Reviewer also suggests we use time from injury for this analysis. Time from injury can indeed potentially be an important factor. However, this analysis would not be appropriate for our dataset, since the effective variation in recovery stage within our population is limited: our sample is essentially chronic (only two patients were examined within the subacute stage – at 2 and 3 months after stroke - with everybody else examined more than a year after stroke) with the “positive” elements of their phenotype (and FM-UE itself) essentially plateaued (Twitchell, 1951; Cortes et al., 2017). We thus would not expect to see any meaningful effects of time from injury within our population. It would be an excellent question for future work to investigate both resting biases and their relationship to reaching in acute/subacute patients, and examine whether the trajectory of resting biases (both emergence and abatement due to recovery) follows the one for abnormal synergies.

It is not clear that even in the static perturbation trials that the hold (and subsequent move from perturbation) is being driven by reticulospinal projections. Given a task where ~20% of the trials are going to be perturbed, there is likely a significant amount of anticipatory or preparatory signaling from the CST. How does this balance with any proposed contribution that the RST may have with increased grip?

Response 3.8. We included our response to this as part of Response 3.2. In brief, while we cannot rule out that these tasks may recruit increased CST signaling, this would tend to increase, rather than reduce, the effects of post-stroke impairment: the requirement for increased signaling from a CST that is damaged would magnify the effects of this damage, in turn leading to increased recruitment of other tracts, such as the RST.

In general, the weakness of the interpretation of the results with respect to the CST/RST framework is that it is necessary to ascribe relative contributions of different tracts to different phases of movement and hold using limited or indirect measures. Barring any quantification of this data during these tasks, different investigators are likely to assess these contributions in different ways and proportions limiting the framework's utility.

Response 3.9. We believe that our Reponses 3.2-3.6 put our findings in fair perspective, and the edits undertaken based on the Reviewer’s comments have clarified our position as to how the dissociation between holding and moving control may break down. We do agree, however, that our framework would be strengthened by the use of direct measures of CST/RST connectivity in future research. We present our conceptual model as a comprehensive explanation of our findings and how they blend with current hypotheses regarding the role of these two tracts in motor control after stroke.  As such, it provides a blueprint towards future research that more directly measures or modulates CST and RST involvement, using tools such as tractography or non-invasive brain stimulation.

Recommendations for the authors:   

Reviewer #1 (Recommendations For The Authors):

L226 “…of this issue, we repeated the analysis of Figure 7F (a) by excluding these four patients…”.  Should this be three, based on the previous sentence? 

Response 1.A.1. Thank you for pointing this typo, which is now corrected. The analysis in question (Figure S1 in the original submission, now re-numbered as Figure S7-4), excluded the three patients mentioned in the previous sentence.

L254 “…the hand was held in a more distal position. The postural force biases were strongest when…”  Could this be "extended" rather than distal? See my later comment about the inadequate description of targets.

Response 1.A.2. The reviewer is correct that, the arm will tend to be more extended in the distal targets. However, since these positions were defined in extrinsic coordinates, we think the terms distal/proximal are also appropriate. In either case, we now clarify these definitions in the text (see Response 1.A.3 below).

L263 “…contained both distal and proximal targets, and, importantly, they were also the movement…”.  Distal/proximal targets were never described as part of the task. 

Response 1.A.3. We improved our description by (i) changing the wording above to “represented positions both distal and proximal to the body,”, (ii) doing the same in our Methods (line 175) and (iii) indicating distal/proximal targets in Figure 3A (bottom right of panel A).

L378 “…the pulse perturbation. We hypothesized that, should resting postural forces play a role, they…”  L379 “…would tend to reduce the effect of the pulse if they were in the opposite direction, and…”  Not really obvious why. A reduction in the displacement caused by a force pulse might be caused by different stiffness or viscosity, but not by a linear, time-invariant force bias. This situation is different from that of "moving the arm through a high-postural bias area vs. a low-postural bias area" where it would encounter time- (actually spatially) varying forces and varying amounts of displacement. Clarify the logic if this is a critical point.

Response 1.A.4. We thank the Reviewer for highlighting this point of potential confusion. We now clarify that these postural bias forces are neuromuscular in origin (Kanade-Mehta et al., 2023), and likely result from an expression of abnormal synergy, at least under static conditions. In this case, we hypothesized that force pulses acting against the gradient of the postural bias field would act to stretch the already active muscles, which would lead to a further increase in postural resistance due to inherent length-tension properties of active muscle. By contrast, force pulses acting along the gradient of the postural bias field would act to shorten the same active muscles, which would lead to a reduction in postural resistance. The data did not support this in the case of force pulses imposed during movement. We note, however, that similar effects would affect responses to static perturbations as well, wherein we do find an effect of resting biases. We now better explain this reasoning (lines 479482).

L466 “resting postural force). In short, our perturbations revealed that resting flexor biases switched  467 on after movement was over, providing evidence for separate control between moving” and 

L468 “holding still.”

I do not think the authors have presented clear evidence that forces, "switch on", implying the switch to a different controller which they posit. This could as easily be a nonlinear or time-varying property of a single controller (admittedly, the latter possibility overlaps broadly with their idea of distinct, interacting controllers). An example that the authors are certainly aware of is that of muscle "thixotropy" a purely peripheral mechanism due to the dynamics of crossbridge cycling that causes resting muscle to be stiffer than moving muscle, changing with a time constant of ~1-2 seconds. Neither this particular example nor changing levels of contraction (more likely during the unpredictable force perturbations) would be in the direction to explain the main observation here -- a point perhaps worth making, together with the stretch reflex comments. 

Response 1.A.5. Thank you for this perspective. Indeed, it might be that “switching on” represents a shift along a nonlinear property of the same controller: in the extreme, if this nonlinearity is a step (on/off) function, this single controller would be functionally identical to two separate controllers. We thus cannot tell if these controllers are distinct in the strict sense. What we argue here is that, no matter the underlying controller architecture - two distinct controllers or two distinct modes of the same controller - is that the control of reaching vs. holding can be functionally separable even after stroke. In line with this idea, we used a more nuanced phrasing (e.g. “separable functional modes for moving vs. holding”) throughout our manuscript, and we have now edited out a mention of “separate controllers” to be consistent with this.

Moreover, thank you for pointing out the example of thixotropy, showing how peripheral mechanisms could interact with central control. As you point out, this effect would not explain the main observation here: in fact, if stiffness were substantially higher during rest or holding (instead of moving) that would reduce the impact of the static perturbation, making it harder to detect any effects of resting biases compared to the moving perturbation case.

L480 “…during movement (Sukal et al., 2007). Yet, Experiment 2 found no relationship between resting…” L481”… postural force biases and active movement control. To further investigate this apparent…”  The methods of the two studies seem fairly similar, but this question warrants a more careful comparison. How did the size of the two workspaces compare? What about the magnitude of the exerted forces? The movement condition in this study was done with the limb entirely supported. Under that condition, the Sukal study also found fairly small effects of the range of motion.

Response 1.A.6. Sukal et al., 2007 did not directly measure exerted forces, but instead compared the active range of motion under different loading conditions. They used the extent of reach area to quantify the effect of abnormal synergies, with a more extended active range of motion signifying reduced effect of abnormal synergies. As the Reviewer points out, Sukal et al. found fairly small effects of synergies upon the range of motion when arm support was provided (the reach area for the paretic side was found to be about 85% of the nonparetic side under full arm support, though they were statistically significantly different, Figure 5 of their paper). They found increasing effect of synergies as arm support was reduced: on average, the reach area when participants had to fully support the arm was less than 50% the reach area when full arm support was given (comparing the 0% vs. 100% active support conditions [i.e. 100% vs. 0% external support] in their Figure 5). As we discuss in our paper, this effect of arm support upon synergy mirrors the one we found for resting postures.

To compare our workspace with the one in Sukal et al., we overlaid our workspace (the array of positions for which the posture biases were measured, for a typical participant from Experiment 1) on the one they used as shown in their Figure 4. Note that their figure only shows an example participant, and thus our ability to compare is limited by the fact that each participant can vary widely in terms of their impairment, and assumptions had to be made to prepare this overlay (e.g. that (0,0) represents the position of the right acromion point). 

For this example, and our assumptions, our workspace was smaller, with the main points of interest (red dots, the movement start/end points used for Experiment 2) within the Sukal et al. workspace. That our workspace is smaller is not surprising, given that the area in Sukal et al. represents the limit of what can be reached, and thus motor control *has* to be examined in a subset of that area.

Author response image 1.

Comparing the two study methodologies, however, suggests an advantage of measuring resting biases in terms of sensitivity and granularity: first, resting biases can be clearly detected even under arm support (something we point out in our Discussion, lines 715-717); second, they can measure abnormalities at any point in the workspace, rather than a binary within/without the reach area. The resting bias approach may thus be a more potent tool to probe the shared bias/synergy mechanisms we propose here.

Figure 2 

Needs color code. 

The red dots could be bigger.

Response 1.A.7. We have increased the size of the red dots and added a color code to explain the levels illustrated by the contours. We also expanded our caption to better explain this illustration.

Figure 3

Labeling is confusing. Drop the colored words (from both A and B), and stick to the color legend. Consider using open and filled symbols (and bars) to represent arm support or lack thereof. The different colored ovals are very hard to distinguish.

Response 1.A.8. We find these recommendations improve the readability of Figure 3 and we have thus adopted them - see updated Figure 3.

Figure 4

Not terribly necessary.  

Response 1.A.9. While this figure is indeed redundant based our descriptions in the text, we kept it as we believe it can be useful in clarifying the different stages of movement we examine.

Figure 5 

Tiny blue and green arrows are impossible to distinguish. 

Although the general idea is clear, E and H are not terribly intuitive.  Add distance scale bars for D-I. 

Response 1.A.10. For improved contrast, we now use red and blue (also in line with comment below regarding Figure 7), and switched to brighter colors in general. To make E and H more intuitive and easier to follow, we expanded the on-panel legend. Thank you for pointing out that distance scale bars are missing; we have now added them (panels EFHI).

Figure 6 

Panel E inset is too small. 

Response 1.A.11. We have now moved the inset to the right and enlarged it.

Figure 7 

Green and blue colors are not good. 

Response 1.A.12. For improved contrast, we now use red and blue.

Figure 8 

Delete or move to supplement? 

Response 1.A.13. We respectfully disagree. While the relationships on these data are also captured by the ANOVA, we believe these scatter plots offer a better overview of the relationships between force biases and FM-UE across different conditions.

Really minor

L113 “…participants' lower arm was supported using a custom-made air-sled (Figure 1C). Above the  participant's…” 

Response 1.A.14. We put the apostrophe after the s so to refer to participants in general (plural).

L117 ”…subject-produced forces on the handle were recorder using a 6-axis force transducer.”  recorded 

Response 1.A.14. Thank you for pointing out this error which we have now corrected.

L136 “…2013), Experiment 1 assessed resting postural forces by passively moving participants to>…”  The experiment did not move the participant. 

Response 1.A.15. We now fix this issue: “by having the robot passively move…”

L248 “…experiment blocks: two with each arm, with or without arm weight support (provided by an air experimental…”

Response 1.A.16. We have now corrected this.

L364 “…responses to mid-movement perturbations. In 1/3 of randomly selected reaching movements…”  Obviously, you mean 1/3 of all movements: "One-third of the reaching movements were chosen randomly"  

Response 1.A.17. We now clarify: “In 1/3 of reaching movements in Experiment 2, chosen randomly”. Also please note our response to Reviewer 2, point 10: we now report the exact number of trials for which each kind of perturbation was present.

L609 “Damage to the CST after stroke reduces its moderating influence upon the RST (Figure 9,…”  "its" refers to the subject, "Damage", not "CST".

Response 1.A.18. We have changed this to “Post-stroke damage to the CST reduces the moderating influence the CST has upon the RST”.

Reviewer #2 (Recommendations For The Authors):

(1) Throughout, the authors cleverly selected the most opposed and most aligned resting postural force biases to perform a within-subject analysis. However, this approach excludes a lot of data. The authors could perform an additional within-subject analysis. For each participant they could correlate lateral resting posture force bias to each dependent variable, utilizing all the trials of a participant. 

Response 2.A.1a. Thank you for your appreciating our analysis design, and suggesting additional analyses. We focused our within-subject analysis design on the most extreme instances, as we believe that this approach would offer the best opportunity to detect any potential effects of resting biases. We reasoned that, since resting biases tend to be relatively small for most locations in the workspace, taking all biases into account would inject a disproportionate amount of noise in our analysis, which would in turn diminish our ability to detect any potential relationships. This could be because small biases lead to small effects but also small biases may themselves be more likely to reflect measurement noise in the first place. Note that our study talks about separability of active reaching from resting abnormalities based on lack of relationships between the two. While one cannot definitely prove a negative, it is also important to take the approach that maximizes the ability to detect any such relationship if there were one. We believe taking the most extreme instances fulfills that role.

However, as the Reviewer points out, this approach also excludes a substantial amount of data. We agree that our findings could be further strengthened by exploring additional within-subject analyses that utilize all trials. Thus, following the reviewer’s suggestion, we estimated the sensitivity of each dependent variable to lateral resting posture force bias. Specifically, we estimated the slope of this relationship for each individual (separately for paretic and non-paretic data) using linear regression, and assessed whether the average slope is significant for each group (paretic data, non-paretic data, and control data).

This secondary analysis replicated our main findings: lack of relationship between posture biases and active reaching control (both for unperturbed and perturbed movement), and a significant relationship between posture biases and active holding control. In addition, in line with main point 2.1 by the reviewer, we performed the same analyses for non-paretic and control data. While there are no definitive conclusions to be made for these cases (as was likely, given that the resting force biases are smaller, as also pointed out by the Reviewer in 2.1) these data are worthy of discussion, with potentially interesting insights (for example, there are hints that the connection between resting biases and active holding control is present in the non-paretic arm as well, and may be explored in future research).

We have included these analyses in the supplementary materials, and we point to them in the main text. Specifically:

First, in line with our main analyses in Figure 5, we find no effect (the average slope is insignificant) for start and endpoint biases upon the corresponding reaching angles. This is now mentioned in lines 425-434 of the Results, and illustrated in Figure S5-2. There was a lack of effect for the non-paretic and control data as well.

Second, in line with our main analyses in Figure 6, we find no effect of start biases upon responses to the pulse (Figure S6-2, mentioned in lines 513-517 of the Results). As above, there was no effect of non-paretic or control data either.

And, finally, in line with our main analysis in Figure 7, we find an effect of resting biases upon performance for the static perturbation (Figure S7-2, mentioned in lines 578-586 of the Results). Interestingly, there is a suggestion that resting biases may affect static perturbation responses in the non-paretic data as well based on the relationship between posture bias and maximum deviation, but not the other two metrics. Given the lack of consistency of resting bias effects for all three different dependent variables examined, however, our current data are thus unable to give a definite answer as to whether there is the connection between resting biases and active holding control is also present in the non-paretic side. Our hypothesis is that, since resting abnormalities and their effects are the pathological over-manifestations of mechanisms inherent in the motor system in general, then such a relationship would exist. Answering this question, however, would require an experiment design better tailored to detect relationships in the non-paretic arm, where resting biases are weaker.

We thank the Reviewer for their suggestions and believe that these additional analyses provide a more complete picture of the data, and their consistency with our main results reinforces the message of the paper.

Then, they can report the percentage of participants that display significant correlations separately for the paretic, nonparetic, and control arms. 

Response 2.A.1b. We note that, even in cases where the average slope (across individuals) is significant, the individual slopes themselves are usually not significant, likely due to the large amount of noise for datapoints corresponding to weak resting biases. To further examine this, we performed additional analyses whereby we examined slopes by (a) pooling all participant data together (centered separately for each individual), and then (b) took a further step to normalize each participant’s data not only by centering but by also adjusting by each individual’s variability along each axis (i.e. assess the slope between z-scores of resting bias vs. z-scores of each dependent variable). These two analyses confirmed our finding that resting biases interacted with active motor control, with significant slopes between resting biases and outcome variables. (a) Pooling all data together: path to stabilization: p = 0.032; time to stabilization: p = 1.4x10-5; maximum deviation: p = 0.021. (b) Pooling and normalizing: path to stabilization: p = 0.0013; time to stabilization: p = 8.6x10-6; maximum deviation: p = 0.00056. The latter analysis showed even stronger connection between resting bias and active holding control, probably due to better accounting for differences in the range of resting biases across participants). For simplicity, however, we only provide the across-individual slope comparisons in the paper.

(2) An important aspect of all the analyses is that they rely heavily on estimates of the resting postural force bias. How stable are these resting postural force biases at the individual level? The authors could assess this by reporting within-subject variance for both the magnitude and direction of the resting postural force bias.

Response 2.A.2. Thank you for your suggestion. We now assess the individual-level variance in error across measurements for patients’ paretic data using an ANOVA: the variance that remains after all other factors (same probe location; same arm support condition; same participant) are taken into account. We found that individual level measurement variance explained a mere 9.0% of total variance for resting bias magnitude. (We note that the same figure was 20.2% for the non-paretic data, in line with the weaker average biases which would be more susceptible to noise). We now note this in the Methods, as part of the new subsection “Stability of resting posture bias measurements in Experiment 1” (lines 266-273).

(3) Does resting postural force bias influence hand movement immediately following force release from the postural perturbation? This could be assessed before any volitional responses by examining the velocity of the hand during the first 50 ms following the postural perturbation.

Response 2.A.3. The influence seems fairly rapid, within the first 100ms as shown to the right. Here we plot hand deviation in the direction of the perturbation for the most-opposed (red) vs. most-aligned (blue) instances to examine when these curves become different. The bottom plots show the difference between these two, whereas shading indicates SEM (note that these curves are referenced to the average deviation in the last 0.5 s before force release). The rightmost plots zoom in to make it easier to see how responses to the most opposed vs. most aligned instances diverge.

To detect the earliest post-perturbation timepoint for which this effect was significant, we performed paired t-tests at each timestep, and found that the two responses were systematically statistically different 95ms after perturbation onset onwards. For reference, the same method detected a response at 25ms for the most aligned instances and 40ms for the most opposed instances.

We have now added Supplementary Figure S7-4 with short commentary in the Supplementary Materials.

(4) Abstract. lines 7-9. At a glance (and when reading the manuscript linearly) this sentence is unclear. If the paretic arm is compromised across rest and movement, how does that afford the opportunity to address the relationship between reaching, stopping, and stabilizing when all could be impacted? It might be useful to specify that these factors may impacted differently relative to one another with stroke, providing an opportunity to better understand the differences between movement and postural control. 

Response 2.A.4. Thank you for pointing out this issue (also related to Reviewer 1’s point – Response 1.1). We have changed this to more clearly reflect our reasoning and highlight that the issue is that stroke can differentially impact reaching vs. holding, copied below:

“The paretic arm after stroke exhibits different abnormalities during rest vs. movement, providing an opportunity to ask whether control of these behaviors is independently affected in stroke.”

(5) Line 27. It is perhaps more appropriate to say conceptual model than simply 'model'.  

Response 2.A.5. Thank you for your suggestion, which we have adopted throughout the manuscript.

(6) Line 122-125. Figure 1A caption. The authors should specify that resting posture force biases occur when the limb or hand is physically constrained in a specific position. 

Response 2.A.6. Thank you for pointing this out – we have clarified the caption:

“If one were to physically constrain the hand in a position away from the resting posture, the torques involved in each component of the abnormal resting posture translate to a force on the hand (blue arrow);”

(7) Line 147. Why was the order not randomized or counterbalanced? 

Response 2.A.7. We prioritized paretic data, as the primary analyses and comparisons in our paper involved resting posture biases and active movement with the paretic arm. We note that our primary analyses, which rely on paretic-paretic comparisons, would not be affected by paretic vs. non-paretic ordering effects. However, ordering effects could potentially affect comparisons between paretic and non-paretic data. We now note the reasoning behind the absence of counterbalancing, and mention the potential limitation in interpreting paretic to non-paretic comparisons in lines 124-129 of the Methods.

(8) Line 172. 12N is the peak force of the pulse?

Response 2.A.8. The reviewer is correct; we have clarified our description (line 463 in the updated manuscript):

“a 70 ms bell-shaped force pulse which was 12N at its peak”

(9) Line 175. What is a clockwise pulse? Was the force vector rotating in direction over time so that it was always acting orthogonally to the movement, or did it always act leftwards or rightwards?

Response 2.A.9. The force vector was not rotating in direction over time. Here, we used clockwise/counterclockwise to indicate rightwards/leftwards with respect to the ideal movement direction – the line from start position to target (which is what we understand the Reviewer means by “always act rightwards or leftwards”). We have clarified the text to indicate this (lines 193-195):

…was applied by the robot lateral to the ideal movement direction (i.e. the direction formed between the center of the start position and the center of the target) after participants reached 2cm away from the starting position (Smith and Shadmehr, 2005; Fine and Thoroughman, 2006).

(10) Lines 177-182. It might be useful to explicitly mention the frequency of each of the perturbations, just for ease of the reader. 

Response 2.A.10. We have added this information to our Methods (lines 206-210):

Thus, in summary, each 96-movement block consisted of 64 unperturbed movements and 32 movements perturbed with a force pulse (16 clockwise, and 16 counter-clockwise). For 20 out of the 96 movements in each block, the hold period was extended to test the hold perturbation (4 trials for each of the 5 target locations, each one of the 4 trials testing one perturbation direction as shown in Figure 7C).

(11) Line 191. Lines 188-190. It would be useful to see a sample of several of these force traces over time (0-5s) that were used to make the average for a position. That would give insight into the stability of the forces of a participant for one of the postures. These traces could be shown in Figure 2.

Response 2.A.11. Thank you for your suggestion. We have added these panels to Figure 1, (as Figure 2 was already large). Each panel illustrates the three measurements taken at similar positions (closest to midline, distal from the body) and the same condition (paretic arm, with arm support given) for one participant (same participants as in Figure 2). Solid lines indicate the force on the x-axis (positive values indicate forces towards the left), whereas dashed lines indicate the force on the y-axis (positive values indicate forces towards the body). The shaded area indicates the part averaged in order to estimate the resting bias, illustrating how resting biases were relatively stable by the 2s mark. Note that these examples include one trial (blue traces in the third panel) which was rejected following visual inspection as described in Materials and Methods – Data Exclusion Criteria (“trials where forces appeared unstable and/or there was movement during the robot hold period”). We find this helpful as this illustrates (and motivates) one component of our methodology. 

(12) Line 196. Figure 1D (not 1E).  

Response 2.A.12. Thank you for catching this error, which we have now corrected.

(13) Line 215: The authors mentioned similar results. Were there any different results that impacted interpretation? Some evidence of this, similar to and in addition to Supplementary 1, would be helpful. 

Response 2.A.13. We repeated our analyses without these exclusion criteria, with no impact to the interpretation. We now include versions of the main outcome panels from Figures 5, 6, and 7 in the supplementary materials calculated without this outlier exclusion (Figures S5-E, S6-E, and S7-E, respectively). 

(14) Line 231: Perhaps better to explicitly state the furthest three positions are being across as the distal targets for the ANOVA. 

Response 2.A.14. Thank you for your suggestion. We now explicitly clarify this in line 276:

“distal targets [furthest three positions] vs. proximal targets [closest two positions]”

(15) Figure 3B, lines 265. Clearly, these are different, but the authors should report statistics. 

Response 2.A.15. We now report these numbers (lines 339-346 of the revised manuscript, which also include statistics related to bias direction as described in 2.A.17 below).

(16) Figure 2 should have a heat map scale.  

Response 2.A.16. We have now added this (also Response 1.A.7), including an explanation of what the heat map represents in the caption.

(17) Figure 3C: It would be useful to quantify and plot the direction of the resting force bias vector. 

Response 2.A.17. Thank you for your suggestion. We have expanded Figure 3 to include the average direction of the resting force bias vector (note the readjustment of colors following Reviewer 1’s comment: striped bars indicate No Support data, and full bars indicate Support data, with the colors being the same). The direction of the force bias vector, however, may not be very informative in cases where the magnitude is small (and the signal-to-noise ratio is small), whereas averaging the direction of the force bias vector across different positions for one participant may average out systematic variations in this direction across different locations. Nevertheless, the average direction appears generally towards the body (around -90°, or 6 o’clock) even in the non-paretic and control data (though the noise – as suggested by the size of the errorbars – is much higher in the latter cases, especially when the arm is supported). This is a (weak) suggestion that these resting biases may be present, though much subdued, in the nonparetic limb and healthy individuals; further work will be needed to elucidate this.

(18) Line 428. It is not significantly longer compared to controls. Can the authors slightly revise this sentence?

Response 2.A.18. We have revised this sentence (lines 529-532):

Patients showed impaired capacity to resist and recover from this perturbation (the abrupt release of the imposed force). The time to stabilization for the paretic side (0.94±0.05s) was longer compared to the non-paretic side (0.79±0.03s, p = 0.024) and controls (0.78±0.06s, though this was statistically marginal, p = 0.061) as shown in Figure 7E, left.

(19) Line 541. It is unclear how these data support the idea of three distinct controllers. Can the authors please clarify? 

Response 2.A.19. Here, we compared our findings to previous ideas about distinct controllers, and discuss a potential fusion of these ideas with ours. Specifically, we find that holding is distinct from both initial reaching and coming to a stop. Previous work argues that initial reaching and coming to a stop are themselves distinct (Ghez et al., 2007; Jayasinghe et al., 2022). Combining these two sets of arguments, we arrive at the possibility of three distinct controllers. 

(20) It would be useful if the authors provided a definition of synergy, as well as distinguishing between muscle and movement synergies. 

Response 2.A.20. We now provide this in lines 591-594:

Here, “synergies” refer to abnormal co-activation patterns across joints that manifest as the patient tries to move – for example, the elbow involuntarily flexing as the patient tries to abduct their shoulder (Twitchell, 1951; Brunnstrom, 1966). 

(21) Line 592-593. The wording of this sentence could be improved. 

Response 2.A.21. We have switched this sentence to active voice for more clarity:

Thus, while full weight support reduces both resting flexor biases and movement-related flexor synergies, this reduction seems more complete for synergies rather than resting biases.

(22) Figure 9. In the left column, it should read normal synergies and normal resting posture.  

Response 2.A.22. We intentionally used the same terminology, as the idea behind our conceptual model is that these patterns, which manifest as well-recognized abnormal synergies and abnormal resting postures in stroke, may be present in the healthy motor system as well, but kept in check by CST moderating the RST. At the same time, we recognize that, by definition, synergies and posture in controls are the “normal” reference point against which “abnormal” synergies and posture are defined after stroke. To clarify this issue, we thus decided to forgo the use of the terms “abnormal” in the figure, and instead refer to “synergistic movement ” and “synergistic resting posture”.

(23) Figure 9. With stroke, is RST upregulated, a decreased influence of CST, or both? All seem plausible.

Response 2.A.23a. We believe both can be happening. From previous work (e.g. McPherson et al., 2018) it seems safe to say that RST upregulation is the case, whereas one would also expect a decreased CST influence due to its damage due to the stroke. The relative weight of these influences would be interesting to elucidate in future work.

I have not read the paper, but did McPherson et al., 2018 test these different hypotheses?  

Response 2.A.23b. The main point of McPherson et al., 2018 is that increased synergy expression is due to increased RST involvement, rather than reduced CST influence. However, McPherson et al. do not show separate increases/reductions in RST/CST activity; they show that contralesional activity relative to ipsilesional activity is increased (using a laterality index). While it does seem that RST is upregulated in this case, this does not exclude the possibility that CST influence is reduced as well.

We also noticed that the citation itself, while mentioned in the text, was missing from the bibliography. This is now fixed.

For Figure 9, McPherson is cited as they provide evidence for the idea that RST involvement increases when arm support is decreased. This evidence is both direct (e.g. in their Figure 3 where they show that “Stroke participants exhibited increased activity in the contralesional (R) hemisphere as SABD loading increased” [i.e. arm support was reduced]) and indirect: they connect synergies to RST involvement, and also show increased synergies with reduced arm support (also shown multiple times previously). Both these arguments suggest that arm support reduces RST involvement. We have clarified the relevant sentence:

The interesting implication of this conceptual model is that synergies are in fact postural abnormalities that spill over into active movement when the CST can no longer modulate the increased RST activation that occurs when weight support is removed. Supporting this idea, McPherson et al. found increased ipsilateral activity (which primarily represents activation via the descending RST (Zaaimi et al., 2012)) when the paretic arm had reduced support compared to full support (McPherson et al., 2018).

Reviewer #3 (Recommendations For The Authors):

For Experiment 2, it is not immediately clear how the within-subject values are being pooled and compared across the different conditions. For instance, in the static perturbation trials, there are four blocks with 20 perturbation trials per block per arm (80 total per arm) with each location and direction once per block. For each participant, the comparison is between the location/direction that was most opposed (although this doesn't look accurately represented in Fig 7F). Therefore, the within-subject comparison is 4 trials per participant? Were these values averaged or pooled? It is a little odd that the SD for all the within-subjects trials are identical or nearly identical across conditions especially when looking at the example patient data in 7B and 7F.  

Response 3.A.1. For static perturbation trials, the within-subject comparison involves 8 trials per participant: 4 trials corresponding to the perturbation direction/position combination with resting bias most opposed to the perturbation, and 4 trials corresponding to the perturbation direction/position combination with resting bias most aligned with the perturbation. These values were averaged for each individual. We have expanded our methods to make this part of our data analysis clear (lines 284-296) for all types of comparisons (unperturbed movement, pulse perturbation, static perturbations – now referred to as “release perturbation”).

The across-subject SDs for the average resting forces for each one of these two conditions, shown in Figure 7F are indeed identical. This is due to how these two instances (most aligned vs. most resistive) were selected: because the perturbation directions come in pairs that exactly oppose each other (Figure 7B), if one were to select the position with the most opposing resting bias, that would mean that the combination with same position and the oppositely-directed perturbation would be the one with the most assistive resting bias. Hence the resting biases selected for the most opposing/assistive instances would be equal in magnitude and opposite to each other for each participant, as illustrated in Figure 7F, whereby the most-opposed bias for each individual is exactly opposite to the corresponding most-aligned bias for the same individual. We have added a brief commentary about this on the caption (lines 551-554), reproduced below:

Note how the most-opposed resting bias for each patient is equal and opposite to the their mostaligned resting bias. This is because the same resting bias, when projected along the direction of two oppositely-directed perturbations (illustrated in C), it would oppose one with the same magnitude it would align with the other.

Importantly, following suggestions by Reviewer 2 (see point 2.A.1), we now provide supplementary analyses that use the entirety of the relevant data, rather than the most extreme instances, which provide evidence supporting our main findings (Figures S5-2, S6-2, and S7-2).

The printed colors in Figure 3 are very muddled and hard to read/interpret, especially in panel A. 

Response 3.A.2. Thank you for pointing out this issue, also raised by Reviewer 1. We have adjusted the colors to be more distinct from each other and look clear both in print and on-screen, making use of dashed lines and stripes rather than different shades.

I think it would improve readability and interpretation if Figure 8 and the results related to FM-UE were contained within the description of results for Experiment 1.

Response 3.A.3. Thank you for this suggestion. This is actually a debate we had among ourselves earlier, and we can see merits to either ordering. It is very arguable that moving Figure 8 and the FMUE results within the rest of Experiment 1 may improve readability somewhat. However, we believe that presenting these results at the end better serves to illustrate the apparent paradox between the lack of direct connection between resting biases and active movement on one hand, and the relationship between resting biases and abnormal synergies on the other. We believe that this better sets the stage to present our conceptual model, which explains this paradox based on the role arm support plays in modulating the expression of both resting biases and abnormal synergies.

Additional changes/corrections not outlined above

Figure 1D displayed a right arm, but showed a target array (red dots) for a left arm paradigm. We now flip the target array shown for consistency.

We corrected Figure 6C, which accidentally used an earlier definition of settling time which was based on lateral stabilization throughout the entire movement, rather focus on the period immediately following the pulse. The intended definition of settling time (as we had described in the Methods, lines 204-206 of original submission) focuses on lateral corrections specific to the pulse (rather than corrections when the participant approaches the endpoint) and better matches the one for settling time for the release (static) perturbation trials. Note that this change did not affect the (lack of) relationship between settling time and resting force bias, both across individuals (correlation plots now in Figure S6-1) and within individuals (now shown in the right part of panel 6D). Also in panel C, an error in the scaling for the maximum lateral deviation in the pulse direction (right side of the panel) is also now corrected.

In addition, we made minor edits throughout the text to improve readability.

References

Albert ST, Hadjiosif AM, Jang J, Zimnik AJ, Soteropoulos DS, Baker SN, Churchland MM, Krakauer JW, Shadmehr R (2020) Postural control of arm and fingers through integration of movement commands. Elife 9:e52507.

Avni I, Arac A, Binyamin-Netser R, Kramer S, Krakauer JW, Shmuelof L (2024) The Kinematics of 3D Arm Movements in Sub-Acute Stroke: Impaired Inter-Joint Coordination is Attributable to Both Weakness and Flexor Synergy Intrusion. Neurorehabil Neural Repair 38:646–658.

Bourbonnais D, VANDEN NOVEN S, Carey KM, Rymer WZ (1989) Abnormal spatial patterns of elbow muscle activation in hemiparetic human subjects. Brain 112:85–102.

Brunnstrom S (1966) Motor testing procedures in hemiplegia: based on sequential recovery stages. Phys Ther 46:357–375.

Cortes JC, Goldsmith J, Harran MD, Xu J, Kim N, Schambra HM, Luft AR, Celnik P, Krakauer JW,

Kitago T (2017) A Short and Distinct Time Window for Recovery of Arm Motor Control Early After Stroke Revealed With a Global Measure of Trajectory Kinematics. Neurorehabil Neural Repair 31:552–560.

Duque J, Thonnard J, Vandermeeren Y, Sébire G, Cosnard G, Olivier E (2003) Correlation between impaired dexterity and corticospinal tract dysgenesis in congenital hemiplegia. Brain 126:732–747.

Fine MS, Thoroughman KA (2006) Motor Adaptation to Single Force Pulses: Sensitive to Direction but Insensitive to Within-Movement Pulse Placement and Magnitude. J Neurophysiol 96:710–720.

Ghez C, Scheidt R, Heijink H (2007) Different Learned Coordinate Frames for Planning Trajectories and Final Positions in Reaching. J Neurophysiol 98:3614–3626.

Hadjiosif AM, Branscheidt M, Anaya MA, Runnalls KD, Keller J, Bastian AJ, Celnik PA, Krakauer JW (2022) Dissociation between abnormal motor synergies and impaired reaching dexterity after stroke. J Neurophysiol 127:856–868.

Jayasinghe SA, Scheidt RA, Sainburg RL (2022) Neural Control of Stopping and Stabilizing the Arm. Front Integr Neurosci 16.

Kanade-Mehta P, Bengtson M, Stoeckmann T, McGuire J, Ghez C, Scheidt RA (2023) Spatial mapping of posture-dependent resistance to passive displacement of the hypertonic arm post-stroke. J NeuroEngineering Rehabil 20:163.

Lawrence DG, Kuypers HG (1968) The functional organization of the motor system in the monkey: II. The effects of lesions of the descending brain-stem pathways. Brain 91:15–36.

Levin MF (1996) Interjoint coordination during pointing movements is disrupted in spastic hemiparesis. Brain 119:281–293.

Lowrey CR, Bourke TC, Bagg SD, Dukelow SP, Scott SH (2019) A postural unloading task to assess fast corrective responses in the upper limb following stroke. J NeuroEngineering Rehabil 16:1–17.

McPherson JG, Chen A, Ellis MD, Yao J, Heckman C, Dewald JP (2018) Progressive recruitment of contralesional cortico-reticulospinal pathways drives motor impairment post stroke. J Physiol 596:1211–1225.

McPherson LM, Dewald JP (2022) Abnormal synergies and associated reactions post-hemiparetic stroke reflect muscle activation patterns of brainstem motor pathways. Front Neurol 13:934670.

Porter R, Lemon R (1995) Corticospinal function and voluntary movement. Oxford University Press.

Smith MA, Brandt J, Shadmehr R (2000) Motor disorder in Huntington’s disease begins as a dysfunction in error feedback control. Nature 403:544.

Smith MA, Shadmehr R (2005) Intact ability to learn internal models of arm dynamics in Huntington’s disease but not cerebellar degeneration. J Neurophysiol 93:2809–2821.

Tower SS (1940) Pyramidal lesion in the monkey. Brain 63:36–90.

Twitchell TE (1951) The restoration of motor function following hemiplegia in man. Brain 74:443–480.

Wilkins KB, Yao J, Owen M, Karbasforoushan H, Carmona C, Dewald JP (2020) Limited capacity for ipsilateral secondary motor areas to support hand function post-stroke. J Physiol 598:2153– 2167.

Zaaimi B, Edgley SA, Soteropoulos DS, Baker SN (2012) Changes in descending motor pathway connectivity after corticospinal tract lesion in macaque monkey. Brain 135:2277–2289.

I'm pretty sure Zeus did this, too.

eLife Assessment

This study provides important insights into the regulation of a retained intron in the mRNA coding for OGT, a process known to be regulated by the O-GlcNAc cycling system, and highlights the functional role of the splicing regulator SFSWAP. The evidence supporting the claims of the authors is convincing: the authors performed an elegant state-of-the-art CRISPR knockout strategy and sophisticated bioinformatic analysis to identify SFSWAP as a negative regulator of alternative splicing. The work will be of interest to researchers in the fields of splicing and glycobiology.

Reviewer #1 (Public review):

Summary:

Govindan and Conrad use a genome-wide CRISPR screen to identify genes regulating retention of intron 4 in OGT, leveraging an intron retention reporter system previously described (PMID: 35895270). Their OGT intron 4 reporter reliably responds to O-GlcNAc levels, mirroring the endogenous splicing event. Through a genome-wide CRISPR knockout library, they uncover a range of splicing-related genes, including multiple core spliceosome components, acting as negative regulators of OGT intron 4 retention. They choose to follow up on SFSWAP, a largely understudied splicing regulator shown to undergo rapid phosphorylation in response to O-GlcNAc level changes (PMID: 32329777). RNA-sequencing reveals that SFSWAP depletion not only promotes OGT intron 4 splicing but also broadly induces exon inclusion and intron splicing, affecting decoy exon usage. While this study offers interesting insights into intron retention and O-GlcNAc signaling regulation, the RNA sequencing experiments lack the essential controls needed to provide full confidence to the authors' conclusions.

Strengths:

(1) This study presents an elegant genetic screening approach to identify regulators of intron retention, uncovering core spliceosome genes as unexpected positive regulators of intron retention.

(2) The work proposes a novel functional role for SFSWAP in splicing regulation, suggesting that it acts as a negative regulator of splicing and cassette exon inclusion, which contrasts with expected SR-related protein functions.

(3) The authors suggest an intriguing model where SFSWAP, along with other spliceosome proteins, promotes intron retention by associating with decoy exons.

Weaknesses:

(1) The conclusions on SFSWAP impact on alternative splicing are based on cells treated with two pooled siRNAs for five days. This extended incubation time without independent siRNA treatments raises concerns about off-target effects and indirect effects from secondary gene expression changes, potentially limiting confidence in direct SFSWAP-dependent splicing regulation. Rescue experiments and shorter siRNA-treatment incubation times could address these issues.

(2) The mechanistic role of SFSWAP in splicing would benefit from further exploration. Key questions remain, such as whether SFSWAP directly binds RNA, specifically the introns and exons (including the decoy exons) it appears to regulate. Furthermore, given that SFSWAP phosphorylation is influenced by changes in O-GlcNAc signaling, it would be interesting to investigate this relationship further. While generating specific phosphomutants may not yield definitive insights due to redundancy and also beyond the scope of the study, the authors could examine whether distinct SFSWAP domains, such as the SR and SURP domains, which likely overlap with phosphorylation sites, are necessary for regulating OGT intron 4 splicing.

(3) Data presentation could be improved (specific suggestions are included in the recommendations section). Furthermore, Excel tables with gene expression and splicing analysis results should be provided as supplementary datasheets. Finally, a more detailed explanation of statistical analyses is necessary in certain sections.

Reviewer #2 (Public review):

Summary:

The paper describes an effort to identify the factors responsible for intron retention and alternate exon splicing in a complex system known to be regulated by the O-GlcNAc cycling system. The CRISPR/Cas9 system was used to identify potential factors. The bioinformatic analysis is sophisticated and compelling. The conclusions are of general interest and advance the field significantly.

Strengths:

(1) Exhaustive analysis of potential splicing factors in an unbiased screen.

(2) Extensive genome wide bioinformatic analysis.

(3) Thoughtful discussion and literature survey.

Weaknesses:

(1) No firm evidence linking SFSWA to an O-GlcNAc specific mechanism.

(2) Resulting model leaves many unanswered questions.

Reviewer #3 (Public review):

Summary:

The major novel finding in this study is that SFSWAP, a splicing factor containing an RS domain but no canonical RNA binding domain, functions as a negative regulator of splicing. More specifically, it promotes retention of specific introns in a wide variety of transcripts including transcripts from the OGT gene previously studied by the Conrad lab. The balance between OGT intron retention and OGT complete splicing is an important regulator of O-GlcNAc expression levels in cells.

Strengths:

An elegant CRISPR knockout screen employed a GFP reporter, in which GFP is efficiently expressed only when the OGT retained intron is removed (so that the transcript will be exported from the nucleus to allow for translation of GFP). Factors whose CRISPR knockdown causes decreased intron retention therefore increase GFP, and can be identified by sequencing RNA of GFP-sorted cells. SFSWAP was thus convincingly identified as a negative regulator of OGT retained intron splicing. More focused studies of OGT intron retention indicate that it may function by regulating a decoy exon previously identified in the intron, and that this may extend to other transcripts with decoy exons.

Weaknesses:

The mechanism by which SFSWAP represses retained introns is unclear, although some data suggests it can operate (in OGT) at the level of a recently reported decoy exon within that intron. Interesting/appropriate speculation about possible mechanisms are provided and will likely be the subject of future studies.

Overall the study is well done and carefully described but some figures and some experiments should be described in more detail.

onbewuste kennis'

eLife Assessment

This study provides useful findings about the effects of heterozygosity for Trio variants linked to neurodevelopmental and psychiatric disorders in mice. However, the strength of the evidence is limited and incomplete mainly because the experimental flow is difficult to follow, raising concerns about the conclusions' robustness. Clearer connections between variables, such as sex, age, behavior, brain regions, and synaptic measures, and more methodological detail on breeding strategies, test timelines, electrophysiology, and analysis, are needed to support their claims.

Reviewer #1 (Public review):

Summary:

This study explores how heterozygosity for specific neurodevelopmental disorder-associated Trio variants affects mouse behavior, brain structure, and synaptic function, revealing distinct impacts on motor, social, and cognitive behaviors linked to clinical phenotypes. Findings demonstrate that Trio variants yield unique changes in synaptic plasticity and glutamate release, highlighting Trio's critical role in presynaptic function and the importance of examining variant heterozygosity in vivo.

Strengths:

This study generated multiple mouse lines to model each Trio variant, reflecting point mutations observed in human patients with developmental disorders. The authors employed various approaches to evaluate the resulting behavioral, neuronal morphology, synaptic function, and proteomic phenotypes.

Weaknesses:

While the authors present extensive results, the flow of experiments is challenging to follow, raising concerns about the strength of the experimental conclusions. Additionally, the connection between sex, age, behavioral data, brain regions, synaptic transmission, and plasticity lacks clarity, making it difficult to understand the rationale behind each experiment. Clearer explanations of the purpose and connections between experiments are recommended. Furthermore, the methodology requires more detail, particularly regarding mouse breeding strategies, timelines for behavioral tests, electrophysiology conditions, and data analysis procedures.

Reviewer #2 (Public review):

Summary:

The authors generated three mouse lines harboring ASD, Schizophrenia, and Bipolar-associated variants in the TRIO gene. Anatomical, behavioral, physiological, and biochemical assays were deployed to compare and contrast the impact of these mutations in these animals. In this undertaking, the authors sought to identify and characterize the cellular and molecular mechanisms responsible for ASD, Schizophrenia, and Bipolar disorder development.

Strengths:

The establishment of TRIO dysfunction in the development of ASD, Schizophrenia, and Bipolar disorder is very recent and of great interest. Disorder-specific variants have been identified in the TRIO gene, and this study is the first to compare and contrast the impact of these variants in vivo in preclinical models. The impact of these mutations was carefully examined using an impressive host of methods. The authors achieved their goal of identifying behavioral, physiological, and molecular alterations that are disorder/variant specific. The impact of this work is extremely high given the growing appreciation of TRIO dysfunction in a large number of brain-related disorders. This work is very interesting in that it begins to identify the unique and subtle ways brain function is altered in ASD, Schizophrenia, and Bipolar disorder.

Weaknesses:

(1) Most assays were performed in older animals and perhaps only capture alterations that result from homeostatic changes resulting from prodromal pathology that may look very different.

(2) Identification of upregulated (potentially compensating) genes in response to these disorder-specific Trio variants is extremely interesting. However, a functional demonstration of compensation is not provided.

(3) There are instances where data is not shown in the manuscript. See "data not shown". All data collected should be provided even if significant differences are not observed.

I consider weaknesses 1 and 2 minor. While they would very interesting to explore, these experiments might be more appropriate for a follow-up study. I would recommend that the missing data in 3 should be provided in the supplemental material.

Author response:

eLife Assessment

This study provides useful findings about the effects of heterozygosity for Trio variants linked to neurodevelopmental and psychiatric disorders in mice. However, the strength of the evidence is limited and incomplete mainly because the experimental flow is difficult to follow, raising concerns about the conclusions' robustness. Clearer connections between variables, such as sex, age, behavior, brain regions, and synaptic measures, and more methodological detail on breeding strategies, test timelines, electrophysiology, and analysis, are needed to support their claims.

We appreciate the opportunity to address the constructive feedback provided by eLife and the reviewers. Below, we respond to the overall assessment and individual reviewers' comments, clarifying our experimental approach, addressing concerns, and providing additional details where necessary.

We thank the editors for highlighting the significance of our findings regarding the effects of Trio variant heterozygosity in mice. We acknowledge the feedback concerning the experimental flow and agree that clarity is paramount. To address these concerns:

(1) Connections between variables: We will revise the manuscript to explicitly outline and extend explanations and the relationships between sex, age, behavior, brain regions, and synaptic measures, ensuring that the rationale for each experiment and its relevance to the overall conclusions are improved.

(2) Methodological details: Our paper Methods section was formatted to be short with additional details provided in the Supplemental Methods section.  We will merge all into an extended section to improve clarity. We will also expand on our breeding strategies, test timelines, electrophysiological protocols, and data analysis methods in the revised Methods section. These additions aim to enhance the transparency and reproducibility of our study and to ensure full support of our conclusions.

(3) Experimental flow: We will revise and extend our results, methods, and discussion sections to clarify the rationale and experimental design to guide readers through the experimental sequence and rationale.

We are confident these revisions address the concerns raised and enhance the robustness and coherence of our findings.

Public Reviews:

Reviewer #1 (Public review):

Summary:

This study explores how heterozygosity for specific neurodevelopmental disorder-associated Trio variants affects mouse behavior, brain structure, and synaptic function, revealing distinct impacts on motor, social, and cognitive behaviors linked to clinical phenotypes. Findings demonstrate that Trio variants yield unique changes in synaptic plasticity and glutamate release, highlighting Trio's critical role in presynaptic function and the importance of examining variant heterozygosity in vivo.

Strengths:

This study generated multiple mouse lines to model each Trio variant, reflecting point mutations observed in human patients with developmental disorders. The authors employed various approaches to evaluate the resulting behavioral, neuronal morphology, synaptic function, and proteomic phenotypes.

Weaknesses:

While the authors present extensive results, the flow of experiments is challenging to follow, raising concerns about the strength of the experimental conclusions. Additionally, the connection between sex, age, behavioral data, brain regions, synaptic transmission, and plasticity lacks clarity, making it difficult to understand the rationale behind each experiment. Clearer explanations of the purpose and connections between experiments are recommended. Furthermore, the methodology requires more detail, particularly regarding mouse breeding strategies, timelines for behavioral tests, electrophysiology conditions, and data analysis procedures.

We appreciate the reviewer’s recognition of the novelty and comprehensiveness of our approach, particularly the generation of multiple mouse lines and our efforts to model Trio variant effects in vivo.

Weaknesses

(1) Experimental flow and rationale and connection between variables: We will expand on the connections between behavioral data, neuronal morphology, synaptic function, and proteomics in the Results and Discussion sections to clarify how each experiment informs the reasoning and the conclusions and to highlight the relationships between sex, age, behavior, and synaptic measures.

(2) Methodological details: Our paper Methods section was formatted to be short to fulfill word limits on the submitted version, with additional details provided in the Supplemental Methods section. We will merge our Methods and Supplemental Methods sections and expand on our breeding strategies, test timelines, electrophysiological protocols, and data analysis methods in the revised Methods section.  These additions aim to enhance the transparency and reproducibility of our study and to ensure full support of our conclusions.

Reviewer #2 (Public review):

Summary:

The authors generated three mouse lines harboring ASD, Schizophrenia, and Bipolar-associated variants in the TRIO gene. Anatomical, behavioral, physiological, and biochemical assays were deployed to compare and contrast the impact of these mutations in these animals. In this undertaking, the authors sought to identify and characterize the cellular and molecular mechanisms responsible for ASD, Schizophrenia, and Bipolar disorder development.

Strengths:

The establishment of TRIO dysfunction in the development of ASD, Schizophrenia, and Bipolar disorder is very recent and of great interest. Disorder-specific variants have been identified in the TRIO gene, and this study is the first to compare and contrast the impact of these variants in vivo in preclinical models. The impact of these mutations was carefully examined using an impressive host of methods. The authors achieved their goal of identifying behavioral, physiological, and molecular alterations that are disorder/variant specific. The impact of this work is extremely high given the growing appreciation of TRIO dysfunction in a large number of brain-related disorders. This work is very interesting in that it begins to identify the unique and subtle ways brain function is altered in ASD, Schizophrenia, and Bipolar disorder.

Weaknesses:

(1) Most assays were performed in older animals and perhaps only capture alterations that result from homeostatic changes resulting from prodromal pathology that may look very different.

(2) Identification of upregulated (potentially compensating) genes in response to these disorder-specific Trio variants is extremely interesting. However, a functional demonstration of compensation is not provided.

(3) There are instances where data is not shown in the manuscript. See "data not shown". All data collected should be provided even if significant differences are not observed.

I consider weaknesses 1 and 2 minor. While they would very interesting to explore, these experiments might be more appropriate for a follow-up study. I would recommend that the missing data in 3 should be provided in the supplemental material.

We are grateful for the reviewer’s recognition of our study’s significance and methodological rigor. The acknowledgment of Trio dysfunction as a novel and impactful area of research is deeply appreciated.

Weaknesses: 

We agree that focusing on older animals may limit insights into early-stage pathophysiology. However, given the goal of this study was to examine the functional impacts of Trio heterozygosity at an adolescent stage and to reveal the ultimate impact of these alleles on synaptic function, we believe the choice of animal age aligns with our objectives. We agree that future studies of earlier developmental stages will be beneficial and complement these findings.

Functional compensation: In this study, we tested functional compensation through rescue experiments in +/K1431M brain slices using a Rac1-specific inhibitor, NSC, which prevents its activation by Trio or Tiam1. Our findings strongly suggest that increased Rac1 activity, attributed to the proposed compensation, drives the deficiency in neurotransmitter release. Furthermore, this deficiency can be normalized by direct Rac1 inhibition.

Data not shown: We will incorporate all previously shown data into the Supplemental Materials, even when results are nonsignificant. We agree that this ensures full transparency and facilitates a more comprehensive evaluation of our findings.

Pas compris ce qu'il fallait faire précisément.

eLife Assessment

This study investigates the fundamental role of polyunsaturated fatty acids (PUFAs) in membrane biology, using a unique model to perform a thorough genetic screen that highlights that PUFA synthesis defects cannot be compensated for by mutations in other pathways. While the data are solid and generally support the claims, additional experimental validation or more detailed descriptions of their results would strengthen the broader conclusions. This study will appeal to researchers in membrane biology, lipid metabolism, and C. elegans genetics.

Reviewer #1 (Public review):

Summary:<br /> This study addresses the roles of polyunsaturated fatty acids (PUFAs) in animal physiology and membrane function. A C. elegans strain carrying the fat-2(wa17) mutation possess a very limited ability to synthesize PUFAs and there is no dietary input because the E. coli diet consumed by lab grown C. elegans does not contain any PUFAs. The fat-2 mutant strain was characterized to confirm that the worms grow slowly, have rigid membranes, and have a constitutive mitochondrial stress response. The authors showed that chemical treatments or mutations known to increase membrane fluidity did not rescue growth defects. A thorough genetic screen was performed to identify genetic changes to compensate for the lack of PUFAs. The newly isolated suppressor mutations that compensated for FAT-2 growth defects included intergenic suppressors in the fat-2 gene, as well as constitutive mutations in the hypoxia sensing pathway components EGL-9 and HIF-1, and loss of function mutations in ftn-2, a gene encoding the iron storage protein ferritin. Taken together, these mutations lead to the model that increased intracellular iron, an essential cofactor for fatty acid desaturases, allows the minimally functional FAT-2(wa17) enzyme to be more active, resulting in increased desaturation and increased PUFA synthesis.

Strengths:<br /> (1) This study provides new information further characterizing fat-2 mutants. The authors measured increased rigidity of membranes compared to wild type worms, however this rigidity is not able to be rescued with other fluidity treatments such as detergent or mutants. Rescue was only achieved with polyunsaturated fatty acid supplementation.<br /> (2) A very thorough genetic suppressor screen was performed. In addition to some internal fat-2 compensatory mutations, the only changes in pathways identified that are capable of compensating for deficient PUFA synthesis was the hypoxia pathway and the iron storage protein ferritin. Suppressor mutations included an egl-9 mutation that constitutively activates HIF-1, and Gain of function mutations in hif-1 that are dominant. This increased activity of HIF conferred by specific egl-9 and hif-1 mutations lead to decreased expression of ftn-2. Indeed, loss of ftn-2 leads to higher intracellular iron. The increased iron apparently makes the FAT-2 fatty acid desaturase enzyme more active, allowing for the production of more PUFAs.<br /> (3) The mutations isolated in the suppressor screen show that the only mutations able to compensate for lack of PUFAs were ones that increased PUFA synthesis by the defective FAT-2 desaturase, thus demonstrating the essential need for PUFAs that cannot be overcome by changes in other pathways. This is a very novel study, taking advantage of genetic analysis of C. elegans, and it confirms the observations in humans that certain essential PUFAs are required for growth and development.<br /> (4) Overall, the paper is well written, and the experiments were carried out carefully and thoroughly. The conclusions are well supported by the results.

Weaknesses:<br /> Overall, there are not many weaknesses. The main one I noticed is that the lipidomic analysis shown in Figs 3C, 7C, S1 and S3. Whie these data are an essential part of the analysis and provide strong evidence for the conclusions of the study, it is unfortunate that the methods used did not enable the distinction between two 18:1 isomers. These two isomers of 18:1 are important in C. elegans biology, because one is a substrate for FAT-2 (18:1n-9, oleic acid) and the other is not (18:1n-7, cis vaccenic acid). Although rarer in mammals, cis-vaccenic acid is the most abundant fatty acid in C. elegans and is likely the most important structural MUFA. The measurement of these two isomers is not essential for the conclusions of the study, but the manuscript should include a comment about the abundance of oleic vs vaccenic acid in C. elegans (authors can find this information, even in the fat-2 mutant, in other publications of C. elegans fatty acid composition). Otherwise, readers who are not familiar with C. elegans might assume the 18:1 that is reported is likely to be mainly oleic acid, as is common in mammals.

Other suggestions to authors to improve the paper:<br /> (1) The title could be less specific; it might be confusing to readers to include the allele name in the title.<br /> (2) There are two errors in the pathway depicted in Figure 1A. The16:0-16:1 desaturation can be performed by FAT-5, FAT-6, and FAT-7. The 18:0-18:1 desaturation can only be performed by FAT-6 and FAT-7

Reviewer #2 (Public review):

Summary:<br /> The authors use a genetic screen in C. elegans to investigate the physiological roles of polyunsaturated fatty acids (PUFAs). They screen for mutations that rescue fat-2 mutants, which have strong reductions in PUFAs. As a result, either mutations in fat-2 itself, or mutations in genes involved in the HIF-1 pathway, were found to rescue fat-2 mutants.

Strengths:<br /> As C. elegans can produce PUFAs de novo as essential lipids, the genetic model is well suited to study the fundamental roles of PUFAs, and the results are very interesting. The genetic screen finds mutations in convergent pathways, suggesting that it has reached near-saturation. The link between the HIF-1 pathway and lipid unsaturation is very interesting. As many of the mutations found to rescue fat-2 mutants are of gain-of-function, it is unlikely that similar discoveries could have been made with other approaches like genome-wide CRISPR screenings, making the current study distinctive.

Weaknesses:<br /> The authors make very important statements, but some are not sufficiently supported by data. In page 5, they conclude that membrane rigidity is a minor cause of fat-2 mutant defects, which is a relevant observation regarding why PUFAs are important. However, they use treatments that have rescued fluidity in another mutant (paqr-2), but do not test if they have the same fluidifying effects in fat-2 mutants.

The screening results seem to converge into the HIF-1 pathway, which is hypothetically correct according to the literature. However, the authors do not validate this hypothesis, which is a critical limitation, especially because many of the mutations they obtained seem to be of gain-of-function. Therefore, without experimental testing, it cannot be concluded that the mutations have the expected effect on the HIF-1 pathway.

In some of the mutants, the rescues in lipid compositions seem to be weak, and it is arguable whether phenotypic rescues are really via a restoration in lipid compositions.

The hypothesis linking iron homeostasis and the rescue of fat-2 mutants is interesting, but the data of rescue by iron repletion seem to be against it. The results might be due to the inefficiency in iron repletion, as the authors suggest, but this has not been formally addressed.

Therefore, the authors propose multiple very interesting and important hypotheses, but experimental validations remain limited.

Author response:

We thank the editors at eLife and the reviewers for the care with which our mansucript has been reviewed and the constructive feedback that we have received. Both reviewers viewed the manuscript positively and in particular praised the merits of the forward genetic screen that led to the discovery of a new link between the HIF-1 pathway and fatty acid desaturation.

We agree with all points by Reviewer #1. We will modify our manuscript to clarify that two types of C18:1 fatty acids are present in our lipidomics, and that the majority is likely vaccenic acid that is not a FAT-2 substrate. The title will be modified and Fig. 1A corrected.

All points raised by Reviewer #2 are also valid and we will try to address most of them experimentally, though not always as suggested. In particular, we plan to use FRAP to verify that membrane-fluidizing treatments are effective in the fat-2 mutant. We also plan to use qPCR to test whether the novel egl-9(lof) and hif-1(gof) alleles lead to the expected downregulation of ftn-2. We note that the pathway connecting EGL-9, HIF-1 and FTN-2 is well supported by published work and that the alleles isolated in our screen are consistent with it, with the addition that FAT-2 is likely a regulated outcome of FTN-2 inhibition/mutation. We also plan to monitor FAT-2 protein levels using Western blots and thus provide more clarity about the mechanism of action of the novel fat-2(wa17) suppressors. The manuscript will be modified to tone down interpretations not directly supported by experiments.

eLife Assessment

This paper provides a valuable contribution to our understanding of how adenosine acts as a signal of nutrient insufficiency and extends this idea to suggest that adenosine is released by metabolically active cells in proportion to the activity of methylation events. Convincing data support this idea. The authors use metabolic tracing approaches to identify the biochemical pathways that contribute to the regulation of adenosine levels and the S-adenosylmethionine cycle in Drosophila larval hemocytes in response to wasp egg infection.

Reviewer #1 (Public review):

Summary:

In this article, Nedbalova et al. investigate the biochemical pathway that acts in circulating immune cells to generate adenosine, a systemic signal that directs nutrients toward the immune response, and S-adenosylmethionine (SAM), a methyl donor for lipid, DNA, RNA, and protein synthetic reactions. They find that SAM is largely generated through the uptake of extracellular methionine, but that recycling of adenosine to form ATP contributes a small but important quantity of SAM in immune cells during the immune response. The authors propose that adenosine serves as a sensor of cell activity and nutrient supply, with adenosine secretion dominating in response to increased cellular activity. Their findings of impaired immune action but rescued larval developmental delay when the enzyme Ahcy is knocked down in hemocytes are interpreted as due to effects on methylation processes in hemocytes and reduced production of adenosine to regulate systemic metabolism and development, respectively. Overall this is a strong paper that uses sophisticated metabolic techniques to map the biochemical regulation of an important systemic mediator, highlighting the importance of maintaining appropriate metabolite levels in driving immune cell biology.

Strengths:

The authors deploy metabolic tracing - no easy feat in Drosophila hemocytes - to assess flux into pools of the SAM cycle. This is complemented by mass spectrometry analysis of total levels of SAM cycle metabolites to provide a clear picture of this metabolic pathway in resting and activated immune cells.

The experiments show that the recycling of adenosine to ATP, and ultimately SAM, contributes meaningfully to the ability of immune cells to control infection with wasp eggs.

This is a well-written paper, with very nice figures showing metabolic pathways under investigation. In particular, the italicized annotations, for example, "must be kept low", in Figure 1 illustrate a key point in metabolism - that cells must control levels of various intermediates to keep metabolic pathways moving in a beneficial direction.

Experiments are conducted and controlled well, reagents are tested, and findings are robust and support most of the authors' claims.

Weaknesses:

The authors posit that adenosine acts as a sensor of cellular activity, with increased release indicating active cellular metabolism and insufficient nutrient supply. It is unclear how generalizable they think this may be across different cell types or organs.

The authors extrapolate the findings in Figure 3 of decreased extracellular adenosine in ex vivo cultures of hemocytes with knockdown of Ahcy (panel B) to the in vivo findings of a rescue of larval developmental delay in wasp egg-infected larvae with hemocyte-specific Ahcy RNAi (panel C). This conclusion (discussed in lines 545-547) should be somewhat tempered, as a number of additional metabolic abnormalities characterize Ahcy-knockdown hemocytes, and the in vivo situation may not mimic the ex vivo situation. If adenosine (or inosine) measurements were possible in hemolymph, this would help bolster this idea. However, adenosine at least has a very short half-life.

Reviewer #2 (Public review):

Summary:

In this work, the authors wish to explore the metabolic support mechanisms enabling lamellocyte encapsulation, a critical antiparasitic immune response of insects. They show that S-adenosylmethionine metabolism is specifically important in this process through a combination of measurements of metabolite levels and genetic manipulations of this metabolic process.

Strengths:

The metabolite measurements and the functional analyses are generally very strong and clearly show that the metabolic process under study is important in lamellocyte immune function.

Weaknesses:

The gene expression data are a potential weakness. Not enough is explained about how the RNAseq experiments in Figures 2 and 4 were done, and the representation of the data is unclear. The paper would also be strengthened by the inclusion of some measure of encapsulation effectiveness: the authors show that manipulation of the S-adenosylmethionine pathway in lamellocytes affects the ability of the host to survive infection, but they do not show direct effects on the ability of the host to encapsulate wasp eggs.

Reviewer #3 (Public review):

Summary:

The authors of this study provide evidence that Drosophila immune cells show upregulated SAM transmethylation pathway and adenosine recycling upon wasp infection. Blocking this pathway compromises the lamellocyte formation, developmental delay, and host survival, suggesting its physiological relevance.

Strengths:

Snapshot quantification of the metabolite pool does not provide evidence that the metabolic pathway is active or not. The authors use an ex vivo isotope labelling to precisely monitor the SAM and adenosine metabolism. During infection, the methionine metabolism and adenosine recycling are upregulated, which is necessary to support the immune reaction. By combining the genetic experiment, they successfully show that the pathway is activated in immune cells.

Weaknesses:

The authors knocked down Ahcy to prove the importance of SAM methylation pathway. However, Ahcy-RNAi produces a massive accumulation of SAH, in addition to blocking adenosine production. To further validate the phenotypic causality, it is necessary to manipulate other enzymes in the pathway, such as Sam-S, Cbs, SamDC, etc. The authors do not demonstrate how infection stimulates the metabolic pathway given the gene expression of metabolic enzymes is not upregulated by infection stimulus.

Author response:

We would like to thank the editors and reviewers for reviewing our work, for finding it valuable supported by convincing data, which we greatly appreciate, but also for identifying the weaknesses of the manuscript. We plan to address these weaknesses in the revised version, briefly as follows:

(1) In the Discussion, we will elaborate more on a possible generalization of our results, while being aware of the limited space in this experimental paper and therefore intend to address this in more detail and comprehensively in a subsequent perspective article.

(2) In the Discussion, we will more clearly address the limitations of our work, in particular the difference between the measurement of extracellular adenosine production ex vivo and the actual production in vivo, where the measurement is indeed very challenging, and also the limitations of manipulating the SAM pathway only at the Ahcy level.

(3) We will describe in detail and complement the supplementary RNAseq data. The RNAseq data have already been described in detail in our previous paper (doi.org/10.1371/journal.pbio.3002299), but we agree with the reviewers that we should describe the necessary details again here.

(4) We will fill in the missing data on encapsulation efficiency; we agree that it was unfortunate to omit them.

(5) We will supplement the data with methyltransferase expressions and better describe the changes in expression of some SAM pathway genes, which, especially with methyltransferase expressions, also support stimulation of this pathway by changes in expression. Although the goal of this work was to test by 13C-labeling whether SAM pathway activity is upregulated, not to analyze how the activity is regulated, we certainly agree that an explanation of possible regulation, especially in the context of the enzyme expressions we show, should be included in our work.

Konrad Kramar im Kurier zur zerplatzenden Blase „Grüner Wasserstoff“. Von den 10 Mio. Tonnen jährlich, die die EU-Kommission bis 2030 geplant hat, sind erst 7% erreicht, von den geplanten Leitungen erst 3% im Bau. Im Vergleich zu Wärmepumpen bei Heizungen und Batterien bei LKWs ist Wasserstoff unwirtschaftlich. Die Wasserstoff-Lobbies promoten tatsächlich grauen, mit Erdgas produzierten Wasserstoff, der auf absehbare Zeit als einziger konkurrenzfähig ist, aber die fossilen Emissionen nicht verringert [via Sabine Jungwirth auf Facebook]. https://kurier.at/wirtschaft/eu-wasserstoff-klimawandel-solarzellen-pipeline-hydrogen-bank/402990372

Screenshot: https://www.facebook.com/photo/?fbid=10227863164746893&set=a.2637414336971

eLife Assessment

This valuable study investigates the neural basis of causal inference of illness, suggesting that it relies on semantic networks specific to living things in the absence of a generalized representation of causal inference across domains. However, the evidence remains incomplete due to some unjustified design and analysis choices. Moreover, the authors do not fully exploit the potential of multivariate fMRI analyses to rigorously test their main hypothesis.

Reviewer #1 (Public review):

Summary:

In this study, the authors aim to understand the neural basis of implicit causal inference, specifically how people infer causes of illness. They use fMRI to explore whether these inferences rely on content-specific semantic networks or broader, domain-general neurocognitive mechanisms. The study explores two key hypotheses: first, that causal inferences about illness rely on semantic networks specific to living things, such as the 'animacy network,' given that illnesses affect only animate beings; and second, that there might be a common brain network supporting causal inferences across various domains, including illness, mental states, and mechanical failures. By examining these hypotheses, the authors aim to determine whether causal inferences are supported by specialized or generalized neural systems.

The authors observed that inferring illness causes selectively engaged a portion of the precuneus (PC) associated with the semantic representation of animate entities, such as people and animals. They found no cortical areas that responded to causal inferences across different domains, including illness and mechanical failures. Based on these findings, the authors concluded that implicit causal inferences are supported by content-specific semantic networks, rather than a domain-general neural system, indicating that the neural basis of causal inference is closely tied to the semantic representation of the specific content involved.

Strengths:

(1) The inclusion of the four conditions in the design is well thought out, allowing for the examination of the unique contribution of causal inference of illness compared to either a different type of causal inference (mechanical) or non-causal conditions. This design also has the potential to identify regions involved in a shared representation of inference across general domains.

(2) The presence of the three localizers for language, logic, and mentalizing, along with the selection of specific regions of interest (ROIs), such as the precuneus and anterior ventral occipitotemporal cortex (antVOTC), is a strong feature that supports a hypothesis-driven approach (although see below for a critical point related to the ROI selection).

(3) The univariate analysis pipeline is solid and well-developed.

(4) The statistical analyses are a particularly strong aspect of the paper.

Weaknesses:

Based on the current analyses, it is not yet possible to rule out the hypothesis that inferring illness causes relies on neurocognitive mechanisms that support causal inferences irrespective of their content, neither in the precuneus nor in other parts of the brain.

(1) The authors, particularly in the multivariate analyses, do not thoroughly examine the similarity between the two conditions (illness-causal and mechanical-causal), as they are more focused on highlighting the differences between them. For instance, in the searchlight MVPA analysis, an interesting decoding analysis is conducted to identify brain regions that represent illness-causal and mechanical-causal conditions differently, yielding results consistent with the univariate analyses. However, to test for the presence of a shared network, the authors only perform the Causal vs. Non-causal analysis. This analysis is not very informative because it includes all conditions mixed together and does not clarify whether both the illness-causal and mechanical-causal conditions contribute to these results.

(2) To address this limitation, a useful additional step would be to use as ROIs the different regions that emerged in the Causal vs. Non-causal decoding analysis and to conduct four separate decoding analyses within these specific clusters:<br /> (a) Illness-Causal vs. Non-causal - Illness First;<br /> (b) Illness-Causal vs. Non-causal - Mechanical First;<br /> (c) Mechanical-Causal vs. Non-causal - Illness First;<br /> (d) Mechanical-Causal vs. Non-causal - Mechanical First.<br /> This approach would allow the authors to determine whether any of these ROIs can decode both the illness-causal and mechanical-causal conditions against at least one non-causal condition.

(3) Another possible analysis to investigate the existence of a shared network would be to run the searchlight analysis for the mechanical-causal condition versus the two non-causal conditions, as was done for the illness-causal versus non-causal conditions, and then examine the conjunction between the two. Specifically, the goal would be to identify ROIs that show significant decoding accuracy in both analyses.

(4) Along the same lines, for the ROI MVPA analysis, it would be useful not only to include the illness-causal vs. mechanical-causal decoding but also to examine the illness-causal vs. non-causal conditions and the mechanical-causal vs. non-causal conditions. Additionally, it would be beneficial to report these data not just in a table (where only the mean accuracy is shown) but also using dot plots, allowing the readers to see not only the mean values but also the accuracy for each individual subject.

(5) The selection of Regions of Interest (ROIs) is not entirely straightforward:<br /> In the introduction, the authors mention that recent literature identifies the precuneus (PC) as a region that responds preferentially to images and words related to living things across various tasks. While this may be accurate, we can all agree that other regions within the ventral occipital-temporal cortex also exhibit such preferences, particularly areas like the fusiform face area, the occipital face area, and the extrastriate body area. I believe that at least some parts of this network (e.g., the fusiform gyrus) should be included as ROIs in this study. This inclusion would make sense, especially because a complementary portion of the ventral stream known to prefer non-living items (i.e., anterior medial VOTC) has been selected as a control ROI to process information about the mechanical-causal condition. Given the main hypothesis of the study - that causal inferences about illness might depend on content-specific semantic representations in the 'animacy network' - it would be worthwhile to investigate these ROIs alongside the precuneus, as they may also yield interesting results.

(6) Visual representation of results:<br /> In all the figures related to ROI analyses, only mean group values are reported (e.g., Figure 1A, Figure 3, Figure 4A, Supplementary Figure 6, Figure 7, Figure 8). To better capture the complexity of fMRI data and provide readers with a more comprehensive view of the results, it would be beneficial to include a dot plot for a specific time point in each graph. This could be a fixed time point (e.g., a certain number of seconds after stimulus presentation) or the time point showing the maximum difference between the conditions of interest. Adding this would allow for a clearer understanding of how the effect is distributed across the full sample, such as whether it is consistently present in every subject or if there is greater variability across individuals.

(7) Task selection:<br /> (a) To improve the clarity of the paper, it would be helpful to explain the rationale behind the choice of the selected task, specifically addressing: (i) why an implicit inference task was chosen instead of an explicit inference task, and (ii) why the "magic detection" task was used, as it might shift participants' attention more towards coherence, surprise, or unexpected elements rather than the inference process itself.<br /> (b) Additionally, the choice to include a large number of catch trials is unusual, especially since they are modeled as regressors of non-interest in the GLM. It would be beneficial to provide an explanation for this decision.

Reviewer #2 (Public review):

Summary:

In this study, the authors hypothesize that "causal inferences about illness depend on content-specific semantic representations in the animacy network". They test this hypothesis in an fMRI task, by comparing brain activity elicited by participants' exposure to written situations suggesting a plausible cause of illness with brain activity in linguistically equivalent situations suggesting a plausible cause of mechanical failure or damage and non-causal situations. These contrasts identify PC as the main "culprit" in a whole-brain univariate analysis. Then the question arises of whether the content-specificity has to do with inferences about animates in general, or if there are some distinctions between reasoning about people's bodies versus mental states. To answer this question, the authors localize the mentalizing network and study the relation between brain activity elicited by Illness-Causal > Mech-Causal and Mentalizing > Physical stories. They conclude that inferring about the causes of illness partially differentiates from reasoning about people's states of mind. The authors finally test the alternative yet non-mutually exclusive hypothesis that both types of causal inferences (illness and mechanical) depend on shared neural machinery. Good candidates are language and logic, which justifies the use of a language/logic localizer. No evidence of commonalities across causal inferences versus non-causal situations is found.

Strengths:

(1) This study introduces a useful paradigm and well-designed set of stimuli to test for implicit causal inferences.

(2) Another important methodological advance is the addition of physical stories to the original mentalizing protocol.

(3) With these tools, or a variant of these tools, this study has the potential to pave the way for further investigation of naïve biology and causal inference.

Weaknesses:

(1) This study is missing a big-picture question. It is not clear whether the authors investigate the neural correlates of causal reasoning or of naïve biology. If the former, the choice of an orthogonal task, making causal reasoning implicit, is questionable. If the latter, the choice of mechanical and physical controls can be seen as reductive and problematic.

(2) The rationale for focusing mostly on the precuneus is not clear and this choice could almost be seen as a post-hoc hypothesis.

(3) The choice of an orthogonal 'magic detection' task has three problematic consequences in this study:<br /> (a) It differs in nature from the 'mentalizing' task that consists of evaluating a character's beliefs explicitly from the corresponding story, which complicates the study of the relation between both tasks. While the authors do not compare both tasks directly, it is unclear to what extent this intrinsic difference between implicit versus explicit judgments of people's body versus mental states could influence the results.<br /> (b) The extent to which the failure to find shared neural machinery between both types of inferences (illness and mechanical) can be attributed to the implicit character of the task is not clear.<br /> (c) The introduction of a category of non-interest that contains only 36 trials compared to 38 trials for all four categories of interest creates a design imbalance.

(4) Another imbalance is present in the design of this study: the number of trials per category is not the same in each run of the main task. This imbalance does not seem to be accounted for in the 1st-level GLM and renders a bit problematic the subsequent use of MVPA.

(5) The main claim of the authors, encapsulated by the title of the present manuscript, is not tested directly. While the authors included in their protocol independent localizers for mentalizing, language, and logic, they did not include an independent localizer for "animacy". As such, they cannot provide a within-subject evaluation of their claim, which is entirely based on the presence of a partial overlap in PC (which is also involved in a wide range of tasks) with previous results on animacy.

Reviewer #3 (Public review):

Summary:

This study employed an implicit task, showing vignettes to participants while a bold signal was acquired. The aim was to capture automatic causal inferences that emerge during language processing and comprehension. In particular, the authors compared causal inferences about illness with two control conditions, causal inferences about mechanical failures and non-causal phrases related to illnesses. All phrases that were employed described contexts with people, to avoid animacy/inanimate confound in the results. The authors had a specific hypothesis concerning the role of the precuneus (PC) in being sensitive to causal inferences about illnesses.

These findings indicate that implicit causal inferences are facilitated by semantic networks specialized for encoding causal knowledge.

Strengths:

The major strength of the study is the clever design of the stimuli (which are nicely matched for a number of features) which can tease apart the role of the type of causal inference (illness-causal or mechanical-causal) and the use of two localizers (logic/language and mentalizing) to investigate the hypothesis that the language and/or logical reasoning networks preferentially respond to causal inference regardless of the content domain being tested (illnesses or mechanical).

Weaknesses:

I have identified the following main weaknesses:

(1) Precuneus (PC) and Temporo-Parietal junction (TPJ) show very similar patterns of results, and the manuscript is mostly focused on PC (also the abstract). To what extent does the fact that PC and TPJ show similar trends affect the inferences we can derive from the results of the paper? I wonder whether additional analyses (connectivity?) would help provide information about this network.

(2) Results are mainly supported by an univariate ROI approach, and the MVPA ROI approach is performed on a subregion of one of the ROI regions (left precuneus). Results could then have a limited impact on our understanding of brain functioning.

(3) In all figures: there are no measures of dispersion of the data across participants. The reader can only see aggregated (mean) data. E.g., percentage signal changes (PSC) do not report measures of dispersion of the data, nor do we have bold maps showing the overlap of the response across participants. Only in Figure 2, we see the data of 6 selected participants out of 20.

(4) Sometimes acronyms are defined in the text after they appear for the first time.

Author response:

We thank the editors and reviewers for their comments on our manuscript. We found the comments of the reviewers helpful and plan to add new text, analyses, and figures to answer some of the outstanding questions.

In response to the reviewers’ comments, we will clarify the goal of the paper in the introduction: to test the hypothesis that causal knowledge (i.e., an intuitive theory of biology) is embedded in domain-preferring semantic networks (i.e., semantic animacy network). This work links developmental psychology work on intuitive theories and cognitive neuroscience.

As we will emphasize in the revised manuscript, the primary goal of the current paper is to test the claim that semantic networks encode causal knowledge, rather than to rule out the contribution of domain-general reasoning mechanisms to causal inference.

In response to the reviewers’ suggestions, we will add multivariate and univariate whole-cortex analyses that provide further tests for domain-general causality responses. In particular, we will include new figures showing univariate responses to the mechanical inference condition over the non-causal control conditions as well as decoding between these conditions. The reviewers have also asked us to provide individual subject dispersion data. We appreciate this suggestion, and new figures will be added to display this information.

We will also perform additional analysis in the precuneus (PC) to look for shared responses to illness and mechanical inferences. In accordance with our hypotheses, we have shown that the PC responds preferentially to illness inferences. To address the reviewers’ concerns about the selectivity of the PC to illness inferences, we will compare responses to i) illness inferences compared to the noncausal conditions and ii) mechanical inferences compared to the noncausal conditions in the PC to investigate the extent to which a shared response to causal inference across domains emerges in this region.

Critically, we find that the cortical areas that distinguish between causal and non-causal conditions in a ‘domain general manner’ (i.e., for both illness and mechanical inferences) are driven by higher responses to the non-causal condition. Moreover, these responses in prefrontal cortex and elsewhere overlap an RT predictor of neural activity, suggesting that they may reflect difficulty effects.

These results suggest that in the current task, signatures of causal inference are primarily found in domain-preferring semantic networks, rather than in domain-general fronto-parietal reasoning systems. We will provide additional discussion of the argument that the current results do not speak against the role of domain general systems across all types of causal reasoning. Instead, they suggest that the types of implicit causal inferences measured in the current study depend primarily on domain-preferring semantic networks.

The reviewers have asked us to analyze responses to causal inferences about illness in the fusiform face area (FFA). We will perform this analysis. However, we note that univariate and multivariate whole-cortex analyses that are already included in the paper did not identify lateral ventral occipito-temporal cortex as a key region involved in causal inferences about illness. Further, we do not have FFA localizer data in the current participants; therefore, the results cannot be interpreted to reflect activity in functionally defined FFA.

Two reviewers asked us to justify our choice of an implicit magic-detection task, which we will now do more clearly in the manuscript. This task was selected to ensure that participants were attending to the meaning of the vignettes. The goal of the current study was to investigate implicit causal inferences that routinely occur in language comprehension, e.g., when someone is reading a book. Past work has shown that explicitly judging the causality of causal and non-causal stimuli results in differences in response times across conditions (e.g., Kuperberg et al., 2006). In the current study, such judgments would also have introduced a confound between the behavioral decision and the condition of interest: the use of an explicit causal judgment task makes it impossible to know whether any observed neural differences between causal and non-causal conditions are simply due to differences in the selection of task responses. The selection of an orthogonal magic-detection task limits these confounds from complicating our interpretation of the neural data.

One of the reviewers asked us to justify the number of catch trials that we decided to include in our paradigm. Approximately 20% of the vignettes were “magical” vignettes (the same proportion as each of the 4 experimental conditions) to encourage participants to remain attentive throughout the task. Since these catch trials are excluded from analysis, their proportion is unlikely to influence the results of the study. We will clarify this in the manuscript.

A question was raised about the balance of trial numbers across conditions and across runs. To address this, we will include individual comparisons of each causal condition (n=36) with each non-causal condition (n=36; i.e., equal trial counts) where they are not already shown. With regard to runs, each condition is shown either 6 or 7 times per run (maximum difference of 1 trial between conditions), and the number of trials per condition is equal across the whole experiment: each condition is shown 7 times in two of the runs and 6 times four of the runs. This minor design imbalance is typical of fMRI experiments and is unlikely to impact the results. We will clarify this in the manuscript.

We believe that our planned revisions will strengthen the paper and highlight its contributions to our understanding of the neural basis of implicit causal inference.

plutôt : "ne sont pas directement concernées...

Serait-il possible de mettre en italique (par exemple) les montants en point de PIB? Le tableau est peu lisible sinon, car tres grand.e

OK, c'et clair et prudent.

je dis surement une grosse bêtise mais visuellement on a l'impression sur ce graphique en euros par ménage que la somme pour le PLF 2025 faisait beaucoup plus que 20% en plus par rapport au PLS 2025, comment l'expliquer ? (dans le résumé vous comptez 5.2 Md€ sur PLS et 6.5 Md€ sur PLF) Par exemple pour les plus pauvres qu'est ce qui explique que l'effet dans la loi spéciale sera plus de 4 fois plus faible, alors que la hausse des taxes sur électricité et gaz est environ 2 fois plus faible en agrégé (2.2 Md€ vs 4.1 Md€)

La note 2 est dans le texte, donc elle a vocation a être supprimée, je suppose.

Je ne comprends pas le paragraphe. Il s'agit de la prévision d'un PLF en 2025? Cela semble contradictoire avec le paragraphe suivant. Je déconnecterais cela de la question du blocage politique: "dans le cas, d'un gel des dépenses en volume et non en valeur, l'ajustement budgétaire du coté de l'Etat..."

La Note 5 est plus inquietante. Je dirai la loi spéciale est "une solution très transitoire. Pour assurer le fonctionnement normal de l'Etat une loi de Finances est nécessaire en 2025".

La dernière phrase est plus un commentaire politique, qui affaiblit plutôt le resumé, me semble-t-il.

"La loi spéciale empeche de prendre" vous voulez dire "La loi spéciale ne permet pas de prendre des mesures..." (juste pour clarifier que l'on peut prendre des mesures nouvelles par la suite"

eLife Assessment

The authors examine the effect of cell-free chromatin particles (cfChPs) derived from human serum or from dying human cells on mouse cells in culture and propose that these cfChPs can serve as vehicles for cell-to-cell active transfer of foreign genetic elements. The work presented in this paper is intriguing and potentially important, but it is incomplete. At this stage, the claim that horizontal gene transfer can occur via cfChPs would strongly benefit from additional evidence emerging from multiple independent approaches. The evolutionary interpretations associated with the concept of "predatory genome" are premature based on the strength of evidence.

Reviewer #1 (Public review):

Summary:

Horizontal gene transfer is the transmission of genetic material between organisms through ways other than reproduction. Frequent in prokaryotes, this mode of genetic exchange is scarcer in eukaryotes, especially in multicellular eukaryotes. Furthermore, the mechanisms involved in eukaryotic HGT are unknown. This article by Banerjee et al. claims that HGT occurs massively between cells of multicellular organisms. According to this study, the cell free chromatin particles (cfChPs) that are massively released by dying cells are incorporated in the nucleus of neighboring cells. These cfChPs are frequently rearranged and amplified to form concatemers, they are made of open chromatin, expressed, and capable of producing proteins. Furthermore, the study also suggests that cfChPs transmit transposable elements (TEs) between cells on a regular basis, and that these TEs can transpose, multiply, and invade receiving cells. These conclusions are based on a series of experiments consisting in releasing cfChPs isolated from various human sera into the culture medium of mouse cells, and using FISH and immunofluorescence to monitor the state and fate of cfChPs after several passages of the mouse cell line.

Strengths:

The results presented in this study are interesting because they may reveal unsuspected properties of some cell types that may be able to internalize free-circulating chromatin, leading to its chromosomal incorporation, expression, and unleashing of TEs. The authors propose that this phenomenon may have profound impacts in terms of diseases and genome evolution. They even suggest that this could occur in germ cells, leading to within-organism HGT with long-term consequences.

Weaknesses:

The claims of massive HGT between cells through internalization of cfChPs are not well supported because they are only based on evidence from one type of methodological approach: immunofluorescence and fluorescent in situ hybridization (FISH) using protein antibodies and DNA probes. Yet, such strong claims require validation by at least one, but preferably multiple, additional orthogonal approaches. This includes, for example, whole genome sequencing (to validate concatemerization, integration in receiving cells, transposition in receiving cells), RNA-seq (to validate expression), ChiP-seq (to validate chromatin state).

Another weakness of this study is that it is performed only in one receiving cell type (NIH3T3 mouse cells). Thus, rather than a general phenomenon occurring on a massive scale in every multicellular organism, it could merely reflect aberrant properties of a cell line that for some reason became permeable to exogenous cfChPs. This begs the question of the relevance of this study for living organisms.

Should HGT through internalization of circulating chromatin occur on a massive scale, as claimed in this study, and as illustrated by the many FISH foci observed in Fig 3 for example, one would expect that the level of somatic mosaicism may be so high that it would prevent assembling a contiguous genome for a given organism. Yet, telomere-to-telomere genomes have been produced for many eukaryote species, calling into question the conclusions of this study.

Reviewer #2 (Public review):

I must note that my comments pertain to the evolutionary interpretations rather than the study's technical results. The techniques appear to be appropriately applied and interpreted, but I do not feel sufficiently qualified to assess this aspect of the work in detail.

I was repeatedly puzzled by the use of the term "function." Part of the issue may stem from slightly different interpretations of this word in different fields. In my understanding, "function" should denote not just what a structure does, but what it has been selected for. In this context, where it is unclear if cfChPs have been selected for in any way, the use of this term seems questionable.

Similarly, the term "predatory genome," used in the title and throughout the paper, appears ambiguous and unjustified. At this stage, I am unconvinced that cfChPs provide any evolutionary advantage to the genome. It is entirely possible that these structures have no function whatsoever and could simply be byproducts of other processes. The findings presented in this study do not rule out this neutral hypothesis. Alternatively, some particular components of the genome could be driving the process and may have been selected to do so. This brings us to the hypothesis that cfChPs could serve as vehicles for transposable elements. While speculative, this idea seems to be compatible with the study's findings and merits further exploration.

I also found some elements of the discussion unclear and speculative, particularly the final section on the evolution of mammals. If the intention is simply to highlight the evolutionary impact of horizontal transfer of transposable elements (e.g., as a source of new mutations), this should be explicitly stated. In any case, this part of the discussion requires further clarification and justification.

In summary, this study presents important new findings on the behavior of cfChPs when introduced into a foreign cellular context. However, it overextends its evolutionary interpretations, often in an unclear and speculative manner. The concept of the "predatory genome" should be better defined and justified or removed altogether. Conversely, the suggestion that cfChPs may function at the level of transposable elements (rather than the entire genome or organism) could be given more emphasis.

Author response:

Public Reviews:

Reviewer #1 (Public review):

Summary:

Horizontal gene transfer is the transmission of genetic material between organisms through ways other than reproduction. Frequent in prokaryotes, this mode of genetic exchange is scarcer in eukaryotes, especially in multicellular eukaryotes. Furthermore, the mechanisms involved in eukaryotic HGT are unknown. This article by Banerjee et al. claims that HGT occurs massively between cells of multicellular organisms. According to this study, the cell free chromatin particles (cfChPs) that are massively released by dying cells are incorporated in the nucleus of neighboring cells. These cfChPs are frequently rearranged and amplified to form concatemers, they are made of open chromatin, expressed, and capable of producing proteins. Furthermore, the study also suggests that cfChPs transmit transposable elements (TEs) between cells on a regular basis, and that these TEs can transpose, multiply, and invade receiving cells. These conclusions are based on a series of experiments consisting in releasing cfChPs isolated from various human sera into the culture medium of mouse cells, and using FISH and immunofluorescence to monitor the state and fate of cfChPs after several passages of the mouse cell line.

Strengths:

The results presented in this study are interesting because they may reveal unsuspected properties of some cell types that may be able to internalize free-circulating chromatin, leading to its chromosomal incorporation, expression, and unleashing of TEs. The authors propose that this phenomenon may have profound impacts in terms of diseases and genome evolution. They even suggest that this could occur in germ cells, leading to within-organism HGT with long-term consequences.

Weaknesses:

The claims of massive HGT between cells through internalization of cfChPs are not well supported because they are only based on evidence from one type of methodological approach: immunofluorescence and fluorescent in situ hybridization (FISH) using protein antibodies and DNA probes. Yet, such strong claims require validation by at least one, but preferably multiple, additional orthogonal approaches. This includes, for example, whole genome sequencing (to validate concatemerization, integration in receiving cells, transposition in receiving cells), RNA-seq (to validate expression), ChiP-seq (to validate chromatin state).

We agree with the reviewer’s suggestions. We propose to use RNA-seq using an orthogonal platform as a solution. This will allow us to answer multiple questions viz. validation of expression of human DNA in mouse cells, obtaining a detailed insight into genes and pathways driven by human cfChPs and enable us to identify chimeric human and mouse transcripts.

Another weakness of this study is that it is performed only in one receiving cell type (NIH3T3 mouse cells). Thus, rather than a general phenomenon occurring on a massive scale in every multicellular organism, it could merely reflect aberrant properties of a cell line that for some reason became permeable to exogenous cfChPs. This begs the question of the relevance of this study for living organisms.

We agree with the reviewer’s suggestion. We propose to show horizontal transfer of cfChPs using four different cell-lines representing four different species.

Should HGT through internalization of circulating chromatin occur on a massive scale, as claimed in this study, and as illustrated by the many FISH foci observed in Fig 3 for example, one would expect that the level of somatic mosaicism may be so high that it would prevent assembling a contiguous genome for a given organism. Yet, telomere-to-telomere genomes have been produced for many eukaryote species, calling into question the conclusions of this study.

The reviewer is right in expecting that the level of somatic mosaicism may be so high that it would prevent assembling a contiguous genome. This is indeed the case, and we find that beyond ~ 250 passages the genomes of the cfChPs treated NIH3T3 cells begin to die out apparently become their genomes have become too unstable for survival. This point will be highlighted in the revised version. It is likely that cell death resulting from large scale HGT creates a vicious cycle of more cell death induced by cfChPs thereby helping to explain the massive daily turnover of cells in the body (10⁹ – 10¹² cells per day).  

Reviewer #2 (Public review):

I must note that my comments pertain to the evolutionary interpretations rather than the study's technical results. The techniques appear to be appropriately applied and interpreted, but I do not feel sufficiently qualified to assess this aspect of the work in detail.

I was repeatedly puzzled by the use of the term "function." Part of the issue may stem from slightly different interpretations of this word in different fields. In my understanding, "function" should denote not just what a structure does, but what it has been selected for. In this context, where it is unclear if cfChPs have been selected for in any way, the use of this term seems questionable.

We think this is a matter of semantics. We have used the term “function” since cfChPs that enter the cell are biologically active; they transcribe, translate, synthesize, proteins and proliferate. We, therefore feel that the term function is not inappropriate.

Similarly, the term "predatory genome," used in the title and throughout the paper, appears ambiguous and unjustified. At this stage, I am unconvinced that cfChPs provide any evolutionary advantage to the genome. It is entirely possible that these structures have no function whatsoever and could simply be byproducts of other processes. The findings presented in this study do not rule out this neutral hypothesis. Alternatively, some particular components of the genome could be driving the process and may have been selected to do so. This brings us to the hypothesis that cfChPs could serve as vehicles for transposable elements. While speculative, this idea seems to be compatible with the study's findings and merits further exploration.

We take the reviewer’s point. We will replace the term “predatory genome” with a more neutral and factual term “supernumerary genome” in the title and throughout the manuscript in the revised version.

I also found some elements of the discussion unclear and speculative, particularly the final section on the evolution of mammals. If the intention is simply to highlight the evolutionary impact of horizontal transfer of transposable elements (e.g., as a source of new mutations), this should be explicitly stated. In any case, this part of the discussion requires further clarification and justification.

We propose to revise the “discussion” section taking into account the issues raised by the reviewer and highlight the potential role of cfChPs in evolution by acting as vehicles of transposable elements.  

In summary, this study presents important new findings on the behavior of cfChPs when introduced into a foreign cellular context. However, it overextends its evolutionary interpretations, often in an unclear and speculative manner. The concept of the "predatory genome" should be better defined and justified or removed altogether. Conversely, the suggestion that cfChPs may function at the level of transposable elements (rather than the entire genome or organism) could be given more emphasis.

Our responses to this paragraph are given in the two above sections.

Remove this section. Now we have RTS UI to enable CNG replay.

Now we have RTS UI to enable CNG replay, should remove the link to "Web settings" and add link to "Preferences view - Internet protocol"(https://docs-staging.docs.admlabs.aws.swinfra.net/adm-vugen-staginglab/en/latest_staging/help/WebHelp/Content/VuGen/UI/121550_ui_rts_ip_preferences.htm)

This UI change actually is to fix the inconsistent behavior between RTS UI and default.cfg. We'd better remove it from What's new.

Bắn cá 23win - Trò chơi bắn cá đổi thưởng hấp dẫn nhất hiện nay!

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eLife Assessment

Valencia et al. combine elegant in vitro biochemical experiments with functional assays in cardiomyocytes to determine which properties of the FHOD3 formin are essential for sarcomere assembly. Using separation-of-function mutants, they show that FHOD3's elongation activity, rather than its nucleation, capping, or bundling activities, is key to its sarcomeric function. This is an important finding; most of the data presented in the manuscript are convincing, with the exception of two experiments (presence of FHOD3 at the barbed end of actin filaments in the TIRF elongation assays and characterization of the GS-FH1 mutant phenotype) that would merit few additional controls.

Reviewer #1 (Public review):

Summary:

Formins are complex proteins with multiple effects on actin filament assembly, including nucleation, capping with processive elongation, and bundling. Determining which of these activities is important for a given biological process and normal cellular function is a major challenge.

Here, the authors study the formin FHOD3L, which is essential for normal sarcomere assembly in muscle cells. They identify point mutants of FHOD3L in which formin nucleation and elongation/bundling activities are functionally separated. Expression of these mutants in neonatal rat ventricular myocytes shows that the control of actin filament elongation by formin is the major activity required for the normal assembly of functional sarcomeres.

Strengths:

The strength of this work is to combine sensitive biochemical assays with excellent work in neonatal rat ventricular myocytes. This combination of approaches is highly effective for analyzing the function of proteins with multiple activities in vitro.

Weaknesses:

FHOD3L does not seem to be the easiest formin to study because of its relatively weak nucleation activity and the short duration of capping events. This difficulty imposes rigorous biochemical analysis and careful interpretation of the data, which should be improved in this work.

Reviewer #2 (Public review):

This article elucidates the biochemical and cellular mechanisms by which the FHOD-family of formins, particularly FHOD3, contributes to sarcomere formation and contractility in cardiomyocytes. Formins are mainly known to nucleate and elongate actin filaments, with certain family members also exhibiting capping, severing, and bundling activities. Although FHOD3 has been well-established as essential for sarcomere assembly in cardiomyocytes, its precise biochemical functions and contributions to actin dynamics remain poorly understood.

In this study, the authors combine in vitro biochemical assays with cellular experiments to dissect FHOD3's roles in actin assembly and sarcomere formation. They demonstrate that FHOD3 nucleates actin filaments and acts as a transient elongator, pausing elongation after an initial burst of filament growth. Using separation-of-function mutants, they show that FHOD3's elongation activity - rather than its nucleation, capping, or bundling capabilities - is key for its sarcomeric function.

The experiments have been conducted rigorously and well-analyzed, and the paper is clearly written. The data presented support the authors' conclusions. I appreciate the detailed description and rationale behind the FHOD3 constructs used in this study.

However, I was somewhat surprised and a bit disappointed that while the authors conducted single-color TIRF experiments to observe the effects of FHOD3 on single filaments, they did not use fluorescently labeled FHOD3 to directly visualize its behavior. Incorporating such experiments would significantly strengthen their conclusions regarding FHOD3's bursts of elongation interspersed with capping activity. While I understand this might require a few additional weeks of experiments, these data would add considerable value by directly testing the proposed mechanism.

There is a typo in the word "required" in line number 30. The authors also use fit data to extract parameters in several panels (e.g., Figures 2b, 2d, 3a, and 3b). While these fit functions may be intuitive to actin experts, explicitly describing the fit functions in the figure legends or methods would greatly benefit the broader readership.

Reviewer #3 (Public review):

Valencia et al. aim to elucidate the biochemical and cellular mechanisms through which the human formin FHOD3 drives sarcomere assembly in cardiomyocytes. To do so, they combined rigorous in vitro biochemical assays with comprehensive in vivo characterizations, evaluating two wild-type FHOD3 isoforms and two function-separating mutants. Surprisingly, they found that both wild-type FHOD3 isoforms can nucleate new actin filaments, as well as elongate existing actin filaments in conjunction with profilin following barbed-end capping. This is in addition to FHOD3's proposed role as an actin bundler. Next, the authors asked whether FHOD3L promotes sarcomere assembly in cardiomyocytes through its activity in actin nucleation or rather elongation. With two function-separating mutants, the authors evaluated the numbers and morphology of sarcomeres, as well as their ability to beat and generate cardiac rhythm. The authors found that while the wild-type FHOD3L and the K1193L mutant can rescue sarcomere morphology and physiology, the GS-FH1 mutant fails to do so. Given that in GS-FH1 mainly elongation activity is compromised, the authors concluded that the elongation activity of FHOD3 is essential for its role in sarcomere assembly in cardiomyocytes, while its nucleator activity is dispensable. Overall, this important study provided a broadened view on the biochemical activities of FHOD3, and a pioneering view on a possible cellular mechanism of how FHOD3L drives sarcomere assembly. If further validated, this can lead to new mechanistic models of sarcomere assembly and potentially new therapeutic targets of cardiomyopathy.

The conclusions of this paper are mostly well supported by the comprehensive biochemical analyses performed by the authors. However, the sarcomere assembly defect phenotype in the GS-FH1 rescue condition requires further investigation, as the extremely low level of GS-FH1 signal in transfected cells in Figure 6A may reflect a failure of actin-binding by this construct in vivo, rather than its inability to drive elongation. Though the authors do show in Figure 6 that GS-FH1 can bind to normal-looking sarcomeres when they are present, this may be due to a lack of siRNA activity in these cells, such that endogenous FHOD3L is still present. In this possible scenario, GS-FH1 may dimerize with endogenous FHOD3L. The authors should demonstrate that GS-FH1 alone can indeed interact with existing actin filaments in vivo. While this has been clearly demonstrated in vitro, given the more complex biochemical environment in vivo where additional unknown binding partners may present, cautions should be made when extrapolating findings from the former to the latter.

Author response:

We thank the reviewers for their careful readings of our paper and their very positive assessment. Here we address the two major concerns they raised, referring to the revised version of the manuscript that will be submitted:

(1) Important points were raised regarding the brief elongation events we reported. The time resolution and noise in our system reduce the accuracy of the burst velocity measurements. To address this, we have reached out to a colleague who is set up to repeat these measurements with microfluidics-assisted TIRF. The noise should be greatly reduced and the system is also optimal for directly visualizing labeled FHOD3, as suggested. We hope this experimental approach will provide new insights.

In the meantime, we analyzed our data more closely. We were asked about the pauses we observe before bursts of elongation and how we know they are functionally relevant. The short answer is that we do not know. We reported them because they were so common:  in three independent experiments with wild type FHOD3L-CT we analyzed a total of 20 filaments. We detected 112 dim regions and 97 of these were pause/burst events (~87%). Among the cases lacking a pause we include instances of apparent "double bursts" with no time for capping in between (which may be a time resolution issue) and some cases where the burst was in progress when data collection started. In the latter case, we cannot determine whether or not a pause was missed. We cannot rule out that this pause reflects an interaction with the surface but might expect the frequency to be lower if it were. In fact, we did detect pauses in the profilin-actin negative control but only 4 pauses were detected across 21 filaments analyzed compared to 97 pauses observed in the presence of wild type FHOD3L across 20 filaments analyzed. We will revise the text to make our conclusions about pauses more circumspect.

For comparison to our current data, we further analyzed the filaments in TIRF assays with no formin present. As the reviewers point out, inhomogeneities in filament intensity are normal. Thus, we examined any dim spots for pauses and/or bursts. We will report (future Figure 2G) that the velocity of growth of these dim spots was the same as the velocity of the rest of the filament. While our numbers may not be perfectly accurate due to the noise in our system, the difference of 3-4 fold increase versus no detectable change in rate is substantial and statistically different. In addition, we determined the number of dim spots per length of filament. We found a higher frequency of dim spots when FHOD3L-CT or FHOD3S-CT was present vs no formin, as will be shown in Figure 2 – figure supplement 1G and 2D.

We are convinced that the brief dim events we observed in the presence of FHOD3L-CT do, in fact, reflect formin-mediated elongation and hope that the reviewers concur. This does not preclude our interest in the microfluidics and two-color assays, which we will pursue in the future.

(2) The reviewers were concerned about the low protein levels in the GS-FH1 rescue experiments as reflected in the HA fluorescence intensity distributions shown in Fig. 5 – figure supplement 2A. While the scenario proposed could explain our observations with the GSFH1 rescues, it is quite complex and does not preclude the conclusion that the FH1 domain is critical. One limit of this scenario would be that the protein levels in the GS-FH1 cells reflect completely inactive protein, as opposed to FHOD3L that cannot elongate (by design). Given that the C-terminal half of the protein folds and functions and that the changes are made within an intrinsically disordered region, we do not favor this model. The reviewers suggest that the mutant protein detected in the few cells with (probably residual) sarcomeres could be stabilized, in part or entirely, by heterodimerization with residual endogenous wild type protein. We agree that heterodimerization is possible. The question becomes, how active is a heterodimer? If heterodimers have any activity, it seems far from sufficient to rescue sarcomere formation, suggesting that two functional FH1 domains are critical. To confirm this possibility, we would have to be able to determine whether the few sarcomeres present in these cases are residual and/or the new sarcomeres the low level of heterodimers could make. That said, we do not see evidence of correlation between protein levels and rescue at the level present in these cells (addressed below). Unfortunately, the proposed IP to test whether FHOD3L binds actin in vivo would only potentially report on filament side binding (both direct and indirect). It would not address whether the GS-FH1 mutant functions as a nucleator, elongator, bundler and/or capping protein in vivo.

If we assume that the protein present is active, the critical question that we can address is whether the phenotype is due to low protein levels or if the phenotype is due to loss of elongation activity by FHOD3L. To address this question, we revisited our data.

First, we plotted the distributions of the intensities of the cells we analyzed further, in addition to the automated readout of all the cells in the dish we originally presented (e.g. Fig. 4 – figure supplement 2A,B). These cells were selected randomly and, as should be the case, the distributions of their intensities agree well with the original distributions for the three different rescue constructs: FHOD3L, K1193L, and GS-FH1 (Fig. 6 – figure supplement 1A,B). We then asked whether there was any correlation between HA intensities with the sarcomere metrics. Consistent with in our pilot data, no correlation is evident in any of the three cases across the range of intensities we collected (400 – 2700 a.u.) (Fig. 6 – figure supplement 1C,D,E). We were originally satisfied with the GS-FH1 data, despite the low average intensity levels, because the intensities were well within the range that we established in pilot studies. These data reconfirm that the intensity levels are reasonable in a larger study.

To more specifically address the question of whether low HA fluorescence intensity is likely to reflect sufficient protein levels to build sarcomeres, we re-examined two data sets from the FHOD3L WT rescue data. We found that, by chance, the first replicate of data from the wild type rescue has a comparable intensity distribution to that of the GSFH1 rescues (580 +/- 261 / cell vs. 548 +/- 105 / cell). In addition, we collected all of the data from cells with intensity levels <720, selected to mimic the distribution of the GS-FH1 cells (Fig. 6 – figure supplement 3A). We then compared the sarcomere metrics (sarcomere number, sarcomere length, sarcomere width) between the full data set and the two low intensity subsets using statistical tests as reported for the rest of the cell biology data set:

· Sarcomere number is the only non-normal metric. We therefore used the Mann Whitney U test for each pairwise comparison, which shows no difference between all 3 WT distributions.

· We compared Z-line lengths by Student’s two-sample, unpaired t-test for each pairwise comparison, again finding no significant difference for all distributions.

· Sarcomere length shows a weakly significant difference (p=0.017 (compared to 0.033 for 3 treatment groups based on Bonferroni correction)) between the whole WT data set and bio rep 1, but no difference between the whole WT data set and the HA<720 group via Student’s two-sample, unpaired t-test.

An alternate statistical analysis approach, one-way ANOVA and Tukey post hoc tests, gave similar results. Thus, cells expressing wild type FHOD3L at levels comparable to levels detected in GS-FH1 mutant rescues, are fully rescued. Based on these findings we conclude that the expression levels in the GS-FH1 are high enough to rescue the FHOD3 knock down, supporting our conclusion that the defect is due to loss of elongation activity. We will add this analysis and discussion to the revised manuscript.

In future studies we will design less severe mutations to the FH1 domain. We hope to identify one with a strong effect on elongation and another with an intermediate effect. Once the best candidates are characterized in vitro, we will test them in our rescue experiments. If the strong mutant mimics the GS-FH1 rescue and the intermediate mutant is less severe, we will have strengthened our conclusion that elongation is a critical FHOD3L activity in sarcomere formation.

Additional improvements will be made to the manuscript based on recommendations we received from the reviewers.

The Atlantic. Review of Plutarch’s Lives, by Arthur Hugh Clough, John Dryden, and Plutarch. January 1860. https://www.theatlantic.com/magazine/archive/1860/01/plutarchs-lives/627616/

Some excellent quotes and evidence for the importance of Plutarch's Lives, almost more so than the importance of this particular translation.

for - definition - nyam - Tibetan term for intense psychophysical meditative experience - from Medium article - Nyams I have known and loved - Alexander Vezhnevets - 2022, Apr 28

DOI: 10.7554/eLife.98514.2

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DOI: 10.7554/eLife.98514.2

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DOI: 10.14309/01.ajg.0000966496.32906.2f

Resource: None

Curator: @sjvitug

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DOI: 10.14309/01.ajg.0000966496.32906.2f

Resource: (RCB Cat# RCB2767, RRID:CVCL_0594)

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DOI: 10.14309/01.ajg.0000966496.32906.2f

Resource: (KCLB Cat# 10171, RRID:CVCL_0440)

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DOI: 10.14309/01.ajg.0000966496.32906.2f

Resource: (RCB Cat# RCB0461, RRID:CVCL_0021)

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DOI: 10.14309/01.ajg.0000966496.32906.2f

Resource: (NIH-ARP Cat# 2188-324, RRID:CVCL_0022)

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DOI: 10.1186/s13062-024-00578-8

Resource: (NCI-DTP Cat# BT-549, RRID:CVCL_1092)

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DOI: 10.1186/s13062-024-00578-8

Resource: (NCI-DTP Cat# MCF7, RRID:CVCL_0031)

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DOI: 10.1186/s13062-024-00578-8

Resource: (RRID:CVCL_0179)

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Traceback (most recent call last): File "/home/ubuntu/dashboard/py/create_release_tables.py", line 54, in format_anno_for_release parsedanno = HypothesisAnnotation(anno) File "/home/ubuntu/dashboard/py/hypothesis.py", line 231, in init self.links = row['document']['link'] TypeError: string indices must be integers

Traceback (most recent call last): File "/home/ubuntu/dashboard/py/create_release_tables.py", line 54, in format_anno_for_release parsedanno = HypothesisAnnotation(anno) File "/home/ubuntu/dashboard/py/hypothesis.py", line 231, in init self.links = row['document']['link'] TypeError: string indices must be integers

Traceback (most recent call last): File "/home/ubuntu/dashboard/py/create_release_tables.py", line 54, in format_anno_for_release parsedanno = HypothesisAnnotation(anno) File "/home/ubuntu/dashboard/py/hypothesis.py", line 231, in init self.links = row['document']['link'] TypeError: string indices must be integers

Traceback (most recent call last): File "/home/ubuntu/dashboard/py/create_release_tables.py", line 54, in format_anno_for_release parsedanno = HypothesisAnnotation(anno) File "/home/ubuntu/dashboard/py/hypothesis.py", line 231, in init self.links = row['document']['link'] TypeError: string indices must be integers

DOI: 10.1126/scisignal.ado6057

Resource: RRID:IMSR_JAX:000664

Curator: @sjvitug

SciCrunch record: RRID:IMSR_JAX:000664

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DOI: 10.1126/scisignal.adn2616

Resource: (Cell Signaling Technology Cat# 8707, RRID:AB_2722660)

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DOI: 10.1126/scisignal.adn2616

Resource: (Sigma-Aldrich Cat# A2228, RRID:AB_476697)

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DOI: 10.1126/scisignal.adn2616

Resource: (Millipore Cat# MAB377, RRID:AB_2298772)

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DOI: 10.1126/scisignal.adk4122

Resource: (RRID:CVCL_4784)

Curator: @sjvitug

SciCrunch record: RRID: CVCL_4784

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What is this?

DOI: 10.1126/scisignal.adk4122

Resource: (Abcam Cat# ab133273, RRID:AB_11156085)

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What is this?

DOI: 10.1126/scisignal.adk4122

Resource: (Sigma-Aldrich Cat# A5441, RRID:AB_476744)

Curator: @sjvitug

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What is this?

DOI: 10.1126/scisignal.adk4122

Resource: (Cell Signaling Technology Cat# 2535, RRID:AB_331250)

Curator: @sjvitug

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DOI: 10.1126/scisignal.adk4122

Resource: (Thermo Fisher Scientific Cat# 32-3300, RRID:AB_2533074)

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What is this?

DOI: 10.1126/scisignal.adk4122

Resource: (Cell Signaling Technology Cat# 9661, RRID:AB_2341188)

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DOI: 10.1126/scisignal.adk4122

Resource: None

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DOI: 10.1126/scisignal.adi8753

Resource: (BioLegend Cat# 110730, RRID:AB_1134168)

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DOI: 10.1126/scisignal.adi8753

Resource: (Cell Signaling Technology Cat# 5018, RRID:AB_10695861)

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DOI: 10.1126/scisignal.adi8753

Resource: (BioLegend Cat# 100414, RRID:AB_312699)

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What is this?

DOI: 10.1126/scisignal.adi8753

Resource: (BioLegend Cat# 110720, RRID:AB_492864)

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What is this?

DOI: 10.1126/scisignal.adi8753

Resource: (BioLegend Cat# 116204, RRID:AB_313705)

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What is this?

DOI: 10.1126/scisignal.adi8753

Resource: (BioLegend Cat# 100724, RRID:AB_389326)

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What is this?

DOI: 10.1126/scisignal.adi8753

Resource: (BioLegend Cat# 108404, RRID:AB_313369)

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What is this?

DOI: 10.1126/scisignal.adi8753

Resource: (BioLegend Cat# 103030, RRID:AB_830787)

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What is this?

DOI: 10.1126/scisignal.adi8753

Resource: (BioLegend Cat# 202507, RRID:AB_492885)

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What is this?

DOI: 10.1126/scisignal.adi8753

Resource: (BioLegend Cat# 117304, RRID:AB_313773)

Curator: @inessasarian

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What is this?

DOI: 10.1126/scisignal.adi8753

Resource: (BioLegend Cat# 100732, RRID:AB_893423)

Curator: @inessasarian

SciCrunch record: RRID:AB_893423

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What is this?

DOI: 10.1126/scisignal.adi8753

Resource: (BioLegend Cat# 104408, RRID:AB_313095)

Curator: @inessasarian

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What is this?

DOI: 10.1126/scisignal.adi8753

Resource: (BioLegend Cat# 100434, RRID:AB_893324)

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What is this?

DOI: 10.1126/scisignal.adi8753

Resource: (BioLegend Cat# 100422, RRID:AB_312707)

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What is this?

DOI: 10.1126/sciimmunol.adp6529

Resource: None

Curator: @inessasarian

SciCrunch record: RRID:AB_1227785

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What is this?

DOI: 10.1126/sciimmunol.adp6529

Resource: (BioLegend Cat# 675504 (also 675503), RRID:AB_2571894)

Curator: @inessasarian

SciCrunch record: RRID:AB_2571894

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What is this?

DOI: 10.1126/sciimmunol.adp6529

Resource: None

Curator: @inessasarian

SciCrunch record: RRID:AB_2892311

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What is this?

DOI: 10.1126/sciimmunol.adp6529

Resource: (BioLegend Cat# 134712, RRID:AB_2563088)

Curator: @inessasarian

SciCrunch record: RRID:AB_2563088

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What is this?

DOI: 10.1126/sciimmunol.adp6529

Resource: (BioLegend Cat# 102407, RRID:AB_312902)

Curator: @inessasarian

SciCrunch record: RRID:AB_312902

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What is this?

DOI: 10.1126/sciimmunol.adp6529

Resource: (BioLegend Cat# 140004, RRID:AB_10643583)

Curator: @inessasarian

SciCrunch record: RRID:AB_10643583

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What is this?

DOI: 10.1126/sciimmunol.adp6529

Resource: (BD Biosciences Cat# 556028, RRID:AB_396304)

Curator: @inessasarian

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What is this?

DOI: 10.1126/sciadv.ads0652

Resource: RRID:BDSC_78316

Curator: @inessasarian

SciCrunch record: RRID:BDSC_78316

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What is this?

DOI: 10.1126/sciadv.adr5947

Resource: (RRID:CVCL_0062)

Curator: @inessasarian

SciCrunch record: RRID:CVCL_0062

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What is this?

Traceback (most recent call last): File "/home/ubuntu/dashboard/py/create_release_tables.py", line 54, in format_anno_for_release parsedanno = HypothesisAnnotation(anno) File "/home/ubuntu/dashboard/py/hypothesis.py", line 231, in init self.links = row['document']['link'] TypeError: string indices must be integers

Traceback (most recent call last): File "/home/ubuntu/dashboard/py/create_release_tables.py", line 54, in format_anno_for_release parsedanno = HypothesisAnnotation(anno) File "/home/ubuntu/dashboard/py/hypothesis.py", line 231, in init self.links = row['document']['link'] TypeError: string indices must be integers

DOI: 10.1085/jgp.202313488

Resource: (Vector Laboratories Cat# H-1500, RRID:AB_2336788)

Curator: @sjvitug

SciCrunch record: RRID:AB_2336788

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What is this?

DOI: 10.1085/jgp.202313488

Resource: (IMSR Cat# JAX_012569,RRID:IMSR_JAX:012569)

Curator: @sjvitug

SciCrunch record: RRID:IMSR_JAX:012569

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What is this?

DOI: 10.1085/jgp.202313488

Resource: (Molecular Probes Cat# A-21424, RRID:AB_141780)

Curator: @sjvitug

SciCrunch record: RRID:AB_141780

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What is this?

DOI: 10.1085/jgp.202313488

Resource: (BioLegend Cat# 801201, RRID:AB_2313773)

Curator: @sjvitug

SciCrunch record: RRID:AB_2313773

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What is this?

Traceback (most recent call last): File "/home/ubuntu/dashboard/py/create_release_tables.py", line 54, in format_anno_for_release parsedanno = HypothesisAnnotation(anno) File "/home/ubuntu/dashboard/py/hypothesis.py", line 182, in init if 'link' in row['document']: TypeError: argument of type 'NoneType' is not iterable

Traceback (most recent call last): File "/home/ubuntu/dashboard/py/create_release_tables.py", line 54, in format_anno_for_release parsedanno = HypothesisAnnotation(anno) File "/home/ubuntu/dashboard/py/hypothesis.py", line 182, in init if 'link' in row['document']: TypeError: argument of type 'NoneType' is not iterable

Traceback (most recent call last): File "/home/ubuntu/dashboard/py/create_release_tables.py", line 54, in format_anno_for_release parsedanno = HypothesisAnnotation(anno) File "/home/ubuntu/dashboard/py/hypothesis.py", line 182, in init if 'link' in row['document']: TypeError: argument of type 'NoneType' is not iterable

Traceback (most recent call last): File "/home/ubuntu/dashboard/py/create_release_tables.py", line 54, in format_anno_for_release parsedanno = HypothesisAnnotation(anno) File "/home/ubuntu/dashboard/py/hypothesis.py", line 231, in init self.links = row['document']['link'] TypeError: string indices must be integers

DOI: 10.1038/s41467-024-54852-4

Resource: (Abcam Cat# ab37185, RRID:AB_778944)

Curator: @sjvitug

SciCrunch record: RRID:AB_778944

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What is this?

DOI: 10.1038/s41467-024-54852-4

Resource: (RRID:CVCL_UR31)

Curator: @sjvitug

SciCrunch record: RRID:CVCL_UR31

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What is this?

DOI: 10.1038/s41467-024-54852-4

Resource: (RRID:CVCL_5J07)

Curator: @sjvitug

SciCrunch record: RRID:CVCL_5J07

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What is this?

DOI: 10.1038/s41467-024-54852-4

Resource: None

Curator: @sjvitug

SciCrunch record: RRID:CVCL_C9CY

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What is this?

DOI: 10.1038/s41467-024-54852-4

Resource: (Cell Signaling Technology Cat# 7074, RRID:AB_2099233)

Curator: @sjvitug

SciCrunch record: RRID:AB_2099233

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What is this?

Traceback (most recent call last): File "/home/ubuntu/dashboard/py/create_release_tables.py", line 54, in format_anno_for_release parsedanno = HypothesisAnnotation(anno) File "/home/ubuntu/dashboard/py/hypothesis.py", line 231, in init self.links = row['document']['link'] TypeError: string indices must be integers

Traceback (most recent call last): File "/home/ubuntu/dashboard/py/create_release_tables.py", line 54, in format_anno_for_release parsedanno = HypothesisAnnotation(anno) File "/home/ubuntu/dashboard/py/hypothesis.py", line 231, in init self.links = row['document']['link'] TypeError: string indices must be integers

Traceback (most recent call last): File "/home/ubuntu/dashboard/py/create_release_tables.py", line 54, in format_anno_for_release parsedanno = HypothesisAnnotation(anno) File "/home/ubuntu/dashboard/py/hypothesis.py", line 231, in init self.links = row['document']['link'] TypeError: string indices must be integers

DOI: 10.1021/acs.nanolett.3c03509

Resource: (Abcam Cat# ab190565, RRID:AB_2732785)

Curator: @sjvitug

SciCrunch record: RRID:AB_2732785

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What is this?

DOI: 10.1021/acs.nanolett.3c03509

Resource: (BioLegend Cat# 644710, RRID:AB_2566685)

Curator: @sjvitug

SciCrunch record: RRID:AB_2566685

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What is this?

DOI: 10.1021/acs.nanolett.3c03509

Resource: (BioLegend Cat# 102523, RRID:AB_2572181)

Curator: @sjvitug

SciCrunch record: RRID:AB_2572181

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What is this?

DOI: 10.1021/acs.nanolett.3c03509

Resource: (BioLegend Cat# 156604, RRID:AB_2783138)

Curator: @sjvitug

SciCrunch record: RRID:AB_2783138

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What is this?