https://www.nytimes.com/2024/07/18/climate/china-greenhouse-gas-emissions-plateau.html

What a brilliant idea!

Goede definitie van kennis!

Much longer list than the one in our slide deck!

Maybe an AY 24-25 PMP goal ....

point of need!

my downfall

I like to combine the smaller segments I've created into tutorials instead of creating new tutorials from scratch whenever possible.

This can be a challenge, but is so helpful!

RUTHLESSLY EDIT

26:30 Brings up progress traps of this new technology

26:48

question How do we shift our (human being's) relationship with the rest of nature

27:00

metaphor - interspecies communications - AI can be compared to a new scientific instrument that extends our ability to see - We may discover that humanity is not the center of the universe

32:54

Question - Dr Doolittle question - Will we be able to talk to the animals? - Wittgenstein said no - Human Umwelt is different from others - but it may very well happen

34:54

species have culture - Marine mammals enact behavior similar to humans

• Unknown unknowns will likely move to known unknowns and to some known knowns

36:29

citizen science bioacoustic projects - audio moth - sound invisible to humans - ultrasonic sound - intrasonic sound - example - Amazonian river turtles have been found to have hundreds of unique vocalizations to call their baby turtles to safety out in the ocean

41:56

ocean habitat for whales - they can communicate across the entire ocean of the earth - They tell of a story of a whale in Bermuda can communicate with a whale in Ireland

43:00

progress trap - AI for interspecies communications - examples - examples - poachers or eco tourism can misuse

44:08

progress trap - AI for interspecies communications - policy

45:16

whale protection technology - Kim Davies - University of New Brunswick - aquatic drones - drones triangulate whales - ships must not get near 1,000 km of whales to avoid collision - Canadian government fines are up to 250,000 dollars for violating

50:35

environmental regulation - overhaul for the next century - instead of - treatment, we now have the data tools for - prevention

56:40 - ecological relationship - pollinators and plants have co-evolved

1:00:26

AI for interspecies communication - example - human cultural evolution controlling evolution of life on earth

Auf gut Norddeutsch: Morgen ist auch noch ein Tag.

in case we version a slot

https://munk.org/typecast/2011/04/25/1964-nomda-blue-book-remington-font-styles/

Satan expects these creatures to sympathize with their stance. Will ask them to join the fight.

Satan plans for his forces to regroup so they can come up with new plans and refreshed energy to carry them out.

Relates to line 157 "Fall'n Cherub" to better showcase Satan's fall from grace and helps create a clearer understanding of how Satan chooses to be seen.

Satan plans to turn god's plans around in such a way that he will be able to accomplish his own goals.

Relates to line 108 "courage never to submit or yield." Satan refuses to lose lying down.

God has used force to gain what he thinks is respect, but it is actually mere control. God wants Satan and his peers to suffer for their disobedience.

To stand against the god character, one must have courage. This implies that all those who follow the god character and his rules are spineless and weak.

Letting the reader know these names may both be used for the same character

Milton and Dante agree on depiction of Hell

Reference to Hell

Showing that the person in that role can be overthrown or replaced

Motivation or goal

有一个患者是没有分割数据的

very organized

max_samples: I reduce a fixed number. max_depth: I reduce an upper bound. Both are called "max", because they have default values of infinity.

If the federal government had access to every email you’ve ever written and every phone call you’ve ever made, it’s almost certain that they could find something you’ve done which violates a provision in the 27,000 pages of federal statues or 10,000 administrative regulations. You probably do have something to hide, you just don’t know it yet.

It is important to understand the difference between correlation and causation because it would be impossible to find the true cause for a trend. https://www.lukeworthington.com/wp-content/uploads/2020/01/CC.png

проблема один, если данные отправить на второй микросервис, он может их просто потерять. проблема два. а если эти данные нужно не одному микросервесу а сразу многим.

броке сообщений берёт всю ответсвтенност за отправку данных на себя

Based on the labels on typewriter ribbon donated by Octavia Butler, she got her ribbon from Office Machines Incorporated which had locations at 543 S. Spring Street, Los Angeles, CA and 433 N. Beverly Drive, Beverly Hills, CA.

via https://anacostia.si.edu/collection/object/acm_2004.0007.0002

Prueba anotación

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Reply to the reviewers

__Dear Reviewers, __

We would like to thank you for the time and attention dedicated to reviewing our manuscript. We have taken all the questions and comments into consideration, which we believe have helped us to improve the paper.

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

The study by Aguirre-Botero et al. shows the dynamics of 3D11 anti-CSP monoclonal antibody (mAb) mediated elimination of rodent malaria Plasmodium berghei (Pb) parasites in the liver. The authors show that the anti-CSP mAb could protect against intravenous (i.v.) Pb sporozoite challenge along with the cutaneous challenge, but requires higher concentration of antibody. Importantly, the study shows that the anti-CSP mAb not only affects sporozoite motility, sinusoidal extravasation, and cell invasion but also partially impairs the intracellular development inside the liver parenchyma, indicating a late effect of this antibody during liver stage development. While the study is interesting and conducted well, the only novel yet very important observation made in this manuscript is the effect of the anti-CSP mAb on liver stage development.

Major This observation is highlighted in the manuscript title but is supported by only limited data. A such it needs to be substantiated and a mechanism should be investigated. The phenomenon of intracellular effects of the anti-CSP mAb should be analyzed in much more detail. For example, can the authors demonstrate uptake of the Ab together with the parasite during hepatocyte invasion? What cellular mechanism leads to elimination?

Lines 234 - 243; 308 - 325: These results are the gist of the entire study and also defined the title of the manuscript. Thus, it would be pre-mature to claim the substantial effect of 3D11 antibody in late killing of the parasite in the infected hepatocytes just by looking at the decreased GFP fluorescence. The authors need to at least verify the fitness of the liver stages by measuring the size of the developing parasites as well as using different parasite specific markers (UIS4, MSP1, HSP70 etc.) in immunofluorescence assays on the infected liver sections and in vitro infections.

Response

We greatly appreciate the comments. We have taken the suggestions into consideration and will add data, perform as well as improve the text to clarify some key concepts. We hope that these changes will increase the impact of our results. We are adding extra data showing the percentage of UIS4+ intracellular parasites at 2, 4, and 44h in control and 3D11 groups. In addition, the effect of 2h incubation of sporozoites with 3D11 on the parasite size, GFP intensity and UIS4-staining at 44h post-infection was quantified. Briefly, the percentage of UIS4-negative intracellular parasites is significantly higher than the control, but not at 44h, indicating that part of the parasite clearance is due to the absence of UIS4 secretion that could be related to the neutralizing Ab effect or the inhibition of parasites during HepG2 cell traversal. At 44h, the in vitro data show that 3D11 only significantly decreases GFP intensity without affecting size and UIS4-staining. Since (i) the inhibition of invasion parallels parasite killing in the titration curve (Fig. 4A), (ii) the post-invasion parasite elimination occurs as early as 15h (Fig. 4C), (iii) the decrease in parasite GFP intensity occurs without a change in UIS4-staining and parasite size at 48h, and (iv) we cannot dissociate non-cytotoxic from cytotoxic 3D11 effects, we concluded for the moment that the 3D11 post-invasion “late effect” is probably due to mAb cytotoxicity, which is decreasing the SPZ fitness with measurable consequences in the percentage of surviving EEF and EEF GFP intensity. However, since we cannot exclude a non-cytotoxic effect of 3D11, which its cytotoxic activity could mask, we are addressing this question in another manuscript using hmAbs against PfCSP with different cytotoxicity levels.

We will also test the potential post-invasion neutralizing effect of 3D11 in vitro. However, since the mAb does not affect the in vivo parasite growth from 24h to 44h, it is unlikely that it affects late intracellular development.

____Reviewer #1__ Minor comments __

• Line 44 - 43: The statement is applicable only to the rodent infecting Plasmodium parasites. The authors need to clarify that.

Responses

This is an important clarification. We have modified the text that now reads:

“The sporozoite surface is covered by a dense coat of the immunodominant circumsporozoite protein (CSP), shown to be an immunodominant protective antigen using a rodent malaria model”.

• Line 68: Replace the second 'against' after the CSP with 'of'.

It is done.

• Line 141 - 143: The 3D11 mAb does affect the homing and killing in the blood of cutaneous injected sporozoites. The authors need to clearly state that the statement is true only for i.v. injected sporozoites.

Thank you for the comment. Now the text reads “Altogether, these data indicate that 3D11 rather than having an early effect on i.v. inoculated sporozoites, e.g. in the homing or killing in the blood, requires more than 4 h to eliminate most parasites in the liver.”

• Figure 3B: The numbers of sporozoites detected in the experiment varies from 0 h (line 172) to 2 h (line 184). Therefore, the numbers need to be mentioned on all the bars of each timepoint.

This information was missing but now we have added the numbers to Figure 3B.

• Figure 3C: If the authors have used flk1-GFP mice, then how well they were able to detect the Pb-PfCSP GFP parasites in the vessel vs. parenchyma in the intravital imaging? The representative images for Pb-PfCSP GFP should also be included.

Since 3D11 does not target PbPf parasites most of them are motile in the movies, making them easily distinguishable from the endothelial cells. In addition, the stronger GFP intensity of sporozoites make them easily detectable in the sinusoids. Representative images were added in the supplementary figures (now Figure S3).

• It is not mentioned anywhere how the viability of the sporozoites was determined. This has to be described especially in the methods section.

• Also, the flow acquisition and data analysis of the sporozoites and infected HepG2 cells must be described in the method section.

We briefly mentioned it in the results (line 228- 230): “In addition, by comparing the total number of recovered GFP+ sporozoites at 2 h in the two studied conditions, we measured the early lethality (%viable sporozoites, Figure 4B) of the anti-CSP Ab on the extracellular forms of the parasite (Figure 4A).”

A more detailed description has been added in the methods section that now reads:

“After 2 h, the supernatant and the trypsinized cells were analyzed by flow cytometry to quantify the amount of GFP+ events inside and outside the cells. Viability was then quantified by adding the total number of sporozoites (GPF+ events) in the supernatant, inside and outside the cells. We calculated the percentage viability by comparing the average of the total number of sporozoites in the treated samples to the average in controls using three technical replicates in each condition. Additionally, we quantify the percentage of infected cells using the total number of GFP+ events in the HepG2 gate (Figure S4). To compare the biological replicates, we further normalized to the control of each experiment. For the samples used to analyze parasite development, the cells were incubated for 15 or 44 h after sporozoite addition, and the medium was changed after 2 and 24 h. The cells were trypsinized and the percentage of intracellular parasites was determined by flow cytometry as described above (Figure S4). The prolonged effect between 2 h - 15/44 h was calculated by normalizing to the percentage of infected cells at 2 h.”

• Figure 4: The flow layouts should be included for at least comparing the 0 vs. 5 μg/ml of 3D11 mAb concentrations.

Flow layouts were added in the supplementary figures.

• Line 651 (Figure S1 legend): Typographical error '14'.

Thank you for noticing. We corrected it.

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

Aguirre-Botero and collaborators report on the dynamics of Plasmodium parasite elimination in the liver using the 3D11 anti-CSP monoclonal antibody (mAb). By using microscopy and bioluminescence imaging in the P. berghei rodent malaria model, the authors first demonstrate that higher antibody concentrations are required for protection against intravenous sporozoite challenge, when compared to cutaneous challenge, which is not surprising. The study also shows that the 3D11 mAb reduces sporozoite motility, impairs hepatic sinusoidal barrier crossing, and more relevantly inhibits intracellular development of liver stages through its cytotoxic activity. These findings highlight the role of this specific monoclonal antibody, 3D11 mAb against CSP, in targeting sporozoites in the liver.


Major Comments

The study provides valuable insights into the mechanisms of protection conferred by the 3D11 anti-CSP monoclonal antibody against P. berghei sporozoites and this finding allow the field to speculate that other monoclonal antibodies against CSP of P. Falciparum may act similarly. However, an important experiment is missing that would significantly strengthen the conclusions. Specifically, the authors should perform experiments where the monoclonal antibody is added immediately after the sporozoites have completed invasion. This should be done both in vitro and in vivo to show whether the antibody has any effect on intracellular development of liver stages when added after invasion.

While the claims are generally supported by the data presented, to comprehensively conclude the late cytotoxic effects of 3D11, the additional experiment of post-invasion antibody application is relevant. This would help determine if the observed effects are due to the antibody's action during invasion or its continued action post-invasion.

The data and methods are presented in a manner that allows for reproducibility. The use of microscopy and bioluminescence imaging is well-documented. The experiments appear adequately replicated, and statistical analyses are appropriate.

Response

We thank reviewer 2 for important suggestions. To be sure that the effect might not come from the internalization of the antibodies after sporozoite invasion, we will test the potential post-invasion neutralizing effect of 3D11 in vitro. However, since the mAb does not affect the in vivo parasite growth from 24h to 44h, it is unlikely that it affects late intracellular development.

Indeed, it is important to corroborate this effect might have a different effect using antibodies targeting P. falciparum CSP. That is why we are working in parallel with anti-PfCSP antibodies, but these data will be included in a different manuscript.

__Reviewer #2 ____Minor Comments __

• The text and figures are clear and accurate. Some minor typographical errors should be corrected.

Thank you for the remark, we have worked on that and hope that the text reads better now.

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

Aguirre-Botero et al have studied the effect of a potent monoclonal antibody against the circumsporozoite protein, the major surface protein of the malaria sporozoite. This is an elegantly designed, performed, and analyzed study. They have efficiently delineated the mode of action of anti-CSP repeat mAb and confirmed previous in vitro work (not cited) that demonstrated the same intracellular effect. Specific comments :

• Line 51: The authors claim a correlation between high antibody levels and protection. However, they did not provide direct proof that these antibodies were responsible for protection, nor did they establish a cut-off level of anti-CSP antibodies that would distinguish between protected and unprotected individuals.

We would first like to thank reviewer 3 for the comments. Indeed, we agree with reviewer 3, these are correlative studies where the causality cannot be established. We modified the ensuing sentence to specify the causality between anti-CSP mAbs and in vivo protection against sporozoite infection. Now the text reads: “Extensive research has demonstrated a positive correlation between high levels of anti-CSP antibodies (Abs) induced by the RTS,S/AS01 vaccine and its efficacy against malaria 11–13. Remarkably, anti-CSP monoclonal Abs (mAbs) have been proven to protect in vivo against malaria in various experimental settings, including, mice 14–21, monkeys 23, and humans 24–26”

• Line 326: The late intrahepatic effect of mAb against the CSP repeat has been previously reported (see Figure 2, Nudelman et al, J Immunol, 1989). The effect was shown to affect the transition from liver trophozoites to liver schizonts. This study should be cited and discussed.

Thank you for the remark. Now the text reads: Notably, a similar effect has been previously reported using sera from mice immunized with PfCSP or mAb against P. yoelii (Py) CSP. Incubation of Pf or Py sporozoites with the immune sera or mAbs not only affected sporozoite invasion in vitro but continued to affect intracellular forms for several days after invasion38,39. Additionally, using anti-PfCSP sera, it was also observed that late EEFs from sera-treated sporozoites had abnormal morphology38. Altogether, it was thus concluded that the anti-CSP Abs present in the sera had a long-term effect on the parasites38,39.

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Referee #3

Evidence, reproducibility and clarity

Aguirre-Botero et al have studied the effect of a potent monoclonal antibody against the circumsporozoite protein, the major surface protein of the malaria sporozoite. This is an elegantly designed, performed, and analyzed study. They have efficiently delineated the mode of action of anti-CSP repeat mAb and confirmed previous in vitro work (not cited) that demonstrated the same intracellular effect.

Specific comments

Line 51: The authors claim a correlation between high antibody levels and protection. However, they did not provide direct proof that these antibodies were responsible for protection, nor did they establish a cut-off level of anti-CSP antibodies that would distinguish between protected and unprotected individuals.

Lone 326: The late intrahepatic effect of mAb against the CSP repeat has been previously reported (see Figure 2, Nudelman et al, J Immunol, 1989). The effect was shown to affect the transition from liver trophozoites to liver schizonts. This study should be cited and discussed.

Significance

A well-done study that elucidates the mechanisms of a protective monoclonal antibody against malaria sporozoites. These data are important and will interest a large audience of researchers working in infectious diseases and immunology.

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Referee #2

Evidence, reproducibility and clarity

Aguirre-Botero and collaborators report on the dynamics of Plasmodium parasite elimination in the liver using the 3D11 anti-CSP monoclonal antibody (mAb). By using microscopy and bioluminescence imaging in the P. berghei rodent malaria model, the authors first demonstrate that higher antibody concentrations are required for protection against intravenous sporozoite challenge, when compared to cutaneous challenge, which is not surprising. The study also shows that the 3D11 mAb reduces sporozoite motility, impairs hepatic sinusoidal barrier crossing, and more relevantly inhibits intracellular development of liver stages through its cytotoxic activity. These findings highlight the role of this specific monoclonal antibody, 3D11 mAb against CSP, in targeting sporozoites in the liver.


Major Comments

The study provides valuable insights into the mechanisms of protection conferred by the 3D11 anti-CSP monoclonal antibody against P. berghei sporozoites and this finding allow the field to speculate that other monoclonal antibodies against CSP of P. Falciparum may act similarly. However, an important experiment is missing that would significantly strengthen the conclusions. Specifically, the authors should perform experiments where the monoclonal antibody is added immediately after the sporozoites have completed invasion. This should be done both in vitro and in vivo to show whether the antibody has any effect on intracellular development of liver stages when added after invasion.

While the claims are generally supported by the data presented, to comprehensively conclude the late cytotoxic effects of 3D11, the additional experiment of post-invasion antibody application is relevant. This would help determine if the observed effects are due to the antibody's action during invasion or its continued action post-invasion.

The data and methods are presented in a manner that allows for reproducibility. The use of microscopy and bioluminescence imaging is well-documented. The experiments appear adequately replicated, and statistical analyses are appropriate.

Minor Comments

The text and figures are clear and accurate. Some minor typographical errors should be corrected.

Significance

The study's strengths lie in its detailed analysis of the 3D11 mAb's effect on sporozoite motility and liver stage development. The use of advanced imaging techniques adds robustness to the findings. The main limitation is the lack of data on the antibody's effect post-invasion. Additionally, the study's conclusions are based on a single monoclonal antibody and its target region, which may not be representative of other anti-CSP antibodies. Still, the findings offer insights into the cytotoxic action of anti-CSP antibodies, which could inform the development of more effective malaria vaccines and therapeutic antibodies.

This research will primarily interest a specialized audience in malaria research, particularly those focused on vaccine development and antibody therapeutics. It also holds value for broader audiences in immunology and infectious disease research.

My expertise: Malaria research and liver invasion by Plasmodium sporozoites

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Referee #1

Evidence, reproducibility and clarity

The study by Aguirre-Botero et al. shows the dynamics of 3D11 anti-CSP monoclonal antibody (mAb) mediated elimination of rodent malaria Plasmodium berghei (Pb) parasites in the liver. The authors show that the anti-CSP mAb could protect against intravenous (i.v.) Pb sporozoite challenge along with the cutaneous challenge, but requires higher concentration of antibody. Importantly, the study shows that the anti-CSP mAb not only affects sporozoite motility, sinusoidal extravasation, and cell invasion but also partially impairs the intracellular development inside the liver parenchyma, indicating a late effect of this antibody during liver stage development. While the study is interesting and conducted well, the only novel yet very important observation made in this manuscript is the effect of the anti-CSP mAb on liver stage development.

Major

This observation is highlighted in the manuscript title but is supported by only limited data. A such it needs to be substantiated and a mechanism should be investigated.

• The phenomenon of intracellular effects of the anti-CSP mAb should be analyzed in much more detail. For example, can the authors demonstrate uptake of the Ab together with the parasite during hepatocyte invasion? What cellular mechanism leads to elimination?

Minor

• Line 44 - 43: The statement is applicable only to the rodent infecting Plasmodium parasites. The authors need to clarify that.
• Line 68: Replace the second 'against' after the CSP with 'of'.
• Line 141 - 143: The 3D11 mAb does affect the homing and killing in the blood of cutaneous injected sporozoites. The authors need to clearly state that the statement is true only for i.v. injected sporozoites.
• Figure 3B: The numbers of sporozoites detected in the experiment varies from 0 h (line 172) to 2 h (line 184). Therefore, the numbers need to be mentioned on all the bars of each timepoint.
• Figure 3C: If the authors have used flk1-GFP mice, then how well they were able to detect the Pb-PfCSP GFP parasites in the vessel vs. parenchyma in the intravital imaging? The representative images for Pb-PfCSP GFP should also be included.
• It is not mentioned anywhere how the viability of the sporozoites was determined. This has to be described especially in the methods section.
• Also, the flow acquisition and data analysis of the sporozoites and infected HepG2 cells must be described in the method section.
• Figure 4: The flow layouts should be included for at least comparing the 0 vs. 5 μg/ml of 3D11 mAb concentrations.
• Lines 234 - 243; 308 - 325: These results are the gist of the entire study and also defined the title of the manuscript. Thus, it would be pre-mature to claim the substantial effect of 3D11 antibody in late killing of the parasite in the infected hepatocytes just by looking at the decreased GFP fluorescence. The authors need to at least verify the fitness of the liver stages by measuring the size of the developing parasites as well as using different parasite specific markers (UIS4, MSP1, HSP70 etc.) in immunofluorescence assays on the infected liver sections and in vitro infections.
• Line 651 (Figure S1 legend): Typographical error '14'.

Significance

The phenomenon of intracellular effects of the anti-CSP Ab is the only novel observation and as such, should be analyzed in much more detail. For example, can the authors demonstrate uptake of the Ab together with the parasite during hepatocyte invasion? What cellular mechanism leads to elimination?

I see what the author is going for but just because a journal is published in Malaysia doesn't mean it has "no international significance whatsoever".

https://www.youtube.com/watch?v=dgVFuPtgMdE

https://www.youtube.com/watch?v=dgVFuPtgMdE

Hi Nuno

Author response:

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

eLife assessment:

The statistical analyses are incomplete.

I find that the eLife assessment mentions “statistical analyses are incomplete” while the reviewers mention that the data are compelling and results are technically solid. Besides all observations in the manuscript are presented with robust and established norms of statistical analysis.

Public Reviews:

Reviewer #1 (Public Review):

Strengths:

The use of data from before COVID-19 is both a strength and a weakness. Because COVID had effects on vascular health and had higher death rates for groups with the comorbidities of interest here, it has likely shifted the demographics in ways that would shift the results in unpredictable ways if the analysis were repeated with current data. This can be a strength in providing a reference point for studying those changes as well as allowing researchers to study differences between regions without the complication of different public health responses adding extra variation to the data. On the other hand, it limits the usefulness of the data in research concerned with the current status of the various populations.

We completely agree with the observation, but were restricted as the purpose was to use the most robust and technically qualified data from GBD. The post COVID19 GBD data has not yet been released, but I am sure the observations made in the study can help in guiding the issues in the post COVID era too, because genetics is not going to change in these population groups.

However, we did highlight this aspect of COVID19 even in our original version and also in the revised version.

Reviewer #2 (Public Review):

Weaknesses:

The presentation is not focused. It would be better to include p-values and focus presentation on the main effects from the dataset analysis.

The significant p-values were restricted to public health data only to identify and distinguish differences in incidence, prevalence and mortality and how they differ across world populations. These differences have often been interpreted from socio-economic point of view, while our manuscript presents the reasons for differences for main condition (Stroke) and its comorbid condition among different ethnicities from a genetic perspective. This genetic perspective was further explored to identify unique ethnic specific variants and their patterns of linkage disequilibrium in distinguishing the phenotypic variations. Considering the quantum and diversity of data, both for public health and GWAS data, there can be several directions but for presentation we focused only on the most distinguishing and established phenotypic differences. I am sure this will open up avenues for several future investigations including COVID, as has been highlighted by the reviewers too. All observations were presented with robust and established norms of statistical analysis.

---

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

Thanks for the constructive observations on strengths and weaknesses of our manuscript. Interestingly, some of the weaknesses mentioned here also turns out to be the strength of the article. For example COVID19 has been mentioned by the reviewer as a driver to increase the mortality in some comorbid conditions and stroke. Firstly, I must clarify that, our data is from PreCOVID era and we indeed mention that in COVID era, COVID-19 might differentially impact the risk of stroke. Possibly this differential influence on the comorbidities of stroke, is likely to be influenced by its underlying genetics of stroke and its comorbidities.

I have tried to address the concerns raised by the reviewers, which ideally doesn’t impact the original manuscript. Statistical limitation has been commented pertaining to P-values, which has been clarified here. However, certain minor concerns such as abbreviations have been resolved in the revised manuscript. My response to weakness and reviewer’s comments are mentioned below.

Reviewer #1 (Public Review):

Strengths:

The data provided here will provide a foundation for a lot of future research into the causes of the observed correlations as well as whether the observed differences in comorbidities across regions have clinically relevant effects on risk management.

Weaknesses:

• As with any cross-national analysis of rates, the data is vulnerable to differences in classification and reporting across jurisdictions.

GBD data is the most robust and most comprehensive data resource which has been used and accepted globally in predicting the health metrics statistics.

GBD data indeed considers normalisations, regarding classification and reporting.

To the best of our knowledge this is the best available resource to consider all health metrics analysis.

• Furthermore, given the increased death rate from COVID-19 associated with many of these comorbid conditions and the long-term effects of COVID-19 infection on vascular health, it is expected that many of the correlations observed in this dataset will shift along with the shifting health of the underlying populations.

I must clarify that we have used data prior to COVID-19.

But yes the patterns after COVID19 will shift due to the impact of covid. This makes the study even more relevant as the comorbid conditions of stroke are also the risk drivers for COVID19 and mortality. This shift has been reported by some authors, which has been discussed in the discussion.

Therefore, understanding the genetic factors underlying stroke and its comorbid conditions might help in resolving how COVID19 might differentially impact on health outcome.

We did highlight this aspect of COVID19 even in our original version.

Introduction 1st para:

“It is the accumulated risk of comorbid conditions that enhances the risk of stroke further. Are these comorbid conditions differentially impacted by socio-economic factors and ethnogeographic factors. This was clearly evident in COVID era, when COVID-19 differentially impacted the risk of stroke, possibly due to its differential influence on the comorbidities of stroke.”

Discussion 3rd para:

“Studies reported reduction in life expectancy in 31 of 37 high-income countries, deduced to be due to COVID-191 . However, it would be unfair to ignore the comorbid conditions which could also be the critical determinants for reduced life expectancy in these countries.”

Recommendations For The Authors:

On page 5, the authors make a note about Africa and the Middle East having the highest ASMR for high SBP and comment about the relative populations of these regions. The populations of the regions are irrelevant to the rate.

Since the study is on comorbid factors of stroke and its impact on mortality therefore, relative burden seems critical. This has been further elaborated here to justify the comment, which indeed is an integral part of the original manuscript.

Paragraph referred – Results section 2:

“Ethno-regional differences in mortality and prevalence of stroke and its major comorbid conditions

We observed interesting patterns of ASMRs of stroke, its subtypes and its major comorbidities across different regions over the years as shown in figure 1a, table 1 and supplementary files S2 & S3. When assessed in terms of ranks, high SBP is the most fatal condition followed by IHD in all regions, except Oceania (OCE) where IHD and high SBP swap ranks. Africa (AFR; 206.2/100000, 95%UI 177.4-234.2) and Middle East (MDE; 198.6/100000, 95%UI 162.8-234.4) have the highest ASMR for high SBP, even though they rank as only the third and sixth most populous continents (fig. S2), respectively.”

On page 17, the authors are alarmed by a large ratio between prevalence rates and mortality rates for certain conditions. This is confusing since this indicates that these conditions are not as dangerous as the other conditions.

This has been further elaborated here to justify the comment, which indeed is an integral part of the original manuscript.

Paragraph referred – Discussion para 1:

“While the global stroke prevalence is nearly 15 times its mortality rate, prevalence of comorbid conditions such as high SBP, high BMI, CKD, T2D are alarmingly 150- to 500-fold higher than their mortality rates. These comorbid conditions can drastically affect the outcome of stroke.”

In Figure 4, the colors are not defined.

In Structure plot colours are assigned as per each K, it doesn’t directly refer to any population. But the plot distinguishes the stratification of populations as per K. Ramasamy, R.K., Ramasamy, S., Bindroo, B.B. et al. STRUCTURE PLOT: a program for drawing elegant STRUCTURE bar plots in user friendly interface. SpringerPlus 3, 431 (2014). https://doi.org/10.1186/2193-1801-3-431

Reviewer #2 (Public Review):

Strengths:

The idea is interesting and the data are compelling. The results are technically solid.

The authors identify specific genetic loci that increase the risk of a stroke and how they differ by region.

Weaknesses:

The presentation is not focused. It would be better to include p-values and focus presentation on the main effects of the dataset analysis.

I presume the comment is made with reference to results with significant p-values.

P-values are mentioned in the main text when referring to significant decrease or increase with respect to global rates and time e.g. P-values for comparison of a year 2019, are based on regional rates to global rates of 2019. Supplementary table S2a (mortality) and S3a (prevalence) e.g. P-values for comparison between year is based on 2019 rates to 2009 rates in Supplementary table S2b (mortality) and S3b (prevalence) e.g. P-values for proportional mortality and proportional prevalence in Supplementary table S4 and S5 is also based on global rates.

Recommendations For The Authors:

It would be better to minimize the use of acronyms. Often one has to go back to decipher what the acronym stands for. It is fine to have acronyms in figure legends, if necessary. However, at least for regions, please do not use acronyms.

In the revised version we have tried to minimise the Acronyms.

Removed the acronyms for regions and other places wherever possible however, the diseases acronyms have been maintained as per the GBD terms.

Please focus the presentation on the main results. Currently, the presentation wanders and repeats itself a lot.

Since the manuscript tries to address the global and regional rates of prevalence, mortality and its relationship to genetic correlates, it is difficult not to repeat the same to stress the significant observations coming out of different analysis methods. This might reflect on some amount of repetitiveness but the intention was to stress the significant observations.

I would also recommend acknowledging and discussing socioeconomic factors earlier in the manuscript.

Current mention happens in 3rd para of Discussion

“The changing dynamics of stroke or its comorbid conditions can be attributed to multitude of factors. Often global burden of stroke has been discussed from the point of view of socio-economic parameters. Studies indicate that half of the stroke-related deaths are attributable to poor management of modifiable risk factors 8,9. However, we observe that different socio-economic regions are driven by different risk factors.”

just like how our brain works

i hope they can do that before theon raids

:((

its ill omen to look at the dead cat :(

:((

older than her father??

he knowss

uhhh im scared

theon...

oh cuz his other sons dead (presumably)

its crazy how the tullys have teld riverrun for so long when none of them are particulary strong

i'd say maybe jaime

yeah but atleast they failed

dos he plant to make him heir or something or is it just baratheon loyalty

he'd be proud

she better be the one to murder him in the end she has to

YES I LOVED THIS DUO IN THE SHOW

exactly its not about who's the true heir its about who can fight for it and maintain it

i love seeing her more vicious side

:((

yess grey wind is so smart

yay he got a song

noo rains of castamere

was gonna say maybe arya's one but 100 is too many

for the plot :(

oh wtf

i love how sansa and arya are always so confident in robb

i reallyyy hope its revealed how exactly he did that but his wolf warging an stuff def helped

woah??

stopp i forgot about that lmao

i hate how similar their names are

oh no oh no

ughrugh fuck you

something about cat hating jon and ned being distant from theon which leads both boys to leave and try to find their place in the world

oh ik his name

i would feel bad but theon lierally couldve hurt bran or even killed him instead

because your betraying everyone

youll learn eventually...i hope

can't even kill a man smh

ugh

oh yeah that IS strange

harrenhall...

nooo

free my girl

lmao

melisandre!!

not if you interpret it differently

WOAH

哥德尔机是存在的

符号 ∈ ∈ 是集合论中的一个基本符号，表示“属于”或“是……的元素”。

“离散”在这里的意思是指时间步是分开的、独立的、非连续的。也就是说，每个时间步都是一个单独的、不可再分的时间点，彼此之间没有连贯的连续时间段。

方法章节

具体路径

关于懂的定义

bla bla bla. wer ein problem hat mit dem globalen massenmord in seiner aktuellen version,<br /> der soll eine bessere lösung vorschlagen, wie wir die faktor 20 übervölkerung lösen sollen.<br /> oder wer zu blöd ist für so große themen, einfach mal die fresse halten : )

https://www.youtube.com/watch?v=dgVFuPtgMdE

https://www.youtube.com/watch?v=dgVFuPtgMdE

https://www.youtube.com/watch?v=dgVFuPtgMdE

https://www.youtube.com/watch?v=dgVFuPtgMdE

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

Learn more at Review Commons

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Reply to the reviewers

1. General Statements

We are grateful for the valuable, constructive comments of the reviewers, which helped to substantially improve the quality of our manuscript. We particularly agree that the original structure of the manuscript was confusing and in parts misleading, since we followed the history of the project, which first identified the RBM39 mediated impact on IRF3 expression, whereas the -omics studies, identifying additional factors, were done at a far later point. Many discrepancies further arose from the low sensitivity of our initial proteomics analysis, which we now repeated, thereby obtaining far more sensitive detection of the key factors we also found in the transcriptomics data.

We have re-structured the entire manuscript by moving the -omics data from the end of the paper towards the middle and provide similar depth downstream analysis of all relevant key factors identified (RIG-I/MDA5, IFN receptors, STAT1/2), to reduce the focus on IRF3, as suggested. We further changed the title and abstract to reflect this major conceptual change. Thanks to this helpful comment, we think that our manuscript is now conceptually much clearer.

We further added new data to support the central claims of our manuscript, including a repetition of the proteomics study. Proteomics and transcriptomics now consistently demonstrate the impact of RMB39 knockdown as well as indisulam treatment on several key factors of innate immunity, including IRF3, STAT1/2, RIG-I and MDA5 (now in Fig. 5), with IFNAR2 and IL10RB additionally found in transcriptomics. We provide additional functional evidence that IRF3 is the key factor affected in the TLR3 pathway (IRF3 overexpression, Fig. 6B, C), whereas diminished abundance of RIG-I/MAD5 is equally important in the respective pathway, thereby also affecting NF-κB response (Fig. 6F-I). We further show the functional significance of IFN-receptor/STAT downregulation on type I and III IFN responses (Fig. 7E-G).

The reviewers also pointed to some datasets showing the expected trends, but in some cases lacking statistical significance, due to variability in knockdown efficiency. We repeated all mentioned datasets with new batches of siRNA with sufficient biological replicates (n=3). We thereby obtained consistent, statistically significant data in all cases. Importantly, all experiments implementing the RMB39.esc control now show consistent rescue (Fig2. A-E).

To generate a homogenous experimental design for virus infections, we further added new data showing a comparable impact of siRNA knockdown (Fig. 3F) and indisulam treatment (new Fig. 3G) on Sendai virus infection in A549 cells and took this as a rationale to consistently use indisulam for all other infections.

2. Point-by-point description of the revisions

__Reviewer #1 (Evidence, reproducibility and clarity (Required)): __ This manuscript by Li and colleagues examines the role of RBM39 in innate immune signaling. Splicing factor RBM39 was identified through a genome wide screen with a death reporter under control of the IFIT1 promoter that got stimulated with pIC in a TLR3-dependent manner. Besides IFIT1, further experiments showed that RBM39 is also involved in optimal expression of other innate immunity genes like IFNB, CXCL10, RIG-I or MDA5. While NFkB-dependent genes seem not to depend on RBM39, for IRF3 it was shown that protein levels decrease under conditions of RBM39 depletion, because IRF3 mRNAs are (slightly) reduced and spliced differently. The sulfonamid Indisulam could largely recapitulate the phenotype of RBM39 depletion. Further analyses using proteomics and transcriptomics showed that RBM39 is required for mRNA splicing and expression of a large set of other proteins. Altogether, this well designed and written study highlights the fundamental role played by RBM39 in in maintaining the pathways of immunity and metabolism. The key conclusions are convincing but some additional experiments would strengthen them further.

We are grateful for the very positive general comments of this reviewer.

Major comments: - For the statistics, authors seem not to have done multiple tests but rather tested individual datasets within larger graphs against each other. Please explain where this is the case and use corrections if multiple testing was done

We apologize for not have been clearer here, we indeed used multiple testing. In the proteomics, statistical significance was evaluated by "two-sample tests" (Student's T-test with permutation-based FDR 0.05 and 250 number of randomizations). For the analysis of RNAseq data, p values were calculated with the Wald test and corrected for multiple testing according to Benjamini-Hochberg. We have now included this information in the materials and methods section and in the respective figure legends.

• Fig. 4 shows that RBM39 depletion reduces IFIT expression in virus infected cells and slightly increases virus replication. RBM39 has a major effect on IRF3 levels, but also on other players in innate immunity. What happens if IRF3 is ectopically expressed as in figure 5? With this experiment one could measure how high the contribution of IRF3 miss-splicing is to innate immunity.

We thank this reviewer for the valuable suggestion. We restructured the entire manuscript, to address several reviewer comments regarding the focus on IRF3 and the lack of data on other factors in the pathway. We now clearly demonstrate that ectopic IRF3 expression entirely rescues the TLR3 response to poly(I:C) in PH5CH cells (Fig. 6B-C), which also explains the lack of impact on the NF-κB pathway (Fig. 2G-H). In contrast, overexpression of IRF3 does not rescue the RIG-I/MDA5 response in A549 cells (new data, Fig. 6F-I). Here, also the NF-κB pathway is affected by knockdown of RBM39, suggesting that reduced RIG-I/MDA5 abundance upon RMB39 knockdown substantially contributed to the diminished innate immune response.

• Fig. 4 A uses siRNAs but B, C and D only indisulam treatment. It would be better if siRNAs would also be used for the other viruses.

We agree that a homogenous setup for virus infection would be favorable, however, the use of different cell lines was authorative due to limited permissivess of the used cell types towards virus infection and it appeared challenging to achieve similar knockdown efficiencies. To generate a homogenous experimental design, we now added new data showing a comparable impact of siRNA knockdown (Fig. 3F) and indisulam treatment (new Fig. 3G) on Sendai virus infection in A549 cells and took this as a rationale to consistently use indisulam for all other infections.

• RBM39 depletion strongly reduces IRF3 levels in the WB, but not so much in RT-PCR and not at all in proteomics. Is the antibody used for WB perhaps recognizing a domain that is underrepresented in isoforms after disturbed splicing? Please clarify.

Our previous proteomics data suffered from a very low sensitivity, therefore we missed clear detection of many factors, including IRF3. We repeated the whole proteomics analysis with siRNA and indisulam treatment (new Fig. 5A, B) and now found significantly reduced IRF3 protein levels in both conditions (new Fig. S5C), in agreement with the WB data. The lower impact on IRF3 mRNA abundance is due to the additional contribution of alternative splicing (Fig. 6A, Fig. S6A-D), which both in combination affect protein abundance.

• Volcano plots in figure 7 show a lot of hits obtained after both RBM38 siRNA and indisulam (green dots), and some that are additionally identified in transcriptomes and in proteomes (red dots). Nonetheless only innate immunity and stress response genes are marked, although they do not belong to these highly conserved classes. Please elaborate more on the most RBM39-dependent genes, e.g. by presenting them in a heat map.

To our knowledge, our study is the first with a comprehensive comparison on the impact of RBM39 knockdown and indisulam treatment on the host cell proteome and transcriptome. However, several studies already did -omics studies on individual conditions/readouts (e.g. (Coomar et al, 2023; Dou et al, 2023; Mai et al, 2016; Nijhuis et al, 2022)). These studies already identified and described in detail key changes in transcriptome and proteome e.g. affecting genes involved in cell cycle control and metabolism, which we find as well. However, the novelty of our paper is the impact on innate immune response, we therefore rather decided to put an even stronger focus on these genes and to omit other factors, like stress response pathway components, etc.. This strategy is supported by the higher sensitivity of our new proteome analysis, which now generated a far better overlap with the transcriptomics, favoring a display setting on highlighting only those factors that were further analyzed in detail in the volcano blots (Fig. 5). Still, interested readers will find the comprehensive list of data in the supplementary Excel-datasheets as well as in our primary data in online depositories.

Minor comments: - Some abbreviations are not explained, like PGK, siNT, siVTN

We apologize and have added the missing explanation of abbreviations.

• Welsch should read Welch

Corrected.

• Fig. 2H: were cells also stimulated and if yes, how?

These were unstimulated conditions, to show the impact of RBM39 on basal expression of the IFNlambda receptor chains. However, we deleted this dataset due to the re-organisation of the manuscript. The analysis of the type I and type III receptor and STAT1/2 expression is now comprehensively shown in Fig. 7/S6E, F, solely based on the transcriptomic data for consistency reasons, along with the functional impact on the IFN response.

• Fig. 6E: I cannot see a difference between to IRF3-203 and 228 isoforms. And what are the white boxes?

• Also 6E: Location of the primers is barely visible

Due to the re-organization of the manuscript these data are now shown in Fig. S6D. Both isoforms are indeed very similar and only differ by a very small (16nt) additional exon in isoform 228. The white boxes are exons not translated in the respective isoforms. We have included this important information in the legend to Fig. S6 and increased the arrows indicating the positions of the primer.

• Some materials are not properly referenced, like the death reporter, the lentiviral system, or the Rift Valley fever luciferase virus

We are sorry for the missing information, which has now been added to the materials and methods section.

• Supplement has no page numbers

We have added page numbers to the supplementary information.

Reviewer #1 (Significance (Required)):

The study advances our knowledge about the regulation of innate immunity. Strengths are the discovery of a novel layer of innate immunity regulation by splicing and the in-depth analysis of the importance of RBM39 for cellular gene expression. A potential weakness might be the focus on innate immunity as other biological functions seem even more dependent on RBM39. However, this reviewer sees the necessity that covering all aspects of RBM39 finction would be beyond the scope of a single study. The relevant literature is appropriately cited (except for some materials, see minor comments). Results will be of interest not only to people doing basic research on innate immunity, but also to those interested in gene regulation in general or to cancer researchers using indisulam

__Reviewer #2 (Evidence, reproducibility and clarity (Required)): __ The authors performed a CRISPR-based screen for genes required for TLR3-mediated signaling and gene expression in Hepatoma cells. Interferon-stimulated expression of an apoptosis inducer was used as a read-out system. A number of candidate genes were identified and one of these, RBM39, investigated in detail. The protein has previously been linked to both transcriptional control and RNA processing. Validation studies confirm that reduction of cellular RBM39 results in less TLR3-mediated IFN-beta synthesis and lower levels of ISG mRNA synthesis. Initial studies suggest a role of RBM39 in regulating of IRF3 levels, the transcription factor activated by TLR3 signaling to induce IFN-beta synthesis. However, the effect is variable and poorly supported by transcriptomic and proteomic data. Moreover, only one out of four cell-based viral infection models reports a substantial effect of the RBM39 knockdown.

We apologize for the lack of consistency among several datasets, which was mainly due to the low sensitivity of the proteomic analysis. This has been repeated and now fully confirms all other data. In part due to the comments of this reviewer, we further broadened the scope of the manuscript away from IRF3, including a change of the title.

Major comments:

1. The data do not support the claim that RBM39 is a broadly acting player in innate immune responses. In addition, they suggest that IRF3 may not be the only relevant RBM39 target. The most informative knockdown control in this regard would be IRF3 siRNA.

We have re-structured the entire manuscript and added new data to support the central claims of our manuscript, including a repetition of the proteomics study. Proteomics and transcriptomics now consistently demonstrate the impact of RMB39 knockdown as well as indisulam treatment on several key factors of innate immunity, including IRF3, STAT1/2, RIG-I and MDA5 (now in Fig. 5), with IFNAR2 and IL10RB additionally found in transcriptomics. We further provide functional evidence that IRF3 is the key factor affected in the TLR3 pathway (IRF3 overexpression, Fig. 6B, C), whereas diminished abundance of RIG-I/MAD5 is equally important in the respective pathway, thereby also affecting NF-κB response (Fig. 6F-I). We further show the functional significance of IFN-receptor/STAT downregulation on type I and III IFN responses (Fig. 7E-G). We hope this reviewer now agrees with our claim that RBM39 is a broadly acting player in innate immune responses.

1. The structure of the manuscript is rather confusing because IRF3 is presented as the main RBM39 target in figures 3-6, but the -omics data in figures 7 and 8 do not support this view. The authors argue different sensitivities of the experimental approaches, but I think few people would agree that western blots are more sensitive than MS. To my opinion a narrative with less focus on IRF3 and a broader integration of candidates of the -omics approaches would be preferable.

We are grateful for this valuable comment and fully agree that the original structure of the manuscript was confusing and in parts misleading, which was mainly due to the fact that we followed the history of the project, which first identified the RBM39 mediated impact on IRF3 expression, whereas the -omics studies, identifying additional factors, were done at a far later point. Many discrepancies further arose from the low sensitivity of our proteomics analysis, which we now repeated, thereby obtaining far more sensitive detection of the key factors we also found in the transcriptomics data. We now moved the -omics data from the end of the paper towards the middle and provide similar depth downstream analysis of all relevant key factors identified (RIG-I/MDA5, IFN receptors, STAT1/2, to reduce the focus on IRF3, as suggested. We further changed the title and abstract to reflect this major conceptual change. Thanks to this helpful comment, we think that our manuscript is now conceptually much clearer.

Investigating the role of RBM39 by RNA-seq in pIC-treated cells would further strengthen the manuscript. It will yield a broader view of the protein's role in induced innate immunity.

We did not add pIC treatment to the RNA-seq analysis, since, based on own experience and numerous papers, this will change the expression of literally thousands of genes. Based on the key factors of the pIC response modulated by RBM39 (RLRs and IRF3), this would very likely simply result in reduced induction of the whole ISG panel (as exemplified for IFIT1, ISG15, MxA and CXCL10 in Fig. 2B-E).

3.The results in figures 6A-C are confusing for two reasons. First, the siRNA-mediated knockdown should result in reduced RBM39 protein as well (as shown in Fig. 3A) and, therefore, in an increase in RBM39 levels. Second, why was this effect not noted in the experiments shown in figs. 1-5? To avoid this confusion it might be good to mention which IRF3 splice isoforms are detected by the primers and antibodies used in these figures.

Unfortunately, the reviewer seems to have conceptually misinterpreted Fig. 6A-C of the original paper, which did not show protein, but transcriptome data. We now added the corresponding data of the proteomic analysis in the new Fig. S5, for all detectable, relevant candidates, showing consistency to all previous data. The confusing point in previous Fig. 6B, which the reviewer appears to refer to, is the upregulation of RBM39 transcript levels upon indisulam treatment, which was not apparent in previous experiments, since we always used WB to show diminished RBM39 protein levels upon indisulam treatment. This increase in RBM39 mRNA is due to an autoregulation of RBM39 mRNA by protein abundance, which has been reported in literature (Campagne et al, 2023). Since this is rather confusing and not relevant for our study, we removed previous Fig. 6B and show this aspect only in the volcano blot in Fig. 5D, mentioning and citing the paper on autoregulation.

Minor comments.

1. Fig S1: the figure panels and legend are inconsistent. IFIT1 is labeled as ISG56 in panel S1A.

We apologie for this inconsistency and now use IFIT1 throughout the paper.

1. Data with the siRNA escape mutant of RBM39 are inconsistent. For example, why is its effect significantly different only in 1 out of 4 ISG in figures S2A-D?

We apologize for the inconsistency, which is due to variability of silencing efficiency. We repeated the entire set of experiments (n=3) with a new batch of siRNA and obtained comparable, significant differences for all ISGs analyzed (new Fig. 2B-E).

1. Line 164: the statement that TRIF and RBM39 siRNAs produce effects of similar magnitude is incorrect for the IFIT1 gene in figure S2A.

This experiment was repeated (see previous point), now obtaining significant, more homogenous data. We have modified the text accordingly.

4.Fig. 2H: In absence of additional evidence for functional implications, the data showing reduced IL10RB expression should be omitted.

We omitted the data, as suggested by the reviewer, however, we provide a more in depth analysis of the type I and III IFN response in Fig. 7, based on the transcriptomic data and a functional analysis.

5.Fig. 3: More datapoints would be needed in panel A to sustain the lack of significant difference between the untreated and escape mutant samples. Are the viability data in panels B and C normalized to untreated cells to control for Indisulam toxicity? In figure S3A the effect of the mutant is rather small. To allow for comparison, the Indisulam titration curves should be adapted to the concentrations used in Fig. 3.

Fig. 3 (now Fig. 4) was replaced by another representative experiment, now also containing the quantification of the shown western blots, however, the statistical analysis shown in the previous version was and is based on three independent biological replicates, as indicated in the figure legend. Viability data was normalized to controls and this information is now added to the figure lengend as well. The mutant analyzed in Fig. S3A (now S4A) confers only partial resistance, which explains the limited but clear rescue. We did not include higher indisulam concentrations here due to the increased cytotoxicity of concentration above 5 µM in PH5CH, in the absence of pronounced additional effects on RBM39 abundance (Fig. 4B).

6.RNA-seq measures steady-state RNA, not transcription.

This is of course correct, we changed all sentences, where our wording might have indicated that we are measuring transcription by RNAseq. However, we still need to differentiate between the role of RBM39 in transcriptional regulation and splicing, where changes in RNA abundance found in RNAseq rather point to transcriptional regulation.

Reviewer #2 (Significance (Required)):

The identification of RBM39 as a candidate player in innate immune responses is of interest to a large scientific community with interest in signalling by pattern recognition receptors. Its role should be strengthened with additional infection models. It is puzzling that three out of four viruses don't benefit from the reduced IFN-beta synthesis in the RBM39 knockdown. Moreover, the data are not convincing (or too diverse) to nail down IRF3 as a major, or the most relevant, RBM39 target.

__Reviewer #3 (Evidence, reproducibility and clarity (Required)): __ CRISPR Screen for factors that are required for dsRNA-dependent ISG production. Found a large number of hits but most did not validate in subsequent assays. The authors follow up the one candidate that did pass secondary screening criteria, RBM39, although re-expression of RBM39 only rescues the phenotype of the siRNAs against RBM39 (siRBM39) in one of the two cell lines tested. Additionally, siRBM39 impacts only a subset of polyIC-induced ISGs and does not regulate NFkB-driven gene expression. They go on to attempt to investigate the impact of siRBM39 on other key innate immune genes and proteins, although many key controls and appropriate methods are missing.

We thank this reviewer for pointing at inconsistencies and missing controls in our manuscript. We have critically re-evaluated the respective datasets.

Major comments: 1) The authors propose some rationale for the limited success of the screen, however, while RBM39 may have a role in dsRNA-induced innate immunity, in general the screen seems to have limited value.

The aim of our CRISPR/Cas9 death reporter screen was the identification of so far unknown contributors to innate immune response. This was achieved by identifying a critical role of RBM39, followed by an in depth validation focusing on RBM39. We further found known components of the TLR3 pathway in our candidate list (e.g. TRIF and UNC93B1), pointing to the overall quality of the experimental setup. At no point of the manuscript we claim that our screen aimed for or delivered a comprehensive overview on innate immunity pathways. Honestly, no comparable screen (e.g. on cytopathic viruses) has delivered such data.

2) Given that the siRBM39 clearly has off-target effects (since expression of a resistant RBM39 cDNA only gives limited rescue in many cases - Fig S2), each of the experiments in which siRBM39 is used (i.e. Fig 2) should have the RBM39.esc control - especially those that drive subsequent experiments such as the expression of IFNbeta and IFNLR1 (Fig 2a, h)

The inconsistency in some datasets, showing all the same trends, but in some cases lacking statistical significance was due to variability in knockdown efficiency. We repeated all mentioned datasets with new batches of siRNA with sufficient biological replicates (n=3) with now all of them revealing consistent, statistically significant data. Importantly, all experiments implementing the RMB39.esc control now show consistent rescue.

3) Since RBM39 reduction has an apparent impact even if IFNLR1-deficient cells (although need the rescue control to know if this is real) the authors conclude that RBM39 regulates the initial wave of dsRNA signaling-events, but this should be tested with the use of Ruxilitinib to block JAK-STAT signaling.

Due to the general major re-organization of the manuscript, aiming for a less confusing data presentation and consistency towards depth of candidate evaluation, we have removed the data on the IFNLR-deficient cell line. The claim that RBM39 affects the initial wave of ISG responses is based on reduced IFNb expression, which is exclusively induced by the initial wave of ISG response and by the general impact on ISG expression, which we measure at 6h after induction, too early for autocrine IFN stimulation (Burkart et al, 2023). However, we further demonstrate that downregulation of type I and type III IFN receptors in conjunction with STAT1/2 affect the type I and the type III IFN response as well (Fig. 7E-G, in part new data). Therefore, RBM39 affects both, the intial wave and the auto-/paracrine IFN response, and we therefore undertook no further efforts to separate these effects.

4) IRF3 expression in the Indisulam-treated cells more closely tracks cell viability than RBM39 expression. For example in Fig 3C 10 microM gives 50% IRF3 expression and 50% viability but still 95% RBB39 expression - arguing that the impact of siRBM39 on IRF3 might be very indirect (and error bars on rescue are large so unclear if the rescue really worked in Fig 3A).

Based on this reviewer comment we re-evaluated the quantification in previous Fig. 3C (now Fig. 4C), which combines data from three independent experiments. We deeply apologize, but the initial quantification proved to be wrong, due erroneous background subtraction, which was relatively high in one of the PHH-replicates (Replicate 1, see Reviewer Fig. 1 in uploaded file). The re-evaluated quantification revealed 55% for the RBM39 abundance at 10µM indisulam, which better reflects the data shown and is now in line with the impact on cytotoxicity and IRF3 abundance.

5) It is unclear in Fig 4 why some cell/virus combinations are tested with siRBM39 and others are tested with Indisulam. Also the conclusion that RBM39 "substantially contributes to the cell intrinsic innate immune response to viral infections" is greatly overstated given that the differences are between ~3 fold and non-significant.

We agree that a homogenous setup for virus infection would be favorable, however, the use of different cell lines was authoritave due to limited permissivess of the used cell types towards virus infection and it appeared challenging to achieve similar knockdown efficiencies. To generate a homogenous experimental design, we now added new data showing a comparable impact of siRNA knockdown (Fig. 3F) and indisulam treatment (new Fig. 3G) on Sendai virus infection in A549 cells and took this as a rationale to consistently use indisulam for all other infections. Overall, the aim of the virus infection experiments was using a variety of natural triggers of innate immunity beyond synthetic poly(I:C). Here we found indeed significant reductions of ISG induction for all viruses tested, similar to poly(I:C), this is the basis for the statement that RBM39 contributes the cell intrinsic innate immune response to viral infections. Our experimental design did not intend to see pronounced effects on viral replication, this was only measured to secure that reduced ISG induction was not due to inhibition of viral replication. We have explained this strategy now clearer and tuned down corresponding statements, to exclude potential overinterpretation of the data.

6) Neither DTU/DRIMseq or qPCR are valid methods to measure splice isoform differences. The authors need to use rMATS or MAJIQ and validate by gel-based RT-PCR.

Output generated by modern alignment algorithms like salmon is suitable for studies on an isoform level (Love et al, 2018) and has been used in a variety of studies (e.g.(Jabs et al, 2020; Xiong et al, 2023). MAJIQ and rMATS are only superior tools if the detection of so far unknown isoforms is of interest (Love et al., 2018), which is beyond the scope of this project. We have validated the data for IRF3 in RT-qPCR, showing close to identical results to the DTU analysis (compare Fig. 6A and S6D). We disagree that a gel-based RT-PCR analysis would be superior here, due to the lack of quantification.

7) The conclusions from the proteomic and transcriptomic analyses should be treated with extreme caution given the caveats of methodology and controls discussed above.

We are aware of the caveats of these technologies. The previous proteomic analysis indeed suffered from low sensitivity, failing to detect essential candidates like IRF3. The repetition of the experiment (new Fig. 5A, B, new Fig. S5) now revealed data very consistent with the transcriptomic data. Overall, the strength of our approach is the direct comparison of siRNA based RBM39 knockdown and RBM39 depletion by indisulam throughout transcriptomics and proteomics analyses. The wide overlap argues for the validity of our data and suggests that we thereby circumvented many caveats.

Reviewer #3 (Significance (Required)):

Innate immune signaling is a complex and essential pathway for maintaining health. While much is known about key components of this pathway, additional regulators are likely to exist. This manuscript describes an attempt to identify new regulators of dsRNA-mediated gene expression.

References

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Campagne S, Jutzi D, Malard F, Matoga M, Romane K, Feldmuller M, Colombo M, Ruepp MD, Allain FH (2023) Molecular basis of RNA-binding and autoregulation by the cancer-associated splicing factor RBM39. Nat Commun 14: 5366

Coomar S, Mota P, Penson A, Schwaller J, Abdel-Wahab O, Gillingham D (2023) Overlaid Transcriptional and Proteome Analyses Identify Mitotic Kinesins as Important Targets of Arylsulfonamide-Mediated RBM39 Degradation. Mol Cancer Res 21: 768-778

Dou Z, Zhang X, Su W, Zhang T, Ye F, Zhao D, Chen X, Li Q, Zhang H, Di C (2023) Indisulam exerts anticancer effects via modulation of transcription, translation and alternative splicing on human cervical cancer cells. Am J Cancer Res 13: 2922-2937

Jabs S, Biton A, Becavin C, Nahori MA, Ghozlane A, Pagliuso A, Spano G, Guerineau V, Touboul D, Giai Gianetto Q et al (2020) Impact of the gut microbiota on the m(6)A epitranscriptome of mouse cecum and liver. Nat Commun 11: 1344

Love MI, Soneson C, Patro R (2018) Swimming downstream: statistical analysis of differential transcript usage following Salmon quantification. F1000Res 7: 952

Mai S, Qu X, Li P, Ma Q, Cao C, Liu X (2016) Global regulation of alternative RNA splicing by the SR-rich protein RBM39. Biochim Biophys Acta 1859: 1014-1024

Nijhuis A, Sikka A, Yogev O, Herendi L, Balcells C, Ma Y, Poon E, Eckold C, Valbuena GN, Xu Y et al (2022) Indisulam targets RNA splicing and metabolism to serve as a therapeutic strategy for high-risk neuroblastoma. Nat Commun 13: 1380

Xiong L, Liu J, Han SY, Koppitch K, Guo JJ, Rommelfanger M, Miao Z, Gao F, Hallgrimsdottir IB, Pachter L et al (2023) Direct androgen receptor control of sexually dimorphic gene expression in the mammalian kidney. Dev Cell 58: 2338-2358 e2335

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Referee #3

Evidence, reproducibility and clarity

CRISPR Screen for factors that are required for dsRNA-dependent ISG production. Found a large number of hits but most did not validate in subsequent assays. The authors follow up the one candidate that did pass secondary screening criteria, RBM39, although re-expression of RBM39 only rescues the phenotype of the siRNAs against RBM39 (siRBM39) in one of the two cell lines tested. Additionally, siRBM39 impacts only a subset of polyIC-induced ISGs and does not regulate NFkB-driven gene expression. They go on to attempt to investigate the impact of siRBM39 on other key innate immune genes and proteins, although many key controls and appropriate methods are missing.

Major comments:

1. The authors propose some rationale for the limited success of the screen, however, while RBM39 may have a role in dsRNA-induced innate immunity, in general the screen seems to have limited value.
2. Given that the siRBM39 clearly has off-target effects (since expression of a resistant RBM39 cDNA only gives limited rescue in many cases - Fig S2), each of the experiments in which siRBM39 is used (i.e. Fig 2) should have the RBM39.esc control - especially those that drive subsequent experiments such as the expression of IFNbeta and IFNLR1 (Fig 2a, h)
3. Since RBM39 reduction has an apparent impact even if IFNLR1-deficient cells (although need the rescue control to know if this is real) the authors conclude that RBM39 regulates the initial wave of dsRNA signaling-events, but this should be tested with the use of Ruxilitinib to block JAK-STAT signaling.
4. IRF3 expression in the Indisulam-treated cells more closely tracks cell viability than RBM39 expression. For example in Fig 3C 10 microM gives 50% IRF3 expression and 50% viability but still 95% RBB39 expression - arguing that the impact of siRBM39 on IRF3 might be very indirect (and error bars on rescue are large so unclear if the rescue really worked in Fig 3A).
5. It is unclear in Fig 4 why some cell/virus combinations are tested with siRBM39 and others are tested with Indisulam. Also the conclusion that RBM39 "substantially contributes to the cell intrinsic innate immune response to viral infections" is greatly overstated given that the differences are between ~3 fold and non-significant.
6. Neither DTU/DRIMseq or qPCR are valid methods to measure splice isoform differences. The authors need to use rMATS or MAJIQ and validate by gel-based RT-PCR.
7. The conclusions from the proteomic and transcriptomic analyses should be treated with extreme caution given the caveats of methodology and controls discussed above.

Significance

Innate immune signaling is a complex and essential pathway for maintaining health. While much is known about key components of this pathway, additional regulators are likely to exist. This manuscript describes an attempt to identify new regulators of dsRNA-mediated gene expression.

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Referee #2

Evidence, reproducibility and clarity

The authors performed a CRISPR-based screen for genes required for TLR3-mediated signaling and gene expression in Hepatoma cells. Interferon-stimulated expression of an apoptosis inducer was used as a read-out system. A number of candidate genes were identified and one of these, RBM39, investigated in detail. The protein has previously been linked to both transcriptional control and RNA processing. Validation studies confirm that reduction of cellular RBM39 results in less TLR3-mediated IFN-beta synthesis and lower levels of ISG mRNA synthesis. Initial studies suggest a role of RBM39 in regulating of IRF3 levels, the transcription factor activated by TLR3 signaling to induce IFN-beta synthesis. However, the effect is variable and poorly supported by transcriptomic and proteomic data. Moreover, only one out of four cell-based viral infection models reports a substantial effect of the RBM39 knockdown.

Major comments:

1. The data do not support the claim that RBM39 is a broadly acting player in innate immune responses. In addition, they suggest that IRF3 may not be the only relevant RBM39 target. The most informative knockdown control in this regard would be IRF3 siRNA.
2. The structure of the manuscript is rather confusing because IRF3 is presented as the main RBM39 target in figures 3-6, but the -omics data in figures 7 and 8 do not support this view. The authors argue different sensitivities of the experimental approaches, but I think few people would agree that western blots are more sensitive than MS. To my opinion a narrative with less focus on IRF3 and a broader integration of candidates of the -omics approaches would be preferable. Investigating the role of RBM39 by RNA-seq in pIC-treated cells would further strengthen the manuscript. It will yield a broader view of the protein's role in induced innate immunity.
3. The results in figures 6A-C are confusing for two reasons. First, the siRNA-mediated knockdown should result in reduced RBM39 protein as well (as shown in Fig. 3A) and, therefore, in an increase in RBM39 levels. Second, why was this effect not noted in the experiments shown in figs. 1-5? To avoid this confusion it might be good to mention which IRF3 splice isoforms are detected by the primers and antibodies used in these figures.

Minor comments.

1. Fig S1: the figure panels and legend are inconsistent. IFIT1 is labeled as ISG56 in panel S1A.
2. Data with the siRNA escape mutant of RBM39 are inconsistent. For example, why is its effect significantly different only in 1 out of 4 ISG in figures S2A-D?
3. Line 164: the statement that TRIF and RBM39 siRNAs produce effects of similar magnitude is incorrect for the IFIT1 gene in figure S2A.
4. Fig. 2H: In absence of additional evidence for functional implications, the data showing reduced IL10RB expression should be omitted.
5. Fig. 3: More datapoints would be needed in panel A to sustain the lack of significant difference between the untreated and escape mutant samples. Are the viability data in panels B and C normalized to untreated cells to control for Indisulam toxicity? In figure S3A the effect of the mutant is rather small. To allow for comparison, the Indisulam titration curves should be adapted to the concentrations used in Fig. 3.
6. RNA-seq measures steady-state RNA, not transcription.

Significance

The identification of RBM39 as a candidate player in innate immune responses is of interest to a large scientific community with interest in signalling by pattern recognition receptors. Its role should be strengthened with additional infection models. It is puzzling that three out of four viruses don't benefit from the reduced IFN-beta synthesis in the RBM39 knockdown. Moreover, the data are not convincing (or too diverse) to nail down IRF3 as a major, or the most relevant, RBM39 target.

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

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Referee #1

Evidence, reproducibility and clarity

This manuscript by Li and colleagues examines the role of RBM39 in innate immune signaling. Splicing factor RBM39 was identified through a genome wide screen with a death reporter under control of the IFIT1 promoter that got stimulated with pIC in a TLR3-dependent manner. Besides IFIT1, further experiments showed that RBM39 is also involved in optimal expression of other innate immunity genes like IFNB, CXCL10, RIG-I or MDA5. While NFkB-dependent genes seem not to depend on RBM39, for IRF3 it was shown that protein levels decrease under conditions of RBM39 depletion, because IRF3 mRNAs are (slightly) reduced and spliced differently. The sulfonamid Indisulam could largely recapitulate the phenotype of RBM39 depletion. Further analyses using proteomics and transcriptomics showed that RBM39 is required for mRNA splicing and expression of a large set of other proteins.

Altogether, this well designed and written study highlights the fundamental role played by RBM39 in in maintaining the pathways of immunity and metabolism. The key conclusions are convincing but some additional experiments would strengthen them further.

Major comments:

• For the statistics, authors seem not to have done multiple tests but rather tested individual datasets within larger graphs against each other. Please explain where this is the case and use corrections if multiple testing was done
• Fig. 4 shows that RBM39 depletion reduces IFIT expression in virus infected cells and slightly increases virus replication. RBM39 has a major effect on IRF3 levels, but also on other players in innate immunity. What happens if IRF3 is ectopically expressed as in figure 5? With this experiment one could measure how high the contribution of IRF3 miss-splicing is to innate immunity.
• Fig. 4 A uses siRNAs but B, C and D only indisulam treatment. It would be better if siRNAs would also be used for the other viruses.
• RBM39 depletion strongly reduces IRF3 levels in the WB, but not so much in RT-PCR and not at all in proteomics. Is the antibody used for WB perhaps recognizing a domain that is underrepresented in isoforms after disturbed splicing? Please clarify.
• Volcano plots in figure 7 show a lot of hits obtained after both RBM38 siRNA and indisulam (green dots), and some that are additionally identified in transcriptomes and in proteomes (red dots). Nonetheless only innate immunity and stress response genes are marked, although they do not belong to these highly conserved classes. Please elaborate more on the most RBM39-dependent genes, e.g. by presenting them in a heat map.

Minor comments:

• Some abbreviations are not explained, like PGK, siNT, siVTN
• Welsch should read Welch
• Fig. 2H: were cells also stimulated and if yes, how?
• Fig. 6E: I cannot see a difference between to IRF3-203 and 228 isoforms. And what are the white boxes?
• Also 6E: Location of the primers is barely visible
• Some materials are not properly referenced, like the death reporter, the lentiviral system, or the Rift Valley fever luciferase virus
• Supplement has no page numbers

Significance

The study advances our knowledge about the regulation of innate immunity. Strengths are the discovery of a novel layer of innate immunity regulation by splicing and the in-depth analysis of the importance of RBM39 for cellular gene expression. A potential weakness might be the focus on innate immunity as other biological functions seem even more dependent on RBM39. However, this reviewer sees the necessity that covering all aspects of RBM39 finction would be beyond the scope of a single study.

The relevant literature is appropriately cited (except for some materials, see minor comments). Results will be of interest not only to people doing basic research on innate immunity, but also to those interested in gene regulation in general or to cancer researchers using indisulam

Il manque la balise auto-fermante, non ?

for - sustainable building - engineered stone - health ban - sustainable construction - engineered stone - health ban

for - stone age 2.0 - stone and lime - new stone age - stone and lime - sustainable building - stone and lime - post-modern construction - sustainable construction - stone and lime - post-modern construction

for - sustainable building - stone age 2.0 - quote - stone age 2.0 - post-modern stone building

quote - stone age 2.0 - post modern stone building - What is required in the first half of the 21st century is a new form of post-modern construction, - relevant to contemporary needs but as - sustainable and as - environmentally benign - as pre-industrial traditional building used to be.

for - post-modern sustainable building - RIBA President Alex Gordon - 1970s proposal for stone system - ignored

for - sustainable building - stone age 2.0 - stone for compression - post-tensioned steel rods for tension

for - sustainable building - lime is better than cement - it faciliates reuse

for - sustainable building - cement mortar is the worst thing for re-using bricks

for - quote - sustainable building - concrete paradox

quote - sustainable building - concrete paradox - It does not make sense today to: - quarry limestone, - burn it with aluminium and a few other ingredients at extremely high temperatures to create a powder that is - mixed with - water, - sand and - gravel - to convert it back into a solid material. - And concrete is not good in tension. - It has to be reinforced with steel in order to build with it.

https://www.theguardian.com/us-news/ng-interactive/2024/jul/11/tim-dunn-texas-oil-billionaire-trump

eLife assessment

This fundamental study provides a near-comprehensive anatomical description and annotation of neurons in a male Drosophila ventral nerve cord, based on large-scale circuit reconstruction from electron microscopy. This connectome resource will be of substantial interest to neuroscientists interested in sensorimotor control, neural development, and analysis of brain connectivity. However, although the evidence is extensive and compelling, the presentation of results in this very large manuscript lacks clarity and concision.

Reviewer #1 (Public Review):

Summary: The authors present a close to complete annotation of the male Drosophila ventral nerve cord, a critical part of the fly's central nervous system.

Strengths: The manuscript describes an enormous amount of work that takes the first steps towards presenting and comprehending the complexity and organization of the ventral nerve cord. The analysis is thorough and complete. It also makes the effort to connect this EM-centric view of the nervous system to more classical analyses, such as the previously defined hemilineages, that also describe the organization of the fly nervous system. There are many, many insights that come from this work that will be valuable to the field for the foreseeable future.

Weaknesses: With more than 60 primary figures, the paper is overwhelming and cannot be read and digested in a single sitting. The result is more like a detailed resource rather than a typical research paper.

Reviewer #2 (Public Review):

Summary and strengths:<br /> This massive paper describes the identity and connectivity of neurons reconstructed from a volumetric EM image volume of the ventral nerve cord (VNC) of a male fruit fly. The segmentation of the EM data was described in one companion paper; the classification of the neurons entering the VNC from the brain (descending neurons or DNs) and the motor neurons leaving the VNC was described in a second companion paper. Here, the authors describe a system for annotating the remaining neurons in the VNC, which include intrinsic neurons, ascending neurons, and sensory neurons, representing the vast majority of neurons in the dataset. Another fundamental contribution of this paper is the identification of the developmental origins (hemilineage) of each intrinsic neuron in the VNC. These comprehensive hemilineage annotations can be used to understand the relationship between development and circuit structure, provide insight into neurotransmitter identity, and facilitate comparisons across insect species.Many sensory neurons are also annotated by comparison to past literature. Overall, defining and applying this annotation system provides the field with a standard nomenclature and resource for future studies of VNC anatomy, connectivity, and development. This is a monumental effort that will fundamentally transform the field of Drosophila neuroscience and provide a roadmap for similar connectomic studies in other organisms.

Weaknesses:<br /> Despite the significant merit of these contributions, the manuscript is challenging to read and comprehend. In some places, it seems to be attempting to comprehensively document everything the authors found in this immense dataset. In other places, there are gaps in scholarship and analysis. As it is currently constructed, I worry that the manuscript will intimidate general readers looking for an entry point to the system, and ostracize specialized readers who are unable to use the paper as a comprehensive reference due to its confusing organization.

The bulk of the 559 pages of the submitted paper is taken up by a set of dashboard figures for each of ~40 hemilineages. Formatting the paper as an eLife publication will certainly help condense these supplemental figures into a more manageable format, but 68 primary figures will remain, and many of these also lack quality and clarity. Without articulating a clear function for each plot, it is hard to know what the authors missed or chose not to show. As an example, many of the axis labels indicate the hemilineage of a group of neurons, but are ordered haphazardly and so small as to be illegible; if the hemilineage name is too small, and in a bespoke order for that data, then is the reader meant to ignore the specific hemilineage labels?

The text has similar problems of emphasis. It is often meandering and repetitive. Overlapping information is found in multiple places, which causes the paper to be much longer than it needs to be. For example, the concept of hemilineages is introduced three times before the subtitle "Introduction to hemilineage-based organisation". When cell typing is introduced, it is unclear how this relates to serial motif, hemilineage, etc; "Secondary hemilineages" follow the Cell typing title. Like the overwhelming number of graphical elements, this gives the impression that little attention has been paid to curating and editing the text. It is unclear whether the authors intend for the paper to be read linearly or used as a reference. In addition, descriptions of the naming system are often followed by extensive caveats and exceptions, giving the impression that the system is not airtight and possibly fluid. At many points, the text vacillates between careful consideration of the dataset's limitations and overly grandiose claims. These presentation flaws overshadow the paper's fundamental contribution of describing a reasonable and useful cell-typing system and placing intrinsic neurons within this framework.

References to past Drosophila literature are inconsistent and references to work from other insects are generally not included; for example, the extensive past work on leg sensory neurons in locusts, cockroaches, and stick insects. Such omissions are understandable in a situation where brevity is paramount. However, this paper adopts a comprehensive and authoritative tone that gives the reader an impression of completeness that does not hold up under careful scrutiny.

The paper accompanies the release of the MANC dataset (EM images, segmentation, annotations) through a web browser-based tool: clio.janelia.org. The paper would be improved by distilling it down to its core elements, and then encouraging readers to explore the dataset through this interactive interface. Streamlining the paper by removing extraneous and incomplete analyses would provide the reader with a conceptual or practical framework on which to base their own queries of the connectome.

Author response:

eLife assessment

This fundamental study provides a near-comprehensive anatomical description and annotation of neurons in a male Drosophila ventral nerve cord, based on large-scale circuit reconstruction from electron microscopy. This connectome resource will be of substantial interest to neuroscientists interested in sensorimotor control, neural development, and analysis of brain connectivity. However, although the evidence is extensive and compelling, the presentation of results in this very large manuscript lacks clarity and concision.

We thank the reviewers for their detailed and thoughtful feedback and the time that they invested to provide it. Organising this manuscript (which is clearly not a standard research article) was quite challenging as it had to fulfil a number of functions: presenting a guide to the system of annotations and the associated online resources; providing an atlas for the annotated cell types; and showcasing various analyses to illustrate the value of the dataset as well as just a few of the many questions it can be used to address. We gave careful consideration to its structure and attempted to signpost the sections that would be most useful to particular types of readers. Nevertheless we can see that this was not completely successful and we thank the reviewers for their suggestions for improvement.

We acknowledge that the resulting manuscript was very large and will endeavour to streamline our text in the revision without compromising the accessibility of the data. We do note that there is some precedent for comprehensive and lengthy connectome papers going all the way back to White et al. 1986 which took 340 pages to describe the 302 neurons of the C. elegans connectome. More recently, we can compare the “hemibrain papers” published in eLife: Scheffer et al., 2020, Li et al., 2020, Schlegel et al., 2021, Hulse et al., 2021. These papers would also be difficult to digest at a single sitting but were game-changing for the Drosophila neuroscience field and have already been cited hundreds of times, a testament to their utility. In the same way that these papers provided the first comprehensively proofread and annotated EM connectome for (a large part of) the adult fly brain, our work now provides the first fully proofread and annotated EM connectome for the nerve cord. Given the pioneering nature of this dataset we feel that the lengthy but highly structured atlas sections of the paper are justified and will prove impactful in the long term.

Whilst no EM dataset is perfect, we have endeavoured to make this one as comprehensive as possible. We found 74.4 million postsynapses and 15,765 neurons of VNC origin, all of which have been carefully proofread, reviewed, annotated and typed. For comparison, the female adult nerve cord dataset (FANC, Azevedo et al., Nature, 2024) contains roughly 45 million synapses and 14,600 neuronal cell bodies of which at the time of writing 5576 have received preliminary proofreading and 222 high quality proofreading. We emphasise that these are highly complementary datasets, given the difference in sex and the fact that each dataset has different artefacts (MANC has poorer preservation of neurons in the leg nerves; FANC is missing part of the abdominal ganglion and has lower synapse recovery). We reconstructed 5484 sensory neurons from the thoracic nerves, 84% of the ~6500 estimated from FANC. The overall recovery rate was ~86.5% if we include the ~1100 sensory neurons from abdominal nerves, which were in excellent condition.

Reviewer #1 (Public Review):

Summary:

The authors present a close to complete annotation of the male Drosophila ventral nerve cord, a critical part of the fly's central nervous system.

Strengths:

The manuscript describes an enormous amount of work that takes the first steps towards presenting and comprehending the complexity and organization of the ventral nerve cord. The analysis is thorough and complete. It also makes the effort to connect this EM-centric view of the nervous system to more classical analyses, such as the previously defined hemilineages, that also describe the organization of the fly nervous system. There are many, many insights that come from this work that will be valuable to the field for the foreseeable future.

We thank the reviewer for acknowledging the enormous collaborative effort represented by this manuscript. We tried to synthesise decades of light-level work by neuroscientists and developmental biologists working in Drosophila and other insects in order to create a standard, systematic nomenclature for >22,000 neurons, most of which had not been typed at light level. We hope that the MANC dataset and this guide to its contents will prove to be useful resources to Drosophila neurobiologists and the wider neuroscience field.

Weaknesses:

With more than 60 primary figures, the paper is overwhelming and cannot be read and digested in a single sitting. The result is more like a detailed resource rather than a typical research paper.

In writing this paper, we had two aims: first, to describe and validate our extensive biological annotation of the connectome and second, to provide interesting illustrative examples of the many analyses that could be carried out on this dataset using the atlas we generated. The resulting paper is intended primarily as a detailed reference rather than a typical research paper. At the end of the Introduction, we outline the structure of the paper and explicitly direct non-specialist readers to focus on the initial and concluding sections for orientation to the dataset so that they would not get bogged down in the details. We will review our section organisation and headings to try to make the paper more straightforward to navigate, and we will add specific figure numbers to the outline.

Reviewer #2 (Public Review):

Summary and strengths:

This massive paper describes the identity and connectivity of neurons reconstructed from a volumetric EM image volume of the ventral nerve cord (VNC) of a male fruit fly. The segmentation of the EM data was described in one companion paper; the classification of the neurons entering the VNC from the brain (descending neurons or DNs) and the motor neurons leaving the VNC was described in a second companion paper. Here, the authors describe a system for annotating the remaining neurons in the VNC, which include intrinsic neurons, ascending neurons, and sensory neurons, representing the vast majority of neurons in the dataset. Another fundamental contribution of this paper is the identification of the developmental origins (hemilineage) of each intrinsic neuron in the VNC. These comprehensive hemilineage annotations can be used to understand the relationship between development and circuit structure, provide insight into neurotransmitter identity, and facilitate comparisons across insect species.Many sensory neurons are also annotated by comparison to past literature. Overall, defining and applying this annotation system provides the field with a standard nomenclature and resource for future studies of VNC anatomy, connectivity, and development. This is a monumental effort that will fundamentally transform the field of Drosophila neuroscience and provide a roadmap for similar connectomic studies in other organisms.

We thank the reviewer for acknowledging the enormous collaborative effort represented by this manuscript. We tried to synthesise decades of light-level work by neuroscientists and developmental biologists working in Drosophila and other insects in order to create a standard, systematic nomenclature for >22,000 neurons, most of which had not been typed at light level. We hope that the MANC dataset and this guide to its contents will prove to be useful resources to Drosophila neurobiologists and the wider neuroscience field.

Weaknesses:

Despite the significant merit of these contributions, the manuscript is challenging to read and comprehend. In some places, it seems to be attempting to comprehensively document everything the authors found in this immense dataset. In other places, there are gaps in scholarship and analysis. As it is currently constructed, I worry that the manuscript will intimidate general readers looking for an entry point to the system, and ostracize specialized readers who are unable to use the paper as a comprehensive reference due to its confusing organization.

In writing this paper, we had two aims: first, to describe and validate our extensive biological annotation of the connectome and second, to provide interesting illustrative examples of the many analyses that could be carried out on this dataset using the atlas we generated. The resulting paper is intended primarily as a detailed reference rather than a typical research paper. At the end of the Introduction, we outline the structure of the paper and explicitly direct non-specialist readers to focus on the initial and concluding sections for orientation to the dataset so that they would not get bogged down in the details. We will review our section organisation and headings to try to make the paper more straightforward to navigate, and we will add specific figure numbers to the outline.

The bulk of the 559 pages of the submitted paper is taken up by a set of dashboard figures for each of ~40 hemilineages. Formatting the paper as an eLife publication will certainly help condense these supplemental figures into a more manageable format, but 68 primary figures will remain, and many of these also lack quality and clarity. Without articulating a clear function for each plot, it is hard to know what the authors missed or chose not to show. As an example, many of the axis labels indicate the hemilineage of a group of neurons, but are ordered haphazardly and so small as to be illegible; if the hemilineage name is too small, and in a bespoke order for that data, then is the reader meant to ignore the specific hemilineage labels?

We will contact eLife professional editing staff to determine whether the paper can be streamlined by moving more material to supplemental without making it difficult to locate the detailed catalogues of neurons that will be of interest to specialist readers. Based on the typical eLife format, we suspect that retaining the dashboard main figures for each hemilineage will be necessary to maintain its utility as a reference. We will, however, shorten the associated main text by, for example, moving background material used to assign the hemilineages to the Methods section and moving specific results to the figure legends where possible.

We articulated the function for each plot as follows: "Below we describe in more depth every hemilineage that produces more than one or two secondary neurons. For each of these 35 hemilineages, we show (A) the overall morphology of the secondary population, (B) representative individual neurons (as estimated by highest average NBLAST score to other members of the hemilineage), and (C) specific notable examples (which in some cases are primary). We then report (D) the locations of their connectors (postsynapses and presynapses), (E) their upstream and downstream partners by class, and (F) their upstream and downstream partners by finer subdivisions corresponding to their systematic types (secondary hemilineage, target, or sensory modality). We also provide supplementary figures showing the morphology and normalised up- and downstream connectivity of all systematic types for each hemilineage."

We have plotted every secondary neuron in each hemilineage, every predicted synapse for those neurons with confidence >0.5, every connection to partner neurons by class (no threshold applied), and then the same information organised by hemilineage in a heatmap (and including partners from all birthtimes and partners of unknown hemilineage). Then the supplementary figures show all connectivity, organised in the same way, for every individual cell type assigned to the hemilineage, including both primary and early secondary neurons. We will add more detail to the figure legends to clarify these points.

We apologise that you were unable to read some of the axis labels in the review copy of the manuscript; we did submit high resolution versions of the figures as a supplemental file, but perhaps this did not reach you; they can also be found at https://www.biorxiv.org/content/10.1101/2023.06.05.543407v2.supplementary-material. The hemilineages are in a conserved (alphanumerical) order for all hemilineage-specific plots and many others. The exceptions arise when neurons are clustered based on their connectivity to hemilineages, in which case the order of the labels necessarily follows the structure of the resulting clusters.

The text has similar problems of emphasis. It is often meandering and repetitive. Overlapping information is found in multiple places, which causes the paper to be much longer than it needs to be. For example, the concept of hemilineages is introduced three times before the subtitle "Introduction to hemilineage-based organisation". When cell typing is introduced, it is unclear how this relates to serial motif, hemilineage, etc; "Secondary hemilineages" follow the Cell typing title. Like the overwhelming number of graphical elements, this gives the impression that little attention has been paid to curating and editing the text. It is unclear whether the authors intend for the paper to be read linearly or used as a reference. In addition, descriptions of the naming system are often followed by extensive caveats and exceptions, giving the impression that the system is not airtight and possibly fluid. At many points, the text vacillates between careful consideration of the dataset's limitations and overly grandiose claims. These presentation flaws overshadow the paper's fundamental contribution of describing a reasonable and useful cell-typing system and placing intrinsic neurons within this framework.

Because we intended this paper to be read primarily as a reference, we tried to make each section stand on its own, which we agree resulted in some redundancy (with more details appearing where relevant). However, we will do our best to tighten the text for the version of record.

Our description immediately under the Cell typing title includes the use of hemilineage, serial (not serial motif, which was not used), and laterality (left-right homologues) in the procedure to assign cell types. We will change this to “Cell typing of intrinsic, ascending, and efferent neurons” for clarity. The “Secondary hemilineages” title marks the start of a new section that serves as a reference for each of the secondary hemilineages; we will change this to “Secondary hemilineage catalogue” or similar for clarity.

References to past Drosophila literature are inconsistent and references to work from other insects are generally not included; for example, the extensive past work on leg sensory neurons in locusts, cockroaches, and stick insects. Such omissions are understandable in a situation where brevity is paramount. However, this paper adopts a comprehensive and authoritative tone that gives the reader an impression of completeness that does not hold up under careful scrutiny.

We did not attempt to review the sensory neuron literature in this manuscript but rather cited those specific papers which included the axon morphology data that informed our modality, peripheral origin, and cell type assignments. Most of these came from the Drosophila literature due to the availability of genetic tools used for sparse labelling of specific populations as well as the greatly increased likelihood of conserved morphology. However we certainly agree that decades of sensory neuron work in larger insects were foundational for this subfield and will add a sentence to this effect in the introduction to our sensory neuron typing.

The paper accompanies the release of the MANC dataset (EM images, segmentation, annotations) through a web browser-based tool: clio.janelia.org. The paper would be improved by distilling it down to its core elements, and then encouraging readers to explore the dataset through this interactive interface. Streamlining the paper by removing extraneous and incomplete analyses would provide the reader with a conceptual or practical framework on which to base their own queries of the connectome.

We certainly hope that this paper will encourage readers to explore the MANC dataset. Indeed, as we state in the Discussion, "Moreover, its ultimate utility depends on how widely it is leveraged in the future experimental and computational work of the entire neuroscience community. We have only revealed the tip of the iceberg in this report, with a wealth of opportunities now available in this publicly available dataset for forthcoming connectomic analyses that will feed into testable functional hypotheses." In the first few sections of the Results, we include a visual introduction to annotated features, a glossary of annotation terms, a visual guide to our cell typing nomenclature, and two video tutorials on the use of Clio Neuroglancer to query the dataset. To further encourage exploration, we have also included illustrative examples of just a few of the many analyses that can now be performed with this comprehensive and publicly available dataset.

Joint Public Review:

Summary:

This paper by Beath et. al. identifies a potential regulatory role for proteins involved in cytoplasmic streaming and maintaining the grouping of paternal organelles: holding sperm contents in the fertilized embryos away from the oocyte meiotic spindle so that they don't get ejected into the polar body during meiotic chromosome segregation. The authors show that by time-lapse video, paternal mitochondria (used as a readout for sperm and its genome) is excluded from yolk granules and maternal mitochondria, even when moving long distances by cytoplasmic streaming. To understand how this exclusion is accomplished, they first show that it is independent of both internal packing and the engulfment of the paternal chromosomes by the maternal endoplasmic reticulum creating an impermeable barrier. They then test whether the control of cytoplasmic steaming affects this exclusion by knocking down two microtubule motors, Katanin and kinesis I. They find that the ER ring, which is used as a proxy for paternal chromosomes, undergoes extensive displacement with these treatments during anaphase I and interacts with the meiotic spindle, supporting their hypothesis that the exclusion of paternal chromosomes is regulated by cytoplasmic streaming. Next, they test whether a regulator of maternal ER organization, ATX-2, disrupts sperm organization so that they can combine the double depletion of ATX-2 and KLP-7, presumably because klp-7 RNAi (unlike mei-1 RNAi) does not affect polar body extrusion and they can report on what happens to paternal chromosomes. They find that the knockdown of both ATX-2 and KLP-7 produces a higher incidence of what appears to be the capture of paternal chromosomes by the meiotic spindle (5/24 vs 1/25). However, this capture event appears to halt the cell cycle, preventing the authors from directly observing whether this would result in the paternal chromosomes being ejected into the polar body.

The authors addressed the vast majority of the Reviewer's comments including the addition of new figures, re-wording of data interpretation and discussion points to better reflect the claims of the paper. There remain a few outstanding points which were not addressed.

In many cases the number of embryos analyzed or events capture remains low and the authors conclude that these sample sizes prevented statistical significance. It's not clear if more embryos were analyzed or if more capture would lead to statistical significance. Language capturing this caveat should also be included in the manuscript. A specific example of this is given below:

In the double knockdown of ATX-2 and KLP-7, there was no significant difference between single and double knockdowns and the ER ring displacement was not analyzed in this double mutant. Further, there was no difference in the frequency of sperm capture between single and double ATX-2 and KLP-7 due to low sample size, the the strength of the conclusion of this manuscript would be greatly improved if both of these results were further explored.

Author response:

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

Public Reviews: 

Reviewer #1 (Public Review): 

Summary: 

This paper by Beath et. al. identifies a potential regulatory role for proteins involved in cytoplasmic streaming and maintaining the grouping of paternal organelles: holding sperm contents in the fertilized embryos away from the oocyte meiotic spindle so that they don't get ejected into the polar body during meiotic chromosome segregation. The authors show that by time-lapse video, paternal mitochondria (used as a readout for sperm and its genome) is excluded from yolk granules and maternal mitochondria, even when moving long distances by cytoplasmic streaming. To understand how this exclusion is accomplished, they first show that it is independent of both internal packing and the engulfment of the paternal chromosomes by maternal endoplasmic reticulum creating an impermeable barrier. They then test whether the control of cytoplasmic steaming affects this exclusion by knocking down two microtubule motors, Katanin and kinesis I. They find that the ER ring, which is used as a proxy for paternal chromosomes, undergoes extensive displacement with these treatments during anaphase I and interacts with the meiotic spindle, supporting their hypothesis that the exclusion of paternal chromosomes is regulated by cytoplasmic streaming. Next, they test whether a regulator of maternal ER organization, ATX-2, disrupts sperm organization so that they can combine the double depletion of ATX-2 and KLP-7, presumably because klp-7 RNAi (unlike mei-1 RNAi) does not affect polar body extrusion and they can report on what happens to paternal chromosomes. They find that the knockdown of both ATX-2 and KLP-7 produces a higher incidence of what appears to be the capture of paternal chromosomes by the meiotic spindle (5/24 vs 1/25). However, this capture event appears to halt the cell cycle, preventing the authors from directly observing whether this would result in the paternal chromosomes being ejected into the polar body. 

Strengths: 

This is a useful, descriptive paper that highlights a potential challenge for embryos during fertilization: when fertilization results in the resumption of meiotic divisions, how are the paternal and maternal genomes kept apart so that the maternal genome can undergo chromosome segregation and polar body extrusion without endangering the paternal genome? In general, the experiments are well-executed and analyzed. In particular, the authors' use of multiple ways to knock down ATX-2 shows rigor. 

Weaknesses: 

The paper makes a case that this regulation may be important but the authors should do some additional work to make this case more convincing and accessible for those outside the field. In particular, some of the figures could include greater detail to support their conclusions, they could explain the rationale for some experiments better and they could perform some additional control experiments with their double depletion experiments to better support their interpretations. Also, the authors' inability to assess the functional biological consequences of the capture of the sperm genome by the oocyte spindle should be discussed, particularly in light of the cell cycle arrest that they observe. 

These general comments are addressed in the more specific critiques below.

Reviewer #2 (Public Review): 

Summary 

In this manuscript, Beath et al. use primarily C. elegans zygotes to test the overarching hypothesis that cytoplasmic mechanisms exit to prevent interaction between paternal chromosomes and the meiotic spindle, which are present in a shared zygotic cytoplasm after fertilization. Previous work, much of which by this group, had characterized cytoplasmic streaming in the zygote and the behavior of paternal components shortly after fertilization, primarily the clustering of paternal mitochondria and membranous organelles around the paternal chromosomes. This work set out to identify the molecular mechanisms responsible for that clustering and test the specific hypothesis that the "paternal cloud" helps prevent the association of paternal chromosomes with the meiotic spindle. 

Strengths 

This work is a collection of technical achievements. The data are primarily 3- and 4-channel time-lapse images of zygotes shortly after fertilization, which were performed inside intact animals. There are many instances in which the experiments show extreme technical skill, such as tracking the paternal chromosomes over large displacements throughout the volume of the embryo. The authors employ a wide variety of fluorescent reporters to provide a remarkably clear picture of what is going on in the zygote. These reagents and the novel characterization of these stages that they provide will be widely beneficial to the community. 

The data provide direct visualization of what had previously been a mostly hypothetical structure, the "paternal cloud," using simultaneous labeling of paternal DNA and mitochondria in combination with a variety of maternal proteins including maternal mitochondria, yolk granules, tubulin, and plasma membrane. Together, these images provided convincing evidence of the existence of this specified cytoplasmic domain. They go on to show that the knockdown of the ataxin-2 homolog ALX-2, a protein previously shown to affect ER dynamics, disrupted the paternal cloud, identifying a role for ER organization in this structure. 

The authors then used the system to test the functional consequences of perturbing the cytoplasmic organization. Consistent with the paternal cloud being a stable structure, it stayed intact during large movements the authors generated using previously published knockdowns (of mei-1/katanin and kinesin-13/kpl-7) that increased cytoplasmic streaming. They used this data to document instances in which the paternal chromosomes were likely to have been attached to the spindle. They concluded with direct evidence of spindle fibers connecting to the paternal chromatin upon knockdown of ATX-2 in combination with increased cytoplasmic streaming, providing strong, direct support for their overarching hypothesis. 

Weaknesses 

While the data is convincing, the narrative of the paper could be streamlined to highlight the novelty of the experiments and better articulate the aims. For example, the cloud of paternal mitochondria and membranous organelles was previously shown, but Figures 1-2 largely reiterate that observation. The innovation seems to be that the combination of ER, yolk, and maternal mitochondrial markers makes the existence of a specified domain more concrete. There are also some instances where more description is needed to make the conclusions from the images clear. 

These general comments are addressed in the more specific critiques below.

The manuscript intersperses what read like basic characterizations of fluorescent markers that, as written, can distract from the main story. The authors characterized the dynamics of ER organization throughout the substages of meiosis and the permeability of the envelope of ER that surrounds the paternal chromatin, but it could be more clearly established how the ability to visualize these structures allowed them to address their aims.

We have added the following after the initial description of ER morphology changes: (ER morphology was used to determine cell-cycle stages during live imaging reported below in Fig. 6.)

More background on what was previously known about ER organization in M-phase and the role of ataxin proteins specifically may help provide more continuity. 

We have added references to transitions to ER sheets during mitotic M-phase in HeLa cells and Xenopus extracts.

Reviewer #3 (Public Review): 

Summary: 

This study by Beath et al. investigated the mechanisms by which sperm DNA is excluded from the meiotic spindle after fertilization. Time-lapse imaging revealed that sperm DNA is surrounded by paternal mitochondria and maternal ER that is permeable to proteins. By increasing cytoplasmic streaming using kinesin-13 or katanin RNAi, the authors demonstrated that limiting cytoplasmic streaming in the embryo is an important step that prevents the capture of sperm DNA by the oocyte meiotic spindle. Further experiments showed that the Ataxin-2 protein is required to hold paternal mitochondria together and close to the sperm DNA. Finally, double depletion of kinesin-13 and Ataxin-2 suggested an increased risk of meiotic spindle capture of sperm DNA. 

Overall, this is an interesting finding that could provide a new understanding of how meiotic spindle capture of sperm DNA and its accidental expulsion into the polar body is prevented. However, some conceptual gaps need to be addressed and further experiments and improved data analyses would strengthen the paper. 

- It would be helpful if the authors could discuss in good detail how they think maternal ER surrounds the sperm DNA

We have added 2 references to papers about nuclear envelope re-assembly from Shirin Bahmanyar’s lab and suggest the ER envelope is a halted intermediate in nuclear envelope reassembly.

and why is it not disrupted following Ataxin disruption. 

We have been attempting to disrupt ER structures in the meiotic embryo for the last 5 years by depleting profilin, BiP, atlastin, ATX-2 and by optogenetically packing ER into a ball in the middle of the oocyte.  None of these treatments prevent envelopment of the sperm DNA by maternal ER.  None of these treatments remove ER from the spindle envelope and none remove ER from the plasma membrane.  These treatments mostly result in “large aggregates” of ER that we have not examined by EM.  Wild speculation: any disruption of the ER strong enough to prevent ER envelopment around chromatin would be sterile because the M to S transition in the mitotic zone of the germline would be blocked.  Rapid depletion of ATX-2 to the extent shown by rigorous data in this manuscript does not prevent ER envelopment around chromatin.  We chose not to speculate about the reasons for this because we do not know why.

- Since important phenotypes revealed in RNAi experiments (e.g. kinesin-13 and ataxin-2 double depletion) are not very robust, the authors should consider toning down their conclusions and revising some of their section headings. I appreciate that they are upfront about some limitations, but they do nonetheless make strong concluding sentences. 

We have changed the discussion of the klp-7 atx-2 double depletion to: “The capture of the sperm DNA by the meiotic spindle in ATX-2 KLP-7 double depleted embryos suggests that the integrity of the exclusion zone around the sperm DNA might insulate the sperm DNA from spindle microtubules.  However, a much larger number of klp-7(RNAi) singly depleted and atx-2(degron) singly depleted time-lapse sequences are needed to rigorously support this idea. “

- The discussion section could be improved further to present the authors' findings in the larger context of current knowledge in the field. 

We have expanded the discussion as suggested.

- The authors previously demonstrated that F-actin prevents meiotic spindle capture of sperm DNA in this system. However, the current manuscript does not discuss how the katanin, kinesin-13 and Ataxin-2 mechanisms could work together with previously established functions of F-actin in this process. 

We have added pfn-1(RNAi) to the discussion section.

- How can the authors exclude off-target effects in their RNAi depletion experiments? Can kinesin-13, katanin, and Ataxin phenotypes be rescued for instance? 

For ataxin-2 phenotypes, two completely independent controls for off target effects are shown.  GFP(RNAi) on a strain with and endogenous ATX-2::GFP tag vs GFP(RNAi) on a strain with no tag on the ATX-2.  ATX-2::AID with or without auxin.  For kinesin-13 and katanin, we did not do a rigorous control for off-target effects of RNAi.  However, the effects of these depletions on cytoplasmic microtubules have been previously reported by others

- How are the authors able to determine if the paternal genome was actually captured by the spindle? Does lack of movement definitively suggest capture without using a spindle marker? 

mKate::tubulin labels the spindle in each capture event.  This can be seen in Video S3. for mei-1(RNAi) and Figure 9 for atx-2 klp-7 double depletions.

(1) Major issues: 

The images provided are not convincing that mitochondria are entirely excluded from the regions with yolk granules from the images provided. Please provide insets of magnified images of the paternal mitochondria in Figure 1E to more clearly show the exclusion even when paternal mitochondria are streaming. Providing grayscale images, individual z-sections and/or some quantification of this data might also be more convincing to this reviewer. 

We have modified Fig. 1 by adding single wavelength magnified insets to more clearly show that paternal mitochondria are in a “black hole” in the maternal yolk granules during  cytoplasmic streaming.

Figure 2 -This figure can be retitled to highlight that the paternal organelle cloud is impermeable to mitochondria and conserved. 

The legend has been re-titled as suggested.

Figure 3B, An image of the DNA within the ring of maternal ER especially since the maternal ER ring is used as a proxy for the paternal chromosomes in later figures would strengthen the authors' claims.

We have added a panel showing DAPI-stained DNA in the center of the ER ring and paternal mitochondria cloud. 

Why is the faster time scale imaging significant? I think this could be more clearly set up in the paper. Perhaps rapid imaging of maternal mito-labeled kca-1(RNAi) embryos would better show the difference in time scale, with the expectation that the paternal cloud forms and persists while the ER invades. 

We are not sure what the reviewer means.  5 sec time intervals were used throughout the paper.  We are also not sure how kca-1(RNAi) would help.  Movement of the entire oocyte into and out of the spermatheca is what limits the ability to keep a fusing sperm in focus.  kca-1(RNAi) would prevent cytoplasmic streaming but not ovulation movements.

Figure 4 - The question about the permeability of the ER envelope seems to come out of nowhere as written. It isn't clear how it contributes to the larger story about preventing sperm incorporation in the spindle.

This section of the results is introduced with: “If the maternal ER envelope around sperm DNA was sealed and impermeable during meiosis, this could both prevent the sperm DNA from inducing ectopic spindle assembly and prevent the sperm DNA from interacting with meiotic spindle microtubules.” 

The data in Figure 4 would probably not be expected to be in this paper based on the paper title. Maybe the title needs something about ER dynamics? "eg. ATX-2 but not an ER envelope" isolates the paternal chromatin? 

In Figure 5, it seems that RNAi of klp-7 and Mei-1 had slightly different effects on short-axis displacement of the ER envelope (klp-7 affecting it more dramatically than mei-1) and slightly different effects on interaction with the meiotic spindle (capture vs streaming past the spindle). The authors mention in their discussion that the difference in the interaction with the meiotic spindle might reflect the effects that loss of Mei-1 may have on the spindle but could it also be a consequence of the differences in cytoplasmic streaming observed?

With our current data, the only statistically significant difference between cytoplasmic streaming of the sperm contents in mei-1(RNAi) vs klp-7(RNAi) is that excessive streaming persists longer into metaphase II in klp-7(RNAi).  We have added a sentence describing this difference to the results.  If differences in streaming were the cause of different capture frequencies, then klp-7(RNAi) would cause more capture events than mei-1(RNAi) but the opposite was observed.  We have avoided too much discussion here because the frequency of capture events is too low to demonstrate statistically significant differences between mei-1(RNAi), klp-7(RNAi), and atx-2(degron) + klp-7(RNAi) without a very large increase in the number of time-lapse sequences.  

Also, the authors should find a way to represent this interaction with the meiotic spindle in a quantitative or table form to allow the reader to observe some of the patterns they report more easily.

We have added a table to Fig. 9 that summarizes capture data.

Finally, can the authors report when they observe the closest association with the meiotic spindle: Does it correlate with the period of greatest displacement (AI) or are they unlinked? 

The low frequency of capture events makes it difficult to test this rigorously.

Figure 6- 'Endogenously tagged ATX-2 was observed throughout oocytes and meiotic embryos without partial co-localization with ER.' How can the authors exclude co-localization with ER? 

We have changed the wording to: “Endogenously tagged ATX-2 was observed throughout oocytes and meiotic embryos (Fig. 6A; Fig. S2).  ATX-2 did not uniquely  co-localize with ER (Fig. S2).“

The rationale for why the authors think that the integrity of sperm organelles is important to keep the genomes apart is not clear to this reviewer and needs to be explained better. Moving the discussion of the displacement experiments in Figure S3 from the end of the results section to the ATX-2 knockdown section would help accomplish this. 

We have added the sentence: “The frequency of sperm capture by the meiotic spindle (Fig. 9D) was significantly higher than wild-type controls in klp-7(RNAi) atx-2(AID) double depleted embryos (p=0.011 Fisher’s exact test).   Although the number of single mutant embryos analyzed was too low to demonstrate a significant difference between single and double mutant embryos,  these results qualitatively support the hypothesis that limiting cytoplasmic streaming and maintaining the integrity of the ball of paternal mitochondria are both important for preventing capture events between the meiotic spindle and sperm DNA.”

It looks like, in the double knockdown of ATX-2 and KLP-7, the spread of paternal mitochondria is less affected than when only ATX-2 is depleted. What effect does this result have on the observation that the incidence of sperm capture appears to increase in the double depletion? What does displacement of the ER ring look like in the double depletion? Is it additive, consistent with their interpretation that both limiting cytoplasmic streaming and maintaining the integrity of the ball of paternal mitochondria is required to keep the genomes separate? 

We cannot show a significant difference between single a double knockdowns without increasing n by alot.  We did not analyze ER ring displacement in the double mutant.

Is the increased incidence of capture in the double-depleted embryos significant? 

We have added the sentence: “The frequency of sperm capture by the meiotic spindle (Fig. 9D) was significantly higher than wild-type controls in klp-7(RNAi) atx-2(AID) double depleted embryos (p=0.011 Fisher’s exact test).   Although the number of single mutant embryos analyzed was too low to demonstrate a significant difference between single and double mutant embryos,  these results qualitatively support the hypothesis that limiting cytoplasmic streaming and maintaining the integrity of the ball of paternal mitochondria are both important for preventing capture events between the meiotic spindle and sperm DNA.”

What do the authors make of the cell cycle arrest observed when paternal chromosomes are captured? Is there an argument to be made that this arrest supports the idea that preventing this capture is actively regulated and therefore functionally important? 

We chose not to discuss the mechanism of this arrest because considerably more work would be required to prove that it is not caused by a combination of imaging conditions and genotype.  The low frequency of these capture + arrest events would make it very difficult to show that the arrest does not occur after depleting a checkpoint protein.

(2) Minor concerns: 

Top of page 4: "streaming because depletion tubulin stops cytoplasmic streaming (7)" should be "streaming because depletion of tubulin stops cytoplasmic streaming (7)" 

The ”of” has been inserted.

Page 6: "This result indicated that the volume of paternal mitochondria excludes maternal mitochondria and yolk granules but not maternal ER." The authors have only shown this for maternal mitochondria, not yolk granules. 

We have deleted the mention of yolk granules here.

Page 7: "These results suggest that all maternal membranes are initially excluded from the sperm at fusion." Should be "These results show that maternal ER are initially excluded from the sperm at fusion. Since maternal mitochondria and yolk granules are excluded later, this suggests that all maternal membranes are initially excluded from the sperm at fusion." 

We have changed this sentence as suggested.

It's not clear why the authors show other types of movement that might be quantified when cytoplasmic streaming is affected in Figure 5A and only quantify long-axis and short-axis displacement. 

We have deleted the other types of movement from the schematic.  Although these parameters were quantified, we did not include this data in the results so it would be confusing for the reader to have them in the schematic.

Bottom of page 7: Mention that the GFP::BAF-1 was maternally provided. 

We have added “Maternally provided..”

Missing an Arrow on Figure 1A 9:20. 

We removed the text citation to an arrow in Fig. 1A because we moved most of the description of the ER ring to Fig. 3 to address other reviewer suggestions.

Supplemental videos should be labeled appropriately to indicate what structures are labeled. It is currently difficult to understand what is being shown. 

(3) Issues with the Discussion section: 

"The simplest explanation is that cytoplasm does not mix during the 45 min from GVBD to pronucleus formation due to the high viscosity of cytoplasm." - Citation page 12. 

We have changed the sentence to: “The simplest hypothesis is that maternal and paternal cytoplasm might not mix during the 45 min from GVBD to pronucleus formation due to the high viscosity of cytoplasm.” 

"The higher frequency of capture of the sperm DNA by the meiotic spindle in ATX-2 KLP-7 double depleted embryos compared with either single depletion suggests that the integrity of the exclusion zone around the sperm DNA may insulate the sperm DNA from spindle microtubule" - Pages 12-13 reference the figures. 

This sentence has been rewritten in response to other comments but the new sentence now references revised Fig. 9.

"ATX-2 is required to maintain the integrity of the ball of paternal mitochondria around the sperm DNA, but the mechanism is unknown." - Page 13 reference figure. 

A reference to Figs 7 and 8 has been inserted.

" In control embryos, the sperm contents rarely came near the meiotic spindle in agreement with a previous study that found that male and female pronuclei rarely form next to each other (6). Streaming of the sperm contents was most commonly restricted to a jostling motion with little net displacement, circular streaming in the short axis of the embryo, or long axis streaming in which the sperm turned away from the spindle before the halfway point of the embryo. Depletion of MEI-1 or KLP-7 resulted in longer excursions of the sperm contents in the long axis of the embryo toward the spindle but frequent capture of the sperm by the spindle was only observed in mei-1(RNAi)." - Page 13, the corresponding figures need to be referenced for these sentences. 

We have inserted figure references.

"In capture events observed after double depletion of ATX-2 and KLP-7, a bundle of microtubules was discernible extending from the spindle into the ER envelope surrounding the sperm DNA. Such bundles were not observed in mei-1(RNAi) capture events, likely because of the previously reported low density of microtubules in mei-1(RNAi) spindles (36, 37)." - Pages 13-14 references figures here. 

We have inserted figure references.

"The higher frequency of capture of the sperm DNA by the meiotic spindle in ATX-2 KLP-7 double depleted embryos compared with either single depletion suggests that the integrity of the exclusion zone around the sperm DNA may insulate the sperm DNA from spindle microtubules." - This should be toned down since this phenotype is not robust. 

We have changed this to: “The capture of the sperm DNA by the meiotic spindle in ATX-2 KLP-7 double depleted embryos suggests that the integrity of the exclusion zone around the sperm DNA might insulate the sperm DNA from spindle microtubules.  However, a much larger number of klp-7(RNAi) singly depleted and atx-2(degron) singly depleted time-lapse sequences are needed to rigorously support this idea. “

ATX-2 depletion alters ER morphology but does not impact the maternal ER envelope - could the authors provide a potential explanation for this? 

In the discussion, we cite papers showing that ATX-2 depletion affects many different cellular processes so the effect we see on paternal mitochondria might have nothing to do with the ER ring.   We have been attempting to disrupt ER structures in the meiotic embryo for the last 5 years by depleting profilin, BiP, atlastin, ATX-2 and by optogenetically packing ER into a ball in the middle of the oocyte.  None of these treatments prevent envelopment of the sperm DNA by maternal ER.  None of these treatments remove ER from the spindle envelope and none remove ER from the plasma membrane.  These treatments mostly result in “large aggregates” of ER that we have not examined by EM.  Wild speculation: any disruption of the ER strong enough to prevent ER envelopment around chromatin would be sterile because the M to S transition in the mitotic zone of the germline would be blocked.  Rapid depletion of ATX-2 to the extent shown by rigorous data in this manuscript does not prevent ER envelopment around chromatin.  We chose not to speculate about the reasons for this because we do not know why.

It would be good to have representative images of what the altered spindle looks like in MEI-1-depleted oocytes. 

The structure of MEI-1-depleted spindles has been described in the cited references.

"Depletion of MEI-1 or KLP-7 resulted in longer excursions of the sperm contents in the long axis of the embryo toward the spindle but frequent capture of the sperm by the spindle was only observed in mei-1(RNAi)" - It is intriguing that this does not happen in the double depletion experiments of kinesin-13 and ATX-2. The authors should perhaps discuss this. 

This does happen in KLP-7 ATX-2 double depleted embryos as shown in Fig. 9.

(4) Missing citations: 

"This analysis was restricted to embryos from anaphase I through anaphase II because our streaming data and that of Kimura 2020 indicate that the sperm contents have not moved significantly before anaphase I." - This needs an appropriate citation. Page 10. 

We have inserted citations here.

" The simplest explanation is that cytoplasm does not mix during the 45 min from GVBD to pronucleus formation due to the high viscosity of cytoplasm." - Citation page 12. Not referencing figures in the discussion. 

We have changed the sentence to: “The simplest hypothesis is that maternal and paternal cytoplasm might not mix during the 45 min from GVBD to pronucleus formation due to the high viscosity of cytoplasm.” 

"The higher frequency of capture of the sperm DNA by the meiotic spindle in ATX-2 KLP-7 double depleted embryos compared with either single depletion suggests that the integrity of the exclusion zone around the sperm DNA may insulate the sperm DNA from spindle microtubule" - Pages 12-13 reference the figures. 

A reference to the revised Fig. 9 has been inserted in the revised version of this sentence.

"ATX-2 is required to maintain the integrity of the ball of paternal mitochondria around the sperm DNA, but the mechanism is unknown." 

References to Figs. 7 and 8 have been inserted.

Page 13 reference figure 

" In control embryos, the sperm contents rarely came near the meiotic spindle in agreement with a previous study that found that male and female pronuclei rarely form next to each other (6). Streaming of the sperm contents was most commonly restricted to a jostling motion with little net displacement, circular streaming in the short axis of the embryo, or long axis streaming in which the sperm turned away from the spindle before the halfway point of the embryo. Depletion of MEI-1 or KLP-7 resulted in longer excursions of the sperm contents in the long axis of the embryo toward the spindle but frequent capture of the sperm by the spindle was only observed in mei-1(RNAi)." Page 13, the corresponding figures need to be referenced for these sentences. 

We have inserted citations here.

"In capture events observed after double depletion of ATX-2 and KLP-7, a bundle of microtubules was discernible extending from the spindle into the ER envelope surrounding the sperm DNA. Such bundles were not observed in mei-1(RNAi) capture events, likely because of the previously reported low density of microtubules in mei-1(RNAi) spindles (36, 37)." Pages 13-14 references figures here. 

We have inserted citations here.

(5) Referencing wrong figures in the text: 

Figure 5 - In the figure legend there is a 5C but there is no 5C panel in the figure. 

A C has been inserted in Fig. 5.

Figure 6A - "Dark holes were observed suggesting exclusion from the lumens of larger membranous organelles (Fig. 6A; Fig. S2)." Page 10. 

6A has been changed to 6C.

Figure 6A is showing background autofluorescence in WT oocytes so I am not certain why it is cited here. 

The Figure citation has been corrected to 6B, C.

Figure 8 - I could not find the supplemental data file with the individual mitochondria distance measurements. 

We are including the Excel file with the revised submission.

The last sentence of the first paragraph should be re-worded to be more concise ". In C. elegans, the nucleus is positioned away from the site of future fertilization so that the meiosis I spindle assembles at the opposite end of the ellipsoid zygote from the site of fertilization (2-4). " 

Every word of this sentence is important.

Last sentence second paragraph typo "These microtubules are thought to drive meiotic cytoplasmic streaming because depletion tubulin stops cytoplasmic streaming (7) and depletion of the microtubule-severing protein katanin by RNAi results in an increased mass of cortical microtubules and an increase in cytoplasmic streaming (8)." Pages 3-4. 

“of” has been inserted.

(6) Typos in the introduction should be corrected: 

Ataxin or kinesin-13 are not mentioned in the introduction but these are a big focus of the paper. 

Gong et al 2024 written instead of number citation (page 5), no citation in References.

This has been corrected. 

Supplemental videos should be labeled appropriately to indicate what structures are labeled. It is currently difficult to understand what is being shown.

Reviewer #2 (Public Review):

Summary:<br /> The authors used four datasets spanning 30 countries to examine funding success and research quality score for various disciplines. They examined whether funding or research quality score were influenced by majority gender of the discipline and whether these affected men, women, or both within each discipline. They found that disciplines dominated by women have lower funding success and research quality score than disciplines dominated by men. These findings are surprising because even the men in women-dominated fields experienced lower funding success and research quality score.

Strengths:<br /> - The authors utilized a comprehensive dataset covering 30 countries to explore the influence of the majority gender in academic disciplines on funding success and research quality scores.<br /> - Findings suggest a systemic issue where disciplines with a higher proportion of women have lower evaluations and funding success for all researchers, regardless of gender.<br /> - The manuscript is notable for its large sample size and the diverse international scope, enhancing the generalizability of the results.<br /> - The work accounts for various factors including age, number of research outputs, and bibliometric measures, strengthening the validity of the findings.<br /> - The manuscript raises important questions about unconscious bias in research evaluation and funding decisions, as evidenced by lower scores in women-dominated fields even for researchers that are men.<br /> - The study provides a nuanced view of gender bias, showing that it is not limited to individuals but extends to entire disciplines, impacting the perception and funding and quality or worth of research.<br /> - This work underscores the need to explore motivations behind gender distribution across fields, hinting at deep-rooted societal and institutional barriers.<br /> - The authors have opened a discussion on potential solutions to counter bias, like adjusting funding paylines or anonymizing applications, or other practical solutions.<br /> - While pointing out limitations such as the absence of data from major research-producing countries, the manuscript paves the way for future studies to examine whether its findings are universally applicable.<br /> - The study carefully uses the existing data (including PBRF funding panel gender diversity) to examine gender bias. These types of datasets are often not readily accessible for analysis. Here, the authors have used the available data to the fullest extent possible.

The authors have addressed the concerns I had about the original version.

Author response:

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

Public Reviews:

Reviewer #2 (Public Review):

Summary:

The authors used four datasets spanning 30 countries to examine funding success and research quality score for various disciplines. They examined whether funding or research quality score were influenced by majority gender of the discipline and whether these affected men, women, or both within each discipline. They found that disciplines dominated by women have lower funding success and research quality score than disciplines dominated by men. These findings, are surprising because even the men in women-dominated fields experienced lower funding success and research quality score.

Strengths:

- The authors utilized a comprehensive dataset covering 30 countries to explore the influence of the majority gender in academic disciplines on funding success and research quality scores.

- Findings suggest a systemic issue where disciplines with a higher proportion of women have lower evaluations and funding success for all researchers, regardless of gender.

- The manuscript is notable for its large sample size and the diverse international scope, enhancing the generalizability of the results.

- The work accounts for various factors including age, number of research outputs, and bibliometric measures, strengthening the validity of the findings.

- The manuscript raises important questions about unconscious bias in research evaluation and funding decisions, as evidenced by lower scores in women-dominated fields even for researchers that are men.

- The study provides a nuanced view of gender bias, showing that it is not limited to individuals but extends to entire disciplines, impacting the perception and funding and quality or worth of research.

- This work underscores the need to explore motivations behind gender distribution across fields, hinting at deep-rooted societal and institutional barriers.

- The authors have opened a discussion on potential solutions to counter bias, like adjusting funding paylines or anonymizing applications, or other practical solutions.

- While pointing out limitations such as the absence of data from major research-producing countries, the manuscript paves the way for future studies to examine whether its findings are universally applicable.

Weaknesses:

- The study does not provide data on the gender of grant reviewers or stakeholders, which could be critical for understanding potential unconscious bias in funding decisions. These data are likely not available; however, this could be discussed. Are grant reviewers in fields dominated by women more likely to be women?

- There could be more exploration into whether the research quality score is influenced by inherent biases towards disciplines themselves, rather than only being gender bias.

- The manuscript should discuss how non-binary gender identities were addressed in the research. There is an opportunity to understand the impact on this group.

- A significant limitation is absence of data from other major research-producing countries like China and the United States, raising questions about the generalizability of the findings. How comparable are the findings observed to these other countries?

- The motivations and barriers that drive gender distribution in various fields could be expanded on. Are fields striving to reach gender parity through hiring or other mechanisms?

- The authors could consider if the size of funding awards correlates with research scores, potentially overlooking a significant factor in the evaluation of research quality. Presumably there is less data on smaller 'pilot' funds and startup funds for disciplines where these are more common. Would funding success follow the same trend for these types of funds?

- The language used in the manuscript at times may perpetuate bias, particularly when discussing "lower quality disciplines," which could influence the reader's perception of certain fields.

- The manuscript does not clarify how many gender identities were represented in the datasets or how gender identity was determined, potentially conflating gender identity with biological sex.

Reviewer #3 (Public Review):

This study seeks to investigate one aspect of disparity in academia: how gender balance in a discipline is valued in terms of evaluated research quality score and funding success. This is important in understanding disparities within academia.

This study uses publicly available data to investigate covariation between gender balance in an academic discipline and:

i) Individual research quality scores of New Zealand academics as evaluated by one of 14 broader subject panels.

ii) Funding success in Australia, Canada, Europe, UK.

The study would benefit from further discussion of it limitations, and from the clarification of some technical points (as described in the recommendations for the authors).

Recommendations For The Authors:

Reviewer #2 (Recommendations For The Authors):

This is a very nice study as-is. In the following comments, I have mainly put my thoughts as I was reading the manuscript. If there are practical ways to answer my questions, I think they could improve the manuscript but the data required for this may not be available.

Are there any data on the gender of grant reviewers or stakeholders who make funding decisions?

The research quality score metrics seem to be more related to unconscious bias. The funding metrics may also, but there are potentially simple fixes (higher paylines for women or remove gender identities from applications).

We have included some details about PBRF funding panel gender diversity. These panels are usually more gender balanced than the field they represent, but in the extreme cases (Engineering, Education, Mathematics) they are skewed as would be expected. Panels for other award decision makers was not available.

I wonder if the research score metric isn't necessarily reflecting on the gender bias in the discipline but rather on the discipline itself? Terms like "hard science" and "soft science" are frequently used and may perpetuate these biases. This is somewhat supported by the data - on line 402-403 the authors state that women in male-dominated fields like Physics have the same expected score as a man. Could it be that Physics has a higher score than Education even if Physics was woman-dominated and Education was man-dominated? Are there any instances in the data where traditionally male- or female-dominated disciplines are outliers and happen to be the opposite? If so, in those cases, do the findings hold up?

Overall we would love to answer this question! But our data is not enough. We mention these points in the Discussion (Lines 472-466). We have extended this a little to cover the questions raised here.

How are those with non-binary gender identities handled in this article? If there is any data on the subject, I would be curious to know how this effects research score and funding success.

These data were either unavailable or the sample size was too small to be considered anonymously (Mentioned on Lines 74-76).

A limitation of the present article is a lack of data on major research-producing countries like China and the United States. Is there any data relevant to these or other countries? Is there reason to believe the findings outlined in this manuscript would apply or not apply to those countries also?

We would be very excited to see if the findings held up in other countries, particularly any that were less European based. Unfortunately we could not find any data to include. Maybe one day!

What are the motivations or other factors driving men to certain fields and women to certain fields over others? What are the active barriers preventing all fields from 50% gender parity?

Field choice is a highly studied area and the explanations are myriad we have included a few references in the discussion section on job choice. I usually recommend my students read the blog post at

https://www.scientificamerican.com/blog/hot-planet/the-people-who-could-have-done-science-didnt/

It is very thoughtful but unfortunately not appropriate to reference here.

The authors find very interesting data on funding rates. Have you considered funding rates and the size of funding awards as a factor in research score? Some disciplines like biomedical science receive larger grants than others like education.

A very interesting thought for our next piece of work. We would definitely like to explore our hypothesis further.

There are instances where the authors writing may perpetuate bias. If possible these should be avoided. One example is on line 458-459 where the authors state "...why these lower quality disciplines are more likely..." This could be re-written to emphasize that some disciplines are "perceived" as lower quality. Certainly those in these discipline would not characterize their chosen discipline as "low quality".

Well-spotted! Now corrected as you suggest.

Similar to the preceding comment, the authors should use care with the term "gender". In the datasets used, how many gender identities were captured? How many gender identity options were given in the surveys or data intake forms? Could individuals in these datasets have been misgendered? Do the data truly represent gender identity or biological sex?

We know that in the PBRF dataset gender was a binary choice and transgender individuals were able to choose which group they identified with. There was no non-binary option (in defence the latest dataset there is from 2018 and NZ has only recently started updating official forms to be more inclusive) and individuals with gender not-stated (a very small number) were excluded. ARC did mention that a small number of individuals were either non-binary or gender not stated, again these are not included here for reasons of anonymity. This is now mentioned on Lines 74-76. The effects on this group are important and understudied likely because, as here, the numbers are too small to be included meaningfully.

Reviewer #3 (Recommendations For The Authors):

Major revisions:

Could you add line numbers to the Supplementary Materials for the next submission?

Yes! Sorry for the omission.

(1) In the main text L146 and Figure 1, it is not clear why the expected model output line is for a 50 year old male from University of Canterbury only, but the data points are from disciplines in all eight universities in New Zealand. I think it would be more clear and informative to report the trend lines that represent the data points. At the moment it is hard to visualise how the results apply to other age groups or universities.

As age and institution are linear variables with no interactions they are only a constant adjustment above or below this line and the adjustment is small in comparison to the linear trend. Unfortunately, if they were included graphically they do not aid understanding. We agree that indluded raw data with an adjusted trend line can be confusing buy after a lor of between-author discussion this was the most informative compromise we could find (many people like raw data so we included it).

(2) Does your logistic regression model consider sample size weighting in pmen? Weighting according to sample sizes needs to be considered in your model. At the moment it is unclear and suggests a proportion between 0 and 1 only is used, with no weighting according to sample size. If using R, you can use glm(cbind(nFem, nMalFem).

Yes. All data points were weighted by group size exactly as you suggest. We have updated the text on Lines 317 to make this clear.

(3) For PBRF, I think it is useful to outline the 14 assessment panels and the disciplines they consider. Did you include the assessment panel as an explanatory variable in your model too to investigate whether quality is assessed in the same manner between panels? If not, then suggest reasons for not doing so.

We have now included more detail in main text on the gender split of the panels. They were not included as an explanatory variable. In theory there was some cross-referencing of panel scores to ensure consistency as part of the PBRF quality assurance guidelines.

(4) There are several limitations which should be discussed more openly:

Patterns only represent the countries studied, not necessarily academia worldwide.

Mentioned on Line 485-487.

Gender is described as a binary variable.

Discussed on Line 74-76.

The measure of research evaluation as a reflection of academic merit.

This is acknowledged in the data limitations paragraph in the discussion, at the end of the discussion

Minor revisions:

(1) L186. Why do you analyse bibliometric differences between individuals from University of Canterbury only? It would be helpful to outline your reasons.

Although bibliometric data is publicly available it is difficult to collect for a large number of individuals. You also need some private data to match bibliometrics with PBRF data which is anonymous. We were only able to do this for our own institution with considerable internal support.

(2) How many data records did you have to exclude in L191 because they could not be linked? This is helpful to know how efficient the process was, should anyone else like to conduct similar studies.

We matched over 80% of available records (384 individuals). We have mentioned this on Line 194.

(3) Check grammar in the sentence beginning in L202.

Thank-you. Corrected.

(4) Please provide a sample size gender breakdown for "University of Canterbury (UC) bibliometric data", as you do for the preceding section. A table format is helpful.

Included on Line 194.

(5) L377 I think this sentence needs revision.

Thank you, we have reworked that paragraph.

(6) L389-392 Is it possible evaluation panels can score women worse than men and that because more women are present in female-biassed disciplines, the research score in these are worse? Women scoring worse between fields, may be a result of some scaling to the mean score.

No.  This is not possible because women in male-dominated fields score higher.

(7) L393 Could you discuss explanations for why men outperform women in research evaluation scores more when disciplines are female dominated?

Unfortunately, we don’t have an explanation for this and can’t get one from our data. We hope it will be an interesting for future work.

(8) Could the figures be improved by having the crosses, x and + scaled, for example, in thickness corresponding to sample size? Alternatively, some description of the sample size variation? Sorting the rows by order of pmen in Table E1 would also be helpful for the reader.

As with the previous figure we have tried many ways of presenting it (including tis one). Unfortunately nothing helped.

We have provided Table E1 as a spreadsheet to allow readers to do this themselves.

(9) Please state in your methods section the software used to aid repeatability.

This is now in Supplementary Materials (Matlab 2022b).

(10) It is great to report your model findings into real terms for PBRF and ARC. Please can you extend this to CIHR and EIGE. i.e. describing how a gender skew increase of x associates with a y increase in funding success chance.

We have added similar explanations for both these datasets comparing the advantage of being male with the advantage of working in a male dominated discipline.

(11) I would apply care to using pronouns "his" and "her" in L322-L324 and avoid if at all possible, instead, replacing them with "men" and "women".

We have updated the text to avoid there pronouns in most places.

The article in general would benefit from a disclosure statement early on conceding that gender investigated here is only as a binary variable, discounting its spectrum.

See Line 74-76.

Please also report how gender balance is defined in the datasets as in the data summary in supplementary materials, within the main text.

Our definition of gender balance (proportion of researchers who are men, ) is given on Line 103.

(12) The data summary Table S1 could benefit from explaining the variables in the first column. It is currently unclear how granularity, size of dataset and quotas/pre-allocation? are defined.

These lines have been removed as they information they contained is included elsewhere in the table with far better explanations!

(13) There are only 4 data points for investigating covariation between gender balance and funding success in CIHR. This should be discussed as a limitation.

The small size of the dataset is now mentioned on Line 348.

(14) L455 "Research varies widely across disciplines" in terms of what?

This sentence has been extended

.

(15) L456 Maybe I am missing something but I don't understand the relevance of "Physicists' search for the grand unified theory" to research quality.

Removed.

(16) Can you provide more discussion into the results of your bibliographic analysis and Figure 2? An explanation into the relationships seen in the figure at least would be helpful.

Thank you we have clarified the relationships seen in each of figures 2A (Lines 226-235), 2B (Lines 236-252), and 2C (lines  260-268).

(17) It would be helpful to include in the discussion a few more sentences outlining:

- Potential future research that would help disentangle mechanisms behind the trends you find.

- How this research could be applied. Should there be some effort to standardise?

We have added a short paragraph to the discussion about implications/applications, and future research (Lines 481-484).

(18) The introduction could benefit from discussing and explaining their a priori hypotheses for how research from female-biassed disciplines may be evaluated differently.

While not discussed in the introduction, possible explanations for why and how research in female dominated fields might be evaluated differently are explored in some detail in the Discussion.  We think once is enough, and towards the end is more effective than at the beginning.

(19) L16 "Our work builds on others' findings that women's work is valued less, regardless of who performs that work." I find this confusing because in your model, there is a significant interaction effect between gender:pmen. This suggests that for female-biassed disciplines, there is even more of a devaluation for women, which I think your lines in figure 1 suggest.

Correct but men are still affected, so the sentence is correct.  What is confusing is that the finding is counter to what we might expect.

eLife Assessment

This study provides convincing evidence that the quality of research in female-dominated fields of research is systematically undervalued by the research community. The authors' findings are based on analyses of data from a research assessment exercise in New Zealand and data on funding success rates in Australia, Canada, the European Union and the United Kingdom. This work is an important contribution to the discourse on gender biases in academia, underlining the pervasive influence of gender on whole fields of research, as well as on individual researchers.

Reviewer #3 (Public Review):<br /> This study seeks to investigate one aspect of disparity in academia: how gender balance in a discipline is valued in terms of evaluated research quality score and funding success. This is important in understanding disparities within academia.<br /> This study uses publicly available data to investigate covariation between gender balance in an academic discipline and:<br /> individual research quality scores of New Zealand academics as evaluated by one of 14 broader subject panels.<br /> [ii] funding success in Australia, Canada, Europe, UK.

The authors have addressed the concerns I had about the original version

eLife assessment

This study presents a valuable development of endometrial organoid culture methodology that mimics the window of implantation. Functional validation to demonstrate its robustness is lacking; therefore, the study is considered incomplete. The data may be interesting to embryologists and investigators working on reproductive biology and medicine.

Reviewer #1 (Public Review):

This study generated 3D cell constructs from endometrial cell mixtures that were seeded in the Matrigel scaffold. The cell assemblies were treated with hormones to induce a "window of implantation" (WOI) state.

The authors did their best to revise their study according to the reviewers' comments. However, the study remains unconvincing and at the same time too dense and not focused enough.

(1) The use of the term organoids is still confusing and should be avoided. Organoids are epithelial tissue-resembling structures. Hence, the multiple-cell aggregates developed here are rather "co-culture models" (or "assembloids"). It is still unexpected (unlikely) that these structures containing epithelial, stromal and immune cells can be robustly passaged in the epithelial growth conditions used. All other research groups developing real organoids from endometrium have shown that only the epithelial compartment remains in culture at passaging (while the stromal compartment is lost). If authors keep to their idea, they should perform scRNA-seq on both early and late (passage 6-10) "organoids". And they should provide details of culturing/passaging/plating etc that are different with other groups and might explain why they keep stromal and immune cells in their culture for such a long time. In other words, they should then in detail compare their method to the standard method of all other researchers in the field, and show the differences in survival and growth of the stromal and immune cells.<br /> (2) The paper is still much too dense, touching upon all kind of conclusions from the manifold bioinformatic analyses. The latter should be much clearer and better described, and then some interesting findings (pathways/genes) should be highlighted without mentioning every single aspect that is observed. The paper needs a lot of editing to better focus and extract take-home messages, not bombing the reader with a mass of pathways, genes etc which makes the manuscript just not readable or 'digest-able'. There is no explanation whatever and no clear rationale why certain genes are included in a list while others are not. There is the impression that mass bioinformatics is applied without enough focus.<br /> (3) The study is much too descriptive and does not show functional validation or exploration (except glycogen production). Some interesting findings extracted from the bioinformatics must be functionally tested.<br /> (4) In contrast to what was found in vivo (Wang et al. 2020), no abrupt change in gene expression pattern is mentioned here from the (early-)secretory to the WoI phase. Should be discussed. Although the bioinformatic analyses point into this direction, there are major concerns which must be solved before the study can provide the needed reliability and credibility for revision.<br /> (5) All data should be benchmarked to the Wang et al 2020 and Garcia-Alonso et al. 2021 papers reporting very detailed scRNA-seq data, and not only the Stephen R. Quake 2020 paper.<br /> (6) Fig. 2B: Vimentin staining is not at all clear. F-actin could be used to show the typical morphology of the stromal cells?<br /> (7) Where does the term "EMT-derived stromal cells" come from? On what basis has this term been coined?<br /> (8) CD44 is shown in Fig. 2D but the text mentions CD45 (line 159)?<br /> (9) All quantification experiments (of stainings etc) should be in detail described how this was done. It looks very difficult (almost not feasible) when looking at the provided pictures to count the stained cells.<br /> (10) Fig. 3C: it is unclear how quantification can be reliably done. Moreover, OLFM4 looks positive in all cells of Ctrl, but authors still see an increase?<br /> (11) Fig. 3F: Met is downregulated which is not in accordance with the mentioned activation of the PI3K-AKT pathway.<br /> (12) Lines 222-226: transcriptome and proteome differences are not significant; so, how meaningful are the results then? Then, it is very hard to conclude an evolution from secretory phase to WoI.<br /> (13) WoI organoids show an increased number of cilia. However, some literature shows the opposite, i.e. less ciliated cells in the endometrial lining at WoI (to keep the embryo in place). How to reconcile?<br /> (14) How are pinopodes distinguished from microvilli? Moreover, Fig. 3 does not show the typical EM structure of cilia.<br /> (15) There is a recently published paper demonstrating another model for implantation. This paper should be referenced as well (Shibata et al. Science Advances, 2024).<br /> (16) Line 78: two groups were the first here (Turco and Borreto) and should both be mentioned.<br /> (17) Line 554: "as an alternative platform" - alternative to what? Authors answer reviewers' comments by just changing one word, but this makes the text odd.

Reviewer #2 (Public Review):

In this research, Zhang et al. have pioneered the creation of an advanced organoid culture designed to emulate the intricate characteristics of endometrial tissue during the crucial Window of Implantation (WOI) phase. Their method involves the incorporation of three distinct hormones into the organoid culture, coupled with additives that replicate the dynamics of the menstrual cycle. Through a series of assays, they underscore the striking parallels between the endometrial tissue present during the WOI and their crafted organoids. Through a comparative analysis involving historical endometrial tissue data and control organoids, they establish a system that exhibits a capacity to simulate the intricate nuances of the WOI.

The authors made a commendable effort to address the majority of the statements. Developing an endometrial organoid culture methodology that mimics the window of implantation is a game-changer for studying the implantation process. However, the authors should strive to enhance the results to demonstrate how different WOI organoids are from SEC organoids, ensuring whether they are worth using in implantation studies, or a proper demonstration using implantation experiments.

Author response:

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

Reviewer #1 (Public Review): 

Q1: First of all, the term organoid must be discarded. The authors just seed the endometrial cell mixture which assembles and aggregates into a 3D structure which is then immediately used for analysis. Organoids grow from tissue stem cells and must be passage-able (see their own description in lines 69-71). So, the term organoid must be removed everywhere, to not confuse the organoid field. It is not shown that the whole 3D assembly is passageable, which would be very surprising given the fact that immune and stromal cells do not grow in Matrigel because of the unfavorable growing conditions (which are targeted to epithelial cell growth).

We appreciate for your highlighting concerns regarding our organoid construction.

(1) The organoids in our system were originated from tissue stem cells.

We induced adult stem cells derived from endometrial tissue to construct organoids in vitro by various small molecules (such as Noggin, EGF, FGF2, WNT-3A and R-Spondin1), which involves a complex self-assembly process rather than a mere cellular assembly. Initially, there are single cells and small cell clusters in the system two days after the planting. On the fourth day, the glandular epithelial cells gradually assembled to glands, while the stromal cells spontaneously organized themselves around the glands.  On the eleventh day, the endometrial glands enlarged, epithelial cells organized in a paving stone arrangement, and stromal cells established an extensive network. (Author response image1) (Figure 1C)

(2) The organoids we constructed are passage-able.  

Most organoids were used for experiments up to the fifth generation, while some are extended to the 10th generation and cryopreserved. (Response Figure 1B, C)

(3) Immune and stromal cells are present in our system from the primary to the fourth generation. In our study, immune and stromal cells were identified not only from scRNA-seq data (third generation of organoids) (Figure 2A), but also from the morphology using 3D transparent staining and light sheet microscopy imaging (third generation of organoids), with Vimentin marking stromal cells, CD45 designating immune cells, and FOXA2 identifying glands. Further, flow cytometric analysis was applied to verify immune cells within the organoids (third generation of organoids). (Response Figure 1D, E, F)  

Moreover, Immune cells and stromal cells can grow in Matrigel, which was also found in the study of organoid pioneer Hans Clevers (Hans Clevers et al., Nature Reviews Immunology 2019).

Author response image 1.

(A) The growth condition of endometrial cells was observed from day2 to day11 after plating under an inverted microscope. Scale bar = 200 μm. (B) The endometrial organoids of different passages were observed from P1 to P5. Scale bar = 200 μm. (C) Stromal cells formed an extensive network (down). The arrowhead indicates dendritic stromal cells. Scale bar = 100 μm (left), Scale bar = 50 μm (right). (D) Exhibition of stromal cells marked by vimentin. Nuclei were counterstained with DAPI. The arrow indicates stromal cells. Scale bar = 40 μm (up), Scale bar = 30 μm (down). (E)Exhibition of immune cells marked by CD45 and endometrial gland marked by FOXA2. Nuclei were counterstained with DAPI. The arrow indicates immune cells. Scale bar = 50 μm. (F) Flow cytometric analysis of T cells and macrophages in the endometrial organoid. Gating strategy used for determining white blood cells (CD45+ cells), T cells (CD45+CD3+ cells) and macrophages (CD45+CD68+CD11b+ cells).

Q2: Second, the study remains fully descriptive, bombing the reader with a mass of bioinformatic analyses without clear descriptions and take-home messages. The paper is very dense, meaning readers may give up. Moreover, functional validation, except for morphological and immunostaining analyses (which are posed as "functional" but actually are only again expression) is missing, such as in vivo functionality (after transplantation e.g.) and embryo interaction. Importantly, the 3D structure misses the right architecture with a lining luminal epithelium which is present in the receptive endometrium in vivo and needed as the first contact site with the embryo. So, in contrast to what the authors claim, this is not the best model to study embryo interaction, or the closest model to the in vivo state (line 318, line 326).

Thank you.

(1) We have made the following improvements. Firstly, we have conducted additional experiments to validate the bioinformatics analysis. Secondly, the structure of the manuscript has been refined to ensure logical coherence and clear transitions between paragraphs. Thirdly, important findings have been emphasized to ensure readers’ comprehension and inspiration. Furthermore, the manuscript was revised by both domestic and international experts to enhance the readability and clarity.

(2)  For the functional validation, in vivo transfer could not be carried out so far due to ethical limitation. But human embryos are able to develop and grow more efficiently in combining with the receptive endometrial organoids we generated (unpublished data).

(3) As you suggested, we replaced the “closest” with “closer”. It is undeniable that the model cannot completely simulate the in vivo implantation process that the luminal epithelium of the endometrium contacts the embryo first.  

Q3: Third, receptive endometrial organoids (assembloids; Rawlings et al., eLife 2021) and receptive organoid-derived "open-faced endometrial layer" (Kagawa et al., Nature 2022) have already been described, which is in contrast to what the authors claim in several places that "they are the first" (e.g. lines 87-88, 316-319, etc). These studies used real organoids to achieve their model (and even showed embryo interaction), while in the present study, different cell types are just seeded and assembled. Hence, logically, immune cells are present which are never found in real organoid models. The only original aspect in the present study is the use of hormones to enhance the WOI phenotype. However, crucial information on this original aspect is missing such as concentration of the hormones, refreshment schedule, all 3 hormones added together or separately, and all 3 required?

Thank you for pointing out these researches referring to endometrial organoids.

(1) While we didn’t explicitly state "the first", we should be careful to use the expressions similar to "the first". It has been changed to a gentle and modest expression, as follows “we are far from understanding how embryo implantation occurs during the WOI due to ethical limitations and fewer in vitro receptive endometrial model” and “which confirms that they are closer to the in vivo state”.

(2) The definition of organoids and the existence of immune cells have been detailed addressed in the first question.

(3) In terms of hormone scheme, hormone concentrations have been detailed in Table S2 of Supplementary. Estrogen was supplemented to the basal medium for the initial two days, after which a combination treatment of MPA, cAMP, PRL, hPL, and HCG was administered for the subsequent six days. The medium was refreshed every two days.

All three hormones were deemed necessary, which was validated by multiple group comparisons. Only the organoids treated with all six hormones together exhibited an endometrial receptivityrelated gene expression profile. (Author response image 2).

Author response image 2.

Heatmap showing receptivity related gene expression profile of organoids in each hormone regimen.  

Q4: Moreover, it is not a "robust" model at all as the authors claim, given the variability of the initial cell mixture (varying from patient to patient). Actually, the reproducibility is not shown. The proportions of the different cell types seeded in the Matrigel droplet will be different with every endometrial biopsy. It would be much better to recombine epithelial (passageable) organoids with stromal and immune cells in a quantified, standardized manner to establish a "robust" model.

Thanks for your suggestion.  

Firstly, the constructed endometrial organoids generally consist of epithelial, stromal, and immune cells. However, it is undeniable that the cell proportions may vary slightly among different patients. Secondly, the term "robust" is intended to convey strong support for embryo development, which will be supported by our next study (unpublished data). Therefore, robust is replaced here as alternative. Thirdly, as for "reproducibility", the hormone-treated organoids from different women exhibited similarity to the in vivo receptive endometrium through multi-omics analysis, ERT, and various other experiments.  

Reviewer #2 (Public Review):

Q1: With endometrial receptivity analysis, they suggest a successful formation of the implantation window in vitro, but this result is difficult to interpret.

Thanks for your question.  

We understand that the most effective way to demonstrate endometrial receptivity is embryo implantation, which was conducted simultaneously and will be presented in our next study. In this study, we validated the receptivity based on the current researches.

(1) At the single-cell transcriptome level, the cellular composition and function of the receptive endometrial organoids were similar to those of the in vivo implantation window (Stephen R. Quake et al, 2020).

(2) At the whole organoids level, the receptive endometrial organoids exhibited the similar characteristics in transcriptome and proteome to the in vivo mid-secretory endometrium (Andres Salumets 2017, Qi Yu 2018, Triin Laisk 2018, Edson Guimarães Lo Turco 2018, Xiaoyan Chen 2020, Francisco Domínguez 2020, DavidW. Greening 2021, Norihiro Sugino 2023). The receptive endometrial organoids were also validated by endometrial receptivity test (ERT), which utilized high-throughput sequencing and machine learning to assess endometrial receptivity (Yanping Li et al., 2021).  

(3) At the microstructural level under electron microscope, the receptive endometrial organoids exhibited characteristics of the implantation window, such as pinopodes, glycogen particles, microvilli, and cilia.

Overall, the receptive organoids we constructed closely resemble the in vivo implantation window at the single-cell, organoids, and microstructural levels based on existing researches.

Q2: Analyzing transcriptome and proteome information of WOI organoids, authors demonstrate a strong response to estrogen and progesterone, but some comparisons are made with CTRL and SEC, and others only with CTRL, which limits the power of some results. In the same way, some genes related to Cilia and pinopodes appear dominant in WOI organoids, but the comparison by electron microscopy is made only against CTRL organoids.  

In subsequent analysis, WOI organoids showed a marked differentiation from proliferative to secretory epithelium, and from proliferative epithelium to EMT-derived stromal cells than SEC organoids. These statements are based on their upregulation of monocarboxylic acid and lipid metabolism, their enhanced peptide metabolism and mitochondrial energy metabolism, or their pseudotime trajectories. However, other analyses (such as the accumulation of secretory epithelium or decreased proliferative epithelium, the increased ciliated epithelium after hormonal treatment, or the presence of EMT-derived stromal cells) show only small differences between SEC and WOI organoids.

Thank you for raising these important questions.

(1) At the organoid level, the differences in transcriptome and proteome between SEC and WOI organoids are not significant. This is understandable because WOI organoids are further induced towards the implantation window based on the secretory phase (i.e. SEC organoids), and both are similar at the overall organoid level.  

(2) At the single-cell level, the accumulation of secretory epithelium, decreased proliferative epithelium, increased ciliated epithelium post hormonal treatment, or the presence of EMTderived stromal cells are the fundamental features of the secretory endometrium. Therefore, these features are present in both WOI and SEC organoids. However, the most notable differences lie in the more comprehensive differentiation and varied cellular functions exhibited by WOI organoids compared to SEC organoids.

(3) Regarding electron microscopy, we have now quantitatively compared the presence of various characteristic structures such as microvilli, cilia, pinopodes and glycogen in the CTRL, SEC and WOI groups. It has been observed that WOI organoids possess longer microvilli and increased cilia, glycogen, and pinopodes compared to SEC organoids (Fig2H).

Reviewer #1 (Recommendations For The Authors):

Q1: Several of the key methods are performed by companies, hence not in detail described and therefore not verifiable which is essential for reviewers and readers.

We are grateful for the suggestion. Specific methods have now been incorporated into the "Supporting Information" section. (Line91~102, Line 107~123, Line 132~139)

Q2 - Line 49: It is not shown in the present study whether the WOI organoids are a 'robust' platform.

- Line 76: There is a study (Dolat L., Valdivia RH., Journal of Cell Science, 2021) that developed a co-culture with endometrial organoids and immune cells (neutrophils) which should be mentioned.:

We have reweighed the word and now replace 'robust' with 'alternative' (Line 54).  We have considered the reviewer's suggestion and added this citation (Line 82-83) about the cocultivation of immune cells with endothelial organoids, which was not previously cited mainly because the research model was mouse.

Q3: Figure 1: Endometrial organoids possess endometrial morphology and function. - The authors should further explain their decision to add PRL, hCG, and hPL to the organoid culture. Why these particular compounds? What is their specific role during the WOI?

In terms of hormone scheme, estrogen and progesterone promote the transition of endometrial organoids into the secretory phase, and on this basis, pregnancy hormones can further promote their differentiation. PRL promotes immune regulation and angiogenesis during implantation, HCG improves endometrial thickness and receptivity, and HPL promotes the development and function of endometrial glands. Our constructed WOI organoid is in a state conducive to embryo implantation. We aim to develop an in vitro model for embryo implantation study. The detailed explanation of this aspect was initially provided in the Discussion section (Lines 298–313). To enhance the clarity for reviewers and readers regarding the selection of the hormonal regimen, we have now articulated it in the Results section (Lines 124–130).

When selecting hormone formulations, multiple group comparisons were made. It was found that the number, area, and average intensity of organoids in these groups were similar over time. But the WOI organoids showed endometrial receptivity related gene expression profile, which highly expressed genes positively correlated with endometrial receptivity, and lowly expressed genes negatively correlated with receptivity, compared to the other hormone formulations (added to Figure S1E, S1F). Hormone dosage was primarily based on peri-pregnant maternal body or localized endometrium levels (Margherita Y. Turco et al., Nature Cell Biology 2017).

-  Line 108: "the endometrial cells" instead of "endometrial organoid"? Because the authors also refer to the stromal cells.

You should be referring to this sentence “The endometrial organoid, consisting of vesicle-like glands, fibrous stromal cells, and other surrounding cells, developed into a 3D structure with the support of Matrigel”. Organoid, a self-assembled 3D structure, consists of multiple cells and closely resembles in vivo tissue or organ. It offers high expansibility, phenotypic, and functional properties. Here, we aim to delineate the endometrial organoid, comprising epithelial cells, stromal cells, and other cellular components that assemble to form intricate 3D structures. Hence, the term "endometrial organoid" is more appropriate.

-  Line 110: "the endometrial glands", do the authors mean the endometrial organoids? The authors also mention they enlarge, which must be quantified.

You should be referring to this sentence “As the organoids grew and differentiated, the endometrial glands enlarged, epithelial cells adopted a paving stone arrangement, and stromal cells formed an extensive network”. Here, we mean the “endometrial glands” grow progressively in the organoids. We agree with your suggestion to quantify the change of organoids’ area over time, and found that they increased progressively in all three groups (shown as follows) (Fig.S1E) (Line130-131) 

Author response image 3.

The dynamic changes of the area of organoids over time in the CTRL, SEC and WOI organoids.

-  Line 112: E-cadherin is a general epithelial marker, not a glandular marker.

We agree with your suggestion and now change to ‘The epithelium marker E-cadherin’ (Line110).

-  Line 116: Which group was used for KI67 and CC3 staining?

The CTRL organoids were used for Ki67 and CC3 staining. We have modified this expression in the Figure 1E Legend.

-  Line 123: Organoid size (diameter or area) needs to be quantified to claim that WOI organoids grow slower than SEC/CTRL organoids. The same goes for Ki67+ cells for proliferation. In the legend of Fig 1B, the authors in contrast state that the organoids show a similar growth pattern.

We are extremely grateful to you for pointing out this problem. We quantitatively analyzed the size of organoids in the three groups. The area was found to be increasing over time, with the three groups growing the most vigorously in the CTRL group, followed by the SEC group and the WOI group, but the differences were not statistically significant. Relevant results have been added to Figure S1E (Line130-131). There were no significant differences in Ki67 expression of these organoids. Therefore, the three groups of organoids showed a similar growth pattern. We decided to delete the statement “Following hormonal stimulation, WOI organoids exhibited slower growth than SEC and CTRL organoids, while CTRL organoids maintained robust proliferative activity (Fig. 1B)”.

Author response image 4.

The dynamic changes of the area of organoids over time in the CTRL, SEC and WOI organoids.

-  Line 126: Fourteen days of organoid treatment is a very long time. Growing organoids may already be dying which should be checked by CC3 staining to prove that organoids are still fully viable.

Endometrial organoids are vigorous in proliferation and have a long survival period due to the presence of adult stem cells. To address your queries effectively, we conducted CC3 staining on the organoids treated for 14 days, revealing negligible expression levels (shown as below).

Author response image 5.

Figure note: The Ki67 and CC3 immunostaining on the organoids after 14-day hormone treatment.

-  Line 128: Changes in hormone receptors should be supported by RT-qPCR data to be more convincing

We agree with your suggestion. Here we supplemented the RT-PCR results of hormone receptors as follows (Figure S1D) (Line119-121). PAEP and PGR are associated with progesterone, and OLFM4 and EGR1 are associated with estrogen.

-  1A: Are authors able to see and characterize decidualized stromal cells as indicated in the illustration?

Upon the reviewer's inquiry, we carefully observed the morphology of stromal cells in hormone-treated organoids. Regrettably, the morphology of decidualized stromal cells was not ascertainable through light microscopy in our endometrial organoids.

-  1C: Which treatment condition are the organoids in these images?

This figure showed the bright-field morphology of the CTRL organoids, which is now noted in the Figure 1C legend.

-  1D: PAS staining should be quantified to support the claims.

We agree with your suggestion. The quantitative comparison of PAS staining was conducted in these three groups of organoids (Figure S1G) (Line142-143)

-  1D: Where are the stromal cells in the model? There should be vimentin-positive cells outside of the glands.

The figure 1D illustrates the outcomes of section staining, which owned limitation to displaying stromal cells around the gland. Considering the 3D structure of organoids, we conducted organoid clearing and staining, and observed stromal cells (marked by Vimentin) under light sheet microscope (shown as below). The stromal cells were also presented using this method in the original Figure 2B.

Author response image 6.

Exhibition of stromal cell marked by vimentin of CTRL organoid through whole-mount clearing, immunostaining and light sheet microscopy imaging. Nuclei were counterstained with DAPI. The arrowhead indicates stromal cells. Scale bar = 70 μm.

Figure 2: Developing receptive endometrial organoids in vitro mimicking the implantation window endometrium.

-  Line 142: CD44 is not an exclusive marker for immune cells. It has been shown to be expressed in glandular secretory epithelial cells (Fonseca et al., 2023). The authors also mention that CD44 is expressed in stromal cells (line 265). Staining for CD45 (or another immune-specific marker) is needed to demonstrate the presence of immune cells. 

We appreciated your suggestions. We demonstrated the distribution of immune cells in organoids using the organoid clearing technique in combination with light-sheet microscopy imaging, using CD45 as a marker (Figure 2C).

-  Line 144: What are the proportions of the immune cells? What is the variation between patient samples?

We assessed the proportion of immune cells with the help of flow cytometry and analyzed the proportion of Macrophages and T cells in organoids derived from 8 patients. The proportion of WBC in organoids was about 3%~4% (Figure 2D), among which macrophages were less than 1% and T cells less than 2% (Figure S2E). There existed a very few patients with large heterogeneity, and the proportion of immune cells in most patients was

relatively stable.

-  Line 161: What is the endometrial receptivity test (ERT)? Not explained at all.

Endometrial Receptivity Test (ERT) is a kind of gene analysis-based method for detecting endometrial receptivity, which combines high-throughput sequencing and machine learning to analyze the expression of endometrial receptivity-related genes, allowing for a relatively accurate assessment of endometrial receptivity. It is currently used in clinical practice to determine endometrial receptivity and guide personalized embryo transfer (Yanping Li et al., J Transl Med 2021). (line179-183)

-  2A: The authors' dataset is compared to a published dataset. How were they combined? Were they merged, mapped on each other, or integrated? Were all cells employed from the published dataset or specific cell types? Much detail to evaluate the analysis is missing.

We are very grateful for your comments.  

(1) The four raw datasets (CTRL, SEC and WOI organoids, and mid-secretory endometrium) underwent batch correction and integration using Harmony. Subsequently, the integrated dataset underwent dimensionality reduction via  PCA. The soft k-means clustering algorithm was employed to address batch effects and clustering, utilizing a clustering parameter resolution of 0.5. Finally, the clustering results were visualized using tSNE based on the cell subpopulation classification. (“Methods” Line164-175)

(2) The Figure 2A displayed comparison of glandular and luminal epithelium, secretory epithelium, LGR5 epithelium, EMT-derived stromal cells, ciliated epithelium, and glandular secretory epithelium (shown as Figure S2C~S2D) (Line150-154)

- 2E: Please add the cell type names above the heatmaps to improve readability.

Thanks to your suggestion, we have added the cell type names above the heatmaps.

- 2G: The difference between the left and right graphs is not clear from the figure itself. Improve by adding a title and more explanation.

Thanks for your careful review. We have added the title to the left and right graphs.

Supplementary Figure 3 is referenced with Figure 2. Supplementary Figure 2 is referenced with Figure 3. The order needs to be changed.

Thanks for your careful review. We have changed the order.

- S3B: Typical markers for annotation of the different cell clusters are not included and therefore it is not convincing enough that annotations are correct. E.g. Epithelial markers (EPCAM, CDH1), Stromal cells (VIM, PDGFRA), SOX9+LGR5+ cells (SOX9, LGR5). How were the EMT-derived stromal cells designated? It is not clear from the data whether they are in fact EMT-derived or whether they show epithelial markers as well (stated in line 246).

We deeply appreciate your suggestion. We provided more details to describe the cell clustering as the following. Single-cell transcriptomics analysis referred to CellMarker, PanglaoDB, Human Cell Atlas, Human Cell Landscape, and scRNASeqDB, and previous endometrium related studies. (W. Wang et al., Nat Med 2020, P. D. Harriet C. Fitzgerald et al., PNAS 2019, K. M. Thomas, M Rawlings et al., eLife 2021, L. Garcia-Alonso et al., Nat Genet 2021) 

(1) SOX9+LGR5+ cells: SOX9 and LGR5 are both proliferative markers. SOX9 is expressed in all clusters dispersedly. LGR5 is mainly expressed in two clusters, one of which is stem derived epithelium, and the other cluster expresses LGR5 in a scattered manner. Refer to the markers of SOX9+LGR5+ cells, SOX9+LGR5- cells, and SOX9+ proliferative cells in 2021 Nature Genetics (L. Garcia-Alonso et al., Nat Genet 2021), the cells in this cluster expressed high levels of NUAK2, CNKSR3, FOS and LIF, which was consistent with the expression profiles of SOX9+LGR5+ cells and SOX9+ proliferative cells. However, considering that the number of cells expressing LGR5 was relatively small, this cluster of cells was renamed SOX9+ proliferative epithelium.

Figure 3: Receptive endometrial organoids recapitulate WOI-associated biological characteristics. - Line 173-174: The WOI organoids should be compared in detail to the SEC organoids in addition to the CTRL organoids, to show that this WOI model and new hormonal treatment is providing better results compared to the SEC organoids and the results obtained in previous studies.

Thanks for your suggestion. At the organoid level, the differences in transcriptome and proteome between SEC and WOI organoids are not significant. This is understandable because WOI organoids are further induced towards the implantation window based on the secretory phase (i.e. SEC organoids), which prompted us to continue exploring at the single-cell level.

- Line 190: Quantification of pinopodes is required to claim that they are more densely arranged in WOI organoids. 

- Line 190-191: Again, is there a difference in pinopode presence between the WOI and SEC organoids to show that the WOI organoids are really distinct and a better model?

We agree with the reviewer’s suggestion and quantified the pinopodes. The CTRL, SEC and WOI organoids were found to have increasing numbers of pinopodes, with WOI organoid owning the most abundant pinopodes under electron microscope. (Figure 2H) (Line184-186)

- Line 194: Also here, quantification of the glycogen particles is missing.

We agree with your suggestion. We have quantified the area of glycogen particles under electron microscope in the CTRL, SEC and WOI organoids. It was found that WOI organoid had the most glycogen particles. (Figure 2H) (Line184-186)

- 3C: There is no difference between SEC and WOI organoids condition for OLFM4 and PRA/B. What is the purpose then of adding extra hormones if no difference is present?

The figure 3C indicated that there was no significant difference in OLFM4 and PRA/B level (reflecting estrogen and progesterone responsiveness) in SEC and WOI organoids at the organoids level. It is understandable because WOI organoids are induced further into the implantation window on the basis of the secretory phase (i.e., SEC organoids), and both are similar at the overall level of organoids. Based on this, we further explored the differences between WOI organoids and SEC organoids at the single-cell level.

- 3G: A higher magnification is necessary to evaluate cilia staining. From these images, it seems like CTRL organoids also express acetyl-a-tubulin.

Thanks for your suggestion. The figure has been enlarged and shown as below. The acetyl-a-tubulin of WOI organoids is different from that of CTRL organoids in morphology and expression level. The glands of WOI organoids have small green tips (expressing acetyl-α-tubulin) convex toward the lumen. WOI organoids expressed higher level of acetyl-α-tubulin than CTRL organoids. (Now replaced with Figure 3G in the revised draft).

Figure 4: Structural cells construct WOI with functionally dynamic changes

- Line 211: To which figure are these claims referring to?

You should be referring to this sentence “In terms of energy metabolism, the WOI organoids exhibited upregulation of monocarboxylic acid and lipid metabolism, and hypoxia response”. Up-regulation of monocarboxylic acid and lipid metabolism in WOI organoids is reflected in Figure 3B, and up-regulation of hypoxia responses is reflected in Figure S3F.

- In general, it should be stated in the text that CellPhoneDB is a useful tool to investigate ligandreceptor interactions, however, it only proposes potential interactions. To validate such interactions, stainings and functional assays are required.

Thanks for your suggestion. The CellphoneDB was briefly introduced in the "Methods" section of "Supporting information" originally. Now it has been explained in the line 256-257 of main text.

We agree that staining and functional assays are required to validate the ligand-receptor interactions. Therefore, we used the proximity ligation assay (PLA) to verify the trend of interaction. (Figure S2J, Line259-261, Line 277-279, Line 285-288）

- Line 243: Please describe the process of EMT in the endometrium more specifically.

EMT is a common and crucial biological event in the endometrium during the implantation window. During the EMT process, epithelial cells lose their epithelial characteristics while gaining migratory and invasive properties of fibroblasts.

During the attachment and adhesion phases of embryo implantation, interaction mediated by trophoblastic factors (e.g. integrins) and maternal ECM factors (e.g. fibronectin) induce the eventual EMT in the trophectoderm. During the peri-implantation period, microRNAs, (e.g. miR429 and miR-126a-3p) which regulate EMT, are expressed in the maternal luminal epithelium to different degrees, mediating its transformation process as the blastocyst invades the maternal decidua. The epithelium of endometrium transforms to epithelioid stromal cells with increased migratory and invasive capacities through the EMT process. The decidual stromal cells migrate away from the implantation site, having acquired increased motility. (Line 265-267)

- Lines 247-251 and 313-316: the claim that proliferative epithelium transforms into EMT derived stromal cells by pseudotime trajectory is too bold and must be underpinned by other means. Pseudotime analysis only suggests and is by definition biased since the first/originating population must be defined by the operator.

In addition to pseudotime analysis based on monocle, RNA rate analysis based on scVelo is also used for cell evolution analysis. They can prove each other if both analyses indicate the transformation from proliferative epithelium to EMT-derived stromal cell. RNA rate analysis automatically determines the direction of differentiation, which can be used as evidence to determine the starting point of pseudotime analysis.

RNA rate analysis showed that the EMT derived stromal cell was most closely connected to the proliferative epithelium. Besides, the pseudotime point plot inferred that the proliferative epithelium was the root cell. It can be mutually proved with pseudotime analysis that the transformation from proliferative epithelium to EMT-derived stromal cell.

Author response image 7.

RNA rate junction diagram (To infer intercellular connectivity)

Author response image 8.

Time differentiation of cells

Discussion

- Line 300-302: It would be interesting to investigate ATP production and IL8 release in the WOI organoids to validate with findings from in vivo.

To answer this point of your interest, we purposely examined ATP production and IL8 release. It was found that WOI organoids indeed produced much more ATP and IL8 than CTRL and

SEC organoids (Figure S3L) (Line323-324)

- Line 313-316: Do the WOI organoids lose polarity and cell-to-cell junctions?

Transcriptome sequencing revealed downregulation of cell adhesion and RHO GTPase signaling in WOI organoids (Figure 3B). Electron microscopy revealed that the cellular arrangement of WOI organoids was slightly looser than that of CTRL organoids, but the microvilli were still oriented toward the medial side of the glands and did not undergo polarity reversal (shown as below).

Author response image 9.

Electron micrograph of the CTRL (left), and WOI (right) endometrial organoid. Scale bar = 5 μm.  

- Line 322: Where is the data that shows that 'a decreased abundance of immune cells', is observed?  

A decreased abundance of immune cells was observed through single-cell transcriptome sequencing and flow cytometry. The number of immune cells was reduced in WOI organoids compared to CTRL organoids in single-cell sequencing results (Figure 4A). Besides, flow cytometry also showed that the percentage of WBCs in WOI organoids was lower than that in CTRL organoids (Figure S2F).  

- Line 324: Elaborate more on how the immune cell composition differs from the endometrium.

The differences of immune cell composition between organoids and endometrium were mainly reflected in the proportion of WBC, the proportion of immune cell subtypes and the changes of T cells after entering the implantation window.

Firstly, the proportion of WBCs in organoids was lower than that in endometrium. Flow cytometry showed that the proportion of WBC in organoids was about 3%~4% (Figure 2D), but the proportion of WBCs in endometrium was about 8% (W. Wang et al., Nat Med 2020). Secondly, the proportions of T cells and macrophages in organoids were about 2%~3% and 1% (Figure 2D), respectively, but the proportions of lymphocytes and macrophages in endometrium were 7%~8% and 0.6%~0.7% (W. Wang et al., Nat Med 2020). Besides, after entering the implantation window, T cells in WOI organoids decreased (Figure S2F), while T cells in endometrium increased (W. Wang et al., Nat Med 2020). These three aspects have differences in vivo and in vitro. (Line347353)

Material and Methods

-  What are the concentrations of all medium components?

Thanks to your suggestions. The concentrations of all medium components have now been refined in Table S1.

-  Authors mention 10x while Smartseq2 is mentioned in Dataset S7?

Thanks for your careful review. Single cell transcriptome sequencing in this study was done using 10X Genomics. Smartseq2 was used to sequence the transcriptome of a gland and its surrounding cells, which can be regarded as small bulk RNA sequencing. A small number of cells are utilized in Smartseq2 to construct a full-length mRNA library with enhanced transcript sequencing coverage, making it particularly well-suited for small-scale samples such as organoids.

The data in Dataset S7 are acquired from small bulk RNA-seq with Smartseq2.  

Reviewer #2 (Recommendations For The Authors):

Q1: The theoretical choice of extra reagents added to the WOI organoids culture (PRL, hCG, and hPL) is theoretically justified, but not experimentally. On what previous studies, or performed experiments, are the choice of conditions used based?

When selecting hormone formulations, multiple group comparisons were made. It was found that the number, area, and average intensity of organoids in these groups were similar over time. But the WOI organoids showed endometrial receptivity related gene expression profile, which highly expressed genes positively correlated with endometrial receptivity, and lowly expressed genes negatively correlated with receptivity, compared to the other hormone formulations (added to Figure S1E, S1F). Hormone dosage was primarily based on peri-pregnant maternal body or localized endometrium levels (Margherita Y. Turco et al., Nature Cell Biology 2017).

Q2: Text in line 111 indicates that "stromal cells formed an extensive network", but vimentin fluorescence is not present on any image surrounding organoids in that figure. This assertion could only be supported by the subsequent results in Figure 2B. In addition, it is not indicated what kind of organoids have been used for these experiments

The stromal cells arranged around the glands in the 3D structure (as shown in Figure 1C and Figure 2B), where bright-field high magnification photography, clearing staining of the organoids, and light microscopy imaging were used, respectively. However, there are many steps of fixation, embedding, staining and elution during the immunostaining of sections. It is difficult to preserve the arrangement and morphology of the stromal cells in the slice, so the stromal cells were not intentionally captured in the other images.  

Figure 1C and Figure 2B are both CTRL organoids, which are now noted in the corresponding figure legend section.  

Q3: It is not clear how glycogen secretion into the lumen is assessed in Figure 1D.

Glycogen from the subnuclear region of the glandular cells gradually reaches the top of the cells, i.e., the supranuclear region, and is discharged into the glandular lumen as parietal plasma secretion. Glycogen-containing eosinophilic secretion can be seen in the glandular lumen in Figure1D.

Q4: Assertions about differences in proliferation between groups are purely subjective; some kind of measurement and analysis would be necessary to be sure that there is differential proliferation based on Figure 1B.

We are extremely grateful to you for pointing out this problem. We quantitatively analyzed the size of organoids in the three groups. The area was found to be increasing over time, with the three groups growing the most vigorously in the CTRL group, followed by the SEC group and the WOI group, but the differences were not statistically significant. Relevant results have been added to Figure S1E (Line130-131).

Q5: For progesterone receptor expression analysis organoids are cultured for fourteen days. What is the basis for this change in culture time? 

The choice of time point here is based on the secretary period of 14 days in the female menstrual cycle, when the endometrium is stimulated by estrogen and progesterone to maximized

level.

Q6: "n" number of individuals analysed through single-cell transcriptomics is not indicated.

One patient's endometrium was simultaneously constructed into CTRL, SEC and WOI organoids, which were then subjected to single-cell transcriptome sequencing. This is described in the Supporting Information (Line 141-142).

Q7: Where does the classification of EMT-derived stromal cells come from?

EMT is a common and crucial biological event in the endometrium during the implantation window. During the EMT process, epithelial cells lose their epithelial characteristics while gaining migratory and invasive properties of fibroblasts.

This cluster of cells expresses both epithelium markers CDH1 and EPCAM, and specifically expresses high levels of the EMT-related stromal cell markers AURKB, HJURP and UBE2C. During endometrial EMT, AURKB upregulates MMP2, VEGFA/Akt/mTOR and Wnt/β-catenin/Myc pathways to induce EMT (Zhen Wang et al., Cancer Manag Res 2020). HJURP also activates Wnt/β-catenin signaling to promote EMT (Y Wei et al., Eur Rev Med Pharmacol Sci 2019, Tianchi Chen et al., Int J Biol Sci 2019). UBE2C is upregulated by estrogen to promote EMT (Yan Liu et al., Mol Cancer Res 2020). Therefore, this cluster was defined as "EMT-derived stromal cells”.

Q8: In the endometrial receptivity test (ERT), endometrium sample data matches with prereceptive endometrium and WOI organoids data matches with a receptive endometrium, but why there is no information about CTRL and SEC organoids?

We performed ERT on these samples at a time when our hospital has a cooperative project with Yikon Genomics (Jiangsu, China). However, only endometrium and WOI organoids were sent for testing due to the limited quotas. Considering the end of cooperation and batch effect, no more CTRL and SEC organoids were tested. Moreover, the current ERT is a machine learning model based on the sequencing data of endometrium samples. But there are still differences in cellular composition between endometrial organoids and endometrium. Thus, the results need to be interpreted in conjunction with other results.

Q9: When analysing the transcriptome and proteome, some comparisons are made between WOI vs CTRL and SEC, or just WOI vs CTRL. It would be interesting to have all the comparisons since the power of WOI organoids lies in their differences with SEC organoids.

Thanks for your suggestion. At the organoid level, the differences in transcriptome and proteome between SEC and WOI organoids are not significant. This is understandable because WOI organoids are further induced towards the implantation window based on the secretory phase (i.e. SEC organoids), which prompted us to continue exploring at the single-cell level.

Q10: Electron microscopy comparisons with respect to pinopods, cilia, and microvilli are only performed between WOI and CTRL. It would be interesting to check it with SEC.

We now quantitatively compared the presence of various characteristic structure like microvilli, cilia, pinopodes and glycogen in the CTRL, SEC and WOI organoids. It was found that WOI organoid had longer microvilli and increased cilia, glycogen, and pinopodes (Figure 2H).

Q11: Line 190 states that pinopods are arranged more densely in WOI organoids than in CTRL organoids. Seems to be a subjective observation. Is there an objective method to quantify this?

We agree with the reviewer’s suggestion and quantified the pinopodes. The CTRL, SEC and WOI organoids were found to have increasing numbers of pinopodes, with WOI organoid owning the most abundant pinopodes. (Figure 2H) (Line184-186)

Q12: Some characteristics are very similar between WOI and SEC organoids (such as the accumulation of secretory epithelium or decreased proliferative epithelium, the increased ciliated epithelium after hormonal treatment, or the presence of EMT-derived stromal cells). The authors should complement the discussion by objectively justifying the use of WOI versus SEC organoids. Would they be useful in more specific cases or at a general level when studying implementation?

Thanks for your comments. WOI organoids are differentiated from SEC organoids towards the implantation window. Therefore, WOI organoids are suitable for studying periimplantation physiological changes or exploring pathological mechanisms. SEC organoids can be used when studying only a range of pathological problems such as endometrial secretory phase changes or hormone reactivity. (Line 365-368)

Q13：ExM media is described in Table S1, but it does not include the concentration of the different reagents in the culture medium, which is the most interesting data about the ExM medium.

Thanks to your suggestions. The concentrations of all medium components have now been refined in Table S1.

Q14: It is not specified which organoid pass is used in each experiment. Is it always the same pass?

Our experiments were conducted using P1~P3 generation endometrial organoids, as specified in the “Supporting Information” Line 54~55.

Q15: As a protocol for freezing organoids is included in materials and methods, do the authors use freshly cultured organoids or do they cryopreserve them and thaw them for culturing?

Thanks for your question. We used freshly cultured organoids in the manuscript. We listed the freezing protocol to illustrate that the constructed organoids can be frozen and recovered for special experimental needs and the establishment of sample banks.

Q16: The most important point: Neither of the two studies that developed human endometrial organoids from tissue biopsies (Boretto et al. 2017 and Turco et al. 2017), observed stromal cell growth in culture. They disappeared between the first and second pass (as indicated by Turco et al. 2017). How do the authors justify the presence of stromal cells in their organoid culture if they rely on the protocols previously described by these research groups? If it is the case that they can only use the initial pass (freshly planted cells from endometrium), it does not make sense to include the freezing of the different passes in materials and methods, since the expansion capacity of the culture would be lost, which implies a major limitation of the model.

Thanks for your question.  

(1) We did not completely follow the protocols of these research groups. To maximize the recovery of both epithelial and stromal cells, we optimized key steps such as tissue digestion and cell strainer filtration. We shortened the digestion time to 20 minutes to protect cells from the digestion solution and retain some cell aggregates, which are beneficial for maintaining cell stemness and preserving stromal and immune cells cluster. The 40 μm filter membrane was used to isolate the endometrial cells, which may acquire both epithelial, and stromal cells.

(2) Our experiments were conducted using P1~P3 generation of freshly constructed organoids. However, we also used recovered organoids when fresh endometrial samples were not available due to the COVID-19 epidemic. It was found that the organoids (e.g., P0~P5) still exhibited vigorous growth condition after recovery and could continue to be cultured by passaging (shown as below).

The recovered organoids can be used for special experiments and biobank establishment.

Author response image 10.

The endometrial organoids of different passages were observed before cryopreservation and after recovery. Scale bar = 200 μm.

Q17: It is not clear which organoids include Figure S2F. Does it include the three types of organoids or just WOI organoids?

This circle diagram showed the functions of upregulated genes in the WOI group compared to CTRL group from combined transcriptome and proteome analysis, which has been labeled in the figure legend section.

eLife assessment

Therapeutic treatments for congenital and acquired craniofacial (CF) bone abnormalities are not well developed. This study provides convincing evidence for an innovative regenerative treatment for pediatric craniofacial bone loss using Jagged1-PEG-MAL hydrogel with pediatric human bone cells. The report is a valuable advance in this field.

Reviewer #1 (Public Review):

Summary:

In this manuscript, the authors conducted an important study that explored an innovative regenerative treatment for pediatric craniofacial bone loss, with a particular focus on investigating the impacts of JAGGED1 (JAG1) signaling.

Strengths:

Building on their prior research involving the effect of JAG1 on murine cranial neural crest cells, the authors demonstrated successful bone regeneration in an in vivo murine bone loss model with a critically-sized cranial defect, where they delivered JAG1 with pediatric human bone-derived osteoblast-like cells in the hydrogel. Additionally, their findings unveiled a crucial mechanism wherein JAG1 induces pediatric osteoblast commitment and bone regeneration through the phosphorylation of p70 S6K. This discovery offers a promising avenue for potential treatment, involving targeted delivery of JAG1 and activation of downstream p70 s6K, for pediatric craniofacial bone loss. Overall, the experimental design is appropriate, and the results are clearly presented.

Reviewer #2 (Public Review):

The current manuscript undoubtedly demonstrates that JAG1 can induced osteogenesis via non-canonical signaling. In fact, using the mouse-calvarial critical defect model, the authors have clearly shown the anabolic regenerative effect of JAG1 in via non-canonical pathways. Exploring the molecular mechanisms, the authors have shown that non-canonically JAG1 is regulating multiple pathways including STAT5, AKT, P38, JNK, NF-ĸB, and p70 S6K, which together possibly culminate to the activation of p70 S6K. In summary these findings have significant implications in designing new approaches for bone regenerative research.

Author response:

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

Reviewer #1 (Recommendations For The Authors):

Major comments:

(1)  Regarding the cell studies of human pediatric bone-derived osteoblast-like cells (HBO), the authors should provide a rationale for their selection of specific cell lines (15,16, 17, 19, 20, 23, 24) in this study. As for animal studies, could the authors clarify which cell lines were utilized in the murine in vivo experiments?

We appreciate the opportunity to address this. To reduce confusion, we have numbered the patient primary cell lines used in these studies sequentially from 1 – 7. Additionally, we have added “HBO cell lines used for experiments were selected based on the ability of the primary cell line to proliferate and mineralize in culture” to the Methods section. 

In vivo experiments: “HBO cell lines 2, 6 and 7 from separate individuals were selected for these experiments based on similar growth and passage characteristics.” This statement is included in the Methods section.

(2)  In this study, the authors performed the murine in vivo experiments using both male and female mice. Could the author clarify if any difference was observed between male and female mice in the findings? This information would contribute to a more comprehensive understanding of the study.

We agree and have added the following to the Results section: “There was no sex-based difference in regenerated bone volume.”

(3)  Although the histological results showed an elevated collagen expression in mice treated with BMP2, JAG1, and JAG1 + DAPT compared to those treated with the cells alone, the differences among groups were subtle. The authors should consider the immunohistochemical (IHC) staining for collagen 1 on the samples, allowing for a quantitative assessment of collagen 1 expression.

Thank you for this comment. The differences between BMP2, JAG1, and JAG1 + DAPT are indeed subtle. We have added Supplementary Figure 5, showing collagen staining of sections from the same FFPE blocks that were sectioned and stained with Masson Trichrome in Figure 2C. 

Minor Comments:

(4)  Please specify which cell lines are represented in the staining results shown in Fig.1A and Fig. 5A, respectively.

In Fig 1A the representative images are of HBO2. Fig 5A representative images are of HBO7. We have added this information to the figure legends for these figures. 

(5)  There appears to be a discrepancy in the specified size of the critical defect. The manuscript states that the size is 4mm, while Supplemental Figure 3 indicates 3.5mm.

Thank you for this catch! Yes, it should be 4mm. This has been corrected in Supplementary Figure 3.

(6)  The scale bar for Figure 2 C is missing.

Scale bars have been added which also gave us an opportunity to brighten the images equally, allowing for better distinction between the different colors of the Masson Trichrome staining.

(7)  In the methodological section 2.5 for JAG1 delivery, it would be helpful if the authors could review the initial dosage of JAG1 delivery to confirm if HBO cells were included or not, given that the MicroCT results indicate that all groups incorporated HBO cells. 

We appreciate this suggestion. In response to another question, we have added Supplementary Figure 4 which includes an “Empty Defect” condition with no HBO cells, making the original method statement accurate.

Reviewer #2 (Recommendations For The Authors):

In the current study, using in vitro and in vivo models the authors clearly show that JAG1 can enhance osteogenesis and thus can be helpful in designing new therapeutic approaches in the field of bone regenerative research. The in vivo mouse CF model is very convincing and shows that JAG1 promotes osteogenesis via non-canonical signaling. Mechanistically it seems that JAG1 activates STAT5, AKT, P38, JNK, NF-ĸB, and p70 S6K. However, additional evidence is needed to convincingly conclude that all the non-canonical pathways activated via JAG1 converge at p70 S6K activation. The following concerns need to be addressed.

(1) In Fig 1A: Even though the Jag1-Fc shows a very significant increase in HBO mineralization, there are no significant increases in cells in osteogenic media when compared to control growth media. Even though the different conditions were subjected to RNAseq analysis in the later figures, qPCR analysis of some osteogenic genes in Figure 1 might be helpful. 

We appreciate the opportunity to explore this question further. We conducted mineralization experiments in triplicate and performed qRT-PCR, assessing for gene expression of 5 osteogenic genes: ALPL, BGLAP (osteocalcin), COL1A1, RUNX2, and SP7. Results are shown in Figure 1C and this text was added to Results: “Additionally, PCR analysis of HBO1 cells from a repeat experiment collected at days 7, 14, and 21 showed significantly increased expression of osteogenic genes with JAG1-bds stimulation (Figure 1C). ALPL was significantly expressed at Day 7, with a 3.5-fold increase (p=0.0004) compared to HBO1 cells grown in growth media. In contrast, significant expression levels of COL1A1 and BGLAP were observed at 14 days, with a 5.1-fold increase (p=0.0021) of COL1A1 and a 12.3-fold increase (0.0002) of BGLAP when compared to growth media conditions. Interestingly, while some mineralization is observed in the osteogenic media and Fc-bds

(Figure 1A) conditions, there were no significant increases in osteogenic gene expression (Figure

1C). Expression of RUNX2 and SP7 was not significantly altered across all conditions and time points (not shown).”

(2) In Fig 2: even though not needed in respect to the hypothesis, was there any Control group without any cells or JAG1 beads? What were the changes in between that group and cells cells-only group?

We have not observed differences between the “Empty Defect” group and the “Cells alone” group.

We have addressed the reviewer’s comments by adding this comparison in Supplementary Figure 4.

(3) Transcriptional profiling and ELISA (Fig 3 and 4) show upregulation of NF-ĸB signaling in response to JAG1. In the discussion, the authors have referenced a previous study showing NF-ĸB as prosurvival in human OB cells. However, based on many published reports, NF-ĸB activation has been shown to inhibit OB function. Does JAG1 regulate HBO cell survival via NF-ĸB activation?

Experimenting using NF-ĸB inhibitor can be helpful to show that JAG1 mediates NF-ĸB activation is anabolic in this experimental setup.

We thank the reviewer for this excellent suggestion. We are eager to explore this new direction for our research in a subsequent study. We have added this to our future directions. 

(4) Fig 5: 

(A)  Condition showing JAG1+ DAPT is needed to compare between JAG1 canonical and noncanonical signaling. 

Thank you for pointing this out. We have added Supplementary Figure 6, which includes a dose response experiment for JAG1 + DAPT.

(B)  S6K18 alone seems to be increasing OB mineralization. Is that statistically significant?  

No, and we have added the statistical analysis for S6K-18 to Figure 5B.

(C)  Fc alone condition seems to have a very significant increase in OB mineralization. Does Fc alone upregulate OB function? 

We do see some upregulation of mineralization with Fc in vitro, which we also observed in our previous studies with mouse neural crest cells, but we have not found it to be osteogenic in vivo. We have added a statement to this effect, with references. Additionally, osteogenic gene expression was not upregulated in our in vitro mineralization experiments with Fc.  See Revised Figure 1.

(D)  Although overall quantification shows that S6K18 partially inhibits HBO mineralization, the representative images do not represent the quantification. Transcriptional analysis (qPCR) is required to validate these findings.

We performed qRT-PCR on cells from a repeat mineralization assay, collecting cells at 9, 14, and 21 days. We have added the following to the Results:” While inhibition of NOTCH and p70 S6K decreased mineralization in our mineralization assay, there are no statistically significant changes in gene expression for ALPL, COL1A1, or BGLAP (Supplementary Figure 7). These results suggest that the HBO cells phenotypes are maturing into osteocytes and that inhibiting p70 S6K hinders the cellular ability to mineralize but not the cell phenotype progression.”

(5) Finally, to convincingly conclude the data from Fig 5, the mouse CF model can be helpful to support the authors' claim that JAG1 acts via p70 S6K.

Thank you for this feedback. We have modified our conclusions to reflect that p70 S6K is one of the non-canonical pathways that JAG1 may be activating in bone regeneration.

Thank you very much for your consideration of our revised manuscript.

eLife assessment

This useful manuscript describes a proteomic analysis of plasma from subjects before and after an exercise regime consisting of endurance and resistance exercise. The work identifies a putative new exerkine, CD300LG, and finds associations of this protein with aspects of insulin sensitivity and angiogenesis. The characterization remains incomplete at present. Because CD300LG may have a transmembrane domain, one possibility is that exercise causes the release of extracellular vesicles containing this protein. As this study reports associations, additional studies will be needed to establish causality. The paper will hopefully prompt further studies to more fully elucidate the underlying biology.

Reviewer #1 (Public Review):

Summary:

In this paper, proteomics analysis of the plasma of human subjects that underwent an exercise training regime consisting of a combination of endurance and resistance exercise led to the identification of several proteins that were responsive to exercise training. Confirming previous studies, many exercise-responsive secreted proteins were found to be involved in the extra-cellular matrix. The protein CD300LG was singled out as a potential novel exercise biomarker and the subject of numerous follow-up analyses. The levels of CD300LG were correlated with insulin sensitivity. The analysis of various open-source datasets led to the tentative suggestion that CD300LG might be connected with angiogenesis, liver fat, and insulin sensitivity. CD300LG was found to be most highly expressed in subcutaneous adipose tissue and specifically in venular endothelial cells. In a subset of subjects from the UK Biobank, serum CD300LG levels were positively associated with several measures of physical activity - particularly vigorous activity. In addition, serum CD300LG levels were negatively associated with glucose levels and type 2 diabetes. Genetic studies hinted at these associations possibly being causal. Mice carrying alterations in the CD300LG gene displayed impaired glucose tolerance, but no change in fasting glucose and insulin. Whether the production of CD300LG is changed in the mutant mice is unclear.

Strengths:

The specific proteomics approach conducted to identify novel proteins impacted by exercise training is new. The authors are resourceful in the exploitation of existing datasets to gain additional information on CD300LG.

Weaknesses:

While the analyses of multiple open-source datasets are necessary and useful, they lead to relatively unspecific correlative data that collectively insufficiently advance our knowledge of CD300LG and merely represent the starting point for more detailed investigations. Additional more targeted experiments of CD300LG are necessary to gain a better understanding of the role of CD300LG and the mechanism by which exercise training may influence CD300LG levels. One should also be careful to rely on external data for such delicate experiments as mouse phenotyping. Can the authors vouch for the quality of the data collected?

Reviewer #2 (Public Review):

Summary:

This manuscript from Lee-Odegard et al reports proteomic profiling of exercise plasma in humans, leading to the discovery of CD300LG as a secreted exercise-inducible plasma protein. Correlational studies show associations of CD300LG with glycemic traits. Lastly, the authors query available public data from CD300LG-KO mice to establish a causal role for CD300LG as a potential link between exercise and glucose metabolism. However, the strengths of this manuscript were balanced by the moderate to major weaknesses. Therefore in my opinion, while this is an interesting study, the conclusions remain preliminary and are not fully supported by the experiments shown so far.

Strengths:

(1) Data from a well-phenotyped human cohort showing exercise-inducible increases in CD300LG.

(2) Associations between CD300LG and glucose and other cardiometabolic traits in humans, that have not previously been reported.

(3) Correlation to CD300LG mRNA levels in adipose provides additional evidence for exercise-inducible increases in CD300LG.

Weaknesses:

(1) CD300LG is by sequence a single-pass transmembrane protein that is exclusively localized to the plasma membrane. How CD300LG can be secreted remains a mystery. More evidence should be provided to understand the molecular nature of circulating CD300LG. Is it full-length? Is there a cleaved fragment? Where is the epitope where the o-link is binding to CD300LG? Does transfection of CD300LG to cells in vitro result in secreted CD300LG?

(2) There is a growing recognition of specificity issues with both the O-link and somalogic platforms. Therefore it is critical that the authors use antibodies, targeted mass spectrometry, or some other methods to validate that CD300LG really is increased instead of just relying on the O-link data.

(3) It is insufficient simply to query the IMPC phenotyping data for CD300LG; the authors should obtain the animals and reproduce or determine the glucose phenotypes in their own hands. In addition, this would allow the investigators to answer key questions like the phenotype of these animals after a GTT, whether glucose production or glucose uptake is affected, whether insulin secretion in response to glucose is normal, effects of high-fat diet, and other standard mouse metabolic phenotyping assays.

(4) I was unable to find the time point at which plasma was collected at the 12-week time point. Was it immediately after the last bout of exercise (an acute response) or after some time after the training protocol (trained state)?

Author response:

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

Reviewer #1 (Public Review):

Summary:

In this paper, proteomics analysis of the plasma of human subjects that underwent an exercise training regime consisting of a combination of endurance and resistance exercise led to the identification of several proteins that were responsive to exercise training. Confirming previous studies, many exercise-responsive secreted proteins were found to be involved in the extra-cellular matrix. The protein CD300LG was singled out as a potential novel exercise biomarker and the subject of numerous follow-up analyses. The levels of CD300LG were correlated with insulin sensitivity. The analysis of various open-source datasets led to the tentative suggestion that CD300LG might be connected with angiogenesis, liver fat, and insulin sensitivity. CD300LG was found to be most highly expressed in subcutaneous adipose tissue and specifically in venular endothelial cells. In a subset of subjects from the UK Biobank, serum CD300LG levels were positively associated with several measures of physical activity - particularly vigorous activity. In addition, serum CD300LG levels were negatively associated with glucose levels and type 2 diabetes. Genetic studies hinted at these associations possibly being causal. Mice carrying alterations in the CD300LG gene displayed impaired glucose tolerance, but no change in fasting glucose and insulin. Whether the production of CD300LG is changed in the mutant mice is unclear.

Strengths:

The specific proteomics approach conducted to identify novel proteins impacted by exercise training is new. The authors are resourceful in the exploitation of existing datasets to gain additional information on CD300LG.

Weaknesses:

While the analyses of multiple open-source datasets are necessary and useful, they lead to relatively unspecific correlative data that collectively insufficiently advance our knowledge of CD300LG and merely represent the starting point for more detailed investigations. Additional more targeted experiments of CD300LG are necessary to gain a better understanding of the role of CD300LG and the mechanism by which exercise training may influence CD300LG levels. One should also be careful to rely on external data for such delicate experiments as mouse phenotyping. Can the authors vouch for the quality of the data collected. 

Thank you for the valuable feedback on our manuscript. We recognize concerns about the specificity of correlative data from open-source datasets and the limitations it presents for understanding CD300LG's role. To address this, we have expanded the manuscript with a paragraph in the discussion regarding the need of targeted experiments confirm CD300LG’s functions and relationship with glucose metabolism. We also emphazise caution regarding external data reliance and we acknowledge the need for generating primary data including direct phenotyping of mice with CD300LG gene alterations to better understand its regulatory mechanisms and effects on glucose tolerance. Please see lines 446-456.

Reviewer #2 (Public Review):

Summary:

This manuscript from Lee-Odegard et al reports proteomic profiling of exercise plasma in humans, leading to the discovery of CD300LG as a secreted exercise-inducible plasma protein. Correlational studies show associations of CD300LG with glycemic traits. Lastly, the authors query available public data from CD300LG-KO mice to establish a causal role for CD300LG as a potential link between exercise and glucose metabolism. However, the strengths of this manuscript were balanced by the moderate to major weaknesses. Therefore in my opinion, while this is an interesting study, the conclusions remain preliminary and are not fully supported by the experiments shown so far.

Strengths:

(1) Data from a well-phenotyped human cohort showing exercise-inducible increases in CD300LG.

(2) Associations between CD300LG and glucose and other cardiometabolic traits in humans, that have not previously been reported.

(3) Correlation to CD300LG mRNA levels in adipose provides additional evidence for exercise-inducible increases in CD300LG.

Weaknesses:

(1) CD300LG is by sequence a single-pass transmembrane protein that is exclusively localized to the plasma membrane. How CD300LG can be secreted remains a mystery. More evidence should be provided to understand the molecular nature of circulating CD300LG. Is it full-length? Is there a cleaved fragment? Where is the epitope where the o-link is binding to CD300LG? Does transfection of CD300LG to cells in vitro result in secreted CD300LG?

(2) There is a growing recognition of specificity issues with both the O-link and somalogic platforms. Therefore it is critical that the authors use antibodies, targeted mass spectrometry, or some other methods to validate that CD300LG really is increased instead of just relying on the O-link data.

(3) It is insufficient simply to query the IMPC phenotyping data for CD300LG; the authors should obtain the animals and reproduce or determine the glucose phenotypes in their own hands. In addition, this would allow the investigators to answer key questions like the phenotype of these animals after a GTT, whether glucose production or glucose uptake is affected, whether insulin secretion in response to glucose is normal, effects of high-fat diet, and other standard mouse metabolic phenotyping assays.

(4) I was unable to find the time point at which plasma was collected at the 12-week time point. Was it immediately after the last bout of exercise (an acute response) or after some time after the training protocol (trained state)?

We acknowledge the importance of understanding the molecular form of CD300LG in circulation. We have expanded the discussion with a paragraph regarding the need of follow-up experiments on whether circulating CD300LG is full-length or a cleaved fragment, to identify the epitope for O-link binding, and assess CD300LG secretion in vitro through transfection experiments. We also discuss the need of targeted mass spectrometry and antibody-based validation of O-link measurements of CD300LG, and the need for more validation experiments on CD300LG-deficient mice. Please see lines 446-456.

The plasma collected post-intervention is in a state that reflects the new baseline trained condition of the subjects, 3 days after the last exercise session during the intervention. We have clarified this in our manuscript. The information is updated in line 491-493.

Reviewer #1 (Recommendations For The Authors):

In the present form, the paper raises interest in the potential role of CD300LG in the response to exercise training but unfortunately does not provide clear answers. The authors should focus their efforts on firmly validating the status of CD300LG as an exercise biomarker in humans and carefully examine the function of CD300LG through mechanistic and animal-based studies.

The authors are encouraged to acquire CD300LG-deficient mice and perform specific experiments to validate hypotheses forthcoming from the analysis of the open-source datasets. In addition, it needs to be validated that the cd300lgtm1a(KOMP)Wtsi mice are actually deficient in CD300LG. It is not uncommon that Tm1a mice have (almost) normal expression of the targeted gene.

We have now revised the manuscript and added a new section to the discussion regarding the limitations with open-source data, cd300lgtm1a(KOMP)Wtsi mice and the need for more validation experiments on CD300LG-deficient mice. Please see lines 446-456.

The value of the correlative data presented in Figure 5 is rather limited. The same can be argued for the data presented in Supplementary Figure 2. If CD300LG is expressed in endothelial cells, it stands to reason that its expression is correlated with angiogenesis. Hence, this observation does not really carry any additional value.

We agree that correlations cannot imply causality. However, similar patterns were observed in several tissues and across different data sets, which at least suggest a role CD300LG related to angiogesis. We have included a section in the discussion were we clarify that our observations should only be regarded as indications and that follow-up studies are needed to confirm any causal role for CD300LG on angiogenesis/oxidativ capacity. Please see lines 446-456.

Figure 6 may be better accommodated in the supplement.

Figure 6 is now moved to the supplement.

Figure 3A and B are a bit awkward. The description "no overlap" is confusing. Isn't it more accurate to say "no enrichment" or "no over-representation"? There will always be some overlap with certain pathways. However, there may be no enrichment. Furthermore, the use of arrows to indicate No overlap is visually not very appealing. Maybe the numbers can be given a specific color?

We have now removed the arrows and text, and rather stated in the text that there were no enrichements other than for the proteins down-regulated in the overweight group.

The description of the figure legend of figure 5E-H is incomplete.

The description is now completed.

eLife assessment

This study provides useful insights into inter- and intra-site B cell receptor repertoire heterogeneity, noting that B cell clones from the tumour interact more with their draining lymph node than with the blood and that there is less mutation/expansion/activation of B cell clones in tumours. Unfortunately, the main claims are incomplete and only partially supported. The work could be of interest to an audience including medical biologists/immunologists and computational biologists across cancer specialities.

Reviewer #3 (Public Review):

In multiple cancers, the key roles of B cells are emerging in the tumor microenvironment (TME). The authors of this study appropriately introduce that B cells are relatively under-characterised in the TME and argue correctly that it is not known how the B cell receptor (BCR) repertoires across tumor, lymph node and peripheral blood relate. The authors therefore supply a potentially useful study evaluating the tumor, lymph node and peripheral blood BCR repertoires and site-to-site as well as intra-site relationships. The authors employ sophisticated analysis techniques, although the description of the methods is incomplete.

Major strengths:

(1) The authors provide a unique analysis of BCR repertoires across tumor, dLN, and peripheral blood. The work provides useful insights into inter- and intra-site BCR repertoire heterogeneity. While patient-to-patient variation is expected, the findings with regard to intra-tumor and intra-dLN heterogeneity with the use of fragments from the same tissue are of importance, contribute to the understanding of the TME, and will inform future study design.

(2) A particular strength of the study is the detailed CDR3 physicochemical properties analysis which leads the authors to observations that suggest a less-specific BCR repertoire of TIL-B compared to circulating B cells.

Concerns and comments on current version:

The revision has improved the manuscript but, in my opinion, remains inadequate. While most of my requested changes have been made, I do not see an expansion of Fig1A legend to incorporate more details about the analysis. Lacking details of methodology was a concern from all reviewers. Similarly, the 'fragmented' narrative was a concern of all reviewers. These matters have not been dealt with adequately enough - there are parts of the manuscript which remain fragmented and confusing. The narrative and analysis does not explain how the plasma cell bias has been dealt with adequately and in fact is simply just confusing. There is a paragraph at the beginning of the discussion re the plasma cell bias, which should be re-written to be clearer and moved to have a prominent place early in the results. Why are these results not properly presented? They are key for interpretation of the manuscript. Furthermore, the sorted plasma cell sequencing analysis also has only been performed on two patients. Another issue is that some disease cohorts are entirely composed of patients with metastasis, some without but metastasis is not mentioned. Metastasis has been shown to impact the immune landscape.

A reviewer brought up a concern about the overlap analysis and I also asked for an explanation on why this F2 metric chosen. Part of the rebuttal argues that another metric was explored showing similar results, thus conclusion reached is reasonable. Remarkably, these data are not only omitted from the manuscript, but is not even provided for the reviewers.

This manuscript certainly includes some interesting and useful work. Unfortunately, a comprehensive re-write was required to make the work much clearer and easier to understand and this has not been realised.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The authors attempt to fully characterize the immunoglobulin (Ig) heavy (H) chain repertoire of tumor-infiltrating B cells from three different cancer types by identifying the IgH repertoire overlap between these, their corresponding draining lymph nodes (DLNs), and peripheral B cells. The authors claim that B cells from tumors and DLNs have a closer IgH profile than those in peripheral blood and that DLNs are differentially involved with tumor B cells. The claim that tumor-resident B cells are more immature and less specific is made based on the characteristics of the CDR-H3 they express.

Strengths:

The authors show great expertise in developing in-house bioinformatics pipelines, as well as using tools developed by others, to explore the IgH repertoire expressed by B cells as a means of better characterizing tumor-associated B cells for the future generation of tumor-reactive antibodies as a therapy.

Weaknesses:

This paper needs major editing, both of the text and the figures, because as it stands it is convoluted and extremely difficult to follow. The conclusions reached are often not obvious from the figures themselves. Sufficient a priori details describing the framework for their analyses are not provided, making the outcome of their results questionable and leaving the reader wondering whether the findings are on solid ground.

The authors are encouraged to explain in more detail the premises used in their algorithms, as well as the criteria they follow to define clonotypes, clonal groups, and clonal lineages, which are currently poorly defined and are crucial elements that may influence their results and conclusions.

In response to this comment, we significantly expanded the paragraph dedicated to the tumor and non-tumor repertoire overlap and isotype composition. The following sections were added:

First, we characterized the relative similarity of IGH repertoires derived from tumors, DLN, and PBMC on the individual CDR-H3 clonotype level. We define clonotype as an instance with an identical CDR-H3 nucleotide sequence  and identical V- and J- segment attribution (isotype attribution may be different). Unlike other authors, here we do not pool together similar CDR-H3 sequences to account for hypermutation. (Hypermutation analysis is done separately and defined as clonal group analysis. )

As overlap metrics are dependent on overall repertoire richness, we normalized the comparison using the same number of top most frequent clonotypes of each isotype from each sample (N = 109). Repertoire data for each sample were split according to the immunoglobulin isotype, and the F2 metric was calculated for each isotype separately and plotted as an individual point.

We also analyzed D metric, which represents the relative overlap diversity uninfluenced by clonotype frequency (Dij\=dij/(di*dj), where dij is the number of clonotypes present in both samples, while di and dj are the diversities of samples i and j respectively). The results for D metric are not shown, as they indicate a similar trend to that of F2 metric. This observation allows us to conclude that tumor IGH repertoires are more similar to the repertoires of lymph nodes than to those of peripheral blood, both if clonotype frequency is taken into account, and when it is not.

Having excluded the IGHD gene segment from some of their analyses (at least those related to clonal lineage inference and phylogenetic trees), it is not well explained which region of CDR-H3 is responsible for the charge, interaction strength, and Kidera factors, since in some cases the authors mention that the central part of CDR-H3 consists of five amino acids and in others of seven amino acids.

We considered different ways of calculating amino acid properties of CDR3 and used different parameters for sample-average and individual-sequence CDR3s. Now plots for Fig S6 C are updated  for consistency and the parameters depicted there are now calculated using 5 central amino acids, as in other sections.

How can the authors justify that the threshold for CDR-H3 identity varies according to individual patient data? 

Ideal similarity threshold may depend on several factors, such as sampling, sequencing depth etc. For example, imagine a sample picking up 100% of the clonal lineage sequences which differ only 1 amino acid from each other, and a worse quality sample/sequencing picking up only every other sequence. Obviously, the minimal threshold required to accumulate these into a cluster/clonal group  would be different for these two cases (1aa for the former, and ~2 aa for the latter for single-linkage clustering). Or, in other words, the more the sequencing depth, the more dense the clusters will be. The method of individual threshold tailoring relies on the following: https://changeo.readthedocs.io/en/latest/examples/cloning.html

Although individual kidera factors that are significant in the context of our analysis are described in the text one by one on their first appearance, we now also added a sentence to describe Kidera factor analysis in general (page 8):

Kidera factors are a set of scores which quantify physicochemical properties of protein sequences (Nakai et al. 1988). 188 physical properties of the 20 amino acids are encoded using dimension reduction techniques.

Throughout the analyses, the reasons for choosing one type of cancer over another sometimes seem subjective and are not well justified in the text.

Whenever possible, we pooled all patients with all cancer types together, because the number of available samples did not allow us to draw any significant conclusions comparing between individual cancer types. When analyzing and showing individual patient data, we also did not attempt to depict any cancer-type-specific findings, but it is inevitable that we name a specific cancer type when labelling a sample coming from a specific tumor.

Overall, the narrative is fragmented. There is a lack of well-defined conclusions at the end of the results subheadings.

In addition to the described above, a conclusion was added to the paragraph describing hypermutation analysis:

IGHG clonotypes from lung cancer samples show higher number of hypermutations, possibly reflecting high mutational load found in lung cancer tissue. For melanoma, another cancer known for high mutational load, no statistically significant difference was found. This may be due to higher variance between melanoma samples, which hinders the analysis, or due to the small sample size.

The exact same paragraph is repeated twice in the results section.

Corrected.

The authors have also failed to synchronise the actual number of main figures with the text, and some panels are included in the main figures that are neither described nor mentioned in the text  (Venn diagram Fig. 2A and phylogenetic tree Fig. 5D). Overall, the manuscript appears to have been rushed and not thoroughly read before submission.

Corrected.

Reviewers are forced to wade through, unravel, and validate poorly explained algorithms in order to understand the authors' often bold conclusions.

We hope that the aforementioned additions to the text and also addition to the Figure 1 make the narrative more easily understandable.

Reviewer #2 (Public Review):

Summary:

The authors sampled the B cell receptor repertoires of Cancers, their draining lymph nodes, and blood. They characterized the clonal makeup of all B cells sampled and then analyzed these clones to identify clonal overlap between tissues and clonal activation as expressed by their mutation level and CDR3 amino acid characteristics and length. They conclude that B cell clones from the Tumor interact more with their draining lymph node than with the blood and that there is less mutation/expansion/activation of B cell clones in Tumors. These conclusions are interesting but hard to verify due to the under-sampling and short sequencing reads as well as confusion as to when analysis is across all individuals or of select individuals.

Strengths:

The main strength of their analysis is that they take into account multiple characteristics of clonal expansion and activation and their different modes of visualization, especially of clonal expansion and overlap. The triangle plots once one gets used to them are very nice.

Weaknesses:

The data used appears inadequate for the conclusions reached. The authors' sample size of B cells is small and they do not address how it could be sufficient. At such low sampling rates, compounded by the plasmablast bias they mention, it is unclear if the overlap trends they observe show real trends. Analyzing only top clones by size does not solve this issue. As it could be that the top 100 clones of one tissue are much bigger than those of another and that all overlap trends are simply because the clones are bigger in one tissue or the other. i.e there is equal overlap of clones with blood but blood is not sufficiently sampled given its greater diversity and smaller clones.

Regarding the number of clonotypes to be taken into account,  we were limited by the B cell infiltration of tumor samples and our ability to capture their repertoire. However, we use technical replicates on the level of cell suspension to ensure that at least top clonotypes are consistently sampled. So, this is how the data should be interpreted - as describing the most abundant clones in the repertoire (which also may be considered the most functionally relevant in case of tumor infiltrating lymphocytes).

To analyze the repertoire overlap, we generally use the F2 metric that takes clone size into account - because we think that clone size is an important functional factor. However, we have now added the description of using D metric (does not include clone frequency as a parameter) - which shows exactly the same trend as F2 metric. So, both F2 and D overlap metrics support our conclusion of higher overlap between tumor and LN.

The following text was added:

We also analyzed D metric, which represents the relative overlap diversity uninfluenced by clonotype frequency (Dij\=dij/(di*dj), where dij is the number of clonotypes present in both samples, while di and dj are the diversities of samples i and j respectively). The results for D metric are not shown, as they indicate a similar trend to that of F2 metric. This observation allows us to conclude that tumor IGH repertoires are more similar to the repertoires of lymph nodes than to those of peripheral blood, both if clonotype frequency is taken into account, and when it is not.

All in all, of course, the deeper the better, but given the data we were able to generate from the samples, this was the best approach to normalization that could be used.

Similarly, the read length (150bp X2) is too short, missing FWR1 and CDR1 and often parts of FWR2 if CDR3 is long. As the authors themselves note (and as was shown in (Zhang 2015 - PMC4811607) this makes mutation analysis difficult.

Indeed, we are aware of this problem, and therefore only a small part of the manuscript is dedicated to the hypermutation analysis. However, as the CDR-H3 region is the most mutated part, we still can capture significant diversity of mutations. To address the question of applicability of our data for the hypermutation phylogeny analysis, we compare the distribution of physico-chemical properties along the trees of hypermutation using the 150+150 and 300+300 data from the same donor and the same set of samples. The main conclusion is that neither for long, nor for short datasets could any correlation of physicochemical properties of the CDR-H3 region with the rank of the clonotype on the tree be found.  

It also makes the identification of V genes and thus clonal identification ambiguous. This issue becomes especially egregious when clones are mutated.

Again, this would be important for clonotype phylogeny analysis. However, for the simple questions that we address with our clonal group analysis, such as clonal group overlap between tissues etc, we consider this data acceptable, because if any mislabelling of V segment occurs, it is a) rare and b) is equally frequent in all types of samples. Therefore, any conclusions made are still valid despite this technical drawback.

To directly address the question of mislabelling of V-genes in our data, we looked at the average number of different  V-genes attributed to the same nucleotide sequence of CDR-H3 region in the short (150+150) and long (300+300) datasets from the same donor. Indeed, some ambiguity of V-gene labelling is observed (see below), but we think that it is unlikely to influence any of our cautious conclusions.

Author response image 1.

Finally, it is not completely clear when the analysis is of single individuals or across all individuals. If it is the former the authors did not explain how they chose the individuals analyzed and if the latter then it is not clear from the figures which measurements belong to which individual (i.e they are mixing measurements from different people).

We addressed this issue by adding a comment to each figure caption, describing whether a particular figure or panel describes individual or pooled data, and also whether the analysis is done on individual clonotype or clonal group level.

Also, in case pooled data were used, we added the number of patients that was pooled for a particular type of analysis. This number differs from one type of analysis to the other, because not all the patients had a complete set of tissues, and also not all samples passed a quality check for a particular analysis.

Here are the numbers listed:

Fig 2A: N=6 (we were only considering those who had all three tissues)

Fig 2C, N=14 (all)

2D: N=14 (all)

2E N=7 (have both tum and PBMC).

2F N=9 (have both tum and PBMC).

2G N=9 (have both tum and PBMC)

2H N=7 (have both tum and LN)

3A N=14 (all)

3B N=11 (only those with tumor)

3E - N=14

7F N=11 (all that have tumor)

Reviewer #3 (Public Review):

In multiple cancers, the key roles of B cells are emerging in the tumor microenvironment (TME). The authors of this study appropriately introduce that B cells are relatively under-characterised in the TME and argue correctly that it is not known how the B cell receptor (BCR) repertoires across tumors, lymph nodes, and peripheral blood relate. The authors therefore supply a potentially useful study evaluating the tumor, lymph node, and peripheral blood BCR repertoires and site-to-site as well as intra-site relationships. The authors employ sophisticated analysis techniques, although the description of the methods is incomplete. Among other interesting observations, the authors argue that the tumor BCR repertoire is more closely related to that of draining lymph node (dLN) than the peripheral blood in terms of clonal and isotype composition. Furthermore, the author's findings suggest that tumor-infiltrating B cells (TIL-B) exhibit a less mature and less specific BCR repertoire compared with circulating B cells. Overall, this is a potentially useful work that would be of interest to both medical and computational biologists working across cancer. However, there are aspects of the work that would have benefitted from further analysis and areas of the manuscript that could be written more clearly and proofread in further detail.

Major Strengths:

(1) The authors provide a unique analysis of BCR repertoires across tumor, dLN, and peripheral blood. The work provides useful insights into inter- and intra-site BCR repertoire heterogeneity. While patient-to-patient variation is expected, the findings with regard to intra-tumor and intra-dLN heterogeneity with the use of fragments from the same tissue are of importance, contribute to the understanding of the TME, and will inform future study design.

(2) A particular strength of the study is the detailed CDR3 physicochemical properties analysis which leads the authors to observations that suggest a less-specific BCR repertoire of TIL-B compared to circulating B cells.

Major Weaknesses:

The study would have benefitted from a deeper biological interpretation of the data. While given the low number of patients one can plausibly understand a reluctance to speculate about clinical details, there is limited discussion about what may contribute to observed heterogeneity.

We indeed do not want to overinterpret our data, especially where it comes to the difference between types of cancer. On the other hand, extracting similar patterns between different cancer types allows to pinpoint mechanisms that are more general and do not depend on cancer type. As for the potential source of intratumoral heterogeneity that we observe, we think that it may be coming from the selective sampling of tertiary lymphoid structures. We include IHC data for TLS detection in the supplementary Fig.5.  Also, tumor mutation clonality may correlate with differential antibody response (i.e. different IGH clonotypes developing to recognize different antigens) – as has been previously described for TCRs by the lab of B.Chain in https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6890490/.

For example, for the analysis of three lymph nodes taken per patient which were examined for inter-LN heterogeneity, there is a lack of information regarding these lymph nodes.

Unfortunately no clinical information about the lymph nodes was available.

'LN3' is deemed as exhibiting the most repertoire overlap with the tumor but there is no discussion as to why this may be the case.

The following phrases describes this in the “LN-to-LN heterogeneity in colorectal cancer” paragraph:

Similarly, an unequal interaction of tumors with DLNs was observed at the level of hypermutating clonal groups.

Functionally, this may again indicate that within a group of DLNs, nodes are unequal in terms of access to tumor antigens, and this inequality shapes the BCR repertoires within these lymph nodes.

(2) At times the manuscript is difficult to follow. In particular, the 'Intra-LN heterogeneity' section follows the 'LN-LN heterogeneity in colorectal cancer' section and compares the overlap of LN fragments (LN11, LN21, LN31) with the tumor in two separate patients (Fig 6A). In the previous section (LN-LN), LN11, LN21, LN31 are names given to separate lymph nodes from the same patient. The fragments are referred to as 'LN2' and the nodes in the previous section are referred to similarly. This conflation of naming for nodes and fragments is confusing.

We corrected this.

(3) There is a duplicated paragraph in 'Short vs long trees' and the following section 'Productive involvement in hypermutation lineages depends on CDR3 characteristics.

Corrected.

Reviewer #1 (Recommendations For The Authors):

- Figures:

Figure 1A lacks resolution

Corrected

Figure 2A, Venn diagram: What do the colors indicate?

Corrected

Figure 5D, why include this tree when there is no mention of it in the text?

Described

Figures 8, 9, and 10 are not to be found. One should not have to figure out that they became supplementary in the end.

Corrected

Regarding the physicochemical properties of CDR-H3, what do the authors mean by "the central part"? Do the authors refer to the CDR-H3 loop, and if so, how is that defined when the IGHD gene segment is excluded from the analyses? Is it 5 amino acids (Productive involvement in hypermutating lineages depends on CDR3 characteristics, Page 21/39 in merged document) and (CDR3 properties, Page 8/39 in merged document), or 7 amino acids (Short vs long trees phylogeny analysis, Page 19/39 in merged document)? Please clarify.  

We considered different ways of calculating amino acid properties of CDR3 and used different parameters for sample-average and individual-sequence CDR3s. Now plots for Fig S6 C are updated for consistency. IGHD segment was not excluded from the analysis. The reviewer might be confused by our description of phylogenetic inference, when an artificial outgroup with D segment deleted is added to the clonal group to facilitate the inference process. All other sequences were analyzed in their original form with the D segment. This way, we could avoid biases in phylogeny introduced by misassignment of D gene germline to the outgroup.

What was the threshold for CDR-H3 identity in their analyses? How can the authors justify that this value changes according to individual patient datasets? (Materials & methods, Clonal lineage inference Page 29/39 in merged document).

As described earlier, ideal similarity threshold may depend on several factors, such as sampling, sequencing depth etc. For example, imagine a sample picking up 100% of the clonal lineage sequences which differ only 1 amino acid from each other, and a worse quality sample/sequencing picking up only every other sequence. Obviously, the minimal threshold required to accumulate these into a clonotype would be different for these two cases (1aa for the former, and ~2 aa for the latter for single-linkage clustering). The method of individual threshold tailoring relies on this: https://changeo.readthedocs.io/en/latest/examples/cloning.html

What is the difference between tumor-induced and tumor-infiltrating B cells? How can the authors discriminate between the two? Page 6/39 in the merged document.

corrected to tumor-infiltrating

"Added nucleotides" meaning N additions? Page 3/39 in the merged document.

yes

How many cancer patients were enrolled? 17 or 14(Materials & methods page 27/39 in the merged document)? Please clarify.   

In the current project 14 patients were enrolled. The appropriate changes have been introduced in the final text. Supplementary table 2 has been added with the patient data.

Abbreviations are used without full descriptions.

According to reviewer’s recommendation, a list of abbreviations was added in the manuscript, and also full descriptions were added in the text upon first mentioning of the term.

Use either CDR3 or CDR-H3

We corrected the text to use CDR-H3 abbreviation throughout the text.

Reviewer #2 (Recommendations For The Authors):

I would like to start by apologizing for the time it took me to review.

As I mentioned above there are issues with the clonal sampling of the sequencing length and the statistics in this paper. From reading the paper I am not sure if they are fixable but there are some things that could be tried.

(1) The authors mention the diversity of their individual analysis - 17 individuals across 3 cancer types, but do not then systematically show us how the different things they measure track across the different individuals and cancer types. it is possible that some trends would be more convincing if we saw them happening again and again across all individuals. But, as I said above, the authors do not identify individuals clearly across all their types of analysis nor do they explain why sometimes they show analysis of specific individuals.

For overlap analysis (Fig. 2 except panel B), CDR3 properties analysis (Fig. 3, Fig. S7), clonal group analysis (Fig. 4) we used pooled data on all cancers, unless it is indicated otherwise on the panel. For overlap analysis, we used Cytoscape graph (Fig. 2B) for one patient, mp3, to illustrate the findings that were made on pooled data. For other types of analysis, such as overlap between individual lymph nodes, or tumor fragments (Fig. 5, 6, 7 except panel F) pooled analysis is not possible due to the individual nature of the processes in question.

(2) The authors do not address how lacking their sampling is nor the distribution of clone sizes in different tissues/ individuals/ subsets. Without such a discussion it is not clear how tenuous or convincing their conclusions are.

(3) The short sequencing lengths limit the ability to exactly identify V and thus the germline root of clones, whose positions are mutated and clonal association of sequences. The authors appear to be aware of this as they often use the most common ancestor as the start of their analysis... however, again there are inconsistencies that are not clearly described in the text. in creating trees with change they defined roots as the putative germline and at least in most cases also in clone association although in some analyses potentially similar clones were collapsed into clonotypes. Again it is not clear when one method was used or the other and how the choice was made what to choose.

Here we can only state that we consistently used the approach described in the Methods section, which was the following:

First, the repertoires were clustered into clonal lineages using the criteria described in “Methods: Clonal lineage inference” Assuming that each clonotype sequence in the clonal lineage originated from the same ancestor, we try to recover the phylogeny. Please note that we refer to the individual BCR sequences as “clonotypes”, and to a group of clonotypes that presumably share a common ancestor - as “clonal lineage” or “clonal group”.

The phylogeny of B-cell hypermutations was inferred for each clonal lineage of size five or more using the maximum likelihood method and the GTR GAMMA nucleotide substitution model. To find the most recent common ancestor (MRCA) or “root” of the tree, we used an artificial outgroup constructed as a conjugate of germline segments V and J defined by MIXCR and added it to the clonal lineage. The D segment was excluded from the outgroup formation, as there was insufficient confidence in the germline annotations due to its short length and high level of mutations. The rest of the clonotypes were still analyzed in their original form with D segment in place. Deleting D segment from the outgroup simply eliminates the risk of biasing the phylogeny by missasigning D segment germline sequence to the outgroup. The MUSCLE tool was used for multiple sequence alignment and RAxML software was used to build and root phylogenetic trees.

(4) Beyond the statistical issues mentioned above: the unclear selection of individual examples for comparison and significance testing, the mixing of individuals and cancer types without clear identification, etc. there is in general a lack of coherence in the statistical analysis performed. specifically:

(a) the authors should choose one cutoff for significance (0.01 for instance) and then just mention when things are significant and when not. There is no need and it is confusing to add the p-value for every comparison. P-values are not good measures of effect size.

We corrected the figures and left p-values only where they are below significance threshold.

(b) the Bonferroni correction used is not well characterized. For an alpha of 0.01 in Figures 3 C and D how many tests were performed?

The number of tests performed that was used for Bonferroni-Holm correction equals the number of comparisons on the heatmap which makes it 39 for each heatmap on Fig 3C and 13 for Fig 3D.

Finally some minor issues -

(1) Not all acronyms are described, for instance, TME and TIL. The first time any acronym is used it should be spelled out.  -> Katya B- список сокращений

(2) The figure captions are not all there...

(a) there is no caption for Figure 3E.

corrected

(b) there are Figure 7 F and G panels but no Figure 7E panel and Figure F is described after Figure G.

corrected

(3) A few problems with wording -

(a) bottom paragraph of page 3 - instead of :

"different lymph nodes from one draining lymph node pool may be more or less involved"

Corrected to "different lymph nodes from one draining lymph node pool may be differentially involved"

(b) figure caption for figure 3a: instead of:

"CDR3 are on average significantly higher in tumor"

Corrected to "CDR3 are on average significantly longer in tumor"

Reviewer #3 (Recommendations For The Authors):

- FIG1A - Suggest expanding the legend to include more information on the computational analyses.

added

- PAGE SIX: Suggest adding a table or some text on patient characteristics. Numbers of unique clonotypes per sample etc. Are there differences in age/sex that need to be considered? Some clonotype information is available in S1 but some summary and statistics would be appreciated.

Added patient information as Supplementary table 2.

- PAGE SIX: F2 Metric, suggestion to explain why this was used vs. other metrics.

We expanded the following paragraph to include information about F2 metric and D metric, and the reason why we are using F2.

Repertoire data for each sample were split according to the immunoglobulin isotype, and the F2 metric was calculated for each isotype separately and plotted as an individual point. We used the repertoire overlap metric F2 (Сlonotype-wise sum of geometric mean frequencies of overlapping clonotypes), which accounts for both the number and frequency of overlapping clonotypes (Fig. 2A). As expected, significantly lower overlaps were observed between the IGH repertoires of peripheral blood and tumors compared to LN/tumor overlaps. The LN/PBMC overlap also tended to be lower, but the difference was not statistically significant. We also analyzed D metric, which represents the relative overlap diversity uninfluenced by clonotype frequency (Dij\=dij/(di*dj), where dij is the number of clonotypes present in both samples, while di and dj are the diversities of samples i and j respectively). The results for D metric are not shown, as they indicate a similar trend to that of F2 metric. This observation allows us to conclude that tumor IGH repertoires are more similar to the repertoires of tumor-draining LNs than to those of peripheral blood, both if clonotype frequency is taken into account, and when it is not.

- PAGE SIX: Make clear in the text that mp3 is a patient.

Added “melanoma patient mp3”

- PAGE EIGHT: Suggest explaining kidera factors at first use - not all readers will know what they are.

We expanded the following paragraph to add more information about Kidera factors:

To explore CDR-H3 physicochemical properties, we calculated the mean charge, hydropathy, predicted interaction strength, and Kidera factors 1 - 9 (kf1-kf9) for five central amino acids of the CDR-H3 region for the 100 most frequent clonotypes of each sample using VDJtools. Kidera factors are a set of scores which quantify physicochemical properties of protein sequences 61. 188 physical properties of the 20 amino acids are encoded using dimension reduction techniques, to yield 9 factors which are used to quantitatively characterize physicochemical properties of amino acid sequences.

- Fig 5D is not referred to.

Corrected

🐐

• org structures expands the bandwith of management to focus on their goals
• Changes done Kranti: staff capacity dev, M&E frameworks, more streamlined and improved programmatic planning and work, improved financial management.
• To face challenges - have a dialogue with personnels on the need and importantance of such changes
• Advice to NGO: give impo to formal processes, focus on the representation on your work
• Advice to Funders - Fund grassroots org directly, focus on commitment and potentialal instead of turnovers

eLife assessment

This paper describes an important advance in a 2D in vitro neural culture system to generate mature, functional, diverse, and geometrically consistent cultures, in a 384-well format with defined dimensions and the absence of the necrotic core, which persists for up to 300 days. The well-based format and conserved geometry make it a promising tool for arrayed screening studies. Some of the evidence is incomplete and could benefit from a more direct head-to-head comparison with more standard culture methods and standardization of cell seeding density as well as further data on reproducibility in each well and for each cell line.

Reviewer #1 (Public Review):

Summary:

Kroeg et al. describe a novel method for 2D culture human induced pluripotent stem cells (hiPSCs) to form cortical tissue in a multiwell format. The method claims to offer a significant advancement over existing developmental models. Their approach allows them to generate cultures with precise, reproducible dimensions and structure with a single rosette; consistent geometry; incorporating multiple neuronal and glial cell types (cellular diversity); avoiding the necrotic core (often seen in free-floating models due to limited nutrient and oxygen diffusion). The researchers demonstrate the method's capacity for long-term culture, exceeding ten months, and show the formation of mature dendritic spines and considerable neuronal activity. The method aims to tackle multiple key problems of in vitro neural cultures: reproducibility, diversity, topological consistency, and electrophysiological activity. The authors suggest their potential in high-throughput screening and neurotoxicological studies.

Strengths:

The main advances in the paper seem to be: The culture developed by the authors appears to have optimal conditions for neural differentiation, lineage diversification, and long-term culture beyond 300 days. These seem to me as a major strength of the paper and an important contribution to the field. The authors present solid evidence about the high cell type diversity present in their cultures. It is a major point and therefore it could be better compared to the state of the art. I commend the authors for using three different IPS lines, this is a very important part of their proof. The staining and imaging quality of the manuscript is of excellent quality.

Weaknesses:

(1) The title is misleading: The presented cultures appear not to be organoids, but 2D neural cultures, with an insufficiently described intermediate EB stage. For nomenclature, see: doi: 10.1038/s41586-022-05219-6. Should the tissue develop considerable 3D depth, it would suffer from the same limited nutrient supply as 3D models - as the authors point out in their introduction.

(2) The method therefore should be compared to state-of-the-art (well-based or not) 2D cultures, which seems to be somewhat overlooked in the paper, therefore making it hard to assess what the advance is that is presented by this work.

(3) Reproducibility is prominently claimed throughout the manuscript. However, it is challenging to assess this claim based on the data presented, which mostly contain single frames of unquantified, high-resolution images. There are almost no systematic quantifications presented. The ones present (Figure S1D, Figure 4) show very large variability. However, the authors show sets of images across wells (Figure S1B, Figure S3) which hint that in some important aspects, the culture seems reproducible and robust.

(4) What is in the middle? All images show markers in cells present around the center. The center however seems to be a dense lump of cells based on DAPI staining. What is the identity of these cells? Do these cells persist throughout the protocol? Do they divide? Until when? Addressing this prominent cell population is currently lacking.

(5) This manuscript proposes a new method of 2D neural culture. However, the description and representation of the method are currently insufficient.<br /> (a) The results section would benefit from a clear and concise, but step-by-step overview of the protocol. The current description refers to an earlier paper and appears to skip over some key steps. This section would benefit from being completely rewritten. This is not a replacement for a clear methods section, but a section that allows readers to clearly interpret results presented later.<br /> (b) Along the same lines, the graphical abstract should be much more detailed. It should contain the time frames and the media used at the different stages of the protocol, seeding numbers, etc.

Reviewer #2 (Public Review):

Summary:

In this manuscript, van der Kroeg et al have developed a method for creating 3D cortical organoids using iPSC-derived neural progenitor cells in 384-well plates, thus scaling down the neural organoids to adherent culture and a smaller format that is amenable to high throughput cultivation. These adherent cortical organoids, measuring 3 x 3 x 0.2 mm, self-organize over eight weeks and include multiple neuronal subtypes, astrocytes, and oligodendrocyte lineage cells.

Strengths:

(1) The organoids can be cultured for up to 10 months, exhibiting mature dendritic spines, axonal myelination, and robust neuronal activity.

(2) Unlike free-floating organoids, these do not develop necrotic cores, making them ideal for high-throughput drug discovery, neurotoxicological screening, and brain disorder studies.

(3) The method addresses the technical challenge of achieving higher-order neural complexity with reduced heterogeneity and the issue of necrosis in larger organoids. The method presents a technical advance in organoid culture.

(4) The method has been demonstrated with multiple cell lines which is a strength.

(5) The manuscript provides high-quality immunostaining for multiple markers.

Weaknesses:

(1) Direct head-to-head comparison with standard organoid culture seems to be missing and may be valuable for benchmarking, ie what can be done with the new method that cannot be done with standard culture and vice versa, ie what are the aspects in which new method could be inferior to the standard.

(2) It would be important to further benchmark the throughput, ie what is the success rate in filling and successfully growing the organoids in the entire 384 well plate?

(3) For each NPC line an optimal seeding density was estimated based on the proliferation rate of that NPC line and via visual observation after 6 weeks of culture. It would be important to delineate this protocol in more robust terms, in order to enable reproducibility with different cell lines and amongst the labs.

Reviewer #3 (Public Review):

Summary:

Kroeg et al. have introduced a novel method to produce 3D cortical layer formation in hiPSC-derived models, revealing a remarkably consistent topography within compact dimensions. This technique involves seeding frontal cortex-patterned iPSC-derived neural progenitor cells in 384-well plates, triggering the spontaneous assembly of adherent cortical organoids consisting of various neuronal subtypes, astrocytes, and oligodendrocyte lineage cells.

Strengths:

Compared to existing brain organoid models, these adherent cortical organoids demonstrate enhanced reproducibility and cell viability during prolonged culture, thereby providing versatile opportunities for high-throughput drug discovery, neurotoxicological screening, and the investigation of brain disorder pathophysiology. This is an important and timely issue that needs to be addressed to improve the current brain organoid systems.

Weaknesses:

While the authors have provided significant data supporting this claim, several aspects necessitate further characterization and clarification. Mainly, highlighting the consistency of differentiation across different cell lines and standardizing functional outputs are crucial elements to emphasize the future broad potential of this new organoid system for large-scale pharmacological screening.

Author response:

Public Reviews:

Reviewer #1 (Public Review):

Summary: 

Kroeg et al. describe a novel method for 2D culture human induced pluripotent stem cells (hiPSCs) to form cortical tissue in a multiwell format. The method claims to offer a significant advancement over existing developmental models. Their approach allows them to generate cultures with precise, reproducible dimensions and structure with a single rosette; consistent geometry; incorporating multiple neuronal and glial cell types (cellular diversity); avoiding the necrotic core (often seen in free-floating models due to limited nutrient and oxygen diffusion). The researchers demonstrate the method's capacity for long-term culture, exceeding ten months, and show the formation of mature dendritic spines and considerable neuronal activity. The method aims to tackle multiple key problems of in vitro neural cultures: reproducibility, diversity, topological consistency, and electrophysiological activity. The authors suggest their potential in high-throughput screening and neurotoxicological studies.

Strengths: 

The main advances in the paper seem to be: The culture developed by the authors appears to have optimal conditions for neural differentiation, lineage diversification, and long-term culture beyond 300 days. These seem to me as a major strength of the paper and an important contribution to the field. The authors present solid evidence about the high cell type diversity present in their cultures. It is a major point and therefore it could be better compared to the state of the art. I commend the authors for using three different IPS lines, this is a very important part of their proof. The staining and imaging quality of the manuscript is of excellent quality.

We thank the reviewer for the positive comments on the potential of our novel platform to address key problems of in vitro neural culture, highlighting the longevity and reproducibility of the method across multiple cell lines.

Weaknesses: 

(1) The title is misleading: The presented cultures appear not to be organoids, but 2D neural cultures, with an insufficiently described intermediate EB stage. For nomenclature, see: doi: 10.1038/s41586-022-05219-6. Should the tissue develop considerable 3D depth, it would suffer from the same limited nutrient supply as 3D models - as the authors point out in their introduction. 

We appreciate the opportunity to clarify this point. We respectfully disagree that the cultures do not meet the consensus definition of an organoid. In fact, a direct quote from the seminal nomenclature paper referenced by the reviewer states: “We define organoids as in vitro-generated cellular systems that emerge by self-organization, include multiple cell types, and exhibit some cytoarchitectural and functional features reminiscent of an organ or organ region. Organoids can be generated as 3D cultures or by a combination of 3D and 2D approaches (also known as 2.5D) that can develop and mature over long periods of time (months to years).” (Pasca et al, 2022 doi10.1038/s41586-022-05219-6). Therefore, while many organoid types indeed have a more spherical or globular 3D shape, the term organoid also applies to semi-3D or non-globular adherent organoids, such as renal (Czerniecki et al 2018, doi.org/10.1016/j.stem.2018.04.022) and gastrointestinal organoids (Kakni et al 2022, doi.org/10.1016/j.tibtech.2022.01.006). Accordingly, the adherent cortical organoids described in the manuscript exhibit self-organization to single radial structures consisting of multiple cell layers in the z-axis, reaching ~200um thickness (therefore remaining within the limits for sufficient nutrient supply), with consistent cytoarchitectural topology and electrophysiological activity, and therefore meet the consensus definition of an organoid.

(2) The method therefore should be compared to state-of-the-art (well-based or not) 2D cultures, which seems to be somewhat overlooked in the paper, therefore making it hard to assess what the advance is that is presented by this work. 

It was not our intention to benchmark this model quantitatively against other culture systems. Rather, we have attempted to characterize the opportunities and limitations of this approach, with a qualitative contrast to other culture methods. Compared to state-of-the-art 2D neural network cultures, adherent cortical organoids provide distinct advantages in:

(1) Higher order self-organized structure formation, including segregation of deeper and upper cortical layers.

(2) Longevity: adherent cortical organoids can be successfully kept in culture up to 1 year where 2D cultures typically deteriorate after 8-12 weeks.

(3) Maturity, including the formation of dendritic mushroom spines and robust electrophysiological activity.

(4) Cell type diversity including a more physiological ratio of inhibitory and excitatory neurons (10% GAD67+/NeuN+ neurons in adherent cortical organoids, vs 1% in 2D neural networks) and the emergence of oligodendrocyte lineage cells.

On the other hand, limitations of adherent cortical organoids compared to 2D neural network cultures are:

(1) Culture times for organoids are much longer than for 2D cultures and the method can therefore be more laborious and more expensive.

(2) Whole cell patch clamping is not easily feasible in the organoids because of the restricting dimensions of the 384well plates.

(3) Reproducibility is prominently claimed throughout the manuscript. However, it is challenging to assess this claim based on the data presented, which mostly contain single frames of unquantified, high-resolution images. There are almost no systematic quantifications presented. The ones present (Figure S1D, Figure 4) show very large variability. However, the authors show sets of images across wells (Figure S1B, Figure S3) which hint that in some important aspects, the culture seems reproducible and robust. 

We made considerable efforts to establish quantitative metrics to assess reproducibility. We applied a quantitative scoring system of single radial structures at different time points for multiple batches of all three lines as indicated in Figure S1D. This figure represents a comprehensive dataset in which each dot represents the average of a different batch of organoids containing 10-40 organoids per batch. To emphasize this, we will adapt the graph to better reflect the breadth of the dataset. Additional quantifications are given in Figure S2 for progenitor and layer markers for Line 1 and in Figure S5 for interneurons across all three lines, showing relatively low variability. That being said, we acknowledge the reviewer’s concerns and will modify the text to reduce the emphasis of this point, pending more extensive data addressing reproducibility across a wide range of parameters.

(4) What is in the middle? All images show markers in cells present around the center. The center however seems to be a dense lump of cells based on DAPI staining. What is the identity of these cells? Do these cells persist throughout the protocol? Do they divide? Until when? Addressing this prominent cell population is currently lacking. 

A more comprehensive characterization of the cells in the center remains a significant challenge due to the high cell density hindering antibody penetration. However, dye-based staining methods such as DAPI and the LIVE/DEAD panel confirm a predominance of intact nuclei with very minimal cell death. The limited available data suggest that a substantial proportion of the cells in the center are proliferative neural progenitors, indicated by immunolabeling for SOX2 and Ki67. We will add additional figures to support these findings. Furthermore, we are currently optimizing the conditions to perform single cell / nuclear RNA sequencing to further characterize the cellular composition of the organoids.

(5) This manuscript proposes a new method of 2D neural culture. However, the description and representation of the method are currently insufficient. <br /> (a) The results section would benefit from a clear and concise, but step-by-step overview of the protocol. The current description refers to an earlier paper and appears to skip over some key steps. This section would benefit from being completely rewritten. This is not a replacement for a clear methods section, but a section that allows readers to clearly interpret results presented later.

We will revise the manuscript to include a more detailed step-by-step overview of the protocol.

(b) Along the same lines, the graphical abstract should be much more detailed. It should contain the time frames and the media used at the different stages of the protocol, seeding numbers, etc. 

As suggested, we will also adapt the graphical abstract to include more detail.

Reviewer #2 (Public Review): 

Summary: 

In this manuscript, van der Kroeg et al have developed a method for creating 3D cortical organoids using iPSC-derived neural progenitor cells in 384-well plates, thus scaling down the neural organoids to adherent culture and a smaller format that is amenable to high throughput cultivation. These adherent cortical organoids, measuring 3 x 3 x 0.2 mm, self-organize over eight weeks and include multiple neuronal subtypes, astrocytes, and oligodendrocyte lineage cells.

Strengths: 

(1) The organoids can be cultured for up to 10 months, exhibiting mature dendritic spines, axonal myelination, and robust neuronal activity. 

(2) Unlike free-floating organoids, these do not develop necrotic cores, making them ideal for high-throughput drug discovery, neurotoxicological screening, and brain disorder studies.

(3) The method addresses the technical challenge of achieving higher-order neural complexity with reduced heterogeneity and the issue of necrosis in larger organoids. The method presents a technical advance in organoid culture.

(4) The method has been demonstrated with multiple cell lines which is a strength. 

(5) The manuscript provides high-quality immunostaining for multiple markers. 

We appreciate the reviewer’s acknowledgement of the strengths of this novel platform as a technical advance in organoid culture that reduces heterogeneity and shows potential for higher throughput experiments.

Weaknesses: 

(1) Direct head-to-head comparison with standard organoid culture seems to be missing and may be valuable for benchmarking, ie what can be done with the new method that cannot be done with standard culture and vice versa, ie what are the aspects in which new method could be inferior to the standard.

In our opinion, it would be extremely difficult to directly compare methods because of substantial differences. Most notably, whole brain organoids grow to large and irregular globular shapes, while adherent cortical organoids have a highly standardized shape confined by the limits of a 384-well. Moreover, it was not our intention to benchmark this model quantitatively against other culture systems. Rather, we have attempted to characterize the opportunities and limitations of this approach, with a qualitative contrast to other culture methods.

(2) It would be important to further benchmark the throughput, ie what is the success rate in filling and successfully growing the organoids in the entire 384 well plate? 

Figure S1D shows the success rate of organoid formation and stability of the organoid structures over time. In addition, we will add the number of wells that were filled per plate.

(3) For each NPC line an optimal seeding density was estimated based on the proliferation rate of that NPC line and via visual observation after 6 weeks of culture. It would be important to delineate this protocol in more robust terms, in order to enable reproducibility with different cell lines and amongst the labs. 

Figure S1C provides the relationship between proliferation rate and seeding density, allowing estimation of seeding densities based on the proliferation rate of the NPCs. However, we appreciate the reviewers feedback and will modify the methods to provide more detail.

Reviewer #3 (Public Review): 

Summary: 

Kroeg et al. have introduced a novel method to produce 3D cortical layer formation in hiPSC-derived models, revealing a remarkably consistent topography within compact dimensions. This technique involves seeding frontal cortex-patterned iPSC-derived neural progenitor cells in 384-well plates, triggering the spontaneous assembly of adherent cortical organoids consisting of various neuronal subtypes, astrocytes, and oligodendrocyte lineage cells. 

Strengths: 

Compared to existing brain organoid models, these adherent cortical organoids demonstrate enhanced reproducibility and cell viability during prolonged culture, thereby providing versatile opportunities for high-throughput drug discovery, neurotoxicological screening, and the investigation of brain disorder pathophysiology. This is an important and timely issue that needs to be addressed to improve the current brain organoid systems. 

We thank the reviewer for highlighting the strengths of our novel platform. We appreciate that all three reviewers agree that the adherent cortical organoids presented in this manuscript reliably demonstrate increased reproducibility and longevity. They also commend its potential for higher throughput drug discovery and neurotoxicological/phenotype screening purposes.

Weaknesses: 

While the authors have provided significant data supporting this claim, several aspects necessitate further characterization and clarification. Mainly, highlighting the consistency of differentiation across different cell lines and standardizing functional outputs are crucial elements to emphasize the future broad potential of this new organoid system for large-scale pharmacological screening.

We appreciate the feedback and will add more detail on consistency and standardization of functional outputs.

Propositions de titres : - colloque hybride : un jeu multijoueurs ? - réflexions sur l'hybridité et parrallèle avec le game design - hydridation des interactions : concevoir un "potentiel hybride" - Colloque hybride : expérience vécue et investissement interactionnel des participants. - Hybridité et game design : réfexions à partir de l'exemple d'un colloque hybride.

j'ai rédigé cette partie, à améliorer par la suite.

Note

redefreiheit muss her, inklusive legalisierung von beleidigungen und "hassrede".<br /> warum soll ich meine revolutionspläne, gewaltfantasien, todeslisten, ... geheim halten?<br /> bei dem punkt sind die USA weiter, aber der globale trend geht eher richtung china : /

从大时间框看形态之后 去小时间框架去看形态形成的情况

斐波那契： 左边高点则是以高点开始 低点结束 右边高点则是以低点开始 高点结束 左高起，右高尾

sissies with platform shoes 穿着厚底鞋的娘娘腔

normal people with sneakers

interior

tài liệu

on the edge of change comparable to the rise of human life on Earth

contradictions between: - be equitable and shift power - building trusted relationships between philanthropists and grantees - enable system change - increase risk appetite of donors and catalyse innovation - to do everything based on data and evidence

for shift of power, there need to be focus on unrestricted funding with generous coverage of operational costs with minimal reporting requirements. But at the same time philanthropists want to enable systems change - which requires aligned strategic approach - i.e. trusted partnership and collaboration. Now this collaboration requires more, time and money and also so NGO's need to be up for it or it wouldn't be an equitable partnership. Minimal reporting can make it difficult to track down progress made by systems change, and counters evidence based approach to philanthropy. Focus can be made on learning. System change when translated as scale can itself exclude many org by prioritising scale, and without changing its understanding, it can be exclusionary for potential grantees and prevent their access to ‘systems change’ funding calls. Take more risks yet strategies formulated by those without lived experiences of the issues at hand. At the same time, philanthropy has a unique role to play in such partnerships to avoid this dynamic: it can leverage its influence and networks to ensure more equitable, power-sensitive approaches while building critical bridges between the profit-driven motivations of the private sector, the social goals of governments, and the justice goals of civil society actors. And so right questions need to be asked to give way to such transformation.

控制代码的调用顺序，这个方式值得学习，装波13

看代码先看这几个文件

eLife assessment

This important study reports that a transcription factor that stimulates mRNA synthesis can stabilize its target transcripts, possibly through co-transcriptional assembly and action in the cytoplasm. While the primary observation is solid, whether an association of Sfp1 with specific transcripts in the cytoplasm is the critical step in transcript stabilization is not entirely clear. If confirmed by independent means, the authors would have found a novel mechanistic link between mRNA synthesis and cytoplasmic mRNA stability for specific transcripts. Such a finding would be of broad interest to the field of molecular biology.

Reviewer #2 (Public Review):

Summary:

The manuscript by Kelbert et al. presents results on the involvement of the yeast transcription factor Sfp1 in the stabilisation of transcripts whose synthesis it stimulates. Sfp1 is known to affect the synthesis of a number of important cellular transcripts, such as many of those that code for ribosomal proteins. The hypothesis that a transcription factor can remain bound to the nascent transcript and affect its cytoplasmic half-life is attractive. However, the association of Sfp1 with cytoplasmic transcripts remains to be validated, as explained in the following comments:

A two-hybrid based assay for protein-protein interactions identified Sfp1, a transcription factor known for its effects on ribosomal protein gene expression, as interacting with Rpb4, a subunit of RNA polymerase II. Classical two-hybrid experiments depend on the presence of the tested proteins in the nucleus of yeast cells, suggesting that the observed interaction occurs in the nucleus. Unfortunately, the two-hybrid method cannot determine whether the interaction is direct or mediated by nucleic acids. The revised version of the manuscript now states that the observed interaction could be indirect.

To understand to which RNA Sfp1 might bind, the authors used an N-terminally tagged fusion protein in a cross-linking and purification experiment. This method identified 264 transcripts for which the CRAC signal was considered positive and which mostly correspond to abundant mRNAs, including 74 ribosomal protein mRNAs or metabolic enzyme-abundant mRNAs such as PGK1. The authors did not provide evidence for the specificity of the observed CRAC signal, in particular what would be the background of a similar experiment performed without UV cross-linking. This is crucial, as Figure S2G shows very localized and sharp peaks for the CRAC signal, often associated with over-amplification of weak signal during sequencing library preparation.

In a validation experiment, the presence of several mRNAs in a purified SFP1 fraction was measured at levels that reflect the relative levels of RNA in a total RNA extract. Negative controls showing that abundant mRNAs not found in the CRAC experiment were clearly depleted from the purified fraction with Sfp1 would be crucial to assess the specificity of the observed protein-RNA interactions (to complement Fig. 2D). The CRAC-selected mRNAs were enriched for genes whose expression was previously shown to be upregulated upon Sfp1 overexpression (Albert et al., 2019). The presence of unspliced RPL30 pre-mRNA in the Sfp1 purification was interpreted as a sign of co-transcriptional assembly of Sfp1 into mRNA, but in the absence of valid negative controls, this hypothesis would require further experimental validation. Also, whether the fraction of mRNA bound by Sfp1 is nuclear or cytoplasmic is unclear.

To address the important question of whether co-transcriptional assembly of Spf1 with transcripts could alter their stability, the authors first used a reporter system in which the RPL30 transcription unit is transferred to vectors under different transcriptional contexts, as previously described by the Choder laboratory (Bregman et al. 2011). While RPL30 expressed under an ACT1 promoter was barely detectable, the highest levels of RNA were observed in the context of the native upstream RPL30 sequence when Rap1 binding sites were also present. Sfp1 showed better association with reporter mRNAs containing Rap1 binding sites in the promoter region. Removal of the Rap1 binding sites from the reporter vector also led to a drastic decrease in reporter mRNA levels. Co-purification of reporter RNA with Sfp1 was only observed when Rap1 binding sites were included in the reporter. Negative controls for all the purification experiments might be useful.

To complement the biochemical data presented in the first part of the manuscript, the authors turned to the deletion or rapid depletion of SFP1 and used labelling experiments to assess changes in the rate of synthesis, abundance and decay of mRNAs under these conditions. An important observation was that in the absence of Sfp1, mRNAs encoding ribosomal protein genes not only had a reduced synthesis rate, but also an increased degradation rate. This important observation needs careful validation, as genomic run-on experiments were used to measure half-lives, and this particular method was found to give results that correlated poorly with other measures of half-life in yeast (e.g. Chappelboim et al., 2022 for a comparison). As an additional validation, a temperature shift to 42{degree sign}C was used to show that , for specific ribosomal protein mRNA, the degradation was faster, assuming that transcription stops at that temperature. It would be important to cite and discuss the work from the Tollervey laboratory showing that a temperature shift to 42{degree sign}C leads to a strong and specific decrease in ribosomal protein mRNA levels, probably through an accelerated RNA degradation (Bresson et al., Mol Cell 2020, e.g. Fig 5E). Finally, the conclusion that mRNA deadenylation rate is altered in the absence of Sfp1, is difficult to assess from the presented results (Fig. 3D).

The effects of SFP1 on transcription were investigated by chromatin purification with Rpb3, a subunit of RNA polymerase, and the results were compared with synthesis rates determined by genomic run-on experiments. The decrease in polII presence on transcripts in the absence of SFP1 was not accompanied by a marked decrease in transcript output, suggesting an effect of Sfp1 in ensuring robust transcription and avoiding RNA polymerase backtracking. To further investigate the phenotypes associated with the depletion or absence of Sfp1, the authors examined the presence of Rpb4 along transcription units compared to Rpb3. An effect of spf1 deficiency was that this ratio, which decreased from the start of transcription towards the end of transcripts, increased slightly. To what extent this result is important for the main message of the manuscript is unclear.

Suggestions: a) please clearly indicate in the figures when they correspond to reanalyses of published results. b) In table S2, it would be important to mention what the results represent and what statistics were used for the selection of "positive" hits.

Strengths:

- Diversity of experimental approaches used.<br /> - Validation of large-scale results with appropriate reporters.

Weaknesses:

- Lack of controls for the CRAC results and lack of negative controls for the co-purification experiments that were used to validate specific mRNA targets potentially bound by Sfp1.<br /> - Several conclusions are derived from complex correlative analyses that fully depend on the validity of the aforementioned Sfp1-mRNA interactions.

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geometric calibration

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