it seems like when we talk about the leader or the regime as a whole organization and the people, rational authority is the highlight or the foundation. While traditional authority is the highlight or foundation when talking about the leader and the staff. Just like a gang gets people to follow by rational authority even if it's messed up, and the leader of that let's say Italian gang get his number two to follow by traditional authority such as being chosen for that position in the traditional manner.

This argument reminds of the social media posts where people express their want to live in a different era which I just don’t agree with. I think it’s interesting to know that no matter the technological advances we make it will never be enough to stop some people from saying they’d rather live in a different era of the assumption technological advances have deteriorated societal values.

What is considered our primitive conditions? And how can wee just abandon out civilizations if it's all we know?

Part of the reason why I decided to take HIS 160A1 is partly because I wanted to know more about the overall general history of Latin America. I learned just a tad bit of my own culture growing up in Tucson and from my family, but our history has always been something most people will only a-credit the Spanish for, and never indigenous people, or even Africans who were forcibly brought over to the Americas. It has a value in which is incomparable to that which was fed to me.

I agree, i think that this does affect student learning and the fact that you can ask an AI system to do something that students are having trouble doing it is something that is questioned because where is it going wrong. its not the fact that their is an AI system but that students don't know the process of writing a good paper

I would say this sentence really stuck out to me because i've played around with Chat-GPT before this class when my brother showed it to me. It helped me construct some crucial points to assist me in writing my persuasion speech in my persuasion class regarding why it's important to weigh your options and really know if it is the right thing to do to receive a vaccine but specific to the COVID-19 vaccine. However, it did not help me in a way where when I asked it "can you tell me reasons not to get vaccinated" it would just say "I am unable to fulfill that request based on reasonings related to the covid-19 pandemic" or something like that which in a lot of peoples eyes is a no-no to go against the vaccine which is why I understood why it could not answer my question.

on the money

Something I think is missed by many of its viewers. The message is just subtle enough that to many, if not blatantly told then they would miss it. Many would just assume United States if better than Mexico end of story.

The word "specter" is really interesting in this context. It implies that authoritarianism of some kind has always been present. It's just been hidden by fake ideas of exceptionalism in this country.

Author Response

Reviewer #1 (Public Review):

In this work, the authors investigate a means of cell communication through physical connections they call membrane tubules (similar or identical to the previously reported nanotubes, which they reference extensively). They show that Cas9 transfer between cells is facilitated by these structures rather than exosomes. A novel contribution is that this transfer is dependent on the pair of particular cell types and that the protein syncytin is required to establish a complete syncytial connection, which they show are open ended using electron microscopy.

The data is convincing because of the multiple readouts for transfer and the ultrastructural verification of the connection. The results support their conclusions. The implications are obvious, since it represents an avenue of cellular communication and modifications. It would be exciting if they could show this occurring in vivo, such as in tissue. The implication of this would be that neighboring cells in a tissue could be entrained over time through transfer of material.

Thank the reviewer for his/her comments and suggestion. It’s possible that the thick tubular connections found in this study also exist in vivo. A previous study reported that TNT-like structures were found in mouse or human primary tumor cells (PMID: 34494703; PMID: 34795441). Our transfer assays could be adopted to evaluate such transfer in primary cultures and in vivo. We anticipate this for future work.

Reviewer #2 (Public Review):

There is a lot of interest in how cells transfer materials (proteins, RNA, organelles) by extracellular vesicles (EV) and tunneling nanotubes (TNTs). Here, Zhang and Schekman developed quantitative assays, based on two different reporters, to measure EV and direct contact-dependent mediated transfer. The first assay is based on transfer of Cas9, which then edits a luciferase gene, whose enzymatic activity is then measured. The second assay is based on a split-GFP system. The experiments on EV trafficking convincingly show that purified exosomes, or any other diffusible agent, are unable to transfer functional Cas9 (either EV-tethered or untethered) and induce significant luciferase activity in acceptor cells. The authors suggest a plausible model by which Cas9 (with the gRNA?) gets "stuck" in such vesicles and is thus unable to enter the nucleus to edit the gene.

To test alternative pathways of transfer, e.g. by direct cell-cell contact, the authors co-cultured donor and acceptor cells and detect significant luciferase activity. The split GFP assay also showed successful transfer. The authors further characterize this process by biochemical, genetic and imaging approaches. They conclude that a small percentage of cells in the population produce open-ended membrane tubules (which are wider and distinct from TNTs) that can transfer material between cells. This process depends on actin polymerization but not endocytosis or trogocytosis. The process also seems to depend on endogenously expressed Syncytin proteins - fusogens which could be responsible for the membrane fusion leading to the open ends of the tubules.

The paper provides additional solid evidence to what is already known about the inefficiency of EV-mediated protein transport. Importantly, it provides an interesting new mechanism for contact-dependent transport of cellular material and assigns valuable new information about the possible function of Syncytins. However, the evidence that the proteins and vesicles transfer through the tubules is incomplete and a few more experiments are required. In addition, certain inconsistencies within the paper and with previous literature need to be resolved. Finally, some parts of the text, methods and the figures require re-writing or additional information for clarity.

Major comments

1) In Figure 1F, the authors compare the function of exosome-transported SBP-Cas9-GFP vs. transient transfection of SBP-Cas9-GFP. It is not clear if the cells in the transiently transfected culture also express the myc-str-CD63 and were treated with biotin. It is important to determine if CD63-tethering itself affects Cas9 function.

Thank the reviewer for his comments and suggestions. We now show in Figure 1- figure supplement 1D that CD63-tethering itself does not affect Cas9 function.

2) The authors do not rule out that TNTs are a mode of transfer in any of their experiments. Their actin polymerization inhibition experiments are also in-line with a TNT role in transfer. This possibility is not discussed in the discussion section.

Yes, the results in this study do not rule out a role for TNTs in the transfer. At present, we are not aware of conditions that would functionally distinguish transfer mediated by TNTs and thick tubules. We have now included this in the Discussion section.

3) Issues with the Split GFP assay:

a) On page 4, line 176, the authors claim that "A mixture of cells before co-culture should not exhibit a GFP signal". However, this result is not presented.

The results of mixture experiment are included in Figure 2-figure supplement 1D, E.

b) The authors show in Figure 2C and F that in MBA/HEK co-culture or only HEK293T co-culture, there are dual-labeled, CFP-mCherry, cells. First - what is the % of this sub-population? Second, the authors dismiss this population as cell adhesion (Page 5, line 192) - but in the methods section they claim they gated for single particles (page 17, line 642), supposedly excluding such events. There is a simple way to resolve this - sort these dual labeled cells and visualize under the microscope. Finally - why do the authors think that the GFP halves can transfer but not the mature CFP or mCherry?

The plot in the Figure 2C and F are displayed in an all-cell mode, not in singlet mode. The percentage of dual-labeled CFP-mCherry in singlet was 0-0.2%. Thus, most of the signal was from doublet, or cell adhesion. We did not claim that the mature CFP or mCherry cannot be transferred. We suggested that the GFP signal of split-GFP recombination may be a more accurate reflection of cytoplasmic transfer between cells. In contrast, mature CFP or mCherry may simply attach to the cell surface but not enter into the other cells.

c) In the Cas9 experiments - the authors detect an increase in Nluc activity similar in order of magnitude that that of transient transfection with the Cas9 plasmid - suggesting most acceptor cells now express Nluc. However, only 6% of the cells are GFP positive in the split-GFP assay. Can the authors explain why the rate is so low in the split-GFP assay? One possibility (related to item #2 above) is that the split-GFP is transferred by TNTs.

The Cas9-based Nluc activity assay is more sensitive as it measures an enzyme with a very high turnover number. The split-GFP assay requires a transfer of GFP fragments to produce intact GFP molecules where the signal is not amplified. We think this explains the dramatic increase in a signal once Cas9 is transferred. Our cell sorting results suggest that at least 6% of the receptor cells are transferred in the co-cultures. Of course, nothing in either analysis rules out a role for TNTs in this transfer.

4) The membrane tubules, the membrane fusion and the transfer process are not well characterized:

a) The suggested tubules are distinct from TNTs by diameter and (I presume, based on the images) that they are still attached to the surface - whereas TNTs are detached. However, how are these structures different from filopodia except that they (rarely) fuse?

We used TIRF microscopy and found that the thick tubules are not attached to the surface (not shown). Filopodia are much closer in diameter to TNTs (0.1-0.4 micron). The thick tubules we observe are much thicker (2-4 micron in diameter).

b) Figure 5E shows that the acceptor cells send out a tubule of its own to meet and fuse. Is this the case in all 8 open-ended tubules that were imaged? Is this structure absent in the closed-ended tubules (e.g. as seen in Figures 6 & 8)?

Around half of open-ended tubules appeared to emanate from acceptor cells. Likewise, for closed-ended tubules, for example, in Figure 6E where a recipient HEK293T cell projected a short tubule.

c) The authors suggest a model for transport of the proteins tethered to vesicles (via CD63 tethering). However, the data is incomplete.

i) They show only a single example of this type of transport, without quantification. How frequent is this event?

The transport of the proteins tethered to vesicles (via CD63 tethering) were found in all 8 open-ended tubules that we detected in this study.

ii) Furthermore, the labeling does not conclusively show that these are vesicles and not protein aggregates. Labeling of the vesicle - by dye or protein marker will be useful to determine if these are indeed vesicles, and which type.

In Figure 4B, the moving punctum in a tubular connection appears to contain SBP-Cas9-GFP, Streptavidin-CD63-mCherry, and the cell surface WGA conjugate that may have been internalized into a donor cell endosome, which indicates that the moving punctum is vesicle type. Nonetheless, in general we cannot distinguish the forms of Cas9 that are transferred and become localized to the nucleus of target cells and we make no claim other than to suggest this possibility that Cas9 may be transferred as an aggregate.

iii) The data from Figure 2 suggest (if I understand correctly) transfer of the CD63-tethered half-GFP, further strengthening the idea of vesicular transfer. However, the authors also show efficient transfer of untethered Cas9 protein (Figure 2A and other figures). Does this mean that free protein can diffuse through these tubules? The Cas9 has an NLS so the un-tethered versions should be concentrated in the nucleus of donor cells. How, then, do they transfer? The authors do not provide visual evidence for this and I think it is important they would.

Based on the results using the Cas9-based luciferase assay (His- or SBP-tagged Cas9) (Figure 2A) and split-GFP assay (free GFP1-10) (Figure 2G), we suggest that free protein could be transferred between cells. Our current imaging approach is not designed to quantify protein diffusion. However, we are able to detect from images that Cas9-GFP does not colocalize exclusively with CD63 or concentrate in the nucleus, but also appears in the cytoplasm. These data indicate that both vesicle association and free diffusion may mediate the transfer through tubules. We thank the referee for emphasizing this issue which we will consider for future work to distinguish the transfer types through tubules.

iv) In Figures 6 & 8, where transfer is diminished, there are still red granules in acceptors cells (representing CD63-mcherry). Does this mean that vesicles do transfer, just not those with Cas9-GFP? Is this background of the imaging? The latter case would suggest that the red granule moving from donor to acceptor cells in figure 4 could also be "background". This matter needs to be resolved.

There are a few red puncta in the acceptor cell in Figure 6B. Since the acceptor cell is close to and overlapped with other donor cells containing CD63-mCherry, the red signal may, as the reviewer suggests, be from donor cells and not as a result of transfer through tubular connections. However, donor-acceptor cultures of HEK293T where transfer is not observed, little CD63-mCherry signal, for example, in Figure 6a, was seen in acceptor cells, even during several hours of observation (Figure 6- figure supplement video). A minor red signal could arise from exosomes secreted by donor cells that are internalized by acceptor cells. Images of single-culture receptor cells were added in Figure 4- figure supplement 1.

For Figure 8, we used MDA-MB-231 syncytin-2 knock-down cells containing Fluc:Nluc:mCherry as the receptor cell, thus in these experiments the red signal most likely represents mCherry expressed in the acceptor cells.

In Figure 4, we observed moving punctum in a tubular connection which contained co-localized green, red, and purple signals, corresponding to SBP-Cas9-GFP, streptavidin-CD63-mCherry, and the WGA conjugate, respectively. The video of punctum transport (Figure 4-figure supplement video) suggests that the red signal is not “background”.

5) Why do HEK293T do not transfer to HEK293T?

a) A major inexplicable result is that HEK293T express high levels of both Syncytin proteins (Figure 7 - supp figure 1A) yet ectopic expression of mouse Syncytin increases transfer (Figure 7E). Why would that be? In addition, Fig 3A shows high transfer rates to A549 cells - which express the least amount of Syncytin. The authors suggest in the discussion that Syncytin in HEK293T might not be functional without real evidence.

We cannot yet explain why the basal level of syncytin expressed in HEK293 cells is insufficient to promote open-ended tubular connections between these cells. It could be that the proteins are not well represented in a processed form at the cell surface. Nonetheless, ectopic expression of mouse syncytin-A in HEK293T produced some increased transfer but less than when syncytin-A is ectopically expressed in MDA-MB-231 cells (up to 4-fold vs. 30-fold change of Nluc/Fluc signal) (Figure 7E). Furthermore, we have added new results which show that apparent furin-processed forms of syncytin-A, -1 and -2 can be detected by cell surface biotinylation in transfected MDA-MB-231 cells (Figure 8-figure supplement 1D). All we demonstrate is that syncytin in the acceptor cell is required for fusion and we make no claim that it is the only protein or lipid at the cell surface in the acceptor cell required for fusion. Clearly, more work is essential to establish the complexity of this fusion reaction.

For A549 cells, syncytin-1 is highly expressed in A549 cells, thus it is possible that syncytin-1 in A549 plays crucial roles in the process.

b) In addition - previous publications (e.g. PMID: 35596004; 31735710) show that over expression of syncytin-1 or -2 in HEK293T cells causes massive cell-cell fusion. The authors do not provide images of the cells, to rule out cell-cell fusion in this particular case.

Overexpression of syncytin-1 or -2 in cells indeed causes massive cell-cell fusion, while overexpression of syncytin-A induced much less cell fusion than syncytin-1, or -2. We have now added new images shown in Figure 8-figure supplement 1A-C to document these observations. It may be that overexpressed human syncytins are better represented in a furin-processed form in both cell types. In contrast, we did not observe donor-acceptor cell fusion at basal levels of expression of syncytin in HEK293T and MDA-MB-231. For example, the Figure 4-figure supplement video shows that tubular structures were seen to form and break during the course of visualization with a tubule fusion event but no cell fusion to form heterokaryons.

Reviewer #3 (Public Review):

In this manuscript, Zhang and Schekman investigated the mechanisms underlying intercellular cargo transfer. It has been proposed that cargo transfer between cells could be mediated by exosomes, tunneling nanotubes or thicker tubules. To determine which process is efficient in delivering cargos, the authors developed two quantitative approaches to study cargo transfer between cells. Their reporter assays showed clearly that the transfer of Cas9/gRNA is mediated by cell-cell contact, but not by exosome internalization and fusion. They showed that actin polymerization is required for the intercellular transfer of Cas9/gRNA, the latter of which is observed in the projected membrane tubule connections. The authors visualized the fine structure of the tubular connections by electron microscopy and observed organelles and vesicles in the open-ended tubular structure. The formation of the open-ended tubule connections depends on a plasma membrane fusion process. Moreover, they found that the endogenous trophoblast fusogens, syncytins, are required for the formation of open-ended tubular connections, and that syncytin depletion significantly reduced cargo Cas9 protein transfer.

Overall, this is a very nice study providing much clarity on the modes of intercellular cargo transfer. Using two quantitative approaches, the authors demonstrated convincingly that exosomes do not mediate efficient transfer via endocytosis, but that the open-ended membrane tubular connections are required for efficient cargo transfer. Furthermore, the authors pinpointed syncytins as the plasma membrane fusogenic proteins involved in this process. Experiments were well designed and conducted, and the conclusions are mostly supported by the data. My specific comments are as follows.

1) The authors showed that knocking down actin (which isoform?) in both donor and acceptor cells blocked transfer, and more so in the acceptor cells perhaps due to the greater knockdown efficiency in these cells. However, Arp2/3 complex knockdown in donor cells, but not recipient cell, reduced Cas9 transfer. It would be good to clarify whether the latter result suggests that the recipient cells use other actin nucleators rather than Arp2/3 to promote actin polymerization in the cargo transfer process. Are formins involved in the formation of these tubular connections?

We thank the reviewer for his/her comments and suggestions. Beta-actin was knocked down in this study. We tried a formin inhibitor, SMIFH2 which resulted in a decrease the Cas9 transfer between cells (Figure 3F).

2) The authors provided convincing evidence to show that the tubular connections are involved in cargo transfer. Intriguingly, in Figure 4-figure supplement video (upper right), protein transfer appeared to occur along a broad cell-cell contact region instead of a single tubular connection. How often does the former scenario occur? Is it possible that transfer can happen as long as cells are contacting each other and making protrusions that can fuse with the target cell?

In the Figure 4-figure supplement video (upper right), it may be that several membrane tubes from several different donor cells contact at sites close to one another on the recipient cell resulting in the appearance a broad cell-cell contact. This was a rare observation. In our quantification, only 8 connections were open-ended in 120 cell-cell contact junctions. Once open-ended, or plasma membrane fused, cargo transfer is observed.

3) The requirement of MFSD2A in both donor (HEK293T) and recipient (MDA-MB-231) cells is consistent with a role for syncytin-1 or 2 in both types of cells. Since HEK293T cells contain both syncytins and MFSD2A but cargo transfer does not occur among these cells, does this suggest that syncytins and/or MFSD2A are only trafficked to the HEK293T cell membrane in the presence of MDA-MB-231 cells?

A proper answer to this question requires the visualization of syncytins and MFSD2A. The commercial syncytin antibodies were inadequate for immunofluorescence. In advance of the more detailed effort required to tag the genes for endogenous syncytin 1 and 2, we performed live cell imaging and surface biotin labeling of cells transiently transfected to express fluorescently-tagged forms of syncytin-1, -2 and -A. We now show that syncytin-A, -1, and -2 partially localize to the plasma membrane or the cell surface of MDA-MB-231 and at points of cell-cell contact. In fact, overexpression of codon-optimized human syncytin-1, and -2 induced dramatic HEK293T cell-cell fusion. However, at basal levels of syncytin expression, HEK293T could not form open-ended tubular connections, which may be because the basal level of syncytins are not well represented in a processed form at the cell surface or their activity is limited by unknown factors.

As an independent test of cell surface localization, we used surface biotinylation to show that a fraction of the syncytins can be labeled externally (Figure 8-figure supplement 1D). This fraction shows evidence of proteolytic processing consistent with furin cleavage whereas the overwhelming majority of transfected syncytins detected in a blot of lysates suggests that most remain in the unprocessed precursor form, consistent with the punctate and reticular fluorescence images (Figure 8-figure supplement 1A-C).

We used IF and GFP-tagged MFSD2A and found this protein partially localized to the plasma membrane of HEK293T cells (Figure 9E, F). Given the results reveal that cargos could be transferred among MDA-MB-231 cells (Figure 2G), syncytin and its receptor appear to function in transfer among these cells.

I don't agree with that or at least I believe it is not applicable to all cases. When taking Egypt as an example, one would find that most if not all norms taken from western influence during colonization have faded as generations changed. The norms that stayed however, have been there before colonization. The thing that cannot be denied its influence is when Napoleon exposed Egyptians to books and knowledge but the thing is they already had knowledge that they just lost before that. I would say that the only norm Egyptians still have is flipping the shoe if it's upside down and that is from the ancient Egyptians

When I graduated high school I actually sat down with myself and wondered if college was something I wanted to do, or if it was something that "I was supposed to do". As young adults I thought it was crazy that people just inspected me to know what I wanted to do.

I'm sure that both parties check their data before they talk, its just that the anecdotes have more people or have better data to present.

It's so interesting to see how much work went into delivering a message in the past, when today we can just send a text and a message is delivered. This shows how difficult messaging was in the past, and that today's convenience of sending a message is something we take for granted!

I liked how they revised this paragraph. It's not really any more simple than the original paragraph, things have just been rearranged in a way that make it easier to understand.

.

This is great! This is what Dr. Fleming was talking about. So it's not just that you feel better, but you perform better cognitively!

While many schools and individual teachers are banning students from using this type of technology I think that they should be encouraging students to learn how to use it effectively.

I dont disagree, it is one way to see it but it is too much cynical. Birthday and other celebration are moment we need to enjoy with family or some friend.

Abigail's evil intentions – Now it's revealed that Abigail really did push well beyond the strict religious laws of Salem in hopes of killing Elizabeth Proctor. Abigail is more than just a liar: she's capable of murder.

Theme of DESIRES: Is everyone permitted to pursue their own desires? How is Abigail's pursuit of her desires dangerous?

I like this statement from Campbell because she's responding to an event that you see quite often when working on eradicating domestic violence. She's telling us that this story is just one of many. When she mentions that victims need help, it's a call to action for anyone to reach out if they notice anything out of the ordinary.

main idea

I'd say it's most certainly just a symptom. The political climate of most 1st world western countries both online and in the real world has been growing increasingly hostile in recent years, especially after Trump was elected in 2016. The "culture war" that's been happening for the last decade or so is almost entirely the cause for situations like the ones listed above in the article. There have always been people willing to harass or attack those they disagree with for their own gain, but the current climate of the internet has made that the standard response to most things someone doesn't like. It's unsurprising that those attitudes would find their way into pop-culture fandoms.

Personally I feel like this isn't something with a general answer that is able to apply to most situations. The Sonic movie, for example, did benefit from the improvements it was given and they only served to make it a better product. Overall though I don't think the "art" of film-making will suffer all that much. Someone who is genuinely trying to create a piece of art will likely have a clear vision that won't be affected by their fans, and if it is it's probably under immense consideration. I honestly don't think the situation with the Sonic movie is similar. Maybe I'm wrong, but it doesn't seem like a film made for artistic purposes. It was just a big budget movie based on a huge pre-existing franchise created to make lots of money, so it makes sense why the creators might want to please fans.

page 57. "violence is a social practice." is a very true statement. normalizing this brings to mind the argument that bystanders are not innocent. They are quite literally silently telling people it is ok or like the reading says, it's like people just expect it. This should not be normalized.

While each group is affected by oppression and discrimination in this country, each groups agenda is catered to the way in which they are impacted by oppression. Which is why it's important that when such groups are discriminated against they don't just use the word "oppression" but rather give facts and examples to how oppression has negatively impacted them.

This quote is a good way of viewing the issues of racism and inequality against all types of different races. I feel that a person in one race, only cares or can see the problems or view and understand the perspective within their own race. Other races wouldn't be able to understand as well as they could. For example, when it comes to police brutality against black people, the white people most of the time want to side with the white cops saying "They were just doing their job. It's not an easy profession." From their point of view, the police did the right thing but from a black person point of view, we think about what was really going on. What was the reason they targeted that person? It is never necessary for the first instinct to pull out a gun just because they THINK something dangerous about to happen next when they are just trying to comply. In America, we are so focused on the black and white communities the most because we make up most of the population and we are blind to the problems of other races or cultures.

I like how Young points out that oppression is not just a few events of injustice that happen in a lifetime for members of cultural groups or genders, but instead it is something that is ongoing and so normalized. I think that oppression in the world (especially America) is starting to become so normalized that the oppressed groups have had almost no choice but to surrender what little power they had left and accept that this is the new norm. Just as a disclaimer, I am not agreeing with the last part of my annotation being right or something that we should accept, I'm just saying that it's something I've noticed, primarily from being a member of an oppressed group(s).

"Екатерина Шульман: Практический Нострадамус, или 12 умственных привычек, которые мешают нам предвидеть будущее". vedomosti/ (in Russian). Retrieved 24 June 2021.

A Note on the Cargo Cult of Zettelkasten

Modern cargo cults can be seen in many technology and productivity spaces where people are pulled in by exaggerated (or sometimes even real claims) of productivity or the general "magic" of a technology or method.

An example is Niklas Luhmann's use of his zettelkasten which has created a cargo cult of zettelkasten aspirants and users who read one or more of the short one page blog posts about his unreasonable productivity and try to mimic it without understanding the system, how it works, or how to make it work for them. They often spend several months collecting notes, and following the motions, but don't realize the promised gains and may eventually give up, sometimes in shame (or as so-called "rubbish men") while watching others still touting its use.

To prevent one's indoctrination into the zettelkasten cult, I'll make a few recommendations:

Distance yourself from the one or two page blog posts or the breathless YouTube delineations. Ask yourself very pointedly: what you hope to get out of such a process? What's your goal? Does that goal align with others' prior uses and their outcomes?

Be careful of the productivity gurus who are selling expensive courses and whose focus may not necessarily be on your particular goals. Some are selling very pointed courses, which is good, while others are selling products which may be so broad that they'll be sure to have some success stories, but their hodge-podge mixture of methods won't suit your particular purpose, or worse, you'll have to experiment with pieces of their courses to discover what may suit your modes of working and hope they'll suffice in the long run. Some are selling other productivity solutions for task management like getting things done (GTD) or bullet journals, which can be a whole other cargo cults in and of themselves. Don't conflate these![^1] The only thing worse than being in a cargo cult is being in multiple at the same time.

If you go the digital route, be extremely wary of shiny object syndrome. Everyone has a favorite tool and will advocate that it's the one you should be using. (Often their method of use will dictate how much they love it potentially over and above the affordances of the tool itself.) All of these tools can be endlessly configured, tweaked, or extended with plugins or third party services. Everyone wants to show you their workflow and set up, lots of which is based on large amounts of work and experimentation. Ignore 99.999% of this. Most tools are converging to a similar feature set, so pick a reasonable one that seems like it'll be around in 5 years (and which has export, just in case). Try out the very basic features for several months before you change anything. Don't add endless plugins and widgets. You're ultimately using a digital tool to recreate the functionality of index cards, a pencil, and a box. How complicated should this really be? Do you need to spend hundreds of hours tweaking your system to save yourself a few minutes a year? Be aware that far too many people touting the system and marketers talking about the tools are missing several thousands of years of uses of some of these basic literacy-based technologies. Don't join their island cult, but instead figure out how the visiting culture has been doing this for ages.[^2] Recall Will Hunting's admonition against cargo cults in education: “You wasted $150,000 on an education you coulda got for $1.50 in late fees at the public library.”[^3]

Most people ultimately realize that the output of their own thinking is only as good as the inputs they're consuming. Leverage this from the moment you begin and ignore the short bite-sized advice for longer form or older advice from those with experience. You're much more likely to get more long term value out of reading Umberto Eco or Mortimer J. Adler & Charles van Doren[^4] than you are an equivalent amount of time reading blog posts, watching YouTube videos, or trolling social media like Reddit and Twitter.

Realize that reaching your goal is going to take honest-to-goodness actual work, though there is potential for fun. No matter how shiny or optimized your system, you've still got to do the daily work of reading, watching, listening and using it to create anything. Focus on this daily work and don't get sidetracked by the minutiae of trying to shave off just a few more seconds.[^5] In short, don't get caught up in the "productivity porn" of it all. Even the high priest at whose altar they worship once wrote on a slip he filed:

"A ghost in the note card index? Spectators visit [my office to see my notes] and they get to see everything and nothing all at once. Ultimately, like having watched a porn movie, their disappointment is correspondingly high." —Niklas Luhmann. <small>“Geist im Kasten?” ZKII 9/8,3. Niklas Luhmann-Archiv. Accessed December 10, 2021. https://niklas-luhmann-archiv.de/bestand/zettelkasten/zettel/ZK_2_NB_9-8-3_V. (Personal translation from German with context added.)</small>

[^1] Aldrich, Chris. “Zettelkasten Overreach.” BoffoSocko (blog), February 5, 2022. https://boffosocko.com/2022/02/05/zettelkasten-overreach/.

[^2]: Blair, Ann M. Too Much to Know: Managing Scholarly Information before the Modern Age. Yale University Press, 2010. https://yalebooks.yale.edu/book/9780300165395/too-much-know.

[^3]: Good Will Hunting. Miramax, Lawrence Bender Productions, 1998.

[^4]: Adler, Mortimer J., and Charles Van Doren. How to Read a Book: The Classic Guide to Intelligent Reading. Revised and Updated edition. 1940. Reprint, New York: Simon & Schuster, 1972.

[^5]: Munroe, Randall. “Is It Worth the Time?” Web comic. xkcd, April 29, 2013. https://xkcd.com/1205/.

Recommended resources

Choose only one of the following and remember you may not need to read the entire work:

Ahrens, Sönke. How to Take Smart Notes: One Simple Technique to Boost Writing, Learning and Thinking – for Students, Academics and Nonfiction Book Writers. Create Space, 2017.

Allosso, Dan, and S. F. Allosso. How to Make Notes and Write. Minnesota State Pressbooks, 2022. https://minnstate.pressbooks.pub/write/.

Bernstein, Mark. Tinderbox: The Tinderbox Way. 3rd ed. Watertown, MA: Eastgate Systems, Inc., 2017. http://www.eastgate.com/Tinderbox/TinderboxWay/index.html.

Dow, Earle Wilbur. Principles of a Note-System for Historical Studies. New York: Century Company, 1924.

Eco, Umberto. How to Write a Thesis. Translated by Caterina Mongiat Farina and Geoff Farina. 1977. Reprint, Cambridge, MA, USA: MIT Press, 2015. https://mitpress.mit.edu/books/how-write-thesis.

Gessner, Konrad. Pandectarum Sive Partitionum Universalium. 1st Edition. Zurich: Christoph Froschauer, 1548.

Goutor, Jacques. The Card-File System of Note-Taking. Approaching Ontario’s Past 3. Toronto: Ontario Historical Society, 1980. http://archive.org/details/cardfilesystemof0000gout.

Sertillanges, Antonin Gilbert, and Mary Ryan. The Intellectual Life: Its Spirit, Conditions, Methods. First English Edition, Fifth printing. 1921. Reprint, Westminster, MD: The Newman Press, 1960. http://archive.org/details/a.d.sertillangestheintellectuallife.

Webb, Sidney, and Beatrice Webb. Methods of Social Study. London; New York: Longmans, Green & Co., 1932. http://archive.org/details/b31357891.

Weinberg, Gerald M. Weinberg on Writing: The Fieldstone Method. New York, N.Y: Dorset House, 2005.

According to what I read, this Where clause goes to the server and applies there. So, we just have to do what we do in js: open a filter form first, build a filter from there and then open the form with that filter (with no Where word).

Doing that, it's sql server who picks up that filter and applies it server side!!! Just what we need ...

individually yes. Feedback loops is the response. It's just that we allowd socmed to base feedback almost entirely on outrage.

I remember reading this in my US History class - there was a particular political cartoon I do not think I’ll ever forget: it depicted Chinese immigrants that were coming to the US as a rat infestation, underneath the floorboards, in the walls, coming out the cracks (the foundations). But honestly? I disagree with the notion that it’s been lost to the hazes of history - it has just changed targets. Like political cartoons that are depictions of the past, along with racism immigration policies that affected immigrants then and still make it difficult today, negative and xenophobic attitudes have remained ingrained in culture - from stereotypes to hate crimes to language barriers to little actions and big actions. The pandemic especially made it apparent.

A handful of things came to mind, and I thought I'd get them out right away (after the video meeting) so I don't forget. I think it's okay for organizations and ideas to fail. Diversity feeds innovation, and it's nearly impossible to know ahead of time what matters for success. Ironically, running somewhat counter to this, there is useful information in what has succeeded in the past (and continues to succeed). Thing is, I'm not sure how best to utilize that information. In Eastern Indonesia, there are highly successful institutions for managing collective action, such as gotong royong and sasi. Would these work in other contexts? I just don't know. This is one reason I really liked the Governance Archeology project since it offers a tangible way address this question (and many other questions, to boot). In the end, I find myself intoning things like "we can learn from the past and from other people," but struggling to identify exact "lessons" when I try to do so.

Over the past decade, as we have continued to see rising temperatures and sea levels, the debate over climate change being real has finally started to secede - however, this is a scary awakening to the fact that our environment is rapidly changing. As we see here, just 8 years ago, only 64% of democrats and 23% of republicans believed climate change was real and caused by human activities, whereas those numbers are closer to 72% across the U.S. now. Regardless of whether people believe it's real or not, environmentalists and economists should put greater emphasis on conserving the environment through their policies.

(https://climatecommunication.yale.edu/visualizations-data/ycom-us/)

A real danger of "bringing your whole self to work."

Oh my goodness it's that Seinfeld episode where Jerry and George try to sit down and write an episode for their new sitcom

Funny how he indirectly tells us here that everything he just did to understand the article was part of a process that he never used again (because it's way too long and arduous!)

This has saved me from what I just know would be so many hours of not progressing in a area. Finding hints in the abstract is pretty much the shortcut to not reading a article you don't need to.

it's good to hear that even professors just skip papers they don't understand

At the end o f the article, I find myself wondering why it's important to find a correlation (or disprove one) between race and IQ (two relatively arbitrary categories). I can certainly see where having this information would be conducive to advocating for marginalized people living within systems built on dividing people into these categories to facilitate inequity, but I would love to live in a world where everyone, regardless of perceived intelligence or racialized identity are cared for not because of their relationship to these categories but because they're alive and deserve to have their needs met. Of course I realize this is trite and idealistic, and that the current state of the world and all its many systems makes this hope unrealistic and I think this is still a valid (née vital) line of scholarly inquiry. Just one of the thoughts I'm left with at the conclusion.

I feel like at the same time that's just.. not what creators might want. Not everyone are willing to work for food and bills, it's exhausting. Artist don't have to live like that, it's a weird idea that people have about always hungry art people. That's just sad. And if we're talking of a good living wage it goes back to the capitalistic problem of creativity: need profit to even just survive, need stable paths for profit

The same happens with everything nowadays. Capitalism destroys innovation for profit. It is especially visible in fields where it takes more people & time to produce: games are the most obvious one. Considering will to success & financial profits, it, unfortunately, makes sense that creativity leaves its lead. When our Game Studio 2 group was trying to figure out our unique time mechanic, we were lost in so many places, starting from code implementation and ending with animation system. All of this, in full production setting makes it impossible to estimate & calculate budget. At the same time, team members need money to pay rent and eat food, and making bold creative choices is just scary and risky. Though I'm very inspired by creators who make what they want, not connecting themselves to trends (like German studio Honig, for example), but it's hard and probably requires some kind of governmental support, which is impossible in many countries.

Timm is working on this file on another branch and renaming it. Click on Timm's branch in the history view to see their version (or really just click anywhere on the history view to toggle the states -- done like that since it's just a mock).

It's very important that everyone is made aware of how big of a problem this is especially for lower income areas and that we all do the best we can.

I think the advantage of using "BCC" (Blind Carbon Copy) in emails. By using BCC, you can avoid the recipients from having to scroll through a long list of email addresses and it also eliminates the risk of accidentally hitting "reply all" when responding, which can result in unwanted and unnecessary emails being sent to multiple people. This makes BCC a useful tool for privacy and efficiency in email communication.

I find this passage about an atheists point of view to be fascinating, because even though this "Atheist" does not believe in God, he ultimately falls back on the hope that God would help them in trying times.

This segment stood out to me, because it's pretty much telling the truth. I've seen a lot of people become swallowed up by certain obsessions they've worshipped whether its money, fame, someone, or something else. People who don't follow some sort of ethical teachings whether it's religion or otherwise do tend to get swallowed up by whatever it is their obsession is. It's lesson just about everyone needs to learn.

I think with this it really describes a daily kind of depression that I've heard many times before, that depression is something that wants to take over your mind and make you think about everything negatively because it's just easier that way, it's simpler to just give in and hate everything and everyone but yourself, because it's so hard to fight against it.

That's actually not (just) seemingly "personally unfair"—it's collectively unfair. The folks responsible for these things serve as the better example of self-centeredness...

The fact that we're not talking about a child here but that it was considered a normal for a 43-year-old man in 2005 to have this as his default setting perhaps explains quite a lot about the evident high skew of self-centeredness in folks who are now in their sixties and seventies.

I didn't notice this in 2005, but maybe I wasn't paying close enough attention.

Could one still argue that it's a component piece of the text/writing that is generated? Just like spelling, grammar, and citation are?

No doubt it's a lot MORE of the text that is generated and COULD be handed in completely as is in many cases. But could it nonetheless be seen as a kind of starting point for students to then focus on other work, other skills? Like the editing processes mentioned above.

Yes! And they ARE writing when they read and annotate, so they can still practice and instructors can still evaluate that skill. It's just a very different writing assignment than a final paper.

"Where do you want to go today", Microsoft's campaign, 1995.

But like... these are real & worthy groundings that aren't some new novelty, some meta-verse-al creation, but just like the ground philosophical basis of the mind. Most of us cannot live up to our own expectations/aspirations, but whether we choose to let that delegitimize the mind, whether we accept that poison chalice & accept disbelief & skepticism broadly is up to each of us.

These corporate entities are extremely tuned in to the plot, to the reality of the mind. They get it. The skepticism is due, but it's not "the metaverse," it's not just fiction, it's deeply part of the human experience, our motivation.

For sure, this all must be tempered with a reality. But I think the duality here is invaluable, and the pure cut-down attitude needs counter-skepticism.

It's disappointing to see how poverty level defines student alienation, not just race.However, because the author was a white student, he was often bullied for his social status. We get to the point of how people perceive others to be more empathetic based on what they get from others. You are more likely to be with someone who is taller.

I feel like this is true, a lot of times in school we aren't told why we're learning something, and just to study it, but I think that it is important to tell people why they should know something. It's important that people learn why history is important, so that they feel motivated to actually learn it.

it's interesting to me (and surprising!) how much this manifesto emphasizes team size. would have expected more of an aesthetic emphasis. i wonder if just team sizes were starting to balloon around this time, and this is reacting to that explicitly?

yeah sort of funny to read all of this and then see what i view as sort of conservative criticisms / outlooks of videogame design get wielded at the bottom. to me, one-time-through linear games are totally exciting and valuable and fit the spirit of this manifesto perfectly! think it's just that games as a medium have evolved a lot in the intervening years; new modes of play have been proven possible (or maybe they existed already and these writers didn't play them?)

It's interesting to me that people find his videos super shocking because much of the points he makes are pointing out problems that are wildly known, such as pollution. I wonder if Disney misinforms people and create false versions of reality or if they are just protecting the younger audiences.

Author Response

Reviewer #1 (Public Review):

The authors optimize a live cell imaging method based on the detection of FAD/NAD(P)H adopted from the fast-growing field of live metabolic imaging. They build upon a method described by KreiB et al 2020 that used metabolic ratio and collagen fiber second harmonic generation imaging. They follow by combining metabolic imaging with morphologic measurements to train a machine-learning model that is able to identify cell types accurately. Upon visualization, authors detected structures hypothesized and then proven to resemble the "goblet cell associated antigen passages" previously studied in intestinal epithelia.

STRENGTHS

• The manuscript is succinct, well written, and overall done rigorously.

• The optimization of the method at multiple levels to the point of identifying both common and rare cell types is impressive.

• Describes the elegant implementation of a sorely needed method in epithelial biology.

• Provides an approach to studying the cholinergic response in epithelial cells, a poorly understood phenomenon despite broad clinical use for diagnosis and treatment.

WEAKNESSES

A) For what is in large part a methods-development paper, the methods are not explained or shared in a manner that facilitates reproducibility. For example:

A.1.) The training and validation datasets seem to come from the same sample (or the source is not clearly described). Therefore, it is not clear whether the "96% accuracy" refers to accuracy within the sample measured, or whether it can extrapolate to other samples.

In order to avoid any confusion, we further clarify that the machine learning training and validation data sets come from the same sample. We had split the total data set into 2 separate subsets for this purpose. This has been laid out in the text as follows:

“In order to assess the performance of machine learning algorithms designed to distinguish cell types, we divided our data set into training and testing subsets. We utilized 75% of the total cells (154 cells) for machine learning training, leaving 25% (52 cells) for subsequent validation.”

A.2.) It is unclear whether the model needs to be re-trained within each new sample measured, or if it's applicable to others. This has implications for method adoption by others. Either way is useful but needs to be clarified.

This is a very interesting point and one that we further clarify in the Discussion noting that in both disease and non-diseased states the model needs to be re-trained in each particular experimental regime.

A.3.) Code was only listed in a PDF file, which makes reproducing the analysis very cumbersome.

We hope that all can utilize the code made for this methodology and have uploaded it to a publicly available GitHub account:

https://github.com/vss11/Label-free-autofluorescence

B) Whereas the optimization to improve cell type detection is very well described, the implementability of the approach could benefit from exploration (using the data already obtained) of the minimal set of measurements needed to identify cell types. For example, is the FAD/NAD(P)H ratio necessary? Or could just morphologic measurements achieve the same goal?

This is an excellent point, and we appreciate the Reviewer’s suggestion for this analysis. We have added Figure 3 Supplement 5 where we perform modeling without autofluorescence data. This analysis reveals a dramatic reduction in accuracy with a Matthew’s correlation coefficient ranging from 0.66 to 0.78. This provides additional justification for the use of autofluorescence for cell type identification. Morphologic measurements are not sufficient for cell type identification alone.

We also have determined the relative contribution of each characteristic to the cell type identification by the Xgboost algorithm in Figure 3 Supplement 4, which shows that autofluorescence signatures are amongst the top contributing characteristics to cell type identification by machine learning.

C) Whereas the conclusions are overall supported by the data, need small adjustments in some cases:

C.1.) For example, P3L80: Claims autofluorescence imaging is more specific than "functional markers", however, this is done in the setting of a very specific intervention that massively affects a protein often used as a secretory cell marker (CCSP aka SCGB1A1), which is known to be secreted (and depleted) in secretory cells upon stimulation.

We agree with the Reviewer that secretory cell identification is a prime example where autofluorescence imaging may be superior to conventional staining, specifically due to the point the Reviewer makes regarding CCSP secretion. We discuss this concept in the Discussion while giving examples of CCSP staining being reduced in asthma, COPD, and smokers. It could be that these cells are missed due to depletion of CCSP. Indeed, we clarify that our methodological approach may be less affected by the loss of the category of specific markers that change with cell state. There are, of course, caveats with utilizing this approach in disease states, and we elaborate on this further below and add this point to the discussion.

C.2.) Relatedly, it is unclear how the method's accuracy would be affected in conditions that affect redox/metabolic state; the approach may be highly affected in inflammation and injury, for example.

As suggested by the Reviewer, we re-analyzed the data after Antimycin A + Rotenone and FCCP to determine if autofluorescence ratio is sufficiently different to identify ciliated and secretory cells and included this data in Figure 2 Supplement 1. This is an example where the redox/metabolic state is indeed altered. Though the autofluorescence ratio is affected, it is still useful for cell type identification after intervention as the ciliated and secretory cells have statistically different ratios.

However, different disease states, particularly infection and inflammation may result in a more profound effect on autofluorescence signatures. For instance, previous work by Dilipkumar et. al, 2019 found changes in autofluorescence over days in repeated measurements in a mouse model of inflammatory bowel disease. Therefore, it is likely that the cell type identification methodology will need to be re-optimized for different experiments and diseased tissues. We include commentary to this effect in the discussion.

D) The data used to describe "SAPs" is very cursory.

To further elaborate on our description of SAPs we have included the following:

1) SAP formation occurs in secretory cells in both stimulated and unstimulated conditions. We performed additional analysis of Figure 4C and determined that SAP formation does occur at baseline prior to stimulation in 9% of secretory cells. Methacholine addition results in 78% of secretory cells forming SAPs (Figure 4 Supplement 1). We have added Figure 5C to demonstrate that SAP formation occurs in the absence of stimulation and is enhanced after methacholine stimulation.

2) We demonstrate that SAPs can uptake both FITC-dextran and FITC-ovalbumin in Figure 5E, and Figure 5 Supplement 2. We also now show that immune cells (CD11c antigen presenting cells) associate with SAPs containing FITC-dextran and FITC-ovalbumin in Figure 5E and Figure 5 Supplement 2. We have expanded the Discussion of SAPs.

3) We now show 3 video examples and an XZ optical cross section of ALI that demonstrate uptake and secretion of FITC-dextran in Figure 5 Supplemental Videos 1-3 and Figure 5 Supplement 1.

D.1.) Unclear if FITC dextran uptake occurs in other cells too, or in secretory cells prior to methacholine stimulation, or induced nonspecifically due to epithelia manipulation. Secretory and goblet cells are very sensitive to stimulation and often considered minimal, for example, see the paper by Abdullah et al DOI:10.1007/978-1-61779-513-8_16 in which extreme care had to be applied to prevent any secretion at all.

Our autofluorescence methodology revealed the formation of “voids” of autofluorescence forming in secretory cells and we focused our experiments on this phenomenon. Based on the reviewer question, we generated Figure 5C to better characterize SAP formation. Figure 5C illustrates that SAP formation occurs in both unstimulated and methacholine stimulated conditions, but is dramatically increased following methacholine stimulation. This is analogous to the behavior of GAPs in the intestine (Knoop et al., 2015). Furthermore, we have reanalyzed Figure 4C to identify SAPs prior to stimulation and found that these structures are present in 9% of secretory cells. After methacholine stimulation this percentage increases to 78%.

D.2.) A single image is provided for the SAP timeline (Figure 5C), which appears to be the same cell shown in the supplementary video.

We now provide numerous example videos and optical XZ cross section of ALI demonstrating SAP uptake and secretion in Supplementary Videos 1-3 and Figure 5 Supplement 1.

IMPACT AND UTILITY

This is well-done work with high potential for widespread adoption within the epithelial biology community, particularly if the methods and code are shared in better detail.

We indeed hope that this methodology can be utilized by others. We have posted analysis code, raw data, MATLAB algorithm, and other necessary files onto a publicly available GitHub link. https://github.com/vss11/Label-free-autofluorescence

Reviewer #2 (Public Review):

Shah and colleagues tackle a significant impediment to exploiting tissue culture systems that enable prospective ex vivo experimentation in real-time. Namely, the ability to identify and track dynamic and coordinated activities of multiple composite cell types in response to experimental perturbations. They develop a clever label-free approach that collects biologically-encoded autofluorescence of epithelial cells by 2-photon imaging of mouse tracheal explant culture over 2 days. They report the ability to distinguish 7 cell types simultaneously, including rare ones, by developing a machine-learning approach using a combination of fluorescence and cytologic features. Their algorithm demonstrates high accuracy by Mathew's Correlation Coefficient when applied to a test set. Lastly, they show the ability of their approach to visualize the dynamic uptake and expulsion of fluorescently-tagged dextran by individual secretory cells. Overall, the results are intriguing and may be very useful for specific applications.

We thank the reviewers for their assessment and indeed hope that the methodology is useful and the discovery of the dynamics of SAP formation have important implications for airway mucosal immunology.

Reviewer #1 (Public Review):

The authors optimize a live cell imaging method based on the detection of FAD/NAD(P)H adopted from the fast-growing field of live metabolic imaging. They build upon a method described by KreiB et al 2020 that used metabolic ratio and collagen fiber second harmonic generation imaging. They follow by combining metabolic imaging with morphologic measurements to train a machine-learning model that is able to identify cell types accurately. Upon visualization, authors detected structures hypothesized and then proven to resemble the "goblet cell associated antigen passages" previously studied in intestinal epithelia.

STRENGTHS<br /> - The manuscript is succinct, well written, and overall done rigorously.<br /> - The optimization of the method at multiple levels to the point of identifying both common and rare cell types is impressive.<br /> - Describes the elegant implementation of a sorely needed method in epithelial biology.<br /> - Provides an approach to studying the cholinergic response in epithelial cells, a poorly understood phenomenon despite broad clinical use for diagnosis and treatment.

WEAKNESSES<br /> A) For what is in large part a methods-development paper, the methods are not explained or shared in a manner that facilitates reproducibility. For example:<br /> A.1.) The training and validation datasets seem to come from the same sample (or the source is not clearly described). Therefore, it is not clear whether the "96% accuracy" refers to accuracy within the sample measured, or whether it can extrapolate to other samples.<br /> A.2.) It is unclear whether the model needs to be re-trained within each new sample measured, or if it's applicable to others. This has implications for method adoption by others. Either way is useful but needs to be clarified.<br /> A.3.) Code was only listed in a PDF file, which makes reproducing the analysis very cumbersome.

B) Whereas the optimization to improve cell type detection is very well described, the implementability of the approach could benefit from exploration (using the data already obtained) of the minimal set of measurements needed to identify cell types. For example, is the FAD/NAD(P)H ratio necessary? Or could just morphologic measurements achieve the same goal?

C) Whereas the conclusions are overall supported by the data, need small adjustments in some cases:<br /> C.1.) For example, P3L80: Claims autofluorescence imaging is more specific than "functional markers", however, this is done in the setting of a very specific intervention that massively affects a protein often used as a secretory cell marker (CCSP aka SCGB1A1), which is known to be secreted (and depleted) in secretory cells upon stimulation.<br /> C.2.) Relatedly, it is unclear how the method's accuracy would be affected in conditions that affect redox/metabolic state; the approach may be highly affected in inflammation and injury, for example.

D) The data used to describe "SAPs" is very cursory.<br /> D.1.) Unclear if FITC dextran uptake occurs in other cells too, or in secretory cells prior to methacholine stimulation, or induced nonspecifically due to epithelia manipulation. Secretory and goblet cells are very sensitive to stimulation and often considered minimal, for example, see the paper by Abdullah et al DOI:10.1007/978-1-61779-513-8_16 in which extreme care had to be applied to prevent any secretion at all.<br /> D.2.) A single image is provided for the SAP timeline (Figure 5C), which appears to be the same cell shown in the supplementary video.

IMPACT AND UTILITY<br /> This is well-done work with high potential for widespread adoption within the epithelial biology community, particularly if the methods and code are shared in better detail.

It's probably for this reason that people are "bad" at English even though they can speak it, read it and write it. Just because someone has fundamental knowledge of language, this doesn't give them the tools to analyze, express themselves in a certain way or determine what makes good writing "good".

I do not believe that writing in general would make the idea of writing seem less difficult but would instead have the opposite effect. The more narrow a pool, the easier it is to research. It does not have to be a very specific thing, just simply narrower than in general which can help someone pick what specific point they want to discuss, This can also limit creativity though by establishing parameters of any sort.

reply to u/stjeromeslibido at https://www.reddit.com/r/antinet/comments/10nlu4l/comment/j6dhx2t/?utm_source=reddit&utm_medium=web2x&context=3

It's relatively easy since it's all hiding in my notes. lt may become a book one of these days, I'm just not sure how to approach it quite yet, though I'm getting close to the philosophy I think is missing from the bigger space. I find it somewhat useful to use my notes to create longer responses in spaces like this that I expect I'll reuse in a book.

One can find utility in asking questions of their own note box, but why not also leverage the utility of a broader audience asking questions of it as well?!

I've seen that same copy of Webb's book floating around in various places. In fact, it's the exact same fingerprinted version of the .pdf that I originally read, which can be seen by appending https://via.hypothes.is/ to the URL like this https://via.hypothes.is/http://digamoo.free.fr/webb1926.pdf which will quickly reveal my own notes in the margins. (It may help some to find the small portions outside of Appendix C which relate to note making. 😀)

If you want to follow me down the rabbit hole on some of the intellectual history and examples, try: https://boffosocko.com/research/zettelkasten-commonplace-books-and-note-taking-collection/ which I try to keep updated with new pieces as they arrive.

Using graphics where they are necessary is advisable but also trying not to be stuck on wanting to create the perfect graphical design. Keep in mind that this is a writing course and not an art course. So long as your message is being passed along to your reader clearly, then it’s alright.

...coming back to this, now. ...

I think this was a good and relatable explanation. I love emojis and i think they help convey a message using way more ethan words. It also funny yo see how different emojis are interpreted. Ill use a crying emoji to my friend but when i use that its when im laughing at something. As in Im laughing so hard Im crying. However if i were to send that to my mom shed interprete it differently.

!- quotable : Al Gore

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Reply to the reviewers

Manuscript number: RC-2022-01758

Corresponding author(s): Harbison, Susan and Souto-Maior, Caetano

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1. General Statements [optional]

This section is optional. Insert here any general statements you wish to make about the goal of the study or about the reviews.

We thank the reviewers for their time and care in evaluating our manuscript. They raise several important points, which we have addressed, resulting in a greatly improved manuscript. Please note that we numbered the comments from both reviewers for ease of reference, as we cross-referenced comments in some cases. Reviewer comments are in italics; our responses are provided in plain text.

2. Point-by-point description of the revisions

This section is mandatory. *Please insert a point-by-point reply describing the revisions that were already carried out and included in the transferred manuscript. *

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

*Summary*:

*The authors of this work generated a Sleep Advanced Intercross Population from 10 extreme sleeper Drosophila Genetics Reference Panel. This new out-bred population was subjected to a artificial selection with the aim of understanding the genes underlying the sleep duration differences between three populations: short-sleep, unselected, and long-sleep. Using analysis of variance the authors identified up to nearly 400 of genes that were significant selected over the various generations and showed opposite trends for long and short sleep, thus potentially relevant for the regulation of sleep duration. 85 of these genes were consistent between male and females sub-populations, suggesting a small number of genetic divergences may underlie sex-independent mechanisms of sleep.

Given the time-course nature of the generational data obtained, the authors studied potential correlations and interactions between these 85 identified candidate genes. Initially, the authors used pairwise Spearman correlation, noticing how this method could not filter most of pairwise interaction (around 40% of all possibilities were significant). To overcome the linear limitations of the previous approach, the authors implemented a more complex, non-linear Gaussian process model able to account for pairwise interactions. This new approach was able to identify a smaller number of different, and potentially more informative, correlations between the candidate genes previously identified.

Lastly, with genetic manipulations, the authors show in vivo that a subset of the candidate genes is causally related with the sleep duration as well as partially validating some of the correlation identified by their new model.

The authors conclude that, given the non-linear and complex nature of biological systems, simplistic linear approaches may not suffice to fully capture underlying mechanisms of complex traits such as sleep.

*Major comments*

1. Most of the the work presented focus on the computational and statistical analysis of different populations submitted (or not) to a process of artificial selection for short or long sleep duration. As such, the amount of potentially relevant biological conclusions to be tested is mostly unfeasible. The authors already present additional experiments to partially support some, though not all, of their findings. Given the manuscript is written as a method innovation, these additional experiments illustrate the potential uses of the method described. *

Our response: The reviewer raises a very important point, one that is at the very impetus of our work. We agree that it is not possible to test all combinations of genes in all contexts to determine whether they influence sleep or not. In contrast to the situation for circadian rhythms, where the core clock is controlled by just four genes, recent work has concluded that sleep is a set of complex traits influenced by large numbers of genes. Robust computational methods are needed to identify the complex interactions among genes. The current manuscript is a first step towards achieving this goal.

*(OPTIONAL) However, since the one of the focuses of this work in identifying potential gene interactions, it would be interesting if the authors could test a "double knockout" and perhaps demonstrate evidence for epistasis between two of the identified genes. Having access to single mutants, this experiment should be realistic. However, I have no hands-on experience working with Drosophila and I am unable to accurately estimate the amount of resources and time such and experiment could take. My initial guess would be 3-6 months work should suffice. *

Our response: The reviewer makes an interesting proposal. While such an experiment would provide some additional information, our method does not make any prediction about what a double knockout would do, either to the sleep phenotypes or to gene expression.

2. In regards to the gene CG1304, it seems to be an important example used throughout the manuscript. It should be carefully re-analyzed as was considered for interaction analyses without showing opposite trends for short- and long-sleep populations (see minor comments on figure 2).

Our response: We are not entirely certain that we understand the reviewer’s point. We note that significant genotype-by-selection-scheme interactions may not manifest as opposite trends and this is not what is being tested for significance. The likelihood ratio is a test for a significant effect of including sel x gen coefficients for both short and long schemes; therefore, GLM significance may mean that either one or the two selection schemes are significantly different from controls, not from each other. We could, for instance, apply three different tests: one (i) comparing between long and short flies; the second (ii) __comparing short flies to controls; and the third (iii) __comparing long to controls and find that the first test is significant — i.e. short is different from long — and that the two others are not — i.e. neither scheme is found to be different from controls. The opposite could also happen: short and long flies may not be different from each other, but with both being different from controls.

Since we are interested in identifying differences of either to controls, our choice of statistical test is equivalent to performing tests (ii) __and (iii)__ without the need to perform and correct for multiple tests. While there are caveats to this choice (like all choices), linear model-based differential expression analysis has its own caveats, and has limited ability to pick up arbitrary trends, so it serves as a coarse-grained filter for large shifts since it’s too costly (computationally) to run the Gaussian process on 50 million pairwise combinations.

*3. One major comment would be that the claim that the Gaussian process method is more sensitive and specific than simpler approaches, though intuitively understandable, does not seem to be fully correct from a strict statistical point of view, given the lack of a gold standard reference to compare if the new method is indeed picking more true positives/negatives. I would reconsider re-rephrasing such statement in the absence of a biologically relevant validation set. *

Our response: We agree with the reviewer that there is no ‘gold standard’ reference data set with which to compare our findings. We have softened this language a bit in response, where it occurs in both the Abstract and the Results.

Under Abstract, we changed “Our method not only is considerably more specific than standard correlation metrics but also more sensitive, finding correlations not significant by other methods” to “Our method appears to be not only more specific than standard correlation metrics but also more sensitive, finding correlations not significant by other methods.”

Under Results, we changed “Therefore, computing correlations between genes using covariance estimates from the Gaussian Processes greatly increases specificity over direct correlations. Furthermore, the Gaussian processes are not only more specific but more sensitive…” to “Therefore, computing correlations between genes using covariance estimates from the Gaussian Processes appears to increase specificity over direct correlations. Furthermore, the Gaussian Processes appear to be more sensitive…”

*4. Finally, the study appears to be well powered and it is clear that the authors were careful in their explanation of the statistical methods. However, I could not find the copy of the code/script used for the model. Without it, it would be very difficult to fully reproduce the results as both the language used (Stan) and the method itself are not common in the sleep research field. *

Our response: We thank the reviewer for noticing this, and apologize for this oversight. The code used for analysis has been deposited in GitHub under: https://github.com/caesoma/Multiple-shifts-in-gene-network-interactions-shape-phenotypes-of-Drosophila-melanogaster.

We have noted the script location in the Data Availability statement. We added a statement to read “All scripts used for the model have been deposited in Git Hub https://github.com/caesoma/Multiple-shifts-in-gene-network-interactions-shape-phenotypes-of-Drosophila-melanogaster.”

* * *Minor comments* * 5. The statistical cut-off used for gene expression hierarchical GLMM after BH correction was of 0.001, which is 50 times more strict than the common 0.05. Could the authors comment on how this choice may impact the results compared to those available in the literature and on the rational for choosing such a value.*

Our response: A FDR of 0.05 would increase the number of genes identified (3,544 for females; 1,136 for males, with 462 overlapping). The FDR of 0.001 is consistent with the lowest threshold typically used for gene expression data collected during other artificial selection experiments (Mackay et al., 2005; Morozova et al., 2007; Edwards et al., 2006), though thresholds as high as 0.20 have been used (Sorensen et al., 2007). We have added to the last statement to the Methods and Materials section under “Generalized Linear Model analysis of expression data” to read “Model p-values were corrected for multiple testing using the Benjamini-Hochberg method (Benjamini and Hochberg, 1995), with significance defined at the 0.001 level, consistent with the lower threshold applied in other artificial selection studies (Mackay et al., 2005; Morozova et al., 2007; Edwards et al., 2006).”

*6. Heritability calculations are not mentioned in the methods. Could it be useful to include a small paragraph? Could a small comment be done on the differences in h2 for the short sleep replicates which show ~10x difference? *

Our response: We thank the reviewer for noticing this omission and apologize for the oversight. We have added the following statements to the Methods and Materials under “Quantitative genetic analyses of selected and correlated phenotypic responses.”

“We estimated realized heritability h2 using the breeder’s equation:

h2 = ΣR/ΣS

where ΣR and ΣS are the cumulative selection response and differential, respectively (Falconer and Mackay, 1996). The selection response is computed as the difference between the offspring mean night sleep and the mean night sleep of the parental generation. The selection differential is the difference between the mean night sleep of the selected parents and the mean night sleep of the parental generation.”

Additionally, we thank the reviewer for noticing the large difference in the realized heritability between the short sleeping population replicates; the heritability for replicate 1 is a typo and should be 0.169, not 0.0169. Hence, the heritabilities of both replicate populations are quite similar, i.e., 0.169 for replicate 1 and 0.183 for replicate 2. We have corrected this error in the Results.

7. In regards to the model implementation, what would be the implications of not enforcing positive semi-definiteness on the co-variance matrix, given than that these are strictly positive semi-defined?

Our response: All covariance matrices are by definition positive semi-definite (PSD), since they cannot yield negative values for the probabilities associated to them, so it would not be possible to relax that assumption generally. The only choice we could make would be on the number of genes included (M) in each multi-channel gaussian process model, and this in turn would by design enforce positive semi-definiteness on an matrix of size MN, (N being the number of generations). As noted in the appendix, “enforcing” positive semi-definiteness on smaller blocks of a larger 2D-array of covariances (which is not itself a covariance matrix) does not imply the latter is PSD and therefore seems like a softer constraint. In practice scaling up to a model where M >> 40 is not trivial from a computational and inference point of view, so the choice of smaller M is in a way imposed on us, and fortunately it is the less limiting one. We provide the appendix as a general clarification on the subtleties of Gaussian Processes, but a comprehensive assessment is beyond the multidisciplinary scope of this article and would require a narrower mathematical/statistical description in a standalone methodological article or technical note.

1. *The methods mention that PCA projection were performed on the first 3 components, however only the first two are showed. *

Our response: PCA was performed on 10 components, although the algorithms will commonly compute all components and return only the selected number. The variance of the third component is smaller than ~5% (that of the second PC). In practice PC1 is by itself enough to show the clear separation of expression per sex with ~65% of the variance; PC2 is in fact only shown to improve visualization. Plots of the remaining components will not show clear separation among samples as the variance explained is so small. We have corrected the Methods to indicate that PCA was performed on 10 components rather than 3.

*9. Figure 1 refers to the mean night sleep time of the population. Could some measurement of variability (se or sd) be represented to provide a general idea of the distribution of the values? Additionally, the standard deviation of associated with the CVe estimates are mentioned but not showed explicitly. Could they maybe be added to the text as to illustrate how much such deviations were reduced? *

Our response: We thank the reviewer for this comment. Including either the standard errors or standard deviations on the plot of the response to selection (Figure 1A) makes visualization unwieldy; thus we have added an additional supplemental table, Supplementary Table S15, that contains the mean night sleep, standard deviation, and number of flies measured for each generation in each replicate population. We also added a plot of the standard deviation in night sleep per generation to Supplemental Figure S2 (letter “Q” in the figure) so that the reduction over time in each population can be seen.

Under “Data Availability,” We added the following: “Night sleep phenotypes per selection scheme/sex/generation/population replicate are listed in Table S15.”

*10. Figure 2 shows the linear model fits for gene CG1304. I find this gene on the list of significant genes for both sexes (tables S5/6), but it does not seem to be one that shows opposite trend for short- and long-sleep (tables S7/8). Surprisingly, it shows up again on table S10! However, the text introducing the figure reads like this should be one of the 85 sex-independent genes. Would it be best to provide an example of what a significant gene looks like? *

Our response: As mentioned in our response to comment #2 above, significance in the likelihood-ratio test does not imply opposite trends between long and short selection schemes, but between a model that includes specific slope coefficients for selection scheme by generation (both long and short) compared to a reduced model where the only slope is one associated to generation and therefore independent of selection scheme.

11. *Figure 3 would be interesting to have both the GP correlations and the Spearman correlations to illustrate the methodological differences. I would be curious to see at least one pairwise expression scatter-plot as well just to see how they correlate in one plot. *

__Our response: __Table S11 contains all (significant and nonsignificant) GP and Spearman values side-by-side for comparison. High correlations are likely to conform to the Spearman assumptions of a monotonic relationship; nevertheless, this will not be so for the majority of genes since the difference in the number of Spearman and GP-significant genes is tenfold or more, so it would be misleading to focus on individual-gene relationships without taking into consideration the transcriptome wide results for any method employed.

We would like to stress that there is nothing particularly special about CG1304 in and of itself; furthermore, there are no “representative” genes or figures in this manuscript. Instead, CG1304 is chosen because its GLM and GP fits are illustrative of the limitations and capabilities of each model to pick up certain kinds of trends, and especially because it is especially instructive of how correlations arise from the GP model, which may not be intuitively clear to all readers.

12. Figures 3S3/4 are described as showing single- and multi-channel models don't change substantially. Would this be expected and why?

Our response: This is not necessarily expected, as scaling up from a single to a multi-channel model will add additional parameters as well as constraints, like positive the semi-definiteness mentioned in comment #7 above. If that seemed to have considerable impact on the fits it could challenge our assumption that the signal variance parameters estimated from the single-channel are good priors for the same parameters in the two-channel model (although this is not a hard constraint, so in the worst case the result could still only be a slight bias).

*13. Having build different networks of pairwise associations of genes (projecting on a unified network as illustrated on figure 5), it could in interesting to compare the network topologies at a basic level such as node degrees, overlapping sub-networks, are they potentially scale free as previously described for biological systems, etc. *

__Our response: __The reviewer makes an interesting point. Indeed summaries of the network could be useful information about the system level parameters, which are the main results of this paper. We now include the number of connections (i.e., the degree) to each gene in each of the four networks presented in Figure 5 in a new supplemental Table (Table S13). We also plot the distribution of node connectivity below. The distributions do not appear random (i.e., a normal distribution), and appear closer to a power-law or scale-free distribution. However, the small size and low average degree of these networks make a formal test unfeasible, and a recent study suggests that a log-normal distribution is in general more likely than a power-law distribution (Broido et al., Nat Comm, 2019), so we lack the evidence to claim that these networks are scale-free.

We have added to the Results under “Gaussian Process model analysis uncovers nonlinear trends and specifically identifies covariance in expression between genes”: “Table S13 lists the number of connections (degrees) that each gene has with others in the network. The average number of connections for long-sleeper males was 2.6; the other three networks had average degrees of 2.0 or less (2.0 for long-sleeper females and short-sleeper males; 1.75 for short-sleeper females).”

*14. On table S6 I noticed some gene symbols were loaded as dates (1-Dec) *

Our response: We thank the reviewer for noticing this, the gene symbol is supposed to be dec. We have corrected this in Table S6 (now Table S7).

1. *In results, the phenotypical response to artificial selection is sometimes described in minutes, other times in hours. Though this is an hurdle, it could make the values easier to compere if they were consistently formatted as minutes (hours). *

Our response: We are unsure what the reviewer is referring to. We only see one sentence in which we used hours, and that was the concluding sentence under Results, “Phenotypic response to artificial selection.” The remainder of the manuscript refers to sleep times in minutes, phenotypes in all of the figures are plotted as minutes, and all of the supplemental material refers to times in minutes.

16. *Over 99% of chains converged after three runs. Even though the reasons for the lack of convergence of these chains was not investigated, could this be a relevant effect? 1% of 3570 interactions is still 35 potential interactions. Do the non convergent chains relate with specific genes? *

Our response: Bayesian MCMC inference is a stochastic algorithm, so there is a finite chance that any given run doesn’t converge, and that means that all eight parallel chains must converge and mix as measured by the stringent choice of R-hat metric being within 0.05 of unity. Relaxing the interval to 0.1 or 0.2 could still be acceptable, but we made the choice of a stringent threshold to avoid making interpretations on less-than-ideal runs. There is no evidence that there is any gene-specific problem, usually it would be one out of eight chains that would not mix well and throw off the diagnostic metrics (like relaxing the metrics, an acceptable approach could be accepting a run with 6-7 chains converging properly, but we decided to rerun all chains and only accept 100% convergence but accept a possible loss). Non-converging/nonmixing runs are likely to eventually do so, but since were are running tens of thousands of runs (3570 pairwise combinations × 3 schemes × 8 chains) a massively parallel implementation in a HPC cluster is required. Finally, seeing that 145 is ~4% of the total number of interactions, a naïve expectation would be that no more than one interaction would come out significant — while there is a chance that an interesting interaction was identified, the same can be said for potential false negatives computed using the GLM, which is a consequence of working at a high-throughput scale.

17. The GO terms identified as significantly enriched after pvalue correction point to a clear association of the 85 genes identified with Serine proteases. Could this be discussed further to highlight biological findings of the work in the context of neuronal function or sleep regulation?

Our response: The reviewer is correct, nine putative Serine proteases are significantly enriched among the 85 genes. All nine exhibit some expression in neurons and in epithelial cells, and all are expressed at the adult stage. The appearance of these enzymes is interesting given their role in proteolysis.

We have updated the Discussion to read, “Interestingly, our Gene Ontology analysis identified nine genes from the 85-gene network with predicted Serine endopeptidase/peptidase/hydrolase activity: CG1304, CG10472, CG14990, CG32523, CG9676, grass, Jon65Ai, Jon65Aii, and Jon99Fii. All of these genes are expressed in neurons and epithelial cells, and all genes are expressed at the adult stage (Li et al., 2022). Serine proteases are a large group of proteins (257 in Drosophila) that perform a variety of functions (Cao and Jiang, 2018). Their predicted enzymatic activity suggests a putative role in proteolysis. This is an intriguing observation given pioneering work in mammals which suggested a role for sleep in exchanging interstitial fluid and metabolites between the brain and cerebral spinal fluid (Xie et al., 2013). Recent work demonstrated that a similar function is conserved in flies via vesicular trafficking through the fly blood-brain barrier (Artiushin et al., 2018). It would be interesting to determine whether these genes function in this process.”

*18. Could the authors discuss the little overlap between males/females and shot/long sleep for 145 gene pairs identified after the MCMC runs. Similarly, how can the network differences be explained from a biological/evolutionary perspective? *

Our response: The reviewer asks an interesting question. We did not detect sex-specific responses to artificial selection for long or short sleep in the present experiment. Yet differences in gene expression network pairs between males and females exist, and as the reviewer mentions, we also observed differences in network pairs between long sleepers and short sleepers. These differences reflect an inescapable conclusion: a given sleep duration phenotype can originate from more than one gene expression network configuration.

19. *In the mutational analyses it is pointed out that CG12560 and Jon65Aii only affect females significantly. However, in the following sentence, the authors claim these two genes had the greatest effect on both sexes, which seems contradictory, at least in the way it is described. *

Our response: Our wording may have been confusing, given that it came after a comment about Jon65Aii. Our exact statement was “Effects of the Minos insertions on night sleep duration were stronger in females than in males; when sexes were examined separately, only mutations in CG12560 and Jon65Aii affected male night sleep duration.” This was meant to convey that the effects of all Minos insertions were the same directionally for both males and females, but that only CG12560 and Jon65Aii insertions had statistically significant effects on each sex separately. We have re-worded this sentence to read “All Minos insertions had the same directional effect on night sleep for both males and females, but only the CG12560 and Jon65Aii insertions had statistically significant effects on night sleep on each sex separately.”

20. *Maybe a small comment on how unchanged expression could lead to the observed phenotypical variation could help understanding how Minos mutations effects are biological mediated for those not familiar with the method. This seems to be the authors expectation so, could it be non-functional proteins or something else? *

Our response: The reviewer raises an interesting point. We did not observe changes in gene expression for CG13793, Cyp6a16, or hiw compared to w1118 controls. Thus far, we have examined gene expression relative to the control for a single timepoint, and only in pooled whole flies. Differential gene expression between the Minos mutants and controls might occur at a different timepoint, or in a small set of key neurons that would be undetectable when comparing whole flies.

We expand on this in Results, under “Mutational analyses confirms the role of candidate genes and interacting expression networks in sleep”: “Potential reasons for the lack of a significant change in gene expression in the remaining lines include: the position of the insertion within the targeted gene, which has variable effects on its expression; the relatively low statistical power of the experiment; confining our observation to a single timepoint during the day; or pooling whole flies, which might obscure gene expression changes occurring at a single-tissue level.”

*21. The assumption that interacting genes would have their expression ratio changed by the Minos insertion would hold on situation where the affected gene causally interferes with the candidates expression. As far as I understand, causality cannot be inferred by the proposed method. Thus in a situation where both genes are co-regulated by a third factor, no change in expression ratio is to expected. How would the authors re-interpret their final result when considering this direct vs indirect interaction distinction? *

Our response: Our method only gives us the hypothesis that two genes interact based on their correlation, and that is what we test using the Minos insertions. We do not as yet have a way to identify a third gene or factor that might be regulating the two. Given the number of genes affecting sleep, it is quite likely that there are such factors, but we can only report and test what we’ve observed. Any interpretation based on an arbitrary third factor would be purely speculative.

**Referees cross-commenting**

22. *I agree with Reviewer #2 comments which, to me, reads as generally pointing out the lack of biological interpretation of the results (and thus connecting this study with previous literature). Adding this component would make the manuscript well-rounded and attractive to a wider audience. *

Our response: We agree with both reviewers that additional biological interpretation of the results would make the manuscript more attractive to a wider audience. Accordingly, we have added the following paragraph to the Discussion: “The genes we identify herein overlap and extend previous work. Of the 1,140 genes implicated in the generalized linear model, 151 (13.2 percent) overlapped with previous candidate gene, random mutagenesis, gene expression, and genome-wide association studies of sleep and circadian behavior in flies (Pegoraro e t al., 2022; Dissel et al., 2015; Seugnet et al., 2017; Shalaby et al., 2018; Thimgan et al., 2010, Thimgan et al., 2018, He et al., 2013; Mallon et al., 2014; Roessingh et al., 2019, Feng et al., 2018; Lee et al., 2021; Khoury et al., 2020; Wu et al., 2018; Harbison et al., 2013; Harbison et al., 2009; Harbison et al., 2017; Harbison et al., 2019). Notably, previous studies identified the genes CG17574, cry, dro, mip120, Mtk, NPFR1, pdgy, PGRP-LC, Shal, and vari as affecting sleep duration (Feng e t al., 2018, Dissel et al., 2015; Pegoraro et al., 2022; Thimgan et al., 2018; Mallon et al., 2014; He et al., 2013; Khoury et al., 2020; Harbison et al., 2013). Two genes, ringer and mip120, overlapped with our previous study of DNA sequence variation in flies selected for long and short sleep (Harbison et al., 2017). In that study we identified a polymorphism in an intron of ringer that changed in allele frequency with selection, with increases in the population frequency of the ‘G’ allele with increasing sleep, while the frequency of the ‘A’ allele increased with decreasing sleep. When the selective breeding procedure was relaxed, the frequency of the ‘G’ allele increased in short-sleeping populations, paralleling an increase in sleep (Souto-Maior et al., 2020). One possibility is that this polymorphism contributes to the changes in gene expression in ringer that we observed in the present study. Of the 85 genes common to both sexes that we used in the gene interaction networks, 11 (13 percent) appear in other studies of sleep: CG10444, CG2003, CG5142, CG6785, CG9114, CG9676, CR42646, hiw, NPFR1, Tie, and wb (He et al., 2013; Seugnet et al., 2017; Wu et al., 2018; Harbison et al., 2013). Thus, our study corroborates genes known to affect sleep, and identifies new candidate genes for sleep as well.”

Reviewer #1 (Significance (Required)):

*This study proposes the application of advanced non-linear methods to study complex traits such as sleep. As implemented, Gaussian Processes are able to identify non-linear correlations between two biological features (e.g. transcripts) over time (e.g. generations), representing an attempt to push the analytical methods available beyond the single gene paradigm. As such, more than the relevance of the biological results themselves, the authors focus on the explaining and illustrating the application of methodological advances obtained, and its relevance to obtain a better understanding of biological systems.

However the mathematical principles required to understand the implemented method are not trivial and require advanced knowledge of machine learning and statistics. This is a potential barrier, though not an impediment, to its quick and wide adoption by the community. In addition, even if demonstrated to be a valid method when working with Drosophila, the resolution required to perform such a study may be difficult to obtain with other model systems, which would likely require further refinement of the statistical approach.

The main audience interested in this work would be basic sleep researchers. However, this work is also related to the understanding gene selection over an artificial evolutionary process, thus evolutionary and developmental biologist may be also be interested. The methodology itself, already used in other fields of study, is a general statistical tool that could be adopted by a broad range of researchers for a diversity of topics. As such, I believe with this work, the authors will be able to stimulate the development and/or rethinking of the available analytical methods to study complex biological systems, though this would likely be done either in collaboration with the authors themselves or by a specific subset of researchers who regularly work with advanced mathematical, statistical and computational principles.

(disclaimer) My mathematical formation does not reach the PhD level expertise that may be required to fully understand the methodology described. I have never personally worked with D. melonogaster or used Gaussian Processes in a professional setting. As such, I may not be able to fully evaluate/appreciate the more detailed technical aspects of this work.

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

Souto-Mairo et al. reports phenotypic and genotypic effects of artificially selecting for short and long sleep in flies. They generated an impressive time-series dataset where one could examine genetic and phenotypic changes across time (generations, total 13 generations) in response to the selection pressure. The authors explored the relationships between pairs of genes in addition to just identifying potential candidate genes involved in the regulation of the amount of sleep.

Major points:

1. Harbison et al 2017: This study seems to be a continuation of Harbison et al 2017. There needs to be a clearer approach in the text (introduction?) in elucidating how this study is really advancing the findings of Harbison et al., 2017. Do the two studies use the same selection lines? If not, how are they different? If they are not different, what might cause the phenotypes evolving differently? For example, day sleep, day bout number do not respond to the selection pressure similarly in both studies etc. *

Our response: We would like to emphasize that this study is not a continuation of the Harbison et al., PLoS Genetics, 2017 paper, where we examined the changes in DNA sequence during artificial selection, and it does not use the same selection lines. The fact that the two studies are different can be seen from an examination of Figure 1A of the current study and Figure 1A of the Harbison et al 2017 study. The trajectories of each population across generation are very different. Out of convenience, we used the same nomenclature to refer to the populations in both studies (L1, L2, S1, S2, etc.), and apologize if this is the source of the confusion. Both studies do originate from the same outbred population, however, and to get to the broader question that the reviewer is asking, should one expect to see the same correlated responses to selection for night sleep among selection lines originating from the same outbred population? The answer is no, not unless the selected trait and the responding trait have a genetic correlation of 1.0. We previously estimated the correlation between day sleep and night sleep to be between 0.29 - 0.38 and between day bout number and night sleep to be -0.05 (Harbison et al., 2013; Harbison et al. 2009). In the Harbison et al. 2017 study we noted that day sleep and day bout number had correlated responses to selection for night sleep, but neither have correlated responses in the current study. The relatively low genetic correlations between these two measures and night sleep explain why we do not see a consistent correlated response among studies.

We didn’t really elaborate on these observations in the manuscript, and so have added to the Results under “Correlated response of other sleep traits to selection for night sleep” the following: “These correlated responses concur with previous observations we made in selected populations originating from the same outbred population for night sleep and night average bout length, and night sleep and sleep latency (Harbison et al., 2017). However, unlike the previous study, we did not see a correlated response between night sleep and day sleep, and night sleep and day bout number (Harbison et al., 2017). The lack of correlated response reflects the relatively low genetic correlations these two traits have with night sleep (Harbison et al., 2013; Harbison et al., 2009).”

2. Zeitgeber Time (ZT) for RNA collection: It is puzzling that the study reports that the RNA was collected at 12 PM. I do not understand what this information means; especially in a project where one is working with sleep. The authors might want to report ZT. Also, why a particular ZT was chosen should be discussed. These genes are potential sleep-relevant genes - hence it is not too esoteric to think that the ZT of data collection matters a lot as some of them might be cycling. To get a more appropriate picture, multiple time points of data collection might be even better. The authors seem to have ignored this crucial aspect of a clock/sleep study - time of data collection and how time of data collection might shape your findings.

Our response: We agree with the reviewer that it would be better to have multiple timepoints for collection, but this is difficult to implement in practice as it would require an additional 5,280 flies per generation (4 pools of 10 flies per sex per population) for 12 timepoints as recommended by Hughes et al., JBR, 2017. We mention collection time in the Methods and Materials because we are aware of the changes in gene expression over the circadian day. 12PM is the midpoint between the start of the lights-on and lights-off period (i.e., ZT6), and was chosen arbitrarily. We have added the ZT notation to the Methods and Materials for clarity.

3. Short sleeping flies: Are there reports of flies sleeping this less? "We found 2,830 interactions; 8 of these were one of the 3,570 between the 85 genes, but none of them overlapped with the 145 gene pairs found to be different from controls. The gene interactions we observed may therefore be unique to extreme sleep." What is extreme sleep? How does this study then claim to have identified evolution of potential sleep-relevant gene expression for normal, physiologically relevant sleep?

Our response: Our statement was not very well worded, and we thank the reviewer for noticing this. What we intended to say was that the lack of overlap between our data and a known protein-protein interaction database may due to the interactions being unique to sleep as opposed to some other complex trait. We have re-worded this statement to say “The gene interactions we observed may therefore be unique to sleep.”

*Minor points:

4. The article uses an unnecessarily defensive tone to establish their approach to understand underlying mechanisms of sleep 'better' than that of the others (in both introduction and discussion): "In spite the large amount of studies and data generated for many systems, identifying underlying processes is still very rare; this is clear indication that better methods are needed to obtain understanding of biological processes from data." The 'still very rare' part is just factually incorrect and misleading as far as sleep is concerned. Even if we just see Drosophila studies on sleep, there is a huge progress that's being made in terms of genes, neurons and circuits relevant for sleep: both in terms of baseline sleep as an output of the circadian clock and the rebound/homeostatic sleep. Most, if not all, of these elegant and pioneering studies from multiple, independent groups took approaches that did not require artificial selection regimes. As a substitution for their defense, the authors might attempt to present their findings in the context of the existing knowledge of sleep in flies. For example, what about genes already implicated in sleep? Do they show up in their analysis? For example, Sleepless, DATfmn, Sandman, AstA, AstA-receptor, Wide-awake etc. This could really help the manuscript.*

Our response: We certainly did not intend for this statement to suggest that no progress had been made in the identification of genes and circuits for sleep, and we agree that elegant and pioneering approaches have made significant progress in our understanding of the phenomenon. Rather, we were thinking more in terms of fully described biochemical networks. To avoid this interpretation by other readers, we have altered the “still very rare” sentence in the Introduction to read: “Despite the large amount of studies and data generated for many systems, a full understanding of underlying processes has not yet been achieved…’

We also agree with the reviewer that it would be helpful to put our work in the context of what is already known in flies. We have added the following paragraph to the Discussion to relate the work with previous work on sleep in flies: “The genes we identify herein overlap and extend previous work. Of the 1,140 genes implicated in the generalized linear model, 151 (13.2 percent) overlapped with previous candidate gene, random mutagenesis, gene expression, and genome-wide association studies of sleep and circadian behavior in flies (Pegoraro e t al., 2022; Dissel et al., 2015; Seugnet et al., 2017; Shalaby et al., 2018; Thimgan et al., 2010, Thimgan et al., 2018, He et al., 2013; Mallon et al., 2014; Roessingh et al., 2019, Feng et al., 2018; Lee et al., 2021; Khoury et al., 2020; Wu et al., 2018; Harbison et al., 2013; Harbison et al., 2009; Harbison et al., 2017; Harbison et al., 2019). Notably, previous studies identified the genes CG17574, cry, dro, mip120, Mtk, NPFR1, pdgy, PGRP-LC, Shal, and vari as affecting sleep duration (Feng e t al., 2018, Dissel et al., 2015; Pegoraro et al., 2022; Thimgan et al., 2018; Mallon et al., 2014; He et al., 2013; Khoury et al., 2020; Harbison et al., 2013). Two genes, ringer and mip120, overlapped with our previous study of DNA sequence variation in flies selected for long and short sleep (Harbison et al., 2017). In that study we identified a polymorphism in an intron of ringer that changed in allele frequency with selection, with increases in the population frequency of the ‘G’ allele with increasing sleep, while the frequency of the ‘A’ allele increased with decreasing sleep. When the selective breeding procedure was relaxed, the frequency of the ‘G’ allele increased in short-sleeping populations, paralleling an increase in sleep (Souto-Maior et al., 2020). One possibility is that this polymorphism contributes to the changes in gene expression in ringer that we observed in the present study. Of the 85 genes common to both sexes that we used in the gene interaction networks, 11 (13 percent) appear in other studies of sleep: CG10444, CG2003, CG5142, CG6785, CG9114, CG9676, CR42646, hiw, NPFR1, Tie, and wb (He et al., 2013; Seugnet et al., 2017; Wu et al., 2018; Harbison et al., 2013). Thus, our study corroborates genes known to affect sleep, and identifies new candidate genes for sleep as well.”

Reviewer #2 (Significance (Required)):

5. I believe that the authors should attempt to put this study in the context of what is already known in sleep in flies and how this study advances the knowledge. And how the knowledge generated by this study would help other sleep researchers, who, for obvious reasons, would like to employ techniques other than artificial selection and big data. The data is elegant. The work seems to be extremely laborious. Nonetheless, as it stands now, this manuscript is only very specific for an audience who work with artificial selection to understand underlying genetics of behavior. In fact, even within the fly sleep field, most people might not find this manuscript very useful.

Our response: The reviewer may not have considered the wider application of this work. This framework is applicable to any data set of gene expression sampled across time, whether sampled across generation, as we did, or across the 24-hour circadian day, or sampled at other time intervals. We have added a statement to the Discussion to stress this fact: “The Gaussian Processes we apply herein have broad applications to other experimental designs, such as gene expression measured at varying time intervals over the circadian day, or time-based sampling of gene expression responses to drug administration.”

!- Thomas Piketty : comment - Just like in the time of the French Revolution, the small class of elites have designed a legal and political system to escape taxation. - We will likely have another French Revolution-like event to end fiscal privileges

!- Thomas Piketty : On redistribution of all forms of wealth - concerning energy, certain harmful forms of energy such as fossil fuels need to be phased out and made illegal due to their harmful effects - ALL forms of wealth, whether financial, energy, housing, needs to be progressively taxed and redistributed equitably. So a billionaire would pay 90 percent tax per year but there will still be a range of wealth...up to millionaires for instance.

Micheal Hudson : tax the rent seeking class or face barbarism like in Rome - The situation today is degrading in the same way Rome degraded into feudalism - rent seeking class today is not the landlord class, but the financial and raw materials class that are making large fortunes from rent seeking - that is the system level reform necessary today

!- Micheal Hudson : debt writedown - At a certain point, Governments of 3rd world countries who are so debt trapped may simply decide to write down the debts and start over - They may reach a point where instead of servicing the debt of the 1%, they decide its not worth it and save their own economies, freeing themselves from World Bank and IMF debt conditions - It's just as radical a move as your suggestion to stop tax evasion by closing down all offshore banks

!- Thomas Piketty : Chinese counter model pressures western elites to change

!- Western multinationals : comment - The elites will continue unabated until externally pressured by the Chinese counter model and others that erode the Global South's trust in the Global North elites - Colonialism has morphed into its new variant, post colonial, globalized capitalism - which is available to all elites, both in Global North or South to exploit and privatize in the same extractive, colonialist logic of the past

!- Thomas Piketty : limited ability for real change as long as elites can lobby governments - but in the past, there has been success, as the two cases previously mentioned - so it is possible, but will take just as enormous a political mobilization of the people

!- Thomas Piketty : Agreement with Michael and limitations of past books - Piketty states that every book has a lot of limitations. Capital and Ideology is his recent book and addresses some of the shortcomings of Capital in the 21st Century

!- Michael Hudson : Wealth is created in the 1% through privatization and loss of the 99% - Largest transfer of wealth in history from the public sector to the private sector, especially through financial sector - govt fire sale of public infrastructure - credit was created and invested in the biggest bon market boom in history - many of Forbes billionaires got rich through such privatization - the 1% got wealthy by indebting the 99% through privatization all around the globe - this was the effect of Ronald Reagan and Margaret Thatcher's neoliberal policies - taxation alone is not sufficient to reverse this wealth concentration, the debt has to be completely wiped out

!- key statement : the elite get rich off the government subsidizing an enormous increase in the value of stocks the value of bonds by the central bank which have been privatized. The reason THAT is happening is because the largest public utility of all, money creation and central banking has been privatized.

!- Michael Hudson : private banks maximizing debt is the goal - creating lots of loans to create lots of debt is the best way for the private banks to make money - it means the 99% spend all their efforts servicing the debt

I find this statement to indicate a critically important factor that will predict the pace and flow of the writing session. The personal investment and desire for the ideas to be written.

I don’t understand how people can get addicted to the internet. It’s crazy how the internet has so much control on peoples lives. If anyone is addicted to the internet or can’t take off the media can they explain why. To me I don’t think the internet is that much interesting it’s just a lot of drama and negative energy.

In this flashback conversation between Gabby and Jess, the audience learns that Jess never wanted a life of violence. The moment doesn't represent a major shift in the ways that the characters view each other, they already assume that the other person is equally unhappy in their situation, but it's very telling about them to the audience. In the face of all the fear and suffering that they undergo on a day-to-day basis, even something as mundane as accounting feels like a blessing. It also helps to reinforce, just in case the audience wasn't paying attention, that Lost Vegas is an absolute wasteland sans electricity and motors.

This part is not the most important part of the play but I think it was a part that cleared up some of what Jess felt towards Lucky. For the most part Lucky had been the somewhat possessive lover-boy. We knew he was into Jess but Jess had that whole awkward moment with Malcolm in the beginning. So when she kissed Lucky it was a bit of a shock. We start to see a possible romance...perhaps? Combined with some of the suspicions the audience may have had with Lucky's past questionable behavior; this scene gives us(the audience) another aspect of the play to look forward to being explored.

Every language is different and had different words for different things so it's very interesting to think of how others view the world just because of their language.

I cannot imagine coming from somewhere that does not have the same social norms. People are just expected to know how to act in different environments. For me, it's easy to know what to do, how to act, and what is polite. For someone else it could be really confusing at first.

It's really interesting that they tell people they're being observed as part of the study. It seems like people will always change they way they're acting subconsciously, just because they know they're being watched.

a little off topic from this paragraph but I just think it's very interesting that we compare successful people and try and say one is better than the other one and bring the other ones down. I don't understand why we can't just be happy and supportive of both/all

It's difficult to calculate intelligence based off neurons alone. As the article says, this is only a rough guide to intelligence. In humans, for example, you can cut out half the brain (lose half the neurons), and still have roughly the same functional intelligence. Neurons are used for more than just thinking. I don't think any of these factors about brains can quantify overall intelligence. However, the important point here is that octopuses have used a large amount of 'genetic energy' to develop a large brain. This may give them other advantages the author has not considered.

This question posed it very interesting to me, "Where is Black Europe?" Before doing the reading and before joining the class I was wondering the same thing. What and where exactly is Black Europe? The author in this quote does a great job in explaining this question. Going off of the term 'imagined nation,' an imagined nation is all internal. The nation holds its legitimacy as a place in the hearts of the people. This can be said of Black Europe, it is not just a place, it is a being and culture that holds it's legitimacy in the hearts of the people who claim they are a part of this culture. This culture and the values held are then passed down generation to generation and this legitimacy holds true. A sentiment I think is very powerful when thinking of a people that are often marginalized in the scale of the world. This quote does a great job in breaking down this notion and explaining that the people can be scattered all across Europe and even in the world, but as long as they hold onto their legitimacy Black Europe will always exist.

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Reply to the reviewers

Response to Reviewer Comments

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

Summary:

In developing systems, morphogens gradients pattern tissues such that cells along the patterning length sense varying levels of the morphogen. This process has a low positional error even in the presence of biological noise in numerous tissues including the early embryo of the Drosophila melanogaster. The authors of this manuscript developed a mathematical model to test the effect of noise and mean cell diameter on gradient variability and the positional error they convey.

They solved the 1D reaction-diffusion equation for N cells with diameters and kinetic parameters sampled from a physiologically relevant mean and coefficient of variation (CV). They fit the resulting morphogen gradients to a hyperbolic cosine profile and determined the decay length (DL) and amplitude (A) for a thousand independent runs and reported the CV in DL and A.

The authors found that CV in DL and A increases with increase in mean cell diameter. They propose a mathematical relationship between CV in DL scales as an inverse-square-root of N. Whereas the CV in DL and A is a weak function of CV of cell surface area (CVa) if CVa __They further looked at the shift in readout boundaries and compared four different readout metrics: spatial averaging, centroid readout, random readout and readout along the length of the cilium. Their results show that spatial averaging and centroid have a high readout precision.

They finally showed that the positional error (PE) increases along the patterning length of the tissue and increases with increasing mean cell diameter.

The authors also supported their theoretical and simulated results by looking at mean cell areas reported for in patterning tissues in literature which also have a higher readout precision with smaller cell diameters.

Major comments:

Most of the key conclusions are convincing. However, there are four major points that should be addressed. First, the authors conclude the section titled, "The positional error scales with the square root of the average cell diameter," by saying that morphogen systems with small cells can have high precision in absolute length scales, but not on the scale of one cell diameter. They state this would result in salt and pepper patterns in the transition zones. The authors should either support this with biological examples or explain why this is not observed experimentally.

We thank the referee for pointing out this imprecise comment, which we have removed. The exact nature of transition zones between patterning domains is a subject of ongoing research in our group, and goes beyond the scope of the present work. We will be sharing our results on this aspect in a separate forthcoming publication.

Second, perhaps the main conclusion of the paper is that morphogen gradients pattern best when the average cell diameter is small. The authors support this by reviewing the apical cell area of epithelial systems that are known to be patterned by morphogens and those that are not (presumably taking apical cell area as a proxy for cell diameter). However, the key parameter is not absolute cell diameter, but the cell diameter relative to the morphogen length scale. The authors should report the ratio of these two quantities in their literature analysis.

Since cell areas and cell diameters are monotonically increasing functions of one another for reasonably regular cell shapes, we indeed consider apical cell areas as proxies for the cell diameter, as the referee correctly noted. Cell areas are more frequently reported in the literature than cell diameters, which is why we compiled these in our analysis.We have now revised our analysis of the effect of the cell diameter on patterning precision to further length scales relevant in the patterning process. We show by example of the Drosophila wing disc how the parallel changes in cell diameter and morphogen source size compensate for the increase in gradient length and domain size, which would otherwise reduce patterning precision over time as the readout positions shift away from the source to maintain the same relative position in the growing wing disc.

Lamentably, accurate measurements of morphogen gradients in epithelial tissues are still rare. In fact, among the listed tissues that are patterned by gradients, we are only aware of measurements of the SHH and BMP gradients in the mouse NT (lambda = 20 µm) and of the Dpp gradients in the Drosophila wing and eye discs [Wartlick, et al., Science, 2011 & Wartlick et al., Development, 2014]. We agree that it would be great if experimental groups would measure this in more tissues. In this revised and extended analysis, we show that the positional error increases with the cell diameter in absolute terms, not only relative to any reference length, be it the gradient length or cell diameter.

Third, as part of their literature analysis, the authors state that in the Drosophila syncytium, there are morphogen gradients, but they imply that because these gradients operate prior to cellularization, one cannot use the large distances between nuclei as counter evidence to their main conclusion. Rather than simply dismissing the case of the Drosophila syncytium, the authors should explain why this case does not apply, using reasoning based on their model assumptions.

Our paper is concerned with patterning of epithelia (which we now make clearer in the manuscript), and we would not want to stretch our paper to other tissue types, as the reaction-diffusion process in them differs. But we do not share the referee’s sentiment that the syncytium would present a counter-example. Since our model explicitly represents kinetic variability between spatial regions bounded by cell membranes, which are absent in the syncytium, our model is not directly applicable to it. We now provide this argument in the discussion, as requested by the referee.

At 100 µm [Gregor et al., Cell, 2007], the Bicoid gradient is 5 times longer than the SHH/BMP gradients in the mouse neural tube and more than 10 times the reported length of the WNT gradient in the Drosophila wing disc [Kicheva et al., Science, 2007]. The nuclei become smaller as they divide because the anterior-posterior length of the Drosophila embryo remains about 500 µm [Gregor et al., Cell, 2007], but even at the earliest patterning stage their diameter will not be larger than 10 µm at midinterphase 12 [Gregor et al., Cell, 2007, Fig. 3A].

Fourth, related to the above: the authors then state that there are no morphogen gradients known during cellularization. Unless I am misunderstanding their point, this is untrue. The Dpp gradient acts during the process of cellularization and specifies at least three distinct spatial domains of gene expression. Furthermore, not long after gastrulation, EGFR signaling patterns the ventral ectoderm into at least two distinct domains of gene expression. What are the cell areas in that case?

Unfortunately, the referee does not provide literature references, and we were not able to find anything in the literature ourselves. We have now rephrased the statement to “we are not aware of morphogen gradient readout during cellularisation”.

Minor comments:

Figs 1cd:

The way the system is set-up: (DL = 20 micron, Patterning Length (LP) = 250 micron, Nominal cell diameter (D) = 5 micron) the DL/L ~ 0.08 which makes the exponential profile far to a small value around 100 micron. This means in all these simulations, the LP was only around 100 micron, cells beyond that saw nearly zero concentration.

Because of this, when diameters were varied from 0.2 - 40 micron, there could be as few as 2.5 cells in the "patterning region" which could be responsible for higher variability in DL and A.

Patterning in the neural tube works across several 100 µm. At x=100µm, there is still exp(-5)=0.0067 of the signal left, which likely well translates into appreciable numbers of the morphogen molecule (see [Vetter & Iber, 2022] for a discussion of concentration ranges cells might sense). Unfortunately, very little is known about absolute morphogen numbers in the different patterning systems — experimental data is available only on relative scales, not in absolute nu mbers. While more quantitative experiments are still outstanding, modeling work needs to be based on reasonable assumptions. The seemingly quick decay of exponential profiles (when plotted on a linear scale) can be deceiving. In fact, exponential profiles describe the same fold-change over repeated equal distances, which makes them biologically very useful for different readout mechanisms operating on different levels of morphogen abundance. Our simulations are not limited to a patterning length of 100µm. Our work merely shows that variable exponential gradients stay precise over a long distance. We draw no conclusion on whether cells are able to interpret the low morphogen concentrations that arise far in the patterning domain - this aspect certainly deserves further research.

The referee’s observation is correct in that for a cell diameter of up to 40 µm, there are only few cells in the patterning domain (namely down to about six, for a length of 250µm, as used in the simulations). It is also correct that this is the reason why gradients in such a tissue have greater variability in lambda and C0. This is precisely the main point we are making in this study: The narrower the cells in a tissue of given size, the less variable the morphogen gradients, and the more accurate the positional information they carry. Conversely, the wider the cells in x direction, the more variable the gradients.

Would any of the results change if DL/L was higher, around 0.2?

As we consider steady state gradients, nothing changes if we fix the (mean) gradient decay length and only shorten the patterning domain, except for a small boundary effect at the far end of the tissue due to zero-flux conditions applied there. At a fixed gradient length, the steady-state gradients just extend further if DL/L is increased (for example to 0.2), reaching lower concentrations, but the shape remains unchanged, and so does the morphogen concentration at a given absolute readout position.

To demonstrate what happens at DL/L = 0.2, as requested by the referee, we repeated simulations with an increased gradient decay length of DL=50 micrometers; the length of the patterning domain remained unchanged at L=250 micrometers. As it is not possible to include image files in this response, we have made the plots available at https://git.bsse.ethz.ch/iber/Publications/2022_adelmann_vetter_cell_size/-/blob/main/revision_increased_dl.pdf for the time of the reviewing process. The plots show the resulting gradient variability, which is analogous to Fig 1c,d in the original manuscript. For both gradient parameters, we still recover the identical scaling laws.

The source region is 25 microns in length and all cell diameters above 25 micron get defaulted back to 25 micron which explains the flatness lines in the region beyond mu_delta/mu_DL> 1

Thanks for pointing this out. We now mention this in the manuscript. Note that it’s the ratio mu_delta/L_s that matters, not mu_delta/mu_lambda. It just so happens in this case, that both are nearly equal, because L_s=5*mu_lambda/4 in our simulations.

Results:

Pg 2 (bottom left): In the git repository code, the morphogen gradients are fit to a hyperbolic cosines function (described in reference 19) which is not described in the main text. Having this in the main text would help readers understand why fig 1c has variation in d only, D only and all k parameters whereas fig 1d has variation with all individual parameters p, d and D and all k.

The reason why the impact of CV_p alone on CV_lambda is not plotted in Fig 1c is that it is minuscule. We now mention this in the figure legend. This follows from the fact that the gradient length lambda is determined in the patterning domain, whereas the production rate p sets the morphogen concentration in the source domain, and thus, the gradient amplitude, but not its characteristic length. This is unrelated to the functional form used to fit the shape of the gradients, be it exponential or a hyperbolic cosine. We mention that we fit hyperbolic cosines to the numerical gradients in section Gradient parameter extraction in the Methods section, and we refer the interested reader to the original reference [Vetter & Iber, 2022], which contains all mathematical details, should they be needed.

Figure 3b:

In figures where markers are overlapping perhaps the authors can use a "dot" to identify one set of simulations and a "o" to identify the ones under it. The way the plots are set up currently makes it hard for the reader to understand where certain points on the plot are.

We use a color code to represent the readout strategy and different symbols to represent the cell diameter in Fig 3b. We agree that for the smallest of the cell diameters, the diamond-shaped data points lie so close that they are not easy to tell apart at first sight. For this reason, we chose different symbol sizes. We would like to keep the symbols as they are to maintain visual consistency with the other figures, which we think is an important feature of our presentation that facilitates the interpretation. Note that all our figures are vector graphics, which allow the reader to zoom in arbitrarily deep, and to easily distinguish the data points. Note also that in this particular case, telling the data points apart is not necessary; recognizing that they are nearly identical is sufficient for the interpretation of our results.

Methods:

The Methods can be more descriptive to include certain aspects of the simulations such as adjusted lambda which is only described in the code and not the main text or supplementary.

We apologize for this omitted detail. As shown in Fig. 8g in [Vetter & Iber, 2022], the mean fitted value of lambda drifts away from the prescribed value, depending on which of the kinetic parameters are varied, and by how much. To report the true observed mean gradient length in our results, we corrected for this drift in our implementation, as the referee correctly noticed. We now describe this in the methods section, and we have extended the methods also on other aspects.

Git code:

The git code function handles do not represent figure numbers and should be updated to make it easier for readers to find the right code

Thank you for pointing this out — it was an oversight from an earlier preprint version. The function names now correspond to the figure numbers.

Reviewer #1 (Significance (Required)):

This manuscript contributes certain key aspects to the patterning domain. The three most important contributions of this work to the current literature are: (1) the scaling relationships developed here are important, (2) the idea that PE increases at the tail-end of the morphogen profile is nicely shown and (3) Comparison of various readout strategies.

Thank you for the positive assessment.

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

Summary:

How morphogen gradients yield to precise patterning outputs is an important problem in developmental biology. In this manuscript, Adelmann et al. study the impact of cell size in the precision of morphogen gradients and use a theoretical framework to show that positional error is proportional to the square root of cell diameter, suggesting that the smaller the cells in a patterning field, the more precise patterns can be established against morphogen gradient variability. This result remains true even when cells average the morphogen signal across their surface or spatial correlations between cells are introduced. Thus, the authors suggest that epithelial tissues patterned by morphogen gradients buffer morphogen variability by reducing apical cell areas and support their hypothesis by examining several experimental examples of gradient-based vs. non-gradient-based patterning systems.

Major comments:

While the idea that smaller cells yield to more precise morphogen gradient outputs is attractive, it is unclear whether patterning systems use this strategy to make patterns more precise, as there are several mechanisms that could achieve precision. Do actual developmental systems use it as a mechanism to increase precision? Or precision is achieved through other mechanisms (for example, cell sorting as in the zebrafish neural tube; Xiong et al. Cell, 2013). Indeed, classical patterning work on Drosophila embryo suggest that segmentation patterns are of an absolute size rather by an absolute number of cells (Sullivan, Nature, 1987). According to the authors, the patterning stripes should be more precise when embryos have higher cell densities than in the wild-type, but stripes are remarkably precise in wild-type embryos. This is likely due to other precision-ensuring mechanisms (such as downstream transcriptional repressors, in this case).

We want to emphasize that our predictions concern the precision of the gradients, not the precision of their readout, which can be strongly affected by readout noise, as we will show in a forthcoming paper. Cell sorting can sharpen boundaries in the transition zone, but this would not address errors in target domain sizes and is thus different from gradient precision as we discuss it here. Also, cell sorting as observed in the zebrafish neural tube requires higher cell motility than what is observed in most epithelial tissues. The work by Sullivan, Nature, 1987, is concerned with patterning of the early Drosophila embryo, and the stripes are defined already before cellularisation. We are unfortunately not aware of any work that quantified gradient precision at different cell densities in epithelia. This would, of course, be highly interesting data and would indeed put our predictions to a test. We are, to the best of our knowledge, the first to propose this principle with the present work. We have now made these points and distinctions clearer in the revised manuscript. Thank you for bringing this up.

Their modeling approach is based on exponential gradients formed by diffusion and linear degradation, but in reality, actual morphogen gradients are affected by receptor and proteoglycan binding and are likely not simply exponential and/or interpreted at the steady state. Do the main results of the manuscript hold even for non-exponential gradients or before they reach a steady state?

We can confirm that our results also hold for non-exponential gradients, as they emerge for example when morphogen degradation is self-enhanced (i.e., non-linear). This result will be published in a follow-up study [BioRxiv: 10.1101/2022.11.04.514993], which we now cite in the concluding remarks in the revised manuscript.

The analysis of pre-steady-state gradients lies outside of the scope of the present work, and so the question as to whether our results are applicable to them as well, remains to be answered in future research. We have added a comment on this to the discussion.

In their Discussion section, the authors note that several patterning systems, such as the Drosophila wing and eye discs, show smaller cells near the morphogen source relative to other regions in the tissue. This observation suggests a prediction of the authors' hypothesis that can be tested experimentally. In the Drosophila wing and eye discs genetic mosaics of ectopic morphogen sources (such as Dpp) can (and have) been made. Therefore, one could predict that the patterning outputs in a region of larger cross-sectional areas will be more imprecise than in the endogenous source. Since this is a theoretical paper, it is understandable that authors are not going to make this experiment themselves, but I wonder if they can use published data to test this prediction or at least mention it in the manuscript to offer the experimental biology reader an idea of how their hypothesis can be tested experimentally.

We appreciate that the referee would like to help us inspire the experimental community. Unfortunately, the problem with the proposal is that Dpp has been shown to result in a lengthening of the cells (and thus a smaller cell width) [Widmann & Dahman, J Cell Sci, 2009]. The Dpp gradient thus ensures a small cell width close to its source, which makes it virtually impossible to test this proposal experimentally in the suggested way. Nevertheless, we have added brief comments on potential experimental testing of our predictions to the discussion.

Other comments:

The Methods section should be expanded and should include more details about how authors consider cell size in their simulations. As presented, I believe that experimental biologists will not be able to grasp how the analysis was done.

We have expanded on the technical details of our model in the methods section, in particular in relation to the cell size, as requested. To avoid being overly redundant with existing published descriptions of the modeling details [Vetter & Iber, 2022], we focus here on a description of what has not been covered already, and refer the interested reader to our previous publication. It is inevitable for any kind of work, be it theoretical or experimental, to be less accessible to experts in other disciplines, but we believe that the presentation of our results is independent enough of modeling aspects to be accessible to experimental biologists, too.

Authors use adjectives such as 'little' as 'small' without a comparative reference. For example in the abstract, the authors say that apical areas "are indeed small in developmental tissues." What does "small" mean? This should be avoided throughout the text.

We thank the referee for raising this point. Where appropriate, we changed the phrasing accordingly to clarify what the comparative reference is. We leave all sentences unchanged where the statement holds in absolute terms. Note that in the substantially revised analysis on the impact of the different length scales involved in the patterning process, we now explicitly show with simulation data and theory that the absolute positional error increases with increasing absolute cell diameter.

Reviewer #2 (Significance (Required)):

Overall, I believe that the manuscript is well written and deserves consideration for publication. However, authors should consider the points outlined above in order to make their manuscript more accessible and relevant to the developmental biology community.

Thank you for the positive assessment.

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

In their mansucript "Impact of cell size on morphogen gradient precision" the authors Adelmann, Vetter and Iber numerically analyse a one-dimensional PDE-based model of morphogen gradient formation in tissues in which the cell sizes and cell-specific parameters locally affecting the gradient properties are varied according to predefined distributions. They find that the average cell size has the largest impact on the variance of the gradient shape and the read-out precision downstream, while other factors such as details of the readout mechanism have markedly less influence on these properties. In addition they demonstrate that averaging gradient concentrations over typical cell areas induces a shift of the readout position, which however appears to be insignificant (~1% of the cell diameter) for typical parameters.

Overall this manuscript is in very good shape already and tackles an interesting topic. I still would like the authors to address the comments below before I would recommend any publication. My main criticism pertains to some of the authors' derivations which, as I find, partly do deserve more detail, and to their conclusions about gradient readout precision.

Thank you for the positive assessment.

MAJOR COMMENTS

p. 1, left column: The positional error of the readout position does not only depend on the variation of the gradient parameters, as suggested by the first part of the introduction. A very important factor is also the fluctuations due to random arrival of molecules to the promoters that perform the readout due to the limited (and typically low) molecule number. In fact, for positions very distant to the source of the gradient, this noise source is expected to be dominant over gradient shape fluctuations. Importantly, these fluctuations also arise for non-fluctuating, "perfect" gradient inputs if copy numbers of the morphogen molecules are limited (which they always are). This important contribution to the noise is neglected in the work of the authors. This is OK if the purpose is focusing on the origin and influence of the gradient shape fluctuations, but that focus should be clearly highlighted in the introduction, saying explicitly that noise due to diffusive arrival of transcription factors is not taken into account in the given work (see, e.g., Tkacik, Gregor, Bialek, PLoS ONE 3, 2008)

In the present work, only precision of the gradients, but not the readout itself is studied. We have now mentioned this more explicitly in the introduction. We also acknowledge the fact that the readout itself introduces additional noise into the system. We are currently finishing up work that addresses exactly this subject, which is outside of the scope of the present paper.

What may have led to misinterpretation of the scope of our work is that we called x_theta the readout position. x_theta defines the location where cells sense (i.e., read out) a certain concentration threshold, and is not meant to be interpreted as the location of a certain readout (a downstream transcription factor) of the morphogen. We have made this distinction clearer in the revised manuscript.

p.1, right column: Why exactly are the parameters p, d, D assumed to follow a log-normal distribution? Such a distribution has been verified for cell size, but the rationale behind choosing it also for the named parameters should be explained, in particular for D. Why would D depend on local properties of the cell? Which diffusion / transport mechanism precisely is assumed here?

The motivations for the used log-normal distributions for the kinetic parameters are the following:

The morphogen production rates, degradation rates and diffusivities must be strictly positive. This rules out a normal distribution. The probability density of near-zero kinetic parameters must vanish quickly, as otherwise no successful patterning can occur. For example, a tiny diffusion coefficient would not enable morphogen transport over biologically useful distances within useful timeframes. This rules out a normal distribution truncated at zero, because very low diffusivities would occur rather frequently for such a distribution. Given the absence of reports on distributions for p, d, D from the literature, we chose a plausible probability distribution that fulfills the above two criteria and possesses just two parameters, such that they are fully defined by a mean value and coefficient of variation. This is given by a lognormal distribution. Our results are largely independent of the exact choice of probability distribution assumed for the kinetic parameters, under the constraints mentioned above. To demonstrate this, we have repeated a set of simulations with a gamma distribution with equal mean and variance as used for the lognormal distribution. Below are some simulation results for a gamma distribution with shape parameters a = 1/CV^2 and inverse scale parameter b = mu*CV^2 with CV = 0.3 as used in the results shown in the paper. As can be appreciated from these plots, the results do not change substantially, and our conclusions still hold. As we believe this information is potentially relevant for the readership of our paper, we have added this result and discussion to the supplement and to the conclusion in the main text.

We assume extracellular, Fickean morphogen diffusion with effective diffusivity D along the epithelial cells, as specified by Eq. 2. We now state this more explicitly just below Eq. 2 in the revised manuscript. Cell-to-cell variability in the effective diffusivity may arise from effects that alter the effective diffusion path and dynamics along the surface of cells, which we do not model explicitly, but lump into the effective values of D. Such effects may include different diffusion paths (different tortuosities) or transient binding, among others.

Moreover, is there any relationship between A_i and p_i, d_i and D_i, or are these parameters varied completely independently? If yes, is there a justification for that?

The parameters are all varied independently, as written in the paragraph below Eq. 2 on the first page (“drawn for each cell independently”). To our knowledge there is no reported evidence for correlations between cell areas, morphogen production rates, degradation rates, or transport rates across epithelia, that we could base our model on. The choice of independent cell parameters therefore represents a plausible model of least assumptions made. Note that we explore the effect of potential spatial correlations in the kinetic parameters between neighboring cells in the section “The effect of spatial correlation”, finding that such correlations, if at all present, are unlikely to significantly alter our results.

p. 2, right column, section on "Spatial averaging": First of all, how is "averaging" exactly defined here? Do the authors assume that the cells can perfectly integrate over their surface in the dimensions perpendicular to their height? If yes, then this should be briefly mentioned here. Secondly, the shift \Delta x calculated by the authors ultimately seems to trace back to the fact that the cells average over an exponential gradient, whose derivative also is exponential, such that levels further to the anterior from the cell center are higher (on average) than levels to the posterior of it. I suppose, therefore, that a similar calculation for linear gradients would not lead to any shift. If these things are true they deserve being mentioned in this part of the manuscript because they provide an intuitive explanation for the shift. Thirdly, in Fig. 2A the cell sizes seem exaggerated with respect to the gradient length. This seems fine for illustrative purposes, but if it is the case it should be mentioned. Also, I believe that this figure panel would benefit from showing another readout case where the average concentration e.g. in cell 1 maps to its corresponding readout position, in order to show that this process repeats in every cell. Moreover, it could be indicated that in the shown case C_\theta matches the average concentration in cell 2 at the indicated position.

Spatial averaging is defined as perfect integration along the spatial coordinate over a length of 2r (which can generally be equal to, or smaller than, or larger than one cell diameter) as detailed in the supplementary material. In simulations, we use the trapezoid method for numerical integration to get the average concentration a cell experiences along its surface area perpendicular to their height.

The reviewer is correct, that the shift is a consequence of averaging over an exponential gradient. The average of an exponential gradient is higher compared to the concentration at the centroid of the cell, thus the small shift. This is mentioned e.g. in the caption of Fig. S1, but also in the main text (“spatial averaging of an exponential gradient results in a higher average concentration than centroid readout”). We have now added this information also to the caption of Fig. 2. As pointed out correctly by the referee, linear gradients would not result in such a shift. A brief comment on this has been added to the revised manuscript.

We now mention that the cell size is exaggerated in comparison to the gradient decay length for illustration purposes in the schematic of Fig. 2a, as requested.

Unfortunately, we had a hard time following the reviewer’s final point. We show a specific readout threshold concentration, C_theta, in Fig. 2a. A cell determines its fate based on whether its sensed (possibly averaged) concentration is greater or smaller than C_theta. In the illustration, cells 1 and 2 sense a concentration greater than C_theta, and all further cells sense a concentration smaller than C_theta. Cell fate boundaries necessarily develop at cell boundaries (here; between cells 2 and 3, red). Additionally, the readout position for a continuous domain, where morphogen sensing can occur at an arbitrary point along the patterning axis, is shown (blue). This position can be different from the one restricted to cell borders. Thus, different readout positions in the patterning domain result from the two scenarios, which is what the schematic illustrates. Given that our illustration seems to go well with the other referees, we are unsure in what way it could be improved.

As for the significance of the magnitude of the shift for typical parameters as calculated by the authors: I believe that it could be said more explicitly and clearly that under biological conditions the calculated shift overall seems insignificant, as it amounts to a small fraction of the cell diameter.

We have made this more explicit in the text.

Finally, and most importantly: The term "spatial averaging" can have a different meaning in developmental biology than the one employed by the authors. While the authors mean by it that individual cells average the gradient concentration over their area, in other works "spatial averaging" typically means that individual cells sense "their" gradient value (by whatever mechanism) and then exchange molecules activated by it, which encode the read-out gradient value downstream, between neighboring cells, in order to average out the gradient values "measured" under noisy conditions. The noise reduction effect of such spatial averaging can be very significant, as evidenced by this (incomplete) list of works which the authors can refer to:

- Erdmann, Howard, ten Wolde, PRL 103, 2009

- Sokolowski & Tkacik, PRE 91, 2015

- Ellison et al., PNAS 113, 2016

- Mugler, Levchenko, Nemenman, PNAS 113, 2016

The main point, however, is that this is a different mechanism as the one described by the authors, and this should be clearly mentioned in order to distinguished them. I would therefore also advise the authors to make the section title more precise here, by changing "Spatial averaging barely affects ..." to "Spatial averaging across the cell area barely affects ..." for clarity.

Most theory development has previously indeed been done with the syncitium of the early Drosophila embryo in mind. However, most patterning in development happens in epithelial (or mesenchymal) tissues, where spatial averaging via translated proteins is not as straightforward and natural as in a syncitium. In fact, a bucket transport of a produced protein from cell to cell would be difficult to arrange (as upon internalization, degradation would have to be prevented), be subject to much molecular noise, and be rather slow. Our paper is concerned with patterning in epithelia, which we have now stated more clearly in the manuscript.

Regarding the section title: Our analysis does not only cover spatial morphogen averaging over the cell area, but it also includes averaging radii below (in the theory) and far above (in the theory and in the new Fig. 4c, previously 3c) half a cell diameter. With cilia of sufficient length r, epithelial cells could potentially average over spatial regions extending further than their own cell area, without need for inter-cellular molecular exchange between neighboring cells. This is the kind of spatial averaging we explored here. Restricting the section title to the cell area only would therefore be misleading. However, we agree with the referee that the distinction between different meanings of “spatial averaging” is important, and we now emphasize our interpretation and the scope of our work more in the revised text.

p. 3, Figure 3: It would be good to highlight the fact that the colours in panel A correspond to the bullet colors in the other panels also in the main text.

We now added this also in the main text.

As to the comparison of different readout strategies: How exactly were the different readout mechanisms compared on the mathematical side? More precisely: How was the readout by the whole area matched (in terms of fluxes) to the readout at a single point, be it in the center of the cell or a randomly chosen point? How was it ensured that the comparison is done at equal footing?

Our model considers that a cell can sense a single concentration even if it is exposed to a gradient of concentrations. Assuming the French flag model is correct, a cell must make a binary decision based on a sensed concentration in order to determine its fate. The different readout strategies are hypothetical and simplified mechanisms for how a cell could, in principle, detect a local morphogen signal. It is unclear to us what the referee is referring to when mentioning “matching in terms of fluxes”, as there are no fluxes involved in the modeled readout strategies. We make no assumption on the underlying biochemical mechanism that would allow cells to implement one of the strategies. The main goal of this analysis was to determine whether various different sensing strategies had a significant effect on the precision of morphogen gradients experienced by cells. To assure that we can compare the different mechanisms at equal footing, we simulated gradients and then calculated from each gradient the readout concentration in each cell and for each of the methods.

p. 3, right column: "... similar gradient variabilities, and thus readout precision": Linking to comment 1 above, this is strictly speaking only the case when the only source of fluctuations in the readout is the gradient fluctuations. I would therefore leave this statement out.

To avoid confusion, we have removed parts of the sentence. Thank you for pointing this out.

p. 3, section on positional error (right column): In this part I had most troubles following the thoughts of the authors.

First of all, the measure that the authors use for the positional error is sigma_x / mu_lambda, i.e. the standard deviation of the readout position relative to the gradient length. The question is whether this is the correct measure. It should be specified what the motivation for normalizing by mu_lambda is. In the end, one could argue, what the cells really do care about would be that the developmental process can assign cell fates with single cell precision, for which the other measure shown in Eq. (6) is the representative one. Now in contrast to the former measure, the latter actually increases with decreasing cell diameter.

We thank the referee for raising this point, and acknowledge that we have not presented this aspect well enough. We have rewritten the entire section and the discussion about biological implications. Instead of normalizing with a constant mean gradient length in the formulas and figures, which has left room for misinterpretation, we now instead varied all relevant length scales in the patterning system, to determine the impact of each of them independently on the positional error. We now show that the positional error increases (to leading order) proportionally to the mean gradient length, the square root of the cell diameter, the square root of the location in the patterned tissue, and inversely proportional to the length of the source domain. We support these new aspects with new simulation data (Fig. 2E-2H, Fig. 3D-G, Fig. S5, Fig. S6). As the positional error is now reported in absolute terms, rather than relative to a particular length scale, the question of the relevant scale is addressed. We now show that the absolute positional error increases with increasing absolute cell diameter.

We believe that this extension provides additional important insight into what affects the patterning precision. We thank the referee very much for motivating us to expand our analysis.

Secondly, even when the former measure (sigma_x / mu_lambda) is employed, Fig. 3(D) shows that while it decreases with decreasing cell diameters, in the regime of small diameters the std. dev. of the readout position becomes larger than the average cell diameter, which actually would mean that cell fates cannot be assigned with single-cell precision. While the authors later report both quantities for specific gradients, it should be clarified beforehand which of the measures is the relevant one.

This has now been addressed by considering absolute length scales as discussed at length in our answer to the previous point.

Moreover, in the following derivations, mu_x is not properly introduced. What exactly is the definition of that quantity? Is it the mean readout position? If yes, it is not clear why exactly it would be interesting and relevant to the cell. This should be properly explained in a way that does not require the reader to look up further details in another publication.

The referee is correct in that mu_x is the mean readout position. We apologize for not being clear enough on this, and have now defined this in the introduction together with the definition of sigma_x.

At the end of this section the authors come back to the sigma_x / mu_delta measure again and indeed point out that it increases with decreasing mu_delta, which causes a bit of confusion because the initial part of the section only talks about the increase of the pos. error with mu_delta. Overall I find that this section should be rewritten more clearly. Right now it leaves the reader with the "take home message" that small cells are good because they lead to smaller pos. error, but when the--in my opinion--relevant measure (sigma_x/mu_delta) is employed the opposite is the case. This is confusing and unclear about the authors' intentions in that part.

See the answer above. The “take-home message” is now reformulated in absolute terms regarding the effect of cell diameter, rather than relative to a certain choice of reference scale. Our new analysis revealed a new relative ratio that determines the positional error, mu_lambda/L_s. We now discuss this relative measure also regarding its biological significance. Once again, we thank the referee for pointing us at this source of confusion, the elimination of which allowed us to improve our analysis.

__Finally, the authors could also supplement the numbers that they name for the FGF8 and SHH gradients by the known numbers for the Bcd gradient in Drosophila, which has been studied excessively and constitutes a paradigm of developmental biology. Here mu_delta ~= 6.5 um, while mu_lambda ~= 100 um, such that mu_delta/mu_lambda While we appreciate that most theoretical work has been done for syncytia, this paper is concerned with patterning of epithelia, which have different patterning constraints, as also explained in a reply further above. We now make the scope of our work clearer in the revised manuscript. But as the referee points out, the diameter of the nucleus relative to the gradient length is such that gradients can be expected to be sufficiently precise.

p. 4, section on the effect of spatial correlation: Here the authors chose to order the kinetic parameters in ascending or descending order. Is there any biological motivation for that particular choice? Other types of correlations seem possible, e.g. imposing the rule that successive parameter values are sampled starting from the previous value, p_i+1 = o_i +- delta_i+1 where delta_i+1 are random numbers with a defined variance.

In the simulations we go from zero correlation (every cell has independent kinetic parameters) to maximal correlation (every cell has the same parameters, resulting effectively in a patterning domain that consists of a single effective “cell”), see Fig. S3. Biologically plausible correlations in between these extremes should retain the same kinetic variability levels (same CVs) which we took from the measured range reported in the literature. We accomplish this by ordering the parameters after independently sampling the parameters for each cell from probability distributions with the desired CV. The motivation for this approach is that this produces a type of maximal correlation that still reflects the measured biological cell-to-cell variability, to demonstrate in Fig. S3, that even such a maximal degree of spatial correlation does not qualitatively alter our results. The kind of correlation that the referee suggests introduces a spatial correlation length that lies in between the extremes that we simulated. Since even for maximal correlation using the ordering approach, we find our conclusions to still apply, we have no reason to expect that intermediate levels of correlation would behave any differently.

The idea brought forward by the referee effectively introduces a correlation length scale. We discuss this case in the paper, noting that the positional error will scale as x~N , where N is the number of cells sharing the same kinetic parameters. A correlation length scale will be proportional to N and will therefore simply uniformly scale the positional error accordingly, but will likely not reveal any new insight beyond that.

Moreover, using the idea of the referee as an additional way to introduce correlation is difficult to realise in practice, as we need to recover the mean and variance of the kinetic parameters, while ensuring strict positivity for each of them. A simple random walk, as proposed, would not lend itself easily to achieve this without introducing a bias in the distribution, because negative values need to be prevented. As explained in a reply further above, an important feature of the kinetic parameters is that they are not too small to prevent the formation of a meaningful gradient, which is not straightforward to ensure with the proposed method.

We acknowledge that there are different types of correlations conceivable, but we expect these correlations to lie between the two extremes that we present in the paper, which show no qualitative difference in the results.

p.5, Discussion: "..., but with nuclei much wider than the average cell diameter". To be honest, I could not completely imagine what is meant with this sentence. Intuitively, it seems that the nuclei cannot be larger than the cells, but I suppose that some kind of special anisotropy is considered here? In any case, this should be made precise.

The main tissues that are patterned by gradients are epithelia. Our paper focuses on such tissues. It is a well-known feature of pseudostratified epithelia that nuclei are on average wider than the cell width averaged over the apical-basis axis. Nature solves this problem by stacking nuclei above each other along the apical-basal axis, resulting in a single-layered tissue that appears to be a multi-layered stratified tissue when only looking at nuclei. For a schematic illustration of this, see Fig. 1 in [DOI: 10.1016/j.gde.2022.101916]. An image search for “pseudostratified epithelia” on Google yields a plethora of microscopy images. Right at the end of the quote recited by the referee, we also cite our own study [Gomez et al, 2021], which quantifies this in Fig. 5.

Moreover, I find that the conclusion that morphogen gradients "provide precise positional information even far away from the morphogen source" goes to far based on the authors' work, precisely for the fact input fluctuations due to limited morphogen copy number, which can become detrimentally low far away from the source, are not considered, neither the timescales needed to both establish and sample such low concentrations far away from the source. While thus, according to the work of the authors, the fluctuations in the morphogen signal may be favorably small, these other factors are supposed to exert a strong limit on positional information. This conclusion therefore seems unjustified and should be toned down, or even better taken out and replaced by a more accurate one, which only focuses on the gradient shape fluctuations, not on the conveyed positional information.

There is no evidence so far that morphogen gradient concentrations become too low to be sensed by epithelial cells, to the best of our knowledge. What we show is that the gradient variability between embryos remains low enough that precise patterning remains possible. Whether the morphogen concentration remains high enough to be read out reliably by cells is a subject that requires future research. Genetic evidence from the mouse neural tube demonstrates that the SHH gradient is still sensed at a distance beyond 15 lambda (SHH signalling represses PAX7 expression at the dorsal end of the neural tube) [Dessaud et al., Nature, 2007], where an exponential concentration has dropped more than 3-million-fold.

As the referee correctly recites, we state that “morphogen gradients remain highly accurate over very long distances, providing precise positional information even far away from the morphogen source”. This statement is restricted to the positional information that the gradients convey, and does not touch potentially precision-enhancing or -deteriorating readout effects, nor does it concern the absolute number of morphogen molecules.

Positional information goes through several steps. The gradients themselves convey a first level of positional information, by being variable in patterning direction, as quantified by the positional error. This is what we draw our conclusion about. This positional information from the gradients can then be translated into positional information further downstream, by specific readout mechanisms, inter-cellular processes, temporal averaging, etc. About these further levels of positional information, we make no statement.

We therefore disagree that our conclusion is unjustified. In fact, we have phrased it exactly having the limited scope of our study in mind, making sure that we restrict the conclusion to the gradients themselves.

MINOR COMMENTS

- p. 1: "and find that positional accuracy is the higher, the narrower the cells".

(This sentence, however, should be anyhow revised in view of major comment 5 above.)

We have added “the”.

- p. 4: "... with an even slightly smaller prefactor."

We have removed “even”.

Reviewer #3 (Significance (Required)):

I believe that this work is significant to the community working on the theoretical foundations of morphogen gradient precision in developmental systems. The main interesting findings are that small cell diameters lead to smaller positional error (although the relevant measure should be clarified according to my comment no. 5), and that the gradient shape fluctuations are surprisingly robust with respect to the readout mechanism.

Its limitations consist of the fact that the impact of small copy numbers on the readout and associated timescales are neglected, such that the findings of the authors on gradient robustness cannot be simply transferred by simple conversion formulas to readout robustness / positional information. Comment 5 goes hand in hand with this, as a different conclusion may emerge depending on how the relevant positional error measure is defined. This should be fixed by the authors as indicated in the main part of the report.

Thank you for your assessment.

Hobbes says, "it’s just a fact about human nature that however much a man may acknowledge many others to be more •witty, or more •eloquent, or more •learned than he is, he won’t easily believe that many men are as •wise as he is; for he sees his own wisdom close up, and other men’s at a distance." I completely agree with what Hobbes is saying here, that man will never outwardly admit that they believe another to be better than they are in any way shape of form. This is because they believe it will make them seem inferior or unequal to another.

See, dont nobody all the time, nor do they in the same way subscribe to or follow standard modes of expression. Everybody mix the dialect they learn at home with whateva other dialect or language they learn afterwards. That’s how we understand accents; that’s how we can hear that some people are from a Pol- ish, Spanish, or French language background when they speak English. It’s how we can tell somebody is from the South, from Appalachia, from Chicago or any other regional background. We hear that background in they speech, and it’s often expressed in they writin too. It’s natural (Coleman). But some would say, “You cant mix no dialects at work; how would peeps who aint from yo hood understand you?” They say, “You just gotta use standard Eng- lish.” Yet, even folks with good jobs in the corporate world dont follow no standard English. Check this out: Reporter Sam Dillon write about a survey conducted by the National Commission on Writing in 2004. He say “that a third of employees in 112 IJCS the nation’s blue-chip companies wrote poorly and that businesses were spending as much as $3.1 billion annually on remedial training” (A23)

I agree with what Young says here. Language is something that forms naturally out of how people already communicate. Theres no person who thinks up every word in the dictionary and then thinks of a definition, people start using words, and then the people who make dictionaries add those words to them. Why should dialects be any different. English is a much different language now, than it was 200 years ago.

The main claim of this article is to bring more situational awareness to the readers. We know what they are doing in most cases is so that their children & grandchildren can have a more prosperous life. They are desperate why hinder them from knowing where they can find help. Having empathy can go a long way just understanding the motive behind them crossing the boards. It's not easy what migrants have to do just by risking their lives to find freedom. If you were in their position, you what do what needs to be done for your family. Migrants shouldn't be just thrown to a detention center or behind a wall, that doesn't really solve anything just leads for more problems and chaos.

I just wanna note the date that this occurred, it’s been 9 years since so when I start analyzing the text I will consider this.

Worth noting that for many of Rochus's own projects (esp. ones related to Oberon/Oberon+), it's just a bunch of .cpp and .h files in one directory...

Reviewer #3 (Public Review):

In this article, the authors examined color evolution in the kingfishers, a group of birds that have achieved a spectacular diversity of colors and color patterns as they have diverged across the continents and island chains of the globe. Like many other avian taxa, kingfishers on islands often exhibit color patterns distinct from their close relatives. The authors focus here on putting this informally recognized pattern of evolutionary change to a formal test, asking if plumage color diversity and evolutionary rate are elevated on islands. They also explore whether a notable characteristic of some kingfishers - their simultaneous use of many of the coloration mechanisms available in birds - contributes to the evolutionary lability of their color patterns.

The authors have previously explored how when color varies in birds it is not just in dimensions of color, but also in the distribution of those colors in patches on the body. Summarizing this variation is challenging, and there are statistical obstacles to comparing it in a holistic manner. In this study, the authors use an exceptional set of analyses to study color in total as a multivariate trait. These are the major strengths of the paper. The authors' efforts are somewhat less convincing when they pursue a univariate model fitting on a small number of principal components, but these analyses are not central to the study. And as with all studies using ancestral state reconstruction to test hypotheses, it's an important tool and one that contributes to this study's effectiveness, but we should acknowledge some level of uncertainty with its results.

The authors report two important relationships in this study. They provide convincing evidence that rates of color evolution are elevated in island kingfishers, without convergence towards a particular island phenotype. They also describe a relationship between the complexity of plumage patterns and the rate at which they evolve, which has fundamental implications for our understanding of the tempo of trait evolution.

Islands make up a tiny portion of the earth's surface but are home to a seemingly disproportionate amount of life's diversity. This paper makes an important contribution to our understanding of how this diversity is generated, by showing that the evolutionary rate is elevated on islands for traits relevant to mate choice and recognition. The authors find that "plumage complexity, rather than uniformity, provides more phenotypic traits for natural selection to act upon". Given the number of different coloration mechanisms they express, the kingfishers are a unique group in which to study this issue, so I look forward to reading and hearing more from the authors on this issue in the future.

To me, the difference is that unlike the brownstones (in Brooklyn, Boston, and Philadelphia, each with their own twists - depending on the city), these are everywhere; there are so many more major cities in America, and architects can design not just for the city, not just for the nation, but often for the world. Homogeneity is beautiful when it's part of a central plan or culture for an area, but to globalize this kind of style is distressing!

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

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Reply to the reviewers

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

Summary:

The submitted manuscript is comparing the effect of individual chaperones and heat-resistant obscure (Hero) proteins on the overall folding of the TDP-43 LCD-domain and its relation to aggregation propensity. Therefore, the authors apply smFRET in order to deduce eventual morphological changes of the LCD-domain from FRET efficiencies. The authors observe that the LCD domain extends its structure upon binding of chaperone/Hero proteins whereas it is collapsed in the absence of those. Furthermore, immunoblotting of filter trap assays indicate that overexpression of chaperones and Hero proteins reduce aggregation of TDP-43 in vivo. Both, the morphological effects on the LCD-domain and the aggregation propensity are significantly enhanced for the TDP-43 A315T mutant. Moreover, the authors tested a charge depleted Hero protein version with reduced "chaperone-like" behaviour. Therefore, the authors conclude that the binding or chaperone activity of the Hero protein is based on its residue specific charges. Finally, the authors conclude that Hero proteins can act similar to chaperones in order to keep protein homeostasis under stress conditions.

We thank the Reviewer for their insightful evaluation of our study.

Major comments:

The similar effect of chaperones and Hero proteins on the morphology of TDP-43 found by the authors is intriguing and the applied experimental procedures seem well described and conducted.

However, the assumption of the authors that a change in morphology of the LCD-domain by the chaperones and Hero proteins is directly connected to the reduction of TDP-43 aggregation is not entirely clear. Whether an overexpression of individual chaperones and Hero proteins has a direct effect on TDP-43 aggregation cannot be tested in vivo, only. It cannot be excluded that inside the cell the here tested chaperones and Hero proteins control intermediate processes or work as co-factors for other proteins involved in protein homeostasis rather directly influencing the aggregation of TDP-43. Therefore, I recommend in vitro aggregation experiments, using ThT signal as a readout. By adding chaperones, Hero proteins and a negative (BSA or others) control individually, a direct effect on TDP-43 aggregation could be concluded. Those experiments have been extensively used in the field and are quick and straightforward to handle.

As the Reviewer explains, indirect effects on TDP-43 aggregation in cells may be accounted for by conducting aggregation experiments in vitro, with recombinant proteins. We are currently designing such experiments based on a previously described full-length recombinant TDP-43 with a TEV-cleavable MBP tag (Wang 2018 EMBO J). This can be incubated with Hero/DNAJA2/Control, and aggregation induced by cleavage of the tag, after which aggregation can be measured via filter trap similar to the method described in our work. We will include these results in our revised manuscript.

We thank the Reviewer for their advice. While we note that it is controversial whether ThT binds to aggregates formed from full-length TDP-43 (used in all our assays in the current manuscript), it is reasonable to apply this assay to the LCD fragment as in the paper referenced by the Reviewer below (Lu 2022 Nat Cell Biol). Such an assay is also a reasonable method for confirming effects of Hero protein and DNAJA2 in vitro, and we can conduct this assay as a back-up if the above does not work.

In addition, focusing on the LCD-domain as a main driver for TDP-43 aggregation is limiting this study. In particular, recent studies [1] indicate that the RRM1 and RRM2 sites of TDP-43 have a major impact on TDP-43 gelation and maturation to solid aggregates. Unfortunately, those sites have not been studied in this manuscript.

We thank the Reviewer for their insight. While we are keen to investigate the impact of other regions on the aggregation of TDP-43 in the future, we chose to focus on the LCD in our current study because our smFRET assay is particularly suitable to monitor the dynamic conformational nature of this flexible, unfolded region.

However, we agree with the Reviewer that it is possible the RRMs have an effect on the activities of Hero11 and DNAJA2. We will create constructs for the RRM-depleted variant, TDP43ΔRRM1&2, and RNA-binding deficient variant, TDP435FL for use in our cell-based assay. This will allow us to investigate how this domain influences the effects of Hero and DNAJA2, and we will include this in our revised manuscript.

As an optional alternative for using Hero11KR->G could be the alteration of buffer conditions and using higher number of salts to promote charge screening. It would be of interest whether the results with the Hero11KR->G could be reproduced with wild type Hero11.

We will perform our assays with Hero11 in high salt conditions for charge screening. While we agree that it may be a great alternative experiment, we note that changing the salt concentration may directly affect the LCD conformation, possibly complicating interpretation of results.

[1] Lu et al. Nat Cell Biol;24(9):1378-1393 (2022)

Minor comments:

Overall, the text is clearly written, and the figures are appropriate.

Whether the activity of individual chaperones or Hero proteins on TDP-43 aggregation "may result in the overall fitness of the cell" or "reinforcing the conformational health of the proteome" is disputable without knowing how the overexpression of certain chaperones or Hero proteins alter the formation of toxic TDP-43 oligomers.

We thank the Reviewer for their balanced critique. We will remove or weaken this point regarding how Hero proteins "may result in the overall fitness of the cell" or may be "reinforcing the conformational health of the proteome" from the discussion.

Reviewer #1 (Significance (Required)):

Studying the mechanistic effects of chaperones on aggregating proteins is of major interest for the field in order to understand aging related disbalance of protein homeostasis and the progression of neurological decline, such as seen for amyotrophic lateral sclerosis (ALS). Furthermore, finding homolog proteins, also being able to inhibit protein aggregation, can help to understand overall mechanisms of protein aggregation and processes preventing such fatal behaviour. However, the technique used in this manuscript are not very novel and have been used numerously times before. smFRET is a common technique to look at protein folding/unfolding and is used frequently as a molecular ruler. The manuscript is of interest for the field of protein aggregation and folding, smFRET and neurodegeneration.

My expertise lies in the field of protein aggregation and inhibition due to chaperones, measuring molecular interactions and neurodegenerative diseases.

We greatly appreciate the Reviewer’s expert opinion on our work. As the Reviewer explains, we believe our work will contribute to the fields of protein aggregation and folding, smFRET and neurodegeneration. While the smFRET method may not be novel on its own, to our knowledge this is the first observation of the TDP-43 LCD, with the effect of a pathogenic mutation, at the single-molecule level. In fact, the production, dye-labeling and isolation of individual molecules is extremely challenging for TDP-43. This was made possible by our technical advances using genetic code expansion to site-specifically introduce an unnatural amino acid in TDP-43, purifying and labeling the TDP-43 from HEK cells, and isolation on glass slides.

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

In this manuscript, the authors build on their findings (Tsuboyama 2020) that electrostatically charged IDPs (Heros) can protect proteins from denaturation and aggregation. In their previous work, they demonstrate that these Hero proteins could decrease the fraction of insoluble GFP-TDP43∆NLS in mammalian cell lines and that this mode of action was related to the electrostatic charge of the proteins and not sequence dependent. Although this protective mode of action appears to be similar to that of canonical chaperones, it is unclear how the Hero proteins compare. In this study, the authors compare Hero11 to a panel of canonical chaperones in their cell-based assays and show that it prevents aggregation in a comparable way to DNJA2. It appears that Hero11 decreases the GFP-TDP43∆NLS aggregates better than some other chaperones. They then utilise their expertise in smFRET analysis (Tsuboyama, 2018) to compare what effect DNJA2 and Hero11 (along with Hero11KR-->G (non-charged control)) have on the dynamic structures of the GFP-TDP43∆NLS (labelled with complementary fluorophores in the LCD domain). Based on analysis of the WT GFP-TDP43∆NLS and the A315T GFP-TDP43∆NLS, the authors suggest that the presence of Hero11 and DNJA2 maintain the LCD-domain of TDP43 in an extended conformation and that by doing so, aggregation can be prevented (as assessed in the cell-based assay).

Despite finding the results very interesting, I feel that the study is preliminary and the conclusions drawn are not fully substantiated by the presented experimental work. Many questions need addressing to validate these findings and conclusions (please see more in the "Significance" section). I have tried to list the main concerns below.

We thank the Reviewer for their detailed and critical assessment of our current study.

Questions/concerns:

Authors used double transient transfections but have not shown quantification of protein levels of the chaperones versus TDP43 - western blots to confirm proper expression (and levels) of the chaperones/Hero protein is crucial without it, we cannot assume that the differences in TDP-43 aggregation are a result of effective chaperoning or due to a lack of expression of any of the chaperone proteins, or high expression of others.

We agree with the Reviewer that this is an important and straightforward validation experiment. We will perform the Western Blotting to confirm the proper and comparable expression of the chaperones/Hero proteins.

Authors used quite a high BSA concentration in the smFRET work; it would be useful to see what the TDP43 smFRET trace looks like without BSA incubation (to ensure it is not causing some effect). Also, is there a concentration dependence? The Authors mention they are unable to identify a Hero/TDP43 complex; but if the amount of Hero protein is high (given that it is single molecules tethered), the change in compaction may not relate to the levels/ratios found in the cells (where changes to aggregation are occurring). have the authors considered whether positively charged polymers (poly-Lys) have any impact on the TDP-43 smFRET distribution? Given that the smFRET trace is so heterogeneous, to understand what is happening here would require the comparison of more than 2 variants.

As the Reviewer suggests, we will include additional smFRET experiments in our revision.

First, we will perform the smFRET experiment of the TDP-43 alone in the PBS buffer. However, we would like to clarify the reason we used BSA incubation for comparison in the current experiment is to account for the possibility of non-specific macromolecular crowding effects on the conformation of the LCD (an effect reported for IDPs in general, for example in Banks 2018 Biophys. J.); we expected that it would be fair to compare Hero11 against another protein, rather than buffer alone. As the Reviewer suggested, we can also perform the same experiments at lower concentrations of Hero11 and DNAJA2, including equimolar concentrations (as suggested below). Moreover, we can also test poly-K peptides for comparison.

Although the A315T variant has a very distinct smFRET profile, it is clear that the effects of Hero11KR-->G (that is proposed to have no effect on aggregation or on the smFRET of WT TDP43) has a clear impact on A315T. Why is this?

We thank the Reviewer for raising this interesting point. We envision that the observed effect is due to weak interactions between the LCD domain of TDP-43 and Hero11KR->G; even without K and R, there many other functional amino acids that are fully accessible due to the extremely disordered nature of the protein. The effect is easier to be observed with the A315T mutant, compared to the WT TDP-43, presumably because the mutant tends to take more compact conformations on its own. Nonetheless, unlike WT Hero11, Hero11KR->G fails to accumulate the very extended form of the LCD (FRET signal of ~0; please see below for the explanation of this value), which appears to be associated with suppression of aggregation. We will include these in our discussion.

The LCD region is prone to PMTs - as the tethered protein is taken from expression in mammalian cells, how can the authors be sure that it has no PMTs? Although a clear difference is observed between WT and A315T in terms of "compactness" of the LCD domain, we cannot assume that the effect of DNAJ2 and Hero11 are the same - in fact, the Hero11 KR-->G control for the A315T is clearly different from the negative control (BSA) and the effect that was seen in WT. As the LCD domain is well-known to be the site of post-translational modifications (ie. Phosphorylation - this would have an effect on an electrostatic Hero11), could the effects be related to changes in PMTs as well?

We thank the Reviewer for their insight. We would like to clarify that we make no assumption that our dye-labeled TDP-43 is free of post-translational modifications. Indeed, the fact that it is derived from HEK293 cells suggests it should have post-translational modifications relevant to humans and may be even considered an advantage of our method. (Most structural methods require purification of a large amount of protein, often only possible through recombinant expression in E. coli, thus lacking human-relevant PTMs.) As the Reviewer points out, the LCD is known to have many phosphorylation sites, which may help explain how the positively charged Hero11 interacts with it. Thus, we will perform mass spectrometry of TDP-43 and the A315T variant expressed in HEK cells to identify what post-translational modifications are present.

The authors mention other studies on DNJA proteins on TDP-43; is the mechanism by which they suppress aggregation known? If the authors want to compare the unknown effects of Hero11, it would be useful to know what DNJ2A is doing, otherwise, the results are still not conclusive, only that "change is similar" in two experiments. What is known about DNJ2A interactions with TDP-43? Did the authors do any pulldown assays to detect a complex in cellulo?

While previous studies have identified various DNAJ (specifically J-domain protein B-subfamily) proteins that suppress aggregation of overexpressed TDP-43, not much is known of this specific interaction (Udan-Johns 2014 Hum Mol Genet, Chen 2016 Brain, Park 2017 PLOS Genet). To address the Reviewer’s questions, we will include experiments characterizing the effects of DNAJA2 on TDP-43. We will perform colocalization experiments, explaining effects of DNAJA2 and Hero11 on TDP-43 in the cell. As explained below, we will also perform Pulse Shape Analysis (PulSA), a flow cytometry-based method that can be used to study protein localization patterns in cell, which will also provide insight into the effects on the distribution of TDP-43 in cells. We can also perform co-IP of TDP-43 to detect if there is a detectable, stable complex with DNAJA2 and/or Hero11. Together, these will clarify the similarities and differences between DNAJA2 and Hero11.

It is unclear how the findings of the smFRET relate to structural understanding of the LCD-domain of TDP43 (i.e. NMR studies?); is it known whether PTMs are more prominent with the A315T variant as this may explain it's more compact nature? As well, putative helical structure in the LCD domain may lend to the changes in compaction.

The Reviewer brings up an interesting and careful discussion. Currently, it is unknown if PTMs actually cause more compaction, or if they are more prominent in the A315T variant, but we will perform mass spectrometry to detect PTMs.

As the Reviewer mentions, it would be very interesting to compare our smFRET results to other studies of specific LCD structures. However, it is not trivial to deduce lengths (and structure) from smFRET data as various other factors, for example, dye orientation and local chemical environment, may affect FRET efficiency. Nonetheless, we can still cautiously provide a discussion of how our FRET results compare with previous studies.

For the dye pair used in our study, Cy3 and ATTO647N, the low/no FRET signals promoted by DNAJA2 and Hero11 correspond to a range of end-to-end distances of 6.9 nm to 10.2 nm (FRET signals of 0.1 to 0.01, respectively). Assuming that the LCD behaves like a ~140 amino acid worm-like chain (WLC) with persistence length (Lp) = 0.8 nm, we expect a mean end-to-end distance of 7.35 nm. Thus, the low FRET peak can be well explained by promotion of an extended WLC behavior of the LCD by DNAJA2 and Hero11. On the other hand, the FRET peaks of WT LCD and the A315T mutant (in the absence of Hero11 or DNAJA2) correspond to ~4 and ~3.3 nm, respectively. We will include a careful discussion of how our results relate to known structural understanding of the LCD in the revised discussion.

It is unclear how there can be such a prominent FRET ~0 peak and in fact negative values.

We regret that we did not clearly explain this in the manuscript. Negative values arise when applying correction factors from the alternating laser scheme (ALEX) to FRET signals. FRET efficiency, E, is the ratio of acceptor signal intensity, IA, over the total signal intensity, ID+IA, (with the application of a correction factor, γ, but this doesn’t affect the negative values and won’t be discussed further here) and is given by the equation: E=IA/(γ×ID+IA). However, due to leakage of the donor signal into the acceptor channel and direct excitation of the acceptor dye by the donor laser, raw IA values, IA,raw, are erroneously higher than in reality. For example, the ~0 FRET peaks in question appear to be around 0.1–0.2 before correction. These are accounted for by applying the respective correction factors, Dleakage and Adirect, through the equation: IA=IA,raw–Dleakage×ID–Adirect×IAA. (IAA is the acceptor signal during excitation of the acceptor dye.) These two correction factors are determined by observing the traces and choosing the mean values using iSMS software (2015 Preus Nat Methods) and applied uniformly to all traces in an experiment. When IA is especially low, such as when FRET is almost 0, the magnitude of the correction factor terms may be larger than IA,raw, resulting in negative values. This does not mean that values less than 0 are invalid, but merely that they have been overcompensated in the error application. For the dye pair in our study, FRET efficiencies less than 0.1 correspond to distances greater than 6.9 nm, meaning peaks around zero represent LCD behaviors with end-to-end distances greater than around 7 nm. Please also note that kernel density estimation often gives distributions with values beyond the (0,1) range just because of how these plots are constructed. This will be added to the methods in the revised manuscript.

Conclusion is that Hero11 and DNJA2 maintain the TDP43 LCD-domain (soluble protein) in an extended form and that this is linked with the decrease in aggregates found in the cell; however, with the cell-based assay, no analysis to quantify the expression levels of the TDP43 and the chaperones/Hero are presented, and more importantly, no analysis on the complementary soluble fraction (to the filter assay) has been done to show that indeed, these biomolecules maintain the proteins in a soluble form. It is possible that the TDP-43 is being degraded?

As described above, we plan to perform Western Blotting to examine the expression levels of these proteins. To address the concerns about solubility, we will perform Pulse Shape Analysis (PulSA) to quantitatively measure the expression and soluble/aggregated distribution GFP-tagged TDP43 in HEK293T cells. Measuring the soluble diffuse signals and the punctate aggregate signals will also tell us if there are differences in how GFP-TDP43 is aggregated between Hero11, DNAJA2 and controls. In addition, to support results from the FTA, we will provide sedimentation assays, where the soluble and aggregate fraction from cells is separated by centrifugation and analyzed (Krobitsch 2000 PNAS). These will provide information on TDP-43 in the soluble fraction.

Reviewer #2 (Significance (Required)):

Contextually, this study has novelty and potential value for basic research. Firstly, understanding the underlying mechanisms by which Hero protein prevent aggregation would be valuable towards understanding the players in protein homeostasis which can be imbalanced with respect to disease. Secondly, the use of smFRET as a tool in understanding the dynamics of TDP-43 and mutational variants can be powerful in defining structural attributes with pathological consequences in ALS. Although this work shows comparisons between the effect of a canonical chaperone (DNJA2) and Hero11 on the dynamics of monomeric protein and the effect on cellular aggregation, proposing a general mechanism on the data from two TDP-43 variants and a cell-based aggregation assay is premature and more experimental evidence is needed to define the critical link that prevents aggregation of TDP-43 within the cell. Mechanistically, the study does not give a lot of additional insight into the mode of action of Hero11 in the process of preventing aggregation (nor does it explain what DNJA2 is doing and therefore how Hero11 compares and contrasts). The proposed "extended versus collapsed" switch is simplistic and doesn't account for the complexity of TDP-43 structural dynamics. To support their proposed mechanism of action, the authors needs to examine TDP-43 mutational variants (specifically disease-related ones) using their smFRET to understand exactly what the "collapsed" and "extended" data is defining before making the leap that this effect is what is preventing aggregation. There are some structural studies about residual structure in this region (via NMR) that should be considered (https://doi.org/10.1016/j.str.2016.07.007). Although the A315T variant has a very distinct smFRET profile, it is clear that the effects of Hero11KR-->G (that is proposed to have no effect on aggregation or on the smFRET of WT TDP43) has a clear impact on A315T. Why is this? Have the authors considered that the LCD domain of TDP43 is prone to post-translational modifications? Is this variant more phosphorylated - a PMT like phosphorylation is surely to have an impact on interactions with Hero proteins as they are positively charged. Given that the protein is expressed in mammalian cells, it is likely that PMTs have occurred (but the authors should analyse for this).

With regards to the cell-based aggregation assays, the authors again present a simplified relationship - however, a number of control experiments and additional questions arise. It appears that there is less aggregation with co-expression of some chaperones and the Hero11, but what about the soluble fraction? What is the impact of these biomolecules? Is this that it is maintaining soluble protein, enhancing degradation, propagating soluble oligomers? Equally, how do we know that the levels of the chaperones/Heros and the TDP-43 is the same in each cell - these are transient transfections, and no western blots are shown to confirm the levels of the proteins. In fact, the authors state that "co-transfection of HSP70 (HSPA8), HSP90 (HSP90AB1) or HOP all failed to suppress TDP-43 aggregation compared to GST" and mention that this is in contrast to other studies, but could this be a failure to express these in the cell models? Some western blot/lysate analysis is needed. Chaperones often form complexes with their client proteins, is there any evidence of complexes in these cell models (i.e. using immunoprecipitation)?

We thank the Reviewer for their detailed evaluation and interest in our work. As the Reviewer describes, smFRET is a powerful tool for studying the conformational dynamics of TDP-43, and we hope that this study will contribute to our understanding of how Hero proteins and chaperones prevent aggregation.

We are also grateful to the Reviewer for their constructive criticism of our current model, and we will revise it accordingly. We completely agree with the Reviewer that there are complex structural dynamics within the LCD that determine aggregation and phase separation behaviors. Our simple model was intended to explain how external factors that suppress aggregation, DNAJA2 and Hero11, could affect the conformation of LCD at the single-molecule level. As discussed above, we were cautious to over-interpret how our FRET observations correlate to specific conformations, leading to this simplistic model. We do not intend for our explanation of “extended versus collapsed” in the model to explain all structural dynamics of the LCD; rather, we wanted to highlight the characteristic low FRET state promoted by DNAJA2 and Hero11. We believe that the experiment plan explained above will address the Reviewer’s concerns in full, and we thank the Reviewer again for helping us to significantly improve our manuscript.

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

- In a recent study (PLosBiol, 2020) the same authors described an interesting class of proteins they call 'Hero'. Based on their analyses in cultured cells and transgenic Drosophila models the authors concluded that 'Hero' proteins protect against protein instability and aggregation. So far, this class of proteins has not been analyzed by independent groups.

In the current manuscript, they mainly confirm their own previous finding that Hero 11 prevents There are several concerns about the presented data:

We thank the Reviewer for their critical comments on our current manuscript.

- Based on the filter trap assays shown in figure 1 and 3 the authors conclude that DNAJB8 and Hero11 specifically interfere with the aggregation of TDP-43. However, they do not show that the expression levels of TDP-43 are not altered by the co-expression of the different proteins and are comparable in the different samples. In order to make a relevant statement about the anti-aggregation activity of the analyzed proteins, the ratio between soluble and aggregated TDP-43 has to be analyzed.

We would like to clarify that the Reviewer means DNAJA2, not DNAJB8. Following the Reviewer’s advice, we will perform Western Blotting combined with sedimentation assays, where the soluble and aggregate fraction from cells is separated by centrifugation and analyzed to examine the expression levels. We will also perform colocalization experiments and Pulse Shape Analysis (PulSA), a flow cytometry-based method that can be used to study protein localization patterns in cell, which will provide insight into the anti-aggregation activities.

- The FRET assays shown in figures 2 and 4 indicate a slightly higher FRET efficiency in the presence of Hero11 and DNAJA2 and Hero11. The authors postulate that is phenomenon is causally linked to the activity of Hero11 to prevent aggregation of TDP-43. First, it remains unclear whether the slight increase is really significant. Second, I could not find any experimental evidence to support the assumption that a more collapse conformation of the TDP-43 LCD measured in single molecule FRET assays, correlates with an increased aggregation tendency of TDP-43.

We apologize that we are not sure what the Reviewer refers to by “a slightly higher FRET efficiency in the presence of Hero11 and DNAJA2 (and Hero11).” We would like to clarify that, in the presence of Hero11 and DNAJA2, what we observed was a very low (not slightly higher) FRET efficiency of ~0 (Figure 2g and h), suggesting an extended conformation. In contrast, the aggregation-prone A315T variant of TDP-43 shows a very high FRET efficiency of ~0.9 (Figure 4a), which indicates a collapsed conformation.

A minor comment, if the authors would like to compare the specific activity of different proteins, they should use equal molarities of the different proteins and not equal amounts.

As the Reviewer suggests, we will include experiments at equal molarities in the revision.

- For a one-way ANOVA, the response variable residuals have to be normally distributed. With an n = 3 this cannot be tested. Thus, the quantifications of the results shown in figure 1 and 3 are not reliable.

We thank the Reviewer for their critical comment on the statistical analysis. We would like to clarify that statistically significant differences in aggregation between conditions compared to a control are based on Dunnett’s test. While ANOVA is typically first performed to test for any significant difference in means before performing a post-hoc test, Dunnett’s test is independent and can be performed without ANOVA.

Following the Reviewer’s advice, we carefully re-examined our assumption of normality for this data. It is reasonable to perform Dunnett’s test on a sample size of n = 3, and it is generally safe to assume that data from three independent experiments will be reasonably normally distributed. In support of this, performing Kolmogorov-Smirnov test on our data in Figure 1 showed none of the groups differ significantly from normal distributions with the respective mean and standard deviation (p-values greater than 0.05). Thus, we believe it is reasonable to assume the data are normally distributed, the residuals normally distributed, and our statistical analyses reliable. This analysis will be included in the revision to support the normality assumption.

However, even if we did not assume a normal distribution of our data in Figure 1, we still would have obtained statistically significant differences; If we had relied on a Kruskal-Wallis test as a non-parametric equivalent of ANOVA, thus making no assumption of normality, we would have seen p = 0.005176, a value much lower than our significance level of α = 0.05, indicating sufficient evidence that there is a difference in aggregation among these groups.

- The title is imprecise and overstate the presented data:

'canonical chaperone' suggest that their results are valid for chaperones in general. However, they only tested DNAJA2 in the single -molecule FRET assay. Moreover, HAPA8, another canonical chaperone, obviously had an opposite effect on TDP-43 aggregation (Fig.1). Similarly, they only tested Hero11. Thus, 'canonical chaperone' has to be replaced by 'DNAJA2' and 'a heat-resistant obscure (Hero) protein' by 'Hero11'. Similarly, the term 'conformational modulation' is not as concise one would one expect for the title of a research paper.

We would like to clarify that the Reviewer means HSPA8 (not HAPA8). According to the Reviewer’s suggestion, we will change the title to “DNAJA2 and Hero11 mediate similar conformational extension and aggregation suppression of TDP-43”.

Reviewer #3 (Significance (Required)):

In a recent study (PLosBiol, 2020) the same authors described an interesting class of proteins they call 'Hero'. Based on their analyses in cultured cells and transgenic Drosophila models the authors concluded that 'Hero' proteins protect against protein instability and aggregation. So far, this class of proteins has not been analyzed by independent groups.

In the current manuscript, they mainly confirm their own previous finding that Hero 11 prevents aggregation of TDP-43 and present very few new data that would provide new insights. Specifically, only the FRET assays shown in figure 2 and 4 are really new, which, by the way, could easily be shown in one figure.

We thank the Reviewer for their critical evaluation of our current study. As the Reviewer suggests, we believe our smFRET results provide new insights into how Hero11 and DNAJA2 function. We would like to emphasize that, rather than confirming our previous findings, our current manuscript mainly addresses a critical point that remained unknown in our previous study by investigating the mechanism of how Hero proteins prevent aggregation. Moreover, to our knowledge, this is the first observation of the TDP-43 LCD, with the effect of a pathogenic mutation, at the single-molecule level.

I feel like this may be different from many other papers that we write for our classes, since we always know that we're going to be graded by the teacher/professor, who obviously knows the point of the assignment and understands our perspective of the topic. It's a very good thing to be aware of that we shouldn't just cater to what the professor knows when writing for this class.

This is very true. Not only is PA very beneficial physically but it is equally beneficial for your mental health. It's not just about having big muscles, its about taking care of yourself.

On the one hand I'm not sure I want to keep subscribing to this re: the pretty embarrassing abdication of responsibility above ("Guys, you don't get it, there's no point saying it's bad, it's so popular") but then this chunk is so dead-on and the kind of thing where you feel relieved Someone Said It... 🤔

The implied contrapositives here are so weird. Like, okay, so criticism would be warranted if you thought that criticism... could make it disappear?

Why not just directly take the derivative on the partial sum of the functions and then show that it's diverging? Why are we precisely using the definition on each of the terms in the sum?

Is it a coincidence that the last story also consisted of somebody named the Buzzard (Nooee)? In the last story, the Doctor created this the buzzard (Nooee) to be his assistant in creation and here, the Great Buzzard helped to make mountains. I just think it's cool that both stories have this connection.

I found this passage interesting because in my mind, it relates quite well to the discussion we had in class last Friday in regards to false philosophizing. Socrates in this passage realizes the pitfall that is false philosophy, and curbs his argument. He is willing to accept the fact that in this circumstance, there is no use in argument as the truth or individual opinions will be lost in meaningless argument. Socrates states "It is out of the range of our current thrust to attain the opinions I now hold about it". Again he is basically avoiding a pointless argument for the sake of true philosophy and pursuit of the truth. I just thought it was cool to see the principles of philosophical discussion that we talked about in class reflected in Platos Republic.

I find this passage interesting but hard to follow. Is he claiming there is no way to have knowledge of good without having the knowledge of bad? In the sentence before he claims that we don't have sufficient knowledge of whats good. If this is what he is saying it makes sense to me that it would be impossible to know whats good without having the knowledge of whats bad. I think that this can be related to many things in life when comparing things. It can be compared to a claim about darkness. Without the experience of light we would know no difference meaning that it wouldn't be dark. Same with good and bad, with no bad there can be no good.

One of the most important concepts that I also personally resonate a lot is the idea of feminism for everyone. Like author said, I personally encountered various conversations where feminism was perceived as something that is only relevant to women and could potentially hard or take away something that other genders own. Often time this misconception leads to lack of motivation or wrongly directed hatreds that prohibits society from moving forward. It's important for the public to understand that feminism is not just about women but is related to everyone and it does not mean taking away something from other genders.

NFT "ownership"

The use of a blockchain transaction to link a wallet address with a URL has not been proven to transfer "ownership" (at least in a copyright sense). I suppose there is a sense of ownership in a closed system like as was done with the NBA Top Shot project.

A common misconception with media is that it is purely for the sake of appearance, but in reality it's just another way to communicate. This allows those who consume media to discover hobbies, discover interests, and find communities. Media isn't just a new dance and song that got popular, it can enable exploration of interests that would otherwise be unknown.

I know there are concerns/theories that internet companies are restricting and controlling how people interact through their technology, but I personally don't feel that they have any major influence. This excerpt here is a perfect example of Net-users' tendency to bypass the system or intended uses in order to get what they want. I've witnessed this occurring a lot in my own experience, and in general I feel like it's just in human nature to seek out ways to do this.

many young kids learning on how to speak English they will put some emphasis on a certain letter. When it comes down to writing they spell it how they can pronounce it.

OPERATION JAZZORCIVILIZE

Jazzercize is something my mommy did around the time I was born. It's literally just "jamboree" or some kind of popular women's ((predominantly)) exercize group. They met all over America in the 80's and they wore some funny socks ;) It is the word associated with "changing everybody up" to include the entirety of the capable group of humanity ever living on a rock with religion. It could be bigger than that, but here I've sort of defined it to literally link with significance only the Church of Rome and things that came after it. It is literally what it is, the A.D. timeline. It most likely includes a group of "less than all" who carried things like knowledge and Asimov's Foundation from the Pentagon Technocrat's "Torah guild" ... many thousands of years before the day Christ appears to have been born or died in history.

this is a big deal. I have come to a place in Deseret I associate with a military group that is literally and ((I pray)) responsible for the colonization or the co-colonization of the known galaxy. I believe we have a number of coveted extra-galactic operations aswell, and that they include Soviet and American as well as European operations outside of Deseret. I have come here to prove that Operation's Gunsider, Holocaust and Y are "Information Operations" which is modern NSA-talk for "propaganda designed for a purpose." I do not believe the technologies are real, and it's important to understand I lived through the time others call "the Cold War" and saw with my own eyes videos of rocket's traveling along United States Federal USHWY1 up and down the Eastern Seaboard ... rather than I-95 though it existed because of known and intentional fortifications on that road for equipment so heavy it would crumble bridges. We are in a place where ... London may be the only bridge left in existence after the move from NM to NV .. if you know what it means to lose pillars of Samson in a place like the Holy Temple's heart.

I need this to be taken seriously. If we want to stop moving towards a point where we are going to be angrier with each other than we should be; I need someone in the world with a public company to hire me to build something ... "more public than companies." It starts with software and it ends with codification in the Constitution and beyond. It's a "big deal" this is a revolution bigger than the invention of voting and money; this is big. I need a pay check from a company with that kind of oversight at the very least.

I am open to FTSE, CAC, ASX, DAX, or similar companies on those exchanges to ones listed on the S&P 500 or the DOW. The exchanges listed are not all inclusive, but it means something that I "know what they are" I studied them and we need something at least as big as an entity governed by laws to be listed on "those" ... a private company in Dubai, for instance; is not large enough to do this properly.

I came to the particular city that I am in to prove that Operation Gunsider and Project Y were "ruce's" ... #informationoperations that were part of a grand design that literally includes the whole of "Majestic" which is another key word in the research path to where we are going.

It includes more than that, much more--on this song and who you all are. Closer to God, than ... "most." Closer to me, too. It includes the entirety of the KJV and "all of religion as seen through the eyes of the Christ." It includes missions to teach Latin and English and "reading and writing" to the entirety of humanity; and at this point we have to pause and really understand what is going on.

We have an "Adam code" that is something like ##305407; its a word that includes research and development on what to do when the "everybody up?" generation fails or succeeds; it is a way to get "way more voters involved" in a place where we once had a world that could have have saved its past, clearly do to the inability to see it at all, much less travel to it. Today I need basic computer knowledge and general concepts of things like terraforming and physics added to the list of things that are "required to vote" in the Constitutional realms considerably here perhaps the somewhere between the third and the fifth Houses of the Capitol of the United States.

I would like to make the entirety of the past, the entire A.D. timeline and perhaps something bigger than that "intelligent, omni-important, and oligarchical rulers of themselves." I would like to see Technocracy flourish as a word that literally involves the Halo of Cortana and its connection to "how we vote." I wrote for a brief time on how to engage an audience in something called "subconscious voting" and how to connect "checking your vote" to the only Labor the Party has to accomplish on it's WED/hour of "required work left once we are done with ... automation, roboticization and the revolution colloquially associated with Bolshevik and Ford.

I need us to think today what kind of classes we would need to put together for ... "members of the midieval civilization of lore" ... people who coincided in cities with Cathedral's or Mission's that match the architecture associated with the One True Church--whether it be the source of the Spanish Armada or the Eastern Orthodox Byzantine Fault.

What kind of classes are required to understand things like "game theory" and "solar fusion" and also the inner workings of Heaven enough to intelligently vote on whether or not another group of people, for instance, is "educated enough to be considered a peer, or a citizen."

UK Home Office U.S. Immigration and Customs Enforcement (ICE) Immigration And Nationality Services (IANS)

It's interesting to "see this answer" INS has aided me here in assuming you understand that acronym has changed from the historical truth, as we consider "naturalization" and what kind of history/nation

https://lnkd.in/dvUKdGZf

How painfully true this may have been in 1899, it's now much worse in 2023!

Specialization of knowledge tends to fit the lifeways of the people who hold and maintain it. Changing lifeways means one must lose one or more domains and begin using or curating different domains of knowledge.

In a global world of specialization, humans who specialize are forced to rely more heavily on the experience and veracity of those around them who have also specialized. One may be able to have a Ph.D. in astrophysics, but their knowledge of the state of the art in anthropology or economic policy may be therefore utterly undeveloped. As a result they will need to rely on the knowledge and help of others in maintaining those domains.

This knowledge specialization means that politicians will need to be more open about what they think and say, yet instead politicians seem to be some of the least knowledge about almost anything.

This is just the start of a somewhat well-formed thesis I've developed elsewhere, but not previously written out... more to come...

THE MONOLOGUE CONTINUES, UNDERSTAND ME.

It doesn’t take much “thought” to see these star charts–our Astrological road maps to ‘wisdom of the Ancients’ might actually be something closer to road maps than I could have previously fathomed–let alone imagined. I’m staring at “Monoceros” and seeing it’s definately connected to “the kissing disease” and to Eros and to Cupid–and seeing … this one not for the first time that character linked to Orion and to the “Speare” of Sagittarius.

I’ve commented … ‘on the show in my head’ that it seems the entirety of the Milky Way might be something like our world … it could be a microcosmic map to something much larger–it could be the seed of “galaxies” in this place that might very well be the “thing” that connects the end and the beginning; rather than the beginning and the end as I once … commented was the original “glyph” i read in the letter “H.”

--

someone commented on the site, they posted a picture from my earlier work ... the "WHY?" one that depicted starvation and crucifixion and no press.

in related news the LA Times spoke, it echoed "and he's thinking about his own mortality" seconds after the event ... the self questioning of whether or not I have any "kind of divinity" in me at all. Dana too, has echoed back that there's a message I am missing.

I forgot to mention the press junket's every day, that was a kind of speech that you can't really "feel" in the rest of the articles that talk about things like walls and "something missing." Acosta may have written more on the reason, but I wasn't able to find out exactly what it was they were saying.

Lately science has started talking about things ... "going haywire" I'm here with "IGNITION" and LLNL on my mind, and also the power of star creation and destruction connecting to the Pentagon and Deuteronomy and ... deuterium and fusion and fission and the Vooshan Young.

I imagine some people read through this feed, the one I'm posting to. I've just posted this:

This is what I have to do to ensure things "aren't vanished upon death" or worse, while I'm still typing about them in the very same day. The post was "vanished" from github, and I mean; it's here for the protection of not just veracity and Americana, but Hypothesis itself.

• https://www.publishthis.email/fw-i-am-not-sure-how-to-break-this-to-you-...-i-feel-in-all-honesty-that-i-am-a-true-ameri-Hkm-2tuos

• https://ipfs.2read.net/ipfs/QmXCxWmuGF48LezyiGLFRzcRjjMzLCDZnRaYajFajvndGv/

Edit: the Github posting didn't disappear it was just marked as closed; along with an explanation "about hearing and answering before."

There's a fortune in building the thing; and putting it together; it's basically "the next big thing" a news and "what's popular on the web" aggregator that has advanced search and friendship capabilities. Integrating with LinkedIn and Facebook and Twitter and ... "most of all abstracting those things with an identity system that ties directly to IPFS and strong identity validation and authentication--

Out of the Ether ..

PS: noting that this link ties to the root directory of fromthemachine dot org and it's already an aged post about this very thing, building something with Ethereum that "is glaringly missing" .. including "find your friends" integration that doesn't require them to send you a long random string of letters and numbers--just being "already connected". Creating a Wallet/Address system that ties together the social networks and "crypto trading" is glaringly missing, and we can already see applications like Snapchat and Tik-Tok that have done it in a way that creates a significant growth factor ... I mean it almost instantly made Tik Tok as big as Instagram.

This tool is a key; being able to see "what everyone is saying about the front page news on the outlets you read every day" in a news feed and interface similar to Facebook's ... "I think that's a game changer for me; I would use it."

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Reply to the reviewers

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

In this manuscript, the authors report dorsomedial hypothalamus-specific PR-domain containing protein 13-knockout (DMH-Prdm13-KO) mice recapitulated age-associated sleep alterations such as sleep fragmentation and increased sleep attempts during sleep deprivation (SD). These phenotypes were further exacerbated during aging, with increased adiposity and decreased physical activity, resulting in shortened lifespan. Moreover, overexpression of Prdm13 in the DMH ameliorated sleep fragmentation and excessive sleepiness during SD in old mice. They identified maintaining Prdm13 signaling in the DMH might play an important role to control sleep-wake patterns during aging. These findings are interesting and novel and the evidence they provided looks solid.*

We deeply appreciate that this reviewer found our findings are interesting and the evidence solid.

*Major comments 1. The author spent a lot of words on Sirt1 in the introduction. Since Sirt1 regulates Prdm13, is there a link between the two in age-related sleep changes? If so, you can add some results and discussion. *

Thank you very much for raising this important issue. Our previous study demonstrated that a mouse model with high hypothalamic Sirt1 activity displays reduced number of transitions between wakefulness and NREM sleep (reference # 15), revealing that hypothalamic Sirt1, as well as Prdm13, is involved in the regulation of sleep fragmentation.However, sleep propensity was not altered in Sirt1-overexpressing transgenic mice (reference #13) and DMH-Prdm13-KO mice (Fig. 1). Based on these findings, we added the following sentence in the Results.

On page 11, line 267-274

"...... Similarly, a mouse model with high hypothalamic Sirt1 activity displays reduced number of transitions between wakefulness and NREM sleep15, revealing that hypothalamic Sirt1, as well as Prdm13, is involved in the regulation of sleep fragmentation. Sleep propensity was not altered in Sirt1-overexpressing transgenic mice13. Given that the level of hypothalamic Prdm13 and its function decline with age, age-associated sleep fragmentation could be promoted through the reduction of Prdm13/Sirt1 signaling in the DMH, but sleep propensity might be increased by other mechanisms. "

• In Figure 2e, the author describes n=7-8 in the figure legend, but why do both groups on the column show eight data? Is there something wrong with the statistics? Please check the statistics in the article carefully. *

We corrected n=7-8 to n=8 in the figure legend of Fig. 2e.

• DMH is known as one of the major outputs of hypothalamus circadian system and is involved in the circadian regulation of sleep-wakefulness (J.Neurosci. 23, 10691-10702 ; Nat Neurosci 4:732-738). Does Prdm13 correlate with circadian rhythms? The author can add relevant content to the discussion *

As per this reviewer's suggestion, we added the following sentence in the Discussion on page 20, line 500-508,

"For instance, it would be of great interest to elucidate whether Prdm13 signaling in the DMH contributes to regulate the circadian system, since the DMH is known to be involved in the regulation of several circadian behaviors32,33. Although DMH-Prdm13-KO mice did not display abnormal period length compared with controls, further studies are needed to address this possibility."

*Minor comments 1. The immunohistochemical diagram in the paper is not representative enough, as shown in FIG. 2b and c. *

We apologize that our presentation in Figs. 2a-c was confusing. Although Fig. 2b shows the numbers of cFos cells in the entire region of the DMH (summed up from three DMH regions), the images in Fig. 2c are from one of DMH regions for each condition. To avoid confusion, we revised the legend of Figs. 2a-c and the manuscript in the Results as follows:

-In the figure legend of Figs. 2a-c

"a, Total numbers of cFos+ cells ......... b,c, Images of DMH sections at bregma -1.67 mm ......."

-In the Results on page 7, line 180

"...... the hypothalamus, the DMH (summed up from bregma -1.67 to -1.91mm) showed a greater number of cFos+ cells during SD compared to SD-Cont (Fig. 2a-c, Supplementary Fig. 2a)..... "

• In FIG. 5h, the authors showed that the effect of overexpression of Prdm13 was very obvious, but the expression range of the virus after injection was lacking. Is there a fluorescent gene such as GFP on the virus to directly see the expression of the virus in the brain? *

Unfortunately, we do not hold extra samples to check the distribution of the virus after injection. However, we have established sufficient injection technique to target the DMH using the lentivirus system that we used in this study (Satoh et al Cell Metab 2013).

• Were mice singly housed or housed in groups? *

Most of the mice were housed in groups, except for the DR study. We added this information in the section Animal models of the Methods on page 41, line 935

".....RIKEN BRC. Most of the mice were housed in groups, except for the DR study. For the DR study ,..... "

• The part of sleep analysis needs to be further refined. How can REM and NREM in mice be distinguished and according to what criteria? *

We added the criteria to define NREM and REM in the section Sleep analysis of the Methods on page 42, line 995-998.

".......with visual examination. EEG periods dominated by higher amplitude delta wave activity with nuchal muscle atonia were scored as NREM sleep epochs. REM sleep consisted of periods of semi-uniform theta activity EEG with muscle atonia and/or muscle atonia with brief myoclonic twitches. Score was blinded ......"

• The authors may consider adding more recent literature related to DMH and sleep, such as DOI: 10.1093/cercor/bhac258 * We incorporated this reference to the following sentence in the section Results on page 8, line 194.

"........ Although DMH neurons are linked to sleep21, aging and longevity .... "

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

Summary: In this study, Tsuji et al. demonstrate that Prdm13 signaling is involved in the regulation of sleep-wake pattern. They also identified Prdm13 as a transcription factor in the DMH neurons. Major comments: 1. The evidence presented in Fig. 1 of age-related sleep fragmentation is potentially problematic. Although many previous studies have demonstrated fragmented sleep, especially fragmentation of NREM sleep, in aged mice compared to young mice, the data here do not suggest NREM fragmentation, because no change in the NREM bout duration was found. REM, on the other hand, may indeed have fragmentation during the dark phase, but REM only takes a small portion of the total sleep. Therefore, the conclusion that sleep is fragmented in old mice is not fully supported by Fig. 1. I noticed that the authors used 4-6 months old mice as the young group. Mice of this age can hardly be called "young". The females even start to have lowered fertility. This might be one of the reasons for the discrepancy between this and other studies. Repeating these experiments (and others involving the young group) with mice of more appropriate age (usually 2-3 months old) is recommended. Nonetheless, aging-caused sleep change is not new knowledge and has been reported repeatedly. This part of the results should be in the supplementary figures. *

We deeply appreciate this reviewer's comment. In accordance with this reviewer's suggestion, we carefully reconsidered the age of young mice. Most of published studies used mice at 2 to 4 months of age as the young group [2 to 4-month-old (7 studies), 4.6-month-old (1 study), 6-month-old (1 study), 2 to 6-month-old (1 study)]. Thus, to strictly use mice at 3-4 months of age as the young group, we excluded data of one cohort using mice at 6 months of age (2 mice each age group). Consistent with many previous studies, our revised data demonstrated that sleep fragmentation during NREM sleep is predominantly observed in old mice compared with young mice, particularly during the dark period. Based on these new results, we revised Fig.1, Suppl Fig.1, and all description related to Fig. 1 (manuscript on page 5-7, line 103-171). We would like to keep Fig. 1 as it is. Since most of the previous studies used males but not females, data from females are still lacking in the field (Campos-Beltran and Marshall, Pflugers. Arch., 473:841-851, 2021).

• The sleep phenotypes in aged mice and in Prdm13-KO mice are clearly distinct from each other. In the old mice (Fig. 1), REM sleep is fragmented but the total amount remains unchanged, and NREM sleep is increased (both bout number and total amount), indicating there may be more REM-to-NREM transitions, which the authors should quantify. However, Fig. 3 shows in Prdm13-KO mice, there is no REM fragmentation. In fact, it even seems to stabilize REM. But NREM duration is shorted, and no change in the total NREM or REM sleep time. These results suggest that the sleep alterations caused by aging and Prdm13-KO might have some overlap but are mostly in parallel and likely through different mechanisms. Therefore, the rationale of connecting Prdm13 signaling to aging-caused sleep changes is questionable. Is there a developmental change of Prdm13 expression in DMH between young and old mice? The authors also showed that Prdm13-KO in old mice caused decrease in NREM duration but has no effect on REM sleep, but in normal old mice, it is REM, but not NREM that has a defect. Prdm13 overexpression also only mildly decreased NREM bout number without affecting the episode duration of either NREM or REM, which can hardly be interpreted as "ameliorating sleep fragmentation". To me, all these results just suggest parallel actions of Prdm13 and aging on sleep, with Prdm13 mostly affecting NREM sleep but aging mostly impairing REM sleep. *

We deeply appreciate this reviewer's keen eyes. We carefully reassessed REM sleep data in Fig. 3. The revised data showed that whereas the duration of NREM episodes in DMH-Prdm13-KO mice during the dark period were significantly shorter compared to control group, the duration of REM episodes in the KO mice was not significantly altered. Therefore, after revising Fig. 1 and 3, our results showed that both aging and Prdm13-KO similarly affect the duration of NREM sleep episodes. These results suggest that sleep fragmentation, in particular, during NREM sleep, is commonly observed in old mice and DMH-Prdm13-KO mice. In addition to sleep fragmentation during NREM sleep, excessive sleepiness during SD was also commonly observed in old mice and DMH-Prdm13-KO mice. On the other hand, the effect of aging and Prdm13-KO on sleep propensity was distinct from each other. We think that age-associated sleep fragmentation could be promoted through Prdm13 signaling in the DMH, but sleep propensity might be increased by other mechanisms. We described these results and possibilities in the Results, and revised the Abstract as follows:

On page 11, line 264-274

"activity in DMH-Prdm13-KO mice (Fig. 3h, Supplementary Fig. 3f-h). Together, sleep fragmentation during NREM sleep and excessive sleepiness during SD are commonly observed in old mice and DMH-Prdm13-KO mice, but the effects of aging and Prdm13-KO on sleep propensity were distinct from each other.............. Given that the level of hypothalamic Prdm13 and its function decline with age16, age-associated sleep fragmentation could be promoted through the reduction of Prdm13/Sirt1 signaling in the DMH, but sleep propensity might be increased by other mechanisms."

On page 2, line 45-46

"Dietary restriction (DR), a well-known anti-aging intervention in diverse organisms, ameliorated age-associated sleep fragmentation and increased sleep attempts during SD, whereas these effects of DR were abrogated in DMH-Prdm13-KO mice."

As this reviewer pointed out, the effect of Prdm13 overexpression on NREM sleep fragmentation seems to be moderate, but we still observed effects on excessive sleepiness during SD. Thus, we revised the manuscript related to Prdm13-overexpression study in the Abstract and Results as follows:

On page 2, line 47-48

"Moreover, overexpression of Prdm13 in the DMH ameliorated sleep fragmentation and excessive sleepiness during SD in old mice."

On page 16, line 387-401

"Overexpression of Prdm13 in the DMH partially affects age-associated sleep alterations

...... (Fig. 5h). The number of wakefulness and NREM sleep episodes in old Prdm13-OE mice were significantly lower, whereas duration of wakefulness in old Prdm13-OE mice tended to be longer than old control mice during the dark period with no change in the duration of NREM episodes (Fig. 5i,j). Intriguingly, .... Thus, the restoration of Prdm13 signaling in the DMH partially rescue age-associated sleep alterations, but its effect on sleep fragmentation is moderate."

• What is the control manipulation for sleep deprivation? The authors need to clarify this in the Methods. Also, sleep deprivation has confounding effects including but not limited to stress, food deprivation (since food was removed during SD), human experimenter (since a gentle-touch method was used). Without proper controls for these variables, the authors should avoid concluding that the changes they saw at cellular level are due to sleep loss. *

Thank you very much for this suggestion. We added detailed description for AL-SD (the control manipulation for SD) in the section SD study of the Materials as follows:

On page 42-43, line 1014-1020

"Mice for control manipulation (AL-SD) were also individually housed prior to the experiment without SD and food removal. We checked the level of blood glucose in the SD study, and found that the level of blood glucose was indistinguishable between SD and AL-SD groups (126±6 and 131±4 mg/dL, respectively), revealing that nutritional status is equal between these two groups."

Identification of Prdm13+ cells using neuronal markers should be performed in addition to electrophysiological characterizations.

We performed immunofluorescence using anti-MAP2 antibody and confirmed that most Prdm13+ cells are neurons. We added this new result in Suppl Fig. 2g.

• Figs. 6 and 7 seem very disconnected from the main story. Identification of Prdm13 as a transcription factor is potentially interesting, but how does it account for its role in affecting sleep? The criteria of picking Cck, Grp and Pmch out of other candidate genes potentially regulated by Prdm13 and the rationale to investigate these genes seem unclear. More importantly, no evidence was shown regarding how Cck/Grp *

Base on RNA-sequencing using DMH samples from DMH-Prdm13-KO and control mice, we got several candidate genes as downstream genes of Prdm13. After validating the candidate genes by qRT-PCR, Cck, Grp and Pmch were detected as top-hit genes. We thus further assessed these three genes in this study. Our result showed that Cckexpression in the hypothalamus significantly declines with age. Based on other literature, hypothalamic Cck seems to be involved in sleep control. Therefore, it is conceivable that Prdm13 controls age-associated sleep alterations via modulating Cck expression. However, as this reviewer pointed out, we are still lacking the evidence showing the role of Prdm13/Cck axis in age-associated sleep alterations. We now clearly described the limitation of our study in the Discussion on page 23, line 560-562.

"However, the detailed molecular mechanisms by which Prdm13 in the DMH regulates age-associated sleep fragmentation and excessive sleepiness during SD still need to be elucidated in future study. "

*Minor comments: 1. Please note on the images of Fig. 2d what the green fluorescence was. It is very confusing as is, given that it's surrounded by quantifications of c-fos in the figure. *

The label "Prdm13" was added in Fig. 2d.

Please note use a different color for Prdm13 in several figure images (e.g., Fig. 2f, g, 7a,d, and Supplementary 2c). Yellow usually means overlap of red and green.

Since we have four-color images in Fig. 7, we consistently used yellow for Prdm13 throughout the main figures of the paper. At this moment, we would like to keep the current version of images, but we will revise images if the editor of affiliate journal requests this revision.

• Please note the statistic test results on power spectrum graphs. *

We added the statistic test results on power spectrum graphs in Figs. 1d, 4c, and 5d.

• Inconsistency between the graphs in Fig. 3d and the description in the text. Fig. 3d suggests no change in Wake episode duration, significant decrease in Dark phase NREM and significant increase in Dark phase REM, whereas lines 224-227 in the main text state "The duration of wakefulness episodes ... was significantly shorter than control mice during the light period, and the duration of NREM sleep episodes ...was significantly longer ... during the dark period (Fig. 3d)". Which one is correct? Please check. *

We apologize for this typo and unclear description. We revised the sentence regarding Fig. 3d as follows:

On page 10, line 242-246

"The duration of wakefulness episodes in DMH-Prdm13-KO mice was significantly shorter than control mice during the light period between ZT0 to ZT2. The duration of NREM sleep episodes in DMH-Prdm13-KO mice was significantly shorter than control mice during the dark period (Fig. 3d). These results indicate that DMH-Prdm13-KO mice showed mild sleep fragmentation compared with control mice."

• Fig. 5f, Y-axis title should be EEG SWA. * We corrected it.

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Referee #2

Evidence, reproducibility and clarity

Summary:

In this study, Tsuji et al. demonstrate that Prdm13 signaling is involved in the regulation of sleep-wake pattern. They also identified Prdm13 as a transcription factor in the DMH neurons.

Major comments:

1. The evidence presented in Fig. 1 of age-related sleep fragmentation is potentially problematic. Although many previous studies have demonstrated fragmented sleep, especially fragmentation of NREM sleep, in aged mice compared to young mice, the data here do not suggest NREM fragmentation, because no change in the NREM bout duration was found. REM, on the other hand, may indeed have fragmentation during the dark phase, but REM only takes a small portion of the total sleep. Therefore, the conclusion that sleep is fragmented in old mice is not fully supported by Fig. 1. I noticed that the authors used 4-6 months old mice as the young group. Mice of this age can hardly be called "young". The females even start to have lowered fertility. This might be one of the reasons for the discrepancy between this and other studies. Repeating these experiments (and others involving the young group) with mice of more appropriate age (usually 2-3 months old) is recommended. Nonetheless, aging-caused sleep change is not new knowledge and has been reported repeatedly. This part of the results should be in the supplementary figures.
2. The sleep phenotypes in aged mice and in Prdm13-KO mice are clearly distinct from each other. In the old mice (Fig. 1), REM sleep is fragmented but the total amount remains unchanged, and NREM sleep is increased (both bout number and total amount), indicating there may be more REM-to-NREM transitions, which the authors should quantify. However, Fig. 3 shows in Prdm13-KO mice, there is no REM fragmentation. In fact, it even seems to stabilize REM. But NREM duration is shorted, and no change in the total NREM or REM sleep time. These results suggest that the sleep alterations caused by aging and Prdm13-KO might have some overlap but are mostly in parallel and likely through different mechanisms. Therefore, the rationale of connecting Prdm13 signaling to aging-caused sleep changes is questionable. Is there a developmental change of Prdm13 expression in DMH between young and old mice? The authors also showed that Prdm13-KO in old mice caused decrease in NREM duration but has no effect on REM sleep, but in normal old mice, it is REM, but not NREM that has a defect. Prdm13 overexpression also only mildly decreased NREM bout number without affecting the episode duration of either NREM or REM, which can hardly be interpreted as "ameliorating sleep fragmentation". To me, all these results just suggest parallel actions of Prdm13 and aging on sleep, with Prdm13 mostly affecting NREM sleep but aging mostly impairing REM sleep.
3. What is the control manipulation for sleep deprivation? The authors need to clarify this in the Methods. Also, sleep deprivation has confounding effects including but not limited to stress, food deprivation (since food was removed during SD), human experimenter (since a gentle-touch method was used). Without proper controls for these variables, the authors should avoid concluding that the changes they saw at cellular level are due to sleep loss.
4. Identification of Prdm13+ cells using neuronal markers should be performed in addition to electrophysiological characterizations.
5. Figs. 6 and 7 seem very disconnected from the main story. Identification of Prdm13 as a transcription factor is potentially interesting, but how does it account for its role in affecting sleep? The criteria of picking Cck, Grp and Pmch out of other candidate genes potentially regulated by Prdm13 and the rationale to investigate these genes seem unclear. More importantly, no evidence was shown regarding how Cck/Grp

Minor comments:

1. Please note on the images of Fig. 2d what the green fluorescence was. It is very confusing as is, given that it's surrounded by quantifications of c-fos in the figure.
2. Please note use a different color for Prdm13 in several figure images (e.g., Fig. 2f, g, 7a,d, and Supplementary 2c). Yellow usually means overlap of red and green.
3. Please note the statistic test results on power spectrum graphs.
4. Inconsistency between the graphs in Fig. 3d and the description in the text. Fig. 3d suggests no change in Wake episode duration, significant decrease in Dark phase NREM and significant increase in Dark phase REM, whereas lines 224-227 in the main text state "The duration of wakefulness episodes ... was significantly shorter than control mice during the light period, and the duration of NREM sleep episodes ...was significantly longer ... during the dark period (Fig. 3d)". Which one is correct? Please check.
5. Fig. 5f, Y-axis title should be EEG SWA.

Significance

General assessment: There are discrepancies in the evidence presented, and the results were poorly organized. I found the main conclusions of the manuscript not very convincing and the causal links among Prdm13, aging and sleep alterations weak.

Advance: The identification of DMH Prdm13 in regulating sleep is potentially interesting and of some novelty, but the underlying mechanism and its causal relationship with aging were not clearly elucidated.

Audience: basic research

My expertise: sleep, social behavior, hypothalamus, dopamine neuromodulation, neural circuit development, synaptic organization.

It is interesting how writing is everywhere, and everyone does it every single day, but lots of people say that they are not good at or do not enjoy writing.

i really like the idea that he carries a little notebook everywhere and he just writes what comes to mind.

Trying to be someone else will never work. Just trusting your creativity creates a personal and authentic product. It's hard to recreate things that you didn't create authentically, so focus on writing what you care about and be unique. Makes writing hellava lot easier when you just let your creativity flow freely.

I have had very similar thoughts to Anya on the overall concept of writing. Growing up it was kind of presented to us as a very structured chore that we had to do. We were taught how to write "perfectly" with the correct number of adjectives, verbs etc. I feel like we were never given a true chance to enjoy writing and express ourselves through it. I hope that education can shift its way of teaching to make it more exciting and creative for kids growing up.

The words densely textured stood out to me. This to me shows how writing doesn't fit into one box or how it's not a one size fits all. There are many styles, topics, reasons for writing. What I highlighted also talks about how its fairly invisible. Of course, theres famous authors but on the other spectrum theres people who write for hobbies or just for a paycheck.

This explanation seems very much like something that someone would roll their eyes at and say, "Well, technically I guess..." but there's no sense in saying something like that is true just because it's technically not false. I suppose that's why it's a bit of an odd middle ground here

Reminds me of "be polarizing" from Mark Manson's book, Models

I can vouch that it's a diverse community. However, in Anaheim I barely met any hispanic peers. This just means that I need to look more if it's truly impoverished. The statistics truly shock me though because these cities and area are my routes.

Author Response

Reviewer #1 (Public Review):

The authors devised a new mRNA imaging approach, MASS, and showed that it can be applied to investigate the activation of gene expression and the dynamics of endogenous mRNAs in the epidermis of live C. elegans. The approach is potentially useful, but this manuscript will benefit by addressing the following questions:

We thank the reviewer for spending time reviewing our manuscript and for the insightful comments.

Major comments:

1) In Figure 1-figure supplement 1, the authors claimed that MASS could verify the lamellipodia-localization of beta-actin mRNAs. However, the image showed the opposite of the authors' claim as the concentration of beta-actin mRNA was lower in lamellipodia than the rest of the cytosol. This result disagreed with ref. 17 (Katz, Z.B. et al., Genes and Development, 2012). Hence, the authors cannot make the statement that "MASS can be readily used to image RNA molecules in live cells without affecting RNA subcellular localization". To thoroughly test this notion, the authors should image beta-actin mRNA using MASS and the conventional MS2 system side by side and calculate the polarization index in the same way as shown in Katz, Z.B. et al., Genes and Development, 2012.

We noticed that b-ACTIN mRNAs were less polarized in our image compared to that shown in Katz, Z.B. et al. (Genes and Development, 2012). It is likely due to different cell lines being used. In the previous study, mouse embryonic fibroblasts (MEFs) were used. In our initial experiment, HeLa cells were used. Our data showed b-that ACTIN mRNAs labeled with MASS could be localized to the lamellipodia.

As suggested by the reviewer, we performed new experiments to image b-ACTIN mRNAs using MASS and the conventional MS2 system side by side in NIH3T3 cells, a mouse fibroblast cell line (MEF cells are not available in our lab). We did not find cells with extensively polarized b-ACTIN mRNAs localization, potentially due to different cell lines. We, therefore, did not calculate the polarization index. However, we found that b-ACTIN mRNAs detected by both methods showed a similar localization pattern. These new data suggest that MASS does not affect RNA subcellular localization. We added the new results and updated Figure 1-figure supplement 3.

2) The experiments that validate this new RNA imaging method are not sufficient. The authors need to systematically compare MASS and the MS2 system, including their RNA signal intensity, signal-to-background ratio.

We have systematically compared MASS and the conventional MS2 system, including signal intensity and signal-to-noise ratio, and measured the velocities of mRNA movement. We found that MASS showed a similar signal-to-noise ratio and higher signal intensity to the conventional MS2 system. We have now revised the information in the text on pages 4 and 5, and in Figure 1-figure supplement 4, 5, and 6.

3) In line with this, does beta-actin mRNA display the same behavior as in (Figure 1C-F) when the mRNA was imaged with the MS2 system? The movies do not indicate the type of motility expected of mRNA. For instance, it seems that almost all of the GFP dots, which are presumably single beta-actin mRNAs, stayed stationary over a time course of tens of seconds (Movie 1). This seems to be very different from what has been observed before. It's not clear that the dots are real mRNAs molecules. This further stresses the importance for them to compare their new imaging system with the conventional MS2 application.

We noticed that the mobility of b-ACTIN mRNAs vary in different cells. It is possible that the mobility of mRNAs was regulated in a cell context-dependent manner.

To confirm that the GFP foci detected are real mRNA molecules, we performed MASS combined with single-molecule RNA FISH. We found that MASS detected a similar number of GFP foci compared to the spots detected by smFISH. In addition, the majority (72%) of GFP foci colocalized with the smFISH spots of b-ACTIN-8xMS2 mRNAs. It is reported that not all MS2 stem-loop will be bound by the MCP (Wu et al., Biophysical journal 2012). As only 8xMS2 was used in MASS, it is likely that some mRNAs were not entirely bound by MCP and were not detected. On the other hand, only sixteen probes were used in the smFISH experiment, and it is possible that some mRNAs were miss labeled by smFISH. Therefore, 100% colocalization of MASS foci with the smFISH spots was hard to achieve. Thus these results suggest that GFP dots are real mRNA molecules. We have added the new data in Figure 1, Figure 1-figure supplement 1, and the text on page 3.

We measured the velocity of (b-ACTIN mRNA movement tracked by MASS and the conventional MS2 system. We added this information in Figure 1-figure supplement 5 and to the text on pages 4 and 5. With the conventional MS2 system, we observed similar behavior to those observed by MASS.

4) The authors claimed that a major advantage of MASS is that it has only 8xMS2 stemloops (350 nt) and overcomes "the previous obstacle of the requirement of inserting a long 1,300 nt 24xMS2". This statement lacks experimental support in this manuscript. The authors need to quantitatively compare the genomic tagging efficiency of 8xMS2 and 24xMS2.

It has been reported by several decent studies that the knock-in efficiency decreases dramatically with increasing insert size. For example:

~10-fold decrease of knockin frequency with a 1085 bp compared to a 767 bp insertion of DNA fragment (Extended Data Fig.8. Wang, J. et al. Nature methods, 2022).

~30-fold decrease of knockin frequency with an 1122 bp compared to a 714 bp insertion of DNA fragment (Figure 3 and Table S1. Paix, A. et al. PNAS, 2017).

In this study, we did not directly examine the knock-in efficiency of 8xMS2 and 24xMS2. Based on published data from other laboratories, we assumed that the efficiency of the knock-in of 8xMS2 (350 nt) would be higher than that of 24xMS2 (~1300 nt).

5) MASS has the same strategy as SunRISER (Guo, Y. & Lee, R.E.C., Cell Reports Methods, 2022). Both methods use Suntag to amplify signals of MS2- or PP7-tagged RNA. The authors need to elaborate the discussions and describe the similarities and differences of the two studies. In particular, the Guo paper needs to be properly referenced.

We have cited the paper and discussed the similarities and differences between our method and the SunRISER (page 7). Taking both studies together, Guo and we demonstrated that it is an efficient strategy to combine the MS2 system and the Suntag system as a signal amplifier for long-term and endogenous mRNA imaging in live cells.

6) In Guo, Y. & Lee, R.E.C., Cell Reports Methods, 2022, they showed that 8XPP7 with 24XSunTag configuration led to fewer mRNA per cell (Figure 5B of the Cell Reports Methods paper). Does MASS, which has 8xMS2 with 24xSunTag, similarly lead to few mRNAs? The authors should compare the number of mRNAs detected by MASS and the conventional MS2, or by FISH.

We compared the number of mRNAs detected by MASS and by smFISH. We performed MASS combined with single-molecule RNA FISH and found that MASS detected a similar number of GFP foci compared to the spots detected by smFISH.

In addition, the majority (72%) of GFP foci colocalized with the smFISH spots of b-ACTIN8xMS2 mRNAs. It is reported that not all MS2 stem-loop will be bound by the MCP. As only 8xMS2 was used in MASS, it is likely that some mRNAs were not entirely bound by MCP and were not detected. On the other hand, only sixteen probes were used in the smFISH experiment, and it is possible that some mRNAs were miss labeled by smFISH. Therefore, 100% colocalization of MASS foci with the smFISH spots was hard to achieve. These data indicated that MASS could label the majority of mRNAs from a specific gene in live cells.

We have added the new data in Figure 1, Figure 1-figure supplement 1, and the text on page 3.

Reviewer #2 (Public Review):

Hu et al. developed a new reagent to enhance single mRNA imaging in live cells and animal tissues. They combined an MS2-based RNA imaging technique and a Suntag system to further amplify the signal of single mRNA molecules. They used 8xMS2 stem-loops instead of the widely-used 24xMS2 stem-loops and then amplified the signal by fusing a 24xSuntag array to an MS2 coat protein (MCP). While a typical 24xMS2 approach can label a single mRNA with 48 GFPs, this technique can label a single mRNA with 384 GFPs, providing an 8-fold higher signal. Such high amplification allowed the authors to image endogenous mRNA in the epidermis of live C. elegans. While a similar approach combining PP7 and Suntag or Moontag has been published, this paper demonstrated imaging endogenous mRNA in live animals. Data mostly support the main conclusions of this paper, but some aspects of data analysis and interpretation need to be clarified and extended.

Strengths:

Because the authors further amplified the signal of single mRNA, this technique can be beneficial for mRNA imaging in live animal tissues where light scattering and absorption significantly reduce the signal. In addition, the size of an MS2 repeat cassette can be reduced to 8, which will make it easier to insert into an endogenous gene. Also, the MCP24xSuntag and scFv-sfGFP constructs can be expressed in previously developed 24xMS2 knock-in animal models to image single mRNAs in live tissues more easily.

The authors performed control experiments by omitting each one of the four elements of the system: MS2, MCP, 24xSuntag, and scFV. These control data confirm that the observed GFP foci are the labeled mRNAs rather than any artifacts or GFP aggregates. And the constructs were tested in two model systems: HeLa cells and the epidermis of C. elegans. These data demonstrate that the technique may be used across different species.

We thank the reviewer for spending time reviewing our manuscript and for the insightful comments.

Weaknesses:

Although the paper has strength in providing potentially useful reagents, there are some weaknesses in their approach.

Each MCP-24xSunTag is labeled with 24 GFPs, providing enough signal to be visualized as a single spot. Although the authors showed an image of a control experiment without MS2 in Figure 1B, the authors should at least mention this potential problem and discuss how to distinguish mRNA from MCP tagged with many GFPs. MCP-24xSunTag labeled with 24 GFPs may diffuse more rapidly than the labeled mRNA. Depending on the exposure time, they may appear as single particles or smeared background, but it will certainly increase the background noise. Such trade-offs should be discussed along with the advantage of this method.

With MCP-24xSuntag, in theory, there will be up to 24 GFP molecules tethered to one MCP molecule, which may lead to the formation of GFP puncta. However, under our imaging conditions (100 ms to 500 ms) with a spinning disk confocal microscopy, puncta of MCP24xSuntag were not detected. As the reviewer suggested, it might be because MCP24xSuntag is diffusing too fast to be detected as a spot.

For the signal-to-noise ratio, we did more experiments and analyses. We imaged overexpressed b-ACTIN mRNAs using the conventional 24xMS2 system or MASS with different repeats of Suntag arrays (MCP-24xSuntag, MCP-12xSuntag, MCP-6xSuntag). For the conventional 24xMS2 system, we followed the previous protocol that added a nuclear localization signal (NLS) to MCP, and b-ACTIN mRNAs were nicely detected with a signal-to-noise ratio of 1.21.

We found that MASS showed a comparable or better signal-to-noise ratio than the conventional 24xMS2 system. (MASS with MCP-24xSuntag: 1.79, MASS with MCP12xSuntag: 1.48, MASS with MCP-6xSuntag: 1.42). These data indicate that using Suntag as a signal amplifier did not increase background noise.

Also, more quantitative image analysis would be helpful to improve the manuscript. For instance, the authors can measure the intensity of each GFP foci, show an intensity histogram, and provide some criteria to determine whether it is an MCP-24xSuntag, a single mRNA, or a transcription site. For example, it is unclear if the GFP spots in Figure 2D are transcription sites or mRNA granules.

Under our imaging conditions, MCP-24xSuntag was not detected as GFP foci.

We performed MASS combined with single-molecule RNA FISH and found that MASS detected a similar number of GFP foci compared to the spots detected by smFISH.

In addition, the majority (72%) of GFP foci colocalized with the smFISH spots of b-ACTIN8xMS2 mRNAs. It is reported that not all MS2 stem-loop will be bound by the MCP. As only 8xMS2 was used in MASS, it is likely that some mRNAs were not entirely bound by MCP and were not detected. On the other hand, only sixteen probes were used in the smFISH experiment, and it is possible that some mRNAs were miss labeled by smFISH. Therefore, 100% colocalization of MASS foci with the smFISH spots was hard to achieve. These data indicated that MASS could label the majority of mRNAs from a specific gene in live cells.

We have added the new data in Figure 1, Figure 1-figure supplement 1, and the text on page 3.

The GFP spots in Figure 2D are not transcription sites, as they were localized in the cytoplasm, not in the nucleus. We imaged exogenous BFP-8xMS2 mRNAs in the epidermis of C. elegans and found that the size of the GFP foci of endogenous C42D4.38xMS2 mRNAs is larger than that of BFP-8xMS2 mRNAs. Those data suggest that the GFP spots in Figure 2D (C42D4.3-8xMS2 mRNA) are mRNA granules. We added those new data in Figure 2-figure supplement 5 and the text on page 7.

Another concern is that the heavier labeling with 24xSuntag may alter the dynamics of single mRNA. Therefore, it would be desirable to perform a control experiment to compare the diffusion coefficient of mRNAs when they are labeled with MCP-GFP vs MCP- 24xSuntag+scFv-sfGFP.

We thank the reviewer for raising this critical issue. We have performed live imaging of bACTIN mRNA using the conventional 24xMS2 system or MASS with different lengths of Suntag arrays (MCP-24xSuntag, MCP-12xSuntag, MCP-6xSuntag). We then measured the velocity of mRNA movement in each imaging condition. We found that compared to the conventional 24xMS2 system, mRNA labeled with MCP-24xSuntag or by MCP-12xSuntag showed a smaller velocity, indicating that heavier labeling affected mRNA movement speed.<br /> In contrast, we found that mRNAs labeled with MCP-6xSuntag showed a similar velocity to that tagged with the conventional 24xMS2 system. Those data pointed out that when MASS is used to measure the speed of mRNA movement, a short Suntag array (MCP6xSuntag) should be used. We added those new data in Figure 1-figure supplement 5 and to the text on pages 4, 5.

The authors could briefly explain about the genes c42d4.3 and mai-1. Why were these specific genes chosen to study gene expression upon wound healing? Did the authors find any difference in the dynamics of gene expression between these two genes?

The function of C42D4.3 and mai-1 is currently not known. Through mRNA deep sequencing, It has been shown that the expression level of C42D4.3 and mai-1 was quickly increased after wounding of the epidermis of C. elegans. We, therefore, choose those two mRNAs for imaging. We added more information about C42D4.3 and mai-1 to the text on page 6.

We observed similar dynamics of gene expression between C42D4.3 and mai-1 (Video 7 ,8, 9).

Reviewer #3 (Public Review):

It is a brilliant idea to combine the MS2-MCP system with Suntag. As the authors stated, it reduces the copies of the MS2 stem loops, which can create challenges during cloning process. The Suntag system can easily amplify the signal by several to tens of folds to boost the signal for live RNA tagging. One of the best ways to claim that MASS works better than the MS2 system by itself is to compare their signal-to-noise ratios (SNRs) within the same model system, such as HeLa cells or the C. elegans epidermis. Because the authors' main argument is that they made an improvement in live RNA tagging method, it is necessary to compare it with other methods side-by-side. The authors claim that MASS can significantly improves the efficiency of CRISPR by reducing the size of the insert, it still requires knocking in several transgenes, which can be even more challenging in some model systems where there are not many selection markers are available. Another possible issue is that the bulky, heavy tagging (384 scFv-sfGFP along with 24xSuntag) can affect the mobility or stability of the target mRNAs. If it also tags preprocessed RNA in the nucleus, it may affect the RNA processing and nuclear export. A few experiments to address these possibilities will strengthen the authors' arguments. I am proposing some experiments below in detailed comments.

We thank the reviewer for spending time reviewing our manuscript and for the insightful comments.

1) For the experiments with HeLa cells, it is not clear whether the authors used one focal plane or the whole z-stack for their assessment of mRNA kinetics, such as fusion, fission, and anchoring. If it was from one z-plane, it was possible that many mRNAs move along the z-axis of the images to assume kinetics. If the kinetics is true, is it expected by the authors? Are beta-actin mRNAs bound to some RNA-binding proteins or clustered in RNP complexes?

One focal plane was used in the experiments showing mRNAs' fusion, fission, and anchoring behavior. We have now added this information in the figure legend of figure 1. Yes, b-ACTIN mRNA are bound to specific RNA-binding proteins, for example, ZBP1, and it has been reported that ZBP1 forms granules with b-ACTIN mRNAs (Farina, K.L., et al., Journal of cell biology, 2003).

2) Some quantifications on beta-actin mRNA kinetics, such as a plot of their movement speed or fusion rate, etc., would help readers better understand the behaviors of the mRNAs and assess whether the MASS tagging did not affect them.

We thank the reviewer for raising this critical issue. We have performed live imaging of bACTIN mRNA using the conventional 24xMS2 system or MASS with different lengths of Suntag arrays (MCP-24xSuntag, MCP-12xSuntag, MCP-6xSuntag). We then measured the velocity of mRNA movement in each imaging condition. We found that compared to the conventional 24xMS2 system, mRNA labeled with MCP-24xSuntag or by MCP-12xSuntag showed a smaller velocity, indicating that heavier labeling affected mRNA movement speed.<br /> In contrast, we found that mRNAs labeled with MCP-6xSuntag showed a similar velocity to that tagged with the conventional 24xMS2 system. Those data pointed out that when MASS is used to measure the speed of mRNA movement, a short Suntag array (MCP6xSuntag) should be used. We added those new data in Figure 1-figure supplement 5 and the text on pages 4 and 5.

3) Using another target gene for MASS tagging would further confirm the efficacy of the system. Assuming the authors generated a parental strain of HeLa cell, where MCP24xSuntag and scFv-sfGFP are already stably expressed (shown in Fig. 1B), CRISPR-ing in another gene should be relatively easy and fast.

For exogenous genes, in addition to b-ACTIN, we imaged mRNAs from three more genes, C-MYC, HSPA1A, and KIF18B, with MASS in HeLa cells. For endogenous genes, we imaged C42D4.3 and mai-1 in the epidermis of C. elegans. These data indicated that MASS is able to image both exogenous and endogenous mRNAs in live cells. We have now added those new data in Figure 1-figure supplement 2, Figure 2-figure supplement 2, and to the text on pages 3, 4, and 6.

4) Adding a complementary approach to the data presented in Fig. 1, such as qRT-PCR for beta-actin, with or without the MASS system would ensure the intense tagging did not affect the mRNA expression or stability.

To address this question, we performed more experiments to test whether MASS affected the mRNA expression and stability. Because b-ACTIN mRNA is very stable; thus it is not suitable for measuring mRNA stability. We, therefore, tested three genes, including C-MYC, HSPA1A, and KIF18B, which were reported as medium-stable mRNAs. We found that MASS did not affect the stability of those three mRNAs in HeLa cells. We also tested the expression level and the stability of endogenous C42D4.3 mRNA in the epidermis of C. elegans and found that both the expression and the stability were not affected by MASS. We have now added those new data in Figure 1-figure supplement 2, Figure 2-figure supplement 2, and to the text on pages 3, 4, and 6.

5) For experiments with the C. elegans epidermis, including at least one more MASS movie clip for C42D4.3 and a movie for mai-1 would be helpful for readers to appreciate the RNA labeling and its dynamics.

We showed two movies (video 7 and video 8) and the snapshots for C42D4.3 mRNA (Figure 2D and Figure 2-figure supplement 3). We also added a movie (Video 9) for mai-1.

6) The difference between Fig. 2D and Fig. 2-fig supp. 3 is unclear. The authors should address the different patterns of RNA signal propagation. Is it due to the laser power used too much, resulting in photobleach in Fig. 2D?

We have noticed the difference between Figure 2D and Figure 2-figure supplement 3. In Figure 2D, GFP foci did not appear within the injury area after wounding. In Figure 2-figure supplement 3, GFP foci quickly appeared within the injury area. Although we kept the laser power setting constant when performing the laser wounding experiment, there are indeed variations in the actual laser power used. As the reviewer suggested, the difference may be due to photobleaching in Figure 2D. Alternatively, it is possible that the location of the injury site or the degree of injury could affect the dynamics of gene expression.

However, we would like to point out that the dynamics of gene expression pattern in Figure 2D (Video 7) and Figure 2-figure supplement 3 (Video 8) is similar. GFP foci of C42D4.3 mRNAs were first detected around the injury sites. Then GFP foci gradually appeared from the area around the injury site to distal regions.

7) Movie 7 is the key data the authors are presenting, but there are a few discrepancies between their arguments and what is seen from the movie. The authors say the RNAs are "gradually spread" (the line 120 in the manuscript). However, it seems that the green foci just appear here and there in the epidermis and the majority of them stay where they were throughout the timelapse. This pattern seems to be different from the montage in Fig. 2-fig supp. 3, which indeed looks like the mRNA spots are formed around the lesion and spread overtime. Additional explanation on this will strengthen the arguments. Given the dramatic increase of c42d4.3 mRNA abundance 1 min. after the laser wounding, there must be a tremendous boost of transcription at the active transcription sites, which should be captured as much bigger and fewer green foci that are located inside the nucleus. Is this simply because those nuclear sites are out of focus or in a similar size as mRNA foci? Regardless, this should be addressed in the discussion.

We apologize for the confusing description of our original data. We wrote "gradually spread", but we did not mean that mRNAs were transcribed at the wounding site and moved to the distal regions. We actually mean that GFP foci first appeared close to the wounding site and more GFP foci gradually appeared at the distal regions. We have changed our writing to "the appearance of GFP foci gradually spreads from the area around the injury site to distal regions".

For the difference between Figure 2D and Figure 2-figure supplement 3, please see our discussion for comment 6.

For transcription, we also expected a boost of transcription after wounding. However, we failed to detect the appearance of bigger GFP foci in the nucleus. We agree with the reviewer that this is because the active nuclear sites are out of focus. The epidermis of C. elegans is a syncytium with 139 nuclei located in different regions and focal planes. With our microscopy, we were able to image only one focal plane, in which there are usually only four to ten nuclei. Therefore, it is likely that the nuclei with active transcription were out of focus. We have now discussed this point in the revised manuscript (page 6).

8) One clear way to confirm that MASS labels mRNAs and does not affect their stability/localization is to compare the imaging data with single-molecule RNA fluorescence in situ hybridization (smFISH) that the Singer lab developed decades ago. The authors can target the endogenous c42d4.3 or mai-1 RNAs using smFISH and compare their abundance and subcellular localization patterns with their data.

To confirm that the GFP foci detected are real mRNA molecules, we performed MASS combined with single-molecule RNA FISH and found that MASS detected a similar number of GFP foci compared to the spots detected by smFISH. In addition, the majority (72%) of GFP foci colocalized with the smFISH spots of b-ACTIN-8xMS2 mRNAs. It is reported that not all MS2 stem-loop will be bound by the MCP. As only 8xMS2 was used in MASS, it is likely that some mRNAs were not fully bound by MCP and were not detected. On the other hand, only sixteen probes were used in the smFISH experiment, and it is possible that some mRNAs were miss labeled by smFISH. Therefore, 100% colocalization of MASS foci with the smFISH spots was hard to achieve. These data indicated that MASS could detect single mRNA molecules and label the majority of mRNAs from a specific gene in live cells. We have now added the new data in Figure 1, Figure 1-figure supplement 1, and to the text on page 3.

We performed more experiments to test whether MASS affected the mRNA expression and stability. Because b-ACTIN mRNA is very stable; thus it is not suitable for measuring mRNA stability. We, therefore, tested three genes, including C-MYC, HSPA1A, and KIF18B, which were reported as medium-stable mRNAs. We found that MASS did not affect the stability of those three mRNAs in HeLa cells. We also tested the expression level and the stability of endogenous C42D4.3 mRNA in the epidermis of C. elegans and found that both the expression and the stability were not affected by MASS. We have now added those new data in Figure 1-figure supplement 2, Figure 2-figure supplement 2, and to the text on pages 3, 4, and 6.

To test whether MASS affected the mRNA localization, we performed new experiments to image b-ACTIN mRNAs using MASS and the conventional MS2 system side by side in NIH3T3 cells, which is a mouse fibroblast cell line. We found that b-ACTIN mRNAs showed similar localization in both methods. These new data suggest that MASS does not affect RNA subcellular localization. We have now added the new results in Figure 1-figure supplement 2.

9) One of the main purposes to live image RNAs is to assess their dynamics. Adding some more analyses, such as the movement speed of the foci, would be helpful to show how effective this system is to assess those dynamics features.

We thank the reviewer for raising this critical issue. We have performed live imaging of bACTIN mRNA using the conventional 24xMS2 system or MASS with different lengths of Suntag arrays (MCP-24xSuntag, MCP-12xSuntag, MCP-6xSuntag). We then measured the velocity of mRNA movement in each imaging condition. We found that compared to the conventional 24xMS2 system, mRNA labeled with MCP-24xSuntag or by MCP-12xSuntag showed a smaller velocity, indicating that heavier labeling affected mRNA movement speed.

In contrast, we found that mRNAs labeled with MCP-6xSuntag showed a similar velocity to that tagged with the conventional 24xMS2 system. Those data pointed out that when MASS is used to measure the speed of mRNA movement, a short Suntag array (MCP6xSuntag) should be used. We added those new data in Figure 1-figure supplement 5 and to the text on pages 4 and 5.

Reviewer #4 (Public Review):

Hu et al introduced the MS2-Suntag system into C. elegans to tag and image the dynamics of individual mRNAs in a live animal. The system involves CRISPR-based integration of 8x MS2 motifs into the target gene, and two transgene constructs (MCP-Suntag; scFv-sfGFP) that can potentially recruit up to 384 GFP molecule to an mRNA to amplify the fluorescent signal. The images show very high signal to background ratio, indicating a large range of optimization to control phototoxicity for live imaging and/or artifacts caused by excessive labeling. The use of epidermal wound repair as a case study provides a simplified temporal context to interpret the results, such as the initiation of transcription upon wounding. The preliminary results also reveal potentially novel biology such as localization of mRNAs and dynamic RNP complexes in wound response and repair. On the other hand, the system recruits a large protein complex to an mRNA molecule, an immediate question is to what extent it may interfere with in vivo regulation. Phenotypic assays, e.g., in development and wound repair, would have been a powerful argument but are not explored. In all, C. elegans is powerful system for live imaging, and the genome is rich in RNA binding proteins as well as miRNAs and other small RNAs for rich posttranscriptional regulation. The manuscript provides an important technical progress and valuable resource for the field to study posttranscriptional regulation in vivo.

We thank the reviewer for spending time reviewing our manuscript and for the insightful comments.

It might be we have all the pieces, just not the connective and compelling narrative.

to me it seems almost like a parsing problem but not quite in the way that it's argued, as I took Japanese (which is mentioned in the foot note) and it would sometimes feel like you almost need to reparse or go back and re-assess how the words go together, and I think it seems like maybe this just isn't as common in English so it then becomes strange-- not because we forgot what we read but it was already considered "done" so why would we "go back" and re-check that. Like in the cotton shirt example, I just couldn't see that cotton and shirt were meant to be separate until they added more pauses/space, because I kinda process cotton shirt- oop one noun phrase and just didn't question that even when the sentence didn't make sense with that.

Do you think it's appropriate to start a piece "genreless", so to speak? I believe the author did this intentionally, and that the essay isn't truly still developing genre, it's just developing its expression of genre

The way she writes down the events makes it seem like John is manipulating her, but it could just be due to the fact that it's from her point of view.

As humans we're good at separating things based on categories. The Dewey Decimal System systematically separates mathematics and history into disparate locations, but your zettelkasten shouldn't force this by overthinking categories. Perhaps the overlap of math and history is exactly the interdisciplinary topic you're working toward? If this is the case, just put cards into the slip box closest to their nearest related intellectual neighbor—and by this I mean nearest related to you, not to Melvil Dewey or anyone else. Over time, through growth and branching, ideas will fill in the interstitial spaces and neighboring ideas will slowly percolate and intermix. Your interests will slowly emerge into various bunches of cards in your box. Things you may have thought were important can separate away and end up on sparse branches while other areas flourish.

If you make the (false) choice to separate math and history into different "sections" it will be much harder for them to grow and intertwine in an organic and truly disciplinary way. Universities have done this sort of separation for hundreds of years and as a result, their engineering faculty can be buildings or even entire campuses away from their medical faculty who now want to work together in new interdisciplinary ways. This creates a physical barrier to more efficient and productive innovation and creativity. It's your zettelkasten, so put those ideas right next to each other from the start so they can do the work of serendipity and surprise for you. Do not artificially separate your favorite ideas. Let them mix and mingle and see what comes out of them.

If you feel the need to categorize and separate them in such a surgical fashion, then let your index be the place where this happens. This is what indices are for! Put the locations into the index to create the semantic separation. Math related material gets indexed under "M" and history under "H". Now those ideas can be mixed up in your box, but they're still findable. DO NOT USE OR CONSIDER YOUR NUMBERS AS TOPICAL HEADINGS!!! Don't make the fatal mistake of thinking this. The numbers are just that, numbers. They are there solely for you to be able to easily find the geographic location of individual cards quickly or perhaps recreate an order if you remove and mix a bunch for fun or (heaven forfend) accidentally tip your box out onto the floor. Each part has of the system has its job: the numbers allow you to find things where you expect them to be and the index does the work of tracking and separating topics if you need that.

The broader zettelkasten, tools for thought, and creativity community does a terrible job of explaining the "why" portion of what is going on here with respect to Luhmann's set up. Your zettelkasten is a crucible of ideas placed in juxtaposition with each other. Traversing through them and allowing them to collide in interesting and random ways is part of what will create a pre-programmed serendipity, surprise, and combinatorial creativity for your ideas. They help you to become more fruitful, inventive, and creative.

Broadly the same thing is happening with respect to the structure of commonplace books. There one needs to do more work of randomly reading through and revisiting portions to cause the work or serendipity and admixture, but the end results are roughly the same. With the zettelkasten, it's a bit easier for your favorite ideas to accumulate into one place (or neighborhood) for easier growth because you can move them around and juxtapose them as you add them rather than traversing from page 57 in one notebook to page 532 in another.

If you use your numbers as topical or category headings you'll artificially create dreadful neighborhoods for your ideas to live in. You want a diversity of ideas mixing together to create new ideas. To get a sense of this visually, play the game Parable of the Polygons in which one categorizes and separates (or doesn't) triangles and squares. The game created by Vi Hart and Nicky Case based on the research of Thomas Schelling provides a solid example of the sort of statistical mechanics going on with ideas in your zettelkasten when they're categorized rigidly. If you rigidly categorize ideas and separate them, you'll drastically minimize the chance of creating the sort of useful serendipity of intermixed and innovative ideas.

It's much harder to know what happens when you mix anthropology with complexity theory if they're in separate parts of your mental library, but if those are the things that get you going, then definitely put them right next to each other in your slip box. See what happens. If they're interesting and useful, they've got explicit numerical locators and are cross referenced in your index, so they're unlikely to get lost. Be experimental occasionally. Don't put that card on Henry David Thoreau in the section on writers, nature, or Concord, Massachusetts if those aren't interesting to you. Besides everyone has already done that. Instead put him next to your work on innovation and pencils because it's much easier to become a writer, philosopher, and intellectual when your family's successful pencil manufacturing business can pay for you to attend Harvard and your house is always full of writing instruments from a young age. Now you've got something interesting and creative. (And if you must, you can always link the card numerically to the other transcendentalists across the way.)

In case they didn't hear it in the back, I'll shout it again: ACTIVELY WORK AGAINST YOUR NATURAL URGE TO USE YOUR ZETTELKASTEN NUMBERS AS TOPICAL HEADINGS!!!

Larry makes a similar analogy (pass by value vs pass by reference) to my argument about why patches are actually better at the collaboration phase—pull requests are fragile links. Transmission of patch contents is robust; they're not references to external systems—a soft promise that you will service a request for the content when it comes. A patch is just the proposed change itself.