Lipid Droplets Fuel Small Extracellular Vesicle Biogenesis

  1. Géraldine C. Genard
  2. Luca Tirinato
  3. Francesca Pagliari
  4. Jessica Da Silva
  5. Alessandro Giammona
  6. Fatema Alquraish
  7. Marie Bordas
  8. Maria Grazia Marafioti
  9. Simone Di Franco
  10. Jeannette Janssen
  11. Daniel Garcia-Calderón
  12. Rachel Hanley
  13. Clelia Nistico
  14. Yoshinori Fukasawa
  15. Torsten Müller
  16. Jeroen Krijgsveld
  17. Matilde Todaro
  18. Francesco Saverio Costanzo
  19. Giorgio Stassi
  20. Michelle Nessling
  21. Karsten Richter
  22. Kendra K. Maass
  23. Carlo Liberale
  24. Joao Seco

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Abstract

Despite an increasing gain of knowledge regarding small extracellular vesicle (sEV) composition and functions in cell-cell communication, the mechanism behind their biogenesis remains unclear. Here, we revealed for the first time that the sEV biogenesis and release into the microenvironment are tightly connected with another important organelle: Lipid Droplets (LD). We have observed this correlation using different human cancer cell lines as well as patient-derived colorectal cancer stem cells (CR-CSCs). Our results showed that the use of external stimuli such as radiation, pH, hypoxia, or lipid interfering drugs, known to affect the LD content, had a similar effect in terms of sEV secretion. Additional validations were brought using multiple omics data, at the mRNA and protein levels. Altogether, the possibility to fine-tune sEV biogenesis by targeting LDs, could have a massive impact on the amount, the cargos and the properties of those sEVs, paving the way for new clinical perspectives.

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    Reviewer #1

    In this paper, authors report that radiation, acidic pH, hypoxia, and drugs that interfere with lipid synthesis, all of which affect lipid droplets (LD), also affect the production of small extracellular vesicles (sEVs). In addition, they also report that LD content and sEV secretion are also modulated in CR-CSCs. Authors conclude that sEV formation and secretion is directly linked to LDs, and that their studies may open the way to new clinical perspectives. However, some important issues need to be addressed before the paper can be considered for publication.

    __My ____main concern ____is that the notion that LDs and sEVs are linked remains vague. Do cells contain more LDs and secrete more sEVs because these two pathways are selectively up-regulated via some mechanism____s____ that controls both pathways in a concerted manner? Or do cells with more LDs and more sEVs also contain more of everything, perhaps as a result of metabolic activity? __

    We appreciate the Reviewer's observations. Indeed, this comment represents the main pillar of the entire manuscript. We have attempted to uncover the molecular mechanism behind this novel and intriguing organelle connection. First of all, we have adapted the manuscript emphasizing that the LD – sEV connection might be direct or indirect. Our omic data suggested that some proteins belonging to the RAB family, mainly Rab18, Rab7a and Rab5c, could play a pivotal role in the LDs-sEVs axis. To strengthen those results, we have performed additional experiments by silencing the expression of the three candidate Rabs. Rab5c seems to be a good candidate to modulate the LD-sEV connection. We believe that Rab5c is not the only contributor to the LD-sEV connection but is part of a whole set of different elements that regulate this axis. However, it is quite challenging to rule out other molecular candidates as co-contributors to this phenomenon, especially when considering cellular metabolic pathways.

    We recognize that external stimuli, such as radiation, pH, and lipid-interfering drugs, may exert their effects on other cellular organelles, even though we have strived to analyze each individual phenomenon rigorously. We are confident that our work lays the foundation for further research in the field.

    __A direct corollary of this issue is whether increased sEV secretion reflects more endosomes and lysosomes (e.g. LysoTracker-positive compartments) or whether sEV secretion is selectively up-regulated. __

    Thanks to the Reviewer’ suggestion, we have analyzed both the lysosome and endosome contents in our experimental cell systems. These data are now included in the manuscript in Figure S8. We have observed that it is unlikely that lysosomes are directly involved in the LD – sEV connection. However, the expression of Rab7a, a regulator of the late endosomal pathway, correlated with the LD content of the cells and their sEV release. Therefore, the endosomal pathway might be a good candidate to contribute to this LD – sEV connection.

    __At one point, authors argue that cells that secrete more sEVs also contain more MVBs, but this issue remains elusive. To what extent is the increase in LDs and sEVS correlated in particular with an increase in endosome-lysosomes, and ER-Golgi (LDs originate from the ER)? __

    We thank the Reviewer for this comment. We agree that the analyses of sEVs secreted in the media might not reflect the MVB content in the cells. However, two experiments, one on Panc01 cells and another one on MCF7 cells, showed that the number of MVBs, assessed by confocal microscopy using CD63 staining (MCF7) or CD63 and Alix plasmids (PANC-01), was directly correlated with the number of released sEVs in the media (Figure Fig S3C and 4J).

    In addition, we included additional experiments assessing the lysosome content in HT29 LDHigh and LDLowcells. Hereby, we confirmed that HT29 LDHigh cells showed a higher LD content than HT29 LDLow cells. Inversely, by studying the lysotracker area per cell, we showed that HT29 LDLow population has a higher lysosomal content as compared to their counterpart, HT29 LDHigh cells (test = Wilcoxon rank sum test with continuity correction_ W = 85127, p-value = 7.255e-07 for LDs and W = 49321, p-value = 1.14e-11 for Lysotracker). However, we could not demonstrate a clear correlation between the number of LDs in the cell and the lysotracker signal.

    Finally, we have also studied the expression of GM130, a Golgi-shaping protein (Ref. 1) and Rab7, a late-endocytic protein (Fig S8C). While the expression of Rab7 (endosome) seemed to correlate with the LD and sEV contents, the expression of GM130 (Golgi) gave back no coherent results. Indeed, it was inversely correlated to the LD and sEV amount, in accordance with what was already reported elsewhere (Ref 2 and 3)

    • Nakamura N. Emerging new roles of GM130, a cis-Golgi matrix protein, in higher order cell functions. J Pharmacol Sci. (2010) 112:255–64. Doi: 10.1254/jphs.09R03CR
    • Lydia-Ann L.S. Harris, James R. Skinner, Trevor M. Shew, Nada A. Abumrad, Nathan E. Wolins. Monoacylglycerol disrupts Golgi structure and perilipin 2 association with lipid droplets.Doi.org/10.1101/2021.07.09.451829
    • Alvin Kamili, Nuruliza Roslan, Sarah Frost, Laurence C. Cantrill, Dongwei Wang, Austin Della-Franca, Robert K. Bright, Guy E. Groblewski, Beate K. Straub, Andrew J. Hoy, Yuyan Chen, Jennifer A. Byrne; TPD52 expression increases neutral lipid storage within cultured cells. J Cell Sci 1 September 2015; 128 (17): 3223–3238. Doi: 10.1242/jcs.167692

    Authors conclude that the data with lipid inhibitors strengthen the connection between LDs and sEVs (Fig 2 and S2). However, is this regulation selective, or does it merely reflect the general effect of these inhibitors on membrane-related processes? The same comment applies to the role of iron metabolism after knockdown of ferritin heavy chain (Fig 3 and S3), acidic pH and X-ray radiation (Fig 4 and S4)____.

    We thank the Reviewer for the interesting observation. As previously mentioned, we cannot rule out other potential contributors to the LDs-sEVs connection upon lipid inhibitor treatments and/or the others external stimuli applied to our cell systems.

    The data presented in this manuscript merely represent a novel and unexplored (at least so far) organelle connection, direct or indirect, with a broad clinical implication. As the membrane-related processes (such as Endosomes, Golgi apparatus, Exosome (sEV) pathway, Lysosomes and Autophagosome) are all interconnected, in our opinion, it might be quite challenging to make such a definitive statement.

    Such assertion would require extensive further investigation to relate each organelle to the LDs and/or sEVs. However, with our research, we hope to open the door to a new era of investigations regarding the sEV – LDs connection.

    OTHER COMMENTS

    1) Which cell line is used for sEV analysis (markers vs contaminants (Fig S1B)? In any case, the data should be shown for both cell types.

    Our method to isolate sEVs is a standardized method that was already published by our group and collaborators in 2020 (M. Bordas, et al., Optimized Protocol for Isolation of Small Extracellular Vesicles from Human and Murine Lymphoid Tissues. Int J Mol Sci (2020) https:/doi.org/10.3390/ijms21155586.). This protocol was validated on human and mouse tissues, much more complex samples than cell culture supernatant.

    Figure S1C was modified, as requested by the Reviewer, including new data for HT29, Panc01 and MCF7 cell lines to broaden the panel. Those results confirmed the good purity of sEV samples isolated from cell culture supernatant.

    2) The Tsg101 blot is not impressive (Fig S1B): the difference between cells and sEVs is not easy to see. It would be nice if blots were quantified.

    Indeed, the signal obtained for TSG101 for sEVs derived from Panc01 cell line is quite weak. It is important to remember that not all sEV markers are highly expressed in all cell lines and their derived sEVs. Some cell line-derived sEVs show a low or high expression of the diverse sEV markers. To answer the Reviewer #1’s comment, we quantified the expression of TSG101 in Panc01-derived sEVs. The quantification showed that TSG101 is 6.8 times more expressed on Panc01-dervied sEVs as compared to the cell line. However, since the expression is quite low, this quantification should be taken with some caution.

    In light of the Reviewer ‘comment, we have performed the Western Blot analysis on other cell lines (HT29 and MCF7), and we have replaced TSG101 marker with CD9 marker (Figure S1C).

    3) From Fig 1B it cannot be concluded that the size of sEVs ranges from 30 to 200nm: the micrograph only shows a few structures.

    We appreciate the Reviewer's comment and have attempted to provide more clarity. Firstly, we want to highlight that TEM micrographs of sEVs typically show the donut shape, a unique feature of sEVs imaged with TEM, as well as a size range. In Figure 1B micrograph, the sEV size is approximately 100 nm. The size distribution of LoVo and HT29-derived sEVs can be observed from the NTA size measurements in Figure S1B. Indeed, the peak size is 148 nm for LoVo-derived sEVs and 135 nm for HT29, which aligns with the sEV sizes presented in Figure 1B. We have also included multiple micrographs here under. As the number of Supplementary Figures is already large, we have decided to not include those micrographs in the manuscript. The average size of LoVo-derived sEVs, based on TEM micrograph analysis, was 94 ± 41.10 nm, while the average size of HT29-derived sEVs was 76.41 ± 44.22 nm. The size discrepancy between the two methods (NTA versus TEM) can be ascribed to the dehydration step required for TEM, which results in a reduction of the actual sEV size.

    4) HT29 cells contain far more LDs than LoVo cells (Fig 1A). Similarly, sEV proteins (CD63, CD81, CD9, Hsc-70) are more abundant in HT29 sEV____s____ than in LoVo sEVs (Fig 1D). However, the sEV preparation from HT29 cells contains only approx. 50% more total protein than LoVo sEVs (Fig S1D-E). Are sEVs prepared from LoVo cells far more contaminated with cell debris etc.. than sEV fractions from HT29 cells?

    We are confident that our EV isolation method allows us to achieve high yield and excellent purity. It is possible that a lower number of sEVs in samples may lead to increased protein contamination during ultracentrifugation. However, size exclusion chromatography should minimize this protein contamination. It is important to note that the NTA method is significantly more sensitive and accurate than Qubit protein quantification. Consequently, protein concentration and particle concentration should not be directly compared.

    5) LD staining should be shown for the corresponding populations of cells with high/low CD63 (Fig 1E). Cells in culture can be somewhat heterogeneous, but the difference between low and high CD63 is quite extreme (Fig 1E). Is such high heterogeneity also observed with other proteins of the endocytic and biosynthetic pathways? Authors conclude that cells containing high CD63 levels also contain more MVBs (Fig 1E): are all late endosomal proteins (e.g. LAMP1, RAB7) upregulated in cells with high CD63?

    We thank the Reviewer for this comment, and we totally agree with the Reviewer that it would be better to have the LD and CD63 staining on the same images. Unfortunately, the staining for CD63 on LD540-sorted HT29 cells requires a permeabilization step that interferes with the cellular lipid part and could therefore negatively affect the LD imaging by confocal microscopy. To prove that the HT29 LDHigh and HT29 LDLowcontain high and low LD amount respectively, we sorted HT29 cells based on the LD content and, soon after, we observed them at the confocal microscopy. We thus added new images in Figure S1F, corresponding to the LD fluorescence detection. The readers will also appreciate the explanation regarding the inability of observing both LDs and CD63 staining on the same confocal images under the line 165 – 166:

    “As the staining for CD63 required a permeabilization step, and therefore lipid digestion, it was not possible to assess both LDs and CD+MVBs on the same micrographs “.

    In addition, we have added confocal images representing HT29 cells sorted based on their LD content and stained with Hoechst and Lysotracker. A quantification of the Lysotracker fluorescence per cell and the correlation with the number of LDs can also be appreciated in Figure S8A-B.

    Finally, we performed Western Blot analysis to examine Rab7a expression under various conditions described in our manuscript (Figure S8C). In general, Rab7 expression corresponded with LD content, indicating that cells with high LD content exhibited higher Rab7 expression, while cells with low LD amount showed lower Rab7 expression, except for Triacsin-C. The Reviewer can now appreciate the quantification in the graphs provided below (not included in the manuscript).

    Regarding the heterogeneity of LDs, CD63+MVBs, or lysotracker among the cell population, we have indeed noticed heterogeneity observable in these three types of staining in HT29, particularly in the HT29 LDHighpopulation.

    6) Inhibitors of lipid synthesis reduce LD formation (Fig 2B), sEV production and CD63 / CD81/ CD9 secretion (Fig2C-D, Fig S2B). Are the cellular levels of these (and other endosomal) proteins also reduced after inhibitor treatment? Does the stimulation of LD formation with oleic acid also stimulate CD63 synthesis and sEV production?

    We thank the Reviewer for this very interesting comment. To answer this question, we have added a supplementary figure (Figure S2A, S2B) showing the cellular expression of CD63 upon LD inhibition or stimulation.

    During the planning of our experiments, we discussed about the possibility of using oleic acid to induce the formation of Lipid Droplets, which was ultimately not done. This is because the use of oleic acid would have more strongly stimulated the triglyceride pathway, as extensively discussed elsewhere (Mejhert N. et al., The lipid droplet knowledge portal: a resource for systematic analyses of lipid droplet biology, Developmental Cell, 2022). Since Lipid Droplets are made by cholesterol esters and triglycerides, we preferred to use other stimuli (hypoxia, radiation), all of them already discussed in literature, to induce both pathways simultaneously, resulting in the Lipid Droplet formation/induction.

    7) It seems that pH and irradiation increase sEV markers far more significantly (Fig 4 B-C and Fig S4A-E) than FTH1 depletion decreases sEV markers (Fig 3 D-E). In fact, authors mention that they cannot exclude a contamination of sEVs with small apoptotic / autophagic vesicles after irradiation (Fig 4). To facilitate comparison, it would be nice to also show the number of secreted particles per cell (like after FTH1 depletion Fig 3D), as well as the distribution of possible contaminants (e.g. Fig S1). Also, authors state that the increase in the number CD63+ MVBs after irradiation is shown, but this is not the case.

    We apologize to the Reviewer because, in fact, one figure was missing (Figure 4). We have rectified this by increasing the quality of Figure 4 and have added representative images for each acquisition of the number of MVBs, either positive for CD63 or Alix, in transfected Panc01 cells X-ray irradiated (8 Gy) or not (0Gy). In addition, a similar experiment was performed in MCF7 cells transduced with shRNA or shFTH1. CD63+ MVBs were assessed in both cell line and the number of CD63+ puncta (MVBs) were quantified by ImageJ. The results, although not significative, illustrated a trend for MCF7 shFTH1 to contain less CD63+ MVBs than MCF7 shRNA. Furthermore, the quantification of sEVs released in the conditioned media was performed in three independent experiments and demonstrated that significantly less particles (sEVs) were released by MCF7 shFTH1 than MCF7 shRNA.

    8) Are the proteomic data (Fig 6) with LDlow and LDhigh cells obtained after cell sorting, as in Fig 1E? Did authors compare the proteome of LoVo and HT29 sEVs? How do the protein profiles (in particular proteins involved in lipid metabolism) obtained under different conditions compare with each other, in particular after irradiation (Fig4N) and knockdown of ferritin heavy chain (Fig 3, Fig S3)? It would also be interesting to compare these data with the data obtained in CR-CSCs culture under hypoxia (Fig S5). Are common proteins involved in sEV production and LD biosynthesis identified in the analysis of these biological processes? Is there a common set of proteins/genes revealed by this analysis, which may potentially control sEV production and LD biosynthesis?

    We thank the reviewer for this interesting comment.

    Proteomic analyses have been performed on the following conditions:

    • Panc01 (0 Gy – 6 Gy – 8 Gy) for sEV samples

    • MCF7 (shFTH1 and MCF7 shRNA)

    • MCF7 (0 Gy and 6 Gy)

    • MCF7 (Normoxia and Hypoxia)

    • H460 (0 Gy and 6 Gy)

    • H460 (Normoxia and Hypoxia) RNA sequencing was performed on the following conditions:

    • CR-CSCs (#4, #8, #21) Based on all those data, we have analyzed the sEV pathway and how this pathway was modulated in the conditions with high LD content and low LD content. We therefore came up with several proteins, presented in Figure S7. Based on this analysis, we have decided to further investigate the role of RAB18, RAB5c and RAB7a in the connection between LDs and sEVs. Those additional results can be found in Figure 6 and Figure S7A (originally Figure 6). We have found that RAB5c, but not RAB7a or RAB18, seems to be a good candidate to intervene in the LD – sEV connection.

    Minor comments

    1) Some parts of the text are still a bit rough, and should be read and corrected carefully. For example: i) isn't it obvious that a common source of lipids builds up the membrane of sEVS, much like any other membrane (line 90, p.2); ii) what does this sentence mean: "LD have been considered as mere fat storage organelles for a long time, although important evidence could be traced back to the early 1960's". Important evidence for what? iii) why is the acronym AdExo used? iv) (line 138) the text should probably be "sEVs released during 72h were studied" and not "released sEVs were studied ... 72 h after seeding".

    We apologize to the Reviewer if some parts of the paper were a bit rough. We have re-read the entire manuscript and corrected all the parts that needed revision work.

    2) The captions are far too small in most figures and diagrams (for example ____X____ and Y axis in Fig 1C-D, text in Fig 1E; Fig S1; Fig 3C proteins in the heatmap).

    We agree with the Reviewer. All images and their captions were properly revised.

    3) The color code for LoVO and HT29 cells is reversed in Fig S1D-E

    The mistake was corrected.

    4) In Fig 1D, I cannot see CD81 in the LoVo blot.

    In the image below, it is possible to see the LoVo blot.

    5) Wording is not adequate in following sentences: "62.7% of proteins related to the exosomal pathway are downregulated in MCF7 shFTH1 cells" (line 233) and a few lines below: ".. the expression of almost all exosomal markers was downregulated in MCF7 shFTH1 cells" (line 239). Does 62.7% represent all proteins?

    We apologize to the reviewer for the mistake. We rephrased this sentence.

    6) In Fig 3E authors compare sEV markers secreted by cells treated with shFTH1 or control shRNA. The Anx5 and CD63 blots are not very convincing (quantification would be helpful).

    We apologize to the Reviewer for this issue. These Western Blot analyses were performed only once, therefore a quantification in the manuscript would not be relevant. However, we report here the results of the quantification. The expression of Annexin V was 1.58 times higher in MCF7 shRNA than MCF7 shFTH1, while the expression of CD63 was 1.34 time higher in MCF shRNA as compared to MCF7 shFTH1.

    7) The micrographs in Fig 4L are too small: gold particles cannot be seen, even in the high magnification views.

    We thank the Reviewer for her/his comment. We have moved the micrograph and the quantification histogram to the Figure S6. Now, it is possible to discriminate easily gold nanoparticles.

    8) The micrographs showing ALIX and CD63 (Fig 4J) in irradiated and unirradiated Panc01 cells should be shown for comparison.

    We followed the Reviewer’ suggestion as it is possible to note in the Figure below.

    Reviewer #2

    This manuscript describes a relationship between lipid droplet presence in cells and small EV secretion. First, correlations are done between number of lipid droplets and numbers of EVs secreted. Then chemical inhibitors of lipid droplet biosynthesis pathways were shown to reduce small EV secretion. Then various processes known to target lipid droplets, including iron metabolism, irradiation, hypoxia, low pH are used to show concordant effects on lipid droplets and small EV secretion. Proteomic analysis of EVs and cells subjected to some of the treatments are also performed. Overall, it is an interesting line of investigation and the data overall seem solid. Several flaws exist, which can probably be fixed. These include the use of different cell lines for different experiments. It makes it a bit difficult to connect everything together. It could be fixed by adding some extra cell lines to some experiments - for example taking the MCF7 and Panc-01 cells for which proteomics was performed and redoing some of the correlative and causative experiments from Figs 1 and 2.

    We appreciate the Reviewer's insightful observation. Following her/his suggestion, we have conducted additional experiments on MCF7, H460 and PANC-01 cell lines to enhance data consistency and facilitate a smoother transition between different sections of the paper.

    It also would be good to have some more direct evidence of the connection between lipid droplets and EV secretion - one could argue that this was already done in Fig 2 with the chemical inhibitors, I wonder if there is a genetic way to do it too?

    We totally agree with the Reviewer. Indeed, starting from our proteomic data we highlighted some genes belonging to the RAB family as potential candidates to interfere with the LD – sEV connection. The Reviewer can now appreciate in Figure 6 and Figure S7, the results from the additional experiments we carried out on RAB5c, RAB7a and RAB18 silencing in HT29 cells. The former Figure 6 has been moved in the Supplementary part (Figure S7).

    Some tightening up of the writing (especially the Discussion) and the resolution of the figures would also improve the manuscript.

    We apologize to the Reviewer for this issue. We have now re-prepared all Figures by increasing their resolution, as well as reviewing the entire manuscript with the aim of making the reading smoother and simpler.

    __Overall, it is a nice piece of work but there are many minor things to be fixed. __

    __Specific Comments: __

    The sentence in the Introduction: "The non-endosomal pathway generates sEVs devoid of CD63, CD81 and CD9 or sEVs enriched in ECM and serum-derived factors (7)." is not well-supported and should be removed. The idea that you can classify membrane of origin based on markers has not been proven, but rather assumed.

    We agree with the Reviewer. We have rephrased the sentence.

    We thank the Reviewer for this comment. In response to this, we have generated correlation graphs for several of our experiments:

    • HT29 (CTL – Triacsin-C - PF-06424439) in Figure 2E
    • PANC-01 (CTL – 2 – 4 – 6 – 8 Gy) in Figure 4K
    • CR-CSCs (#4, #8, #21) in Figure 5E

    __The Method used for EV purification should be stated in the Results rather than referring to a reference and a Supplemental Figure (S1A) that is too low of a resolution to see. __

    Our method to isolate sEVs is a standardized methods that was already published by our group and collaborators in 2020 (M. Bordas, et al., Optimized Protocol for Isolation of Small Extracellular Vesicles from Human and Murine Lymphoid Tissues. Int J Mol Sci (2020) https:/doi.org/10.3390/ijms21155586.). This protocol was validated on human and mouse tissues, much more complex samples than cell culture supernatant.

    In regard to the Reviewer’s comment, we have added a better description of the protocol in the Results part, referring to the Material and Method. For this reason, we decided to keep the sEV protocol in the SI section. We apologize for the low quality of the Figure S1. In agreement with the Reviewer suggestion, we have modified the image by increasing its quality.

    __Fig 1B would be better to have an image in which the EVs are not aggregated. __

    We thank the Reviewer for this comment and have modified the Figure accordingly.

    __Fig 3 is interesting but jumps cell lines. For better continuity, some of the experiments from Figs 1 and 2 should be repeated in the MCF7 cells to connect with the proteomics. __

    In agreement with the Reviewer’ comment, we decided to perform additional experiment on MCF7, using Triacsin-C. The Reviewer can now appreciate the results in Figure 2F, Figure 2G and Figure S2E.

    __Fig 3C is too low resolution to read, please export at higher resolution. __

    We are sorry for the low-quality Figure. We have modified the image accordingly.

    __Please provide all the raw proteomics data as a supplementary spreadsheet____. __

    We have provided all the raw data regarding our proteomic analyses.

    __Fig 4 panels are low resolution __

    We apologize for the low-resolution Figure. We have modified the figure by increasing the quality.

    Fig 4 again adds new cell lines with H460 and Panc-01

    We thank the reviewer for this comment. In this regard, we have performed additional experiment:

    • Western Blot: comparison cellular and exosomal markers (Figure S1C)
    • MCF7 (CTL - Triacsin) (Figure 2F, Figure 2G and Figure S2E)
    • Western Blot: analysis of RAB7a, GM130

    __The images corresponding to 4J should be shown in a Supp Figure somewhere __

    We thank the reviewer for pointing out this oversight. We have added the confocal images corresponding to the Figure 4J below the quantification.

    The statements: "In addition, the exosomal nature of Panc01-derived vesicles was demonstrated by an analysis of CD63+ or Alix+ multivesicular bodies (MVBs) in unirradiated (0 Gy) or irradiated (8 Gy) pancreatic cancer cells (Fig 4J). Moreover, we confirmed a clear correlation between cellular LD content and sEV biogenesis, as represented in Fig 4K." are overly conclusive. For 4J, one can make a statement about the MVBs but not the EVs as that's not what was measured there. Likewise for 4K, what was measured was how many EVs were released not how many were formed. While the data are suggestive of alteration of exosome biogenesis, they are not conclusive.

    We agree with the reviewer and have performed the necessary changes in the manuscript. The reviewer can see the changes under the lines 282 – 284:

    “In addition, the analysis of CD63+ or Alix+ multivesicular bodies (MVBs) in unirradiated (0 Gy) or irradiated (8 Gy) pancreatic cancer cells revealed an increased number of MVBs after irradiation (Fig____ure 4J).”

    __Western blot is always capitalized by convention - Western not western. __

    We have corrected it accordingly.

    __Fig 5A is too small and low resolution - suggest eliminating and just put info in methods. __

    We are sorry for the low-resolution image. We have followed the Reviewer suggestion. The graphical method has been now moved to the Supplementary Figure S6.

    Fig 5G, many of the genes shown are frequently EV cargoes but most not involved in exosome biogenesis - not sure where the label of Exosome pathway came from but it is not very compelling. Only ANXA2, Arf6, and Rab5C seem related and they are barely elevated.

    We completely agree with the Reviewer's comment. As a result, we have revised the heatmap title to "Exosomal Cargoes and Pathways" instead of "Exosomal Pathway".

    __Most main figures and all supplementary figures are extremely low res - please fix. __

    We are very sorry for the low-quality figures. We have revised all Figures (main text and SI) by increasing their quality.

    __Fig 6 is first mentioned in the Discussion - it should be described in the Results before that (or alternatively removed). __

    We agree with the Reviewer. Our initial idea was to mention perspectives of analyses that could be carried ulteriorly. Nevertheless, we have performed additional experiments in order to get insight on the mechanism involved in the LD – sEV connection. Indeed, based on our proteomic data, we have analyzed the sEV pathway and how this pathway was modulated in the conditions with high LD content and low LD content. We therefore came up with several proteins, presented in Figure S7A (originally Figure 6). Based on this analysis, we have decided to further investigate the role of RAB18, RAB5c and RAB7a in the connection between LDs and sEVs. Those additional results can be found in Figure 6 and Figure S7 in the Results section. We have found that RAB5c, but not RAB7a or RAB18, seems to be a good candidate to intervene in the LD – sEV connection.

    Table S1, also first mentioned in the Discussion, is missing. Either describe in the Results section or remove the callout to it.

    Our apologies for that. The Table S1 has been now mentioned in the Results section and has been properly uploaded.

    __The discussion is too dense with too many trains of thought, often many different directions in the same paragraph. It needs to be streamlined, with a central thought for each paragraph and good transitions between the paragraphs. __

    We apologize to the Reviewer if the Discussion part was a bit confusing. We rewrote the paragraph, streamlining it and making the transitions between its paragraphs smoother.

    Reviewer #2 (Significance (Required)):

    __ Strengths of this manuscript are the interesting connection between lipid droplets and exosomes and the number of experiments to address it. __

    __ Limitations: use of different cell lines for different figures, overall descriptive nature with regard to direct demonstration of connection to lipid droplets -- it's kind of done in Fig 2, but could be possibly bolstered. __

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    Reply to the reviewers

    Reviewer #1:

    In this paper, authors report that radiation, acidic pH, hypoxia, and drugs that interfere with lipid synthesis, all of which affect lipid droplets (LD), also affect the production of small extracellular vesicles (sEVs). In addition, they also report that LD content and sEV secretion are also modulated in CR-CSCs. Authors conclude that sEV formation and secretion is directly linked to LDs, and that their studies may open the way to new clinical perspectives. However, some important issues need to be addressed before the paper can be considered for publication.

    My main concern is that the notion that LDs and sEVs are linked remains vague. Do cells contain more LDs and secrete more sEVs because these two pathways are selectively up-regulated via some mechanism****s that controls both pathways in a concerted manner? Or do cells with more LDs and more sEVs also contain more of everything, perhaps as a result of metabolic activity?

    We appreciate the Reviewer's observations. Indeed, this comment represents the main pillar of the entire manuscript. We have attempted to uncover the molecular mechanism behind this novel and intriguing organelle connection. First of all, we have adapted the manuscript emphasizing that the LD – sEV connection might be direct or indirect. Our omic data suggested that some proteins belonging to the RAB family, mainly Rab18, Rab7a and Rab5c, could play a pivotal role in the LDs-sEVs axis. To strengthen those results, we have performed additional experiments by silencing the expression of the three candidate Rabs. Rab5c seems to be a good candidate to modulate the LD-sEV connection. We believe that Rab5c is not the only contributor to the LD-sEV connection but is part of a whole set of different elements that regulate this axis. However, it is quite challenging to rule out other molecular candidates as co-contributors to this phenomenon, especially when considering cellular metabolic pathways.

    We recognize that external stimuli, such as radiation, pH, and lipid-interfering drugs, may exert their effects on other cellular organelles, even though we have strived to analyze each individual phenomenon rigorously. We are confident that our work lays the foundation for further research in the field.

    A direct corollary of this issue is whether increased sEV secretion reflects more endosomes and lysosomes (e.g. LysoTracker-positive compartments) or whether sEV secretion is selectively up-regulated.

    Thanks to the Reviewer’ suggestion, we have analyzed both the lysosome and endosome contents in our experimental cell systems. These data are now included in the manuscript in Figure S8. We have observed that it is unlikely that lysosomes are directly involved in the LD – sEV connection. However, the expression of Rab7a, a regulator of the late endosomal pathway, correlated with the LD content of the cells and their sEV release. Therefore, the endosomal pathway might be a good candidate to contribute to this LD – sEV connection.

    At one point, authors argue that cells that secrete more sEVs also contain more MVBs, but this issue remains elusive. To what extent is the increase in LDs and sEVS correlated in particular with an increase in endosome-lysosomes, and ER-Golgi (LDs originate from the ER)?

    We thank the Reviewer for this comment. We agree that the analyses of sEVs secreted in the media might not reflect the MVB content in the cells. However, two experiments, one on Panc01 cells and another one on MCF7 cells, showed that the number of MVBs, assessed by confocal microscopy using CD63 staining (MCF7) or CD63 and Alix plasmids (PANC-01), was directly correlated with the number of released sEVs in the media (Figure Fig S3C and 4J).

    In addition, we included additional experiments assessing the lysosome content in HT29 LDHigh and LDLowcells. Hereby, we confirmed that HT29 LDHigh cells showed a higher LD content than HT29 LDLow cells. Inversely, by studying the lysotracker area per cell, we showed that HT29 LDLow population has a higher lysosomal content as compared to their counterpart, HT29 LDHigh cells (test = Wilcoxon rank sum test with continuity correction_ W = 85127, p-value = 7.255e-07 for LDs and W = 49321, p-value = 1.14e-11 for Lysotracker). However, we could not demonstrate a clear correlation between the number of LDs in the cell and the lysotracker signal.

    Finally, we have also studied the expression of GM130, a Golgi-shaping protein (Ref. 1) and Rab7, a late-endocytic protein (Fig S8C). While the expression of Rab7 (endosome) seemed to correlate with the LD and sEV contents, the expression of GM130 (Golgi) gave back no coherent results. Indeed, it was inversely correlated to the LD and sEV amount, in accordance with what was already reported elsewhere (Ref 2 and 3)

    • Nakamura N. Emerging new roles of GM130, a cis-Golgi matrix protein, in higher order cell functions. J Pharmacol Sci. (2010) 112:255–64. Doi: 10.1254/jphs.09R03CR
    • _Lydia-Ann L.S. Harris, James R. Skinner, Trevor M. Shew, Nada A. Abumrad, Nathan E. Wolins. _Monoacylglycerol disrupts Golgi structure and perilipin 2 association with lipid droplets.__Doi.org/10.1101/2021.07.09.451829
    • Alvin Kamili, Nuruliza Roslan, Sarah Frost, Laurence C. Cantrill, Dongwei Wang, Austin Della-Franca, Robert K. Bright, Guy E. Groblewski, Beate K. Straub, Andrew J. Hoy, Yuyan Chen, Jennifer A. Byrne; TPD52 expression increases neutral lipid storage within cultured cells. J Cell Sci 1 September 2015; 128 (17): 3223–3238. Doi: 10.1242/jcs.167692

    Authors conclude that the data with lipid inhibitors strengthen the connection between LDs and sEVs (Fig 2 and S2). However, is this regulation selective, or does it merely reflect the general effect of these inhibitors on membrane-related processes? The same comment applies to the role of iron metabolism after knockdown of ferritin heavy chain (Fig 3 and S3), acidic pH and X-ray radiation (Fig 4 and S4)****.

    We thank the Reviewer for the interesting observation. As previously mentioned, we cannot rule out other potential contributors to the LDs-sEVs connection upon lipid inhibitor treatments and/or the others external stimuli applied to our cell systems.

    The data presented in this manuscript merely represent a novel and unexplored (at least so far) organelle connection, direct or indirect, with a broad clinical implication. As the membrane-related processes (such as Endosomes, Golgi apparatus, Exosome (sEV) pathway, Lysosomes and Autophagosome) are all interconnected, in our opinion, it might be quite challenging to make such a definitive statement.

    Such assertion would require extensive further investigation to relate each organelle to the LDs and/or sEVs. However, with our research, we hope to open the door to a new era of investigations regarding the sEV – LDs connection.

    OTHER COMMENTS

    1) Which cell line is used for sEV analysis (markers vs contaminants (Fig S1B)? In any case, the data should be shown for both cell types.

    Our method to isolate sEVs is a standardized method that was already published by our group and collaborators in 2020 (M. Bordas, et al., Optimized Protocol for Isolation of Small Extracellular Vesicles from Human and Murine Lymphoid Tissues. Int J Mol Sci (2020) https:/doi.org/10.3390/ijms21155586.). This protocol was validated on human and mouse tissues, much more complex samples than cell culture supernatant.

    Figure S1C was modified, as requested by the Reviewer, including new data for HT29, Panc01 and MCF7 cell lines to broaden the panel. Those results confirmed the good purity of sEV samples isolated from cell culture supernatant.

    2) The Tsg101 blot is not impressive (Fig S1B): the difference between cells and sEVs is not easy to see. It would be nice if blots were quantified.

    Indeed, the signal obtained for TSG101 for sEVs derived from Panc01 cell line is quite weak. It is important to remember that not all sEV markers are highly expressed in all cell lines and their derived sEVs. Some cell line-derived sEVs show a low or high expression of the diverse sEV markers. To answer the Reviewer #1’s comment, we quantified the expression of TSG101 in Panc01-derived sEVs. The quantification showed that TSG101 is 6.8 times more expressed on Panc01-dervied sEVs as compared to the cell line. However, since the expression is quite low, this quantification should be taken with some caution.

    In light of the Reviewer ‘comment, we have performed the Western Blot analysis on other cell lines (HT29 and MCF7), and we have replaced TSG101 marker with CD9 marker (Figure S1C).

    3) From Fig 1B it cannot be concluded that the size of sEVs ranges from 30 to 200nm: the micrograph only shows a few structures.

    We appreciate the Reviewer's comment and have attempted to provide more clarity. Firstly, we want to highlight that TEM micrographs of sEVs typically show the donut shape, a unique feature of sEVs imaged with TEM, as well as a size range. In Figure 1B micrograph, the sEV size is approximately 100 nm. The size distribution of LoVo and HT29-derived sEVs can be observed from the NTA size measurements in Figure S1B. Indeed, the peak size is 148 nm for LoVo-derived sEVs and 135 nm for HT29, which aligns with the sEV sizes presented in Figure 1B. We have also included multiple micrographs here under. As the number of Supplementary Figures is already large, we have decided to not include those micrographs in the manuscript. The average size of LoVo-derived sEVs, based on TEM micrograph analysis, was 94 ± 41.10 nm, while the average size of HT29-derived sEVs was 76.41 ± 44.22 nm. The size discrepancy between the two methods (NTA versus TEM) can be ascribed to the dehydration step required for TEM, which results in a reduction of the actual sEV size.

    4) HT29 cells contain far more LDs than LoVo cells (Fig 1A). Similarly, sEV proteins (CD63, CD81, CD9, Hsc-70) are more abundant in HT29 sEV****s than in LoVo sEVs (Fig 1D). However, the sEV preparation from HT29 cells contains only approx. 50% more total protein than LoVo sEVs (Fig S1D-E). Are sEVs prepared from LoVo cells far more contaminated with cell debris etc.. than sEV fractions from HT29 cells?

    We are confident that our EV isolation method allows us to achieve high yield and excellent purity. It is possible that a lower number of sEVs in samples may lead to increased protein contamination during ultracentrifugation. However, size exclusion chromatography should minimize this protein contamination. It is important to note that the NTA method is significantly more sensitive and accurate than Qubit protein quantification. Consequently, protein concentration and particle concentration should not be directly compared.

    5) LD staining should be shown for the corresponding populations of cells with high/low CD63 (Fig 1E). Cells in culture can be somewhat heterogeneous, but the difference between low and high CD63 is quite extreme (Fig 1E). Is such high heterogeneity also observed with other proteins of the endocytic and biosynthetic pathways? Authors conclude that cells containing high CD63 levels also contain more MVBs (Fig 1E): are all late endosomal proteins (e.g. LAMP1, RAB7) upregulated in cells with high CD63?

    We thank the Reviewer for this comment, and we totally agree with the Reviewer that it would be better to have the LD and CD63 staining on the same images. Unfortunately, the staining for CD63 on LD540-sorted HT29 cells requires a permeabilization step that interferes with the cellular lipid part and could therefore negatively affect the LD imaging by confocal microscopy. To prove that the HT29 LDHigh and HT29 LDLowcontain high and low LD amount respectively, we sorted HT29 cells based on the LD content and, soon after, we observed them at the confocal microscopy. We thus added new images in Figure S1F, corresponding to the LD fluorescence detection. The readers will also appreciate the explanation regarding the inability of observing both LDs and CD63 staining on the same confocal images under the line 165 – 166:

    “As the staining for CD63 required a permeabilization step, and therefore lipid digestion, it was not possible to assess both LDs and CD+MVBs on the same micrographs “.

    In addition, we have added confocal images representing HT29 cells sorted based on their LD content and stained with Hoechst and Lysotracker. A quantification of the Lysotracker fluorescence per cell and the correlation with the number of LDs can also be appreciated in Figure S8A-B.

    Finally, we performed Western Blot analysis to examine Rab7a expression under various conditions described in our manuscript (Figure S8C). In general, Rab7 expression corresponded with LD content, indicating that cells with high LD content exhibited higher Rab7 expression, while cells with low LD amount showed lower Rab7 expression, except for Triacsin-C. The Reviewer can now appreciate the quantification in the graphs provided below (not included in the manuscript).

    Regarding the heterogeneity of LDs, CD63+MVBs, or lysotracker among the cell population, we have indeed noticed heterogeneity observable in these three types of staining in HT29, particularly in the HT29 LDHighpopulation.

    6) Inhibitors of lipid synthesis reduce LD formation (Fig 2B), sEV production and CD63 / CD81/ CD9 secretion (Fig2C-D, Fig S2B). Are the cellular levels of these (and other endosomal) proteins also reduced after inhibitor treatment? Does the stimulation of LD formation with oleic acid also stimulate CD63 synthesis and sEV production?

    We thank the Reviewer for this very interesting comment. To answer this question, we have added a supplementary figure (Figure S2A, S2B) showing the cellular expression of CD63 upon LD inhibition or stimulation.

    During the planning of our experiments, we discussed about the possibility of using oleic acid to induce the formation of Lipid Droplets, which was ultimately not done. This is because the use of oleic acid would have more strongly stimulated the triglyceride pathway, as extensively discussed elsewhere (Mejhert N. et al., The lipid droplet knowledge portal: a resource for systematic analyses of lipid droplet biology, Developmental Cell, 2022). Since Lipid Droplets are made by cholesterol esters and triglycerides, we preferred to use other stimuli (hypoxia, radiation), all of them already discussed in literature, to induce both pathways simultaneously, resulting in the Lipid Droplet formation/induction.

    7) It seems that pH and irradiation increase sEV markers far more significantly (Fig 4 B-C and Fig S4A-E) than FTH1 depletion decreases sEV markers (Fig 3 D-E). In fact, authors mention that they cannot exclude a contamination of sEVs with small apoptotic / autophagic vesicles after irradiation (Fig 4). To facilitate comparison, it would be nice to also show the number of secreted particles per cell (like after FTH1 depletion Fig 3D), as well as the distribution of possible contaminants (e.g. Fig S1). Also, authors state that the increase in the number CD63+ MVBs after irradiation is shown, but this is not the case.

    We apologize to the Reviewer because, in fact, one figure was missing (Figure 4). We have rectified this by increasing the quality of Figure 4 and have added representative images for each acquisition of the number of MVBs, either positive for CD63 or Alix, in transfected Panc01 cells X-ray irradiated (8 Gy) or not (0Gy). In addition, a similar experiment was performed in MCF7 cells transduced with shRNA or shFTH1. CD63+ MVBs were assessed in both cell line and the number of CD63+ puncta (MVBs) were quantified by ImageJ. The results, although not significative, illustrated a trend for MCF7 shFTH1 to contain less CD63+ MVBs than MCF7 shRNA. Furthermore, the quantification of sEVs released in the conditioned media was performed in three independent experiments and demonstrated that significantly less particles (sEVs) were released by MCF7 shFTH1 than MCF7 shRNA.

    8) Are the proteomic data (Fig 6) with LDlow and LDhigh cells obtained after cell sorting, as in Fig 1E? Did authors compare the proteome of LoVo and HT29 sEVs? How do the protein profiles (in particular proteins involved in lipid metabolism) obtained under different conditions compare with each other, in particular after irradiation (Fig4N) and knockdown of ferritin heavy chain (Fig 3, Fig S3)? It would also be interesting to compare these data with the data obtained in CR-CSCs culture under hypoxia (Fig S5). Are common proteins involved in sEV production and LD biosynthesis identified in the analysis of these biological processes? Is there a common set of proteins/genes revealed by this analysis, which may potentially control sEV production and LD biosynthesis?

    We thank the reviewer for this interesting comment.

    Proteomic analyses have been performed on the following conditions:

    • Panc01 (0 Gy – 6 Gy – 8 Gy) for sEV samples
    • MCF7 (shFTH1 and MCF7 shRNA)
    • MCF7 (0 Gy and 6 Gy)
    • MCF7 (Normoxia and Hypoxia)
    • H460 (0 Gy and 6 Gy)
    • H460 (Normoxia and Hypoxia)

    RNA sequencing was performed on the following conditions:

    • CR-CSCs (#4, #8, #21)

    Based on all those data, we have analyzed the sEV pathway and how this pathway was modulated in the conditions with high LD content and low LD content. We therefore came up with several proteins, presented in Figure S7. Based on this analysis, we have decided to further investigate the role of RAB18, RAB5c and RAB7a in the connection between LDs and sEVs. Those additional results can be found in Figure 6 and Figure S7A (originally Figure 6). We have found that RAB5c, but not RAB7a or RAB18, seems to be a good candidate to intervene in the LD – sEV connection.

    Minor comments

    1) Some parts of the text are still a bit rough, and should be read and corrected carefully. For example: i) isn't it obvious that a common source of lipids builds up the membrane of sEVS, much like any other membrane (line 90, p.2); ii) what does this sentence mean: "LD have been considered as mere fat storage organelles for a long time, although important evidence could be traced back to the early 1960's". Important evidence for what? iii) why is the acronym AdExo used? iv) (line 138) the text should probably be "sEVs released during 72h were studied" and not "released sEVs were studied ... 72 h after seeding".

    We apologize to the Reviewer if some parts of the paper were a bit rough. We have re-read the entire manuscript and corrected all the parts that needed revision work.

    2) The captions are far too small in most figures and diagrams (for example X and Y axis in Fig 1C-D, text in Fig 1E; Fig S1; Fig 3C proteins in the heatmap).

    We agree with the Reviewer. All images and their captions were properly revised.

    3) The color code for LoVO and HT29 cells is reversed in Fig S1D-E

    The mistake was corrected.

    4) In Fig 1D, I cannot see CD81 in the LoVo blot.

    In the image below, it is possible to see the LoVo blot.

    5) Wording is not adequate in following sentences: "62.7% of proteins related to the exosomal pathway are downregulated in MCF7 shFTH1 cells" (line 233) and a few lines below: ".. the expression of almost all exosomal markers was downregulated in MCF7 shFTH1 cells" (line 239). Does 62.7% represent all proteins?

    We apologize to the reviewer for the mistake. We rephrased this sentence.

    6) In Fig 3E authors compare sEV markers secreted by cells treated with shFTH1 or control shRNA. The Anx5 and CD63 blots are not very convincing (quantification would be helpful).

    We apologize to the Reviewer for this issue. These Western Blot analyses were performed only once, therefore a quantification in the manuscript would not be relevant. However, we report here the results of the quantification. The expression of Annexin V was 1.58 times higher in MCF7 shRNA than MCF7 shFTH1, while the expression of CD63 was 1.34 time higher in MCF shRNA as compared to MCF7 shFTH1.

    7) The micrographs in Fig 4L are too small: gold particles cannot be seen, even in the high magnification views.

    We thank the Reviewer for her/his comment. We have moved the micrograph and the quantification histogram to the Figure S6. Now, it is possible to discriminate easily gold nanoparticles.

    8) The micrographs showing ALIX and CD63 (Fig 4J) in irradiated and unirradiated Panc01 cells should be shown for comparison.

    We followed the Reviewer’ suggestion as it is possible to note in the Figure below.

    Reviewer #2:

    This manuscript describes a relationship between lipid droplet presence in cells and small EV secretion. First, correlations are done between number of lipid droplets and numbers of EVs secreted. Then chemical inhibitors of lipid droplet biosynthesis pathways were shown to reduce small EV secretion. Then various processes known to target lipid droplets, including iron metabolism, irradiation, hypoxia, low pH are used to show concordant effects on lipid droplets and small EV secretion. Proteomic analysis of EVs and cells subjected to some of the treatments are also performed. Overall, it is an interesting line of investigation and the data overall seem solid. Several flaws exist, which can probably be fixed. These include the use of different cell lines for different experiments. It makes it a bit difficult to connect everything together. It could be fixed by adding some extra cell lines to some experiments - for example taking the MCF7 and Panc-01 cells for which proteomics was performed and redoing some of the correlative and causative experiments from Figs 1 and 2.

    We appreciate the Reviewer's insightful observation. Following her/his suggestion, we have conducted additional experiments on MCF7, H460 and PANC-01 cell lines to enhance data consistency and facilitate a smoother transition between different sections of the paper.

    It also would be good to have some more direct evidence of the connection between lipid droplets and EV secretion - one could argue that this was already done in Fig 2 with the chemical inhibitors, I wonder if there is a genetic way to do it too?

    We totally agree with the Reviewer. Indeed, starting from our proteomic data we highlighted some genes belonging to the RAB family as potential candidates to interfere with the LD – sEV connection. The Reviewer can now appreciate in Figure 6 and Figure S7, the results from the additional experiments we carried out on RAB5c, RAB7a and RAB18 silencing in HT29 cells. The former Figure 6 has been moved in the Supplementary part (Figure S7).

    Some tightening up of the writing (especially the Discussion) and the resolution of the figures would also improve the manuscript.

    We apologize to the Reviewer for this issue. We have now re-prepared all Figures by increasing their resolution, as well as reviewing the entire manuscript with the aim of making the reading smoother and simpler.

    **Overall, it is a nice piece of work but there are many minor things to be fixed.
    **
    Specific Comments:

    The sentence in the Introduction: "The non-endosomal pathway generates sEVs devoid of
    CD63, CD81 and CD9 or sEVs enriched in ECM and serum-derived factors (7)." is not well-supported and should be removed. The idea that you can classify membrane of origin based on markers has not been proven, but rather assumed.

    We agree with the Reviewer. We have rephrased the sentence.

    We thank the Reviewer for this comment. In response to this, we have generated correlation graphs for several of our experiments:

    • HT29 (CTL – Triacsin-C - PF-06424439) in Figure 2E
    • PANC-01 (CTL – 2 – 4 – 6 – 8 Gy) in Figure 4K
    • CR-CSCs (#4, #8, #21) in Figure 5E

    The Method used for EV purification should be stated in the Results rather than referring to a reference and a Supplemental Figure (S1A) that is too low of a resolution to see.

    Our method to isolate sEVs is a standardized methods that was already published by our group and collaborators in 2020 (M. Bordas, et al., Optimized Protocol for Isolation of Small Extracellular Vesicles from Human and Murine Lymphoid Tissues. Int J Mol Sci (2020) https:/doi.org/10.3390/ijms21155586.). This protocol was validated on human and mouse tissues, much more complex samples than cell culture supernatant.

    In regard to the Reviewer’s comment, we have added a better description of the protocol in the Results part, referring to the Material and Method. For this reason, we decided to keep the sEV protocol in the SI section. We apologize for the low quality of the Figure S1. In agreement with the Reviewer suggestion, we have modified the image by increasing its quality.

    Fig 1B would be better to have an image in which the EVs are not aggregated.

    We thank the Reviewer for this comment and have modified the Figure accordingly.

    Fig 3 is interesting but jumps cell lines. For better continuity, some of the experiments from Figs 1 and 2 should be repeated in the MCF7 cells to connect with the proteomics.

    In agreement with the Reviewer’ comment, we decided to perform additional experiment on MCF7, using Triacsin-C. The Reviewer can now appreciate the results in Figure 2F, Figure 2G and Figure S2E.

    Fig 3C is too low resolution to read, please export at higher resolution.

    We are sorry for the low-quality Figure. We have modified the image accordingly.

    Please provide all the raw proteomics data as a supplementary spreadsheet****.

    We have provided all the raw data regarding our proteomic analyses.

    Fig 4 panels are low resolution

    We apologize for the low-resolution Figure. We have modified the figure by increasing the quality.

    Fig 4 again adds new cell lines with H460 and Panc-01

    We thank the reviewer for this comment. In this regard, we have performed additional experiment:

    • Western Blot: comparison cellular and exosomal markers (Figure S1C)
    • MCF7 (CTL - Triacsin) (Figure 2F, Figure 2G and Figure S2E)
    • Western Blot: analysis of RAB7a, GM130

    The images corresponding to 4J should be shown in a Supp Figure somewhere

    We thank the reviewer for pointing out this oversight. We have added the confocal images corresponding to the Figure 4J below the quantification.

    The statements: "In addition, the exosomal nature of Panc01-derived vesicles was demonstrated by an analysis of CD63+ or Alix+ multivesicular bodies (MVBs) in unirradiated (0 Gy) or irradiated (8 Gy) pancreatic cancer cells (Fig 4J). Moreover, we confirmed a clear correlation between cellular LD content and sEV biogenesis, as represented in Fig 4K." are overly conclusive. For 4J, one can make a statement about the MVBs but not the EVs as that's not what was measured there. Likewise for 4K, what was measured was how many EVs were released not how many were formed. While the data are suggestive of alteration of exosome biogenesis, they are not conclusive.

    We agree with the reviewer and have performed the necessary changes in the manuscript. The reviewer can see the changes under the lines 282 – 284:

    “In addition, the analysis of CD63+ or Alix+ multivesicular bodies (MVBs) in unirradiated (0 Gy) or irradiated (8 Gy) pancreatic cancer cells revealed an increased number of MVBs after irradiation (Fig****ure 4J).”

    Western blot is always capitalized by convention - Western not western.

    We have corrected it accordingly.

    Fig 5A is too small and low resolution - suggest eliminating and just put info in methods.

    We are sorry for the low-resolution image. We have followed the Reviewer suggestion. The graphical method has been now moved to the Supplementary Figure S6.

    Fig 5G, many of the genes shown are frequently EV cargoes but most not involved in exosome biogenesis - not sure where the label of Exosome pathway came from but it is not very compelling. Only ANXA2, Arf6, and Rab5C seem related and they are barely elevated.

    We completely agree with the Reviewer's comment. As a result, we have revised the heatmap title to "Exosomal Cargoes and Pathways" instead of "Exosomal Pathway".

    Most main figures and all supplementary figures are extremely low res - please fix.

    We are very sorry for the low-quality figures. We have revised all Figures (main text and SI) by increasing their quality.

    Fig 6 is first mentioned in the Discussion - it should be described in the Results before that (or alternatively removed).

    We agree with the Reviewer. Our initial idea was to mention perspectives of analyses that could be carried ulteriorly. Nevertheless, we have performed additional experiments in order to get insight on the mechanism involved in the LD – sEV connection. Indeed, based on our proteomic data, we have analyzed the sEV pathway and how this pathway was modulated in the conditions with high LD content and low LD content. We therefore came up with several proteins, presented in Figure S7A (originally Figure 6). Based on this analysis, we have decided to further investigate the role of RAB18, RAB5c and RAB7a in the connection between LDs and sEVs. Those additional results can be found in Figure 6 and Figure S7 in the Results section. We have found that RAB5c, but not RAB7a or RAB18, seems to be a good candidate to intervene in the LD – sEV connection.

    Table S1, also first mentioned in the Discussion, is missing. Either describe in the Results section or remove the callout to it.

    Our apologies for that. The Table S1 has been now mentioned in the Results section and has been properly uploaded.

    The discussion is too dense with too many trains of thought, often many different directions in the same paragraph. It needs to be streamlined, with a central thought for each paragraph and good transitions between the paragraphs.

    We apologize to the Reviewer if the Discussion part was a bit confusing. We rewrote the paragraph, streamlining it and making the transitions between its paragraphs smoother.

    Reviewer #2 (Significance (Required)):

    **
    Strengths of this manuscript are the interesting connection between lipid droplets and exosomes and the number of experiments to address it.**

    **

    Limitations: use of different cell lines for different figures, overall descriptive nature with regard to direct demonstration of connection to lipid droplets -- it's kind of done in Fig 2, but could be possibly bolstered.**

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    Referee #2

    Evidence, reproducibility and clarity

    This manuscript describes a relationship between lipid droplet presence in cells and small EV secretion. First, correlations are done between number of lipid droplets and numbers of EVs secreted. Then chemical inhibitors of lipid droplet biosynthesis pathways were shown to reduce small EV secretion. Then various processes known to target lipid droplets, including iron metabolism, irradiation, hypoxia, low pH are used to show concordant effects on lipid droplets and small EV secretion. Proteomic analysis of EVs and cells subjected to some of the treatments are also performed. Overall, it is an interesting line of investigation and the data overall seem solid. Several flaws exist, which can probably be fixed. These include the use of different cell lines for different experiments. It makes it a bit difficult to connect everything together. It could be fixed by adding some extra cell lines to some experiments - for example taking the MCF7 and Panc-01 cells for which proteomics was performed and redoing some of the correlative and causative experiments from Figs 1 and 2. It also would be good to have some more direct evidence of the connection between lipid droplets and EV secretion - one could argue that this was already done in Fig 2 with the chemical inhibitors, I wonder if there is a genetic way to do it too? Some tightening up of the writing (especially the Discussion) and the resolution of the figures would also improve the manuscript. Overall, it is a nice piece of work but there are many minor things to be fixed.

    Specific Comments:

    The sentence in the Introduction: "The non-endosomal pathway generates sEVs devoid of
    CD63, CD81 and CD9 or sEVs enriched in ECM and serum-derived factors (7)." is not well-supported and should be removed. The idea that you can classify membrane of origin based on markers has not been proven, but rather assumed.

    Fig 1A and B - to better support the idea of a correlation between LD formation and EV release, more than two cell lines should be used and a linear correlation plot with R2 value shown. Likewise, it would be very interesting to see whether there is really a correlation between LD content and CD63-endosome positivity in a similar manner, given the results in Fig 1E. Also, it would be good to see LD and CD63 in the same cells for Fig 1E from the sorted populations.

    The Method used for EV purification should be stated in the Results rather than referring to a reference and a Supplemental Figure (S1A) that is too low of a resolution to see.

    Fig 1B would be better to have an image in which the EVs are not aggregated.

    Fig 3 is interesting but jumps cell lines. For better continuity, some of the experiments from Figs 1 and 2 should be repeated in the MCF7 cells to connect with the proteomics.

    Fig 3C is too low resolution to read, please export at higher resolution.

    Please provide all the raw proteomics data as a supplementary spreadsheet

    Fig 4 panels are low resolution

    Fig 4 again adds new cell lines with H460 and Panc-01

    The images corresponding to 4J should be shown in a Supp Figure somewhere

    The statements: "In addition, the exosomal nature of Panc01-derived vesicles was demonstrated by an analysis of CD63+ or Alix+ multivesicular bodies (MVBs) in unirradiated (0 Gy) or irradiated (8 Gy) pancreatic cancer cells (Fig 4J). Moreover, we confirmed a clear correlation between cellular LD content and sEV biogenesis, as represented in Fig 4K." are overly conclusive. For 4J, one can make a statement about the MVBs but not the EVs as that's not what was measured there. Likewise for 4K, what was measured was how many EVs were released not how many were formed. While the data are suggestive of alteration of exosome biogenesis, they are not conclusive.

    Western blot is always capitalized by convention - Western not western.

    Fig 5A is too small and low resolution - suggest eliminating and just put info in methods.

    Fig 5G, many of the genes shown are frequently EV cargoes but most not involved in exosome biogenesis - not sure where the label of Exosome pathway came from but it is not very compelling. Only ANXA2, Arf6, and Rab5C seem related and they are barely elevated.

    Most main figures and all supplementary figures are extremely low res - please fix.

    Fig 6 is first mentioned in the Discussion - it should be described in the Results before that (or alternatively removed).

    Table S1, also first mentioned in the Discussion, is missing. Either describe in the Results section or remove the callout to it.

    The discussion is too dense with too many trains of thought, often many different directions in the same paragraph. It needs to be streamlined, with a central thought for each paragraph and good transitions between the paragraphs.

    Significance

    Strengths of this manuscript are the interesting connection between lipid droplets and exosomes and the number of experiments to address it.

    Limitations: use of different cell lines for different figures, overall descriptive nature with regard to direct demonstration of connection to lipid droplets -- it's kind of done in Fig 2, but could be possibly bolstered.

  6. Review Commons

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

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    Referee #1

    Evidence, reproducibility and clarity

    In this paper, authors report that radiation, acidic pH, hypoxia, and drugs that interfere with lipid synthesis, all of which affect lipid droplets (LD), also affect the production of small extracellular vesicles (sEVs). In addition, they also report that LD content and sEV secretion are also modulated in CR-CSCs. Authors conclude that sEV formation and secretion is directly linked to LDs, and that their studies may open the way to new clinical perspectives. However, some important issues need to be addressed before the paper can be considered for publication.

    My main concern is that the notion that LDs and sEVs are linked remains vague. Do cells contain more LDs and secrete more sEVs because these two pathways are selectively up-regulated via some mechanism that controls both pathways in a concerted manner? Or do cells with more LDs and more sEVs also contain more of everything, perhaps as a result of metabolic activity? A direct corollary of this issue is whether increased sEV secretion reflects more endosomes and lysosomes (e.g. LysoTracker-positive compartments) or whether sEV secretion is selectively up-regulated. At one point, authors argue that cells that secrete more sEVs also contain more MVBs, but this issue remains elusive. To what extent is the increase in LDs and sEVS correlated in particular with an increase in endosome-lysosomes, and ER-Golgi (LDs originate from the ER)? Authors conclude that the data with lipid inhibitors strengthen the connection between LDs and sEVs (Fig 2 and S2). However, is this regulation selective, or does it merely reflect the general effect of these inhibitors on membrane-related processes? The same comment applies to the role of iron metabolism after knockdown of ferritin heavy chain (Fig 3 and S3), acidic pH and X-ray radiation (Fig 4 and S4)

    Other comments

    1. Which cell line is used for sEV analysis (markers vs contaminants (Fig S1B)? In any case, the data should be shown for both cell types.
    2. The Tsg101 blot is not impressive (Fig S1B): the difference between cells and sEVs is not easy to see. It would be nice if blots were quantified.
    3. From Fig 1B it cannot be concluded that the size of sEVs ranges from 30 to 200nm: the micrograph only shows a few structures.
    4. HT29 cells contain far more LDs than LoVo cells (Fig 1A). Similarly, sEV proteins (CD63, CD81, CD9, Hsc-70) are more abundant in HT29 sEVS than in LoVo sEVs (Fig 1D). However, the sEV preparation from HT29 cells contains only approx. 50% more total protein than LoVo sEVs (Fig S1D-E). Are sEVs prepared from LoVo cells far more contaminated with cell debris etc.. than sEV fractions from HT29 cells?
    5. LD staining should be shown for the corresponding populations of cells with high/low CD63 (Fig 1E). Cells in culture can be somewhat heterogeneous, but the difference between low and high CD63 is quite extreme (Fig 1E). Is such high heterogeneity also observed with other proteins of the endocytic and biosynthetic pathways? Authors conclude that cells containing high CD63 levels also contain more MVBs (Fig 1E): are all late endosomal proteins (e.g. LAMP1, RAB7) upregulated in cells with high CD63?
    6. Inhibitors of lipid synthesis reduce LD formation (Fig 2B), sEV production and CD63 / CD81/ CD9 secretion (Fig2C-D, Fig S2B). Are the cellular levels of these (and other endosomal) proteins also reduced after inhibitor treatment? Does the stimulation of LD formation with oleic acid also stimulate CD63 synthesis and sEV production?
    7. It seems that pH and irradiation increase sEV markers far more significantly (Fig 4 B-C and Fig S4A-E) than FTH1 depletion decreases sEV markers (Fig 3 D-E). In fact, authors mention that they cannot exclude a contamination of sEVs with small apoptotic / autophagic vesicles after irradiation (Fig 4). To facilitate comparison, it would be nice to also show the number of secreted particles per cell (like after FTH1 depletion Fig 3D), as well as the distribution of possible contaminants (e.g. Fig S1). Also, authors state that the increase in the number CD63+ MVBs after irradiation is shown, but this is not the case.
    8. Are the proteomic data (Fig 6) with LDlow and LDhigh cells obtained after cell sorting, as in Fig 1E? Did authors compare the proteome of LoVo and HT29 sEVs? How do the protein profiles (in particular proteins involved in lipid metabolism) obtained under different conditions compare with each other, in particular after irradiation (Fig4N) and knockdown of ferritin heavy chain (Fig 3, Fig S3)? It would also be interesting to compare these data with the data obtained in CR-CSCs culture under hypoxia (Fig S5). Are common proteins involved in sEV production and LD biosynthesis identified in the analysis of these biological processes? Is there a common set of proteins/genes revealed by this analysis, which may potentially control sEV production and LD biosynthesis?

    Minor comments

    1. Some parts of the text are still a bit rough, and should be read and corrected carefully. For example: i) isn't it obvious that a common source of lipids builds up the membrane of sEVS, much like any other membrane (line 90, p.2); ii) what does this sentence mean: "LD have been considered as mere fat storage organelles for a long time, although important evidence could be traced back to the early 1960's". Important evidence for what? iii) why is the acronym AdExo used? iv) (line 138) the text should probably be "sEVs released during 72h were studied" and not "released sEVs were studied ... 72 h after seeding".
    2. The captions are far too small in most figures and diagrams (for example x and Y axis in Fig 1C-D, text in Fig 1E; Fig S1; Fig 3C proteins in the heatmap).
    3. The color code for LoVO and HT29 cells is reversed in Fig S1D-E
    4. In Fig 1D, I cannot see CD81 in the LoVo blot.
    5. Wording is not adequate in following sentences: "62.7% of proteins related to the exosomal pathway are downregulated in MCF7 shFTH1 cells" (line 233) and a few lines below: ".. the expression of almost all exosomal markers was downregulated in MCF7 shFTH1 cells" (line 239). Does 62.7% represent all proteins?
    6. In Fig 3E authors compare sEV markers secreted by cells treated with shFTH1 or control shRNA. The Anx5 and CD63 blots are not very convincing (quantification would be helpful).
    7. The micrographs in Fig 4L are too small: gold particles cannot be seen, even in the high magnification views.
    8. The micrographs showing ALIX and CD63 (Fig 4J) in irradiated and unirradiated Panc01 cells should be shown for comparison.

    Significance

    The topic of the paper is clearly interesting, since the mechanisms that regulate sEV formation and secretion are not fully understood and since the notion that their fate is linked to LDs is potentially exciting.

    My expertise: subcellular organization, endocytosis, membrane traffic, organelle biogenesis

  7. Version published to 10.1101/2022.10.24.513202v1 on bioRxiv