Qal) to come to an end (

teeth, ivory,

**Qal)perish, die, be exterminated perish, vanish (fig.) be lost, strayed

**(Piel)to destroy, kill, cause to perish, to give up (as lost), exterminate to blot out, do away with, cause to vanish, (fig.) cause to stray, lose

Summer, summer fruit, harvest

Winter, harvest-time

Hiphil)to smite, strike, beat, scourge, clap, applaud, give a thrust to smite, kill, slay (man or beast) to smite, attack, attack and destroy, conquer, subjugate, ravage to smite, chastise, send judgment upon, punish, destroy

Qal)perish, die, be exterminated vanish (fig.) be lost, strayed

Qal)perish, die, be exterminated perish, vanish (fig.) be lost, strayed

hosts, army, war צָבָא

How the Hieroglyphics were decodified?

• Europeans were missing a key piece of the puzzle and had been for 2 000 years. They had been trying to figure out how to read hieroglyphics for centuries but the only instructions on how to do so came from ancient Greek and Roman writers who insisted that they were ideographies using pictures to indicate concepts. While that was true sometimes they could also be phonetic indicating sounds the same way as alphabetic languages do. This misunderstanding was inherited all the way to the 1800s.
• Medieval Muslim researchers tried to crack the code and failed though two did discover that some of the code lined up with Coptic, a descendant of ancient Egyptian. Later when Renaissance alchemists attempted to read the texts hoping to learn ancient spells, healing practices and other wonders, they had even less luck.
• It wasn't in until 1814 that an English polymath named Thomas Young made the first real progress. Young, a medical doctor, scientist and linguist at first just busied himself with translating the Demonic section of the Rosetta Stone. However, after a conversation with another researcher (who suggested that the ptolemies being Greek might have written their names phonetically in hieroglyphics) he decided to jump sections. He reasoned that finding the Royal name should be easy enough since it had been suggested that they were always in a circle that we now know as a Cartouche and sure enough he found the name Ptolemy. Upon further study, Young found 80 similarities between the hieroglyphic section of the stone and the Demonic one.
• Young's work stalled as he incorrectly assumed that hieroglyphics were logographic symbols with each symbol representing a word (like Chinese or Japanese) and that only the Greek names would have phonetic equivalents.

In comes Jean Francois Champollion!

• Champollion had been attempting to translate hieroglyphics from his knowlege of Copic and Demotic believing that they were in fact phonetic. However, being in France he had to work off print copies of the stone and probably never got to see the actual Rosetta Stone.
• champollion used his earlier work on demonic and knowledge of Coptic to reconstruct theoretical cartouches of common Egyptian royal names> His hope was that these cartouches, should he find them in inscriptions, would gradually unlock more hieroglyphic characters. This he did while also feuding with rivals and periodically going into exile for his continued support of Napoleon.
• Then when Banks (see below about Banks) sent him a print of the inscriptions on his Obelisk champollion stopped dead. There on the side was his reconstruction of Cleopatra! He went into a feverish blitz of work and began to realize that Egyptian hieroglyphics were a mix of ideographic and phonetic characters.
• It was i 1822 when it all clicked. He read the name Thutmose from an imported inscription, then checked it against the Rosetta Stone. He then bolted from his desk ran down the street to his brother's house and supposedly screamed "I've got it" before collapsing in a dramatic faint.
• In1829 he fulfilled his lifelong dream of traveling to Egypt. Once there, he found a vanished world beginning to speak to him. Using his dictionary and grammar system, he read the words of Gods and Priests off the temple walls. He uncovered Kings whose names had not been spoken in a millennia and in the Papyrus Scrolls preserved in the Arid deserts of Upper Egypt he found the words of the common people even though he'd never laid eyes on it.

About Banks mentioned above

John Banks was touring Egypt when he fell in love with a 22 foot tall six-ton Obelisk and decided that it would look great in front of his yard as it also had inscriptions in hieroglyphics and Greek. He hoped it would be a second Rosetta Stone. So he did what anyone would do: hired an Italian circus strongman to coordinate hauling it back to his estate in England.

copying sqlite databases from remote sources can be done faster by .dump it to a text file, this will be smaller than the db if you have lots of indexes (to speed queries up) then compress the text file download, unzip, and reconstruct locally as sqlite db

Para Cardumem el enfoque, como he dicho en otros lados es diferente, eligiendo una arquitectura federada, que incluye los servidores ejecutándose localmente y con menores complejidades arquitectónicas.

Una arquitectura donde cada cual pueda fácilmente descargar y ejecutar un servidor completo y comunicarlo con otros, es para efectos prácticos una arquitectura federada, con la posibildad de convertirse en P2P.

Una arquitectura federada/P2P no es garantía de descentralización, como vemos pasó con la web, diría yo debido a la dificultad de montar y desplegar servidores. Y si bien se ejercen fuerzas extremas de centralización sobre sistemas como el correo electrónico y los podcast, estos continúan siendo federados. Además, el fediverso ha adquirido un nuevo auge tras la compra de Twitter, pero enfrenta sus propios desafíos.

Diría que se requiere no sólo una manera frugal de poner a funcionar la tecnología, sino de disponerla a terceros para sus usos colectivos. Acá pareciera ser que el cuello de botella es el hospedaje y habría que mirar cómo hacerlo barato y amigable.

123

The. https://jamaicanpatwah.com/term/di/963

Again, these sentences make a parallel with "Gold comes rushing out the rivers straight into your hands": the all evoke the illusionary hopes and dreams of immigrants entering a new land and abandoning their own.

Thre are existing code libraries for Javascript and PHP (and Python) into Zotero

Zotero api

Calibre API documentation.

Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

Learn more at Review Commons

Reply to the reviewers

Reviewer #1 (Evidence, reproducibility and clarity (Required)):

*The authors have a longstanding focus and reputation on single cell sequencing technology development and application. In this current study, the authors developed a novel single-cell multi-omic assay termed "T-ChIC" so that to jointly profile the histone modifications along with the full-length transcriptome from the same single cells, analyzed the dynamic relationship between chromatin state and gene expression during zebrafish development and cell fate determination. In general, the assay works well, the data look convincing and conclusions are beneficial to the community. *

Thank you for your positive feedback.

*There are several single-cell methodologies all claim to co-profile chromatin modifications and gene expression from the same individual cell, such as CoTECH, Paired-tag and others. Although T-ChIC employs pA-Mnase and IVT to obtain these modalities from single cells which are different, could the author provide some direct comparisons among all these technologies to see whether T-ChIC outperforms? *

In a separate technical manuscript describing the application of T-ChIC in mouse cells (Zeller, Blotenburg et al 2024, bioRxiv, 2024.05. 09.593364), we have provided a direct comparison of data quality between T-ChIC and other single-cell methods for chromatin-RNA co-profiling (Please refer to Fig. 1C,D and Fig. S1D, E, of the preprint). We show that compared to other methods, T-ChIC is able to better preserve the expected biological relationship between the histone modifications and gene expression in single cells.

*In current study, T-ChIC profiled H3K27me3 and H3K4me1 modifications, these data look great. How about other histone modifications (eg H3K9me3 and H3K36me3) and transcription factors? *

While we haven't profiled these other modifications using T-ChIC in Zebrafish, we have previously published high quality data on these histone modifications using the sortChIC method, on which T-ChIC is based (Zeller, Yeung et al 2023). In our comparison, we find that histone modification profiles between T-ChIC and sortChIC are very similar (Fig. S1C in Zeller, Blotenburg et al 2024). Therefore the method is expected to work as well for the other histone marks.

*T-ChIC can detect full length transcription from the same single cells, but in FigS3, the authors still used other published single cell transcriptomics to annotate the cell types, this seems unnecessary? *

We used the published scRNA-seq dataset with a larger number of cells to homogenize our cell type labels with these datasets, but we also cross-referenced our cluster-specific marker genes with ZFIN and homogenized the cell type labels with ZFIN ontology. This way our annotation is in line with previous datasets but not biased by it. Due the relatively smaller size of our data, we didn't expect to identify unique, rare cell types, but our full-length total RNA assay helps us identify non-coding RNAs such as miRNA previously undetected in scRNA assays, which we have now highlighted in new figure S1c .

*Throughout the manuscript, the authors found some interesting dynamics between chromatin state and gene expression during embryogenesis, independent approaches should be used to validate these findings, such as IHC staining or RNA ISH? *

We appreciate that the ISH staining could be useful to validate the expression pattern of genes identified in this study. But to validate the relationships between the histone marks and gene expression, we need to combine these stainings with functional genomics experiments, such as PRC2-related knockouts. Due to their complexity, such experiments are beyond the scope of this manuscript (see also reply to reviewer #3, comment #4 for details).

*In Fig2 and FigS4, the authors showed H3K27me3 cis spreading during development, this looks really interesting. Is this zebrafish specific? H3K27me3 ChIP-seq or CutTag data from mouse and/or human embryos should be reanalyzed and used to compare. The authors could speculate some possible mechanisms to explain this spreading pattern? *

Thanks for the suggestion. In this revision, we have reanalysed a dataset of mouse ChIP-seq of H3K27me3 during mouse embryonic development by Xiang et al (Nature Genetics 2019) and find similar evidence of spreading of H3K27me3 signal from their pre-marked promoter regions at E5.5 epiblast upon differentiation (new Figure S4i). This observation, combined with the fact that the mechanism of pre-marking of promoters by PRC1-PRC2 interaction seems to be conserved between the two species (see (Hickey et al., 2022), (Mei et al., 2021) & (Chen et al., 2021)), suggests that the dynamics of H3K27me3 pattern establishment is conserved across vertebrates. But we think a high-resolution profiling via a method like T-ChIC would be more useful to demonstrate the dynamics of signal spreading during mouse embryonic development in the future. We have discussed this further in our revised manuscript.

Reviewer #1 (Significance (Required)):

*The authors have a longstanding focus and reputation on single cell sequencing technology development and application. In this current study, the authors developed a novel single-cell multi-omic assay termed "T-ChIC" so that to jointly profile the histone modifications along with the full-length transcriptome from the same single cells, analyzed the dynamic relationship between chromatin state and gene expression during zebrafish development and cell fate determination. In general, the assay works well, the data look convincing and conclusions are beneficial to the community. *

Thank you very much for your supportive remarks.

Reviewer #2 (Evidence, reproducibility and clarity (Required)):

*Joint analysis of multiple modalities in single cells will provide a comprehensive view of cell fate states. In this manuscript, Bhardwaj et al developed a single-cell multi-omics assay, T-ChIC, to simultaneously capture histone modifications and full-length transcriptome and applied the method on early embryos of zebrafish. The authors observed a decoupled relationship between the chromatin modifications and gene expression at early developmental stages. The correlation becomes stronger as development proceeds, as genes are silenced by the cis-spreading of the repressive marker H3k27me3. Overall, the work is well performed, and the results are meaningful and interesting to readers in the epigenomic and embryonic development fields. There are some concerns before the manuscript is considered for publication. *

We thank the reviewer for appreciating the quality of our study.

*Major concerns: *

1. • A major point of this study is to understand embryo development, especially gastrulation, with the power of scMulti-Omics assay. However, the current analysis didn't focus on deciphering the biology of gastrulation, i.e., lineage-specific pioneer factors that help to reform the chromatin landscape. The majority of the data analysis is based on the temporal dimension, but not the cell-type-specific dimension, which reduces the value of the single-cell assay. *

We focused on the lineage-specific transcription factor activity during gastrulation in Figure 4 and S8 of the manuscript and discovered several interesting regulators active at this stage. During our analysis of the temporal dimension for the rest of the manuscript, we also classified the cells by their germ layer and "latent" developmental time by taking the full advantage of the single-cell nature of our data. Additionally, we have now added the cell-type-specific H3K27-demethylation results for 24hpf in response to your comment below. We hope that these results, together with our openly available dataset would demonstrate the advantage of the single-cell aspect of our dataset.

1. *The cis-spreading of H3K27me3 with developmental time is interesting. Considering H3k27me3 could mark bivalent regions, especially in pluripotent cells, there must be some regions that have lost H3k27me3 signals during development. Therefore, it's confusing that the authors didn't find these regions (30% spreading, 70% stable). The authors should explain and discuss this issue. *

Indeed we see that ~30% of the bins enriched in the pluripotent stage spread, while 70% do not seem to spread. In line with earlier observations(Hickey et al., 2022; Vastenhouw et al., 2010), we find that H3K27me3 is almost absent in the zygote and is still being accumulated until 24hpf and beyond. Therefore the majority of the sites in the genome still seem to be in the process of gaining H3K27me3 until 24hpf, explaining why we see mostly "spreading" and "stable" states. Considering most of these sites are at promoters and show signs of bivalency, we think that these sites are marked for activation or silencing at later stages. We have discussed this in the manuscript ("discussion"). However, in response to this and earlier comment, we went back and searched for genes that show H3K27-demethylation in the most mature cell types (at 24 hpf) in our data, and found a subset of genes that show K27 demethylation after acquiring them earlier. Interestingly, most of the top genes in this list are well-known as developmentally important for their corresponding cell types. We have added this new result and discussed it further in the manuscript (Fig. 2d,e, , Supplementary table 3).

*Minors: *

1. • The authors cited two scMulti-omics studies in the introduction, but there have been lots of single-cell multi-omics studies published recently. The authors should cite and consider them. *

We have cited more single-cell chromatin and multiome studies focussed on early embryogenesis in the introduction now.

*2. T-ChIC seems to have been presented in a previous paper (ref 15). Therefore, Fig. 1a is unnecessary to show. *

Figure 1a. shows a summary of our Zebrafish TChIC workflow, which contains the unique sample multiplexing and sorting strategy to reduce batch effects, which was not applied in the original TChIC workflow. We have now clarified this in "Results".

1. *It's better to show the percentage of cell numbers (30% vs 70%) for each heatmap in Figure 2C. *

We have added the numbers to the corresponding legends.

1. *Please double-check the citation of Fig. S4C, which may not relate to the conclusion of signal differences between lineages. *

The citation seems to be correct (Fig. S4C supplements Fig. 2C, but shows mesodermal lineage cells) but the description of the legend was a bit misleading. We have clarified this now.

*5. Figure 4C has not been cited or mentioned in the main text. Please check. *

Thanks for pointing it out. We have cited it in Results now.

Reviewer #2 (Significance (Required)):

*Strengths: This work utilized a new single-cell multi-omics method and generated abundant epigenomics and transcriptomics datasets for cells covering multiple key developmental stages of zebrafish. *

*Limitations: The data analysis was superficial and mainly focused on the correspondence between the two modalities. The discussion of developmental biology was limited. *

*Advance: The zebrafish single-cell datasets are valuable. The T-ChIC method is new and interesting. *

*The audience will be specialized and from basic research fields, such as developmental biology, epigenomics, bioinformatics, etc. *

*I'm more specialized in the direction of single-cell epigenomics, gene regulation, 3D genomics, etc. *

Thank you for your remarks.

Reviewer #3 (Evidence, reproducibility and clarity (Required)):

*This manuscript introduces T‑ChIC, a single‑cell multi‑omics workflow that jointly profiles full‑length transcripts and histone modifications (H3K27me3 and H3K4me1) and applies it to early zebrafish embryos (4-24 hpf). The study convincingly demonstrates that chromatin-transcription coupling strengthens during gastrulation and somitogenesis, that promoter‑anchored H3K27me3 spreads in cis to enforce developmental gene silencing, and that integrating TF chromatin status with expression can predict lineage‑specific activators and repressors. *

*Major concerns *

1. *Independent biological replicates are absent, so the authors should process at least one additional clutch of embryos for key stages (e.g., 6 hpf and 12 hpf) with T‑ChIC and demonstrate that the resulting data match the current dataset. *

Thanks for pointing this out. We had, in fact, performed T-ChIC experiments in four rounds of biological replicates (independent clutch of embryos) and merged the data to create our resource. Although not all timepoints were profiled in each replicate, two timepoints (10 and 24hpf) are present in all four, and the celltype composition of these replicates from these 2 timepoints are very similar. We have added new plots in figure S2f and added (new) supplementary table (#1) to highlight the presence of biological replicates.

2. *The TF‑activity regression model uses an arbitrary R² {greater than or equal to} 0.6 threshold; cross‑validated R² distributions, permutation‑based FDR control, and effect‑size confidence intervals are needed to justify this cut‑off. *

Thank you for this suggestion. We did use 10-fold cross validation during training and obtained the R2 values of TF motifs from the independent test set as an unbiased estimate. However, the cutoff of R2 > 0.6 to select the TFs for classification was indeed arbitrary. In the revised version, we now report the FDR-adjusted p-values for these R2 estimates based on permutation tests, and select TFs with a cutoff of padj supplementary table #4 to include the p-values for all tested TFs. However, we see that our arbitrary cutoff of 0.6 was in fact, too stringent, and we can classify many more TFs based on the FDR cutoffs. We also updated our reported numbers in Fig. 4c to reflect this. Moreover, supplementary table #4 contains the complete list of TFs used in the analysis to allow others to choose their own cutoff.

3. *Predicted TF functions lack empirical support, making it essential to test representative activators (e.g., Tbx16) and repressors (e.g., Zbtb16a) via CRISPRi or morpholino knock‑down and to measure target‑gene expression and H3K4me1 changes. *

We agree that independent validation of the functions of our predicted TFs on target gene activity would be important. During this revision, we analysed recently published scRNA-seq data of Saunders et al. (2023) (Saunders et al., 2023), which includes CRISPR-mediated F0 knockouts of a couple of our predicted TFs, but the scRNAseq was performed at later stages (24hpf onward) compared to our H3K4me1 analysis (which was 4-12 hpf). Therefore, we saw off-target genes being affected in lineages where these TFs are clearly not expressed (attached Fig 1). We therefore didn't include these results in the manuscript. In future, we aim to systematically test the TFs predicted in our study with CRISPRi or similar experiments.

4. *The study does not prove that H3K27me3 spreading causes silencing; embryos treated with an Ezh2 inhibitor or prc2 mutants should be re‑profiled by T‑ChIC to show loss of spreading along with gene re‑expression. *

We appreciate the suggestion that indeed PRC2-disruption followed by T-ChIC or other forms of validation would be needed to confirm whether the H3K27me3 spreading is indeed causally linked to the silencing of the identified target genes. But performing this validation is complicated because of multiple reasons: 1) due to the EZH2 contribution from maternal RNA and the contradicting effects of various EZH2 zygotic mutations (depending on where the mutation occurs), the only properly validated PRC2-related mutant seems to be the maternal-zygotic mutant MZezh2, which requires germ cell transplantation (see Rougeot et al. 2019 (Rougeot et al., 2019)) , and San et al. 2019 (San et al., 2019) for details). The use of inhibitors have been described in other studies (den Broeder et al., 2020; Huang et al., 2021), but they do not show a validation of the H3K27me3 loss or a similar phenotype as the MZezh2 mutants, and can present unwanted side effects and toxicity at a high dose, affecting gene expression results. Moreover, in an attempt to validate, we performed our own trials with the EZH2 inhibitor (GSK123) and saw that this time window might be too short to see the effect within 24hpf (attached Fig. 2). Therefore, this validation is a more complex endeavor beyond the scope of this study. Nevertheless, our further analysis of H3K27me3 de-methylation on developmentally important genes (new Fig. 2e-f, Sup. table 3) adds more confidence that the polycomb repression plays an important role, and provides enough ground for future follow up studies.

*Minor concerns *

1. *Repressive chromatin coverage is limited, so profiling an additional silencing mark such as H3K9me3 or DNA methylation would clarify cooperation with H3K27me3 during development. *

We agree that H3K27me3 alone would not be sufficient to fully understand the repressive chromatin state. Extension to other chromatin marks and DNA methylation would be the focus of our follow up works.

*2. Computational transparency is incomplete; a supplementary table listing all trimming, mapping, and peak‑calling parameters (cutadapt, STAR/hisat2, MACS2, histoneHMM, etc.) should be provided. *

As mentioned in the manuscript, we provide an open-source pre-processing pipeline "scChICflow" to perform all these steps (github.com/bhardwaj-lab/scChICflow). We have now also provided the configuration files on our zenodo repository (see below), which can simply be plugged into this pipeline together with the fastq files from GEO to obtain the processed dataset that we describe in the manuscript. Additionally, we have also clarified the peak calling and post-processing steps in the manuscript now.

*3. Data‑ and code‑availability statements lack detail; the exact GEO accession release date, loom‑file contents, and a DOI‑tagged Zenodo archive of analysis scripts should be added. *

We have now publicly released the .h5ad files with raw counts, normalized counts, and complete gene and cell-level metadata, along with signal tracks (bigwigs) and peaks on GEO. Additionally, we now also released the source datasets and notebooks (.Rmarkdown format) on Zenodo that can be used to replicate the figures in the manuscript, and updated our statements on "Data and code availability".

*4. Minor editorial issues remain, such as replacing "critical" with "crucial" in the Abstract, adding software version numbers to figure legends, and correcting the SAMtools reference. *

Thank you for spotting them. We have fixed these issues.

Reviewer #3 (Significance (Required)):

The method is technically innovative and the biological insights are valuable; however, several issues-mainly concerning experimental design, statistical rigor, and functional validation-must be addressed to solidify the conclusions.

Thank you for your comments. We hope to have addressed your concerns in this revised version of our manuscript.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

Referee #3

Evidence, reproducibility and clarity

This manuscript introduces T‑ChIC, a single‑cell multi‑omics workflow that jointly profiles full‑length transcripts and histone modifications (H3K27me3 and H3K4me1) and applies it to early zebrafish embryos (4-24 hpf). The study convincingly demonstrates that chromatin-transcription coupling strengthens during gastrulation and somitogenesis, that promoter‑anchored H3K27me3 spreads in cis to enforce developmental gene silencing, and that integrating TF chromatin status with expression can predict lineage‑specific activators and repressors.

Major concerns

1. Independent biological replicates are absent, so the authors should process at least one additional clutch of embryos for key stages (e.g., 6 hpf and 12 hpf) with T‑ChIC and demonstrate that the resulting data match the current dataset.
2. The TF‑activity regression model uses an arbitrary R² {greater than or equal to} 0.6 threshold; cross‑validated R² distributions, permutation‑based FDR control, and effect‑size confidence intervals are needed to justify this cut‑off.
3. Predicted TF functions lack empirical support, making it essential to test representative activators (e.g., Tbx16) and repressors (e.g., Zbtb16a) via CRISPRi or morpholino knock‑down and to measure target‑gene expression and H3K4me1 changes.
4. The study does not prove that H3K27me3 spreading causes silencing; embryos treated with an Ezh2 inhibitor or prc2 mutants should be re‑profiled by T‑ChIC to show loss of spreading along with gene re‑expression.

Minor concerns

1. Repressive chromatin coverage is limited, so profiling an additional silencing mark such as H3K9me3 or DNA methylation would clarify cooperation with H3K27me3 during development.
2. Computational transparency is incomplete; a supplementary table listing all trimming, mapping, and peak‑calling parameters (cutadapt, STAR/hisat2, MACS2, histoneHMM, etc.) should be provided.
3. Data‑ and code‑availability statements lack detail; the exact GEO accession release date, loom‑file contents, and a DOI‑tagged Zenodo archive of analysis scripts should be added.
4. Minor editorial issues remain, such as replacing "critical" with "crucial" in the Abstract, adding software version numbers to figure legends, and correcting the SAMtools reference.

Significance

The method is technically innovative and the biological insights are valuable; however, several issues-mainly concerning experimental design, statistical rigor, and functional validation-must be addressed to solidify the conclusions.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

Referee #2

Evidence, reproducibility and clarity

Joint analysis of multiple modalities in single cells will provide a comprehensive view of cell fate states. In this manuscript, Bhardwaj et al developed a single-cell multi-omics assay, T-ChIC, to simultaneously capture histone modifications and full-length transcriptome and applied the method on early embryos of zebrafish. The authors observed a decoupled relationship between the chromatin modifications and gene expression at early developmental stages. The correlation becomes stronger as development proceeds, as genes are silenced by the cis-spreading of the repressive marker H3k27me3. Overall, the work is well performed, and the results are meaningful and interesting to readers in the epigenomic and embryonic development fields. There are some concerns before the manuscript is considered for publication.

Major concerns:

1. A major point of this study is to understand embryo development, especially gastrulation, with the power of scMulti-Omics assay. However, the current analysis didn't focus on deciphering the biology of gastrulation, i.e., lineage-specific pioneer factors that help to reform the chromatin landscape. The majority of the data analysis is based on the temporal dimension, but not the cell-type-specific dimension, which reduces the value of the single-cell assay.
2. The cis-spreading of H3K27me3 with developmental time is interesting. Considering H3k27me3 could mark bivalent regions, especially in pluripotent cells, there must be some regions that have lost H3k27me3 signals during development. Therefore, it's confusing that the authors didn't find these regions (30% spreading, 70% stable). The authors should explain and discuss this issue.

Minors:

1. The authors cited two scMulti-omics studies in the introduction, but there have been lots of single-cell multi-omics studies published recently. The authors should cite and consider them.
2. T-ChIC seems to have been presented in a previous paper (ref 15). Therefore, Fig. 1a is unnecessary to show.
3. It's better to show the percentage of cell numbers (30% vs 70%) for each heatmap in Figure 2C.
4. Please double-check the citation of Fig. S4C, which may not relate to the conclusion of signal differences between lineages.
5. Figure 4C has not been cited or mentioned in the main text. Please check.

Significance

Strengths: This work utilized a new single-cell multi-omics method and generated abundant epigenomics and transcriptomics datasets for cells covering multiple key developmental stages of zebrafish. Limitations: The data analysis was superficial and mainly focused on the correspondence between the two modalities. The discussion of developmental biology was limited.

Advance: The zebrafish single-cell datasets are valuable. The T-ChIC method is new and interesting.

The audience will be specialized and from basic research fields, such as developmental biology, epigenomics, bioinformatics, etc.

I'm more specialized in the direction of single-cell epigenomics, gene regulation, 3D genomics, etc.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

Referee #1

Evidence, reproducibility and clarity

The authors have a longstanding focus and reputation on single cell sequencing technology development and application. In this current study, the authors developed a novel single-cell multi-omic assay termed "T-ChIC" so that to jointly profile the histone modifications along with the full-length transcriptome from the same single cells, analyzed the dynamic relationship between chromatin state and gene expression during zebrafish development and cell fate determination. In general, the assay works well, the data look convincing and conclusions are beneficial to the community.

There are several single-cell methodologies all claim to co-profile chromatin modifications and gene expression from the same individual cell, such as CoTECH, Paired-tag and others. Although T-ChIC employs pA-Mnase and IVT to obtain these modalities from single cells which are different, could the author provide some direct comparisons among all these technologies to see whether T-ChIC outperforms?

In current study, T-ChIC profiled H3K27me3 and H3K4me1 modifications, these data look great. How about other histone modifications (eg H3K9me3 and H3K36me3) and transcription factors?

T-ChIC can detect full length transcription from the same single cells, but in FigS3, the authors still used other published single cell transcriptomics to annotate the cell types, this seems unnecessary?

Throughout the manuscript, the authors found some interesting dynamics between chromatin state and gene expression during embryogenesis, independent approaches should be used to validate these findings, such as IHC staining or RNA ISH?

In Fig2 and FigS4, the authors showed H3K27me3 cis spreading during development, this looks really interesting. Is this zebrafish specific? H3K27me3 ChIP-seq or CutTag data from mouse and/or human embryos should be reanalyzed and used to compare. The authors could speculate some possible mechanisms to explain this spreading pattern?

Significance

The authors have a longstanding focus and reputation on single cell sequencing technology development and application. In this current study, the authors developed a novel single-cell multi-omic assay termed "T-ChIC" so that to jointly profile the histone modifications along with the full-length transcriptome from the same single cells, analyzed the dynamic relationship between chromatin state and gene expression during zebrafish development and cell fate determination. In general, the assay works well, the data look convincing and conclusions are beneficial to the community.

Reqestsの章でのタイムアウトが、connect/ read の2つのタイムアウトに触れているので、connect, read, write, pool の各タイムアウトは、httpx.Timeout()で細かく設定できることに軽く触れてもよいかなと思いました。 https://www.python-httpx.org/advanced/timeouts/?utm_source=chatgpt.com

阿部寛！

存在します？

TYPO: HTTPS?

TYPO: True

TYPO: Requests

^

Is India an example here? Looks like it might be appropriate - the linked paper references China.

Wow, storage and transport is two thirds of the cost of h2 for backup power? I had no idea it was such a big share of costs

Post-order traversal（後序走訪） 的順序是：對每個節點都照 左子樹 → 右子樹 → 自己 來處理

Pre-order traversal（先序走訪） 是另一種走二元樹的順序：對每個節點照 自己 → 左子樹 → 右子樹 這個順序處理。

In-order traversal（中序走訪） 是一種走二元樹的順序：對「每一個節點」，都照這個順序處理：左子樹 → 自己 → 右子樹。

You have to train the person doing a think aloud. They also learn to do it if they engage in multiple sessions.

代名詞

優越的

探明

看法

決定

標準

à conjuguer au pluriel : "mettent"

Est-il possible de changer ce terme par "ouvert" ?

à supprimer pour éviter la répétition avec ce qui suit

PEUR BLEUE - Anatomie des violences conjugales et du parcours de sortie

31 275 vues 4 oct. 2023 Ce film a été produit par le Conseil Départemental d'Accès au Droit des Hautes-Pyrénées.

Basé sur des témoignages de victimes et de professionnels, ce film a pour objectif de rendre compte du phénomène des violences et de ses différentes formes, qui peuvent ou non se cumuler.

La victime, dépossédée de sa capacité d'action par les effets de l'emprise et du psycho-trauma, peut cependant trouver des voies de protection et bénéficier d'un accompagnement personnalisé dans un lieu spécialisé.

Retrouvez nous sur Instagram : / cdad65000 <br /> Illustrations faites par Ysar : / __ysar<br /> Ce film a été mis en image et monté par Yannick Chaumeil : / @associationlarrache-temps1871

Pédocriminalité: dans le piège des loverboys | Reportage | ARTE Regards

186 676 vues 25 sept. 2025 #reportage #prostitution #arte Reportage disponible jusqu'au 27/08/2026

En France, près de 20 000 adolescentes seraient sous l’emprise de proxénètes parfois à peine plus âgés qu’elles.

Derrière une apparence séduisante et beaucoup de tchatche, ces jeunes hommes appelés parfois "loverboys" utilisent la manipulation amoureuse comme arme de contrôle.

La méthode des "loverboys" est redoutable : séduire, isoler, détruire pour mieux asservir.

Ce reportage raconte comment, de Paris à Maastricht, trois jeunes filles se sont retrouvées dans les griffes de ces proxénètes qui après les avoir séduites, les ont vendues sur Internet.

Bao, contrainte à la prostitution pendant cinq ans, livre un témoignage bouleversant.

Jennifer Pailhé, mère de l’une des victimes, raconte son combat acharné pour sauver sa fille et faire tomber son agresseur.

Chemelle Jongen, ancienne victime devenue lanceuse d’alerte, lutte aujourd’hui pour sensibiliser le public aux mécanismes de l’emprise et aux ravages causés par ce système.

Tous les pays européens sont confrontés à la prostitution des mineurs. Mais chaque pays a sa propre législation et ses méthodes.

Reportage (France, 2025, 31mn)

prostitution #reportage #arte

Wowie

و بر آن نظارت

DOI: 10.19723/j.issn.1671-167X.2025.06.015

Resource: (RRID:CVCL_KS65)

Curator: @evieth

SciCrunch record: RRID:CVCL_KS65

What is this?

DOI: 10.19723/j.issn.1671-167X.2025.06.015

Resource: (RRID:CVCL_G257)

Curator: @scibot

SciCrunch record: RRID:CVCL_G257

What is this?

DOI: 10.19723/j.issn.1671-167X.2025.06.015

Resource: (KCLB Cat# 80020, RRID:CVCL_9555)

Curator: @scibot

SciCrunch record: RRID:CVCL_9555

What is this?

DOI: 10.1186/s12935-025-04056-7

Resource: (KCLB Cat# 90524, RRID:CVCL_1568)

Curator: @evieth

SciCrunch record: RRID:CVCL_1568

What is this?

DOI: 10.1158/1541-7786.MCR-25-0319

Resource: (RRID:CVCL_0063)

Curator: @evieth

SciCrunch record: RRID:CVCL_0063

What is this?

DOI: 10.1158/1541-7786.MCR-25-0319

Resource: (DSMZ Cat# ACC-357, RRID:CVCL_0292)

Curator: @evieth

SciCrunch record: RRID:CVCL_0292

What is this?

DOI: 10.1158/1541-7786.MCR-25-0319

Resource: (ATCC Cat# CRL-2172, RRID:CVCL_1723)

Curator: @evieth

SciCrunch record: RRID:CVCL_1723

What is this?

DOI: 10.1158/1541-7786.MCR-25-0319

Resource: (NCBI_Iran Cat# C558, RRID:CVCL_0152)

Curator: @evieth

SciCrunch record: RRID:CVCL_0152

What is this?

DOI: 10.1158/1541-7786.MCR-25-0319

Resource: (ATCC Cat# CRL-1997, RRID:CVCL_0313)

Curator: @evieth

SciCrunch record: RRID:CVCL_0313

What is this?

DOI: 10.1158/1541-7786.MCR-25-0319

Resource: (BCRC Cat# 60343, RRID:CVCL_0547)

Curator: @evieth

SciCrunch record: RRID:CVCL_0547

What is this?

DOI: 10.1158/1541-7786.MCR-25-0319

Resource: (CCLV Cat# CCLV-RIE 1035, RRID:CVCL_0023)

Curator: @evieth

SciCrunch record: RRID:CVCL_0023

What is this?

DOI: 10.1158/1541-7786.MCR-25-0319

Resource: (Millipore Cat# SCC109, RRID:CVCL_4U18)

Curator: @evieth

SciCrunch record: RRID:CVCL_4U18

What is this?

DOI: 10.1158/1541-7786.MCR-25-0319

Resource: (RRID:CVCL_0248)

Curator: @evieth

SciCrunch record: RRID:CVCL_0248

What is this?

DOI: 10.1158/1541-7786.MCR-25-0319

Resource: (ATCC Cat# CRM-CRL-1420, RRID:CVCL_0428)

Curator: @evieth

SciCrunch record: RRID:CVCL_0428

What is this?

DOI: 10.1158/1541-7786.MCR-25-0319

Resource: (ATCC Cat# CRL-5895, RRID:CVCL_1495)

Curator: @evieth

SciCrunch record: RRID:CVCL_1495

What is this?

DOI: 10.1158/1541-7786.MCR-25-0319

Resource: RStudio (RRID:SCR_000432)

Curator: @scibot

SciCrunch record: RRID:SCR_000432

What is this?

DOI: 10.1158/1541-7786.MCR-25-0319

Resource: ggplot2 (RRID:SCR_014601)

Curator: @scibot

SciCrunch record: RRID:SCR_014601

What is this?

DOI: 10.1158/1541-7786.MCR-25-0319

Resource: MatPlotLib (RRID:SCR_008624)

Curator: @scibot

SciCrunch record: RRID:SCR_008624

What is this?

DOI: 10.1158/1541-7786.MCR-25-0319

Resource: (Santa Cruz Biotechnology Cat# sc-30, RRID:AB_627865)

Curator: @scibot

SciCrunch record: RRID:AB_627865

What is this?

DOI: 10.1158/1541-7786.MCR-25-0319

Resource: GraphPad Prism (RRID:SCR_002798)

Curator: @scibot

SciCrunch record: RRID:SCR_002798

What is this?

DOI: 10.1158/1541-7786.MCR-25-0319

Resource: (Cell Signaling Technology Cat# 7076, RRID:AB_330924)

Curator: @scibot

SciCrunch record: RRID:AB_330924

What is this?

DOI: 10.1158/1541-7786.MCR-25-0319

Resource: (Proteintech Cat# 18295-1-AP, RRID:AB_2121046)

Curator: @scibot

SciCrunch record: RRID:AB_2121046

What is this?

DOI: 10.1158/1541-7786.MCR-25-0319

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

Curator: @scibot

SciCrunch record: RRID:AB_2099233

What is this?

DOI: 10.1158/1541-7786.MCR-25-0319

Resource: (Santa Cruz Biotechnology Cat# sc-31, RRID:AB_628041)

Curator: @scibot

SciCrunch record: RRID:AB_628041

What is this?

DOI: 10.1158/1541-7786.MCR-25-0319

Resource: Cancer Dependency Map Portal (RRID:SCR_017655)

Curator: @scibot

SciCrunch record: RRID:SCR_017655

What is this?

DOI: 10.1158/1541-7786.MCR-25-0319

Resource: (Santa Cruz Biotechnology Cat# sc-7384, RRID:AB_628252)

Curator: @scibot

SciCrunch record: RRID:AB_628252

What is this?

DOI: 10.1158/1541-7786.MCR-25-0319

Resource: (BioLegend Cat# 901501, RRID:AB_2565006)

Curator: @scibot

SciCrunch record: RRID:AB_2565006

What is this?

DOI: 10.1155/humu/5569005

Resource: (KCLB Cat# 30001, RRID:CVCL_0359)

Curator: @evieth

SciCrunch record: RRID:CVCL_0359

What is this?

DOI: 10.1126/sciadv.adt7982

Resource: (BCRC Cat# 60191, RRID:CVCL_1714)

Curator: @evieth

SciCrunch record: RRID:CVCL_1714

What is this?

DOI: 10.1126/sciadv.adt7982

Resource: (ICLC Cat# HTL95023, RRID:CVCL_0030)

Curator: @evieth

SciCrunch record: RRID:CVCL_0030

What is this?

DOI: 10.1126/sciadv.adt7982

Resource: (RRID:CVCL_0063)

Curator: @evieth

SciCrunch record: RRID:CVCL_0063

What is this?

DOI: 10.1038/s41598-025-27754-8

Resource: Nikon Eclipse E200 microscope (RRID:SCR_021243)

Curator: @evieth

SciCrunch record: RRID:SCR_021243

What is this?

DOI: 10.1038/s41598-025-27754-8

Resource: tidyverse (RRID:SCR_019186)

Curator: @evieth

SciCrunch record: RRID:SCR_019186

What is this?

DOI: 10.1038/s41598-025-27665-8

Resource: (ICLC Cat# HTL98017, RRID:CVCL_0032)

Curator: @evieth

SciCrunch record: RRID:CVCL_0032

What is this?

DOI: 10.1038/s41467-025-66107-x

Resource: RRID:BDSC_32229

Curator: @evieth

SciCrunch record: RRID:BDSC_32229

What is this?

DOI: 10.1016/j.xcrm.2025.102519

Resource: (IZSLER Cat# BS TCL 216, RRID:CVCL_4358)

Curator: @areedewitt04

SciCrunch record: RRID:CVCL_4358

What is this?

DOI: 10.1016/j.str.2025.11.010

Resource: (IMSR Cat# RBRC06344,RRID:IMSR_RBRC06344)

Curator: @areedewitt04

SciCrunch record: RRID:IMSR_RBRC06344

What is this?

DOI: 10.1016/j.str.2025.11.010

Resource: (DSMZ Cat# ACC-305, RRID:CVCL_0045)

Curator: @areedewitt04

SciCrunch record: RRID:CVCL_0045

What is this?

DOI: 10.1016/j.molcel.2025.11.028

Resource: (TKG Cat# TKG 0509, RRID:CVCL_0470)

Curator: @areedewitt04

SciCrunch record: RRID:CVCL_0470

What is this?

DOI: 10.1016/j.molcel.2025.11.028

Resource: (DSMZ Cat# ACC-305, RRID:CVCL_0045)

Curator: @areedewitt04

SciCrunch record: RRID:CVCL_0045

What is this?

DOI: 10.1016/j.molcel.2025.11.028

Resource: (RRID:CVCL_0042)

Curator: @areedewitt04

SciCrunch record: RRID:CVCL_0042

What is this?

DOI: 10.1016/j.molcel.2025.11.028

Resource: (IZSLER Cat# BS CL 15, RRID:CVCL_0214)

Curator: @areedewitt04

SciCrunch record: RRID:CVCL_0214

What is this?

DOI: 10.1016/j.molcel.2025.11.023

Resource: (RRID:CVCL_0134)

Curator: @areedewitt04

SciCrunch record: RRID:CVCL_0134

What is this?

DOI: 10.1016/j.molcel.2025.11.023

Resource: (ATCC Cat# CRL-2149, RRID:CVCL_1701)

Curator: @areedewitt04

SciCrunch record: RRID:CVCL_1701

What is this?

DOI: 10.1016/j.molcel.2025.11.023

Resource: (KCLB Cat# 00423, RRID:CVCL_0366)

Curator: @areedewitt04

SciCrunch record: RRID:CVCL_0366

What is this?

DOI: 10.1016/j.molcel.2025.11.023

Resource: (KCLB Cat# 00398, RRID:CVCL_0077)

Curator: @areedewitt04

SciCrunch record: RRID:CVCL_0077

What is this?

DOI: 10.1016/j.molcel.2025.11.023

Resource: (BCRJ Cat# 0110, RRID:CVCL_0317)

Curator: @areedewitt04

SciCrunch record: RRID:CVCL_0317

What is this?

DOI: 10.1016/j.molcel.2025.11.023

Resource: (KCLB Cat# 88065, RRID:CVCL_0027)

Curator: @areedewitt04

SciCrunch record: RRID:CVCL_0027

What is this?

DOI: 10.1016/j.molcel.2025.11.023

Resource: (RRID:CVCL_0042)

Curator: @areedewitt04

SciCrunch record: RRID:CVCL_0042

What is this?

DOI: 10.1016/j.molcel.2025.11.023

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

Curator: @areedewitt04

SciCrunch record: RRID:CVCL_0031

What is this?

DOI: 10.1016/j.molcel.2025.11.023

Resource: (KCB Cat# KCB 200942YJ, RRID:CVCL_0326)

Curator: @areedewitt04

SciCrunch record: RRID:CVCL_0326

What is this?

DOI: 10.1016/j.molcel.2025.11.023

Resource: (CLS Cat# 300342/p657_SK-OV-3, RRID:CVCL_0532)

Curator: @areedewitt04

SciCrunch record: RRID:CVCL_0532

What is this?

DOI: 10.1016/j.molcel.2025.11.023

Resource: (ATCC Cat# CRL-11233, RRID:CVCL_3804)

Curator: @areedewitt04

SciCrunch record: RRID:CVCL_3804

What is this?

DOI: 10.1016/j.molcel.2025.11.023

Resource: (CLS Cat# 300149/p748_Caki-1, RRID:CVCL_0234)

Curator: @areedewitt04

SciCrunch record: RRID:CVCL_0234

What is this?

DOI: 10.1016/j.molcel.2025.11.023

Resource: (CLS Cat# 300315/p526_PLC-PRF-5, RRID:CVCL_0485)

Curator: @areedewitt04

SciCrunch record: RRID:CVCL_0485

What is this?

DOI: 10.1016/j.molcel.2025.11.023

Resource: (KCLB Cat# 00449, RRID:CVCL_0454)

Curator: @areedewitt04

SciCrunch record: RRID:CVCL_0454

What is this?

DOI: 10.1016/j.molcel.2025.11.023

Resource: (ATCC Cat# HTB-161, RRID:CVCL_0465)

Curator: @areedewitt04

SciCrunch record: RRID:CVCL_0465

What is this?

DOI: 10.1016/j.molcel.2025.11.023

Resource: (ECACC Cat# 96020759, RRID:CVCL_2673)

Curator: @areedewitt04

SciCrunch record: RRID:CVCL_2673

What is this?

DOI: 10.1016/j.molcel.2025.11.023

Resource: (RRID:CVCL_2424)

Curator: @areedewitt04

SciCrunch record: RRID:CVCL_2424

What is this?

DOI: 10.1016/j.molcel.2025.11.023

Resource: (TKG Cat# TKG 0331, RRID:CVCL_0030)

Curator: @areedewitt04

SciCrunch record: RRID:CVCL_0030

What is this?

DOI: 10.1016/j.molcel.2025.11.023

Resource: (IZSLER Cat# BS TCL 165, RRID:CVCL_0201)

Curator: @areedewitt04

SciCrunch record: RRID:CVCL_0201

What is this?

DOI: 10.1016/j.molcel.2025.11.023

Resource: (RRID:CVCL_1056)

Curator: @areedewitt04

SciCrunch record: RRID:CVCL_1056

What is this?

DOI: 10.1016/j.molcel.2025.11.023

Resource: (ATCC Cat# CRL-1932, RRID:CVCL_1051)

Curator: @areedewitt04

SciCrunch record: RRID:CVCL_1051

What is this?

DOI: 10.1016/j.molcel.2025.11.023

Resource: (ATCC Cat# CRL-11732, RRID:CVCL_3768)

Curator: @areedewitt04

SciCrunch record: RRID:CVCL_3768

What is this?

DOI: 10.1016/j.molcel.2025.11.022

Resource: RRID:Addgene_83356

Curator: @areedewitt04

SciCrunch record: RRID:Addgene_83356

What is this?

DOI: 10.1016/j.molcel.2025.11.022

Resource: RRID:Addgene_12521

Curator: @areedewitt04

SciCrunch record: RRID:Addgene_12521

What is this?

DOI: 10.1016/j.molcel.2025.11.022

Resource: RRID:Addgene_14887

Curator: @areedewitt04

SciCrunch record: RRID:Addgene_14887

What is this?

DOI: 10.1016/j.molcel.2025.11.022

Resource: RRID:Addgene_12259

Curator: @areedewitt04

SciCrunch record: RRID:Addgene_12259

What is this?

DOI: 10.1016/j.molcel.2025.11.022

Resource: RRID:Addgene_12260

Curator: @areedewitt04

SciCrunch record: RRID:Addgene_12260

What is this?

DOI: 10.1016/j.molcel.2025.11.022

Resource: RRID:IMSR_GPT:D000521

Curator: @areedewitt04

SciCrunch record: RRID:IMSR_GPT:D000521

What is this?

DOI: 10.1016/j.molcel.2025.11.020

Resource: RRID:Addgene_12253

Curator: @areedewitt04

SciCrunch record: RRID:Addgene_12253

What is this?

DOI: 10.1016/j.molcel.2025.11.020

Resource: (RRID:CVCL_0063)

Curator: @areedewitt04

SciCrunch record: RRID:CVCL_0063

What is this?

DOI: 10.1016/j.molcel.2025.11.020

Resource: (ATCC Cat# CRL-2244, RRID:CVCL_4593)

Curator: @areedewitt04

SciCrunch record: RRID:CVCL_4593

What is this?

DOI: 10.1016/j.molcel.2025.11.020

Resource: None

Curator: @areedewitt04

SciCrunch record: RRID:CVCL_4594

What is this?

DOI: 10.1016/j.molcel.2025.11.020

Resource: (IZSLER Cat# BS CL 15, RRID:CVCL_0214)

Curator: @areedewitt04

SciCrunch record: RRID:CVCL_0214

What is this?

DOI: 10.1016/j.molcel.2025.11.020

Resource: None

Curator: @areedewitt04

SciCrunch record: RRID:Addgene_157623

What is this?

DOI: 10.1016/j.molcel.2025.11.020

Resource: RRID:Addgene_73178

Curator: @areedewitt04

SciCrunch record: RRID:Addgene_73178

What is this?

DOI: 10.1016/j.immuni.2025.11.020

Resource: RRID:IMSR_JAX:012851

Curator: @areedewitt04

SciCrunch record: RRID:IMSR_JAX:012851

What is this?

DOI: 10.1016/j.immuni.2025.11.020

Resource: (IMSR Cat# JAX_008374,RRID:IMSR_JAX:008374)

Curator: @areedewitt04

SciCrunch record: RRID:IMSR_JAX:008374

What is this?

DOI: 10.1016/j.immuni.2025.11.020

Resource: (IMSR Cat# JAX_002014,RRID:IMSR_JAX:002014)

Curator: @areedewitt04

SciCrunch record: RRID:IMSR_JAX:002014

What is this?

DOI: 10.1016/j.immuni.2025.11.020

Resource: RRID:IMSR_JAX:000664

Curator: @areedewitt04

SciCrunch record: RRID:IMSR_JAX:000664

What is this?

DOI: 10.1016/j.ejphar.2025.178484

Resource: RRID:Addgene_214013

Curator: @areedewitt04

SciCrunch record: RRID:Addgene_214013

What is this?

DOI: 10.1016/j.celrep.2025.116757

Resource: RRID:IMSR_JAX:000664

Curator: @areedewitt04

SciCrunch record: RRID:IMSR_JAX:000664

What is this?

DOI: 10.1016/j.celrep.2025.116757

Resource: (KCLB Cat# 80008, RRID:CVCL_0159)

Curator: @areedewitt04

SciCrunch record: RRID:CVCL_0159

What is this?

DOI: 10.1016/j.cej.2025.171980

Resource: (KCLB Cat# 88065, RRID:CVCL_0027)

Curator: @areedewitt04

SciCrunch record: RRID:CVCL_0027

What is this?

DOI: 10.1016/j.bcp.2025.117659

Resource: (RRID:CVCL_6898)

Curator: @areedewitt04

SciCrunch record: RRID:CVCL_6898

What is this?

DOI: 10.1002/ijc.70146

Resource: (RRID:CVCL_0002)

Curator: @evieth

SciCrunch record: RRID:CVCL_0002

What is this?

DOI: 10.1002/eji.70101

Resource: (DSMZ Cat# ACC-794, RRID:CVCL_2H32)

Curator: @evieth

SciCrunch record: RRID:CVCL_2H32

What is this?

DOI: 10.1002/eji.70101

Resource: (DSMZ Cat# ACC-528, RRID:CVCL_1878)

Curator: @evieth

SciCrunch record: RRID:CVCL_1878

What is this?

DOI: 10.1002/eji.70101

Resource: (ICLC Cat# HTL10002, RRID:CVCL_0539)

Curator: @evieth

SciCrunch record: RRID:CVCL_0539

What is this?

DOI: 10.1002/eji.70101

Resource: (DSMZ Cat# ACC-47, RRID:CVCL_1179)

Curator: @evieth

SciCrunch record: RRID:CVCL_1179

What is this?

DOI: 10.1002/eji.70101

Resource: (ICLC Cat# HTL00002, RRID:CVCL_0511)

Curator: @evieth

SciCrunch record: RRID:CVCL_0511

What is this?

DOI: 10.1002/alz.70968

Resource: Northwestern University NUANCE BioCryo Core Facility (RRID:SCR_021288)

Curator: @evieth

SciCrunch record: RRID:SCR_021288

What is this?

DOI: 10.1002/advs.202508948

Resource: (ATCC Cat# TIB-71, RRID:CVCL_0493)

Curator: @evieth

SciCrunch record: RRID:CVCL_0493

What is this?

DOI: 10.7554/eLife.95541

Resource: R Project for Statistical Computing (RRID:SCR_001905)

Curator: @scibot

SciCrunch record: RRID:SCR_001905

What is this?

DOI: 10.7554/eLife.95541

Resource: Agilent Seahorse XF HS Analyzer (RRID:SCR_019540)

Curator: @scibot

SciCrunch record: RRID:SCR_019540

What is this?

DOI: 10.7554/eLife.95541

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

Curator: @scibot

SciCrunch record: RRID:AB_2099233

What is this?

DOI: 10.7554/eLife.95541

Resource: (Abcam Cat# ab40793, RRID:AB_722533)

Curator: @scibot

SciCrunch record: RRID:AB_722533

What is this?

DOI: 10.7554/eLife.95541

Resource: (Cell Signaling Technology Cat# 2148, RRID:AB_2288042)

Curator: @scibot

SciCrunch record: RRID:AB_2288042

What is this?

DOI: 10.7554/eLife.95541

Resource: (Abcam Cat# ab15580, RRID:AB_443209)

Curator: @scibot

SciCrunch record: RRID:AB_443209

What is this?

DOI: 10.7554/eLife.95541

Resource: (Sigma-Aldrich Cat# C2306, RRID:AB_258792)

Curator: @scibot

SciCrunch record: RRID:AB_258792

What is this?

DOI: 10.7554/eLife.95541

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

Curator: @scibot

SciCrunch record: RRID:AB_11156085

What is this?

DOI: 10.7554/eLife.95541

Resource: (Abcam Cat# ab33780, RRID:AB_2191441)

Curator: @scibot

SciCrunch record: RRID:AB_2191441

What is this?

DOI: 10.7554/eLife.95541

Resource: ImageJ (RRID:SCR_003070)

Curator: @scibot

SciCrunch record: RRID:SCR_003070

What is this?

DOI: 10.7554/eLife.95541

Resource: RRID:IMSR_CRL:027

Curator: @scibot

SciCrunch record: RRID:IMSR_CRL:027

What is this?

DOI: 10.7554/eLife.95541

Resource: (IMSR Cat# CRL_022,RRID:IMSR_CRL:022)

Curator: @scibot

SciCrunch record: RRID:IMSR_CRL:022

What is this?

DOI: 10.7554/eLife.106201

Resource: statsmodel (RRID:SCR_016074)

Curator: @scibot

SciCrunch record: RRID:SCR_016074

What is this?

DOI: 10.7554/eLife.106201

Resource: Inkscape (RRID:SCR_014479)

Curator: @scibot

SciCrunch record: RRID:SCR_014479

What is this?

DOI: 10.7554/eLife.106201

Resource: seaborn (RRID:SCR_018132)

Curator: @scibot

SciCrunch record: RRID:SCR_018132

What is this?

DOI: 10.7554/eLife.106201

Resource: DABEST (RRID:SCR_022340)

Curator: @scibot

SciCrunch record: RRID:SCR_022340

What is this?

DOI: 10.7554/eLife.106201

Resource: MatPlotLib (RRID:SCR_008624)

Curator: @scibot

SciCrunch record: RRID:SCR_008624

What is this?

DOI: 10.7554/eLife.106201

Resource: Pingouin (RRID:SCR_022261)

Curator: @scibot

SciCrunch record: RRID:SCR_022261

What is this?

DOI: 10.7554/eLife.106201

Resource: SciPy (RRID:SCR_008058)

Curator: @scibot

SciCrunch record: RRID:SCR_008058

What is this?

DOI: 10.7554/eLife.106201

Resource: NumPy (RRID:SCR_008633)

Curator: @scibot

SciCrunch record: RRID:SCR_008633

What is this?

DOI: 10.7554/eLife.106201

Resource: Python Programming Language (RRID:SCR_008394)

Curator: @scibot

SciCrunch record: RRID:SCR_008394

What is this?

DOI: 10.7554/eLife.106201

Resource: Pandas (RRID:SCR_018214)

Curator: @scibot

SciCrunch record: RRID:SCR_018214

What is this?

DOI: 10.3389/fonc.2025.1659304

Resource: ClinicalTrials.gov (RRID:SCR_002309)

Curator: @scibot

SciCrunch record: RRID:SCR_002309

What is this?

DOI: 10.1523/JNEUROSCI.0508-25.2025

Resource: (MGI Cat# 3694359,RRID:MGI:3694359)

Curator: @scibot

SciCrunch record: RRID:MGI:3694359

What is this?

DOI: 10.1523/ENEURO.0292-25.2025

Resource: GraphPad Prism (RRID:SCR_002798)

Curator: @scibot

SciCrunch record: RRID:SCR_002798

What is this?

DOI: 10.1523/ENEURO.0292-25.2025

Resource: (BD Biosciences Cat# 564907, RRID:AB_2869624)

Curator: @scibot

SciCrunch record: RRID:AB_2869624

What is this?

DOI: 10.1523/ENEURO.0292-25.2025

Resource: (Millipore Cat# AB144P, RRID:AB_2079751)

Curator: @scibot

SciCrunch record: RRID:AB_2079751

What is this?

DOI: 10.1523/ENEURO.0292-25.2025

Resource: (Rockland Cat# 600-402-379, RRID:AB_828391)

Curator: @scibot

SciCrunch record: RRID:AB_828391

What is this?

DOI: 10.1523/ENEURO.0292-25.2025

Resource: Spike2 Software (RRID:SCR_000903)

Curator: @scibot

SciCrunch record: RRID:SCR_000903

What is this?

DOI: 10.1523/ENEURO.0292-25.2025

Resource: (Jackson ImmunoResearch Labs Cat# 705-545-147, RRID:AB_2336933)

Curator: @scibot

SciCrunch record: RRID:AB_2336933

What is this?

DOI: 10.1523/ENEURO.0292-25.2025

Resource: SHapley Additive ExPlanations (RRID:SCR_021362)

Curator: @scibot

SciCrunch record: RRID:SCR_021362

What is this?

DOI: 10.1523/ENEURO.0292-25.2025

Resource: (Jackson ImmunoResearch Labs Cat# 711-165-152, RRID:AB_2307443)

Curator: @scibot

SciCrunch record: RRID:AB_2307443

What is this?

DOI: 10.1371/journal.pone.0338619

Resource: FSL (RRID:SCR_002823)

Curator: @scibot

SciCrunch record: RRID:SCR_002823

What is this?

DOI: 10.1371/journal.pone.0338619

Resource: SPM (RRID:SCR_007037)

Curator: @scibot

SciCrunch record: RRID:SCR_007037

What is this?

DOI: 10.1371/journal.pone.0338619

Resource: Connectivity Toolbox (RRID:SCR_009550)

Curator: @scibot

SciCrunch record: RRID:SCR_009550

What is this?

DOI: 10.1371/journal.pbio.3003566

Resource: University of Tennessee Knoxville Biological and Small Molecule Mass Spectrometry Core Facility (RRID:SCR_021368)

Curator: @scibot

SciCrunch record: RRID:SCR_021368

What is this?

DOI: 10.1242/jcs.264340

Resource: (DSHB Cat# R26.4C, RRID:AB_2205518)

Curator: @scibot

SciCrunch record: RRID:AB_2205518

What is this?

DOI: 10.1242/jcs.264340

Resource: (Santa Cruz Biotechnology Cat# sc-7446, RRID:AB_2267583)

Curator: @scibot

SciCrunch record: RRID:AB_2267583

What is this?

DOI: 10.1242/jcs.264340

Resource: (BD Biosciences Cat# 612562, RRID:AB_399853)

Curator: @scibot

SciCrunch record: RRID:AB_399853

What is this?