break down the 70 page chapter down over so many days (2 or 3)

This textbook being a resource to us is going to help more than I may realize now. The total cost of books throughout a students education can cost upwards of $1,500.

Knowing how to write an appropriate college paper will always be useful now and in the future

Address entire class as "classmates."

I liked that the authors added this graph into the research article. It is a good way to visualize the data that was collected. It also shows the different types of ageism that can occur. By knowing the different types of ageism, us as nurses can be more aware of it when caring for the older population

from the plots, it seems like using S-G for denoising is comparable to RamanMAE. it would be good to know what the advantage is of RamanMAE as a much more computationally intensive algorithm

i am confused how the 2D representation (image) is actually created. does the wrapping go patch-by-patch or by the entire width of the image? perhaps an illustration could be useful.

Both McCormick and John Deere are still the top selling names we hear and see today. I found this really interesting that production exploded over time to the biggest production leading it to the world's breadbasket. The discovery of how to distribute the workers that would benefit the production quicker was a very strategic move that helped the United States placing them above other countries.

I found this information knew to me. It is interesting that Chicago would be the meeting place for the protest and wonder why Chicago. Such a tragedy that so many protesters lost their lives and that so many others had to endure the horrible event.

I find this absolutely baffling to know that a "rifle diet" could be the solution to any problem. It does not seem back in this time that a human life was looked at as such and instead quickly replaceable if it does not benefit the person seeking something. A president suggesting this type of behavior is just as worrying and questionable as to why.

I could not wrap my mind around the idea that this could be a punishment. I feel that humans were not looked at as such instead as easily replaceable. Benefiting off others hard work and time yet lacking the empathy and gesture to pay them accordingly seems to be what happened in that time. It is scary to think that strikes got this violent and chaotic back then and the best solution to end the problem would be death. The Rifle diet method is extreme it gives an idea how life did not mean anything.

In the first sentence it gives us that this is between the end or reconstruction in 1876 and the First World War, this helps us understand a timeline. It also states that we will be reading about how people responded to the challenge of the changing American economy during this time.

This process of production made everyone have their own task which would get people to be more focused on what they needed to accomplish, this made an incredible difference in speeds of producing products forever.

I have heard of tenure before in a LS course. I assumed it just determined how much you get paid based off your years in the career.

This is good to see as a future educator because working jobs have taught me to understand/value written contracts from an employer

This makes me think about how school districts prioritize their safety protocols and their curriculum

This makes sense. I do think that the state oversight in terms of age attendance can be good in terms of allowing children to develop more before they attend a school. But it can cause some students to not be used to interacting with others (which is basic interaction)

This is crucial so that every concern is well-heard out. There can be many changes in education today that cause ripple effects in school sites too.

Check the answers

This is interesting because it shows how GPS isn’t just one system anymore, and it’s a network of different countries satellites working together.

...and still is. One of the biggest learning curves I had during my internships was how hard it was to actually effectively implement movies and videos into the class. As a student, I just thought my teacher was showing us a video so they didn't have to teach a full lesson, or so that they could grade papers. I thought the questions they asked were logical and just thought of on the spot.

I was at a different district working under a Social Studies teacher that always showed movies in their class. It was to the points that the kids didn't want to do it (even though the did not have to fill anything out). The other teachers were saying negative things about the head teacher and I was confused. Once I saw there was not much constructive activity to go along with the movie, I was worried about its effectiveness.

I taught an Immigration unit and showed a video of Ellis Island. I attempted to make Guided Notes for them to fill out. I thought listening to it in pieces would be best. Turns out I needed to have them watch it through once, and then replay the specific clips multiple times. Creating thought provoking questions students could answer after the video was also more difficult than I thought. All of this to say that it is actually not much easier to teach using videos because you need to make sure you are using them in moderation, with the appropriate activities, and in a way that engages students. There are so many factors that videos may not work in a classroom.

I personally agree with this viewpoint. While teachers should have control over the learning activities and means of presentation they use in their own room, they should not be viewed as primary means of presenting instruction. Instead, teachers should be in charge of creating and facilitating activities that help students construct their own learning. I think the best way to do this is to implement diverse forms of instruction and collaborating with other teachers, department members, or administration about what media to utilize.

That’s interesting because it shows how models basically give us a safe shortcut to study big or complex things. Instead of waiting thousands of years to watch erosion or risking damage to the environment, geographers can use tools like stream tables or computer simulations to test out ideas and actually see how things could work out.

That is interesting because it shows how glaciers like Gangotri are not only powerful natural systems shaping the landscape, but also tied to human culture and survival. The fact that Gaumukh is both the visible end of the glacier and the sacred source of the Ganges reminds us how closely geography and humans are connected

she wants to goback

she is talking about Orpheus and how he ignored her which led her to go with hades to the underworld. Even after he made a deal with hades he was so close to bringing Eurydice back but right before he looked back which forever condemned her to the underworld

Finish your homework quickly so we can go out later. ➡️ We need an adverb here (to describe how to finish). The adverb form of quick is quickly.

Are you booking the hotel room today? ➡️ After Are you … today? we need a verb in the -ing form (Present Continuous) to show an action happening now or planned for today.

She played really well in today’s match. ➡️ Good is an adjective, but here we need an adverb (to describe how she played). The adverb form is well.

That’s good news, I’m pleased for him. ➡️ News is a noun, so we need an adjective to describe it. Good news is the correct collocation. (Well news ❌ doesn’t exist).

John and Jess were in Spain last Christmas. ➡️ The subject is plural (John and Jess), and the time is past (last Christmas), so we use were (past simple of be).

I’m studying Music and Art at university. ➡️ After I’m …, we need the -ing form for Present Continuous. It describes what you are doing at university.

Does the model actually predict the correct sequence of AAs as opposed to just the AA content? If so, that is amazing, but I would be very surprised if a short peptide e.g. "RACR" had a different spectrum than a peptide containing the same AAs but in a different order e.g. "RRAC". If this is actually the case, it may be very informative to actually show the predicted spectra!

how does this performance compare to other techniques of predicting sequence from spectra? e.g. a simple linear model that tries to optimally fit known AA spectra to the unknown spectrum?

Great informative figure but the colorscheme is slightly confusing. perhaps dataset titles do not need to be colored to avoid confusion with the colors of the models in the plots?

the re-ranking module seems to look at existing chemical-spectral pairs and rank ones within the training set as more likely (higher score) and those outside the training set as less likely (lower score). if that is the case, does it negatively impact performance when inferring on out-of-distribution data? and what would be the performance of this module alone (i.e. without the encoder/decoder models altogether?

I think this is fair for a B. You have to participate and listen to the people you are responding to; otherwise, it will not benefit the colleague or you. It is important to work as a group for group exercises and constructively respond, especially because this course is online, so there is already limited communication. Overall, I believe that having to specifically and constructively respond is a fair way to earn a B.

I feel that the overall suggestion guideline for our grading in this class is acceptable. To do well in this class, we must go beyond and challenge ourselves. Being curious will allow us to help and assist our classmates. I like having a clear understanding of what is expected from us.

But see Leroy et al., 2025 (https://doi.org/10.1101/2025.08.14.670341) that addresses this issue using an improved pooling operator. However, as they discuss in their preprint, the performance they achieve (exceeding that of the MLE) still likely does not represent a ceiling on their performance here, as the architecture is quite simple. Use of more sophisticated graph-based architectures including graph transformers (which combat oversmoothing and can more readily account for both local and global patterns) will likely increase this performance further.

The first French settlement that was in North America

Funded early exploration

I find it interesting that they criticized the greed of powerful businesses. This really showed how focused they were on holding powerful figures accountable for their issues.

It's crazy how the alliance grew to over a million members. It really shows how much effort and support were among farmers.

This platform helped to ensure the essential services would run in the interests of the people rather than the profits of wealthy investors. This raised need for economic fairness. This also shows growing dissatisfaction with the political system.

This gives a little Insight about what famers were facing during this time. By just reading this it sounds like markets were taking off, which then left famers lowering their prices and falling further behind.

this is another thing i never really acknowledged, or i didn’t know there was a word for it.

i never paid attention to this before, now im hyper aware..

"Improve performance...apply skills...acquire knowledge" I interpret this as providing learners with tools that assist them in best demonstrating their understanding. Often times, this includes technological devices/platforms. For example, some learners excel on computer based assignments versus written assignments.

The 1994 definition proves that you need both parts of the equation for a successful instructional setting. For example, media is a great resource for gathering or organizing information but a teacher not only instructs new users how to operate these devices/platforms but also assists in problem solving,implementing new ideas and encouraging collaboration.

One important aspect of this definition is the addition of human contexts / social experiences in the way that we teach and learn.

This is interesting to me. My co-worker began explaining to students that they cannot learn from her, but rather from the activities she is facilitating. This is another modern mindset shift in the field. It used to be that the teacher was pouring out information to the students, who were learning from them. Now, teachers recognize that it is the way they present the information, or the opportunities they provide for learning that actually drive student success.

This definition combines the processes/procedures associated with the earlier instructional technology definition and the perceived definition of supplemental educational technology/mediums such as devices/presentation.

This made it pretty clear that educational technology and instructional technology go hand in hand. In this context, it implies that educational technology is supplemental to other forms of instructional practices. For example, adding a slide set with images and audio to a story written on paper would allow students to connect with the material through various methods.

When I think of this degree program, this is what I picture. Creating appealing and easy to understand slides, images, videos,etc that help students form the connections between content and imagery.

As previously stated, the field is so broad and includes many different topics. I think people find their "niche" and work to perfect that. I know that I am personally most interested in using visual aids to enhance learning.

This shift has been emphasized in how we teach material to students. The most important part students need to take away is not the answer itself but rather how to get there so that they can apply it to other problems. It is also important to recognize the need for new solutions, even if the problems are the same. This philosophy can explain shifts in perspective and values.

Meaning not only the composition or visual appeal but the best times to use it. I feel that many times online instruction could lose its meaning (just like games and simulations) when overused or used at the wrong time.

Is there a point that becoming too broad is a bad thing? How can we attempt to establish the field without ranking which aspects are 'most important' ?

Initial questions : Can educational technology and instructional technology be used interchangeably? If not, how could we describe the difference?

I love how this was integrated into the writing as a metaphor. Because it is known by many people that a semicolon represent a suicide survivor. I have made connections of how poems can be like people struggles.

I relate to this sentence, because anytime I read a poem in the begging I do not know fully what the poem is about until near the end. It is just like in humanity you do not know a person just by a few sentences and a few conversations you get to know the person after you keep engaging and learning more about them.

Yes, but I think a very effective way to achieve this is by dropping your lowest grade, this could be any missing assignments or large tests that you just were not ready for

That is such a good point to reflect on the evidence as looking outside of the worksheet, paper, etc. can really help see the student.

A couple that come in to mind is letting the students submit late work but at half the full grade or at a lesser deficit.

You know what I just realized, my hometown school doesn't do this and even if they did, there would be a huge margin of parents who simply wouldn't care.

بتا امولویید رو اندازه گیری کردن

a commentary on the research

i.e. Podcasts, Blogs, Digital Humanities. Offering itself for a wider audeince that doesnt include the same level fo crediblity. Is still made by experts but is intended for people who are lookng at it from a glance

Music have knowledge on the industry or topic to understand whats being said

Happiness is a Warm Typewriter: 10½ Years On, the Story Stays the Same<br /> by [[Robert Messenger]]<br /> accessed on 2025-09-05T11:21:50

Just to clarify: are these the absolute pixels in the entire imaging field? or the pixels of individual segmented cells? if segmented cells, was there any registration of the images? Are the cells moving or do you have evidence that some of these changes in the phasor analysis don't result from jitter in the positions of the cells?

Is the expectation here that metabolic changes will cause changes in the refactive index on a time scale of greater than 8 Hz? Is that a realistic time frame for metabolic changes? or are these other structural changes in response to activation?

Poems need vividness and uniqueness to keep the reader's attention!

This was an interesting statement in the poem. In english class it is known that punctuation in any piece of writing and even in a single sentence; punctuation is extremely important. This also helps remind the reader to use descriptive words when writing their own poems to make the reader feel more connected.

The incipit line of Edward Bulwer-Lytton's 1830 novel Paul Clifford.

https://en.wikipedia.org/wiki/Edward_Bulwer-Lytton

naturalism allows the audience to connect more with the film and the actors showing this through there emotions make the film more reliable and realistic.

i think the acting was the most outstanding the carema did add the effects of emotions but her eyes stood out and spoke to the audience a lot more.

I agree in many movies they will blur out objects and create a slow zoom in to show emotions through the actor into the camera then the audience gets to watch this all play out.

Ive read about this earlier on. the greeks create masks and preformed on stage on a theater this was their type of acting. They even created demons and angels out of diffent types of mask to create different characters.

profession actors have to dedicate so much time into there acting and sometimes this can even take on toll on their mental health many create actors have mental health issues from a movie because they had ot put all of their energy into the film. For example the joker heath won a grammy but was never there for it because he took his own life i think his role as the joker had something to do with this.

I believe those with creativity are more open to the world because of their curiosity and eagerness to see the beauty in everything

I usually tend skip the About Us part and head straight to the required skills section to see how I can align my skills with those the job role demands. I think moving forward I am certainly going t dedicate some time to look into this and make sure the organization's values align with mine, which would maybe also prove to be useful during behavioral interview rounds.

It's fascinating how the different shades of lines are used to differentiate the elevation and air pressure on maps.

classes that many deem "useless" are not necessarily required for the content itself, and AI takes away from that

is a student who uses AI more likely to receive a higher grade than one who does not if the grader is also AI?

I think this is completely destroying the purpose of higher education, which will soon mean nothing; has the potential to single-handedly take out universities

it seems like going through the trouble of avoiding AI detection is more work and stress than doing the assignment oneself

it is interesting that students focus more on grades than the actual learning experience, and I think teachers need to consider this when grading assignments

the consequences may not necessarily be immediate

I think that AI will soon diminish the title of the Ivies, as the vast majority of students use AI, regardless of the school's prestige

The author combined the bad news of his brother with music. It speaks to how, even in hard moments, his brain is so entangled in music. In the frequent mentions of music, it also refers back to how it bonds him and his brother.

This sentence makes one imagine and realize how John felt about the vegetative condition in which his brother was, a corpse forced to live by the machines connected to his body.

I was curious what is known about how PARP1 finds it's nicked DNA target? Is it possible that the reduced binding to the nicked substrate is due to a reduction in the efficiency of finding the substrate (i.e. sliding or inter- or intra-strand transfer?).

This is a really elegant biochemical study--congratulations on the work! I was wondering if you could or have confirmed this compaction using an alternate methodology, like sedimentation velocity analytical ultracentrifugation?

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This shows how powerful China was and how much they were producing compared to all of the other European countries.

This showed how after the son and grandson took over they didn't was to go on the expeditions and that was at the bottom of their list of priorities.

This shows how rude Zheng He was and that if he didn't get what he wanted it was not good and some not so good things happened like seizing leaders and bringing them to Beijing.

This expresses how after Confucian died and Hongwu became the ruler the country was much more strict and no one could trust anyone besides close ones like family.

A success story about the impact of effective curriculum development and giving the teachers the needed support to use that curriculum.

Main point when it comes to adapting curriculum for teachers and trying to keep it relevant.

I wish they would give some examples of this.

I do think that this is a weird section to read because this statement is almost repeating itself and can be taken hypocritically.

This is something that I would like the author to write a follow-up article on because this is something that I plan on using a lot in my ag classroom.

Main point of the article and is one of the author's best sentences. States why curriculum design is important to be specialized for different areas since social norms and cultural values are different.

Muy profesional tener un Abstract, que es la introducción pero en inglés, me agrado ver el detalle

What if I annotate this in hypothes.is -- is it preserved usefully?\(CRF(r,n) = \frac{r(1+r)^n}{(1+r)^n - 1} \)

Yes, it seems to stay in the same place in the notebook even if the notebook is edited.

Wait now it's an orphan?

Different methodoloy may be a good step in the direction of treatment, with promotion of cultural sensitivity will help broaden and narrow down research

I feel as though, as solution for clinician stand point as finding different nationality groups that are able to relate to the patient, but keep a professional settings.

this answers my previous question, it would make sense for treatment to be different for others.

i wonder if this applies to different cultural backgrounds may play differently in CBT because ethnic groups beliefs and backrounds.

“we should be found an Insensible people, as not standing before Him with Thanksgiving, as well as lading him with our Complaints in the time of pressing Afflictions” Observation: This seems as he is pleading with his people to give a sacrifice and stand with him. It also seems that the speaker is saying how bad it is ask for help when your in trouble but not help those.

Interpretation- I believe that the author is trying to say how they plead with their god to assist them, they ask for time and his help but to not stand with him when they need him

Context: A war is occurred between the Native Americans and colonist because the colonists had wanted to take over the land, the council is setting a day called “thanksgiving” signifying that the day was marked as the day they got the land

Observation: An observation I had is that the people believe that thankfulness should be spread to God because most people only want to use God for when times are hard so the people believe that they should always celebrate him because if sadness is continuous God make notice. Interpratation: That you shouldn't cry to God when things go wrong in your life you should also thank him when things go right in life. Change: The people of this time are begging the citizens to make a religious change in their life to show gratitude to God

They view the Native Americans as heathens and they are at war with them currently. I interpret this to mean that even though Native American tribes practiced religion they did not recognize their religions and therefore called them heathens (someone who doesn't practice religion). This adds context to the territory source on colonies and how the puritans tried to make the Native Americans convert to their religion because they believed it was the only right religion. This provides early context for colonists making Native Americans convert to Christianity and making their languages and religions illegal.

See previous comment, but the hypothesis link disappeared, so I'm making a new comment:

it's now the opposite where the spacing looks smaller than above and below. Could you make it the same (have 1 spacing instead of having 2 spacing/no spacing at all)

understandable seems forward now

Important factor, I agree

Agreed

This observation sticks out to me because this is where my point of view on Africa came from. Students are only shown African people who are struggling with poverty and lack of education to paint an unrealistic picture of African civilians.

The entire paragraph is basically about how paleontologists used their gifts to give ack to their country and uplift the generation after them. This method is one of the many ways Mohammad Bazzi, Yara Hardy, and others began to “decolonize” Africa.

Acknowledges that some parts of African history can confuse people who are not from the continent because they lack the more authentic stories of Africa.

This list is a plethora of the media’s stereotypes of Africa. They see the continent as incompetent and old-fashioned. When in reality, Modern Africa may be a better living place compared to other places.

This is a list of the world's main knowledge of Africa and it’s citizens lives

what kinds of homework assignments will we be getting regularly ?

excited to learn more

Different communities have different discourse specialized to their own area.

Is a key point to consider in writing

Remember to keep your own biases in check when writing (think about your end goal) and to keep an author's bias in mind because it will put their writing in certain perspectives

often scizhophrenia is used as a metaphor of madness as being woc, and sickness-madness is to be politically present to be mad?

use of the metaphor as sickness is important alos, is being woc beingsick when u are in the world, becaue like either ur crazy and everyone else is normal or they're crazy and ur the only one who is normal, also black women becoming an object of learning for the world, and the decentering of herself in the poem, but it is the larger experience for being woc

the lack of ounctuation shows the exhaustion and urgency in the poem and also the pedestal women of oclour are put on

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

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Reply to the reviewers

Based on the below reviews, we propose the following revision plan. Briefly:

• We will re-focus the manuscript on the developmental data providing a molecular and cellular blueprint __of lining macrophage development. The __novelty and relevance of our developmental data have been highlighted by all three reviewers, and they have also praised the rigor of these experiments and their interpretation. We thus believe that this re-focus will improve the manuscript's message.
• We will include our data on CSF1 as a key signal. Whilst previously appreciated as a factor required for tissue-resident macrophages, including those in the joint, our study is the first to show the requirement of lining macrophages over a complete developmental time course, using modern readouts, and in a model that circumvents the limitations of previously used approaches (see point-by-point response for details).
• However, we will remove the functional data on TGFβ signaling and mechanical loading/mechanosensing. We agree with the reviewers that we would need to generate additional histological and molecular data from conditional knockout mice, antibody and (ant)agonist treatments and the optogenetic model to determine their exact involvement in lining macrophage maturation. These experiments require significant time and other resources. We would therefore like to uncouple this question for a follow-on manuscript, and to re-focus the current study as a developmental atlas. Removal of (some) of these data has been suggested in the reviewers' comments as well.
• To further elevate our developmental atlas, we are proposing to include additional data and new analyses delineating the developmental dynamics of synovial fibroblasts on single cell (transcriptomic) level. This change to the original manuscript had not been requested by the reviewers, but we are proposing this pro-actively because we believe this would be an impactful addition to a revised version of our study, providing data also on the maturation of the synovial (lining) macrophage niche. Again, this will re-focus the manuscript on the developmental data and provide a novel, valuable resource for those interested in joint biology.
• We will otherwise respond to all individual reviewer comments and implement the requested changes, unless technically not possible. We are convinced that this revision plan will result in a manuscript that fits very well with the remit of Genes & Development.

Please find below detailed point-by-point answers.

Reviewer #1

Evidence, reproducibility and clarity

In their manuscript entitled "The synovial lining macrophage layer develops in the first weeks of life in a CSF1- and TGFβ-dependent but monocyte-independent process," the authors explore the developmental trajectory of synovial lining macrophages. They demonstrate that the formation of this specialized macrophage layer is age-dependent and governed by a distinct developmental program that proceeds independently of circulating monocytes. Through scRNA-Seq, the authors show that synovial lining macrophages originate locally from Aqp1⁺ macrophages and are marked by the expression of Csf1r, Tgfbr, and Piezo1. Notably, genetic ablation of each of these factors impaired the development of lining macrophages to varying degrees, suggesting differential contributions of CSF1, TGFβ, and PIEZO1 signaling pathways to their maturation and maintenance.

The manuscript is well written, and the data quality and representation is of a high standard. The authors have employed a sophisticated array of state-of-the-art mouse models and cutting-edge technologies to elucidate the developmental origin of synovial lining macrophages. Notably, the supporting scRNA-Seq datasets are of excellence and provide valuable insights that will likely be of significant interest to researchers in the field of immunology and joint biology. Accordingly, the experimental approach and interpretations regarding macrophage origin are well-founded and compelling. However, in the eye of the reviewer, the section addressing the underlying molecular mechanisms is a bit less convincing. This part of the study appears slightly underdeveloped, and some of the mechanistic claims lack sufficient experimental clarity. A more rigorous experimental investigation would be essential to reinforce the manuscript's conclusions, particularly concerning the data related to Tgfbr and Piezo1, where the current evidence appears insufficiently substantiated.

We thank the reviewer for their positive and constructive evaluation of our manuscript. We agree with them (and the other reviewers) that our functional data on the involvement of TGFβ signaling and mechanical loading/mechanosensing are comparably less convincing and substantiated than our developmental data. We are very grateful for their (and the other reviewers') suggestions to provide more support for the involvement of these factors in lining macrophage development. However, we think that carrying this out to the same high standard will require substantial time and other resources. We have therefore decided to uncouple this from the developmental data and pursue this in follow-up work. We will re-focus the current manuscript on the developmental data. We have proposed to the editors to instead include additional data on synovial fibroblast development, to complement our macrophage data and also delineate the maturation of their niche, thereby providing a conclusive developmental atlas.

Major point:

1. The numbers of VSIG4⁺ macrophages appear either unaffected or only minimally altered in both Csf1rMerCreMer Tgfbr2floxed and Fcgr1Cre Piezo1floxed mouse models, respectively. This raises an important question: was the gene deletion efficiency sufficient in each model? Accordingly, the authors are encouraged to include quantitative data on gene deletion efficiency for both mouse models, as this information is critical for interpreting the observed phenotypic outcomes and validating the conclusions regarding gene function. Furthermore, to better assess the impact of Tgfbr2 and Piezo1 disruption, the authors should provide more comprehensive flow cytometry analyses and histological data for these mouse models. Given the apparent homogeneity of VSIG4⁺ macrophages (as shown by the authors themselves), bulk RNA-Seq of sorted Tgfbr2- and Piezo1-deficient VSIG4⁺ macrophages (or from TGFβ-treated animals) would offer valuable insights into both the effectiveness of gene deletion and the molecular pathways governed by TGFβ and PIEZO1 in lining macrophages.

As outlined above, we have decided to uncouple our functional data on TGFβ, Piezo1 and mechanical loading. The points raised here are all very valid, and we will implement your suggestions in our follow-up functional work focusing on signaling events regulating lining macrophage development. On the suggestion to perform bulk RNA sequencing for VSIG4+ macrophages: This is a good one in principle - although we will not be able to use this strategy where we want to assess the consequences of experimental treatments or genetic models on lining macrophage maturation, because acquisition of VSIG4 is a key maturation event that might be impaired in these conditions.

Minor points:

Consistent usage of Cx3cr1-GFP+ nomenclature (for instance: Fig. S1 legend "adult mouse synovial tissue, showing PDGFRα⁺ fibroblasts (yellow) and CX3CR1-GFP⁺ cells (cyan)." versus Fig. 1 legend "Automated spot detection highlights Cx3cr1-GFP⁺ macrophages)".

We will implement these changes.

Unclear Fig. 3 legend: "Representative immunofluorescence images of synovial tissue from Clec9aCre:Rosa26lsl-tdT mice at 3 weeks and in adulthood, showing and tdTomato (yellow) and stained for DAPI (blue), VSIG4 (cyan)" Check 'showing and tdTomato.'

We will implement these changes.

For greater clarity, it would have been helpful if the transcript names had been directly included within Figures 3C, S3A, and S3C.

We will implement these changes.

Page 24: "(Mki67CreERT2:Rosa26lsl-tdT)" Last bracket not superscript.

We will implement these changes.

Page 25: "we again leveraged our scRNAsequencing dataset" Missing punctuation.

We will implement these changes.

Page 27: Fig. 5C legend: " of synovial tissue of 1 week-old, 3 weeks-old and adult mice." Please specify and change to 'adult Csf1rΔFIRE/ΔFIRE mice'.

We will implement these changes.

Page 30: The outcome observed in the Acta1-rtTA:tetO-Cre:ChR2-V5fl mouse model appears to be inconclusive: "This approach resulted in an increased density of VSIG4+ and total (F4/80+) macrophages in the exposed leg of some 5 days-old pups, but others showed the opposite trend (Figure S5D)." This variability may reflect low efficiency of the model or other technical limitations (e.g. muscle contractions frequency or time point of analysis). Given this ambiguity, it is worth reconsidering whether the data are sufficiently robust to warrant inclusion. Should the authors choose to include these findings, further experimentation of appropriate depth and precision is required to allow a conclusive interpretation (either it increases the density of VSIG4+ macrophages or not). The same applies to the Yoda1-treated mice, for which additional data are needed to determine whether VSIG4⁺ macrophage density is truly affected.

We have decided to remove the data on the optogenetic mouse model and Yoda1 treatment and follow-on separately, implementing these suggestions, including proof of concept data for optogenetically induced muscle contractions.

Significance

General assessment: provide a summary of the strengths and limitations of the study. What are the strongest and most important aspects? What aspects of the study should be improved or could be developed? This is a well-designed study that uses cutting-edge methodologies to investigate the developmental trajectory of synovial lining macrophages under homeostatic conditions. The authors present robust experimental evidence and compelling interpretations concerning synovial macrophage origin, which are both well-substantiated and impactful. Nonetheless, from the reviewer's perspective, the section exploring the molecular mechanisms underlying macrophage differentiation is comparatively less convincing. This section appears somewhat underdeveloped, as some of the mechanistic claims lack sufficient depth and experimental rigor to fully substantiate the conclusions.

Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field: In contrast to earlier studies (PMID: 31391580, 32601335), the inclusion of fate-mapping experiments adds an important dimension, offering novel insight into the ontogeny of synovial macrophages. This expanded perspective may prove particularly valuable in advancing our understanding of joint immunology, especially regarding the local origins and lineage relationships of macrophage populations.

Furthermore, the authors present novel insights into the molecular pathways underlying the differentiation and development of synovial lining macrophages. By demonstrating previously unrecognized regulatory mechanisms, this work significantly deepens our understanding of the cellular and transcriptional programs that drive macrophage specialization within the joint microenvironment.

Place the work in the context of the existing literature (provide references, where appropriate): This study builds upon previous work characterizing the macrophage compartment in the joint (PMID: 31391580, 32601335), yet provides a substantially more comprehensive dataset that spans multiple developmental time points and data on the origin of this specialized macrophage subset.

State what audience might be interested in and influenced by the reported findings: Immunologist, clinicians

Define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate. This study falls well within the scope of the reviewer's expertise in innate immunity.

Reviewer #2

Evidence, reproducibility and clarity

In the manuscript „The synovial lining macrophage layer develops in the first weeks of life in a CSF1- and TGFβ- dependent but monocyte-independent process", Magalhaes Pinto and colleagues carefully employ a wide range of technologies including single cell profiling, imaging and an exceptional combination of fate mapping models to characterize the ontogeny and development of lining macrophages in the joint, thus dissecting their maturation during postnatal development. Over the last decade, several landmark studies highlighted the imprinting of tissue-resident macrophages by a combination of ontogenetic and tissue-specific niche factors during development. So far, the ontogeny and the tissue niche factors governing the development and maturation of lining macrophages have not been described. Therefore, the results of this study offers insights on a small highly adapted macrophage population with relevance in many disease settings in the joint. Furthermore, the findings are nicely showcasing how macrophages are specializing to even very small tissue niches across development within one bigger anatomical compartment to serve dedicated functions within this niche.

This manuscript is beautifully written and highlights many novel, highly relevant findings on lining macrophage biology and the authors employ a wide range of different technologies to carefully dissect the postnatal development of lining macrophages.

In particular, the combination of scRNA-seq and fate mapping is providing a unique the link of transcriptional programs to ontogeny within the tissue niche. Furthermore, the integrative use of distinct fate mapping strategies, transgenic mouse lines, and treatment paradigms to elucidate key niche factors guiding the development and maturation of lining macrophages provides many interesting findings and data that are highly relevant to the field. I really enjoyed reading this manuscript.

Thank you for your complimentary and constructive assessment of our manuscript, and the detailed comments below, which are very helpful. Please find point-by-point responses below.

Major points:

The authors show dynamic regulation of VSIG4 in lining macrophages during development, therefore VSIG4 is maybe not an ideal choice for gating strategies to define lining macrophages or to show as a single markers in immunofluorescence (IF) stainings to demonstrate their abundance across development (even though it is clear that this is the reason why the F4/80 staining is shown next to it). To demonstrate the increase of lining macrophages during development in IF, it would be more helpful if the authors would show quantifications of all F4/80+ cells and additionally VSIG4+ as a proportion of F4/80+ cells (or VSIG4+ F4/80+ and all F4/80+ in a stacked bar plot). We agree with the assessment of VSIG4 not being ideal since this is a key marker of mature lining macrophages only.

We agree with the assessment of VSIG4 not being ideal since this is a key marker of mature lining macrophages only. We will provide additional data and analyses.

In Figure 1C, the authors nicely demonstrate that the lining macrophages get closer in their distance across development to build the epithelial-like macrophage structure along the adult lining. Is the close proximity between lining macrophages already fully "matured" at 3 weeks of age and comparable to adults? Please quantify the distance in adult linings.

We will provide additional data for adult joints.

Can the authors explain how the grouping was performed between the analyzed human fetal joints? It is not clear why the cut was chosen between the groups at 16/17 weeks of age. Maybe it would be also beneficial if the authors would consider not grouping these samples but rather show the specific quantifications for each samples individually and estimate via linear regression the expansion over time across human development. Furthermore, can the authors give additional information about the distancing of lining macrophages in the human fetal samples, it would be great to see if they follow the same dynamics as in mouse. Maybe comparison to human juvenile/adult joints would also add on to substantiate the findings in human samples (if possible).

We will show samples ungrouped and perform new linear regression analysis as suggested.

The scRNA-seq analysis leaves several questions open and some conclusions and workflows cannot be easily followed.

We appreciate this comment and the complexity of the data, and will implement the below recommendations, and clarify the issues raised. Detailed:

a. It is not clear how and especially why the signature genes to define macrophages vs. monocytes were chosen. Especially as the signature genes for monocytes would not include patrolling monocytes and the macrophage signature genes seem to be highly regulated during development, see also Apoe expression in NB vs. adult in Figure S2e. Why did the authors not take classical markers such as Itgam, Fcgr1a, Csf1r?

We will include new analyses using these markers.

b. Can dendritic cell signatures be excluded? Cluster 11 and 12 show indeed some DC markers, are these really macrophages?

We will include new analyses to account for DC markers.

c. The authors provide several figure panels showing TOP marker genes or key marker genes for the identified clusters, however it is not clear if these are TOP DE genes or if the genes were hand chosen. Somehow, the authors give the impression that the clusters were chosen and labeled not based on DE genes, but more on existing literature that previously reported these macrophage populations. DE gene lists for all annotated cell types and macrophage clusters need to be provided within the manuscript.

We will provide the full DEG analysis results.

d. The authors claim that Clusters 1 and 4 are "developing" macrophages. How is this defined? Why are these developing cells compared to other clusters? And why are these clusters later on not considered as progenitors of Aqp1 macrophages and Vsig4 macrophages? Why are Aqp1+ macrophages not labeled as developing when they are later on in the manuscript shown as potential intermediate progenitors of lining macrophages?

As per below comment, we will expand on this and clarify nomenclature and (potential) relationships between these and other macrophages.

e. Furthermore, it is again confusing that markers are used throughout Figure 2 which are labeled as "key marker genes" for a population and then later on they are claimed to be regulated during development within this population, see for example Figure 2D and 2H.

We will clarify this as per above answer.

f. It is appreciated that the authors distinguished cycling clusters such as 8, 9, and 10 based on their cycling gene signature. Here it would be very exciting to see a cell cycle analysis across all clusters and time points to see when exactly the cells are expanding during development; this would also substantiate the data later shown for the Mki67-CreERT2 mouse model.

We will perform the proposed cell cycle analysis, and implement this and the other reviewer's suggestions for marker selection and cluster annotation (this is also covered in below comments from other reviewers).

g. Can the authors identify certain gene modules during development of lining macrophages (and/or their progenitors) which are associated with certain functions (e.g. GO terms, GSEA enrichment)?

This will be included in the revised manuscript.

To determine the actual presence of the identified macrophage clusters from the scRNA-seq as macrophage populations in the joint, the authors should perform IF or FACS for key markers. Especially, Aqp1+ macrophages should be shown in the developing joint.

We will provide additional data on Aqp1+ macrophages in the developing joint, and related these to a study by collaborators currently in revision at Immunity, which characterizes the Aqp1+ population in detail (we are hoping to have a doi available during our revision process).

The authors used a wide range of fate mapping models, which is quite unique and highly appreciated. The obtained results and the conclusions made from the models raise a couple of questions: Whereas contribution of HSC-derived/monocyte-derived macrophages to the lining compartment seems to be minor, there is still labeling across different models. Various aspects would need to be clarified.

We will clarify these data throughout as per below suggestions.

a. For example, the authors employ Ms4a3-Cre as a tracing model for GMP-derived monocytes, however all quantifications of the labeling efficiency are not normalized to the labeling in monocytes or another highly recombined cell population. This should be shown, similar to the other fate mapping models (Figure 3 F-I).

Labelling efficacy for Ms4a3-Cre is near complete for GMP-derived monocytes (and neutrophils) with the Rosa-lsl-tdT (aka Ai14) reporter we have used (see also PMID: 31491389 and doi: 10.1101/2024.12.03.626330); but we will include normalized data as requested.

b. Please show Ms4a3 expression across clusters across time points, to exclude expression in fetal-derived clusters.

We will include this in the revised supplementary information, but there is indeed very little at birth (in line with the original report for other tissues PMID: 31491389).

c. In line with the question raised above, if the authors can exclude a development of the Egfr1+ and Clec4n+ developing macrophages into Aqp1+ macrophages and subsequently into Vsig4 lining macrophages, the obtained data from the Ms4a3-Cre model highly suggests a correlative labeling across these clusters what could implicate a relation. However, the authors do not discuss throughout the manuscript the role of these developing macrophages. It is highly encouraged to include this into the manuscript and it would be of high relevance to understand lining macrophage development.

This is an interesting point and we agree it deserves consideration in the revised manuscript. Indeed, our trajectory analyses do not predict differentiation of the Egfr1+ and Clec4n+ developing macrophages into Aqp1+ macrophages, and hence, ultimately lining macrophages. Conversely, Aqp1+ cells might also convert into Egfr1+ and Clec4n+ developing macrophages. We will elaborate on this more in the revised manuscript.

d. The authors conclude from the pseudo bulk transcriptomic profiling of the different macrophage clusters that TdT+ and TdT- macrophages do not differ in their gene expression profile and that this is due to niche imprinting rather than origin imprinting. Even though the data supports that conclusion, the authors should verify if inkling cells early during development also show this similar gene expression profile and gene expression should be compared at the different developmental time points. Tissue niche imprinting is happening within the niche during development, most likely in a stepwise progress, and therefore there should be differences in the beginning.

This is another important point that we will address in the revised manuscript by performing additional differential gene expression analyses at the different developmental time points, including the earliest stages, as suggested.

The trajectorial analysis using different pseudotime pipelines is very interesting and nicely points out the potential role of Aqp1 macrophages as intermediates of Vsig4 lining macrophages. From my point of view, all trajectories seem to suggest that Egfr1 developing macrophages and Clec4n developing macrophages might differentiate into Aqp1 macrophages, however the authors are not exploring this further and the role of both developing macrophage clusters is not further discussed (see also comments above).

We will address and discuss this in the revised manuscript.

How was the starting point of the trajectorial analyses defined and is it the same for each pipeline used?

We will clarify this in the revised manuscript.

Are there potentially two trajectories? It looks like there is one in the beginning of postnatal life and a second one appearing from the monocyte-compartment later in life. If this is true, that would rather speak for a dual ontogeny of Vsig4+ macrophages, wouldn't it?

We will discuss this in the revised manuscript.

A heatmap (transcriptional shift) of trajectories between more clusters should be shown at least for Cluster 0,1,2, and 3. It is not sufficient to demonstrate this only between two clusters.

We will add these analyses during revision.

To show the similarity between Aqp1 macrophages and proliferating macrophage clusters, the authors should remove the cycling signature and compare these clusters to show that the cycling cells might be Aqp1 macrophages or earlier developing macrophage progenitors aka Clec4n or Egfr1 macrophages.

We will address this in the revised manuscript.

The conclusions made from the Mki67-CreERT2 data are a bit difficult to understand, whereas all progenitors (monocyte progenitors and macrophage progenitors will proliferate at the neonatal time point and no conclusions can be made if the cells expand in the niche. The authors should employ Confetti mice or other models (Ubow mice) to analyze clonal expansion in the niche.

We acknowledge that interpretation of the Mki67-CreERT2 data is complicated by labeling of other cells, and notably, labeling observed in BM-derived cells. To complement the Mki67-CreERT2 data, and specifically account for proliferation of BM-derived cells, we have tried using Ms4a3-Cre:Ubow mice to quantify expansion of the few monocyte-derived macrophages in the joint (lining). However, this yielded

All predicted cell-cell interactions between macrophages and fibroblasts should be provided in a supplementary table. Are the interactions shown in Figure 5 chosen interactions or the TOP predicted ones? Whereas the authors show different numbers of interactions, it is most likely hand-picked and therefore biased.

We will provide a full list of all predicted interactions in the revised supplementary material in addition to a list of the full differential gene expression analysis.

The authors further aim to dissect the factors involved in the developmental niche imprinting of lining macrophages. Even though it is highly appreciated that the authors used so many experimental setups to show the reliance of lining macrophages on Csf1 and TGF-beta as well as mechanosensation, the wide range of models the different methods used and selected developmental time points make it very difficult to really interpret the data. The authors should carefully choose time points and methods (either FACS analysis across all models or IF across all, or both). Often deletion efficiencies for transgenic models and proof of concept that the inhibitors and agonists are working in the treatment paradigm are not provided. For example, Csf1rMer-iCre-Mer Tgfbr2fl/fl mice are used but no deletion efficiency is shown or different time points of analysis, maybe the macrophages are not properly targeted in the set up.

We have decided to uncouple our experimental data on Tgfb, Piezo1 and mechanosensing/mechanical loading, but are taking this into consideration for revision. In many cases, we have in fact performed flow cytometry and imaging analyses, and agree, we should be showing this consistently.

The authors have shown the role of Csf1 and Tgfbr2 only for lining macrophages, is this specific in the joint to this population of are subliming macrophages affected in a similar manner.

We will include data on sublining macrophages in the revised figure (for CSF1; Tgfb data will be uncoupled from this current manuscript).

Can the authors confirm their results in CSF1R-FIRE mice with anti-Csf1 injections or in Csf1op/op mice?

We will expand our discussion of the Csf1 findings, and aim to include data for anti-CSF1 antibody treatment during revision. Csf1 has previously been reported as a key factor required for maintenance of tissue-resident macrophages, including those in the joint (lining). Indeed, Csf1op/op mice are deficient in synovial lining macrophages, from 2 days of age onwards (PMID: 8050349), and lining macrophages are also absent from 2-weeks-old and adult Csf1r-/- mice (PMID: 11756160). However, a full developmental analysis has not been performed. We are thus the first to show a full developmental time course, using state-of-the-art experimental readouts, and specifically focusing on the early postnatal window of lining maturation that we have identified here in this study. Moreover, we have used a more specific model, Csf1rFIRE ko, in which Csf1 deficiency is restricted to myeloid cells. This model circumvents issues with other models, which show many developmental defects, some of which unrelated to macrophages. These include growth retardation and skeletal defects, which may influence joint macrophage development. Therefore, although Csf1 dependence of synovial lining macrophage had indeed been previously reported in principle, our data substantially expand on and solidify these findings, thereby adding novelty.

The setup in Figure S5G is very interesting to test the role of movement and mechanical load on the joint, however, there is basically no data on the model provided showing the efficiency of the induced optogenetic muscle contractions, and only one time point is shown.

Data on mechanical loading will be uncoupled from the current manuscript and substantiated in a separate follow-up.

The results regarding the role of Piezo1 and mechanosensation vary a lot. Could it be that analyses were done too early or that actually proper weight load on the joint must be applied for the maturation of the macrophages? The authors should test this to.

We will uncouple these data from the current manuscript during revision in order to investigate the contribution of these (and other) factors in sufficient detail. However, this is a possibility that we have discussed. In fact, the most appropriate experimental approach to address the involvement of mechanical loading, onset of walking and specifically, weight bearing would be a loss-of-function approach (i.e. paralysis at the newborn stage), for which we unfortunately could not obtain ethics approval from the UK Home Office.

The Rolipram experiment is shown in Figure S5G, but is not described in the result section. It only appears at some point in the discussion part. The authors should move it to results or remove it from the manuscript.

We will incorporate these data with the revised section on developing synovial macrophage populations.

Minor points:

Please reference the Figure panels in numeric order throughout the text.

We will change this where not the case already.

Figure 2a and 2b are a bit out of the storyline, it is not obvious why this is shown here and maybe it would be good to move it to the supplements. Gating strategy is also not used for scRNA-seq. Therefore, it would better fit to the later analysis of joint macrophages across different transgenic mouse models and treatment paradigms. The gating strategies are changing across different experiments throughout the figures, it would be nice to have a similar gating strategy for all experiments, see also Figure 3 where the defining markers for joint macrophages are changing between models.

We will revise Figures 2, 3 and the related supplementary figures.

A lot of figure panels have very small labeling that is basically unreadable. Axes at FACS plots for example. Sometimes, it is even impossible to distinguish cluster labels especially when they have similar colors.

We will revise this, thanks for pointing it out.

In the text on page 14, many markers are named which are specifically regulated during development in lining macrophages, but these factors are not labeled anywhere in the volcano plot. It would be good to showcase at least some of these named genes in the figure panel, e.g. Trem2.

We will do this for revision.

Figure 2F and Figure S2F are really nicely showing the percentage of cells per cluster in each analyzed biological sample. Maybe the authors could additionally consider to show a stacked bar plot with the mean percentage of cells per cluster and how the clusters are distributed across time points?

We will include this in the revised manuscript.

Figure 3A: IF for adult lining macrophages and the quantification are missing.

This will be included in the revised version.

Reviewer #3 - Major

Generally, the story could be more streamlined by introducing earlier reporter lines and lineage-origin logic. Clearly state which reporter/CreERT2 lines and acrosses are used. It was unclear in Figure 2 that cells of the cross of the Cx3cr1-GFP and Ms4a3Cre:Rosa26lsl-tdT reporter lines were used for the scRNA-seq. The principle that there are fetal-derived and bone marrow (GMP)-derived monocytes and macrophages doesn't need to be "hidden" until Figure 3. For example, also the imaging of Ms4a3Cre could be introduced before the scRNA-seq.

We will revise the structure and order of the manuscript during revision. However, we will streamline this between reviewer comments, and would also like to point out that the 2 other reviewers were very complimentary about the writing and clarity, i.e. we may not follow every specific suggestion of reviewer 3, but are very much taking on board their overall comment on structure and clarity.

Figure 1 could benefit from a cartoon visualizing the anatomy of the knee joint. The terms "sublining" and "synovium" are now a bit unclear, as it appears that sometimes the synovium is indicated as sublining and vice versa. Additionally, a schematic developmental timeline could be added to indicate the parallels between mouse and human development (fetal and postnatal development in mouse versus gestational age in human). Also, the various waves of hematopoiesis could be indicated in this timeline, which would be particularly helpful for Figure 3 for the lineage-tracing readouts. Lastly, the authors could end the manuscript (a new Figure 6) with a general cartoon summarizing all the results presented.

We will include these illustrations as suggested.

Figure 1 could be rearranged: first introduce the markers CX3CR1 and VSIG4 (Figure 1D) and then present the quantifications (Figure 1B/E). Where possible, co-visualization CX3CR1-GFP and VSIG4 on tissue sections to strengthen the claims on the relationship between these 2 markers. Tying the scRNA-seq insights (Figure 2) to the imaging would be elegant. Moreover, it would be informative to represent the CX3CR1+ and VSIG4+ macrophages as a percentage of F4/80+ macrophages (Figure 1B/E). Similarly, for the flow cytometry data in Figure 2, the relationship between the markers CX3CR1 and VSIG4 on macrophages could be more clearly displayed and discussed.

Thanks for this remark. We will endeavour to show co-localization and analysis of both markers wherever possible. However, where we did not use Cx3cr1gfp mice, co-staining was limited by antibody choice and availability.

The 3D imaging of the joint is a nice addition to the manuscript, as it provides more context to the anatomical structure; however, while the text suggests several newborn joints were imaged, Figure 1F visualizes (again) the knee joint. Could other joints also be represented by 3D imaging? If the knee joint is the only joint available for imaging, and previous confocal imaging focused specifically on the meniscus in the knee joint, could the meniscus also be highlighted in the lightsheet imaging?

Apologies if this was not clear from the original manuscript text, but we have only imaged the knee joint in 3D. We will clarify this during revision. Whilst we want to maintain the focus on knee joints throughout this manuscript, but we will include additional 3D lightsheet imaging data from micro-dissected knee joints to further substantiate the original data.

Clarification is requested regarding the imaging quantification representation. The M&M section under "Statistical analysis and reproducibility" states that individual data points are displayed, and bars represent the mean. However, some of the Figure legends (e.g., Figures 1B and S1C) specify that each dot corresponds to an individual mouse, with quantification based on 2-3 sections per mouse. While this appears to be a very reasonable representation of the data, does this mean that for each dot, the mean value from the 2-3 sections per mouse was calculated and plotted?

We will clarify this.

It is not clear how the differential expression analysis was performed on the Vsig4+ cells. Please specify if Cluster 0 was used for analysis, or all Vsig4-expressing cells? Not all cells in Cluster 0 have Vsig4+ expression. The authors described the expression dynamics of Aqp1 as intriguing, but lack a reasoning on why this is interesting.

We will revise this section.

Figure S3E: In line with the previous comment, can the authors justify that the tdTomato+/- comparisons are not biased by scRNA-seq dropout (scRNA-seq is zero-inflated, so some tdTomato- cells could be false negatives), and provide methodological details (thresholds, ambient RNA correction, etc.) to support this?

We will clarify this and include additional representations of the tdTomato transcript data.

Although the sex-related differences in macrophage composition and the absence of differential expression are interesting, they distract from the manuscript's main messages. Moreover, the Discussion does not elaborate on how these observations relate to joint (disease) biology. Consider removing this section or integrating it clearly into the relevant biological context.

We will remove this section as suggested.

CreERT2 transgenic lines are often not 100% efficient in recombination, also depending on whether tamoxifen or 4-OHT is used. Could the authors report the percentage of tdTomato+ cells in the joints and compare them to the recombination efficiencies in the monocytes/microglia under the same tamoxifen or 4-OHT conditions? This would help clarify how the interpret the macrophage labeling %'s.

We will report labelling efficacies and/or show normalized data in the revised manuscript.

Could the authors draw parallels between the observations in the mouse knee joint macrophage populations and literature on other joints in mouse and the knee joint in human (for example, as described in Alivernini et al., 2020 and in the very recent Raut et al., 2025)?

We will include a section on this in the revised manuscript.

Reviewer #3 - Minor comments:

In general, the authors should clarify in the Results what each marker used for imaging, flow cytometry, or in the mouse reporter lines delineates. For example, mention that F4/80 is a marker for tissue-resident macrophages (correct?) in immunofluorescence, that IBA1 is a marker for macrophages on human tissue sections (Figure S1), and PDPN is GP38 (Figure S2 - align usage of marker reference across main text and figures).

We will implement this request.

Figure S1B: Is CX3CR1 also restricted to the lining macrophages in human? Could a co-staining with IBA1 be performed to strengthen the species similarities?

To our knowledge, there is no antibody available that works for imaging of human CX3CR1. Moreover, CX3CR1 is only limited to the lining population in adult joints, in fetal and newborn (mouse) joints, all macrophages express this receptor, as do fetal progenitors to macrophages. However, Alivernini and colleagues have reported that TREM2high macrophages are the human counterpart of the mouse CX3CR1+ lining population (PMID: 32601335). We do not have access to postnatal human joint tissue samples, unfortunately, but we will attempt to stain for and quantify TREM2+ macrophages in human fetal joints for the revised manuscript.

Adipocyte diameter quantification: Avoid plotting individual adipocytes from 2 mice without per-mouse visualization. Instead, report the mean adipocyte diameter per mouse and plot those means.

We will implement this change.

A little typo was spotted in the "Statistical analysis and reproducibility" section: it is Dunn's, not Bunn's multiple-comparison correction.

Thanks for spotting this.

Figure 2A: The gating strategy for the CX3CR1-GFP cells is missing.

We will provide this in the revised manuscript or supplementary material.

Improve the visualization of some plots. For example, Figure 2F is hard to read because of the big dot size. The dots seem to add no information to the graph and could be removed. Additionally, for comparing the clusters across the different time points, one could project the cells from the other time points in grey in the background.

We will revise the presentation of these data.

Figure S2: The dotplot is more informative than the heatmap, consider removing the heatmap.

We will do that.

Figure 3A: If technically feasible, image and visualize both the GFP and tdTomato expression. It would be informative to see the Cx3cr1+ and Ms4a3-derived cells in the same specimen.

We will strive to show this in the revised manuscript.

Figure 3C: Highlight that tdTomato expression is visualized here.

We will do that.

Figure 3G,F: The authors should place the schematics and graphs next to each other, so the data points can be more easily compared.

We aim to do this in the revised manuscript.

Figure 4B: Which co-staining was performed for the immunofluorescence to quantify the % of tdTomato+ cells?

We co-stained for F4/80 and assessed localization in the lining or sublining. This will be clarified in the revised Figure legend.

Figure 4C: The trajectory analysis appears to have an arrow pointing from the Ccr2+ macrophages to the Ly6c+ monocytes. Please verify this directionality, as its seems against the known biology.

This will be addressed during revision.

Figure 5 mentions that the Csfr1 levels were reduced in a tissue-specific manner, but it is unclear how this tissue specificity was achieved.

We apologize for this misunderstanding. Csfr1FIRE mice are not tissue-specific knockouts, but they are more specific than global knockout mice, since only a (myeloid-specific) enhancer is affected. We will clarify this in the relevant section.

For the TGFb perturbations (Tgfbr2 KO and systemic TGFb depletion): did the authors validate reduced TGFb pathway activity in the macrophages, for example, reduced pSMAD2/3 levels? This would validate the effectiveness of the perturbations.

This is an important point, and assessing signaling events downstream of TGFb is a very good suggestion. As per above comment, we have decided to uncouple the functional data with exception of CSF1 from the revised version of the current manuscript, but we will be taking this into account for substantiating our functional data in follow-up work.

Figure 5F could benefit from a timeline of the treatment.

As for 15., we will be taking this into account for follow-up work on the uncoupled functional data.

The Methods mention that Gene Ontology analysis was performed on the single-cell data, but the results are not plotted in a figure. It would be informative to include this GO/pathway analysis in the appropriate figure(s).

We will include this in the revised (supplementary) information.

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Referee #3

Evidence, reproducibility and clarity

Summary:

Magalhaes Pinto, Malengier-Devlies, and co-authors investigated the developmental origins and maturation of synovial (lining and sublining) macrophages across embryonic, newborn, and postnatal stages in mouse. The authors used multiple transgenic reporter lines, lineage tracing, scRNA-seq, 2D confocal and 3D lightsheet imaging, and perturbations to delineate the macrophage states and ontogeny. They propose a model in which the majority of the joint lining macrophages has a fetal (EMP-derived) origin and a small proportion has a definitive HSC-derived monocyte origin, which both seed and mature within the synovial space in the postnatal period in the first 3 weeks of life. Using cell-cell communication analysis on their scRNA-seq data, they identified Fgf2, Csf1, and Tgfb as candidate signaling pathways that support (lining) macrophage development and maturation. Functional experiments indicate that the process is CSF1 and TGFb-dependent and also partly dependent on mechanosensing through Piezo1. The key conclusions on the composition of the synovial macrophages are convincing based on the presented results, and are carefully phrased. The study is very comprehensive, yet the description and organization of the results of the different mouse models could be altered to improve the storyline. Several refinements in data presentation, formulation, and minor validation experiments would further improve the clarity of the story, as well as summary recaps of the major findings throughout the text.

Major comments:

1. Generally, the story could be more streamlined by introducing earlier reporter lines and lineage-origin logic. Clearly state which reporter/CreERT2 lines and acrosses are used. It was unclear in Figure 2 that cells of the cross of the Cx3cr1-GFP and Ms4a3Cre:Rosa26lsl-tdT reporter lines were used for the scRNA-seq. The principle that there are fetal-derived and bone marrow (GMP)-derived monocytes and macrophages doesn't need to be "hidden" until Figure 3. For example, also the imaging of Ms4a3Cre could be introduced before the scRNA-seq.
2. Figure 1 could benefit from a cartoon visualizing the anatomy of the knee joint. The terms "sublining" and "synovium" are now a bit unclear, as it appears that sometimes the synovium is indicated as sublining and vice versa. Additionally, a schematic developmental timeline could be added to indicate the parallels between mouse and human development (fetal and postnatal development in mouse versus gestational age in human). Also, the various waves of hematopoiesis could be indicated in this timeline, which would be particularly helpful for Figure 3 for the lineage-tracing readouts. Lastly, the authors could end the manuscript (a new Figure 6) with a general cartoon summarizing all the results presented.
3. Figure 1 could be rearranged: first introduce the markers CX3CR1 and VSIG4 (Figure 1D) and then present the quantifications (Figure 1B/E). Where possible, co-visualization CX3CR1-GFP and VSIG4 on tissue sections to strengthen the claims on the relationship between these 2 markers. Tying the scRNA-seq insights (Figure 2) to the imaging would be elegant. Moreover, it would be informative to represent the CX3CR1+ and VSIG4+ macrophages as a percentage of F4/80+ macrophages (Figure 1B/E). Similarly, for the flow cytometry data in Figure 2, the relationship between the markers CX3CR1 and VSIG4 on macrophages could be more clearly displayed and discussed.
4. The 3D imaging of the joint is a nice addition to the manuscript, as it provides more context to the anatomical structure; however, while the text suggests several newborn joints were imaged, Figure 1F visualizes (again) the knee joint. Could other joints also be represented by 3D imaging? If the knee joint is the only joint available for imaging, and previous confocal imaging focused specifically on the meniscus in the knee joint, could the meniscus also be highlighted in the lightsheet imaging?
5. Clarification is requested regarding the imaging quantification representation. The M&M section under "Statistical analysis and reproducibility" states that individual data points are displayed, and bars represent the mean. However, some of the Figure legends (e.g., Figures 1B and S1C) specify that each dot corresponds to an individual mouse, with quantification based on 2-3 sections per mouse. While this appears to be a very reasonable representation of the data, does this mean that for each dot, the mean value from the 2-3 sections per mouse was calculated and plotted?
6. It is not clear how the differential expression analysis was performed on the Vsig4+ cells. Please specify if Cluster 0 was used for analysis, or all Vsig4-expressing cells? Not all cells in Cluster 0 have Vsig4+ expression. The authors described the expression dynamics of Aqp1 as intriguing, but lack a reasoning on why this is interesting.
7. Figure S3E: In line with the previous comment, can the authors justify that the tdTomato+/- comparisons are not biased by scRNA-seq dropout (scRNA-seq is zero-inflated, so some tdTomato- cells could be false negatives), and provide methodological details (thresholds, ambient RNA correction, etc.) to support this?
8. Although the sex-related differences in macrophage composition and the absence of differential expression are interesting, they distract from the manuscript's main messages. Moreover, the Discussion does not elaborate on how these observations relate to joint (disease) biology. Consider removing this section or integrating it clearly into the relevant biological context.
9. CreERT2 transgenic lines are often not 100% efficient in recombination, also depending on whether tamoxifen or 4-OHT is used. Could the authors report the percentage of tdTomato+ cells in the joints and compare them to the recombination efficiencies in the monocytes/microglia under the same tamoxifen or 4-OHT conditions? This would help clarify how the interpret the macrophage labeling %'s.
10. Could the authors draw parallels between the observations in the mouse knee joint macrophage populations and literature on other joints in mouse and the knee joint in human (for example, as described in Alivernini et al., 2020 and in the very recent Raut et al., 2025)?

Minor comments:

1. In general, the authors should clarify in the Results what each marker used for imaging, flow cytometry, or in the mouse reporter lines delineates. For example, mention that F4/80 is a marker for tissue-resident macrophages (correct?) in immunofluorescence, that IBA1 is a marker for macrophages on human tissue sections (Figure S1), and PDPN is GP38 (Figure S2 - align usage of marker reference across main text and figures).
2. For clarity in the microscopy representation, the single channels should be represented in a grey scale.
3. Figure S1B: Is CX3CR1 also restricted to the lining macrophages in human? Could a co-staining with IBA1 be performed to strengthen the species similarities?
4. Adipocyte diameter quantification: Avoid plotting individual adipocytes from 2 mice without per-mouse visualization. Instead, report the mean adipocyte diameter per mouse and plot those means.
5. A little typo was spotted in the "Statistical analysis and reproducibility" section: it is Dunn's, not Bunn's multiple-comparison correction.
6. Figure 2A: The gating strategy for the CX3CR1-GFP cells is missing.
7. Improve the visualization of some plots. For example, Figure 2F is hard to read because of the big dot size. The dots seem to add no information to the graph and could be removed. Additionally, for comparing the clusters across the different time points, one could project the cells from the other time points in grey in the background.
8. Figure S2: The dotplot is more informative than the heatmap, consider removing the heatmap.
9. Figure 3A: If technically feasible, image and visualize both the GFP and tdTomato expression. It would be informative to see the Cx3cr1+ and Ms4a3-derived cells in the same specimen.
10. Figure 3C: Highlight that tdTomato expression is visualized here.
11. Figure 3G,F: The authors should place the schematics and graphs next to each other, so the data points can be more easily compared.
12. Figure 4B: Which co-staining was performed for the immunofluorescence to quantify the % of tdTomato+ cells?
13. Figure 4C: The trajectory analysis appears to have an arrow pointing from the Ccr2+ macrophages to the Ly6c+ monocytes. Please verify this directionality, as its seems against the known biology.
14. Figure 5 mentions that the Csfr1 levels were reduced in a tissue-specific manner, but it is unclear how this tissue specificity was achieved.
15. For the TGFb perturbations (Tgfbr2 KO and systemic TGFb depletion): did the authors validate reduced TGFb pathway activity in the macrophages, for example, reduced pSMAD2/3 levels? This would validate the effectiveness of the perturbations.
16. Figure 5F could benefit from a timeline of the treatment.
17. The Methods mention that Gene Ontology analysis was performed on the single-cell data, but the results are not plotted in a figure. It would be informative to include this GO/pathway analysis in the appropriate figure(s).

Significance

This work provides a high temporal-resolution and "spatial" resolution reference map of the ontogeny and maturation of the synovial lining macrophages in the knee joint. It complements existing literature that demonstrated the presence of tissue-resident macrophages in the synovial space and lining (Culemann, et al., 2019 and others) by charting the embryonic-to-postnatal emergence of lining and sublining subsets. In particular, this mouse work identified some key signaling pathways in shaping this tissue compartment. This dataset serves as a robust, steady-state reference for joint pathology and can be implemented with human studies on disease biology of the knee joint (e.g., Alivernini et al., 2020; Raut et al., 2025). Insights into the exact developmental origins, mechanisms contributing to diverse or seemingly similar cell types, and distinct maturation processes are crucial to understanding disease biology, in which developmental processes can be hijacked/reactivated.

These findings will interest researchers in joint disease biology (osteoarthritis and immune-mediated arthritides such as RA and psoriasis), macrophage development (tissue-resident vs monocyte-derived lineages), the bone/joint microenvironment, and joint mechanobiology.

The reviewer's expertise is in developmental biology, mesoderm, bone biology, hematopoiesis, and monocyte/macrophage biology in disease

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Referee #2

Evidence, reproducibility and clarity

In the manuscript „The synovial lining macrophage layer develops in the first weeks of life in a CSF1- and TGFβ- dependent but monocyte-independent process", Magalhaes Pinto and colleagues carefully employ a wide range of technologies including single cell profiling, imaging and an exceptional combination of fate mapping models to characterize the ontogeny and development of lining macrophages in the joint, thus dissecting their maturation during postnatal development. Over the last decade, several landmark studies highlighted the imprinting of tissue-resident macrophages by a combination of ontogenetic and tissue-specific niche factors during development. So far, the ontogeny and the tissue niche factors governing the development and maturation of lining macrophages have not been described. Therefore, the results of this study offers insights on a small highly adapted macrophage population with relevance in many disease settings in the joint. Furthermore, the findings are nicely showcasing how macrophages are specializing to even very small tissue niches across development within one bigger anatomical compartment to serve dedicated functions within this niche.

This manuscript is beautifully written and highlights many novel, highly relevant findings on lining macrophage biology and the authors employ a wide range of different technologies to carefully dissect the postnatal development of lining macrophages.

In particular, the combination of scRNA-seq and fate mapping is providing a unique the link of transcriptional programs to ontogeny within the tissue niche. Furthermore, the integrative use of distinct fate mapping strategies, transgenic mouse lines, and treatment paradigms to elucidate key niche factors guiding the development and maturation of lining macrophages provides many interesting findings and data that are highly relevant to the field. I really enjoyed reading this manuscript.

Major points:

1) The authors show dynamic regulation of VSIG4 in lining macrophages during development, therefore VSIG4 is maybe not an ideal choice for gating strategies to define lining macrophages or to show as a single markers in immunofluorescence (IF) stainings to demonstrate their abundance across development (even though it is clear that this is the reason why the F4/80 staining is shown next to it). To demonstrate the increase of lining macrophages during development in IF, it would be more helpful if the authors would show quantifications of all F4/80+ cells and additionally VSIG4+ as a proportion of F4/80+ cells (or VSIG4+ F4/80+ and all F4/80+ in a stacked bar plot).

2) In Figure 1C, the authors nicely demonstrate that the lining macrophages get closer in their distance across development to build the epithelial-like macrophage structure along the adult lining. Is the close proximity between lining macrophages already fully "matured" at 3 weeks of age and comparable to adults? Please quantify the distance in adult linings.

3) Can the authors explain how the grouping was performed between the analyzed human fetal joints? It is not clear why the cut was chosen between the groups at 16/17 weeks of age. Maybe it would be also beneficial if the authors would consider not grouping these samples but rather show the specific quantifications for each samples individually and estimate via linear regression the expansion over time across human development. Furthermore, can the authors give additional information about the distancing of lining macrophages in the human fetal samples, it would be great to see if they follow the same dynamics as in mouse. Maybe comparison to human juvenile/adult joints would also add on to substantiate the findings in human samples (if possible).

4) The scRNA-seq analysis leaves several questions open and some conclusions and workflows cannot be easily followed.

a. It is not clear how and especially why the signature genes to define macrophages vs. monocytes were chosen. Especially as the signature genes for monocytes would not include patrolling monocytes and the macrophage signature genes seem to be highly regulated during development, see also Apoe expression in NB vs. adult in Figure S2e. Why did the authors not take classical markers such as Itgam, Fcgr1a, Csf1r?

b. Can dendritic cell signatures be excluded? Cluster 11 and 12 show indeed some DC markers, are these really macrophages?

c. The authors provide several figure panels showing TOP marker genes or key marker genes for the identified clusters, however it is not clear if these are TOP DE genes or if the genes were hand chosen. Somehow, the authors give the impression that the clusters were chosen and labeled not based on DE genes, but more on existing literature that previously reported these macrophage populations. DE gene lists for all annotated cell types and macrophage clusters need to be provided within the manuscript.

d. The authors claim that Clusters 1 and 4 are "developing" macrophages. How is this defined? Why are these developing cells compared to other clusters? And why are these clusters later on not considered as progenitors of Aqp1 macrophages and Vsig4 macrophages? Why are Aqp1+ macrophages not labeled as developing when they are later on in the manuscript shown as potential intermediate progenitors of lining macrophages?

e. Furthermore, it is again confusing that markers are used throughout Figure 2 which are labeled as "key marker genes" for a population and then later on they are claimed to be regulated during development within this population, see for example Figure 2D and 2H.

f. It is appreciated that the authors distinguished cycling clusters such as 8, 9, and 10 based on their cycling gene signature. Here it would be very exciting to see a cell cycle analysis across all clusters and time points to see when exactly the cells are expanding during development; this would also substantiate the data later shown for the Mki67-CreERT2 mouse model.

g. Can the authors identify certain gene modules during development of lining macrophages (and/or their progenitors) which are associated with certain functions (e.g. GO terms, GSEA enrichment)?

5) To determine the actual presence of the identified macrophage clusters from the scRNA-seq as macrophage populations in the joint, the authors should perform IF or FACS for key markers. Especially, Aqp1+ macrophages should be shown in the developing joint.

6) The authors used a wide range of fate mapping models, which is quite unique and highly appreciated. The obtained results and the conclusions made from the models raise a couple of questions: Whereas contribution of HSC-derived/monocyte-derived macrophages to the lining compartment seems to be minor, there is still labeling across different models. Various aspects would need to be clarified.

a. For example, the authors employ Ms4a3-Cre as a tracing model for GMP-derived monocytes, however all quantifications of the labeling efficiency are not normalized to the labeling in monocytes or another highly recombined cell population. This should be shown, similar to the other fate mapping models (Figure 3 F-I).

b. Please show Ms4a3 expression across clusters across time points, to exclude expression in fetal-derived clusters.

c. In line with the question raised above, if the authors can exclude a development of the Egfr1+ and Clec4n+ developing macrophages into Aqp1+ macrophages and subsequently into Vsig4 lining macrophages, the obtained data from the Ms4a3-Cre model highly suggests a correlative labeling across these clusters what could implicate a relation. However, the authors do not discuss throughout the manuscript the role of these developing macrophages. It is highly encouraged to include this into the manuscript and it would be of high relevance to understand lining macrophage development.

d. The authors conclude from the pseudo bulk transcriptomic profiling of the different macrophage clusters that TdT+ and TdT- macrophages do not differ in their gene expression profile and that this is due to niche imprinting rather than origin imprinting. Even though the data supports that conclusion, the authors should verify if inkling cells early during development also show this similar gene expression profile and gene expression should be compared at the different developmental time points. Tissue niche imprinting is happening within the niche during development, most likely in a stepwise progress, and therefore there should be differences in the beginning.

7) The trajectorial analysis using different pseudotime pipelines is very interesting and nicely points out the potential role of Aqp1 macrophages as intermediates of Vsig4 lining macrophages. From my point of view, all trajectories seem to suggest that Egfr1 developing macrophages and Clec4n developing macrophages might differentiate into Aqp1 macrophages, however the authors are not exploring this further and the role of both developing macrophage clusters is not further discussed (see also comments above).

8) How was the starting point of the trajectorial analyses defined and is it the same for each pipeline used?

9) Are there potentially two trajectories? It looks like there is one in the beginning of postnatal life and a second one appearing from the monocyte-compartment later in life. If this is true, that would rather speak for a dual ontogeny of Vsig4+ macrophages, wouldn't it?

10) A heatmap (transcriptional shift) of trajectories between more clusters should be shown at least for Cluster 0,1,2, and 3. It is not sufficient to demonstrate this only between two clusters.

11) To show the similarity between Aqp1 macrophages and proliferating macrophage clusters, the authors should remove the cycling signature and compare these clusters to show that the cycling cells might be Aqp1 macrophages or earlier developing macrophage progenitors aka Clec4n or Egfr1 macrophages.

12) The conclusions made from the Mki67-CreERT2 data are a bit difficult to understand, whereas all progenitors (monocyte progenitors and macrophage progenitors will proliferate at the neonatal time point and no conclusions can be made if the cells expand in the niche. The authors should employ Confetti mice or other models (Ubow mice) to analyze clonal expansion in the niche.

13) All predicted cell-cell interactions between macrophages and fibroblasts should be provided in a supplementary table. Are the interactions shown in Figure 5 chosen interactions or the TOP predicted ones? Whereas the authors show different numbers of interactions, it is most likely hand-picked and therefore biased.

14) The authors further aim to dissect the factors involved in the developmental niche imprinting of lining macrophages. Even though it is highly appreciated that the authors used so many experimental setups to show the reliance of lining macrophages on Csf1 and TGF-beta as well as mechanosensation, the wide range of models the different methods used and selected developmental time points make it very difficult to really interpret the data. The authors should carefully choose time points and methods (either FACS analysis across all models or IF across all, or both). Often deletion efficiencies for transgenic models and proof of concept that the inhibitors and agonists are working in the treatment paradigm are not provided. For example, Csf1rMer-iCre-Mer Tgfbr2fl/fl mice are used but no deletion efficiency is shown or different time points of analysis, maybe the macrophages are not properly targeted in the set up.

15) The authors have shown the role of Csf1 and Tgfbr2 only for lining macrophages, is this specific in the joint to this population of are subliming macrophages affected in a similar manner.

16) Can the authors confirm their results in CSF1R-FIRE mice with anti-Csf1 injections or in Csf1op/op mice?

17) The setup in Figure S5G is very interesting to test the role of movement and mechanical load on the joint, however, there is basically no data on the model provided showing the efficiency of the induced optogenetic muscle contractions, and only one time point is shown.

18) The results regarding the role of Piezo1 and mechanosensation vary a lot. Could it be that analyses were done too early or that actually proper weight load on the joint must be applied for the maturation of the macrophages? The authors should test this to

19) The Rolipram experiment is shown in Figure S5G, but is not described in the result section. It only appears at some point in the discussion part. The authors should move it to results or remove it from the manuscript.

Minor points:

1) Please reference the Figure panels in numeric order throughout the text.

2) Figure 2a and 2b are a bit out of the storyline, it is not obvious why this is shown here and maybe it would be good to move it to the supplements. Gating strategy is also not used for scRNA-seq. Therefore, it would better fit to the later analysis of joint macrophages across different transgenic mouse models and treatment paradigms. The gating strategies are changing across different experiments throughout the figures, it would be nice to have a similar gating strategy for all experiments, see also Figure 3 where the defining markers for joint macrophages are changing between models.

3) A lot of figure panels have very small labeling that is basically unreadable. Axes at FACS plots for example. Sometimes, it is even impossible to distinguish cluster labels especially when they have similar colors.

4) In the text on page 14, many markers are named which are specifically regulated during development in lining macrophages, but these factors are not labeled anywhere in the volcano plot. It would be good to showcase at least some of these named genes in the figure panel, e.g. Trem2.

5) Figure 2F and Figure S2F are really nicely showing the percentage of cells per cluster in each analyzed biological sample. Maybe the authors could additionally consider to show a stacked bar plot with the mean percentage of cells per cluster and how the clusters are distributed across time points?

6) Figure 3A: IF for adult lining macrophages and the quantification are missing

Significance

This manuscript highlights novel, highly relevant findings on lining macrophage biology and the authors employ a wide range of different technologies to carefully dissect the postnatal development of lining macrophages. Furthermore, this study showcases in a very elegant and detailed way the adaptation of macrophage progenitors to a highly specific anatomical tissue niche.

The manuscript is of high interest to basic scientists focussing on macrophage biology and immune cell development and clinicians and clinician scientists focussing on joint diseases such as RA

Therefore the manuscript is of interest to a wide community working in immunology.

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Referee #1

Evidence, reproducibility and clarity

In their manuscript entitled "The synovial lining macrophage layer develops in the first weeks of life in a CSF1- and TGFβ-dependent but monocyte-independent process," the authors explore the developmental trajectory of synovial lining macrophages. They demonstrate that the formation of this specialized macrophage layer is age-dependent and governed by a distinct developmental program that proceeds independently of circulating monocytes. Through scRNA-Seq, the authors show that synovial lining macrophages originate locally from Aqp1⁺ macrophages and are marked by the expression of Csf1r, Tgfbr, and Piezo1. Notably, genetic ablation of each of these factors impaired the development of lining macrophages to varying degrees, suggesting differential contributions of CSF1, TGFβ, and PIEZO1 signaling pathways to their maturation and maintenance.

The manuscript is well written, and the data quality and representation is of a high standard. The authors have employed a sophisticated array of state-of-the-art mouse models and cutting-edge technologies to elucidate the developmental origin of synovial lining macrophages. Notably, the supporting scRNA-Seq datasets are of excellence and provide valuable insights that will likely be of significant interest to researchers in the field of immunology and joint biology. Accordingly, the experimental approach and interpretations regarding macrophage origin are well-founded and compelling. However, in the eye of the reviewer, the section addressing the underlying molecular mechanisms is a bit less convincing. This part of the study appears slightly underdeveloped, and some of the mechanistic claims lack sufficient experimental clarity. A more rigorous experimental investigation would be essential to reinforce the manuscript's conclusions, particularly concerning the data related to Tgfbr and Piezo1, where the current evidence appears insufficiently substantiated.

Major point:

• The numbers of VSIG4⁺ macrophages appear either unaffected or only minimally altered in both Csf1rMerCreMer Tgfbr2floxed and Fcgr1Cre Piezo1floxed mouse models, respectively. This raises an important question: was the gene deletion efficiency sufficient in each model? Accordingly, the authors are encouraged to include quantitative data on gene deletion efficiency for both mouse models, as this information is critical for interpreting the observed phenotypic outcomes and validating the conclusions regarding gene function. Furthermore, to better assess the impact of Tgfbr2 and Piezo1 disruption, the authors should provide more comprehensive flow cytometry analyses and histological data for these mouse models. Given the apparent homogeneity of VSIG4⁺ macrophages (as shown by the authors themselves), bulk RNA-Seq of sorted Tgfbr2- and Piezo1-deficient VSIG4⁺ macrophages (or from TGFβ-treated animals) would offer valuable insights into both the effectiveness of gene deletion and the molecular pathways governed by TGFβ and PIEZO1 in lining macrophages.

Minor points:

• Consistent usage of Cx3cr1-GFP+ nomenclature (for instance: Fig. S1 legend "adult mouse synovial tissue, showing PDGFRα⁺ fibroblasts (yellow) and CX3CR1-GFP⁺ cells (cyan)." versus Fig. 1 legend "Automated spot detection highlights Cx3cr1-GFP⁺ macrophages)"
• Unclear Fig. 3 legend: "Representative immunofluorescence images of synovial tissue from Clec9aCre:Rosa26lsl-tdT mice at 3 weeks and in adulthood, showing and tdTomato (yellow) and stained for DAPI (blue), VSIG4 (cyan)" Check 'showing and tdTomato.'
• For greater clarity, it would have been helpful if the transcript names had been directly included within Figures 3C, S3A, and S3C.
• Page 24: "(Mki67CreERT2:Rosa26lsl-tdT)" Last bracket not superscript.
• Page 25: "we again leveraged our scRNAsequencing dataset" Missing punctuation.
• Page 27: Fig. 5C legend: " of synovial tissue of 1 week-old, 3 weeks-old and adult mice." Please specify and change to 'adult Csf1rΔFIRE/ΔFIRE mice'.
• Page 30: The outcome observed in the Acta1-rtTA:tetO-Cre:ChR2-V5fl mouse model appears to be inconclusive: "This approach resulted in an increased density of VSIG4+ and total (F4/80+) macrophages in the exposed leg of some 5 days-old pups, but others showed the opposite trend (Figure S5D)." This variability may reflect low efficiency of the model or other technical limitations (e.g. muscle contractions frequency or time point of analysis). Given this ambiguity, it is worth reconsidering whether the data are sufficiently robust to warrant inclusion. Should the authors choose to include these findings, further experimentation of appropriate depth and precision is required to allow a conclusive interpretation (either it increases the density of VSIG4+ macrophages or not). The same applies to the Yoda1-treated mice, for which additional data are needed to determine whether VSIG4⁺ macrophage density is truly affected.

Significance

• General assessment: provide a summary of the strengths and limitations of the study. What are the strongest and most important aspects? What aspects of the study should be improved or could be developed?

This is a well-designed study that uses cutting-edge methodologies to investigate the developmental trajectory of synovial lining macrophages under homeostatic conditions. The authors present robust experimental evidence and compelling interpretations concerning synovial macrophage origin, which are both well-substantiated and impactful. Nonetheless, from the reviewer's perspective, the section exploring the molecular mechanisms underlying macrophage differentiation is comparatively less convincing. This section appears somewhat underdeveloped, as some of the mechanistic claims lack sufficient depth and experimental rigor to fully substantiate the conclusions. - Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field:

In contrast to earlier studies (PMID: 31391580, 32601335), the inclusion of fate-mapping experiments adds an important dimension, offering novel insight into the ontogeny of synovial macrophages. This expanded perspective may prove particularly valuable in advancing our understanding of joint immunology, especially regarding the local origins and lineage relationships of macrophage populations. Furthermore, the authors present novel insights into the molecular pathways underlying the differentiation and development of synovial lining macrophages. By demonstrating previously unrecognized regulatory mechanisms, this work significantly deepens our understanding of the cellular and transcriptional programs that drive macrophage specialization within the joint microenvironment. -Place the work in the context of the existing literature (provide references, where appropriate):

This study builds upon previous work characterizing the macrophage compartment in the joint (PMID: 31391580, 32601335), yet provides a substantially more comprehensive dataset that spans multiple developmental time points and data on the origin of this specialized macrophage subset. - State what audience might be interested in and influenced by the reported findings:

Immunologist, clinicians - Define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate.

This study falls well within the scope of the reviewer's expertise in innate immunity.

testing; specific highlights for some reason aren't working for me, only page notes. anyway, if the library is an example, KOP is the shelving and KOS is the library of congress classification system?

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This lowkey reminds me of the road of el dorado the film. They were trusting because they were expecting their gods to come down and live beside them and Cortez caught onto this and played his part letting him and his men become celebrated and strikes on the day of the celebration. Killing slauthering women and children forcing the Azetcs no choice but to fight. I like it’s from the Azetcs perspective in a sense.

Attitudes are helpful to decide the tome of writing.

Why are you writing?

A person that we imagine the writer is.

The same thing knowing your audience.

Reading like a writer is like not just getting a story in my head but thinking about the authors point of you and getting into the author's head.

Usually when I read, it was just a story in my head.

eLife Assessment

In this fundamental manuscript, Richter et al. present a thorough anatomical characterization of the Drosophila melanogaster larval pharyngeal sensory system, which is involved in taste-guided behaviors. This study fills a major gap in the larval sensory map, providing a compelling neuroanatomical foundation for future investigations into sensory circuits and behavior. The data presented here are of exceptional quality and will be of interest to the Drosophila neurobiology community.

Reviewer #1 (Public review):

Summary:

The authors provide a detailed ultrastructural analysis of the larval pharyngeal sensory organs, including the dorsal pharyngeal sensilla, dorsal pharyngeal organ, ventral pharyngeal sensilla, and posterior pharyngeal sensilla. Using electron microscopy and 3D reconstruction, Richter et al., present a comprehensive mapping and classification of pharyngeal sensory structures, defining mthe orphological type of pharyngeal sensilla based on ultrastructure and generating a neuron-to-sensillum map. These findings significantly advance our understanding of internal larval sensory systems and establish a robust framework for future functional studies in coordination with external sensory systems.

Strengths:

The application of high-resolution electron microscopy and 3D imaging analysis successfully overcomes technical challenges associated with visualizing deep internal structures. This enables an unprecedented level of anatomical detail of the larval pharyngeal sensory system. Thus, the study complements and completes existing maps of larval sensory circuits, contributing a comprehensive neuroanatomical characterization of larval sensory input pathways. These insights will inform future studies on larval behavior, sensory processing, and may also have applied relevance for insect control strategies.

Weaknesses:

While the manuscript is concise, clearly written, and methodologically rigorous, it primarily addresses a specialized readership with expertise in insect neuroanatomy.

Reviewer #2 (Public review):

Summary:

This manuscript documents the structure of the pharyngeal nervous system of the Drosophila larva. The authors wanted to achieve a detailed ultrastructural reconstruction of the gustatory sensory organs in the Drosophila pharynx. Using serial EM and the associated bioinformatics tools, they have achieved their goal. The paper is written clearly and illustrated beautifully with 3D models and annotated sections. The data will significantly enrich the field of Drosophila neurobiology.

Strengths:

Given the dataset, the findings presented are solid and will be an important work of reference for the future.

Weaknesses:

Previous work, including EM, on the pharyngeal sensory organ is not sufficiently referenced and used for comparison with the data presented in this study.

Author Response:

We thank the reviewers and editors for their thoughtful and constructive feedback on our manuscript, “Morphology and ultrastructure of pharyngeal sense organs of Drosophila larvae.” We are pleased that both reviewers found our ultrastructural analysis and 3D reconstructions of the larval pharyngeal sensory system to be of high quality, and we appreciate the recognition of the study’s significance and potential impact on the Drosophila neurobiology field.

We want to address the concern raised regarding the limited referencing and comparison with previous work on pharyngeal sensory organs, particularly in adult Drosophila and other insect species.

As noted by the reviewers, our manuscript is concise and focused. We want to clarify that we initially prepared and submitted this study with the intention of it being considered as a Short Report, which comes with limitations on the number of characters and figures that can be included. During the submission process, we were asked by the editors if we would like to submit our work as a full-length Research Advance, which we agreed to.

That said, we are now happy to expand the discussion in the broader context of related studies — including prior EM and anatomical work — which would enrich the manuscript and provide readers with a deeper comparative perspective.

We are grateful for the positive assessment of our manuscript and for the opportunity to clarify this point.

Sincerely,

Vincent Richter and Andreas S. Thum

Specifically, the contractor must:

• Be a Mitel/Unify Systems certified partner authorized to perform on-network installations and maintenance.

eLife Assessment

This important work provides convincing evidence of the cognitive and neural mechanisms that give rise to feelings of shame and guilt, as well as their transformation into compensatory behavior. The authors combine well-designed manipulations of responsibility and harm with computational cognitive modeling and neuroimaging to provide a comprehensive account of how emotions are experienced and acted upon.

Reviewer #1 (Public review):

Summary:

This work provides important new evidence of the cognitive and neural mechanisms that give rise to feelings of shame and guilt, as well as their transformation into compensatory behavior. The authors use a well-designed interpersonal task to manipulate responsibility and harm, eliciting varying levels of shame and guilt in participants. The study combines behavioral, computational, and neuroimaging approaches to offer a comprehensive account of how these emotions are experienced and acted upon. Notably, the findings reveal distinct patterns in how harm and responsibility contribute to guilt and shame and how these factors are integrated into compensatory decision-making.

Strengths:

(1) Investigating both guilt and shame in a single experimental framework allows for a direct comparison of their behavioral and neural effects while minimizing confounds.

(2) The study provides a novel contribution to the literature by exploring the neural bases underlying the conversion of shame into behavior.

(3) The task is creative and ecologically valid, simulating a realistic social situation while retaining experimental control.

(4) Computational modeling and fMRI analysis yield converging evidence for a quotient-based integration of harm and responsibility in guiding compensatory behavior.

Weaknesses:

(1) Post-experimental self-reports rely both on memory and on the understanding of the conceptual difference between the two emotions. Additionally, it is unclear whether the 16 scenarios were presented in random order; sequential presentation could have introduced contrast effects or demand characteristics.

(2) In the neural analysis of emotion sensitivity, the authors identify brain regions correlated with responsibility-driven shame sensitivity and then use those brain regions as masks to test whether they were more involved in the responsibility-driven shame sensitivity than the other types of emotion sensitivity. I wonder if this is biasing the results. Would it be better to use a cross-validation approach? A similar issue might arise in "Activation analysis (neural basis of compensatory sensitivity)."

Additional comments and questions:

(1) Regarding the traits of guilt and shame, I appreciate using the scores from the subscales (evaluations and action tendencies) separately for the analyses (instead of a composite score). An issue with using the actions subscales when measuring guilt and shame proneness is that the behavioral tendencies for each emotion get conflated with their definitions, risking circularity. It is reassuring that the behavior evaluation subscale was significantly correlated with compensatory behavior (not only the action tendencies subscale). However, the absence of significant neural correlates for the behavior evaluation subscale raises questions: Do the authors have thoughts on why this might be the case, and any implications?

(2) Regarding the computational model finding that participants seem to disregard self-interest, do the authors believe it may reflect the relatively small endowment at stake? Do the authors believe this behavior would persist if the stakes were higher? Additionally, might the type of harm inflicted (e.g., electric shock vs. less stigmatized/less ethically charged harm like placing a hand in ice-cold water) influence the weight of self-interest in decision-making?

Taken together, the conclusions of the paper are well supported by the data. It would be valuable for future studies to validate these findings using alternative tasks or paradigms to ensure the robustness and generalizability of the observed behavioral and neural mechanisms.

Reviewer #2 (Public review):

Summary:

The authors combined behavioral experiments, computational modeling, and functional magnetic resonance imaging (fMRI) to investigate the psychological and neural mechanisms underlying guilt, shame, and the altruistic behaviors driven by these emotions. The results revealed that guilt is more strongly associated with harm, whereas shame is more closely linked to responsibility. Compared to shame, guilt elicited a higher level of altruistic behavior. Computational modeling demonstrated how individuals integrate information about harm and responsibility. The fMRI findings identified a set of brain regions involved in representing harm and responsibility, transforming responsibility into feelings of shame, converting guilt and shame into altruistic actions, and mediating the effect of trait guilt on compensatory behavior.

Strengths:

This study offers a significant contribution to the literature on social emotions by moving beyond prior research that typically focused on isolated aspects of guilt and shame. The study presents a comprehensive examination of these emotions, encompassing their cognitive antecedents, affective experiences, behavioral consequences, trait-level characteristics, and neural correlates. The authors have introduced a novel experimental task that enables such a systematic investigation and holds strong potential for future research applications. The computational modeling procedures were implemented in accordance with current field standards. The findings are rich and offer meaningful theoretical insights. The manuscript is well written, and the results are clearly and logically presented.

Weaknesses:

In this study, participants' feelings of guilt and shame were assessed retrospectively, after they had completed all altruistic decision-making tasks. This reliance on memory-based self-reports may introduce recall bias, potentially compromising the accuracy of the emotion measurements.

In many behavioral economic models, self-interest plays a central role in shaping individual decision-making, including moral decisions. However, the model comparison results in this study suggest that models without a self-interest component (such as Model 1.3) outperform those that incorporate it (such as Model 1.1 and Model 1.2). The authors have not provided a satisfactory explanation for this counterintuitive finding.

The phrases "individuals integrate harm and responsibility in the form of a quotient" and "harm and responsibility are integrated in the form of a quotient" appear in the Abstract and Discussion sections. However, based on the results of the computational modeling, it is more accurate to state that "harm and the number of wrongdoers are integrated in the form of a quotient." The current phrasing misleadingly suggests that participants represent information as harm divided by responsibility, which does not align with the modeling results. This potentially confusing expression should be revised for clarity and accuracy.

In the Discussion, the authors state: "Since no brain region associated with social cognition showed significant responses to harm or responsibility, it appears that the human brain encodes a unified measure integrating harm and responsibility (i.e., the quotient) rather than processing them as separate entities when both are relevant to subsequent emotional experience and decision-making." However, this interpretation overstates the implications of the null fMRI findings. The absence of significant activation in response to harm or responsibility does not necessarily imply that the brain does not represent these dimensions separately. Null results can arise from various factors, including limitations in the sensitivity of fMRI. It is possible that more fine-grained techniques, such as intracranial electrophysiological recordings, could reveal distinct neural representations of harm and responsibility. The interpretation of these null findings should be made with greater caution.

Reviewer #3 (Public review):

Summary:

Zhu et al. set out to elucidate how the moral emotions of guilt and shame emerge from specific cognitive antecedents - harm and responsibility - and how these emotions subsequently drive compensatory behavior. Consistent with their prediction derived from functionalist theories of emotion, their behavioral findings indicate that guilt is more influenced by harm, whereas shame is more influenced by responsibility. In line with previous research, their results also demonstrate that guilt has a stronger facilitating effect on compensatory behavior than shame. Furthermore, computational modeling and neuroimaging results suggest that individuals integrate harm and responsibility information into a composite representation of the individual's share of the harm caused. Brain areas such as the striatum, insula, temporoparietal junction, lateral prefrontal cortex, and cingulate cortex were implicated in distinct stages of the processing of guilt and/or shame. In general, this work makes an important contribution to the field of moral emotions. Its impact could be further enhanced by clarifying methodological details, offering a more nuanced interpretation of the findings, and discussing their potential practical implications in greater depth.

Strengths:

First, this work conceptualizes guilt and shame as processes unfolding across distinct stages (cognitive appraisal, emotional experience, and behavioral response) and investigates the psychological and neural characteristics associated with their transitions from one stage to the next.

Second, the well-designed experiment effectively manipulates harm and responsibility - two critical antecedents of guilt and shame.

Third, the findings deepen our understanding of the mechanisms underlying guilt and shame beyond what has been established in previous research.

Weaknesses:

(1) Over the course of the task, participants may gradually become aware of their high error rate in the dot estimation task. This could lead them to discount their own judgments and become inclined to rely on the choices of other deciders. It is unclear whether participants in the experiment had the opportunity to observe or inquire about others' choices. This point is important, as the compensatory decision-making process may differ depending on whether choices are made independently or influenced by external input.

(2) Given the inherent complexity of human decision-making, it is crucial to acknowledge that, although the authors compared eight candidate models, other plausible alternatives may exist. As such, caution is warranted when interpreting the computational modeling results.

(3) I do not agree with the authors' claim that "computational modeling results indicated that individuals integrate harm and responsibility in the form of a quotient" (i.e., harm/responsibility). Rather, the findings appear to suggest that individuals may form a composite representation of the harm attributable to each individual (i.e., harm/the number of people involved). The explanation of the modeling results ought to be precise.

(4) Many studies have reported positive associations between trait gratitude, social value orientation, and altruistic behavior. It would be helpful if the authors could provide an explanation about why this study failed to replicate these associations.

(5) As the authors noted, guilt and shame are closely linked to various psychiatric disorders. It would be valuable to discuss whether this study has any implications for understanding or even informing the treatment of these disorders.

Should be in line under. See PDF

eLife Assessment

This is a useful analysis of STORM data that characterizes the clustering of active zones in retinogeniculate terminals across ages and in the absence of retinal waves. The design makes it possible to relate fixed time point structural data to a known outcome of activity-dependent remodeling. However, the evidence is incomplete, weakening the claims the authors make regarding how activity influences the clustering of these synapses. This basic criticism has not improved with revisions.

Reviewer #1 (Public review):

Summary

The authors previously published a study of RGC boutons in the dLGN in developing wild-type mice and developing mutant mice with disrupted spontaneous activity. In the current manuscript, they have broken down their analysis of RGC boutons according to the number of Homer/Bassoon puncta associated with each vGlut3 cluster.

The authors find that, in the first post-natal week, RGC boutons with multiple active zones (mAZs) are about a third as common as boutons with a single active zone (sAZ). The size of the vGluT2 cluster associated with each bouton was proportional to the number of active zones present in each bouton. Within the author's ability to estimate these values (n=3 per group, 95% of results expected to be within ~2.5 standard deviations), these results are consistent across groups: 1) dominant eye vs. non-dominant eye, 2) wild-type mice vs. mice with activity blocked, and at 3) ages P2, P4, and P8. The authors also found that mAZs and sAZs also have roughly the same number (about 1.5) of sAZs clustered around them (within 1.5 um).

However, the authors do not interpret this consistency between groups as evidence that active zone clustering is not a specific marker or driver of activity dependent synaptic segregation. Rather, the authors perform a large number of tests for statistical significance and cite the presence or absence of statistical significance as evidence that "Eye-specific active zone clustering underlies synaptic competition in the developing visual system (title)". I don't believe this conclusion is supported by the evidence.

Strengths

The source dataset is high resolution data showing the colocalization of multiple synaptic proteins across development. Added to this data is labeling that distinguishes axons from the right eye from axons from the left eye. The first order analysis of this data showing changes in synapse density and in the occurrence of multi-active zone synapses is useful information about the development of an important model for activity dependent synaptic remodeling.

Weaknesses

In my previous review I argued that it was not possible to determine, from their analysis, whether the differences they were reporting between groups was important to the biology of the system. The authors have made some changes to their statistics (paired t-tests) and use some less derived measures of clustering. However, they still fail to present a meaningfully quantitative argument that the observed group differences are important. The authors base most of their claims on small differences between groups. There are two big problems with this practice. First, the differences between groups appear too small to be biologically important. Second, the differences between groups that are used as evidence for how the biology works are generally smaller than the precision of the author's sampling. That is, the differences are as likely to be false positives as true positives.

(1) Effect size. The title claims: "Eye-specific active zone clustering underlies synaptic competition in the developing visual system". Such a claim might be supported if the authors found that mAZs are only found in dominant-eye RGCs and that eye-specific segregation doesn't begin until some threshold of mAZ frequency is reached. Instead, the behavior of mAZs is roughly the same across all conditions. For example, the clear trend in Figure 4C and D is that measures of clustering between mAZ and sAZ are as similar as could reasonably be expected by the experimental design. However, some of the comparisons of very similar values produced p-values < 0.05. The authors use this fact to argue that the negligible differences between mAZ and sAZs explain the development of the dramatic differences in the distribution of ipsilateral and contralateral RGCs.

(2) Sample size. Performing a large number of significance tests and comparing p-values is not hypothesis testing and is not descriptive science. At best, with large sample sizes and controls for multiple tests, this approach could be considered exploratory. With n=3 for each group, many comparisons of many derived measures, among many groups, and no control for multiple testing, this approach constitutes a random result generator.

The authors argue that n=3 is a large sample size for the type of high resolution / large volume data being used. It is true that many electron microscopy studies with n=1 are used to reveal the patterns of organization that are possible within an individual. However, such studies cannot control individual variation and are, therefore, not appropriate for identifying subtle differences between groups.<br /> In response to previous critiques along these lines, the authors argue they have dealt with this issue by limiting their analysis to within-individual paired comparisons. There are several problems with their thinking in this approach. The main problem is that they did not change the logic of their arguments, only which direction they pointed the t-tests. Instead of claiming that two groups are different because p < 0.05, they say that two groups are different because one produced p < 0.05 and the other produced p > 0.05. These arguments are not statistically valid or biologically meaningful.

To the best of my understanding, the results are consistent with the following model:

• RGCs form mAZs at large boutons (known)

• About a quarter of week-one RGC boutons are mAZs (new observation)

• Vesicle clustering is proportional to active zone number (~new observation)

• RGC synapse density increases during the first post-week (known)

• Blocking activity reduces synapse density (known)

• Contralateral eye RGCs for more and larger synapses in the lateral dLGN (known)

• With n=3 and effect sizes smaller than 1 standard deviation, a statistically significant result is about as likely to be a false positive as a true positive.

• A true-positive statistically significant result does is not evidence of a meaningful deviation from a biological model.

Providing plots that show the number of active zones present in boutons across these various conditions is useful. However, I could find no compelling deviation from the above default predictions that would influence how I see the role of mAZs in activity dependent eye-specific segregation.

Below are critiques of most of the claims of the manuscript.

Claim (abstract): individual retinogeniculate boutons begin forming multiple nearby presynaptic active zones during the first postnatal week.

Confirmed by data.

Claim (abstract): the dominant-eye forms more numerous mAZ contacts,

Misleading: The dominant-eye (by definition) forms more contacts than the non-dominant eye. That includes mAZ.

Claim (abstract): At the height of competition, the non-dominant-eye projection adds many single active zone (sAZ) synapses

Weak: While the individual observation is strong, it is a surprising deviation based on a single n=3 experiment in a study that performed twelve such experiments (six ages, mutant/wildtype, sAZ/mAZ)

Claim (abstract): Together, these findings reveal eye-specific differences in release site addition during synaptic competition in circuits essential for visual perception and behavior.

False: This claim is unambiguously false. The above findings, even if true, do not argue for any functional significance to active zone clustering.

Claim (line 84): "At the peak of synaptic competition midway through the first postnatal week, the non-dominant-eye formed numerous sAZ inputs, equalizing the global synapse density between the two eyes"

Weak: At one of twelve measures (age, bouton type, genotype) performed with 3 mice each, one density measure was about twice as high as expected.

Claim (line 172): "In WT mice, both mAZ (Fig. 3A, left) and sAZ (Fig. 3B, left) inputs showed significant eye-specific volume differences at each age."

Questionable: There appears to be a trend, but the size and consistency is unclear.

Claim (line 175): "the median VGluT2 cluster volume in dominant-eye mAZ inputs was 3.72 fold larger than that of non-dominant-eye inputs (Fig. 3A, left)."

Cherry picking. Twelve differences were measured with an n of 3, 3 each time. The biggest difference of the group was cited. No analysis is provided for the range of uncertainty about this measure (2.5 standard deviations) as an individual sample or as one of twelve comparisons.

Claim (line 174): "In the middle of eye-specific competition at P4 in WT mice, the median VGluT2 cluster volume in dominant-eye mAZ inputs was 3.72 fold larger than that of non-dominant-eye inputs (Fig. 3A, left). In contrast, β2KO mice showed a smaller 1.1 fold difference at the same age (Fig. 3A, right panel). For sAZ synapses at P4, the magnitudes of eye-specific differences in VGluT2 volume were smaller: 1.35-fold in WT (Fig. 3B, left) and 0.41-fold in β2KO mice (Fig. 3B, right). Thus, both mAZ and sAZ input size favors the dominant eye, with larger eye-specific differences seen in WT mice (see Table S3)."

No way to judge the reliability of the analysis and trivial conclusion: To analyze effect size the authors choose the median value of three measures (whatever the middle value is). They then make four comparisons at the time point where they observed the biggest difference in favor of their hypothesis. There is no way to determine how much we should trust these numbers besides spending time with the mislabeled scatter plots. The authors then claim that this analysis provides evidence that there is a difference in vGluT2 cluster volume between dominant and non-dominant RGCs and that that difference is activity dependent. The conclusion that dominant axons have bigger boutons and that mutants that lack the property that would drive segregation would show less of a difference is very consistent with the literature. Moreover, there is no context provided about what 1.35 or 1.1 fold difference means for the biology of the system.

Claim (189): "This shows that vesicle docking at release sites favors the dominant-eye as we previously reported but is similar for like eye type inputs regardless of AZ number."

Contradicts core claim of manuscript: Consistent with previous literature, there is an activity dependent relative increase in vGlut2 clustering of dominant eye RGCs. The new information is that that activity dependence is more or less the same in sAZ and mAZ. The only plausible alternative is that vGlut2 scaling only increases in mAZ which would be consistent with the claims of their paper. That is not what they found. To the extent that the analysis presented in this manuscript tests a hypothesis, this is it. The claim of the title has been refuted by figure 3.

Claim (line 235): "For the non-dominant eye projection, however, clustered mAZ inputs outnumbered clustered sAZ inputs at P4 (Fig. 4C, bottom left panel), the age when this eye adds sAZ synapses (Fig. 2C)."

Misleading: The overwhelming trend across 24 comparisons is that the sAZ clustering looks like mAZ clustering. That is the objective and unambiguous result. Among these 24 underpowered tests (n=3), there were a few p-values < 0.05. The authors base their interpretation of cell behavior on these crossings.

Claim (line 328): "The failure to add synapses reduced synaptic clustering and more inputs formed in isolation in the mutants compared to controls."

Trivially true: Density was lower in mutant.

Claim (line 332): "While our findings support a role for spontaneous retinal activity in presynaptic release site addition and clustering..."

Not meaningfully supported by evidence: I could not find meaningful differences between WT and mutant beside the already known dramatic difference in synapse density.

Reviewer #2 (Public review):

Summary:

In this manuscript, Zhang and Speer examine changes in the spatial organization of synaptic proteins during eye specific segregation, a developmental period when axons from the two eyes initially mingle and gradually segregate into eye-specific regions of the dorsal lateral geniculate. The authors use STORM microscopy and immunostain presynaptic (VGluT2, Bassoon) and postsynaptic (Homer) proteins to identify synaptic release sites. Activity-dependent changes of this spatial organization are identified by comparing the β2KO mice to WT mice. They describe two types of synapses based on Bassoon clustering: the multiple active zone (mAZ) synapse and single active zone (sAZ) synapse. In this revision, the authors have added EM data to support the idea that mAZ synapses represent boutons with multiple release sites. They have also reanalyzed their data set with different statistical approaches.

Strengths:

The data presented is of good quality and provides an unprecedented view at high resolution of the presynaptic components of the retinogeniculate synapse during active developmental remodeling. This approach offers an advance to the previous mouse EM studies of this synapse because of the CTB label allows identification of the eye from which the presynaptic terminal arises.

Weaknesses:

While the interpretation of this data set is much more grounded in this second revised submission, some of the authors' conclusions/statements still lack convincing supporting evidence. In particular, the data does not support the title: "Eye-specific active zone clustering underlies synaptic competition in the developing visual system". The data show that there are fewer synapses made for both contra- and ipsi- inputs in the β2KO mice-- this fact alone can account for the differences in clustering. There is no evidence linking clustering to synaptic competition. Moreover, the findings of differences in AZ# or distance between AZs that the authors report are quite small and it is not clear whether they are functionally meaningful.

Reviewer #3 (Public review):

This study is a follow-up to a recent study of synaptic development based on a powerful data set that combines anterograde labeling, immunofluorescence labeling of synaptic proteins, and STORM imaging (Cell Reports, 2023). Specifically, they use anti-Vglut2 label to determine the size of the presynaptic structure (which they describe as the vesicle pool size), anti-Bassoon to label active zones with the resolution to count them, and anti-Homer to identify postsynaptic densities. Their previous study compared the detailed synaptic structure across the development of synapses made with contra-projecting vs. ipsi-projecting RGCs and compared this developmental profile with a mouse model with reduced retinal waves. In this study, they produce a new detailed analysis on the same data set in which they classify synapses into "multi-active zone" vs. "single-active zone" synapses and assess the number and spacing of these synapses. The authors use measurements to make conclusions about the role of retinal waves in the generation of same-eye synaptic clusters. The authors interpret these results as providing insight into how neural activity drives synapse maturation, the strength of their conclusions is not directly tested by their analysis.

Strengths:

This is a fantastic data set for describing the structural details of synapse development in a part of the brain undergoing activity-dependent synaptic rearrangements. The fact that they can differentiate the eye of origin is what makes this data set unique over previous structural work. The addition of example images from the EM dataset provides confidence in their categorization scheme.

Weaknesses:

Though the descriptions of single vs multi-active zone synapses are important and represent a significant advance, the authors continue to make unsupported conclusions regarding the biological processes driving these changes. Although this revision includes additional information about the populations tested and the tests conducted, the authors do not address the issue raised by previous reviews. Specifically, they provide no assessment of what effect size represents a biologically meaningful result. For example, a more appropriate title is "The distribution of eye-specific single vs multi-active zone is altered in mice with reduced spontaneous activity" rather than concluding that this difference in clustering is somehow related to synaptic competition. Of course, the authors are free to speculate, but many of the conclusions of the paper are not supported by their results.

Author response:

The following is the authors’ response to the previous reviews

Reviewer #1 (Public review)

Summary:

This manuscript addresses the question of whether spontaneous activity contributes to the clustering of retinogeniculate synapses before eye opening. The authors re-analyze a previously published dataset to answer the question. The authors conclude that synaptic clustering is eye-specific and activity dependent during the first postnatal week. While there is useful information in this manuscript, I don't see how the data meaningfully supports the claims made about clustering.

In adult retinogeniculate connections, functionally specificity is supported by select pairings of retinal ganglion cells and thalamocortical cells forming dozens of synaptic connections in subcellular microcircuits called glomeruli. In this manuscript, the authors measure whether the frequency of nearby synapses is higher in the observed data than in a model where synapses are randomly distributed throughout the volume. Any real anatomical data will deviate from such a model. The interesting biological question is not whether a developmental state deviates from random. The interesting question is how much of the adult clustering occurs before eye opening. In trying to decode the analysis in this manuscript, I can't tell if the answer is 99% or 0.001%.

We thank the reviewer for their helpful critique through both rounds of review. We have refocused the manuscript on paired eye-specific measurements of active zone addition and spatial relationships among active zones at each age. All effect sizes and power values for each comparison are now reported in Table S2. These measures allow readers to gauge biological significance more transparently.

Strengths:

The source dataset is high resolution data showing the colocalization of multiple synaptic proteins across development. Added to this data is labeling that distinguishes axons from the right eye from axons from the left eye. The first order analysis of this data showing changes in synapse density and in the occurrence of multi-active zone synapses is useful information about the development of an important model system.

Weaknesses:

I don't think the analysis of clustering within this dataset improves our understanding of how the system works. It is possible that the result is clear to the authors based on looking at the images. As a reader trying to interpret the analysis, I ran into the following problems:

• It is not possible to estimate biologically meaningful effect sizes from the data provided. Spontaneous activity in the post natal week could be responsible for 99% or 0.001% of RGC synapse clustering.

• The sample size is too small for the kinds of comparisons being made. The authors point out that many STORM studies use an n of 1 while the authors have n = 3 for each of their six experimental groups. However, the critical bit is what kinds of questions you are trying to answer with a given sample size. This study depends on determining whether the differences between groups are due to age, genotype, or individual variation. This study also makes multiple comparisons of many different noisy parameters that test the same or similar hypothesis. In this context, it is unlikely that n = 3 sufficiently controls for individual variation.

We have revised the manuscript to focus on eye-specific differences, which are paired measurements collected at each age. We have measured effect sizes and performed power tests for all comparisons presented in the manuscript. These measurements are shown for every figure in a new supplemental table S2.

• There is no clear biological interpretation of the core measure of the publication, the normalized clustering index. The normalized clustering index starts with counting the fraction of single active zone synapses within various distances to the edge of synapses. This frequency is compared to a randomization model in which the positions of synapses are randomized throughout a volume. The authors found that the biggest deviation between the observed and randomized proximity frequency using a distance threshold of 1.5 um. They consider the deviation from the random model to be a sign of clustering. However, two RGC synapses 1.5 um apart have a good chance of coming from the same RGC axon. At this scale, real observations will, therefore, always look more clustered than a model where synapses are randomly placed in a volume. If you randomly place synapses on an axon, they will be much closer together than if you randomly place synapses within a volume. The authors normalize their clustering measure by dividing by the frequency of clustering in the normalized model. That makes the measure of clustering an ambiguous mix of synapse clustering, axon morphology, and synaptic density.

We have removed the “normalized clustering index”. “Clustered” inputs are now defined strictly as those that have a neighboring single active-zone (sAZ) synapse within 1.5 mm. For each type of input (sAZ and mAZ) we show 1) the ratio of clustered to isolated inputs for both eyes, and 2) the number of neighboring sAZs (Figure 4).

We agree with the reviewer that many synapses are likely made nearby along the same axon from an individual RGC. In this scenario, sAZ synapses that are nearby a neighboring mAZ input may be part of the same nascent bouton. And, sAZ synapses nearby other sAZ neighbors may ultimately mature into a mAZ input. At the same time, inputs from one RGC may form nearby other inputs from neighboring RGCs. We discuss these motifs and potential mechanisms of cell-autonomous and non-autonomous development (Lines 300-308).

• Other measures are also very derived. For instance, one argument is based on determining that the cumulative distribution of the distance of dominant-eye multi-active zone synapses with nearby single-active zone synapses from dominant-eye multi-active zone synapses is statistically different from the cumulative distribution of the distance of dominant-eye multi-active zones without nearby single-active zone synapses from dominant-eye multi-active zones. Multiple permutations of this measure are compared.

We have simplified the presentation to show all measured path lengths for every input. This allows the reader to see each of the inputs and their relative distances. We present these data for like-eye type interactions at P4 and P8 (Figures 5 and S5).   

• There are major biological differences between groups that are difficult to control for. Between P2, P4, and P8, there are changes in cell morphology and synaptic density. There are also large differences in synapse density between wild type and KO mice. It is difficult to be confident that these differences are not responsible for the relatively subtle changes in clustering indices.

• Many claims are based on complicated comparisons between groups rather than the predominating effects within the data. It is noted that: "In KO mice, dominant eye projections showed increased clustering around mAZ synapses compared to sAC synapses suggesting partial maintenance of synaptic clustering despite retinal wave defects". In contrast, I did not notice any discussion of the fact that the most striking trend in those measures is that the clustering index decreases from P2 to P8.

Related to the points above, we have revised the manuscript to focus on eye-specific release site addition and spatial relationships. For clarity, we have removed the clustering index and instead present ratios of clustered and isolated inputs, the number of sAZ synapses near each input type, and distance between like-eye mAZ inputs (Figure 4).      

• Statistics are improperly applied. In my first review I tried to push the authors to calculate confidence intervals for two reasons. First, I believed the reader should be able to answer questions such as whether 99% or 0.01% of RGC synaptic clustering occurred in the first postnatal week. Second, I wanted the authors to deal with the fact that n=3 is underpowered for many of the questions they were asking. While many confidence intervals can now be found leading up to a claim, it is difficult to find claims that are directly supported by the correct confidence interval. Many claims are still incorrectly based on which combinations of comparisons produced statistically significant differences and which combinations did not.

We have substantially revised the manuscript to focus on within-group paired effects between eye-of-origin. We performed power tests for all statistical presentations and effect sizes and powers are presented for every figure in a new supplemental table S2. To simplify the manuscript and make it easier to read, we report confidence interval measurements in a separate supplemental table S3.

Reviewer #2 (Public review):

Summary:

This study provides a valuable data set showing changes in the spatial organization of synaptic proteins at the retinogeniculate connection during a developmental period of active axonal and synaptic remodeling. The data collected by STORM microscopy is state-of-the-art in terms of the high-resolution view of the presynaptic components of a plastic synapse. The revision has addressed many, but not all, of the initial concerns about the authors interpretation of their data. However, with the revisions, the manuscript has become very dense and difficult to follow.

We greatly appreciate the reviewer’s thoughtful comments through two rounds of review. To improve the clarity of the manuscript, we have substantially revised the work to streamline the narrative, clearly define terminology, and simplify data presentations, allowing readers to more directly interpret results and their implications.

Strengths:

The data presented is of good quality and provides an unprecedented view at high resolution of the presynaptic components of the retinogeniculate synapse during active developmental remodeling. This approach offers an advance to the previous mouse EM studies of this synapse because the CTB label allows identification of the eye from which the presynaptic terminal arises.

Weaknesses:

From these data the authors conclude that eye-specific increase in mAZ synapse density occur over retinogeniculate refinement, that sAZ synapses cluster close to mAZ synapses over age, and that this process depends on spontaneous activity and proximity to eye-specific mAZ synapses. While the interpretation of this data set is much more grounded in this revised submission, some of the authors' conclusions/statements still lack convincing supporting evidence.

This includes:

(1) The conclusion that multi-active zone synapses are loci for synaptic clustering. This statement, or similar ones (e.g., line 407) suggest that mAZ synapses actively or through some indirect way influence the clustering of sAZ synapses. There is no evidence for this. Clustering of retinal synapses are in part due to the fact that retinal inputs synapse on the proximal dendrites. With increased synaptogenesis, there will be increased density of retinal terminals that are closely localized. And with development, perhaps sAZ synapses mature into mAZ synapses. This scenario could also explain a large part of this data set.

We thank the reviewer for their comment. We have removed the ambiguous phrasing and clarified the manuscript to explicitly discuss alternative interpretations consistent with the results (Lines 300-308). This includes a discussion of sAZ synapse maturation into mAZ inputs (Lines 294-296).

(2) The conclusion that, "clustering depends on spontaneous retinal activity" could be misleading to the reader given that the authors acknowledge that their data is most consistent with a failure of synaptogenesis in the mutant mice (in the rebuttal). Additionally clustering does occur in CTB+ projections around mAZ synapses.

We have removed the highlighted phrase and revised the manuscript to focus on differences in release site addition between eye-of-origin. We clarified our discussion of activity-dependent changes to state that synapses fail to form in the mutant and synaptic clustering was reduced (Lines 324-330).

(3) Line 403: "Since mAZ synapses are expected to have a higher release probability, they likely play an important role in driving plasticity mechanisms reliant on neurotransmission.":What evidence do the authors have that mAZ are expected to have higher release probability?

We thank the reviewer for their careful reading. Because they have several active zones, mAZ synapses are expected to have a higher number of release sites (N), which could be independent of release probability at any individual active zone (Pr). We have removed the reference to release probability. Instead, we maintain focus on active zone number.

Reviewer #3 (Public review):

This study is a follow-up to a recent study of synaptic development based on a powerful data set that combines anterograde labeling, immunofluorescence labeling of synaptic proteins, and STORM imaging (Cell Reports, 2023). Specifically, they use anti-Vglut2 label to determine the size of the presynaptic structure (which they describe as the vesicle pool size), anti-Bassoon to label active zones with the resolution to count them, and anti-Homer to identify postsynaptic densities. Their previous study compared the detailed synaptic structure across the development of synapses made with contra-projecting vs. ipsi-projecting RGCs and compared this developmental profile with a mouse model with reduced retinal waves. In this study, they produce a new detailed analysis on the same data set in which they classify synapses into "multi-active zone" vs. "single-active zone" synapses and assess the number and spacing of these synapses. The authors use measurements to make conclusions about the role of retinal waves in the generation of same-eye synaptic clusters, providing key insight into how neural activity drives synapse maturation.

Strengths:

This is a fantastic data set for describing the structural details of synapse development in a part of the brain undergoing activity-dependent synaptic rearrangements. The fact that they can differentiate eye of origin is what makes this data set unique over previous structural work. The addition of example images from EM data set provides confidence in their categorization scheme.

Weaknesses:

Though the descriptions of synaptic clusters are important and represent a significant advance, the authors conclusions regarding the biological processes driving these clusters are not testable by such a small sample. This limitation is expected given the massive effort that goes into generating this data set. Of course the authors are free to speculate, but many of the conclusions of the paper are not statistically supported.

We thank the reviewer for their helpful comments throughout the revision process. We have substantially modified the manuscript to reframe the work around release site addition during eye-specific competition. Power tests and effect size measurements are presented for every figure in a new supplemental table S2.

Reviewer #2 (Recommendations for the authors):

(1) Authors should discuss that it is not clear what the relationship is between sAZ and mAZ, and sAZ could turn into a mAZ. This is not unreasonable that the number of AZ/bouton increases with development given that in the adult rodent retinogeniculate bouton, there is an average of 27 active zones (Budisantoso et al, 2012).

We thank the reviewer for their helpful suggestion. We have added a discussion of the relationship between sAZ and mAZ inputs and the point that sAZ synapses may mature into mAZ synapses (Lines 294-296). We now reference the work of Budisantoso et al., J. Neurosci. 2012.   

(2) The authors should clarify how the statistics are calculated for the normalized clustering index (figure 3B, C). For ratios of values each with variance, the variance is summed when calculating SEM.

For clarity, we have removed the normalized clustering index analysis. We have simplified the work to present a clear definition of clustered and unclustered inputs, where clustering is defined by the presence of a nearby neighboring synapse within 1.5mm. We present the ratio of clustered and unclustered inputs for each input type and eye-of-origin. We also show the number of sAZ synapses nearby each clustered input (Figure 4).

(3) The authors have significantly clarified the terminology that they use in the text. This is much appreciated. However, it would be helpful to the naïve reader if they could define their use of the word "synapse" as referring to individual active zones/release sites or to terminals/boutons. For example:

Line 378: "Prior electron microscopy studies in the mouse found limited evidence of convergent synaptic clustering from neighboring RGCs at postnatal day 8 (10, 13), suggesting that the mAZ synapses seen in STORM images are single retinogeniculate terminals. The lack of synaptic convergence in prior EM reconstructions at P8 implies that early clustering around mAZ synapses may result from local output clustering within individual RGC arbors.":

What do the authors mean by "convergent synaptic clustering": do they mean clustering of release sites from different RGC inputs? And what does "local output clustering" mean?

We thank the reviewer for their suggestion to use clear terminology. We have revised the manuscript to define our use of the term “synapse” as a single active zone/release site (Lines 134-136). We refer to mAZ boutons in STORM data as “inputs”. We have revised the discussion of prior EM studies (Lines 130-132) and clarified all discussions of synaptic clustering throughout the work.

(4) While the authors argue that the retina-specific β2-nAChR mice exhibit disrupted retinal waves and defects in eye specific segregation, the authors are studying issues of active zone density which may depend on mechanisms depending on the postsynaptic neuron. This should be acknowledged.

We have updated the text to discuss the fact that postsynaptic mechanisms are also critical for the refinement of eye-specific synapses (Lines 332-340). We have added several additional references to the manuscript accordingly.

Reviewer #3 (Recommendations for the authors):

The authors have addressed many of my original concerns. The additional description of criteria for categorizing synapses, showing all the data points, gives the reader a stronger sense of where the numbers in the quantification come from. Replacing the "complex/simple" distinction with the "multi/single active zone" and the other clarifying text was effective. The addition of the EM data was also a very nice example to help interpret STORM images. It does appear there was no quantification on this EM data set and perhaps just a few example images were taken as "proof of principle". If, by chance, the authors have more EM images to make a data set of them that allows for some quantification, that would be great to add.

We thank the reviewer for their helpful comments on the manuscript through both rounds of review. The EM data we collected were 2D images of a subset of physical sections at postnatal day 8. Most dAPEX2(+) profiles had a single active zone, but a definitive identification would require 3D imaging so that each terminal can be assessed in its entirety for release sites that might be missed in a single cross section. Similarly, multi-active zone boutons are positively identified in 2D images, but definitive measurements of AZ number would require 3D information. We analyzed our 2D EM images and present a plot of dAPEX2(+) profile size versus active zone number below. These measures are positively correlated (r = 0.74), with larger profiles containing more active zones.

Author response image 1.<br />

Unfortunately, we are not currently equipped to perform volumetric EM imaging at our home institution and are concerned that analysis of 2D data may be inconclusive. For these reasons, we are opting to maintain a qualitative presentation of our current EM results and we look forward to collaborating with other experts to achieve volumetric EM reconstructions in the future

We should have Skoove expert, especially in Germany, being quoted here. Florian or Dominik, instead of a musician.

Why would those links not lead to Skoove pages instead of Datapulse?

Skoove is not being mentioned in this article, by my opinion this is not enough for Digital PR to have positive impact for us. It has a potential to go broad, but if we are not being mentioned, only linked I think the value of this link without contextual surrounding will be very low for both our SEO and brand reputation efforts.

Analysis done by Skoove and Datapulse, as we want to be mentioned high in the article.

I am really missing Skoove being mentioned in the methodology, what was our role here? Why would anyone give us link or a mention or citation, if we are only mentioned once?

In order to benefit from brand awareness, it would be really nice having Skoove expert being quoted, insteas of a musician. In general, mentioning Skoove in a way that we really participated in this campaign would be really important for us.

Same comment here, seems to be really relevant to Indian media potentially, but our performance and earnings in that market are not giving enough reasons to highlight it.

It would be great if one of our core markets is at the beginning of the article, instead of India and Costa Rica, as we also potentially want to be published in those. At least France or Spain for example.

Unique cultural aspects of Benin traditions. Enjoy local market bargains. Learn about voodoo beliefs. Prepare for the fascinating moto-taxis known as "zem."

A new initiative called the WorkerBee pool. Where users can delegate their WORKERBEE tokens to support a witness. While earning BEE tokens without any fees.

Learn the alchemical concept of "the son of the Sun and Moon." As a symbol of the union of opposites. See the transformative journey to an unknowable source.

Fund smaller projects within the Hive community. Using the Shark Tank Community for project pitches. Also, the #hiveproject hashtag to increase visibility.

Single-cell proteomics: From Protein Variation to Biological Functions

Presentation by Prof. Nikolai Slavov at the MRC Protein Phosphorylation and Ubiquitylation Unit, Dundee University: Technological and biological frontiers for single-cell proteomics: From Protein Variation to Biological Functions. The presented research was performed at Slavov Laboratory, Northeastern University and Parallel Squared Technology Institute.

https://youtu.be/8nhjKSTqeOs?si=kq_eYIjEnpQBmH4Q

It seems in group C; the samples are "most different" at V5. Though no specific taxa or topics are found here (further down in the report)

Some assignments are going to be from a general understanding but always pay attention to the text or to your instructor presenting the assignments.

PF Chang’s Menu with Prices https://pfchangmenu.com/

You don't need to be a formal expert to teach others. You can be in the top 10% of knowledgeable people in any field. Learn, share, and grow your expertise.

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

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Reply to the reviewers

Manuscript number: RC-2025-02879 Corresponding author(s): Matteo Allegretti; Alia dos Santos

1. General Statements

In this study, we investigated the effects of paclitaxel on both healthy and cancerous cells, focusing on alterations in nuclear architecture. Our novel findings show that:

• Paclitaxel-induced microtubule reorganisation during interphase alters the perinuclear distribution of actin and vimentin. The formation of extensive microtubule bundles, in paclitaxel or following GFP-Tau overexpression, coincides with nuclear shape deformation, loss of regulation of nuclear envelope spacing, and alteration of the nuclear lamina.

• Paclitaxel treatment reduces Lamin A/C protein levels via a SUN2-dependent mechanism. SUN2, which links the lamina to the cytoskeleton, undergoes ubiquitination and consequent degradation following paclitaxel exposure.

• Lamin A/C expression, frequently dysregulated in cancer cells, is a key determinant of cellular sensitivity to, and recovery from, paclitaxel treatment.

Collectively, our data support a model in which paclitaxel disrupts nuclear architecture through two mechanisms: (i) aberrant nuclear-cytoskeletal coupling during interphase, and (ii) multimicronucleation following defective mitotic exit. This represents an additional mode of action for paclitaxel beyond its well-established mechanism of mitotic arrest.

We thank the reviewers for their time and constructive feedback. We have carefully considered all comments and have carried out a full revision. The updated manuscript now includes additional data showing:

• Overexpression of microtubule-associated protein Tau causes similar nuclear aberration phenotypes to paclitaxel. This supports our hypothesis that increased microtubule bundling directly leads to nuclear disruption in paclitaxel during interphase.

• Paclitaxel's effects on nuclear shape and Lamin A/C and SUN2 expression levels occur independently of cell division.

• Reduced levels of Lamin A/C and SUN2 upon paclitaxel treatment occur at the protein level via ubiquitination of SUN2.

• The effects of paclitaxel on the nucleus are conserved in breast cancer cells.

Full Revision

We have also edited our text and added further detail to clarify points raised by the reviewers. We believe that our revised manuscript is overall more complete, solid and compelling thanks to the reviewers' comments.

1. Point-by-point description of the revisions

Reviewer #1 Evidence, reproducibility and clarity

This description of the down-regulation of the expression of lamin A/C upon treatment with paclitaxel and its sensitivity to SUN2 is quite interesting but still somehow preliminary. It is unclear whether this effect involves the regulation of gene expression, or of the stability of the proteins. How SUN2 mediates this effect is still unknown.

We thank the reviewer for this valuable comment. To elucidate the mechanism behind the decrease in Lamin A/C and SUN2 levels, we have now performed several additional experiments. First, we performed RT-qPCR to quantify mRNA levels of these genes, relative to the housekeeping gene GAPDH (Supplementary Figure 3B and O). The levels of SUN2 and LMNA mRNA remained the same between control and paclitaxel-treated cells, indicating that this effect instead occurs at the protein level. We have also tested post-translational modifications as a potential regulatory mechanism for Lamin A/C and SUN2. In addition to the phosphorylation of Ser404 which we had already tested (Supplementary Figure 3C), we have now included additional Phos-tag gel and Western blotting data showing that the overall phosphorylation status of Lamin A/C is not affected by paclitaxel (Supplementary Figure 3E and F). We also pulled-down Lamin A/C from cell lysates and then Western blotted for polyubiquitin and acetyl-lysine, which showed that the ubiquitination and acetylation states of Lamin A/C are also not affected by paclitaxel (Supplementary Figure 3G-I). However, Western blots for polyubiquitin of SUN2 pulled down from cell lysates showed that paclitaxel treatment results in significant SUN2 ubiquitination (Figure 3M and N). Therefore, we propose that the downregulation of SUN2 following paclitaxel treatment occurs by ubiquitin-mediated proteolysis.

The roles of free tubulins and polymerized microtubules, and thus the potential role of paclitaxel, need to be uncovered.

We addressed this important point by using an alternative method to stabilise/bundle microtubules in interphase, namely by overexpressing GFP-Tau, as suggested by reviewer 2. Following GFP- Tau overexpression, large microtubule bundles were observed throughout the cytoplasm (Figure 4A), and this resulted in a significant decrease in nuclear solidity (Figure 4B). Furthermore, in cells where microtubule bundles extensively contacted the nucleus, the nuclear lamina became unevenly distributed and appeared patchy (Figure 4C). This supports our hypothesis that the aberrations to nuclear shape and Lamin A/C localisation in paclitaxel-treated cells are due to the presence of microtubules bundles surrounding the nucleus.

The doses of paclitaxel at which occur the effects described in the paper are not fully consistent with all the conclusions. Most experiments have been done at 5 nM. However, at this dose the effect of lamin A/C over or down expression on the growth (differences in the slopes of the curves in Figure 4A) are not fully convincing and not fully consistent with the clear effect on viability as well (in addition, duration of treatments before assessing vialbility are not specified). At 1 nM, cell growth is reduced and the rescuing effect of lamin over-expression is much more clear (Fig 4A), and the nucleus deformation clear (Fig 2A) but this dose has no effect on lamin A/C expression (Fig 3C), which questions how lamins impact nucleus shape and cell survival. Cytoskeleton reorganisation in these conditions is not described although it could clarify the respective role of force production (suggested in figure 1) and nuclei resistance (shown in figure 2) in paclitaxel sensitivity.

We thank the reviewer for raising this important point. We have addressed this by conducting additional repeats for the cell confluency measurements to increase the statistical power of our experiments (Figure 5A). Our data now show that GFP-lamin A/C had a statistically significant effect on rescuing cell growth at both 1 nM and 5 nM paclitaxel, while Lamin A/C knockdown exacerbated the inhibition of cell growth at 5 nM paclitaxel but not 1 nM paclitaxel (Figure 5A). In addition, we note that the duration of paclitaxel treatment before assessing viability was specified in the figure legend: "Bar graph comparing cell viability between wild-type (red), GFP-Lamin A/C overexpression (green), and Lamin A/C knockdown (blue) cells following 20 h incubation in 0, 1, 5, or 10 nM paclitaxel." We also repeated cell viability analysis after 48 h incubation in paclitaxel instead of 20 h to allow for a longer time for differences to take effect (Figure 5B).

We also added figures showing the cytoskeletal reorganisation at both 1 and 10 nM in addition to 0 and 5 nM (Supplementary Figure 1A) showing that microtubule bundling and condensation of actin into puncta correlated with increased paclitaxel concentration. Vimentin colocalised well with microtubules at all concentrations.

We have also included in our results section further clarification for the use of 5nM paclitaxel in this study. The new section reads as follows: "Experiments were performed at 5 nM paclitaxel (with additional experiments to determine dose relationships at 1 and 10 nM) because this aligns with previous studies7,14,24. Furthermore, previous analysis of patient plasma reveals that typical concentrations are within the low nanomolar range8, and concentrations of 5-10 nM are required in cell culture to reach the same intracellular concentrations observed in vivo in patient tumours9. This aligns with in vitro cytotoxic studies of paclitaxel in eight human tumour cell lines which show that paclitaxel's IC50 ranges between 2.5 and 7.5 nM41."

Finally, although the absence of role of mitotic arrest is clear from the data, the defective reorganisation of the nucleus after mitosis still suggest that the effect of paclitaxel is not independent of mitosis.

We thank the reviewer for pointing out the need for clarification in the wording of our manuscript. We have reworded the title and relevant sections of our abstract, introduction, and discussion to make it clearer that the effects of paclitaxel on the nucleus are due to a combination of aberrant nuclear cytoskeletal coupling during interphase and multimicronucleation following mitotic slippage. We have also added additional data in support of the effect of paclitaxel on nuclear architecture during interphase. For this, we used serum-starved cells (which divide only very slowly such that the majority of cells do not pass through mitosis during the 16 h incubation in paclitaxel [Supplementary Figure 2D]). Our new data confirmed that paclitaxel's effects on nuclear solidity, and Lamin A/C and SUN2 proteins levels can occur independently of cell division (Figure 2C; Figure 3H-J). Finally, when we overexpressed GFP-Tau (as discussed above) we observed similar aberrations to nuclear solidity and Lamin A/C localisation. This indicates that these effects occur due to microtubule bundling in interphase, especially as in our study GFP-Tau did not lead to multimicronucleation or appear to affect mitosis (Figure 4).

Below are the main changes to the text regarding the interphase effect of paclitaxel:

• Title: "Paclitaxel compromises nuclear integrity in interphase through SUN2-mediated cytoskeletal coupling"

• Abstract: "Overall, our data supports nuclear architecture disruption, caused by both aberrant nuclear-cytoskeletal coupling during interphase and exit from defective mitosis, as an additional mechanism for paclitaxel beyond mitotic arrest."

• Introduction: "Here we propose that cancer cells have increased vulnerability to paclitaxel both during interphase and following aberrant mitosis due to pre-existing defects in their NE and nuclear lamina."

• Discussion: "Overall, our work builds on previous studies investigating loss of nuclear integrity as an anti-cancer mechanism of paclitaxel separate from mitotic arrest14,20,21. We propose that cancer cells show increased sensitivity to nuclear deformation induced by aberrant nuclear-cytoskeletal coupling and multimicronucleation following mitotic slippage. Therefore, we conclude that paclitaxel functions in interphase as well as mitosis, elucidating how slowly growing tumours are targeted."

minor: a more thorough introduction of known data about dose response of cells in culture and in vivo would help understanding the range of concentrations used in this study.

As mentioned above, we have now included additional information in our Results section to clarify our paclitaxel dose range: "Experiments were performed at 5 nM paclitaxel (with additional experiments to determine dose relationships at 1 and 10 nM) because this aligns with previous studies7,14,24. Furthermore, previous analysis of patient plasma reveals that typical concentrations are within the low nanomolar range8, and concentrations of 5-10 nM are required in cell culture to reach the same intracellular concentrations observed in vivo in patient tumours9. This aligns with in vitro cytotoxic studies of paclitaxel in eight human tumour cell lines which show that paclitaxel's IC50 ranges between 2.5 and 7.5 nM41."

Significance

In this manuscript, Hale and colleagues describe the effect of paclitaxel on nucleus deformation and cell survival. They showed that 5nM of paclitaxel induces nucleus fragmentation, cytoskeleton reorganisation, reduced expression of LaminA/C and SUN2, and reduced cell growth and viability. They also showed that these effects could be at least partly compensated by the over-expression of lamin A/C. As fairly acknowledged by the authors, the induction of nuclear deformation in paclitaxel-treated cells, and the increased sensitivity to paclitaxel of cells expressing low level of lamin A/C are not novel (reference #14). Here the authors provided more details on the cytoskeleton changes and nuclear membrane deformation upon paclitaxel treatment. The effect of lamin A/C over and down expression on cell growth and survival are not fully convincing, as further discussed below. The most novel part is the observation that paclitaxel can induce the down-regulation of the expression of lamin A/C and that this effect is mediated by SUN2.

We appreciate the reviewer's summary and thank them for their time. We believe our comprehensive revisions have addressed all comments, strengthening the manuscript and making it more robust and compelling.

Reviewer #2 Evidence, reproducibility and clarity This study investigates the effects of the chemotherapeutic drug paclitaxel on nuclear-cytoskeletal coupling during interphase, claiming a novel mechanism for its anti-cancer activity. The study uses hTERT-immortalized human fibroblasts. After paclitaxel exposure, a suite of state- of-the-art imaging modalities visualizes changes in the cytoskeleton and nuclear architecture. These include STORM imaging and a large number of FIB-SEM tomograms.

We thank the reviewer for the summary and for highlighting our efforts in using the latest imaging technical advances.

Major comments:

The authors make a major claim that in addition to the somewhat well-described mechanism of paclitaxel on mitosis, they have discovered 'an alternative, poorly characterised mechanism in interphase'.

However, none of the data proves that the effects shown are independent of mitosis. To the contrary, measurements are presented 48 hours after paclitaxel treatment starts, after which it can be assumed that 100% of cells have completed at least one mitotic event. The appearance of micronuclei evidences this, as discussed by the authors shortly. It looks like most of the results shown are based on botched mitosis or, more specifically, errors on nuclear assembly upon exit from mitosis rather than a specific effect of paclitaxel on interphase. The readouts the authors show just happen to be measurements while the cells are in interphase.

Alternative hypotheses are missing throughout the manuscript, and so are critical controls and interpretations.

We thank the reviewer for highlighting the lack of clarity in our wording. We have revised the title, abstract and relevant sections of the introduction and discussion to clarify our message that the effects of paclitaxel on the nucleus arise from a combination of aberrant nuclear-cytoskeletal coupling during interphase and multimicronucleation following exit from defective mitosis. We have also included additional data where we used slow-dividing, serum-starved cells (under these conditions, the majority of cells do not undergo mitosis during the 16 h incubation in paclitaxel [Supplementary Figure 2D]). Our new data show that even in these cells there is a clear effect of paclitaxel on nuclear solidity, and Lamin A/C and SUN2 protein levels, further supporting our hypothesis that these phenotypes can occur independently of cell division (Figure 2C; Figure 3H-J). Furthermore, we performed additional experiments where we used overexpression of GFP-Tau as an alternative method of stabilising microtubules in interphase and observed similar aberrations to nuclear solidity and Lamin A/C localisation. As GFP-Tau overexpression did not lead to micronucleation or appear to affect mitosis, these data support the hypothesis that nuclear aberrations occur due to microtubule bundling in interphase (Figure 4). We discuss these experiments in more detail below. Finally, we have reworded the introduction to better introduce alternative hypotheses and mechanisms for paclitaxel's activity.

The authors claim that 'Previously, the anti-cancer activity of paclitaxel was thought to rely mostly on the activation of the mitotic checkpoint through disruption of microtubule dynamics, ultimately resulting in apoptosis.' The authors may have overlooked much of the existing literature on the topic, including many recent manuscripts from Xiang-Xi Xu's and another lab.

We would like to note that the paper from Xiang-Xi Xu's lab (Smith et al, 2021) was cited in our original manuscript (reference 14 in both the original and revised manuscripts). We have now also included additional review articles from the Xiang-Xi Xu lab (PMID:36368286 20 and PMID: 35048083 21). Furthermore, we have clarified the wording in both the introduction and discussion to better reflect the current understanding of paclitaxel's mechanism and alternative hypotheses.

The data, e.g. in Figure 1, does not hold up to the first alternative hypothesis, e.g. that paclitaxel stabilizes microtubules and that excessive mechanical bundling of microtubules induces major changes to cell shape and mechanical stress on the nucleus. Even the simplest controls for this effect (the application of an alternative MT stabilizing drug or the overexpression of an MT stabilizer, e.g., tau).

We thank the reviewer for suggesting this control experiment using the microtubule stabiliser Tau. We have now included these experiments in the revised version of the manuscript (Figure 4). The overexpression of GFP-Tau supports our hypothesis that cytoskeletal reorganisation in paclitaxel exerts mechanical stress on the nucleus during interphase, resulting in nuclear deformation and aberrations to the nuclear lamina. In particular, GFP-Tau overexpression resulted in large microtubule bundles throughout the cytoplasm (Figure 4A). Notably, in cells where these bundles extensively contacted the nucleus, we observed a significant decrease in nuclear solidity (Figure 4B) accompanied by changes in nuclear lamina organisation, including a patchy lamina phenotype, similar to that induced by paclitaxel (Figure 4C).

The focus on nuclear lamina seems somewhat arbitrary and adjacent to previously published work by other groups. What would happen if the authors stained for focal adhesion markers? There would probably be a major change in number and distribution. Would the authors conclude that paclitaxel exerts a specific effect on focal adhesions? Or would the conclusion be that microtubule stabilization and the following mechanical disruption induce pleiotropic effects in cells? Which effects are significant for paclitaxel function on cancer cells?

We thank the reviewer for raising important points regarding the specificity of paclitaxel's effects. We agree that microtubule stabilisation can induce myriad cellular changes, including alterations to focal adhesions and other cytoskeletal components. Our focus on Lamin A/C and nuclear morphology is grounded both in the established clinical relevance of nuclear mechanics in cancer and builds on mechanistic work from other groups.

Lamin A/C expression is commonly altered in cancer, and nuclear morphology is frequently used in cancer diagnosis35. Lamin A/C also plays a crucial role in regulating nuclear mechanics32 and, importantly, determines cell sensitivity to paclitaxel14. However, the mechanism by which Lamin A/C determines sensitivity of cancer cells to paclitaxel is unclear.

Our data are consistent with Lamin A/C being a determinant of paclitaxel survival sensitivity. We also provide evidence that paclitaxel itself reduces Lamin A/C protein levels and disrupts its organisation at the nuclear envelope. We directly link these effects to microtubule bundling around the nucleus and degradation of force-sensing LINC component SUN2, highlighting the importance of nuclear architecture and mechanics to overall cellular function. Furthermore, we show that recovery from paclitaxel treatment depends on Lamin A/C expression levels. This has clinical relevance, as unlike cancer cells, healthy tissue with non-aberrant lamina would be able to selectively recover from paclitaxel treatment.

Minor comments:

While I understand the difficulty of the experiments and the effort the authors have put into producing FIB-SEM tomograms, I am not sure they are helping their study or adding anything beyond the light microscopy images. Some of the images may even be in the way, such as supplementary Figure 6, which lacks in quality, controls, and interpretation. Do I see a lot of mitochondria in that slice?

We agree with the reviewer that Supplementary Figure 6 does not add significant value to the manuscript and thank the reviewer for pointing this out. We have removed it from the manuscript accordingly.

I may have overlooked it, but has the number of cells from which lamellae have been produced been stated?

We thank the reviewer for pointing out the missing information. For our cryo-ET experiments, we collected data from 9 lamellae from paclitaxel-treated cells and 6 lamellae from control cells, with each lamella derived from a single cell. This information has now been added to the figure legend (Figure 2F).

Significance

The significance of studying the effect of paclitaxel, the most successful chemotherapy drug, should be broad and of interest to basic researchers and clinicians.

As outlined above, I believe that major concerns about the design and interpretation of the study hamper its significance and advancements.

We appreciate the reviewer's concerns and have performed major revisions to strengthen the significance of our study. Specifically, we conducted two key sets of experiments to validate our original conclusions: serum starvation to control for the effects of cell division, and overexpression of the microtubule stabiliser Tau to demonstrate that paclitaxel can affect the nucleus via its microtubule bundling activity in interphase.

By elucidating the mechanistic link between microtubule stabilisation and nuclear-cytoskeletal coupling, our findings contribute to our understanding of paclitaxel's multifaceted actions in cancer cells.

My areas of expertise could be broadly defined as Cell Biology, Cytoskeleton, Microtubules, and Structural Biology.

Reviewer #3 Evidence, reproducibility and clarity The manuscript presents interesting new ideas for the mechanism of an old drug, taxol, which has been studied for the last 40 years.

We thank the reviewer for the positive feedback.

Although similar ideas are published, which may be suitable to be cited? • Paclitaxel resistance related to nuclear envelope structural sturdiness. Smith ER, Wang JQ, Yang DH, Xu XX. Drug Resist Updat. 2022 Dec;65:100881. doi: 10.1016/j.drup.2022.100881. Epub 2022 Oct 15. PMID: 36368286 Review. • Breaking malignant nuclei as a non-mitotic mechanism of taxol/paclitaxel. Smith ER, Xu XX. J Cancer Biol. 2021;2(4):86-93. doi: 10.46439/cancerbiology.2.031. PMID: 35048083 Free PMC article.

We thank the reviewer for bringing to our attention these important review articles. In our initial manuscript, we only cited the original paper (14, also reference 14 in the original manuscript). We have now included citations to the suggested publications (20,21).

We would also like to emphasise how our manuscript distinguishes itself from the work of Smith et al.14,20,21:

• Cell-type focus: In their study 14, Smith et al. examined the effect of paclitaxel on malignant ovarian cancer cells and proposed that paclitaxel's effects on the nucleus are limited to cancer cells. However, our data extends these findings by demonstrating paclitaxel's effects in both cancerous and non-cancerous backgrounds.

• Cytoskeletal reorganisation: Smith et al. show reorganisation of microtubules in paclitaxel-treated cells14. Our data show re-organisation of other cytoskeletal components, including F-actin and vimentin.

• Multimicronucleation: Smith et al. propose that paclitaxel-induced multimicronucleation occurs independently of cell division14. Although we observe progressive nuclear abnormalities during interphase over the course of paclitaxel treatment, our data do not support this conclusion; we find that multimicronucleation occurs only following mitosis.

• Direct link between microtubule bundling and nuclear aberrations: We show that nuclear aberrations caused by paclitaxel during interphase (distinct from multimicronucleation) are directly linked to microtubule bundling around the nucleus, suggesting they result from mechanical disruption and altered force propagation.

• Lamin A/C regulation: Consistent with Smith et al.14, we show that Lamin A/C depletion leads to increased sensitivity to paclitaxel treatment. However, we further demonstrate that paclitaxel itself leads to reduced levels of Lamin A/C and that this effect occurs independently of mitosis and is mediated via force-sensing LINC component SUN2. Upon SUN2 knockdown, Lamin A/C levels are no longer affected by paclitaxel treatment.

• Recovery: Finally, our work reveals that cells expressing low levels of Lamin A/C recover less efficiently after paclitaxel removal. This might help explain how cancer cells could be more susceptible to paclitaxel.

Only one cell line was used in all the experiments? "Human telomerase reverse transcriptase (hTERT) immortalised human fibroblasts" ? The cells used are not very relevant to cancer cells (carcinomas) that are treated with paclitaxel. It is not clear if the observations and conclusions will be able to be generalized to cancer cells.

We thank the reviewer for this comment. Our initial study aimed to understand the effects of paclitaxel on nuclear architecture in non-aberrant backgrounds. To show that the observed effects of paclitaxel are also applicable to cancer cells, we have now repeated our main experiments using MDA-MB-231 human breast cancer cells (Supplementary Figure 1B; Supplementary Figure 3P-T). Similar to our findings in human fibroblasts, paclitaxel treatment of MDA-MB-231 led to cytoskeletal reorganisation (Supplementary Figure 1B), a decrease in nuclear solidity (Supplementary Figure 3P), aberrant (patchy) localisation of Lamin A/C (Supplementary Figure 3Q), and a reduction in Lamin A/C and SUN2 levels (Supplementary Figure 3R-T).

"Fig. 1. (B) STORM imaging of α-tubulin immunofluorescence in cells fixed after 16 h incubation in control media or 5 nM paclitaxel. Lower panels show α-tubulin clusters generated with HDBSCAN analysis. Scale bars = 10 μm." It needs explanation of what is meaning of the different color lines in the lower panels, just different filaments?

We have added further detail to the figure legend for clarification: "Lower panels show α-tubulin clusters generated with HDBSCAN analysis. Different colours distinguish individual α-tubulin clusters, representing individual microtubule filaments or filament bundles."

Generally, the figures need additional description to be clear.

We have added further clarification and detail to our figure legends.

"Figure 3 - Paclitaxel results in aberrations to the nuclear lamina." The sentence seems not to be well constructed. "Paclitaxel treatment causes ..."?

We changed this sentence to: "Figure 3 - Paclitaxel treatment results in aberrant organisation of the nuclear lamina and decreased Lamin A/C levels via SUN2."

Lamin A and C levels are different in different images (Fig. 3B, H): some Lamin A is higher, and sometime Lamin C is higher? This may possibly due to culture condition or subtle difference in sample handling?.

We thank the reviewer for pointing this out and we agree that the ratio of Lamin A to Lamin C can vary with culture conditions. To confirm that paclitaxel treatment reduces total Lamin A/C levels regardless of this ratio, we repeated the Western blot analysis in three additional biological replicates using cells in which Lamin C levels exceeded Lamin A levels. These experiments confirmed a comparable decrease in total Lamin A/C levels. Figure 3B and 3C have been updated accordingly.

Also, the effect on Lamin A/C and SUN2 levels are not significant of robust.

Decreased Lamin A/C and SUN2 levels following paclitaxel treatment were consistently seen across three or more biological repeats (Figure 3B-C), and this could be replicated in a different cell type (MDA-MB-231) (Supplementary Figure 3R-T). Furthermore, Western blotting results are consistent with the patchy Lamin A/C distribution observed using confocal and STORM following paclitaxel treatment (Figure 3A; Supplementary Figure 3A), where Lamin A/C appears to be absent from discrete areas of the lamina.

Any mechanisms are speculated for the reason for the reduction?

We have now included additional data which aims to shed light on the mechanism behind the decrease in Lamin A/C and SUN2 levels following paclitaxel treatment. We found that SUN2 is selectively degraded during paclitaxel treatment. Immunoprecipitation of SUN2 followed by Western blotting against Polyubiquitin C showed increased SUN2 ubiquitination in paclitaxel (Figure 3M and N). Furthermore, in our original manuscript, we showed that Lamina A/C levels remained unaltered during paclitaxel treatment in cells where SUN2 had been knocked down. We propose that changes in microtubule organisation affect force propagation to Lamin A/C specifically via SUN2 and that this leads to Lamina A/C removal and depletion. Future work will be needed to fully understand this mechanism.

In addition to the findings described above, we report no significant changes in mRNA levels for LMNA or SUN2 in paclitaxel (Supplementary Figure 3B and O). Phos-tag gels followed by Western blotting analysis for Lamin A/C also did not detect changes to the overall phosphorylation status of Lamin A/C due to paclitaxel treatment. This is in agreement with our initial data showing no changes to Lamin A/C Ser 404 phosphorylation levels (Supplementary Figure 3E and F). Finally, Lamin A/C immunoprecipitation experiments followed by Western blotting for Polyubiquitin C and acetyl-lysine showed no significant changes in the ubiquitination and acetylation state of Lamin A/C in paclitaxel-treated cells (Supplementary Figure 3G-I).

Also, the about 50% reduction in protein level is difficult to be convincing as an explanation of nuclear disruption.

The nuclear lamina and LINC complex proteins play a critical role in regulating nuclear integrity, stiffness and mechanical responsiveness to external forces28,31-33,54,75, as well as in maintaining the nuclear intermembrane distance69,74. In particular, SUN-domain proteins physically bridge the nuclear lamina to the cytoskeleton through interactions with Nesprins, thereby preserving the perinuclear space distance30,69,74. Mutations in Lamins have been shown to disrupt chromatin organization, alter gene expression, and compromise nuclear structural integrity, and experiments with LMNA knockout cells reveal that nuclear mechanical fragility is closely coupled to nuclear deformation47. Furthermore, nuclear-cytoskeletal coupling is essential during processes such as cell migration, where cells undergo stretching and compression of the nucleus; weakening or loss of the lamina in such cases compromises cell movement47,73. In our work, we show that alterations to nuclear Lamin A/C and SUN2 by paclitaxel treatment coincide with nuclear deformations (Figure 2A-D, F, G; Figure 3A-D, F, G; Supplementary Figure 3A, P-T) and that these deformations are reversible following paclitaxel removal (Supplementary Figure 4B-D). Our experiments also demonstrate that Lamin A/C expression levels significantly influence cell growth, cell viability, and cell recovery in paclitaxel (Figure 5). Therefore, drawing on current literature and our results, we propose that, during interphase, paclitaxel induces severe nuclear aberrations through the combined effects of: i) increased cytoskeletal forces on the NE caused by microtubule bundling; ii) loss of ~50% Lamin A/C and SUN2; iii) reorganisation of nucleo-cytoskeletal components.

Significance

The manuscript presents interesting new ideas for the mechanism of an old drug, taxol, which has been studied for the last 40 years.

The data may be improved to provide stronger support.

Additional cell lines (of cancer or epithelial origin) may be repeated to confirm the generality of the observation and conclusions.?

We thank the reviewer for the feedback and valuable suggestions. In response, we have included experiments using human breast cancer cell line MDA-MB-231 to further corroborate our findings and interpretations. We believe these additions have improved the clarity, robustness and impact of our manuscript, and we are grateful for the reviewer's contributions to its improvement.

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Referee #3

Evidence, reproducibility and clarity

The manuscript presents interesting new ideas for the mechanism of an old drug, taxol, which has been studied for the last 40 years. Although similar ideas are published, which may be suitable to be cited?

• Paclitaxel resistance related to nuclear envelope structural sturdiness. Smith ER, Wang JQ, Yang DH, Xu XX. Drug Resist Updat. 2022 Dec;65:100881. doi: 10.1016/j.drup.2022.100881. Epub 2022 Oct 15. PMID: 36368286 Review.
• Breaking malignant nuclei as a non-mitotic mechanism of taxol/paclitaxel. Smith ER, Xu XX. J Cancer Biol. 2021;2(4):86-93. doi: 10.46439/cancerbiology.2.031. PMID: 35048083 Free PMC article.

Only one cell line was used in all the experiments? "Human telomerase reverse transcriptase (hTERT) immortalised human fibroblasts" ? The cells used are not very relevant to cancer cells (carcinomas) that are treated with paclitaxel. It is not clear if the observations and conclusions will be able to be generalized to cancer cells.

"Fig. 1. (B) STORM imaging of α-tubulin immunofluorescence in cells fixed after 16 h incubation in control media or 5 nM paclitaxel. Lower panels show α-tubulin clusters generated with HDBSCAN analysis. Scale bars = 10 μm." It needs explanation of what is meaning of the different color lines in the lower panels, just different filaments?

Generally, the figures need additional description to be clear.

"Figure 3 - Paclitaxel results in aberrations to the nuclear lamina." The sentence seems not to be well constructed. "Paclitaxel treatment causes ..."?

Lamin A and C levels are different in different images (Fig. 3B, H): some Lamin A is higher, and sometime Lamin C is higher? This may possibly due to culture condition or subtle difference in sample handling?. Also, the effect on Lamin A/C and SUN2 levels are not significant of robust. Any mechanisms are speculated for the reason for the reduction? Also, the about 50% reduction in protein level is difficult to be convincing as an explanation of nuclear disruption.

Significance

The manuscript presents interesting new ideas for the mechanism of an old drug, taxol, which has been studied for the last 40 years.

The data may be improved to provide stronger support.

Additional cell lines (of cancer or epithelial origin) may be repeated to confirm the generality of the observation and conclusions.?

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Referee #2

Evidence, reproducibility and clarity

This study investigates the effects of the chemotherapeutic drug paclitaxel on nuclear-cytoskeletal coupling during interphase, claiming a novel mechanism for its anti-cancer activity. The study uses hTERT-immortalized human fibroblasts. After paclitaxel exposure, a suite of state-of-the-art imaging modalities visualizes changes in the cytoskeleton and nuclear architecture. These include STORM imaging and a large number of FIB-SEM tomograms.

Major comments:

The authors make a major claim that in addition to the somewhat well-described mechanism of paclitaxel on mitosis, they have discovered 'an alternative, poorly characterised mechanism in interphase'.

However, none of the data proves that the effects shown are independent of mitosis. To the contrary, measurements are presented 48 hours after paclitaxel treatment starts, after which it can be assumed that 100% of cells have completed at least one mitotic event. The appearance of micronuclei evidences this, as discussed by the authors shortly. It looks like most of the results shown are based on botched mitosis or, more specifically, errors on nuclear assembly upon exit from mitosis rather than a specific effect of paclitaxel on interphase. The readouts the authors show just happen to be measurements while the cells are in interphase.

Alternative hypotheses are missing throughout the manuscript, and so are critical controls and interpretations.

The authors claim that 'Previously, the anti-cancer activity of paclitaxel was thought to rely mostly on the activation of the mitotic checkpoint through disruption of microtubule dynamics, ultimately resulting in apoptosis.' The authors may have overlooked much of the existing literature on the topic, including many recent manuscripts from Xiang-Xi Xu's and another lab.

The data, e.g. in Figure 1, does not hold up to the first alternative hypothesis, e.g. that paclitaxel stabilizes microtubules and that excessive mechanical bundling of microtubules induces major changes to cell shape and mechanical stress on the nucleus. Even the simplest controls for this effect (the application of an alternative MT stabilizing drug or the overexpression of an MT stabilizer, e.g., tau).

The focus on nuclear lamina seems somewhat arbitrary and adjacent to previously published work by other groups. What would happen if the authors stained for focal adhesion markers? There would probably be a major change in number and distribution. Would the authors conclude that paclitaxel exerts a specific effect on focal adhesions? Or would the conclusion be that microtubule stabilization and the following mechanical disruption induce pleiotropic effects in cells? Which effects are significant for paclitaxel function on cancer cells?

Minor comments:

While I understand the difficulty of the experiments and the effort the authors have put into producing FIB-SEM tomograms, I am not sure they are helping their study or adding anything beyond the light microscopy images. Some of the images may even be in the way, such as supplementary Figure 6, which lacks in quality, controls, and interpretation. Do I see a lot of mitochondria in that slice?

I may have overlooked it, but has the number of cells from which lamellae have been produced been stated?

Significance

The significance of studying the effect of paclitaxel, the most successful chemotherapy drug, should be broad and of interest to basic researchers and clinicians.

As outlined above, I believe that major concerns about the design and interpretation of the study hamper its significance and advancements.

My areas of expertise could be broadly defined as Cell Biology, Cytoskeleton, Microtubules, and Structural Biology.

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Referee #1

Evidence, reproducibility and clarity

This description of the down-regulation of the expression of lamin A/C upon treatment with paclitaxel and its sensitivity to SUN2 is quite interesting but still somehow preliminary. It is unclear whether this effect involves the regulation of gene expression, or of the stability of the proteins. How SUN2 mediates this effect is still unknown. The roles of free tubulins and polymerized microtubules, and thus the potential role of paclitaxel, need to be uncovered.

The doses of paclitaxel at which occur the effects described in the paper are not fully consistent with all the conclusions. Most experiments have been done at 5 nM. However, at this dose the effect of lamin A/C over or down expression on the growth (differences in the slopes of the curves in Figure 4A) are not fully convincing and not fully consistent with the clear effect on viability as well (in addition, duration of treatments before assessing vialbility are not specified). At 1 nM, cell growth is reduced and the rescuing effect of lamin over-expression is much more clear (Fig 4A), and the nucleus deformation clear (Fig 2A) but this dose has no effect on lamin A/C expression (Fig 3C), which questions how lamins impact nucleus shape and cell survival. Cytoskeleton reorganisation in these conditions is not described although it could clarify the respective role of force production (suggested in figure 1) and nuclei resistance (shown in figure 2) in paclitaxel sensitivity.

Finally, although the absence of role of mitotic arrest is clear from the data, the defective reorganisation of the nucleus after mitosis still suggest that the effect of paclitaxel is not independent of mitosis.

minor: a more thorough introduction of known data about dose response of cells in culture and in vivo would help understanding the range of concentrations used in this study.

Significance

In this manuscript, Hale and colleagues describe the effect of paclitaxel on nucleus deformation and cell survival. They showed that 5nM of paclitaxel induces nucleus fragmentation, cytoskeleton reorganisation, reduced expression of LaminA/C and SUN2, and reduced cell growth and viability. They also showed that these effects could be at least partly compensated by the over-expression of lamin A/C. As fairly acknowledged by the authors, the induction of nuclear deformation in paclitaxel-treated cells, and the increased sensitivity to paclitaxel of cells expressing low level of lamin A/C are not novel (reference #14). Here the authors provided more details on the cytoskeleton changes and nuclear membrane deformation upon paclitaxel treatment. The effect of lamin A/C over and down expression on cell growth and survival are not fully convincing, as further discussed below. The most novel part is the observation that paclitaxel can induce the down-regulation of the expression of lamin A/C and that this effect is mediated by SUN2.

eLife Assessment

This manuscript uses modeling approaches to provide mechanistic insight into the structural and dynamic properties of enhancer-promoter interactions in Drosophila. Given the interest in this field, this is a timely approach, and the results give useful insights by providing predictions about the processivity of cohesin loop extrusion in Drosophila and concluding that the compartmental interaction strength is poised near criticality in the coil-globule phase space. The evidence provided to support some of the conclusions is, however, incomplete and would be strengthened by better considering some of the caveats in the data used to constrain the models, such as the use of "homie" genetic elements in the dynamic data. There is insufficient evidence provided for the dynamics being criticality-driven, and in addition, consideration of alternative models would further strengthen the conclusions of the manuscript.

Reviewer #1 (Public review):

Summary:

This computational study investigates the physical mechanisms underlying enhancer-promoter (E-P) interactions across genomic distances in Drosophila chromosomes, motivated by a previously published study that revealed unexpectedly frequent long-range contacts challenging classical polymer models. The authors performed coarse-grained polymer simulations testing three chromatin organization models: ideal polymers, loop extrusion, and compartmental segregation, comparing their predictions to experimental Hi-C contact maps, mean E-P distances, and two-locus mean-squared displacement dynamics. They found that compartmental segregation best captured both the structural and dynamic features observed experimentally, while neither ideal chains nor loop extrusion alone could reproduce all experimental observables. The combination of compartmental segregation with loop extrusion further improved agreement with experimental data, suggesting these mechanisms might be involved in Drosophila chromatin organization.

Strengths:

The paper has two primary strengths:

(1) The simulations are based on biologically interpretable mechanisms (compartmentalization and loop extrusion), which may facilitate making specific experimentally testable predictions.

(2) The work uses a systematic approach to increase model complexity by directly fitting to data, first establishing that simple models fail to capture the data until arriving at a more complex model that does capture the data.

Weaknesses:

I have two major concerns (detailed below) and multiple minor concerns.

Major concerns:

(1) While the upside of the mechanistic simulations is that they are interpretable, the downside is that specific choices for the considered mechanism were made, and conclusions drawn from it are necessarily biased by the initial choices. In this paper, only two mechanisms were considered: loop extrusion and compartmentalization. Yet, it is not clear why these are the most likely underlying mechanisms that might determine the chromosome dynamics. Indeed, previous work (not cited in this paper) showed that Drosophila chromosome structure is not determined by loop extrusion: https://elifesciences.org/articles/94070.

This should be acknowledged, and the main reasons for choosing these particular mechanisms should be laid out. The conclusions of the paper must then necessarily always be seen under the caveat that only these two mechanisms were considered.

(2) Even within the framework of the approach, insufficient evidence is given to support the title of the paper "Criticality-driven enhancer-promoter dynamics in Drosophila chromosomes" for two reasons:

(a) The fact that the best-fit parameters are near a coil-globule transition does not mean that the resulting dynamics are criticality-driven. To claim criticality, one would usually expect much more direct evidence, such as diverging correlation lengths. Furthermore, it would need to be shown that the key features of the dynamics (which should be defined, presumably the static and dynamic exponents) indeed depend on the parameters being at this transition. i.e., when tuning the simulations away from this parameter point, does the behaviour disappear? Only in this case can it be claimed that the behaviour is driven by this phenomenon.

(b) The results section actually contains no mention of the coil-globule transition, and it is not clear in what way the parameters are close to this transition.

Thus, three things are necessary:

(i) How the parameters are close to the transition needs to be explained in detail.

(ii) The divergence of observed dynamics whenever the parameters are tuned away from the transition needs to be demonstrated.

(iii) Even if 1 and 2 are fulfilled, a more careful title should be chosen, such as "Polymer simulations near the coil-globule transition are consistent with enhancer-promoter dynamics in Drosophila chromosomes."

Many of the results in the figures and results section are rather repetitive and could be compressed. The main result of Figure 1 - that the data are not described by an ideal chain - was already fully shown and established in the original paper from which the data are taken. Figure 2 is a negative result with near-identical panels to Figure 3. Figure 4B is hard to interpret.

The paper makes no concrete suggestions for new experiments to test the hypotheses formulated. Since the paper can only claim that the simulations are consistent with the data, it would significantly strengthen the paper if testable predictions could be made.

Reviewer #2 (Public review):

Summary:

In this work, Ganesh and colleagues use experimental data from Hi-C and from live-cell imaging to evaluate different polymer models of 3D genome organization in Drosophila based on both structural and dynamic properties. The authors consider several leading hypotheses, which are examined sequentially in increasing level of complexity - from the minimal Rouse polymer, to a model combining sequence-specific compartmentalization and loop-extrusion without extrusion blockers. They conclude that the combination of both compartmentalization and loop-extrusion gives the best agreement with the data. Their analysis also leads to concrete predictions about the processivity of cohesin loop extrusion in Drosophila, and a conclusion that the compartmental interaction strength is poised near criticality in the coil-globule phase space.

Strengths:

There is considerable interest in the field in understanding the mechanisms responsible for the 3D spatial organization genome and the dynamic movement of the genome, which has major implications for our understanding of long-range transcriptional regulation and other genome behaviors. The live-cell experimental work on which this study draws highlights the limitations of existing models to explain even the dynamic behaviors observed in the data, further exciting interest in further exploration. Therefore, this paper seeks to address an important gap in the field. The work is written in a well-organized, well-illustrated fashion. The text and figures are nicely integrated, easy to read, and explain challenging concepts with elegance and brevity in a manner that will be accessible to a broad audience.

Weaknesses:

The validity and utility of these conclusions are, in my view, substantially undermined by what appears to be unappreciated peculiarities of the live-cell data set that was used to constrain the model. The live-cell data comes from embryos were edited in a way that intentionally substantively changed both the 3D genome structure and dynamics specifically at the loci which are imaged, a case which is not at all explained by any of the models suggested nor acknowledged in the current work, nor compatible with the Hi-C data that simultaneously used to explain these models. As these ignored synthetic alterations have been previously shown to be determinative of transcriptional activity, the relevance of the author's work to transcriptional control (a prime motivation in the introduction) is unclear.

The agreement in 3D organization, as represented in chromosome-scale contact frequency heatmaps, is substantially less impressive than the agreement seen in prior work with similar models. This discrepancy appears to be due in part to the unappreciated effects of the mentioned in the previous limitation, as well as inappropriate choices in metrics used to evaluate agreement. It is also not particularly surprising that combining more models, with more free parameters, results in an improvement in the quality of fit.

Some major results, including both theoretical works and experimental ones, are ignored, despite their relevance to the stated objective of the work. The current manuscript and analysis could be improved substantially by a consideration of these works.

I describe these issues in more detail below.

Major issues:

(1) The genetic element "homie" is present in a subset of the data: The experimental data used in this analysis come from different fly lines, half of which have been edited explicitly to alter genome structure and consequent transcriptional behavior, yet the authors are trying to fit with a common model - a problem which substantially undermines the utility of the analysis.

Specifically, the authors evaluate the various models/simulations by comparing them to Hi-C from wildtype Drosophila embryos on the chromosome scale and 3D distances and dynamics from live cell imaging in genetically edited embryos, to a series of models in turn. The exercise fatally overlooks a critical fact, (admittedly not easily noticed in the work from Bruckner et al), that the fly embryos used for nearly all their analyses contain not only fluorescent labels, but also contain two copies of a powerful genetic sequence, "homie", known for its ability to dramatically change the 3D organization and dynamics of the genome. Whether or not the fluorescent labels themselves used in the study further alter structure and dynamics is not entirely clear (and will require further work beyond the scope of either study), but at least these fluorescent labels aren't known to dramatically affect 3D structure and dynamics the way homie is. The critical problem is that adding or removing the "homie", as shown in a collection of prior works I describe below in more detail, dramatically affects structure, dynamics, and gene expression. Whether or not the genome contains two distal cis-linked copies of homie fundamentally changes genome structure and dynamics, so to use one dataset which has this edit (the live-cell data) and one dataset which lacks it (the Hi-C data) is, in some sense, to guarantee failure of any model to match all the data.

If the authors had chosen instead to focus exclusively on the 'no homie' genetic lines in the Brukner data, they would have a much smaller dataset (just 2 distances), which would not cover all the length scales of interest, but it would at least be a dataset not known to be contradictory to the Hi-C. The two 'no homie' lines make much more plausible candidates for the sort of generalizable polymer dynamics these authors seek to explain, as will hopefully be made more clear by a brief review of what is known about homie. I next describe the published data that support these conclusions about how homie affects 3D genome spatial organization and dynamics:

What is "homie" and how does it affect 3D genome distances, dynamics, and gene expression?

The genetic element "homie" was named by James Jaynes' lab ( Fujioka...Jaynes 2009) in reference to its remarkable "homing" ability - a fascinating and still poorly understood biological observation that some genetic sequences from Drosophila, when cloned on plasmids and reintegrated into the genome with p-elements, had a remarkable propensity to re-integrate near their endogenous sequence, (Hama et al., 1990; Kassis, 2002; Taillebourg and Dura, 1999; Bender and Hudson, 2000; Fujioka...Jaynes 2009). By contrast, most genetic elements tend to incorporate at random across the genome in such assays (with some bias for active chromatin).

The Jaynes lab subsequently showed that flies carrying two copies of homie, one integrated in cis, ~140 kb distal from the endogenous element, formed preferential cis contacts with one another. Indeed, if a promoter and reporter gene were included at this distal integration site, the reporter gene would activate gene expression in the pattern normally seen by the gene, even-skipped. The endogenous copy of homie marks one border of ~16 kb mini-TAD which contains the even-skipped gene, (eve), and its developmental enhancers, so this functional interaction provides further evidence of physical proximity (as was also shown by 3C by Jaynes (Fujioka..., Schedl, Jaynes 2016), and later with elegant live imaging, by Jaynes and Gregor (Chen 2018)).

Critically, if either copy of homie is deleted or substantially mutated, the 3D proximity is lost (Fujioka 2016, Chen 2018, Bruckner 2023), and the expression of the transgene is dramatically reduced (at 58 kb) or lost. Given the author's motivation of understanding "E-P" interactions, the fact that the increased 3D proximity provided by homie is as essential for transcription as the promoter itself at the ~150 kb distance, underscores that these are not negligible changes.

These effects can be seen by plotting the data from Bruckner 2023, which includes data from labels with separations of 58 kb and ~150 kb "no homie" as well as homie. Unfortunately, the authors don't plot this data in the manuscript in the comparison of 3D distances, though the two-point MSD can be seen in Figure S13C, and laudably, the data is made public in a well-annotated repository on Zenodo, noted in the study. Note that the distance data in Figure S13 were filtered to exclude the transcriptionally off state, and are thus not the quantity the current authors are interested in. If they plot the published data for no homie, they will see the clear effect on the average 3D distance, R(s), and a somewhat stronger effect on the contact frequency P(s), which causes significant deviation from the trend-line followed by the homie-containing data.

(2) The agreement between the "best performing" simulations for all models and the Hi-C data is not on par with prior studies using similar approaches, apparently due to some erroneous choices in how the optimization is carried out:

Hi-C-comparison

The 'best fit' simulation Hi-C looks strikingly different from the biological data in all comparisons, with clearly lower agreement than other authors have shown using highly similar methods (e.g., Shi and Thirumalai 2023; Di Pierro et al. 2017; Nuebler et al. 2018; Esposito et al. 2022; Conte et al. 2022), among many others. I believe this results from a few issues with how the current authors select and evaluate the data in their work:

(a) Most works have used Pearson's correlation rather than Spearman's correlation when comparing simulation and Hi-C contact frequencies. Pearson's correlation is more appropriate when we expect the values to be linearly related, which they should be in this case, as they are constructed indeed to be measuring the same thing (contact frequency), just derived from two different methods. Spearman's correlation would have been justifiable for comparing how transcription output correlates with contact frequency. This may fix the bafflingly low correlations reported at lower adhesion values in Figure S2C.

(b) Choice of adhesion strengths - The Hi-C map comparison in Figure 3 strongly suggests that a much more striking visual agreement would have been achieved if much weaker (but still non-zero) homotypic monomer affinity had been selected. In the authors' simulation, the monomer state (A/B identity) strongly dominates polymer position, resulting in the visual appearance of an almost black-and-white checkerboard. The data, meanwhile, look like a weak checkerboard superimposed on the polymer.

(c) A further confounding problem is the aforementioned issue that the Hi-C data don't come from the edited cell lines, and that the interaction of the two Homie sites is vastly stronger than the compartment interactions of this region of the genome.

(3) Some important concepts from the field are ignored:

The crumpled/fractal globule model is widely discussed in the literature (including the work containing the data used in this study) - its exclusion from this analysis thus appears as a substantial gap/oversight:

A natural alternative to the much-discussed Rouse polymer model is the "crumpled polymer" (Grosberg et al. 1988; Grosberg 2016; Halverson et al. 2011; Halverson et al. 2011), also known as the "fractal globule" (Lieberman-Aiden et al. 2009; Mirny 2011; Dekker and Mirny 2016; Boettiger et al. 2016), much discussed for the way it captures the ⅓ scaling of R(s), found for much of the genome (or, equivalently, the -1 exponent of the probability of contact as a function of genome separation, P(s)). Given the 1/3rd scaling in the data, and the fact that the original authors highlighted the crumpled model in addition to the Rouse model, it seems that this comparison would be instructive and the lack of discussion an oversight. Moreover, while prior works (e.g., Buckner, Gregor, 2023) used some traditional simplifying assumptions to estimate the MSD and relaxation time scaling of this model, I believe a more rigorous analysis with explicit simulations (as in Figure 1 for the Rouse model) would be instructive for the crumpled polymer simulations. Note the crumpled globule is not necessarily the same as the globule in the coil-globule transition discussed here - it requires some assumptions about non-entanglement to stay trapped in the meta-stable state which has the 1/3rd R(s) scaling that is indicative of this model, and not the 1/2 exhibited by equilibrium globules (for s<< length of the polymer) and dilute polymers alike.

While the fit in Figure 2 appears to get closer to the 1/3rd exponent (B= 0.32), this appears to be a largely coincidental allusion of agreement - the simulation data in truth shows a systematic deviation, returning to the 1/2 scaling for distances from 500 kb to whole chromosomes. This feature is not very evident as the authors restrict the analysis to only the few points available in the experimental data, though had they tested intervening distances I expect they would show log-log P(s) is nonlinear (non-powerlaw) for distances less than the typical loop length up to a few fold larger than the loop length, and thereafter returns to the scaling provided by the 'base' polymer behavior. This appears to be Rouse-like in these authors' model, with R(s) going like 1/2, even though the data are closer to 1/3rd, as indeed most published simulated P(s) curves based on loop extrusion - e.g., (Fudenberg et al. 2016; Nuebler et al. 2018). In this vein, it would be instructive to the readers if the authors would include additional predictions from the simulation on the plot that lie at genomic separation distances not tested in the data, to better appreciate the predictions.

Minor issues

(1) I think it is too misleading to only describe the experimental data from Brukner as "E-P" interactions from Drosophila. It is important to note somewhere that this is not an endogenous interaction with a functional role in Drosophila - it is a synthetic interaction between enhancers in the vicinity of the eve gene and a synthetic promoter placed at a variable distance away. The uniformity is elegant - (it is the same pair of elements being studied at all distances), but also provides limited scope for generalization as suggested by the current text. Moreover, the enhancers were not directly labeled; rather, the 3D position of nascent RNA transcribed from eve was tracked with an RNA-binding protein and used as a proxy for the 3D position of the enhancers. There is not an individual enhancer at the eve locus that interacts with the transgene, but rather a collection of enhancers is distributed at different positions throughout the entire TAD, which contains eve, and must form separate loops to reach eve. Indeed, it was previously reported that differences in the local position of these enhancers, relative to eve, affect their ability to interact with the distal reporter gene and the endogenous eve gene (Chen 2018). There is also reported competition between these enhancers and the distal gene, which further complicates the analysis (especially since the state of eve and of its enhancers varies among the different cells as a function of stripe position) - see Chen 2018. All of this is ignored in the current work, despite the assertion of the application to understanding E-P interaction. A detailed discussion of these issues is not necessary, but I fear that ignoring them entirely is to invite further confusion and error.

(2) I believe this sentence is overstated, given available data: " TAD borders are characterized by transitions between epigenetic states rather than by preferentially-bound CTCF [4, 23, 24]." Indeed, this claim has been repeatedly made in the literature as cited here. However, other data clearly demonstrate a strong enrichment of CTCF at TAD borders (and at epigenetic borders, which in Drosophila have a high correspondence with TAD borders, as the authors have already appropriately noted). See, for example, Figure 4 of Sexton Cell 2012, and compare to Figure 2 of Dixon 2012. Of minor note, CTCF peaks co-occupied by the Zinc Finger TF CP190 are more likely to be TAD borders than CTCF alone. How big a species-specific difference this is remains unclear, as it appears some mammalian CTCF-marked TAD boundaries may be co-occupied by additional ZNFs. While plenty of Drosophila TAD boundaries indeed lack CTCF, many are marked by CTCF, this is enriched relative to what would be expected by chance (or relative to the alignment of other TFs, like Twist or Eve with TAD boundaries), and it has been shown that CTCF loss is sufficient to remove a subset of these, see for example Figure 5 of (Kaushal et al. 2021) (though it is possible, most will require mutation of the all the border-associated factors that collectively bind many of the borders, dCTCF, CP190, mod(mdg4) and others).

(3) This assertion is overstated given available data: "Although TAD boundaries in Drosophila are often associated with insulator proteins [20], there is no direct evidence that these elements block LEFs in vivo. Therefore, we did not impose boundary constraints in our simulations; LEFs were allowed to move freely unless stalled by collisions with other LEFs, with the possibility of crossover.". Deletion of insulator in Drosophila that lie within a common epigenetic state leads to fusion of TADs (e.g., Mateo et al., 2019 - deletion of the CTCF-marked Fub insulator, in posterior tissues where both flanks of Fub are active; Kaushal, 2021, has examples as well). Loss of CTCF causes a small number of TADs to fuse as measured by Hi-C. This is far from 'direct evidence that insulators block LEFs' - as the authors have already noted, even the idea that cohesin extrudes loops in Drosophila in the first place is indeed controversial. However, LEF activity and stalling at insulators would provide a very natural explanation of why chromatin in a shared epigenetic state should form distinct TADs, and why these TADs should fuse upon insulator deletion. Justifying the lack of stalling sites based on empirical data is thus not very convincing to this reviewer. I believe it would be more apt to simply describe this as a simplifying assumption, rather than the above phrase, which may be misleading.

Author response:

We thank the editors and the reviewers for their constructive comments, which have greatly helped us identify key areas to strengthen the manuscript. We acknowledge the validity of the major points raised, and we plan the following revisions:

Criticality

As suggested by Reviewer #1, we will carefully examine whether the dynamics we observe are indeed poised near criticality. We will perform additional analyses to assess how structural and dynamic features change when parameters are tuned away from the coil–globule transition, and we will revise the title and text to ensure that our claims are appropriately moderated.

Role of the homie element

We agree with Reviewer #2 that the presence of homie elements introduces major modifications to chromosome structure and dynamics. We initially considered that this factor might even explain the paradox described in Gregor’s work. In the first phase of our study, we carried out simulations including homie elements and found that the potential confounding effects are largely resolved if we restrict the analysis to trajectories prior to encounters between the two homie copies. We will include these simulations and expand the discussion accordingly in the revised version.

Comparison to Hi-C data

Both reviewers noted a visual discrepancy between experimental and simulated Hi-C maps. We will address this by testing alternative similarity measures (e.g., Pearson correlation, as suggested) and by exploring parameter ranges that may improve the agreement.<br /> Together, these modifications will strengthen the manuscript, clarify the scope of our conclusions, and directly address the reviewers’ central concerns.

eLife Assessment

This paper explores the role of extracellular vesicles in providing extracellular matrix signals for migration of vascular smooth muscle cells. The evidence, based on cell culture experiments and supporting imaging of human samples, is mostly convincing. The paper will be valuable for researchers investigating cell migration during vessel repair and atherogenesis.

Reviewer #1 (Public review):

In this revised submission from Kapustin et al., the authors have made significant changes to the manuscript. Namely, the authors have addressed several of the major issues with the original submission, providing a more concrete link between fibronectin and the secretion of extracellular vesicles. Additionally, the authors have moderated some of the conclusions to better suit the rigor of the experimental results and limitations of their approach. Generally, the findings convey an interesting cell autonomous pathway in which smooth muscle cells sense fibronectin, which canonically is a proinflammatory substrate with activating properties in many tissues. Fibronectin-mediated integrin signaling stimulates secretion of small extracellular vesicles containing collagen VI which is deposited into the surrounding extracellular matrix. Collagen VI itself gleaned from extracellular vesicle secretion seems to further alter smooth muscle cell morphodynamics. For this later finding, much of the mechanism behind collagen VI vesicle loading and secretion has yet to be worked out. The authors provide evidence of extracellular vesicles containing collagen VI trapped in fibronectin in atherosclerotic plaques providing a nice validation of their in vitro findings in a diseased human cohort. Some limitations do still exist in the manuscript in its current form such as the assessment of the vesicle origins, contents and their association with the actin cytoskeleton; however, the rigor and execution are much improved from the preceding version. Overall, the pathobiology underlying vascular smooth muscle remodeling in disease states is a critical area of research that warrants further exploration.

Reviewer #2 (Public review):

The findings in the current manuscript are interesting and valuable contributions to the fields of vascular biology and extracellular vesicle-related mechanisms. They suggest a potential role for smooth muscle cell-derived extracellular vesicles in presenting Type VI collagen to cells to orchestrate their migration, with proposed relevance to aberrant smooth muscle cell movements in the progression of atherosclerotic lesions. A wide range of assays are utilized to test various aspects of this working model, with the resulting data being largely solid and supporting several of the interpretations articulated by the authors. The revised manuscript has adequately addressed key weaknesses.

The authors present data suggesting a working model in which vascular smooth muscle cells (vSMCs) are stimulated by fibronectin (FN) to generate small extracellular vesicles (sEVs) that harbor Type VI Collagen (collagen VI). These collagen VI-associated sEVs are suggested to accumulate in the extracellular matrix (ECM) and influence cell migration and adhesion dynamics, potentially contributing to disease progression in atherosclerosis. Majors strengths of this manuscript include robust imaging data and the inclusion of human-derived samples in their analysis. The authors also make a reasonable attempt to provide data to support the potential existence of these mechanistic connections, though some minor questions remain regarding data interpretation. The authors largely achieved their aims of finding evidence consistent with their interpretations, and they have presented logical support for their conclusions while acknowledging important limitations and caveats to their current study. This work will likely have a sustained impact on the field of sEV biology and potential intersections with vascular biology, including their methodology e.g., imaging approaches. As biologists continue to explore the role of sEVs in physiological and pathological processes, this work raises an interesting aspect that must be considered more broadly, and that is, what is the role of sEVs that are ECM-associated and not necessarily internalized by recipient cells? Are there discrete mechanisms that govern their role in maintaining and/or disrupting normal physiological processes? This manuscript makes an attempt to address these unresolved yet critical questions.

Author response:

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

Reviewer #1 (Public Review):

Summary

In this investigation Kapustin et al. demonstrate that vascular smooth muscle cells (VSMCs) exposed to the extracellular matrix fibronectin stimulates the release of small extracellular vesicles (sEVs). The authors provide experimental evidence that stimulation of the actin cytoskeleton boosts sEV secretion and posit that sEVs harbor both fibronectin and collagen IV protein themselves which also, in turn, alter cell migration parameters. It is well established that fibronectin is associated with increased cell migration and adherence; therefore, this association with VSMCs is not novel.

The reviewer is correct that FN has been associated with migration and adherence in previous studies.  However we have extended these observations to show that the extracellular fibronectin matrix stimulates small extracellular vesicle (sEVs) secretion by modulating the actin cytoskeleton. We also showed that sEVs are trapped in the extracellular matrix and that by presenting collagen VI induce early focal adhesion formation, reduce excessive cellular spreading and guide cell invasion directionality though a 3D matrix. Hence, sEVs mediate cell-matrix cross talk and change cell behaviour in the context of fibronectin matrix. This is critically important for vasculature where regulated VSMC invasion is essential for repair with its deregulation leading to pathology.

The authors purport that sEV are largely born of filopodia origin; however, this data is not well executed and seems generally at odds with the presented data.

Our experimental data showed that CD63 MVs are associated with filopodia in fixed and live cells (Fig 2E, 2F and Video S1) and that inhibition of filopodia formation using the formin inhibitor, SMIFH2 reduced sEV secretion on FN (Fig 2B). However, we agree with the reviewer that further studies are required to connect sEV secretion to filopodia.  To address this we have provided further data analysis but also toned down our conclusions regarding this point: . Changes include:

(1) Title: Matrix-associated extracellular vesicles modulate smooth muscle cell adhesion and directionality by presenting collagen VI.

(2) Results, section title: 2. FN-induced sEV secretion is modulated by Arp2/3 and formin-dependent actin cytoskeleton remodelling

(3) Results, page 6 Line 27-44 and conclusion page 7, Ln 3 “Interestingly, CD63+ MVBs can be observed in filopodia-like structures suggesting that sEV secretion can also occur spatially via cellular protrusion-like filopodia but more studies are needed to confirm this hypothesis.”

(4) Discussion, page 12, line 19. “Curiously we observed CD63+ MVB transport toward the filopodia tips as well as inhibition of sEV-secretion with filopodia formation inhibitors suggesting that sEV secretion can be directly linked to filopodia but further studies are needed to define the contribution of this pathway to the overall sEV secretion by cells.”

Similarly, the effect of sEVs on parameters of cell migration has almost no magnitude of effect, making mechanism exploration somewhat nebulous.

VSMC are mesenchymal-type cells with a low migration rate and we agree that the changes in the motility are not of great magnitude even for the positive controls suggesting that this is a complex, multifactorial process for VSMCs. In our experiments we collected data from >5000 individual cells to measure the average speed and found that fibronectin matrix on its own increased VSMC speed from ~0.61 um/min to ~0.68 μm/min (~12% raise) which was statistically significant (Fig 5A). Addition of a sEV inhibitor caused a modest but significant decrease in cellular speed. Interestingly, addition of ECM-associated sEVs did not influence cell speed in 2D or 3D assays. However in a 3D model we observed a 22% change in cell directionality (Fig 5G) and  a 235% change in cell alignment index (FMI, Fig 5H) which we believe is very strong evidence that VSMC-derived sEVs are involved in a regulation of VSMC invasion directionality.  These data are also in agreement with sEV effects in tumour cells (Sung et al., 2015) though this previous study did not identify the factor driving the directionality and we think our Collagen VI data extends significantly these previous observations. 

Results, page 9: “Hence, ECM-associated sEVs have modest influence on VSMC speed but influence VSMC invasion directionality.”.  

Lastly, the proposed mechanism of VSMCs responding to, and depositing, ECM proteins via sEVs was not rigorously executed; again, making the conclusions challenging for the reader to interpret.

We appreciate the reviewer’s comment regarding the mechanistic aspects of VSMCs responding to and depositing ECM proteins via sEVs. In our revised manuscript, we have expanded the data demonstrating that sEVs can be retained within the extracellular matrix (see Figs 3A, 3B, S3A, S3B). Additionally, we show that collagen VI is present on the surface of sEVs, where it may modulate cell adhesion and influence the directionality of cell invasion (Fig 7E). Our results further indicate that both fibronectin (FN) and collagen VI can be recycled through multivesicular bodies (see Figs S3C, S3D, S3E–S3G). However, we acknowledge that the precise mechanisms governing the selective loading of ECM proteins onto sEVs, as well as the specific contributions of sEVs to overall ECM organization, remain to be fully elucidated and warrant further investigation. Based on our current evidence, we propose that collagen VI–loaded sEVs act primarily in a signaling capacity by modulating focal adhesion formation but are not directly involved in ECM structural remodeling.

Results, page 7: To quantify ECM-trapped sEVs we applied a modified protocol for the sequential extraction of extracellular proteins using salt buffer (0.5M NaCl) to release sEVs which are loosely-attached to ECM via ionic interactions, followed by 4M guanidine HCl buffer (GuHCl) treatment to solubilize strongly-bound sEVs (Fig S3A) [42]. We quantified total sEV and characterised the sEV tetraspanin profile in conditioned media, and the 0.5M NaCl and GuHCl fractions using ExoView. The total particle count showed that EVs are both loosely bound and strongly trapped within the ECM. sEV tetraspanin profiling showed differences between these 3 EV populations.  While there was close similarity between the conditioned media and the 0.5M NaCl fraction with high abundance of CD63+/CD81+ sEVs as well as CD63+/CD81+/CD9+ in both fractions (Fig S3A). In contrast, the GuHCl fraction was particularly enriched with CD63+ and CD63+/CD81+ sEVs with very low abundance of CD9+ EVs (Fig S3A). The abundance of CD63+/CD81+ sEVs was confirmed independently by a CD63+ bead capture assay in the media and loosely bound fractions (Fig S3B).

Results, page 7: We previously found that the serum protein prothrombin binds to the sEV surface both in the media and MVB lumen showing it is recycled in sEVs and catalyses thrombogenesis being on the sEV surface43. So we investigated whether FN can also be associated with sEV surface where it can be directly involved in sEV-cell cross-talk43.   We treated serum-deprived primary human aortic VSMCs with FN-Alexa568 and found that it was endocytosed and subsequently delivered to early and late endosomes together with fetuin A, another abundant serum protein that is a recycled sEV cargo and elevated in plaques (Figs S3C and S3D). CD63 visualisation with a different fluorophore (Alexa488) confirmed FN colocalization with CD63+ MVBs (Fig S3E). Next, we stained non-serum deprived VSMC cultured in normal growth media (RPMI supplemented with 20% FBS) with an anti-FN antibody and observed colocalization of CD63 and serum-derived FN.  Co-localisation was reducd likely due to competitive bulk protein uptake by non-deprived cells (Fig S3F). Notably, when we compared FN distribution in sparsely growing VSMCs versus confluent cells we found that FN intracellular spots, as well as colocalization with CD63, completely disappeared in the confluent state (Fig S3F and S3G). This correlated with nearly complete loss of CD63+/CD81+ sEV secretion by the confluent cells indicating that confluence abrogates intracellular FN trafficking as well as sEV secretion by VSMCs (Fig S3H). Finally, FN could be co-purified with sEVs from VSMC conditioned media (Fig S3I) and detected on the surface of sEVs by flow cytometry confirming its loading and secretion via sEVs (Fig 3C).

Results: page 10  Collagen VI was the most abundant protein in VSMC-derived sEVs (Fig 7B, Table S7) and  was previously implicated in the interaction with the proteoglycan NG2[53] and suppression of cell spreading on FN[54]. To confirm the presence of collagen VI in ECM-associated sEVs we analysed sEVs extracted from the 3D matrix using 0.5M NaCl treatment and showed that both collagen VI and FN are present (Fig 7D). Next, we analysed the distribution of collagen VI using dot-blot. Alix staining was bright only upon permeabilization of sEV indicating that it is preferentially a luminal protein (Fig 7E). On the contrary, CD63 staining was similar in both conditions showing that it is surface protein (Fig 7E). Interestingly, collagen VI staining revealed that 40% of the protein is located on the outside surface with 60% in the sEV lumen (Fig 7E). 

Discussion page 12. “In fact, we observed that an extensive secretion of sEVs effectively ceased protrusion activity; also VSMCs acquired a rounded morphology when “hovering” over the FN matrix decorated with sEVs (data not shown). Hence, it will be interesting in future studies to investigate whether sEVs can stimulate Rho activity by presenting adhesion modulators—particularly collagen VI—on their surface, thereby guiding cell directionality during invasion..”

Discussion, page 14 “In summary, cooperative activation of integrin signalling and F-actin cytoskeleton pathways results in the secretion of sEVs which associate with the ECM and play a signalling role by controling FA formation and cell-ECM crosstalk. Further studies are needed to test these mechanisms across various cell types and ECM matrices.     

Strengths

The authors provide a comprehensive battery of cytoskeletal experiments to test how fibronectin and sEVs impact both sEV release and vascular smooth muscle cell migratory activation.

We appreciate this comment reflecting our efforts to apply a range of orthogonal methods to show the role of the integrin/actin cytoskeleton in ECM-stimulated sEV secretion.

Weaknesses

Unfortunately, this article suffers from many weaknesses. First, the rigor of the experimental approach is low, which calls into question the merit of the conclusions. In this vein, there is a lack of proper controls or inclusion of experiments addressing alternative explanations for the phenotype or lack thereof.

We acknowledge this comment and agree that there was not sufficient evidence to conclude that sEV secretion occurs via filopodia despite the microscopy/inhibitory data so this claim has now been excluded from the study. However we believe that our experimental data does clearly show that FN stimulates the secretion of collagenVI-loaded sEVs which are trapped by the ECM and have the capacity to modulate VSMC adhesion and invasion directionality. To support this, we have now extended the dataset in the revised version:

(1) In addition to the use of inhibitors and live cell analysis we have added quantitative data confirming that a large proportion of CD63+ endosomes are associated with F-actin/cortactin tails and this colocalization is increased upon the inhibition of sEV secretion with 3-OMS (Fig  2D, Fig S2B).

(2) We developed a method to extract ECM-associated sEVs and quantified/characterized these using ExoView Assays further confirming significant sEV entrapment by the ECM (Figs 3B, S3A, S3B).    

(3) We extended the controls to confirm FN delivery to CD63+ endosomes and showed that FN recycling is stopped upon reaching cell confluence (Figs S3F, S3G and Fig S3H).

(4) We included more intensive characterisation of human atherosclerotic plaque morphology (H&E, Masson’s trichrome staining, Orcein, elastin fibers staining) to confirm predominant accumulation of sEV in the neointima (Figs S4A, S4B and S4C). We also excluded an endothelial origin for the  CD81+ sEVs (Fig 4G).

(5) We included individual cellular tracks to the 2D migration analysis to confirm the statistical significance and concluded that ECM-associated sEVs regulate cell invasion directionality but not the cell speed (Figs 5A and 5B).

(6) We showed surface localisation of collagen VI on sEVs confirming that it can activate signalling pathways leading to early FA formation on the FN matrix  (Figs 7D and 7E).

(7) We included alternative explanations for some of our data in the discussion.      

Reviewer #2 (Public Review):

Extracellular vesicles have recently gained significant attention across a wide variety of fields, and they have therefore been implicated in numerous physiological and pathophysiological processes. When such a discovery and an explosion of interest occur in science, there is often much excitement and hope for answers to mechanisms that have remained elusive and poorly understood. Unfortunately, there is an equal amount of hype and overstatement that may also be put forth in the name of "impact", but this temptation must be avoided so that scientists and the broader public are not misled by overreaching interpretations and statements that lack rigorous and fully convincing evidence.

Thank you for your comment and we agree that investigating sEVs is particularly challenging due to the their heterogeneity and nano-size, as well as complex biogenesis mechanisms. ECM-associated sEVs is a very new direction for the EV field but one that is particularly relevant to the vasculature where cells must invade through a thick ECM and where the accumulation of ECM-bound EVs is a unique and documented phenomenon.  To further strengthen out conclusions we have included new data to support our statements but also excluded statements re: filopodia as the origin of sEVs, that are out of scope of our study and need to be investigated further.

The study presented by Kapustin et al. is certainly intriguing and timely, and it offers an interesting working hypothesis for the fields of extracellular vesicles and vascular biology to consider. The authors do a reasonable job at detecting these small extracellular vesicles, though some aspects of data presentation are missing such as full Western blots with accompanying size markers for the viewer to more fully appreciate that data and comparisons being made (see Figures 1 and 7).

We agree with the reviewer and have now included molecular weight markers (Fig 1F, 7C, 7D, S3I, S4E) and provided all original western blot scans (uncropped and unedited) to the eLife editor. 

Much of the imaging data from cell-based experiments is strong and conducted with many cutting-edge tools and approaches. That said, the static images and the dynamic imaging fall short of being fully convincing that the small extracellular vesicles found in the neighboring extracellular matrix are indeed being deposited there via the smooth muscle cell filopodia. Many of the lines of evidence presented suggest that this could occur, but alternative hypotheses also exist that were not fully ruled out, such as the ECM-deposited vesicles were secreted more from the soma and/or the lamellipodia that are also emitted and retracted from the cells. In particular, the authors show very nice dynamic imaging (Supplementary Figure S2A and Supplemental Video S1) that is interpreted as "extracellular vesicles being released from the cell" and these are seen as "bursts" of fluorescent signal; however, none of these appear to occur in filopodia as they appear within the cell proper (a "burst" of signal vs. a more intense "streak" of signal), which would be a stronger and more consistent observation predicted by the working model proposed by the authors.

Our live and fixed cell microscope data as well as inhibitor analysis showed that sEV secretion can be associated with the filopodia. However we agree with the reviewer that the data generated using pHluoron GFP marker clearly indicate that the majority of sEVs are secreted from the cell soma toward the ECM:

To reflect this, we have added further changes:

(1) Title: Matrix-associated extracellular vesicles modulate smooth muscle cell adhesion and directionality by presenting collagen VI.

(2) Results, section title: 2. FN-induced sEV secretion is modulated by Arp2/3 and formin-dependent actin cytoskeleton remodelling

(3)  Results, page 6 Line 27-36 “Formins and the Arp2/3 complex play a crucial role in the formation of filopodia, a cellular protrusion required for sensing the extracellular environment and cell-ECM interactions36. To test whether MVBs can be delivered to filopodia, we stained VSMCs for Myosin-10 (Myo10)37. We observed no difference between total filopodia number per cell on plastic or FN matrices (n=18±8 and n=14±3, respectively) however the presence of endogenous CD63+ MVBs along the Myo10-positive filopodia were observed in both conditions (Fig 2E, arrows). Filopodia have been implicated in sEV capture and delivery to endocytosis “hot-spots”38, so next we examined the directionality of CD63+ MVB movement in filopodia by overexpressing Myo10-GFP and CD63-RFP in live VSMCs. Importantly, we observed anterograde MVB transport toward the filopodia tip (Fig 2F and Supplementary Video S2) indicative of MVB secretion”.

(4) Results, page 6, Ln 37-44 “We also attempted to visualise sEV release in filopodia using CD63-pHluorin where fluorescence is only observed upon the fusion of MVBs with the plasma membrane39. Using total internal reflection fluorescence microscopy (TIRF) we observed the typical “burst”-like appearance of sEV secretion at the cell-ECM interface in full agreement with an earlier report showing MVB recruitment to invadopodia-like structures in tumor cells18 (Fig S2B and Supplementary Video S1). Although we also observed an intense CD63-pHluorin staining along filopodia-like structures we were not able to detect typical “burst”-like events to confirm sEV secretion in filopodia. (Fig S2C and Supplemental Video S1)”.

(5) Results, page 7 Ln 3 “Interestingly, CD63+ MVBs can be observed in filopodia-like structures suggesting that sEV secretion can also occur spatially via cellular protrusion-like filopodia but more studies are needed to confirm this hypothesis.”

(6) Discussion, page 12, line 19. “Curiously we observed CD63+ MVB transport toward the filopodia tips as well as inhibition of sEV-secretion with filopodia formation inhibitors suggesting that sEV secretion can be directly linked to filopodia but further studies are needed to define the contribution of this pathway to the overall sEV secretion by cells.”

Imaging of related human samples is certainly a strength of the paper, and the authors are commended for attempting to connect the findings from their cell culture experiments to an important clinical scenario. However, the marker selected for marking extracellular vesicles is CD81, which has been described as present on the endothelium of atherosclerotic plaques with a proposed role in the recruitment of monocytes into diseased arteries (Rohlena et al. Cardiovasc Res 2009). More data should address this potentially confounding interpretation of the signals presented in images within Figure 4.

We thank the reviewer for this insightful comment that the  sEV marker CD81 can originate from endothelial cells in agreement with Rohlena et al., 2009.   To address this we investigated the spatial overlap between CD81 and the endothelial marker, CD31. We observed very strong CD81 staining in the intact endothelial cell (intima) layer and occasional CD31 positive cells in the neointima. Importantly, quantification of colocalization confirmed that 80% of CD81 in the neointima does not overlap with CD31 excluding an endothelial origin of these sEVs. (Fig 4G).  Moreover, we included complete morphological characterisation of the atherosclerotic plaques confirming that CD81 sEVs were primarily observed in the neointima where VSMCs constitute the cellular majority (Fig S4A, S4B, S4C and S4D).

On a conceptual level, the idea that the small extracellular vesicles contain Type VI Collagen, and this element of their cargo is modulating smooth muscle cell migration, is an intriguing aspect of the authors' working model. Nevertheless, the evidence supporting this potential mechanism does not quite fit together as presented. It is not entirely clear how the collagen VI within the vesicles is somehow accessed by the smooth muscle cell filopodia during migration. Are the vesicles lysed open once on the extracellular matrix? If so, what is the proposed mechanism for that to occur? If not, how are the adhesion molecules on the smooth muscle cell surface engaging the collagen VI fibers that are contained within the vesicles? This aspect of the model does not quite fit together with the proposed mechanism and may be an interesting speculative interpretation, warranting further investigation, but it should not be considered a strong conclusion with sufficient convincing data supporting this idea.

We thank the reviewer for their insightful comments regarding the mechanism by which collagen VI associated with sEVs could modulate smooth muscle cell adhesion and migration. To clarify, our new data suggest that collagen VI is predominantly present on the surface of the sEVs, as evidenced by Fig 7E. This surface localization strongly implies that collagen VI can be directly accessed by cell surface adhesion receptors, without the need for vesicle lysis or opening. While we cannot entirely rule out all alternative mechanisms, we consider vesicle rupture or lysis within the extracellular matrix to be a highly unlikely route for collagen VI exposure, given the known stability of sEVs under physiological conditions. We have added these points to clarify:

(1) Results, page 10, Ln 45 “To confirm the presence of collagen VI in ECM-associated sEVs we analysed sEVs extracted from the 3D matrix using 0.5M NaCl treatment and showed that both collagen VI and FN are present (Fig 7D). Next, we analysed the distribution of collagen VI using dot-blot. Alix staining was bright only upon permeabilization of sEV indicating that it is preferentially a luminal protein (Fig 7E). On the contrary, CD63 staining was similar in both conditions showing that it is surface protein (Fig 7E). Interestingly, collagen VI staining revealed that 40% of the protein is located on the outside surface with 60% in the sEV lumen (Fig 7E).”

(2) Discussion, page 13, Ln 2 “Hence, it will be interesting in future studies to investigate whether sEVs can stimulate Rho activity by presenting adhesion modulators—particularly collagen VI—on their surface, thereby guiding cell directionality during invasion..”

(3) Discussion, page 14, Ln 30: In addition to collagen VI the unique adhesion cluster in VSMC-derived sEVS also includes EGF-like repeat and discoidin I-like domain-containing protein (EDIL3), transforming growth factor-beta-induced protein ig-h3 (TGFBI) and the lectin galactoside-binding soluble 3 binding protein (LGALS3BP) and these proteins are also directly implicated in activation of integrin signalling and cellular invasiveness85-87. Although we found that collagen VI plays the key role in sEV-induced early formation of FAs in VSMCs, it is tempting to speculate that the high sEV efficacy in stimulating FA formation is driven by cooperative action of this unique adhesion complex on the sEVs surface and targeting this novel sEV-dependent mechanism of VSMC invasion may open-up new therapeutic opportunities to modulate atherosclerotic plaque development or even to prevent undesired VSMC motility in restenosis.    .   

(4) Abstract Figure

On a technical level, some of the statistical analysis is not readily understood from the data presented. It is very much appreciated that the authors show many of the graphs with technical and biological replicate values in addition to the means and standard deviations (though this is not clearly stated in all figure legends). However, in figures such as Figure 5, there are bars shown and indicated to be different by statistical comparison (see panel B in Figure 5). It is not clear how the values for Group 1 (no FN, no 3-OMS, no sEV) are statistically different (denoted by three asterisks but no p value provided in the legend) than Group 3 (no FN, 3-OMS added, no sEV), when their means and standard deviations appear almost identical. If this is an oversight, this needs to be corrected. If this is truly the outcome, further explanation is warranted. A higher level of transparency in such instances would certainly go a long way in helping address the current crisis of mistrust within the scientific community and at the interface with society at-large.

We thank the reviewer for their careful reading and important comments on the statistical analysis. We acknowledge that the technical and biological replicate data were not clearly reported in all figure legends and that the statistical approach for Figures 5A and 5B required clarification. In response, we have made several changes for greater transparency and rigor:

First, we have now explicitly included the numbers of biological replicates (N) and technical replicates (n) in all relevant figure legends for Figures 1–7. In addition, the number of individual cell tracks is now annotated for the migration/invasion analyses, along with the mean values for each dataset.

Upon review, we found that the original statistical analyses for Figures 5A and 5B were conducted using pooled averaged data. To address this, we have repeated the statistical tests using pooled individual cell track data, applying the Kruskal–Wallis test with Dunn’s multiple comparison correction. This more stringent approach revealed revised p-values, which are now indicated in Figures 5A and 5B.

With these corrections, we reconfirm our major findings: In the 2D model, fibronectin (FN) coating promotes VSMC velocity, while inhibition of sEV secretion with 3-OMS leads to reduced cell speed (Fig. 5A). Addition of sEVs to the ECM had no effect on VSMC speed at baseline but did rescue cell speed and distance in the presence of 3-OMS, consistent with EVs acting primarily on invasion directionality rather than speed in both 2D and 3D models (Fig. 5A, 5D). Furthermore, sEVs continue to significantly impact VSMC invasion directionality (Figs. 5G, 5H), in agreement with previous reports in tumor cells (Sung et al., 2015).

In summary, we have implemented the following revisions:

(1) Figures 5A and 5B: Individual cell track data are now shown, and statistical analyses have been repeated using the Kruskal–Wallis test with Dunn’s multiple comparisons.

(2) Figure legends and results sections: Numbers of biological and technical replicates, as well as individual data points, are now clearly stated.

Results, page 9, line 14: The text has been updated to clarify the statistical approach and major findings as described above.

We hope that these changes address the reviewer’s concerns and improve the transparency and reproducibility of our data presentation

Reviewer #1 (Recommendations For The Authors):

We are very thankful for the comprehensive review and comments which helped to improve our data.

Figure 1.<br /> The authors clearly show that FN stimulation (immobilized or cell-derived) promotes sEV secretion via canonical integrin pathways. FN is a promigratory substrate, hence its extensive use as a cell adhesion aid; thus one could assume that simply plating on FN induces a pro-migratory phenotype (later data supports this notion). Does the addition of growth factors also increase sEV release? An endogenous function of FN is siloing of various GFs during clot formation. Also, FAK and SRC networks intersect with canonical RTK signaling in terms of promoting Rac1, CDC42 and other migration mediators. The reason I believe this is important is because the data could be interpreted in two ways: 1) FN induces pro-migration signaling and then sEVs are released, or visa versa, FN induces sEV release and migration is initiated. GF supplementation in the absence of FN would clarify this relationship.

We thank the reviewer for this insightful comment regarding the possible role of growth factors (GFs) and the mechanistic relationship between FN stimulation, sEV secretion, and cell migration. We agree that FN is a well-established promoter of cell migration, and it is important to distinguish whether FN directly induces a pro-migratory phenotype or does so via sEV-mediated signaling.

Our data show that FN stimulation markedly increases VSMC motility, as reflected by enhanced cell speed (Fig. 5A), an increased number of focal adhesions (Fig. 6E), and facilitated centripetal movement of FAs (Fig. 6F). Interestingly, ECM-associated sEVs appear to play a complementary but distinct role: they do not significantly affect cell migration speed (Fig. 5A) but instead guide cell invasion directionality (Figs. 5G, 5H), reduce the number of FAs per cell (Fig. 6E), and promote early peripheral FA formation (Fig. 6F). In light of these findings, we have updated our graphical abstract to reflect the unique cross-talk mediated by sEVs between VSMCs and the ECM.

Regarding the influence of growth factors, we acknowledge that FN can bind and present different GFs, which could also contribute to changes in sEV secretion. Although our inhibition studies and integrin-blocking antibody results support a primary role for β1 integrin activation and actin assembly in triggering sEV secretion, we cannot entirely exclude the possibility that FN-bound growth factors play a role in this process. We have now incorporated this point into the discussion to address the reviewer’s suggestion.

Discussion, page 14 , Ln 7 “Although our small inhibitors and integrin modulating antibody data clearly indicate that β1 activation triggers sEV secretion via activation of actin assembly we cannot fully rule out that FN may also be modulating growth factor activity which in turn contributes to sEV secretion by VSMCs²³.  Excessive collagen and elastin matrix breakdown in atheroma has been tightly linked to acute coronary events hence it will be interesting to study the possible link between sEV secretion and plaque stability as sEV-dependent invasion is also likely to influence the necessary ECM degradation induced by invading cells⁹⁶

Figure 2.<br /> • The authors provide no evidence (or references) that SMIFH2 or CK666 halts filopodia extensions.

Thank you for this important note. We have included the corresponding references:

Results, page 5: “So next we tested the contribution of Arp2/3 and formins by using the small molecule inhibitors, CK666 and SMIFH2, respectively31, 32”.  

• Is there an increase in filopodia density when plated on FN vs plastic? Similarly, if there are more filopodia present is that associated with more sEV? Please provide evidence in this regard.

We agree that connecting the number of filopodia with the secretion level of sEVs may be an important clue if sEV secretion can be driven by FN-induced filopodia formation. However, Myosin10 staining to quantify filopodia (Fig 2E) showed no difference between VSMCs plated on plastic versus FN matrix. Therefore, we agree with the reviewer that the filopodia contribution to sEV secretion needs to be investigated further.  This idea is reflected in the following comments:

(1) Results, page 6, Ln 29 “We observed no difference between total filopodia number per cell on plastic or FN matrices (n=18±8 and n=14±3, respectively) however the presence of endogenous CD63+ MVBs along the Myo10-positive filopodia were observed in both conditions (Fig 2E, arrows).

(2) Results, page 6, Ln 37 “We also attempted to visualise sEV release in filopodia using CD63-pHluorin where fluorescence is only observed upon the fusion of MVBs with the plasma membrane39. Using total internal reflection fluorescence microscopy (TIRF) we observed the typical “burst”-like appearance of sEV secretion at the cell-ECM interface in full agreement with an earlier report showing MVB recruitment to invadopodia-like structures in tumor cells18 (Fig S2B and Supplementary Video S1). Although we also observed an intense CD63-pHluorin staining along filopodia-like structures we were not able to detect typical “burst”-like events to confirm sEV secretion in filopodia. (Fig S2C and Supplemental Video S1)..”

(3) Discussion, page 12, Ln 15 : “Focal complexes either disassemble or mature into the elongated centripetally located FAs48. In turn, these mature FAs anchor the ECM to actin stress fibres and the traction force generated by actomyosin-mediated contractility pulls the FAs rearward and the cell body forward12, 13. Here we report that β1 integrin activation triggers sEV release followed by sEV entrapment by the ECM. Curiously we observed CD63+ MVB transport toward the filopodia tips as well as inhibition of sEV-secretion with filopodia formation inhibitors suggesting that sEV secretion can be directly linked to filopodia but further studies are needed to define the contribution of this pathway to the overall sEV secretion by cells..”

As hinted above, this data could be interpreted in the light of generally inhibiting cell migration to blunt sEV shedding. Does cell confluence affect sEV release? If cells are cultured to 100% confluency this would limit filopodia formation regardless of ECM type. If sEV secretion remains elevated on FN in this culture condition it would suggest a lack of dependency on filopodia.

We thank the reviewer for this thoughtful suggestion regarding the influence of cell confluence on sEV release and filopodia formation. To directly address this hypothesis, we performed additional experiments comparing VSMCs cultured at low and high confluency. As described in the revised Results (page 7, line 39), we found that high cellular confluency reduced FN recycling, as indicated by the marked decrease in intracellular FN-positive spots and loss of colocalization with CD63 (Figs S3F, S3G). Importantly, this was accompanied by a significant reduction in CD63+/CD81+ sEV secretion by confluent cells (Fig S3H). These results suggest that VSMC confluence, which suppresses filopodia formation, nearly abolishes both intracellular FN trafficking and sEV secretion, even in the presence of FN. Thus, under our experimental conditions, sEV secretion by VSMCs appears to be closely linked to dynamic cell–matrix interactions and is dramatically reduced when these processes are limited by confluence:

(1) Results, page 7, Ln 39 : “Notably, when we compared FN distribution in sparsely growing VSMCs versus confluent cells we found that FN intracellular spots, as well as colocalization with CD63, completely disappeared in the confluent state (Fig S3F and S3G). This correlated with nearly complete loss of CD63+/CD81+ sEV secretion by the confluent cells indicating that confluence abrogates intracellular FN trafficking as well as sEV secretion by VSMCs (Fig S3H)..  

• Inhibition of branched actin polymerization has been shown to reduce both exocytic and endocytic activity. Thus, it is hard to interpret the results of Fig. 2B than anything more than a generalized effect of losing actin.

We thank the reviewer for this important point regarding the broad cellular functions of branched actin polymerization, and agree that generalized actin loss can influence both exocytic and endocytic pathways. To address this, we performed additional experiments and analyses to better define the relationship between branched actin structures and sEV-related processes in VSMCs.

As described in the revised Results (page 6), we overexpressed ARPC2-GFP (an Arp2/3 subunit) together with F-tractin-RFP in VSMCs and carried out live-cell imaging. This approach revealed that Arp2/3 and F-actin organize into lamellipodial scaffolds at the cell cortex, as expected (Fig. S2A; Supplementary Video S2). Additionally, and more unexpectedly, we observed numerous Arp2/3– and F-actin–positive dynamic spots within the VSMC cytoplasm. These structures resemble actin comet tails seen in other systems, previously implicated in endosomal propulsion (Fig. S2A, arrow; Supplementary Video S2).

Quantitative analysis confirmed that a substantial fraction of these dynamic F-actin/cortactin spots colocalized with CD63+ endosomes (Fig. 2D), and that these structures are indeed branched actin tails based on cortactin immunostaining. Furthermore, inhibition of SMPD3 (with 3-OMS) induced enlarged cortactin/F-actin/CD63+ complexes, morphologically similar to invadopodia (Fig. 2D, arrowheads), supporting a functional link between actin branching and MVB dynamics.

To quantify the association, we calculated Manders’ colocalization coefficients for F-actin tails and CD63+ endosomal structures in fixed VSMCs, observing that ~50% of F-actin tails were associated with ~13% of endosomes. Upon 3-OMS treatment, this overlap increased further (Fig. S2B).

Finally, using live-cell imaging (Fig 2C; Supplementary Video S4), we directly observed CD63+ MVBs being propelled through the cytoplasm by Arp2/3-driven actin tails, suggesting a mechanistic role for branched actin assembly in MVB intracellular transport, rather than a generalized effect of actin disruption alone.

We believe these combined data reinforce a more specific mechanistic role for Arp2/3-mediated branched actin in MVB/endosome transport and, consequently, in sEV secretion in VSMCs—over and above an indirect effect of global actin loss. We hope these additional experiments and quantitative analyses address the reviewer’s concern and clarify the functional relevance of branched actin structures to sEV trafficking:

(1) Results, page 6, Ln 3 “As regulators of branched actin assembly, the Arp2/3 complex and cortactin are thought to contribute to sEV secretion in tumour cells by mediating MVB intracellular transport and plasma membrane docking[28, 33]. Therefore, we overexpressed the Arp2/3 subunit, ARPC2-GFP and the F-actin marker, F-tractin-RFP in VSMCs and performed live-cell imaging. As expected, Arp2/3 and F-actin bundles formed a distinct lamellipodia scaffold in the cellular cortex (Fig S2A and Supplementary Video S2). Unexpectedly, we also observed numerous  Arp2/3/F-actin positive spots moving  through the VSMC cytoplasm that resembled previously described endosome actin tails observed in Xenopus eggs[33] and parasite infected cells where actin comet tails propel parasites via filopodia to neighbouring cells[34, 35] (Fig S2A, arrow, and Supplementary Video S2). Analysis of the intracellular distribution of Arp2/3 and CD63-positive endosomes in VSMCs showed CD63-MVB propulsion by the F-actin tail in live cells (Fig 2C and Supplementary Video S4).”

(2) Results, New data Fig 2D, page 6, Ln 14. “we observed numerous F-actin spots in fixed VSMCs that were positive both for F-actin and cortactin indicating that these are branched-actin tails (Fig 2D). Moreover, cortactin/F-actin spots colocalised with CD63+ endosomes and addition of the SMPD3 inhibitor, 3-OMS, induced the appearance of enlarged doughnut-like cortactin/F-actin/CD63 complexes resembling invadopodia-like structures similar to those observed in tumour cells (Fig 2D, arrowheads)[18].”

(3) Results, New data Fig S2B, page 6, Ln 19 “To quantify CD63 overlap with the actin tail-like structures, we extracted round-shaped actin structures and calculated the thresholded Manders colocalization coefficient (Fig S2B).  We observed overlap between F-actin tails and CD63 as well as close proximity of these markers in fixed VSMCs (Fig S2B). Approximately 50% of the F-actin tails were associated with 13% of all endosomes (tM1=0.44±0.23 and tM2= 0.13±0.06, respectively, N=3). Addition of 3-OMS enhanced this overlap further (tM1=0.75±0.18 and tM2=0.25±0.09) suggesting that Arp2/3-driven branched F-actin tails are involved in CD63+ MVB intracellular transport in VSMCs”

• In video 1 the author states (lines 8-9; pg6) "intense CD63 staining along filopodia" Although, there is some fluorescence (not strong) in these structures, there was no visible exocytic activity. This data is more suggestive that sEVs (marked by CD63) are not associated with filopodia. The following conclusion statement the authors make is overreaching given this result.

We thank the reviewer for this careful observation and agree that the previous conclusion regarding sEV release from filopodia was overstated. In response, we have revised both the Results and Discussion sections to more accurately reflect the data..

(1) Results, page 6, Ln37 “We also attempted to visualise sEV release in filopodia using CD63-pHluorin where fluorescence is only observed upon the fusion of MVBs with the plasma membrane39. Using total internal reflection fluorescence microscopy (TIRF) we observed the typical “burst”-like appearance of sEV secretion at the cell-ECM interface in full agreement with an earlier report showing MVB recruitment to invadopodia-like structures in tumor cells18 (Fig S2B and Supplementary Video S1). Although we also observed an intense CD63-pHluorin staining along filopodia-like structures we were not able to detect typical “burst”-like events to confirm sEV secretion in filopodia. (Fig S2C and Supplemental Video S1)..”

(2) Discussion, page 12, Ln19 “Curiously we observed CD63+ MVB transport toward the filopodia tips as well as inhibition of sEV-secretion with filopodia formation inhibitors suggesting that sEV secretion can be directly linked to filopodia but further studies are needed to define the contribution of this pathway to the overall sEV secretion by cells.”. 

• Fig 2D and video 2 are wholly unconvincing with regard to sEV secretion sites. The authors could use their CD63-pHluroin construct to count exocytic events in the filopodia vs the whole cell. Given the movie, I have a suspicion this would not be significant. The authors could also perform staining CD63 in non-permeabilized cells to capture and count exocytic events at the plasma membrane as well as their location between groups.

We thank the reviewer for these constructive suggestions and their critical assessment of our current data regarding the sites of sEV secretion. We agree that our CD63-pHluorin approach clearly indicates sEV secretion events in the soma at the cell–ECM interface, while we did not observe comparable events in filopodia. Accordingly, we have clarified these points in the revised manuscript.

(1) Results, page 6, Ln37 “We also attempted to visualise sEV release in filopodia using CD63-pHluorin where fluorescence is only observed upon the fusion of MVBs with the plasma membrane39. Using total internal reflection fluorescence microscopy (TIRF) we observed the typical “burst”-like appearance of sEV secretion at the cell-ECM interface in full agreement with an earlier report showing MVB recruitment to invadopodia-like structures in tumor cells18 (Fig S2B and Supplementary Video S1). Although we also observed an intense CD63-pHluorin staining along filopodia-like structures we were not able to detect typical “burst”-like events to confirm sEV secretion in filopodia. (Fig S2C and Supplemental Video S1)..”

(2) Discussion, page 12, Ln19 “Curiously we observed CD63+ MVB transport toward the filopodia tips as well as inhibition of sEV-secretion with filopodia formation inhibitors suggesting that sEV secretion can be directly linked to filopodia but further studies are needed to define the contribution of this pathway to the overall sEV secretion by cells.”. 

• Fig. 2E and video 4. Again, the conclusions drawn from this data are very strained. First, no co-localization quantification is presented on the proportion of CD63 vesicles with actin. Once again, the movie, if anything convinces the reader that 95-99% of all CD63 vesicles are not associated with actin; therefore, this is an unlikely mechanism of transport.

We thank the reviewer for this valuable comment and for highlighting the need for quantitative co-localization analysis. In response, we developed a method to systematically quantify F-actin and CD63 co-localization in fixed VSMCs, as now presented in new Figures 2D and S2B. We acknowledge that the majority of CD63+ endosomes are not associated with F-actin, consistent with the reviewer’s interpretation. However, our quantitative data now show that a specific subpopulation of MVBs appears to utilize this actin-based mechanism for transport. We believe this addresses the concern and more accurately reflects the prevalence and significance of the mechanism described.

(1) Results, page 6 , Ln 19. “To quantify CD63 overlap with the actin tail-like structures, we extracted round-shaped actin structures and calculated the thresholded Manders colocalization coefficient (Fig S2B).  We observed overlap between F-actin tails and CD63 as well as close proximity of these markers in fixed VSMCs (Fig S2B). Approximately 50% of the F-actin tails were associated with 13% of all endosomes (tM1=0.44±0.23 and tM2= 0.13±0.06, respectively, N=3). Addition of 3-OMS enhanced this overlap further (tM1=0.75+/-0.18 and tM2=0.25+/-0.09) suggesting that Arp2/3-driven branched F-actin tails are involved in CD63+ MVB intracellular transport in VSMCs.”

• Are there perturbations that increase filopodia numbers? A gain of function experiment would be valuable here.

We thank the reviewer for this important suggestion regarding the potential value of gain-of-function experiments to clarify filopodia’s contribution to sEV release. In agreement with the reviewer’s scepticism, we have removed statements linking filopodia to sEV release from both the title and abstract to avoid overinterpretation. At present, our understanding of filopodia biology and the lack of robust tools to selectively and substantially increase filopodia numbers in VSMCs prevent us from directly addressing this question through gain-of-function assays. We acknowledge that future studies using established methods—such as overexpression of filopodia-inducing proteins (e.g., mDia2 or fascin)—could provide insight into whether an increased number of filopodia affects sEV release. However, such experiments are beyond the scope of the current manuscript. We have made the following changes to clarify these points:

(1) Results, page 6, Ln37 “We also attempted to visualise sEV release in filopodia using CD63-pHluorin where fluorescence is only observed upon the fusion of MVBs with the plasma membrane39. Using total internal reflection fluorescence microscopy (TIRF) we observed the typical “burst”-like appearance of sEV secretion at the cell-ECM interface in full agreement with an earlier report showing MVB recruitment to invadopodia-like structures in tumor cells18 (Fig S2B and Supplementary Video S1). Although we also observed an intense CD63-pHluorin staining along filopodia-like structures we were not able to detect typical “burst”-like events to confirm sEV secretion in filopodia. (Fig S2C and Supplemental Video S1)..”

(2) Discussion, page 12, Ln19 “Curiously we observed CD63+ MVB transport toward the filopodia tips as well as inhibition of sEV-secretion with filopodia formation inhibitors suggesting that sEV secretion can be directly linked to filopodia but further studies are needed to define the contribution of this pathway to the overall sEV secretion by cells.”. 

Figure 3<br /> • Fig 3A. The CD63 staining is strongly associated with the entire plasma membrane. How are the authors distinguishing between normal membrane shedding and bona fida sEVs based on this staining alone (?)- this is insufficient as all membrane structures are seemingly positive. Additionally, there are very few sEVs in scrutinizing the provided images. For the "sEV secretion, fold change" graphs in previous figures, could the authors provide absolute values, or an indication of what these values are in absolute terms?

We thank the reviewer for raising this important point regarding the specificity of CD63 staining and the need to distinguish bona fide sEVs from membrane fragments or general membrane shedding. We agree that CD63 staining alone at the plasma membrane or in the extracellular matrix is not sufficient to unequivocally identify sEVs. To address this, we employed several complementary approaches to rigorously characterize ECM-associated sEVs:

First, using high-resolution iSIM imaging, we confirmed the association of CD63-positive particles specifically with the FN-rich matrix, and demonstrated that SMPD3 knockdown significantly reduced the number of CD63+ particles in the matrix (Fig. 3B; revised from Fig. S3A).

Second, by incubating FN matrices with purified and fluorescently labeled sEVs, we directly observed efficient entrapment of these labeled sEVs within the matrices (Fig. 3E), confirming that sEVs can interact with and be retained by the ECM.

Third, we developed and applied a sequential extraction protocol using mild salt buffer (0.5M NaCl) and strong denaturant (4M guanidine HCl) to selectively extract ECM-associated sEVs based on the strength of their association (see new Figs. S3A and S3B). Extracted vesicles were then characterized by ExoView analysis, which demonstrated a tetraspanin profile (CD63+/CD81+/CD9+) closely matching that of sEVs from conditioned media, providing evidence that these particles are true sEVs and not merely membrane debris. We also found that the more weakly bound (NaCl-extracted) fraction closely resembles media-derived sEVs, while the strongly bound (GuHCl-extracted) fraction is more enriched in CD63+ and CD63+/CD81+ sEVs but contains very few CD9+ vesicles, further supporting distinct extracellular vesicle subpopulations within the ECM.

In addition, the abundance of CD63+/CD81+ sEVs in both media and ECM-derived fractions was independently validated by CD63 bead-capture assay (Fig. S3B).

We hope these clarifications and the expanded data set address the reviewer’s concerns about sEV identification and quantification in the extracellular matrix:

(1) Results, page 7, Ln 16. To quantify ECM-trapped sEVs we applied a modified protocol for the sequential extraction of extracellular proteins using salt buffer (0.5M NaCl) to release sEVs which are loosely-attached to ECM via ionic interactions, followed by 4M guanidine HCl buffer (GuHCl) treatment to solubilize strongly-bound sEVs (Fig S3A) 42. We quantified total sEV and characterised the sEV tetraspanin profile in conditioned media, and the 0.5M NaCl and GuHCl fractions using ExoView. The total particle count showed that EVs are both loosely bound and strongly trapped within the ECM. sEV tetraspanin profiling showed differences between these 3 EV populations.  While there was close similarity between the conditioned media and the 0.5M NaCl fraction with high abundance of CD63+/CD81+ sEVs as well as CD63+/CD81+/CD9+ in both fractions (Fig S3A). In contrast, the GuHCl fraction was particularly enriched with CD63+ and CD63+/CD81+ sEVs with very low abundance of CD9+ EVs (Fig S3A). The abundance of CD63+/CD81+ sEVs was confirmed independently by a CD63+ bead capture assay in the media and loosely bound fractions (Fig S3B).

• A control of fig 3b would be helpful to parse out random uptake of extracellular debris verses targeted sEV internalization. It would be helpful if the authors added particles of similar size to that of the sEVs to test whether these structures are endocytosed/micropinocytosed at similar levels.

We thank the reviewer for this useful suggestion regarding the need for better controls to distinguish specific sEV uptake from nonspecific internalization of extracellular debris or similarly sized particles. As a comparison, in our study we analyzed the uptake of both sEVs and serum proteins such as fibronectin and fetuin-A (Figs S3C and S3D), and observed similar patterns of intracellular trafficking. However, we acknowledge that inert nanoparticles or beads of a similar size to sEVs could serve as potential controls to assess nonspecific micropinocytosis or endocytosis.

It is important to note, however, that the uptake of sEVs is strongly influenced by their surface protein composition and the so-called “protein corona.” Recent work from Prof. Khuloud T. Al-Jamal’s group underscores that exosome uptake mechanisms may be highly specific (Liam-Or et al., 2024), and studies from Mattias Belting’s lab have also shown the importance of heparan sulfate proteoglycans in exosome endocytosis (Cerezo-Magana et al., 2021). As a result, uptake comparisons with inert particles or beads may not fully recapitulate the specificity of sEV internalization, and distinct nanoparticle classes may rely on different uptake pathways.

Figure 4<br /> • Fig. 4E,F,G. How are the authors determining the neointima and media compartments without ancillary staining for basement membrane or endothelial markers? Anatomic specific markers need to be incorporated here for the reader to evaluate the specificity of the FN and CD81 staining. It is also hard to understand the severity of the atherosclerotic lesion without a companion H&E cross section.

We thank the reviewer for highlighting the need for more rigorous characterization of atherosclerotic lesion architecture and anatomical compartments in our study. In response, we have incorporated additional histological analyses and now provide ancillary staining and companion images to enable clear identification of the neointima and medial compartments, as well as to assess lesion severity (see new Figs S4A–S4D):

(1)Results, page  8, Ln 28. . “To test if FN associates with sEV markers in atherosclerosis, we investigated the spatial association of FN with sEV markers using the sEV-specific marker CD81. Staining of atherosclerotic plaques with haematoxylin and eosin revealed well-defined regions with the neointima as well as tunica media layers formed by phenotypically transitioned or contractile VSMCs, respectively (Fig S4A). Masson's trichrome staining of atherosclerotic plaques showed abundant haemorrhages in the neointima, and sporadic haemorrhages in the tunica media (Fig S4B). Staining of atherosclerotic plaques with orcein indicated weak connective tissue staining in the atheroma with a confluent extracellular lipid core, and strong specific staining at the tunica media containing elastic fibres which correlated well with the intact elastin fibrils in the tunica media (Figs S4C and S4D). Using this clear morphological demarcation, we found that FN accumulated both in the neointima and the tunica media where it was significantly colocalised with the sEV marker, CD81 (Fig. 4D, 4E and 4F). Notably CD81 and FN colocalization was particularly prominent in cell-free, matrix-rich plaque regions (Figs. 4E and 4F).”

• Figs s4c, S4d- proper controls are not provided. Again, a non-FN internalization control as well as a 4oC cold block negative control is required to interpret this data.

We thank the reviewer for this valuable suggestion. To enhance the rigor of our internalization assays, we have now included several additional controls using alternative treatments, fluorophore combinations, and internalization conditions:

a) We performed FN-Alexa568 uptake assays, followed by immunostaining for CD63 with a distinct fluorophore (Alexa488), to confirm the colocalization of internalized FN with CD63+ endosomal compartments in VSMCs (new Fig. S3E).

b) We also stained VSMCs, cultured under normal growth conditions, with an anti-FN antibody to visualize intracellular serum-derived FN and again observed colocalization with CD63 (new Figs. S3F and S3G). Notably, in cells grown to confluence, we observed a complete loss of intracellular FN staining and FN/CD63 colocalization, suggesting that FN recycling is prominent in sparse, motile cells, but not in confluent populations.

These additional controls strengthen our conclusions regarding FN internalization pathways and the conditions under which FN trafficking to the endosomal system occurs:

(1) Results, page 7, Ln 31  We treated serum-deprived primary human aortic VSMCs with FN-Alexa568 and found that it was endocytosed and subsequently delivered to early and late endosomes together with fetuin A, another abundant serum protein that is a recycled sEV cargo and elevated in plaques (Figs S3C and S3D). CD63 visualisation with a different fluorophore (Alexa488) confirmed FN colocalization with CD63+ MVBs (Fig S3E). Next, we stained non-serum deprived VSMC cultured in normal growth media (RPMI supplemented with 20% FBS) with an anti-FN antibody and observed colocalization of CD63 and serum-derived FN.  Co-localisation was reduced likely due to competitive bulk protein uptake by non-deprived cells (Fig S3F). Notably, when we compared FN distribution in sparsely growing VSMCs versus confluent cells we found that FN intracellular spots, as well as colocalization with CD63, completely disappeared in the confluent state (Fig S3F and S3G)..

• Can the authors please provide live and fixed imaging of FN and CD63-mediate filopodial secretion to amply support their conclusions.

We have observed CD63 MVBs in both fixed (Fig 2E) and live VSMCs (Fig 2F) yet we agree that further studies are required to establish the contribution of filopodia to sEV secretion. Therefore, we have added the following changes:

(1) Results, page 6, Ln37 “We also attempted to visualise sEV release in filopodia using CD63-pHluorin where fluorescence is only observed upon the fusion of MVBs with the plasma membrane39. Using total internal reflection fluorescence microscopy (TIRF) we observed the typical “burst”-like appearance of sEV secretion at the cell-ECM interface in full agreement with an earlier report showing MVB recruitment to invadopodia-like structures in tumor cells18 (Fig S2B and Supplementary Video S1). Although we also observed an intense CD63-pHluorin staining along filopodia-like structures we were not able to detect typical “burst”-like events to confirm sEV secretion in filopodia. (Fig S2C and Supplemental Video S1)..”

(2) Discussion, page 12, Ln19 “Curiously we observed CD63+ MVB transport toward the filopodia tips as well as inhibition of sEV-secretion with filopodia formation inhibitors suggesting that sEV secretion can be directly linked to filopodia but further studies are needed to define the contribution of this pathway to the overall sEV secretion by cells.”. 

Figure 5

• Fig. 5A,B. The authors claim that sEV supplementation enhances VSMC migration speed and distance. The provided graphs show only a marginal increase in speed with sEV addition (A) but, concerningly, there is a four-star significant difference between the FN condition compared with FN+sEV (B) while the means appear the same. How are these conditions statistically different? The statistics seem off for these comparisons.

We thank the reviewer for highlighting concerns regarding the statistical analysis in Figures 5A and 5B. In response, we have carefully re-examined our data and statistical approach to ensure accuracy and transparency.

First, we have now included all individual cell migration tracks in the data representation for these figures. The statistical tests were repeated using the Kruskal–Wallis test with Dunn’s multiple comparison correction across all groups. This more stringent analysis confirmed our key findings: fibronectin (FN) stimulates VSMC migration speed, while inhibition of sEV secretion (with 3-OMS) reduces cellular speed (Fig. 5A). Addition of exogenous ECM-associated sEVs modestly restored cell speed in the presence of 3-OMS, but had no effect on baseline migration speed in 2D or 3D models (Figs. 5A, 5D).

Regarding the four-star significance observed in the original Fig. 5B, the previous result reflected an analysis based on pooled group averages, which may have overstated marginal differences. The revised analysis, based on individual cell tracks, does not support a substantial difference between FN and FN+sEV groups. The revised p-values and comparisons are now provided directly on the figures and described in the figure legends. We also clearly report the numbers of biological replicates, technical replicates, and individual data points for every condition.

Further, the modest effect of ECM-associated sEVs on speed is consistent with our observation that sEVs influence invasion directionality rather than baseline migration velocity, in agreement with previous findings in tumor models (Sung et al., 2015).

The manuscript has been revised accordingly, with updates in:

(1) Figures 5A and 5B: Individual cell track data are now shown, and statistical analyses have been repeated using the Kruskal–Wallis test with Dunn’s multiple comparisons.

(2) Figure legends and results sections: Numbers of biological and technical replicates, as well as individual data points, are now clearly stated.

(3) Results, page 9, line 14:  “FN as a cargo in sEVs promotes FA formation in tumour cells and increases cell speed14, 15. As we found that FN is loaded into VSMC-derived sEVs we hypothesized that ECM-entrapped sEVs can enhance cell migration by increasing cell adhesion and FA formation in the context of a FN-rich ECM. Therefore, we tested the effect of sEV deposition onto the FN matrix on VSMC migration in 2D and 3D models. We found that FN coating promoted VSMC velocity and inhibition of bulk sEV secretion with 3-OMS reduced VSMC speed in a 2D single-cell migration model (Figs. 5A, 5B) in agreement with previous studies using tumour cells14, 15. However, addition of sEVs to the ECM had no effect on VSMC speed at baseline but rescued cell speed and distance in the presence of the sEV secretion inhibitor, 3-OMS suggesting the EVs are not primarily regulating cell speed (Figs 5A and 5B).”

(4) Results, page 9, Ln 29 “Hence, ECM-associated sEVs have modest influence on VSMC speed but influence VSMC invasion directionality.”.

We hope that these changes address the reviewer’s concerns and improve the transparency and reproducibility of our data presentation

• Fig d-h. Generally, the magnitude of the difference between the presented conditions are biologically insignificant. Several of the graphs show a four-star difference with means that appear equivalent with overlapping error bars. Do the authors conclude that a 0.1%, or less, effect between groups is biologically meaningful?

We thank the reviewer for drawing attention to the apparent mismatch between statistical significance and biological relevance in Figures 5d–h. In response, we have reanalyzed the data using individual cell tracks and more stringent non-parametric statistical tests, as described above. This reanalysis confirmed that the magnitude of differences in migration speed and related parameters between the groups is minimal and not biologically meaningful. Thus, we no longer claim that sEVs significantly affect VSMC migration speed under these conditions in either 2D or 3D assays. Our revised manuscript now accurately reflects this finding in both the Results and Discussion sections, and the updated figures and legends clarify the true extent of any differences observed.

Figure 6

• Generally, the author's logic for looking into adhesion, focal adhesion and traction forces is hard to follow. If there are sEV-mediated migration differences, then there would inexorably be focal adhesion alterations. However, the data indicates few differences brought on by sEVs, which speaks to the lack of migration differences presented in Fig. 5. Overall, the sEV migration phenotype has so little of an effect, to then search for a mechanism seems destine to not turn up anything significant.

We thank the reviewer for highlighting the importance of connecting the observed phenotypic effects of sEVs to the investigation of adhesion and focal adhesion mechanisms. While our revised analysis confirms that sEVs have little to no effect on VSMC migration speed or distance in 2D and 3D models, we did observe a robust effect of sEVs on the directionality of cell invasion (Figs. 5G and 5H). This prompted us to look more closely at pathways involved in cell guidance rather than bulk cell motility.

Our proteomic comparison between larger EVs (10K fraction) and sEVs (100K fraction) revealed a unique adhesion complex present specifically on the sEVs—comprising collagen VI, TGFBI, LGALS3BP, and EDIL3 (Figs. 7A–C)—each of which has previously been implicated in integrin signaling, cell adhesion, or invasion. Functional blocking and knockdown studies further identified collagen VI as a key mediator in the regulation of cell adhesion and invasion directionality influenced by sEVs (Figs. 7F and 7I).

In response to this mechanistic insight, we have modified the graphical abstract and discussion to clarify our approach:

We now explicitly state that our focus has shifted from analyzing baseline migration speed to mechanisms guiding invasion directionality, in line with our key phenotypic findings.We highlight that the unique adhesion cluster identified on sEVs—including collagen VI and its cooperative partners—provides a strong mechanistic rationale for examining focal adhesion dynamics and ECM interactions, even in the absence of changes in migration velocity.Discussion excerpts (pages 13–14) have been updated to reflect this rationale and to summarize the potential significance of these findings for vascular biology and disease.

We hope this clarifies the logic underlying our approach and justifies the mechanistic studies performed in this context:

(1) Discussion, page 13, Ln 2  “Hence, it will be interesting in future studies to investigate whether sEVs can stimulate Rho activity by presenting adhesion modulators—particularly collagen VI—on their surface, thereby guiding cell directionality during invasion.”

(2) Discussion, page 13, Ln 30  “In addition to collagen VI the unique adhesion cluster in VSMC-derived sEVS also includes EGF-like repeat and discoidin I-like domain-containing protein (EDIL3), transforming growth factor-beta-induced protein ig-h3 (TGFBI) and the lectin galactoside-binding soluble 3 binding protein (LGALS3BP) and these proteins are also directly implicated in activation of integrin signalling and cellular invasiveness85-87. Although we found that collagen VI plays the key role in sEV-induced early formation of FAs in VSMCs, it is tempting to speculate that the high sEV efficacy in stimulating FA formation is driven by cooperative action of this unique adhesion complex on the sEVs surface and targeting this novel sEV-dependent mechanism of VSMC invasion may open-up new therapeutic opportunities to modulate atherosclerotic plaque development or even to prevent undesired VSMC motility in restenosis”.    . 

(3) Discussion, page 14, Ln 14 “In summary, cooperative activation of integrin signalling and F-actin cytoskeleton pathways results in the secretion of sEVs which associate with the ECM and play a signalling role by controlling FA formation and cell-ECM crosstalk. Further studies are needed to test these mechanisms across various cell types and ECM matrices.     ”.    

Figure 7<br /> • The authors need to provide additional evidence Col IV is harbored in sEVs and not a contaminant of sEV isolation as VSMCs secrete a copious amount of this in culture. For instance, IHC of isolated sEVs stained for CD63 and Col IV as well as single cell staining of the same sort.

We thank the reviewer for this important comment regarding the specificity of collagen VI detection in sEVs. To ensure that collagen VI is associated with bona fide sEVs—rather than being a contaminant resulting from high extracellular abundance—we performed a comparative analysis of vesicles isolated from the same conditioned media. Both proteomic mass spectrometry and western blotting revealed that collagen VI was exclusively present in the small EV (100K pellet) fraction and not in the larger EVs (10K pellet), as shown in Figs. 7B and 7C. Collagen VI was further identified in sEVs extracted from the ECM using our salt/guanidine protocol (new Fig. 7D).

Reviewer #2 (Recommendations For The Authors):

The authors have presented a nice collection of data with strong approaches to address their hypotheses. Nevertheless, an additional section within the Discussion would be welcome in addressing the potential limitations and important caveats to be considered alongside their study. These caveats and limitations could be reshaped by additional data supporting the ideas that: (1) small extracellular vesicles can be directly observed during their secretion from filopodia, (2) CD81 labeling in tissue can be interpreted clearly as extracellular vesicles and not the cell surface of other cell types (co-staining with an endothelial cell marker such as PECAM-1 perhaps), and (3) collagen VI within the vesicles is somehow accessed by adhesion molecules on the cell surface of migrating cells.

We thank the reviewer for these important suggestions and we have now added further studies and modified our conclusions to reflect the data more accurately:

(1) Results. Page 6, Ln37  “We also attempted to visualise sEV release in filopodia using CD63-pHluorin where fluorescence is only observed upon the fusion of MVBs with the plasma membrane39. Using total internal reflection fluorescence microscopy (TIRF) we observed the typical “burst”-like appearance of sEV secretion at the cell-ECM interface in full agreement with an earlier report showing MVB recruitment to invadopodia-like structures in tumor cells18 (Fig S2B and Supplementary Video S1). Although we also observed an intense CD63-pHluorin staining along filopodia-like structures we were not able to detect typical “burst”-like events to confirm sEV secretion in filopodia. (Fig S2C and Supplemental Video S1)”..  

(2) Discussion, page 12, Ln18: “Here we report that β1 integrin activation triggers sEV release followed by sEV entrapment by the ECM. Curiously we observed CD63+ MVB transport toward the filopodia tips as well as inhibition of sEV-secretion with filopodia formation inhibitors suggesting that sEV secretion can be directly linked to filopodia but further studies are needed to define the contribution of this pathway to the overall sEV secretion by cells”..

We quantified the colocalization of CD81 and CD31 to exclude the endothelial cell origin of sEVs and extended the characterisation of the atherosclerotic matrix as well as highlighting any limitations to interpretation ie re  CD81 ECM localisation: 

(1) Results, page 8, Ln 43 “An enhanced expression of CD81 by endothelial cells in early atheroma has been previously reported so to study the contribution of CD81+ sEVs derived from endothelial cells  we investigated the localisation of CD31 and CD8145. In agreement with a previous study, we found that the majority of CD31 colocalises with CD81 (Thresholded Mander's split colocalization coefficient 0.54±0.11, N=6) indicating that endothelial cells express CD81 (Fig 4G)45. However, only a minor fraction of total CD81 colocalised with CD31 (Thresholded Mander's split colocalization coefficient 0.24±0.06, N=6) confirming that the majority of CD81 in the neointima is originating from the most abundant VSMCs.. 

(2) Results, page 8, Ln 28: “To test if FN associates with sEV markers in atherosclerosis, we investigated the spatial association of FN with sEV markers using the sEV-specific marker CD81. Staining of atherosclerotic plaques with haematoxylin and eosin revealed well-defined regions with the neointima as well as tunica media layers formed by phenotypically transitioned or contractile VSMCs, respectively (Fig S4A). Masson's trichrome staining of atherosclerotic plaques showed abundant haemorrhages in the neointima, and sporadic haemorrhages in the tunica media (Fig S4B). Staining of atherosclerotic plaques with orcein indicated weak connective tissue staining in the atheroma with a confluent extracellular lipid core, and strong specific staining at the tunica media containing elastic fibres which correlated well with the intact elastin fibrils in the tunica media (Figs S4C and S4D). Using this clear morphological demarcation, we found that FN accumulated both in the neointima and the tunica media where it was significantly colocalised with the sEV marker, CD81 (Fig. 4D, 4E and 4F). Notably CD81 and FN colocalization was particularly prominent in cell-free, matrix-rich plaque regions (Figs. 4E and 4F). .”

We showed that collagen VI is presented on the surface of sEVs:

(1) Results, page 10, Ln43: “Collagen VI was the most abundant protein in VSMC-derived sEVs (Fig 7B, Table S7) and  was previously implicated in the interaction with the proteoglycan NG253 and suppression of cell spreading on FN54. To confirm the presence of collagen VI in ECM-associated sEVs we analysed sEVs extracted from the 3D matrix using 0.5M NaCl treatment and showed that both collagen VI and FN are present (Fig 7D). Next, we analysed the distribution of collagen VI using dot-blot. Alix staining was bright only upon permeabilization of sEV indicating that it is preferentially a luminal protein (Fig 7E). On the contrary, CD63 staining was similar in both conditions showing that it is surface protein (Fig 7E). Interestingly, collagen VI staining revealed that 40% of the protein is located on the outside surface with 60% in the sEV lumen (Fig 7E)