Single-cell transcriptomics identifies altered neutrophil dynamics and accentuated T-cell cytotoxicity in tobacco flavored e-cigarette exposed mouse lungs

  1. Gagandeep Kaur
  2. Thomas Lamb
  3. Ariel Tjitropranoto
  4. Irfan Rahman

  • Curated by eLife

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    eLife Assessment

    This manuscript by Kaur et al. identifies differential gene expression in distinct cell populations, specifically myeloid and lymphoid cells, following short-term exposure to e-cigarette aerosols with various flavors. Their findings are useful because they provide a single-cell sequencing data resource for assessing which genes and cellular pathways could be affected by e-cig aerosols and their components. However, the evidence is incomplete due to limited number of biological replicates per condition, as well as due to the lack of in vivo validation.

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Abstract

E-cigarettes (e-cigs) are a public health concern for young adults due to their rising popularity despite evidence of harmful effects. Yet an extensive study defining the cell-specific immune changes upon exposure to flavored e-cigs remains elusive. To determine the immunological lung landscape upon acute nose-only exposure of C57BL/6J to flavored e-cig aerosols, we performed single-cell RNA sequencing (scRNA seq). Analyses of the levels of metals in the e-cig aerosol generated daily during exposure revealed a flavor-dependent variation in the day-to-day leaching of the levels of metals like Ni, Cu, K and Zn, among others. scRNA profiles of 71,725 cells generated from control and treatment groups (n=2/sex/group) found maximum dysregulation of (a) myeloid cell function in menthol (324 differentially enriched genes (DEG)) and tobacco (553 DEGs) –flavor exposed and (b) lymphoid cell function in fruit (112 DEGs)-flavored e-cig aerosol exposed mouse lungs as compared to air. Flow cytometry analyses identified marked increase in the neutrophil percentage and a decrease in the eosinophil count in menthol and tobacco-flavored e-cig aerosol exposed mouse lungs which corroborated with our scRNA seq data. We further found an: (a) increase in CD8+ T cell percentages, (b) upregulation of inflammatory genes like Stat4, Il1bos, Il1b, Il1ra and Cxcl3 and (c) enrichment of terms like ‘T-helper cell 1cytokine function’ and ‘NK cell degranulation’ in the lungs of e-cig aerosol exposed mouse when compared to control. Interestingly, the increase in the level of immature neutrophils characterized by Ly6G deficiency and reduction in the S100A8 (marker for neutrophil activation) using immunofluorescence in tobacco-flavored e-cig aerosol exposed mouse lung sections point towards a possible shift in the neutrophil dynamics upon exposure to e-cigs. Overall, this study identifies flavor dependent changes in myeloid and lymphoid-cell mediated responses in the mouse lungs exposed to acute nose-only e-cig aerosols and provides a resource to influence future research in select /specific cell types to understand the immunological implications of long-term use of e-cigs.

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  1. eLife

    eLife Assessment

    This manuscript by Kaur et al. identifies differential gene expression in distinct cell populations, specifically myeloid and lymphoid cells, following short-term exposure to e-cigarette aerosols with various flavors. Their findings are useful because they provide a single-cell sequencing data resource for assessing which genes and cellular pathways could be affected by e-cig aerosols and their components. However, the evidence is incomplete due to limited number of biological replicates per condition, as well as due to the lack of in vivo validation.

  2. eLife

    Reviewer #1 (Public review):

    Summary:

    The authors assess the impact of E-cigarette smoke exposure on mouse lungs using single-cell RNA sequencing. Air was used as control and several flavors (fruit, menthol, tobacco) were tested. Differentially expressed genes (DEGs) were identified for each group and compared against the air control. Changes in gene expression in either myeloid or lymphoid cells were identified for each flavor and the results varied by sex. The scRNAseq dataset will be of interest to the lung immunity and e-cig research communities, and some of the observed effects could be important. Unfortunately, the revision did not address the reviewers' main concerns about low replicate numbers and lack of validations. The study remains preliminary and no solid conclusions could be drawn about the effects of E-cig exposure as a whole or any flavor-specific phenotypes.

    Strengths:

    The study is the first to use scRNAseq to systematically analyze the impact of e-cigarettes on the lung. The dataset will be of broad interest.

    Weaknesses:

    This study had only N=1 biological replicates for the single-cell sequencing data per sex per group and some sex-dependent effects were observed. This could have been remedied by validating key observations from the study using traditional methods such as flow cytometry and qPCR, but the limited number of validation experiments did not support the conclusions of the scRNAseq analysis. An important control group (PG:VG) had extremely low cell numbers and therefore could not be used to derive meaningful conclusions. Statistical analysis is lacking in almost all figures. Overall, this is a preliminary study with some potentially interesting observations.

    (1) The only new validation experiment for this revision is the immunofluorescent staining of neutrophils in Figure 4. The images are very low resolution and low quality and it is not clear which cells are neutrophils. S100A8 (calprotectin) is highly abundant in neutrophils but not strictly neutrophil-specific. It's hard to distinguish positive cells from autofluorescence in both ly6g and S100a8 channels. No statistical analysis is presented for the quantified data from this experiment.

    (2) The relevance of Fig. 3A and B are unclear since these numbers only reflect the number of cells captured in the scRNAseq experiment and the biological meaning of this data is not explained. Flow cytometry quantification is presented as cell counts but percentage of cells from the CD45+ gate should be shown. No statistical analysis is shown, and flow cytometry results do not support the conclusions of scRNAseq data.

  3. eLife

    Reviewer #3 (Public review):

    This work aims to establish cell-type-specific changes in gene expression upon exposure to different flavors of commercial e-cigarette aerosols compared to control or vehicle. Kaur et al. conclude that immune cells are most affected, with the greatest dysregulation found in myeloid cells exposed to tobacco-flavored e-cigs and lymphoid cells exposed to fruit-flavored e-cigs. The up- and down-regulated genes are heavily associated with innate immune response. The authors suggest that a Ly6G-deficient subset of neutrophils is found to be increased in abundance for the treatment groups, while gene expression remains consistent, which could indicate impaired function. Increased expression of CD4+ and CD8+ T cells along with their associated markers for proliferation and cytotoxicity is thought to be a result of activation following this decline in neutrophil-mediated immune response.

    Strengths:

    Single-cell sequencing data can be very valuable in identifying potential health risks and clinical pathologies of lung conditions associated with e-cigarettes considering they are still relatively new.

    Not many studies have been performed on cell-type-specific differential gene expression following exposure to e-cig aerosols.

    The assays performed address several factors of e-cig exposure such as metal concentration in the liquid and condensate, coil composition, cotinine/nicotine levels in serum and the product itself, cell types affected, which genes are up- or down-regulated and what pathways they control.

    Considerations were made to ensure clinical relevance such as selecting mice whose ages corresponded with human adolescents so that data collected was relevant.

    The discussion addresses the limitations of this study.

    Weaknesses:

    The exposure period of 1 hour a day for 5 days is not representative of chronic use and this time point may be too short to see a full response in all cell types. There is no gold standard in the field.

    Most findings are based on scRNA-seq alone, so interpretations should be made with care as some conclusions are observational.

    This paper provides a good foundation for future follow-up studies that will examine the effects of e-cig exposure on innate immunity.

  4. eLife

    Author response:

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

    Public Reviews:

    Reviewer #1 (Public review):

    The authors assess the impact of E-cigarette smoke exposure on mouse lungs using single cell RNA sequencing. Air was used as control and several flavors (fruit, menthol, tobacco) were tested. Differentially expressed genes (DEGs) were identified for each group and compared against the air control. Changes in gene expression in either myeloid or lymphoid cells were identified for each flavor and the results varied by sex. The scRNAseq dataset will be of interest to the lung immunity and e-cig research communities and some of the observed effects could be important. Unfortunately, the revision did not address the reviewers' main concerns about low replicate numbers and lack of validations. The study remains preliminary, and no solid conclusions could be drawn about the effects of E-cig exposure as a whole or any flavor-specific phenotypes.

    Strengths:

    The study is the first to use scRNAseq to systematically analyze the impact of e-cigarettes on the lung. The dataset will be of broad interest.

    Weaknesses:

    scRNAseq studies may have low replicate numbers due to the high cost of studies but at least 2 or 3 biological replicates for each experimental group is required to ensure rigor of the interpretation. This study had only N=1 per sex per group and some sex-dependent effects were observed. This could have been remedied by validating key observations from the study using traditional methods such as flow cytometry and qPCR, but the limited number of validation experiments did not support the conclusions of the scRNA seq analysis. An important control group (PG:VG) had extremely low cell numbers and was basically not useful. Statistical analysis is lacking in almost all figures. Overall, this is a preliminary study with some potentially interesting observations, but no solid conclusions can be made from the data presented.

    The only new validation experiment is the immunofluorescent staining of neutrophils in Figure 4. The images are very low resolution and low quality and it is not clear which cells are neutrophils. S100A8 (calprotectin) is highly abundant in neutrophils but not strictly neutrophil-specific. It's hard to distinguish positive cells from autofluorescence in both Ly6g and S100a8 channels. No statistical analysis in the quantification.

    We thank the reviewer for identifying the strengths of this study and pointing out the gaps in knowledge. Overall, our purpose to present this data is to provide the scRNA seq results as a resource to a wider community. We have used techniques like flow cytometry, multianalyte cytokine array and immunofluorescence to validate some of the results. We agree with the reviewer that we were unable to rightly point out the significance of our findings with the immunofluorescent stain in the previous edit. We have revised the manuscript and included the quantification for both Ly6G+ and S100A8+ cells in e-cig aerosol exposed and control lung tissues. Briefly, we identified a marked decrease in the staining for S100A8 (marker for neutrophil activation) in tobacco-flavored e-cig exposed mouse lungs as compared to controls. Upon considering the corroborating evidence from scRNA seq and flow cytometry with regards to increased neutrophil percentages in experimental group and lowered staining for active neutrophils using immunofluorescence, we speculate that exposure to e-cig (tobacco) aerosols may alter the neutrophil dynamics within the lungs. Also, co-immunofluorescence identified a more prominent co-localization of the two markers in control samples as compared to the treatment group which points towards some changes in the innate immune milieu within the lungs upon exposures. Future work is required to validate these speculations.

    We have now discussed all the above-mentioned points in the Discussion section of the revised manuscript and toned down our conclusions regarding sex-dependent changes from scRNA seq data.

    It is unclear what the meaning of Fig. 3A and B is, since these numbers only reflect the number of cells captured in the scRNAseq experiment and are not biologically meaningful. Flow cytometry quantification is presented as cell counts, but the percentage of cells from the CD45+ gate should be shown. No statistical analysis is shown, and flow cytometry results do not support the conclusions of scRNAseq data.

    We thank the reviewer for this question. However, we would like to highlight that scRNA seq and flow cytometry may show similar trends but cannot be identical as one relies on cell surface markers (protein) for identification of cell types, while other is dependent on the transcriptomic signatures to identify the cell types. In our data, for the myeloid cells (alveolar macrophages and neutrophils), the scRNA and flow cytometry data match in trend. However, the trends do not match with respect to the lymphoid cells being studied (CD4 and CD8 T cells). The possible explanation for such a finding could be possible high gene dropout rates in scRNA seq, different analytical resolution for the two techniques and pooling of samples in our single cell workflow. We realize these shortcomings in our analyses and mention it clearly in the discussion as limitation of our work. It is important to note also that cell frequencies identified in scRNA seq just provide wide and indistinct indications which need to be further validated, which we tried to accomplish in our work to some degree. Our flow-based results clearly highlight the sex-specific variations in the immune cell percentages (something we could not have anticipated earlier). In future studies, we will include more replicates to tease out sex-based variations upon acute and chronic exposure to e-cig aerosols.

    We have now replotted the graphs in Fig 3A and B and plotted the flow quantification as the percentage of total CD45+ cells. The gating strategy for the flow plots is also included as Figure S6 in the revised manuscript.

    Reviewer #2 (Public review):

    This study provides some interesting observations on how different flavour e-cigarettes can affect lung immunology; however, there are numerous flaws, including a low replicate number and a lack of effective validation methods, meaning findings may not be repeated. This is a revised article but several weaknesses remain related to the analysis and interpretation of the data.

    Strengths:

    The strength of the study is the successful scRNA-seq experiment which gives some preliminary data that can be used to create new hypotheses in this area.

    Weaknesses:

    Although some text weaknesses have been addressed since resubmission, other specific weaknesses remain: The major weakness is the n-number and analysis methods. Two biological n per group is not acceptable to base any solid conclusions. Any validatory data was too little (only cell % data) and not always supporting the findings (e.g. figure 3D does not match 3B/4A). Other examples include:

    There aren't enough cells to justify analysis - only 300-1500 myeloid cells per group with not many of these being neutrophils or the apparent 'Ly6G- neutrophils'.

    We thank the reviewer for the comment, but we disagree with the reviewer in terms of the justification of analyses. All the flavored e-cig aerosol groups were compared with air controls to deduce the outcomes in the current study. We already acknowledge low sample quality for PGVG group and have only included the comparisons with PGVG upon reviewer’s request which is open to interpretation by the reader.

    By that measure, each treatment group (except PGVG group) has over 1000 cells with 24777 genes being analyzed for each cell type, which by the standards of single cell is sufficient. We understand that this strategy should not be used for detection of rare cell populations, which was neither the purpose of this manuscript nor was attempted. We conduct comparisons of broader cell types and mention more samples need to be added in the Discussion section of the revised manuscript.

    As for the Ly6G neutrophil category, we don’t only base our results on scRNA analyses but also perform co-immunofluorescence and multi-analyte analyses and use evidence from previous literature to back our outcome. To avoid over-stating our results we have revamped the whole manuscript and ensured to tone down our results with relation to the presence of Ly6G- neutrophils. We do understand that more work is required in the future, but our work clearly shows the shift in neutrophil dynamics upon exposure which should be reported, in our opinion.

    The dynamic range of RNA measurement using scRNAseq is known to be limited - how do we know whether genes are not expressed or just didn't hit detection? This links into the Ly6G negative neutrophil comments, but in general the lack of gene expression in this kind of data should be viewed with caution, especially with a low n number and few cells. The data in the entire paper is not strong enough to base any solid conclusion - it is not just the RNA-sequencing data.

    We acknowledge this to be a valid point and have revamped the manuscript and toned down our conclusions. However, such limitations exist with any scRNA seq dataset and so must be interpreted accordingly by the readers. We do understand that due to the low cell counts and the limitations with scRNA seq we should not perform DESeq2 analyses for Ly6G+ versus Ly6G- neutrophil categories, which was never attempted at the first place. However, our results with co-immunofluorescence, multianalyte assay and scRNA expression analyses in myeloid cluster do point towards a shift in neutrophil activation which needs to be further investigated. Furthermore, Ly6G deficiency has been linked to immature neutrophils in many previous studies and is not an unlikely outcome that needs to be treated with immense skepticism.

    We wish to make this dataset available as a resource to influence future research. We are aware of its limitations and have been transparent with regards to our experimental design, capture strategy, the quality of obtained results, and possible caveats to make it is open for discussion by the readers.

    There is no data supporting the presence of Ly6G negative neutrophils. In the flow cytometry only Ly6G+ cells are shown with no evidence of Ly6G negative neutrophils (assuming equal CD11b expression). There is no new data to support this claim since resubmission and the New figures 4C and D actually show there are no Ly6G negative cells - the cells that the authors deem Ly6G negative are actually positive - but the red overlay of S100A8 is so strong it blocks out the green signal - looking to the Ly6G single stains (green only) you can see that the reported S100A8+Ly6G- cells all have Ly6G (with different staining intensities).

    We thank the reviewer for this query and do understand the skepticism. We have now quantified the data to provide more clarity for interpretation. As we were using paraffin embedded tissues, some autofluorescence is expected which could explain some of reviewer’s concerns. However we expect that the inclusion of better quality images and quantification must address some of the concerns raised by the reviewer.

    Eosinophils are heavily involved in lung macrophage biology, but are missing from the analysis - it is highly likely the RNA-sequence picked out eosinophils as Ly6G- neutrophils rather than 'digestion issues' the authors claim

    We thank the reviewer for raising a valid concern. However, the Ly6G- cluster cannot be eosinophils in our case. Literature suggests SiglecF as an important biomarker of eosinophils which was absent in the Ly6G- cluster our in scRNA seq analyses as shown in File S18 and Figure 6B of the revised manuscript. We have now provided a detailed explanation (Lines 476-488; 503-506) of the observed results pertaining to eosinophil population in the revised manuscript to further address some of the concerns raised by this reviewer.

    After author comments, it appears the schematic in Figure 1A is misleading and there are not n=2/group/sex but actually only n=1/group/sex (as shown in Figure 6A). Meaning the n number is even lower than the previous assumption.

    We concur with reviewers’ valid concern and so are willing to provide this data as a resource for a wider audience to assist future work. Pooling of samples have been practiced by many groups previously to save resources and expense. We did it for the very same reason. It may not be the preferred approach, but it still has its merit considering the vast amount of cell-specific data generated using this strategy. To avoid overstating our results we have ensured to maintain transparency in our reporting and acknowledge all the limitations of this study.

    We do not believe that the strength of scRNA seq lies in drawing conclusive results, but to tease our possible targets and direction that need to be validated with more work. In that respect, our study does identify the target cell types and biological processes which could be of importance for future studies.

    Reviewer #3 (Public review):

    This work aims to establish cell-type specific changes in gene expression upon exposure to different flavors of commercial e-cigarette aerosols compared to control or vehicle. Kaur et al. conclude that immune cells are most affected, with the greatest dysregulation found in myeloid cells exposed to tobacco-flavored e-cigs and lymphoid cells exposed to fruit-flavored e-cigs. The up- and down-regulated genes are heavily associated with innate immune response. The authors suggest that a Ly6G-deficient subset of neutrophils is found to be increased in abundance for the treatment groups, while gene expression remains consistent, which could indicate impaired function. Increased expression of CD4+ and CD8+ T cells along with their associated markers for proliferation and cytotoxicity is thought to be a result of activation following this decline in neutrophil-mediated immune response.

    Strengths:

    Single cell sequencing data can be very valuable in identifying potential health risks and clinical pathologies of lung conditions associated with e-cigarettes considering they are still relatively new.

    Not many studies have been performed on cell-type specific differential gene expression following exposure to e-cig aerosols.

    The assays performed address several factors of e-cig exposure such as metal concentration in the liquid and condensate, coil composition, cotinine/nicotine levels in serum and the product itself, cell types affected, which genes are up- or down-regulated and what pathways they control.

    Considerations were made to ensure clinical relevance such as selecting mice whose ages corresponded with human adolescents so that data collected was relevant.

    Weaknesses:

    The exposure period of 1 hour a day for 5 days is not representative of chronic use and this time point may be too short to see a full response in all cell types. The experimental design is not well-supported based on the literature available for similar mouse models. Clinical relevance of this short exposure remains unclear.

    We thank the reviewer for this query. However, we would like to emphasize that chronic exposure was never the intention of this study. We wished to design a study for acute nose-only exposure owing to which the study duration was left shorter. Shorter durations limit the stress and discomfort to the animal. The in vivo study using nose-only exposure regimen is still developing with multiple exposure regimen being used by different groups. To our knowledge there is no gold standard of e-cig aerosol exposure which is widely accepted other than the CORESTA recommendations, which we followed. Also, we show in our study how the daily exposure to leached metals vary in a flavor-dependent manner thus validating that exposure regime does need more attention in terms of equal dosing, particle distribution and composition- something we have started doing in our future studies. We have included all the explanations in the revised manuscript (Lines 82-85, 425-435, 648-654).

    Several claims lack supporting evidence or use data that is not statistically significant. In particular, there were no statistical analyses to compare results across sex, so conclusions stating there is a sex bias for things like Ly6G+ neutrophil percentage by condition are observational.

    We agree with reviewer’s comment and have taken this into consideration. We have now revamped the whole manuscript and toned down most of the sex-based conclusions stated in this work. Having said that, it is important to note that most of the work relying solely on scRNA seq, as is the case for this study, is observational in nature and needs to be assessed bearing this in mind.

    Overall, the paper and its discussion are relatively surface-level and do not delve into the significance of the findings or how they fit into the bigger picture of the field. It is not clear whether this paper is intended to be used as a resource for other researchers or as an original research article.

    We have now reworked on the Discussion and tried to incorporate more in-depth discussion and the results providing our insights regarding the observations, discrepancies and the possible explanations. We have also made it clear that this paper is intended to be used as a resource by other researchers (Lines 577-579)

    The manuscript has some validation of findings but not very comprehensive.

    We have now revamped the manuscript. We have Included quantification for immunofluorescence data with better representation of the GO analyses. We have worked on the Results and Discussion sections to make this a useful resource for the scientific community.

    This paper provides a strong foundation for follow-up experiments that take a closer look at the effects of e-cig exposure on innate immunity. There is still room to elaborate on the differential gene expression within and between various cell types.

    We thank the reviewer for pointing out the strength of this paper. The reason why we refrained from elaborating of the differential gene expressions within and between various cell types was due to low sample number and sequencing depth for this study. However the raw data will be provided with the final publication, which should be freely accessible to the public to re-analyze the data set as they deem fit.

    Comments on revisions:

    The reviewers have addressed major concerns with better validation of data and improved organization of the paper. However, we still have some concerns and suggestions pertaining to the statistical analyses and justifications for experimental design.

    We appreciate the nuance of this experimental design, and the reviewers have adequately commented on why they chose nose-only exposure over whole body exposure. However, the justification for the duration of the exposure, and the clinical relevance of a short exposure, have not been addressed in the revised manuscript.

    We thank the editor for this query. We have now addressed this query briefly in Lines 82-85, 425-435, 648-654 of the revised manuscript. We would like to add, however, that we intend to design a study for acute nose-only exposure for this project. Shorter durations limit the stress and discomfort to the animal, owing to which a duration of 1hour per day was chosen. The in vivo study using nose-only exposure regimen is still developing with multiple exposure regimen being used by different groups. Ours is one such study in that direction just intended to identify cell-specific changes upon exposure. Considering our results in Figure 1B showing variations in the level of metals leached in each flavor per day, the appropriate exposure regimen to design a controlled, reproducible experiment needs to be discussed. There could be room for improvement in our strategy, but this was the best regimen that we found to be appropriate per the literature and our prior knowledge in the field.

    The presentation of cell counts should be represented by a percentage/proportion rather than a raw number of cells. Without normalization to the total number of cells, comparisons cannot be made across groups/conditions. This comment applies to several figures.

    We thank the editor for this comment and have now made the requested change in the revised manuscript.

    We appreciate that the authors have taken the reviewers' advice to validate their findings. However, we have concerns regarding the immunofluorescent staining shown in Figure 4. If the red channel is showing a pan-neutrophil marker (S100A8) and the green channel is showing only a subset of neutrophils (LY6G+), then the green channel should have far less signal than the red channel. This expected pattern is not what is shown in the figure, with the Ly6G marker apparently showing more expression than S100A8. Additionally, the FACS data states that only 4-5% of cells are neutrophils, but the red channel co-localizes with far more than 4-5% of the DAPI stain, meaning this population is overrepresented, potentially due to background fluorescence (noise). In addition, some of the shapes in the staining pattern do not look like true neutrophils, although it is difficult to tell because there remains a lot of background staining. The authors need to verify that their S100A8 and Ly6G antibodies work and are specific to the populations they intend to target. It is possible that only the brightest spots are truly S100A8+ or Ly6G+.

    We thank the editor for this comment and acknowledge that we may have made broad generalizations in our interpretation of our data previously. We have now revisited the data and quantified the two fluorescence for better interpretation of our results. We have also reassessed our conclusions from this data and reworded the manuscript accordingly. Briefly we believe that Ly6G deficiency could be an indication of the presence of immature neutrophils in the lungs. This is a common process of neutrophil maturation. An active neutrophil population has Ly6G and should also express S100A8 indicating a normal neutrophilic response against stressors. However, our results, despite some autofluorescence which is common with lung tissues, shows a marked decline in the S100A8+ cells in the lung of tobacco-flavored e-cig aerosol exposed mice as compared to air controls. We also do not see prominent co-localization of the two markers in exposed group thus proving a shift in neutrophil dynamics which requires further investigation. We would also like to mention here that S100A8 is predominantly expressed in neutrophils, but is also expressed by monocytes and macrophages, so that could explain the over-representation of these cells in our immunofluorescence results. We have now included this in the Discussion section (Lines 489- 538) of the revised manuscript.

    Paraffin sections do not always yield the best immunostaining results and the images themselves are low magnification and low resolution.

    We agree with the editor that paraffin sections may not yield best results, we have worked on the final figure to improve the quality of the displayed results and zoomed-in some parts of the merged image to show the differences in the co-localization patterns for the two markers in our treated and control groups for easier interpretation.

    Please change the scale bars to white so they are more visible in each channel.

    The merged image in Figure 6C now has a white scale bar.

    We appreciate that this is a preliminary test used as a resource for the community, but there is interesting biology regarding immune cells that warrants DEG analysis by the authors. This computational analysis can be easily added with no additional experiments required.

    We thank the editor for this comment and agree that interesting biology regarding immune cells could be explored upon performing the DEG analyses on individual immune populations. However, due to the small sample size, low sequencing depth and pooling of same sex animals in each treatment group, we refrained from performing that analyses fearing over-representation of our results. We will be providing the link to the raw data with this publication which will be freely accessible to public on NIH GEO resource to allow further analyses on this dataset by the judgement of the investigator who utilizes it as a resource.

    Recommendations for the authors:

    Reviewer #1 (Recommendations for the authors):

    (Minor) The pathway analyses in Fig. 6-8 have different fonts than what's used in all other figures.

    We have now made the requested change in the revised manuscript.

  5. Version published to 10.7554/elife.106380.3 on eLife
  6. Version published to 10.7554/elife.106380 on eLife
  7. Version published to 10.7554/elife.106380.2 on eLife
  8. eLife

    eLife Assessment

    This manuscript by Kaur et al. identifies differential gene expression observed in distinct cell populations, namely myeloid and lymphoid cells, upon short-term exposure to e-cig aerosols with various flavors. Their findings are useful because they provide a single cell sequencing data resource for assessing which genes and cellular pathways are most affected by e-cig aerosols and their components. However, the evidence is incomplete due to limited analyses and replicates per condition, as well as the lack of in vivo validation.

  9. eLife

    Reviewer #1 (Public review):

    Summary:

    The authors assess the impact of E-cigarette smoke exposure on mouse lungs using single cell RNA sequencing. Air was used as control and several flavors (fruit, menthol, tobacco) were tested. Differentially expressed genes (DEGs) were identified for each group and compared against the air control. Changes in gene expression in either myeloid or lymphoid cells were identified for each flavor and the results varied by sex. The scRNAseq dataset will be of interest to the lung immunity and e-cig research communities and some of the observed effects could be important. Unfortunately, the revision did not address the reviewers' main concerns about low replicate numbers and lack of validations. The study remains preliminary and no solid conclusions could be drawn about the effects of E-cig exposure as a whole or any flavor-specific phenotypes.

    Strengths:

    The study is the first to use scRNAseq to systematically analyze the impact of e-cigarettes on the lung. The dataset will be of broad interest.

    Weaknesses:

    scRNAseq studies may have low replicate numbers due to the high cost of studies but at least 2 or 3 biological replicates for each experimental group is required to ensure rigor of the interpretation. This study had only N=1 per sex per group and some sex-dependent effects were observed. This could have been remedied by validating key observations from the study using traditional methods such as flow cytometry and qPCR, but the limited number of validation experiments did not support the conclusions of the scRNAseq analysis. An important control group (PG:VG) had extremely low cell numbers and was basically not useful. Statistical analysis is lacking in almost all figures. Overall, this is a preliminary study with some potentially interesting observations but no solid conclusions can be made from the data presented.

    (1) The only new validation experiment is the immunofluorescent staining of neutrophils in Figure 4. The images are very low resolution and low quality and it is not clear which cells are neutrophils. S100A8 (calprotectin) is highly abundant in neutrophils but not strictly neutrophil-specific. It's hard to distinguish positive cells from autofluorescence in both Ly6g and S100a8 channels. No statistical analysis in the quantification.

    (2) It is unclear what the meaning of Fig. 3A and B is, since these numbers only reflect the number of cells captured in the scRNAseq experiment and are not biologically meaningful. Flow cytometry quantification is presented as cell counts, but the percentage of cells from the CD45+ gate should be shown. No statistical analysis is shown, and flow cytometry results do not support the conclusions of scRNAseq data.

  10. eLife

    Reviewer #2 (Public review):

    This study provides some interesting observations on how different flavour e-cigarettes can affect lung immunology; however, there are numerous flaws, including a low replicate number and a lack of effective validation methods, meaning findings may not be repeated. This is a revised article but several weaknesses remain related to the analysis and interpretation of the data.

    Strengths:

    The strength of the study is the successful scRNA-seq experiment which gives some preliminary data that can be used to create new hypotheses in this area.

    Weaknesses:

    Although some text weaknesses have been addressed since resubmission, other specific weaknesses remain: The major weakness is the n-number and analysis methods. Two biological n per group is not acceptable to base any solid conclusions. Any validatory data was too little (only cell % data) and not always supporting the findings (e.g. figure 3D does not match 3B/4A). Other examples include:

    (1) There aren't enough cells to justify analysis - only 300-1500 myeloid cells per group with not many of these being neutrophils or the apparent 'Ly6G- neutrophils'

    (2) The dynamic range of RNA measurement using scRNAseq is known to be limited - how do we know whether genes are not expressed or just didn't hit detection? This links into the Ly6G negative neutrophil comments, but in general the lack of gene expression in this kind of data should be viewed with caution, especially with a low n number and few cells. The data in the entire paper is not strong enough to base any solid conclusion - it is not just the RNA-sequencing data.

    (3) There is no data supporting the presence of Ly6G negative neutrophils. In the flow cytometry only Ly6G+ cells are shown with no evidence of Ly6G negative neutrophils (assuming equal CD11b expression). There is no new data to support this claim since resubmission and the New figures 4C and D actually show there are no Ly6G negative cells - the cells that the authors deem Ly6G negative are actually positive - but the red overlay of S100A8 is so strong it blocks out the green signal - looking to the Ly6G single stains (green only) you can see that the reported S100A8+Ly6G- cells all have Ly6G (with different staining intensities).

    (4) Eosinophils are heavily involved in lung macrophage biology, but are missing from the analysis - it is highly likely the RNA-sequence picked out eosinophils as Ly6G- neutrophils rather than 'digestion issues' the authors claim

    (5) After author comments, it appears the schematic in Figure 1A is misleading and there are not n=2/group/sex but actually only n=1/group/sex (as shown in Figure 6A). Meaning the n number is even lower than the previous assumption.

  11. eLife

    Reviewer #3 (Public review):

    This work aims to establish cell-type specific changes in gene expression upon exposure to different flavors of commercial e-cigarette aerosols compared to control or vehicle. Kaur et al. conclude that immune cells are most affected, with the greatest dysregulation found in myeloid cells exposed to tobacco-flavored e-cigs and lymphoid cells exposed to fruit-flavored e-cigs. The up- and down-regulated genes are heavily associated with innate immune response. The authors suggest that a Ly6G-deficient subset of neutrophils is found to be increased in abundance for the treatment groups, while gene expression remains consistent, which could indicate impaired function. Increased expression of CD4+ and CD8+ T cells along with their associated markers for proliferation and cytotoxicity is thought to be a result of activation following this decline in neutrophil-mediated immune response.

    Strengths:

    - Single cell sequencing data can be very valuable in identifying potential health risks and clinical pathologies of lung conditions associated with e-cigarettes considering they are still relatively new.

    - Not many studies have been performed on cell-type specific differential gene expression following exposure to e-cig aerosols.

    - The assays performed address several factors of e-cig exposure such as metal concentration in the liquid and condensate, coil composition, cotinine/nicotine levels in serum and the product itself, cell types affected, which genes are up- or down-regulated and what pathways they control.

    - Considerations were made to ensure clinical relevance such as selecting mice whose ages corresponded with human adolescents so that data collected was relevant.

    Weaknesses:

    - The exposure period of 1 hour a day for 5 days is not representative of chronic use and this time point may be too short to see a full response in all cell types. The experimental design is not well-supported based on the literature available for similar mouse models. Clinical relevance of this short exposure remains unclear.

    - Several claims lack supporting evidence or use data that is not statistically significant. In particular, there were no statistical analyses to compare results across sex, so conclusions stating there is a sex bias for things like Ly6G+ neutrophil percentage by condition are observational.

    - Overall, the paper and its discussion are relatively surface-level and do not delve into the significance of the findings or how they fit into the bigger picture of the field. It is not clear whether this paper is intended to be used as a resource for other researchers or as an original research article.

    - The manuscript has some validation of findings but not very comprehensive.

    This paper provides a strong foundation for follow-up experiments that take a closer look at the effects of e-cig exposure on innate immunity. There is still room to elaborate on the differential gene expression within and between various cell types.

    Comments on revisions:

    The reviewers have addressed major concerns with better validation of data and improved organization of the paper. However, we still have some concerns and suggestions pertaining to the statistical analyses and justifications for experimental design.

    - We appreciate the nuance of this experimental design, and the reviewers have adequately commented on why they chose nose-only exposure over whole body exposure. However, the justification for the duration of the exposure, and the clinical relevance of a short exposure, have not been addressed in the revised manuscript.

    - The presentation of cell counts should be represented by a percentage/proportion rather than a raw number of cells. Without normalization to the total number of cells, comparisons cannot be made across groups/conditions. This comment applies to several figures.

    - We appreciate that the authors have taken the reviewers' advice to validate their findings. However, we have concerns regarding the immunofluorescent staining shown in Figure 4. If the red channel is showing a pan-neutrophil marker (S100A8) and the green channel is showing only a subset of neutrophils (LY6G+), then the green channel should have far less signal than the red channel. This expected pattern is not what is shown in the figure, with the Ly6G marker apparently showing more expression than S100A8. Additionally, the FACS data states that only 4-5% of cells are neutrophils, but the red channel co-localizes with far more than 4-5% of the DAPI stain, meaning this population is overrepresented, potentially due to background fluorescence (noise). In addition, some of the shapes in the staining pattern do not look like true neutrophils, although it is difficult to tell because there remains a lot of background staining. The authors need to verify that their S100A8 and Ly6G antibodies work and are specific to the populations they intend to target. It is possible that only the brightest spots are truly S100A8+ or Ly6G+.

    - Paraffin sections do not always yield the best immunostaining results and the images themselves are low magnification and low resolution.

    - Please change the scale bars to white so they are more visible in each channel.

    - We appreciate that this is a preliminary test used as a resource for the community, but there is interesting biology regarding immune cells that warrants DEG analysis by the authors. This computational analysis can be easily added with no additional experiments required.

  12. eLife

    Author response:

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

    Reviewer #1 (Public review):

    Summary:

    The authors tackled the public concern about E-cigarettes among young adults by examining the lung immune environment in mice using single-cell RNA sequencing, discovering a subset of Ly6G- neutrophils with reduced IL-1 activity and increased CD8 T cells following exposure to tobaccoflavored e-cigarettes. Preliminary serum cotinine (nicotine metabolite) measurements validated the effective exposure to fruit, menthol, and tobacco-flavored e-cigarettes with air and PG:VG serving as control groups. They also highlighted the significance of metal leaching, which fluctuated over different exposure durations to flavored e-cigarettes, underscoring the inherent risks posed by these products. The scRNAseq analysis of e-cig exposure to flavors and tobacco demonstrated the most notable differences in the myeloid and lymphoid immune cell populations. Differentially expressed genes (DEGs) were identified for each group and compared against the air control. Further subclustering revealed a flavor-specific rise in Ly6G- neutrophils and heightened activation of cytotoxic T cells in response to tobacco-flavored e-cigarettes. These effects varied by sex, indicating that immune changes linked to e-cig use are dependent on gender. By analyzing the expression of various genes and employing gene ontology and gene enrichment analysis, they identified key pathways involved in this immune dysregulation resulting from flavor exposure. Overall, this study affirmed that e-cigarette exposure can suppress the neutrophil-mediated immune response, subsequently enhancing T cell toxicity in the lung tissue of mice.

    Strengths:

    This study used single-cell RNA sequencing to comprehensively analyze the impact of e-cigarettes on the lung. The study pinpointed alterations in immune cell populations and identified differentially expressed genes and pathways that are disrupted following e-cigarette exposure. The manuscript is well written, the hypothesis is clear, the experiments are logically designed with proper control groups, and the data is thoroughly analyzed and presented in an easily interpretable manner. Overall, this study suggested novel mechanisms by which e-cigs impact lung immunity and created a dataset that could benefit the lung immunity field.

    Weaknesses:

    The authors included a valuable control group - the PG:VG group, since PG:VG is the foundation of the e-liquid formulation. However, most of the comparative analyses use the air group as the control. Further analysis comparing the air group to the PG:VG group, and the PG:VG group to the individual flavored e-cig groups will provide more clear insights into the true source of irritation. This is done for a few analyses but not consistently throughout the paper. Flavor-specific effects should be discussed in greater detail. For example, Figure 1E shows that the Fruit flavor group exhibits more severe histological pathology, but similar effects were not corroborated by the singlecell data.

    We thank the reviewer for this query. We agree that PG:VG group is the foundation of the e-liquid formulation and hence comparisons with this group are of significance to understand the effect of individual flavors on the cell population. Though we compared the flavored e-cig groups with PG:VG group, we did not discuss it in detail within the manuscript to avoid confusions in interpretation for this study. However, we have now included the comparisons with the PG:VG group as a Supplement File S13-S18 in our revised manuscript to facilitate proper interpretation of our omics data to interested readers.

    While we agree that flavor-specific effects might be of interest, we did not delve into exploring them in detail as the fruit flavor e-liquids have now been regulated/banned from sale in the US. Thus, from regulatory point of view, the effects of tobacco-flavored e-liquids hold most interest. Since at the time of conducting this study, fruit flavors were in the market, we have still included the data. However, studying it further was not the focus of this work.

    The characterization of Ly6g+ vs Ly6g- neutrophils is interesting and potentially very impactful. Key results like this from scRNAseq analyses should be validated by qPCR and flow cytometry.

    Also, a recent study by Ruscitti et al reported Ly6g+ macrophages in the lung which can potentially confound the cell type analysis. A more detailed marker gene and sub-population analysis of the myeloid clusters could rule out this potential confounding factor.

    We agree with the reviewer that the loss of Ly6G on neutrophils is a very interesting finding and we have designed a neutrophil specific experiment to study the impact of e-cig exposure on neutrophil maturation and function which will be discussed in subsequent work by our group. To address the concerns raised by the reviewer, we stained the lung tissue samples from air-and tobacco flavored e-cig aerosol exposed mouse lungs with Ly6G and S100A8 (universal marker for neutrophil) to see the infiltration of Ly6G+ vs Ly6G- neutrophils within the lungs of exposed and unexposed mice. Results from this study showed that exposure to tobacco-flavored e-cig aerosol affects the neutrophil population within the mouse lungs. In fact, the changes were more pronounced for female mice. The data have now been shown in Figure 4.

    Reviewer #2 (Public review):

    This study provides some interesting observations on how different flavors of e-cigarettes can affect lung immunology, however there are numerous flaws including a low number of replicates and a lack of effective validation methods which reduces the robustness and rigor of the findings.

    Strengths:

    The strength of the study is the successful scRNA-seq experiment which gives good preliminary data that can be used to create new hypotheses in this area.

    Weaknesses:

    The major weakness is the low number of replicates and the limited analysis methods. Two biological n per group is not acceptable to base any solid conclusions. Any validatory data was too little (only cell % data) and did not always support the findings (e.g. Figure 4D does not match 4C). Often n seems to be combined and only one data point is shown, it is not at all clear how the groups were analyzed and how many cells in each group were compared.

    We thank the reviewer for recognizing the strengths of this manuscript while pointing out the errors to allow us to improve our analyses. We understand that the low number of replicates in this work makes the analyses difficult to draw solid conclusions, but this was a pilot study to identify the changes in the mouse lung upon acute exposures to flavored e-cig aerosols at a single cell level. So far, the e-cig field has been primarily focused on conducting toxicological studies to help regulatory bodies to set standards and enforce laws to better regulate the manufacture, sale and distribution of e-cig products. However, adolescents and young adults are still getting access to these products, and there is little to no understanding of how this may affect the lung health upon acute and chronic exposures. Single cell technology is a powerful tool to analyze the gene expression changes within cell populations to study cell heterogeneity and function. Yet, it is a costly tool owing to which conducting such analyses on large sample sizes is not ideal. This pilot study was designed to get some initial leads for our future studies involving larger sample sizes and chronic exposures. However, due to the vast information that is provided by a single cell RNA sequencing experiment, we intend to share it with a larger audience to support research and further study in this area. We understand that the validations are limited in our current work and so we have now conducted coimmunostaining to validate the Ly6G+ and Ly6G- neutrophil population. We have now included single cell findings with the validating experiments using classical methods of experimentation including ELISA, immunostaining or flow cytometry and revamped the whole manuscript. However, it is important to mention that such validations are sometimes challenging as many of these techniques still investigate the tissue while the changes shown in single cell analyses are mainly pertaining to a single cell type. This could be well-understood by looking at the flow cytometry results for neutrophils where we use Ly6G as a marker to stain for neutrophils which is only found in mature neutrophil population.

    Only 71,725 cells mean only 7,172 per group, which is 3,586 per animal - how many of these were neutrophils, T-cells, and macrophages? This was not shown and could be too low.

    We do agree that the number of cells could be too low. To avoid this, we did not study gene expression variations at the finest level of cell identity. We classified the cell clusters into general annotations -myeloid, lymphoid, endothelial, stromal and epithelial- and identified the changes in the gene expressions. Of these, only two clusters (myeloid and lymphoid) with more than ~1000 cells per cell type per group were studied in detail. We have included the cell count information to allow better interpretation of our results in the revised manuscript. For a single cell point of view, a cell count of ~3500 each with over 20000 features (genes) has good statistical strength and merit in our opinion.

    The dynamic range of RNA measurement using scRNA seq is known to be limited - how do we know whether genes are not expressed or just didn't hit detection? This links into the Ly6G negative neutrophil comment, but in general, the lack of gene expression in this kind of data should be viewed with caution, especially with a low n number and few cells.

    This is a well-taken point, and we thank the reviewer for this comment. We agree that the dynamic range RNA measurement is limited low cell numbers that could lead to bias. However, none of the clusters with counts lower than 150 were included for differential gene analyses. To avoid confusion, we now show immunofluorescence results to validate the findings. We are certain that with the inclusion of these validation experiments, will convince the reviewer about the loss of Ly6G marker from neutrophils and lack of proper neutrophilic response in exposed mouse lungs as compared to the controls.

    There is no rigorous quantification of Ly6G+ and Ly6G- cells int he flow cytometry data.

    We understand that flow-based quantification of our scRNA seq findings would be interesting. However, flow cytometry and single cell suspension to perform sequencing were performed parallelly for this study. We used a basic flow panel using single markers to identify individual immune cell type. We did identify changes in the Ly6G population in our treated and control samples using scRNA seq and intend to exclude it as a marker for our future studies using flow cytometry. Unfortunately, the same analyses could not be performed for the current batch of samples. We have now included results from IHC staining to identify the Ly6G+ and Ly6G- population in the lung tissues from control and treated mice in revised manuscript to address some of the concerns raised here.

    Eosinophils are heavily involved in lung biology but are missing from the analysis.

    We use RBC lysis buffer to remove the excess RBCs during lung digestion for preparation of single cell suspension for scRNA seq in this study. Reports suggest that RBC lysis could adversely affect the eosinophil number and function. We did not identify any cell cluster, representing markers for eosinophils through our scRNA seq data and we believe that our lung digestion protocol could be the reason for it. We have studied the eosinophil changes through flow cytometry in these samples and have found significant changes as well. However, due to our inability to find cell clusters for eosinophil through scRNA seq data, we did not include these results in the final manuscript previously. To avoid confusion and maintain transparency, we have now included the changes in eosinophils through flow cytometry in revised manuscript (Figure S4).

    The figures had no titles so were difficult to navigate.

    We have now revamped the figures to make it easier for the readers to navigate.

    PGVG is not defined and not introduced early enough.

    We have made the necessary changes in the revised manuscript.

    Neutrophils are not well known to proliferate, so any claims about proliferation need to be accompanied by validation such as BrdU or other proliferation assays.

    We have now removed the cell cycle scoring information from the revised manuscript. Performing BrDU assay was not possible for these tissues due to limited samples and resources. However, we may consider performing it in our future studies.

    It was not clear how statistics were chosen and why Table S2 had a good comparison (two-way ANOVA with gender as a variable) but this was not used for other data particularly when looking at more functional RNA markers (Table S2 also lacks the interaction statistic which is most useful here).

    We have now included the two-way ANOVA statistics (Supplementary File S3) for other data included in the revised manuscript. It is important to note that since we did not identify any significant changes upon two-way ANOVA, the interaction statistics were not available for the abovementioned statistical test. We have included the interaction information wherever available.

    Many statistics are only vs air control, but it would be more useful as a flavor comparison to see these vs PGVG. In some cases, the carrier PGVG looks worse than some of the flavors (which have nicotine).

    While we agree with this comment of the reviewer, comparisons with PG:VG were not included due to the low cell numbers for PG:VG samples obtained following quality control and filtering of scRNA seq analyses. However, considering the reviewer’s question we still include the details of comparisons with PG:VG included as supplementary files S13-S18 in the revised manuscript.

    The n number is a large issue, but in Figures such as 4, 6, and 7 it could be a bigger factor. The number of significant genes identified has been determined by chance rather than any real difference, e.g. Is Il1b not identified in Fruit flavor vs air because there wasn't enough n, while in Air vs Tobacco, it randomly hit the significance mark. This is but an example of the problems with the analysis and conclusions.

    While we agree in part with the concern raised here. In our opinion, an omics study is not necessarily aimed at finding the changes at transcript level with absolute certainty, but rather to identify probable cell and gene targets to validate with subsequent work. We did not claim that our findings are absolute outcomes but rather add the limitation of sample number and need for further research at every step. The strength of this work is to be the first study of its kind looking at changes in the lung cell population at single cell level upon e-cig aerosol exposure. This study has provided us with interesting gene and cell targets that we are now validating with future work. We still strongly believe that a dataset like this is a useful resource for a wider audience.

    The data in Figure 7A is confusing, if this is a comparison to air, then why does air vs air not equal 1? Even if this was the comparison to the average of air between males and females, then this doesn't explain why CCL12 is >1 in both. Is this z-score instead? Regardless the data is difficult to interpret in this format.

    We have now changed the format of data representation in the figure.

    Individual n was not shown for almost all experiments - e.g. Figure 1D - what is this representative of? Figure 2D - is this bulk-grouped data for all cells and all mice? The heatmaps are also pooled from 2n and don't show the variability.

    Wherever needed, the n number has been included in the Figure legend. Additionally, the n number is shown in Figure 1A. However, with respect to the second comment we would like to differ from the reviewer’s opinion. Each scRNA seq data had 2 samples – one for male and another for female which has been clearly shown in the current figures. The pooling of cells as mentioned in the comment happened at the stage of preparation of cell suspension from each sex/group at the start of the sequencing. We show the results of the pooled sample showing the variability amongst pooled samples, which we acknowledge is a shortcoming of our work. In terms of representation of the heat maps and data analyses we have included all the needed information to uphold transparency of our study design and data visualization for each figure and would like to stick to the current representations. However, validation cohort does not involve any pooling of sample and still agrees with most of the deductions made from this study. So we are confident that no over statements have been made in this work and we still provide a useful dataset to inform future research in this area.

    Reviewer #3 (Public review):

    This work aims to establish cell-type specific changes in gene expression upon exposure to different flavors of commercial e-cigarette aerosols compared to control or vehicle. Kaur et al. conclude that immune cells are most affected, with the greatest dysregulation found in myeloid cells exposed to tobacco-flavored e-cigs and lymphoid cells exposed to fruit-flavored e-cigs. The up-and-downregulated genes are heavily associated with innate immune response. The authors suggest that a Ly6G-deficient subset of neutrophils is found to be increased in abundance for the treatment groups, while gene expression remains consistent, which could indicate impaired function. Increased expression of CD4+ and CD8+ T cells along with their associated markers for proliferation and cytotoxicity is thought to be a result of activation following this decline in neutrophil-mediated immune response.

    Strengths:

    (1) Single-cell sequencing data can be very valuable in identifying potential health risks and clinical pathologies of lung conditions associated with e-cigarettes considering they are still relatively new.

    (2) Not many studies have been performed on cell-type specific differential gene expression following exposure to e-cig aerosols.

    (3) The assays performed address several factors of e-cig exposure such as metal concentration in the liquid and condensate, coil composition, cotinine/nicotine levels in serum and the product itself, cell types affected, which genes are up- or down-regulated and what pathways they control.

    (4)Considerations were made to ensure clinical relevance such as selecting mice whose ages corresponded with human adolescents so that the data collected was relevant.

    Weaknesses:

    The exposure period of 1 hour a day for 5 days is not representative of chronic use and this time point may be too short to see a full response in all cell types. The experimental design is not well-supported based on the literature available for similar mouse models.

    This study was not designed to study the effects of chronic exposures on lung tissues. We were interested in delineating the effect of acute exposures for which the proposed study design was chosen. Previous work by our group has performed similar exposures and has been well received by the community. We understand that chronic exposures will be interesting to look at, but that was beyond the scope of this pilot study. Longer / chronic exposures will be conducted considering disease modifying effects of e-cigarettes.

    Several claims lack supporting evidence or use data that is not statistically significant. In particular, there were no statistical analyses to compare results across sex, so conclusions stating there is a sex bias for things like Ly6G+ neutrophil percentage by condition are observational.

    We thank the reviewer for this observation, and we have now included the necessary validations and details of the sex-based statistical analyses in the revised version of this manuscript.

    Statistical analyses lack rigor and are not always displayed with the most appropriate graphical representation.

    We thank the reviewer and have included all the necessary statistical details with more details in the revised manuscript.

    Overall, the paper and its discussion are relatively limited and do not delve into the significance of the findings or how they fit into the bigger picture of the field.

    As pointed out by the reviewers themselves the strength of this work is in the first ever scRNA seq analyses of mice exposed to differently flavored e-cig aerosols in vivo. We also show cellspecific differential gene expressions and address some of the major queries made around e-cig research including release of metals on a day-to-day basis from the same coil. The limited sample number makes it difficult to draw solid conclusions from this work, which has been discussed as a shortcoming. Nevertheless, the major strength of this work is not in identifying specific trends, but rather to determine the possible cell and gene targets to expand the study for longer (chronic) exposures with a larger sample group. We have mentioned the significance of the study with respect to vaping effects on cellular heterogeneity leading to deleterious effects.

    The manuscript lacks validation of findings in tissue by other methods such as staining.

    We have now included some validation experiments and revamped the revised manuscript to support scRNA seq findings.

    This paper provides a foundation for follow-up experiments that take a closer look at the effects of e-cig exposure on innate immunity. There is still room to elaborate on the differential gene expression within and between various cell types.

    We thank the reviewer for this observation. The cell numbers for some cell clusters (especially epithelial cells) were too low. So, though we have performed the differential gene expression analyses on all the cell clusters, we refrained from discussing it in the manuscript to avoid over interpretation of our results. Only clusters with high enough (> 150) cells per sex per group were used to plot the heatmaps. We have now included the cell numbers for each cell type in the revisions to allow better interpretation of our data. Furthermore, the raw data from this study will be freely available to the public upon publication of this manuscript. This would enable the interested readers to access the raw data and study the cell types of interest in detail based on their study requirements. This data will be a useful resource for all in this community to inform and design future studies.

    Recommendation For The Author:

    Major comments

    Mouse experiments are extremely variable and an n of 2 is not enough. Because of the complexity of separating male and female mice, the analyses are not adequately powered to support conclusions. The two-way ANOVA style approach to consider sex as a separate variable was a great idea in Table S2 - but this was not used elsewhere, and there is a need to show the interaction statistic (which would say if there is a flavor effect dependent on sex).

    We thank the reviewers for this recommendation. We agree that the experiments are highly variable. However, it is not merely an outcome of a small sample size (which we address as one of the limitations). What is important to mention here is the fact that validating results from single cell technologies using regular molecular biology techniques is challenging and may not completely align. It is because we are comparing single cell population in the former and a heterogeneous cell population in latter. However, considering this comment, we have now toned down our conclusions and performed some extra experiments to validate single cell findings. We also provide the results from two-way ANOVA statistics for all the figures/experiments performed in this work.

    More validatory data with PCR, immunostaining, and flow cytometry would be very helpful. This includes validating the neutrophil functional and phenotype data and the T-cell data by flow cytometry.

    To validate the presence of Ly6G+ and Ly6G- neutrophil population, we performed coimmunostaining experiments and proved that exposure to tobacco-flavored e-cig aerosols results in increase in cell percentages of two neutrophil population in female mice. We also re-analyzed our Flow cytometry data to align with scRNA seq results. Multiplex protein assay was another technique used to show altered innate/adaptive immune responses upon exposure to differently flavored e-cig aerosol. Of note, considering the short duration of exposure we did not identify significant changes in cell numbers or inflammatory responses. But we have now validated our scRNA seq results using various techniques to draw meaningful conclusions.

    The in vivo experimental design seems to model very short-term exposure. In the literature, including the papers cited in the references, much longer time points are used, extending from several weeks to months of exposure. There seem to be few examples of papers using 5-day exposure and those that do are inspired by traditional cigarette smoke rather than e-cig aerosols or model acute exposure by making the daily duration longer. It is important to consider the possibility that the greatest number of up- or down-regulated genes are found in immune cell populations solely because they are the first to be affected by e-cig exposure and the other cell types just do not have time to become dysregulated in 5 days.

    We thank the reviewers for this comment. We do not refute the fact that our observations of major changes in the immune cell population are due to the short duration of exposure. This was one of the first studies using single cell technologies to look at cell specific changes in the mouse lungs exposed to e-cig aerosols. However, the future experiments being conducted in our lab are using more controlled approach to mimic chronic exposures to e-cig aerosols to identify changes in other cell types and long-term effects of e-cig exposures in vivo. However, since this was not the focus of this work, we have not discussed it in detail.

    The validity of the claims pertaining to septal thickening and mean linear intercept (MLI) are questionable due to the poor lung inflation of the treatment group, which the authors acknowledge. Thus, MLI cannot be accurately used. It is contradictory to state that the fruit-flavored treatment group presented challenges with inflation but then concluded that there is a phenotype. In addition, inflation with low-melting agarose is not an ideal method because it does not use a liquid column to maintain constant pressure. For these metrics to be used and evaluated, it is imperative that all lobes are properly inflated. Therefore, these data should either be repeated or removed.

    We agree with this critique and have removed the MLI quantification from the revised manuscripts, we also do not make claims regarding much histological changes upon exposure. We suggest further work in future to get better understanding of the effect of differently flavored e-cig aerosol exposure on mouse lungs.

    What is the purpose of analyzing cell cycle scores? Why is it relevant that neutrophils are in G2M-phase? Figure 3B shows that neutrophils are clearly in both G1- and G2M-phase and this cluster includes both Ly6G+ and Ly6G- subsets, so it does not seem accurate to claim that they are in the G2M-phase of the cell cycle, nor does it reveal anything novel about Ly6G- neutrophils. Is it possible that the cell cycle score is noting a point in differentiation when neutrophils acquire/begin expressing Ly6G? Ly6G expression in neutrophils has been found to be associated with differentiation and maturation. To rule out the possibility that this is a cell state being identified, differential gene expression between the 2 neutrophil subsets should be shown in a volcano plot. It would also be useful to stain for Ly6G+/- neutrophils using either IF or RNAscope to prove they are present. If the claim is that Ly6G- neutrophils are a "unique" population, it must be established to what extent they are unique. Immune cells cluster together on UMAPs, so what if these are a different cell type entirely, like another immature myeloid lineage, and this is an artifact of clustering? This could be clarified with a trajectory analysis and further subsetting of the immune population.

    We thank the reviewers for this comment. We now realize that analyzing the cell cycle scores was not serving the intended purpose in this work. Moreover, due to the use of pooled samples for scRNA seq analyses, it may not be best to perform such downstream analyses in our datasets. We have thus removed these graphs from the revised version and have tried to simplify the conclusions of our study to the readers.

    Our main take home from this study is the increase in number of mature (Ly6G+) and immature (Ly6G-) neutrophils in tobacco-flavored e-cig aerosol exposed mouse lungs as compared to air control. This result was validated using co-immunofluorescence in the revised manuscript (Figure 4).

    In vivo validation of findings should be included, especially for the claimed changes. As of now, this paper serves more as a dataset that could be further explored by other groups, which in itself is valuable, but it is just one single cell sequencing experiment without validation.

    We thank the reviewers for this comment. We have used multiple techniques (flow cytometry, multiplex protein assay, co-immunofluorescence) in the revised manuscript to validate the scRNA seq findings. However, this was a preliminary study which was designed to generate a small dataset for future experiments, and we do not have resources to add more validatory experiments for this study. We are currently designing chronic e-cig exposure studies to elaborate upon certain hypothesis generated through this study in future.

    Minor Comments

    There are several examples of typos or small errors in the text that would benefit from proofreading. Examples: line 51 "in the many countries including (the) United States (US), (the) United Kingdom..."; on line 54, the reference cited states that 9.4% of middle schoolers are daily users, not 9.2%; on line 55 the reference cited states that these are the most commonly used flavors, not the most preferred, which explains why the percentages do not add up to 100; line 120 "the lungs were in a collapsed state than the other groups"; line 127 "to confirm out speculations"; line 136 "PGVG" instead of the previously used "PG:VG"; line 140 "(single cell capture))"; line 999 "result in" rather than "results in" for Figure 4 title, etc.

    We thank the reviewer for this comment. The manuscript has been thoroughly proofread and edited to avoid typos and grammatical errors.

    If this is a "pilot study" (as it is stated in the introduction) it is meant to assess the validity of experimental design on a small scale to later test a hypothesis. The authors should change the phrasing.

    We have now changed the phrasing as suggested.

    The introduction lacked the necessary context and background. Some information described in the results section could be addressed in the intro. For example: What is the significance of neutrophils having a Ly6G deficiency? Why was the exposure duration of 1 hour a day for 5 days chosen? Why use nose-only exposure when many models use whole-body exposure? Why look at cell-type-specific changes?

    We have made the necessary amendments in the introduction.

    Some figure titles only address certain panels rather than summarizing the figure as a whole. For example, the title of Figure 1 only refers to panel D and is unrelated to serum cotinine levels, septa thickening, or mean linear intercept. The text discussed conclusions about septa thickening and Lm values for the fruit-flavored treatment group, so they are equally relevant to the figure compared to the metal levels.

    We have now changed the Figures and Figure legends to summarize the figure.

    significance level is not defined in Figure 1 legend although it is used in Figure 1C.

    The Figure legend has now been updated.

    Figure 1E does not include a scale bar.

    We have now included the scale bar in updated figures.

    The multiplex ELISA shown in the experimental design schematic is not further discussed in the paper. Flow cytometry plots should be displayed in addition to the data they generated.

    The flow cytometry plots have now been included (Figures 3&5) and the results for Multiplex ELISA are shown as Figure S3D and lines 327-342 of the revised manuscript.

    In Figure 1F, a multivariate ANOVA should be used so that multiple groups can be compared across sex, rather than plotting in a sex-specific manner and claiming there exists a sex bias. The small sample size also introduces an issue because a p-value cannot be generated with so few samples.

    Per the suggestions made previously, figure 1F has now been removed from the revised manuscript.

    The protocol for achieving a single-cell suspension should be detailed in the methods section. As is, it only describes the sample collection and preparation. This could help elucidate to the reader why the UMAP shows such a large abundance of immune cells.

    We have now included the protocol in the revised manuscript.

    Clarify whether PG:VG was used as a control in the scRNA sequencing in addition to air to generate the UMAP in Figure 2A.

    Yes, PG:VG was used as one of the controls which has now been illustrated as groupwise comparison in Figure 2D. We have also included the comparisons to identify DEGs in myeloid and lymphoid clusters upon comparison of various treatment groups versus PGVG (Supplementary Files S13-S18)

    A UMAP should be shown for each treatment group/flavor. The overall UMAP in Figure 1A is good, but there could be another panel with separate projections for each condition.

    A groupwise UMAP has now been included in Figure 2D.

    In Figure 2C, relative cell percentage is not a reliable method to quantify cell type and the histogram is not a great way to visualize the data or its statistical significance. These claims should also be validated in tissue.

    We thank the reviewers for this comment and have tried to validate the findings using Flow cytometry. However, we may want to add that the changes observed in single cell technologies cannot be validated using simple molecular biology techniques as the markers used to specify cell clusters in scRNA seq is too specific which was not the case for the design of flow panel in this work. Our major purpose of using cell percentages was to show the flavor-specific changes in generalized cell populations in mouse lungs. So, we have still included these graphs in the revised manuscript.

    Figure 2D could be better illustrated with a volcano plot to show which genes are being dysregulated rather than just how many. Knowing which genes are affected is more valuable than knowing just the number of genes.

    Figure 2D is no longer a part of the revised manuscript. For the other comparisons we have still used heatmaps as they also depict sex-specific changes in gene expressions, which would have been difficult to elucidate using volcano plots.

    Assuming Figure 3C is representative of all conditions, then Figures 3C and D demonstrate that Ly6G- neutrophils are present in all conditions including controls. To see whether they are truly present in different abundances between treatment and control groups, separate UMAPs of the neutrophil subsets should be made per condition or use a dot plot for Figure 3A. This also applies to Figure 3B.

    We thank the reviewers for pointing this out. We have now revamped the whole manuscript and used additional validation experiments to show the presence of Ly6G- and Ly6G+ neutrophil population upon exposure to tobacco-flavored e-cig aerosols.

    Figure 3E shows that there is no statistically significant change in % of Ly6G+ neutrophils across treatment groups, but the text claims that there is "an increase in the levels of Ly6G+ neutrophils in lung digests from mouse lungs exposed to tobacco-flavored e-cig aerosols" (lines 207-209). The text also claims that "The observed increase was more pronounced in males as compared to females" (lines 209-210), but there was no statistical analysis across sexes to support this statement. It is clear that the change in % of Ly6G+ neutrophils is more pronounced in males than females, but it is still not statistically significant. This figure should also be repeated for analysis of Ly6G- neutrophils. Lines 272-274 mention that the % increase is higher for Ly6G- neutrophils than for Ly6G+ neutrophils, but there is not an analogous histogram to demonstrate this. The claims made in lines 275-280 are not clearly shown in any figure.

    We thank the reviewer for this query. This was an error on our part. We have now added sex-specific changes using scRNA seq, flow cytometry and co-immunofluorescence-based experiments to prove that more pronounces changes in the Ly6G+ and Ly6G- neutrophil population occurs in female mice and not males.

    Figures 4 and 6 have an overwhelming amount of heatmaps. Volcano plots with downstream analyses could be used to make some of this data more legible. The main findings should be validated in vivo/in tissue.

    We have now revamped the figures and data distribution to make the data legible and remove overwhelming amount of data from the slides.

    For Figure 5, show cell type by condition and do differential gene expression analysis displayed in a volcano plot. Then, stain tissue to validate the findings. Compare across sex during statistical analysis.

    The necessary changes have been made.

    Figure 6 error: panels E and F should be labeled as "tobacco" rather than "fruit".

    Error has now been fixed.

    Figure 7C can be placed in the supplemental materials.

    It has now been included in supplemental materials.

    The Figure 6E title should have been tobacco instead of fruit.

    This error has now been fixed.

    Line 381 mentioned the wrong subfigure. (Figure 7B instead of 7E).

    We have now made the necessary edits.

  13. eLife

    eLife Assessment

    This manuscript by Kaur et al. identifies differential gene expression observed in distinct mouse lung cell populations, namely myeloid and lymphoid cells, upon short-term exposure to e-cig aerosols with various flavors. Their findings are potentially useful because the single-cell sequencing data provides a reference for future studies of genes and cellular pathways that are most affected by e-cig aerosols and their components. However, the evidence is incomplete due to limited statistical analyses and few biological replicates, as well as a lack of experimental validation.

  14. eLife

    Reviewer #1 (Public review):

    Summary:

    The authors tackled the public concern about E-cigarettes among young adults by examining the lung immune environment in mice using single-cell RNA sequencing, discovering a subset of Ly6G- neutrophils with reduced IL-1 activity and increased CD8 T cells following exposure to tobacco-flavored e-cigarettes. Preliminary serum cotinine (nicotine metabolite) measurements validated the effective exposure to fruit, menthol, and tobacco-flavored e-cigarettes with air and PG:VG serving as control groups. They also highlighted the significance of metal leaching, which fluctuated over different exposure durations to flavored e-cigarettes, underscoring the inherent risks posed by these products. The scRNAseq analysis of e-cig exposure to flavors and tobacco demonstrated the most notable differences in the myeloid and lymphoid immune cell populations. Differentially expressed genes (DEGs) were identified for each group and compared against the air control. Further sub-clustering revealed a flavor-specific rise in Ly6G- neutrophils and heightened activation of cytotoxic T cells in response to tobacco-flavored e-cigarettes. These effects varied by sex, indicating that immune changes linked to e-cig use are dependent on gender. By analyzing the expression of various genes and employing gene ontology and gene enrichment analysis, they identified key pathways involved in this immune dysregulation resulting from flavor exposure. Overall, this study affirmed that e-cigarette exposure can suppress the neutrophil-mediated immune response, subsequently enhancing T cell toxicity in the lung tissue of mice.

    Strengths:

    This study used single-cell RNA sequencing to comprehensively analyze the impact of e-cigarettes on the lung. The study pinpointed alterations in immune cell populations and identified differentially expressed genes and pathways that are disrupted following e-cigarette exposure. The manuscript is well written, the hypothesis is clear, the experiments are logically designed with proper control groups, and the data is thoroughly analyzed and presented in an easily interpretable manner. Overall, this study suggested novel mechanisms by which e-cigs impact lung immunity and created a dataset that could benefit the lung immunity field.

    Weaknesses:

    (1) The authors included a valuable control group - the PG:VG group, since PG:VG is the foundation of the e-liquid formulation. However, most of the comparative analyses use the air group as the control. Further analysis comparing the air group to the PG:VG group, and the PG:VG group to the individual flavored e-cig groups will provide more clear insights into the true source of irritation. This is done for a few analyses but not consistently throughout the paper. Flavor-specific effects should be discussed in greater detail. For example, Figure 1E shows that the Fruit flavor group exhibits more severe histological pathology but similar effects were not corroborated by the single-cell data.

    (2) The characterization of Ly6g+ vs Ly6g- neutrophils is interesting and potentially very impactful. Key results like this from scRNAseq analyses should be validated by qPCR and flow cytometry.

    Also, a recent study by Ruscitti et al reported Ly6g+ macrophages in the lung which can potentially confound the cell type analysis. A more detailed marker gene and sub-population analysis of the myeloid clusters could rule out this potential confounding factor.

  15. eLife

    Reviewer #2 (Public review):

    This study provides some interesting observations on how different flavors of e-cigarettes can affect lung immunology, however there are numerous flaws including a low number of replicates and a lack of effective validation methods which reduces the robustness and rigor of the findings.

    Strengths:

    The strength of the study is the successful scRNA-seq experiment which gives good preliminary data that can be used to create new hypotheses in this area.

    Weaknesses:

    The major weakness is the low number of replicates and the limited analysis methods. Two biological n per group is not acceptable to base any solid conclusions. Any validatory data was too little (only cell % data) and did not always support the findings (e.g. Figure 4D does not match 4C). Often n seems to be combined and only one data point is shown, it is not at all clear how the groups were analysed and how many cells in each group were compared.

    Other specific weaknesses were identified in addition to the ones above:

    (1) Only 71,725 cells means only 7,172 per group, which is 3,586 per animal - how many of these were neutrophils, T-cells, and macrophages? This was not shown and could be too low.

    (2) The dynamic range of RNA measurement using scRNAseq is known to be limited - how do we know whether genes are not expressed or just didn't hit detection? This links into the Ly6G negative neutrophil comment, but in general, the lack of gene expression in this kind of data should be viewed with caution, especially with a low n number and few cells.

    (3) There is no rigorous quantification of Ly6G+ and Ly6G- cells int he flow cytometry data.

    (4) Eosinophils are heavily involved in lung biology but are missing from the analysis.

    (5) The figures had no titles so were difficult to navigate.

    (6) PGVG is not defined and not introduced early enough.

    (7) Neutrophils are not well known to proliferate, so any claims about proliferation need to be accompanied by validation such as BrdU or other proliferation assays.

    (8) It was not clear how statistics were chosen and why Table S2 had a good comparison (two-way ANOVA with gender as a variable) but this was not used for other data particularly when looking at more functional RNA markers (Table S2 also lacks the interaction statistic which is most useful here).

    (9) Many statistics are only vs air control, but it would be more useful as a flavour comparison to see these vs PGVG. In some cases, the carrier PGVG looks worse than some of the flavours (which have nicotine).

    (10) The n number is a large issue, but in Figures such as 4, 6, and 7 it could be a bigger factor. The number of significant genes identified has been determined by chance rather than any real difference, e.g. Is Il1b not identified in Fruit flavour vs air because there wasn't enough n, while in Air vs Tobacco, it randomly hit the significance mark. This is but an example of the problems with the analysis and conclusions

    (11) The data in Figure 7A is confusing, if this is a comparison to air, then why does air vs air not equal 1? Even if this was the comparison to the average of air between males and females, then this doesn't explain why CCL12 is >1 in both. Is this z-score instead? Regardless the data is difficult to interpret in this format.

    (12) Individual n was not shown for almost all experiments - e.g. Figure 1D - what is this representative of? Figure 2D - is this bulk-grouped data for all cells and all mice? The heatmaps are also pooled from 2n and don't show the variability.

  16. eLife

    Reviewer #3 (Public review):

    This work aims to establish cell-type specific changes in gene expression upon exposure to different flavors of commercial e-cigarette aerosols compared to control or vehicle. Kaur et al. conclude that immune cells are most affected, with the greatest dysregulation found in myeloid cells exposed to tobacco-flavored e-cigs and lymphoid cells exposed to fruit-flavored e-cigs. The up-and-down-regulated genes are heavily associated with innate immune response. The authors suggest that a Ly6G-deficient subset of neutrophils is found to be increased in abundance for the treatment groups, while gene expression remains consistent, which could indicate impaired function. Increased expression of CD4+ and CD8+ T cells along with their associated markers for proliferation and cytotoxicity is thought to be a result of activation following this decline in neutrophil-mediated immune response.

    Strengths:

    (1) Single-cell sequencing data can be very valuable in identifying potential health risks and clinical pathologies of lung conditions associated with e-cigarettes considering they are still relatively new.

    (2) Not many studies have been performed on cell-type specific differential gene expression following exposure to e-cig aerosols.

    (3) The assays performed address several factors of e-cig exposure such as metal concentration in the liquid and condensate, coil composition, cotinine/nicotine levels in serum and the product itself, cell types affected, which genes are up- or down-regulated and what pathways they control.

    (4) Considerations were made to ensure clinical relevance such as selecting mice whose ages corresponded with human adolescents so that the data collected was relevant.

    Weaknesses:

    (1) The exposure period of 1 hour a day for 5 days is not representative of chronic use and this time point may be too short to see a full response in all cell types. The experimental design is not well-supported based on the literature available for similar mouse models.

    (2) Several claims lack supporting evidence or use data that is not statistically significant. In particular, there were no statistical analyses to compare results across sex, so conclusions stating there is a sex bias for things like Ly6G+ neutrophil percentage by condition are observational.

    (3) Statistical analyses lack rigor and are not always displayed with the most appropriate graphical representation.

    (4) Overall, the paper and its discussion are relatively limited and do not delve into the significance of the findings or how they fit into the bigger picture of the field.

    (5) The manuscript lacks validation of findings in tissue by other methods such as staining.

    (6) This paper provides a foundation for follow-up experiments that take a closer look at the effects of e-cig exposure on innate immunity. There is still room to elaborate on the differential gene expression within and between various cell types.

  17. eLife

    Author response:

    Public Reviews:

    Reviewer #1 (Public review):

    Summary:

    The authors tackled the public concern about E-cigarettes among young adults by examining the lung immune environment in mice using single-cell RNA sequencing, discovering a subset of Ly6G- neutrophils with reduced IL-1 activity and increased CD8 T cells following exposure to tobacco-flavored e-cigarettes. Preliminary serum cotinine (nicotine metabolite) measurements validated the effective exposure to fruit, menthol, and tobacco-flavored e-cigarettes with air and PG/VG serving as control groups. They also highlighted the significance of metal leaching, which fluctuated over different exposure durations to flavored e-cigarettes, underscoring the inherent risks posed by these products. The scRNAseq analysis of e-cig exposure to flavors and tobacco demonstrated the most notable differences in the myeloid and lymphoid immune cell populations. Differentially expressed genes (DEGs) were identified for each group and compared against the air control. Further sub-clustering revealed a flavor-specific rise in Ly6G- neutrophils and heightened activation of cytotoxic T cells in response to tobacco-flavored e-cigarettes. These effects varied by sex, indicating that immune changes linked to e-cig use are dependent on gender. By analyzing the expression of various genes and employing gene ontology and gene enrichment analysis, they identified key pathways involved in this immune dysregulation resulting from flavor exposure. Overall, this study affirmed that e-cigarette exposure can suppress the neutrophil-mediated immune response, subsequently enhancing T cell toxicity in the lung tissue of mice.

    Strengths:

    This study used single-cell RNA sequencing to comprehensively analyze the impact of e-cigarettes on the lung. The study pinpointed alterations in immune cell populations and identified differentially expressed genes and pathways that are disrupted following e-cigarette exposure. The manuscript is well written, the hypothesis is clear, the experiments are logically designed with proper control groups, and the data is thoroughly analyzed and presented in an easily interpretable manner. Overall, this study suggested novel mechanisms by which e-cigs impact lung immunity and created a dataset that could benefit the lung immunity field.

    We thank the reviewer for identifying the strengths of our work.

    Weaknesses:

    The authors included a valuable control group - the PG/VG group, since PG/VG is the foundation of the e-liquid formulation. However, most of the comparative analyses use the air group as the control. Further analysis comparing the air group to the PG/VG group, and the PG/VG group to the individual flavored e-cig groups will provide more clear insights into the true source of irritation. This is done for a few analyses but not consistently throughout the paper. Flavor-specific effects should be discussed in greater detail. For example, Figure 1E shows that the Fruit flavor group exhibits more severe histological pathology, but similar effects were not corroborated by the single-cell data.

    We thank the reviewer for this query. We agree that PG/VG group is the foundation of the e-liquid formulation and hence comparisons with this group is of significance to understand the effect of individual flavors on the cell population. Though we compared the flavored e-cig groups with PG/VG group, we did not discuss it in detail within the manuscript to avoid confusions in interpretation for such a big dataset. However, we will include the comparisons with the PG/VG group as a Supplement File in our revised manuscript to facilitate proper interpretation of our omics data to interested readers.

    While we agree that flavor-specific effects might be of interest, we did not delve into exploring them in detail as the fruit flavored e-liquids have now been regulated for sale in the US. Thus, from regulatory point of view, the effects of tobacco- and menthol-flavored e-liquids hold most interest. Since at the time of conducting this study, fruit flavors were in the market, we have still included the data. However, studying it further was not the focus of this work. Nevertheless, interested readers of our manuscript can have access to our dataset to allow further analyses and interpretation of our results.

    The characterization of Ly6g+ vs Ly6g- neutrophils is interesting and potentially very impactful. Key results like this from scRNAseq analyses should be validated by qPCR and flow cytometry.

    Also, a recent study by Ruscitti et al reported Ly6g+ macrophages in the lung which can potentially confound the cell type analysis. A more detailed marker gene and sub-population analysis of the myeloid clusters could rule out this potential confounding factor.

    We agree with the reviewer that the loss of Ly6G on neutrophils is a very interesting find and we are in process of designing neutrophil specific experiments to study the impact of e-cig exposure on neutrophil maturation and function which will be discussed in subsequent work by our group. However, to address the concerns raised by the reviewer, we are staining the lung tissue samples from air-and differently flavored e-cig aerosol exposed mouse lungs with Ly6G and S100A8 (universal marker for neutrophil) to see the infiltration of Ly6g+ vs Ly6g- neutrophils within the lungs of exposed and unexposed mice. This would also address the question if these populations were neutrophils or belong to another myeloid origin as suggested by recent publications. We will share the results from our findings in the revised manuscript and update our interpretations accordingly with better validations.

    Reviewer #2 (Public review):

    This study provides some interesting observations on how different flavors of e-cigarettes can affect lung immunology, however there are numerous flaws including a low number of replicates and a lack of effective validation methods which reduces the robustness and rigor of the findings.

    Strengths:

    The strength of the study is the successful scRNA-seq experiment which gives good preliminary data that can be used to create new hypotheses in this area.

    We appreciate the reviewer for recognizing the strength of this work.

    Weaknesses:

    The major weakness is the low number of replicates and the limited analysis methods. Two biological n per group is not acceptable to base any solid conclusions. Any validatory data was too little (only cell % data) and did not always support the findings (e.g. Figure 4D does not match 4C). Often n seems to be combined and only one data point is shown, it is not at all clear how the groups were analyzed and how many cells in each group were compared.

    We thank the reviewer for the critique to allow us to improve our analyses. We understand that the low number of replicates in this work makes the analyses difficult to draw solid conclusions, but this was a pilot study to understand the changes in the mouse lung upon acute exposures to flavored e-cig aerosols at a single cell level. So far, the e-cig field has been primarily focused on conducting toxicological studies to help regulatory bodies to set standards and enforce laws to better regulate the manufacture, sale and distribution of e-cig products. However, adolescents and young adults are still getting access to these products, and there is little to no understanding of how this may affect the lung health upon acute and chronic exposures. Single cell technology is a powerful tool to analyze the gene expression changes within cell populations to study cell heterogeneity and function. Yet, it is a costly tool, owing to which, conducting such analyses on large sample sizes is not ideal. This pilot study was designed to get some initial leads for future studies involving larger sample sizes and chronic exposures. Further, we still intend to share our results with the scientific community due to the value of such a dataset for a wider audience interested in learning about the mechanistic underpinnings of e-cig exposures in vivo.

    We understand that the validations are limited in our current work and so we are in process of conducting some immunostaining to validate a few targets made through this work. We also want to add here that validating single cell findings using any of the classical methods of experimentation including ELISA, qPCR or flow cytometry is sometimes difficult as many of these techniques still investigate the tissue while the changes shown in single cell analyses are mainly pertaining to a single cell type. This could be a probable reason for the scRNA seq results not aligning with our findings from flow cytometry. The data/findings from this pilot study have now allowed us to be better informed to design an effective flow panel for our future studies. In terms of the statistics and the number of cells for each analysis, we will share the detailed account and information for each to allow better interpretation of our results.

    Only 71,725 cells means only 7,172 per group, which is 3,586 per animal - how many of these were neutrophils, T-cells, and macrophages? This was not shown and could be too low.

    We do agree that the number of cells could be too low, but to avoid this we never studied the gene expression variations at the finest level of cell identity. We classified the cell clusters into general annotations -myeloid, lymphoid, endothelial, stromal and epithelial- and identified the changes in the gene expressions. Of these, only two clusters (myeloid and lymphoid) with more than ~1000 cells per cell type per group were studied in detail. We will include the cell count information to allow better interpretation of our results in the revised manuscript.

    The dynamic range of RNA measurement using scRNAseq is known to be limited - how do we know whether genes are not expressed or just didn't hit detection? This links into the Ly6G negative neutrophil comment, but in general, the lack of gene expression in this kind of data should be viewed with caution, especially with a low n number and few cells.

    This is a well-made point, and we thank the reviewer for this comment. We agree that the dynamic range RNA measurement is limited and for low cell numbers that could lead to bias. We are in process of validating the findings regarding the presence of Ly6G+ and Ly6G- cells in our control and treated lungs, the outcome of which will be discussed in the revised manuscript. We will also provide the cell number for the Ly6G- cell cluster for each sample with more detailed discussion of our findings. Due to the small sample size and cell capture, few limitations are hard to overcome which will be further elaborated upon in our revisions.

    There is no rigorous quantification of Ly6G+ and Ly6G- cells in the flow cytometry data.

    We understand that flow-based quantification of our scRNA seq findings would be interesting. However, flow cytometry and single cell suspension to perform sequencing were performed parallelly for this study. We used a basic flow panel using single markers to identify individual immune cell type. We did identify changes in the Ly6G population in our treated and control samples using scRNA seq and intend to include it as a marker for our future studies using flow cytometry. But unfortunately, the same analyses could not be performed for the current batch of samples. We will still include results from IHC staining to identify the Ly6G+ and Ly6G- population in the lung tissues from control and treated mice in revised manuscript to address some of the concerns raised here.

    Eosinophils are heavily involved in lung biology but are missing from the analysis.

    We used RBC lysis buffer to remove the excess RBCs during lung digestion for preparation of single cell suspension for scRNA seq in this study. Reports suggest that RBC lysis could adversely affect the eosinophil number and function. We did not identify any cell cluster, representing markers for eosinophils through our scRNA seq data and we believe that our lung digestion protocol could be the reason for the same. We have studied the eosinophil number changes through flow cytometry in these samples and have found significant changes as well. However due to our inability to find cell clusters for eosinophil through scRNA seq data, we did not include these results in the final manuscript. To avoid confusions and maintain transparency we will include our results from flow cytometry experiments in the revised manuscript.

    The figures had no titles so were difficult to navigate.

    We will make necessary adjustments to the data representation and include the titles to enable easy navigation of the Figures.

    PG/VG is not defined and not introduced early enough.

    We agree that PG/VG is an important control to compare in e-cig studies. This was the reason why this group was included, and we performed comparisons with this group for scRNA seq studies as well. However, to reduce the complexity of the study, we only shared the comparisons with Air control in this manuscript. We will include the comparisons made with PG/VG group as a Supplementary File in the revised manuscript to allow the interested readers have access to the study results and make necessary interpretations for future research.

    Neutrophils are not well known to proliferate, so any claims about proliferation need to be accompanied by validation such as BrdU or other proliferation assays.

    We thank the reviewer for this suggestion; however, we cannot perform the BrDU or other proliferation assay on neutrophils for now. We are planning to include these in the study designs of our future work, however we have limitations of funds to continue further experimentation to support this claim for this study. We mention clearly that this is only a scRNA seq finding and requires further study to avoid over-interpretation of our results.

    It was not clear how statistics were chosen and why Table S2 had a good comparison (two-way ANOVA with gender as a variable) but this was not used for other data particularly when looking at more functional RNA markers (Table S2 also lacks the interaction statistic which is most useful here).

    We thank the reviewer for bringing this concern. We understand that this is a valid point and will include all the necessary information regarding the statistics and other related parameters in the revised manuscript.

    Many statistics are only vs air control, but it would be more useful as a flavor comparison to see these vs PG/VG. In some cases, the carrier PG/VG looks worse than some of the flavors (which have nicotine).

    We will include the comparisons with PG/VG as supplementary file in our revised manuscript, however we do not intend to describe all those changes in detail in the main manuscript.

    The n number is a large issue, but in Figures such as 4, 6, and 7 it could be a bigger factor. The number of significant genes identified has been determined by chance rather than any real difference, e.g. Is Il1b not identified in Fruit flavor vs air because there wasn't enough n, while in Air vs Tobacco, it randomly hit the significance mark. This is but an example of the problems with the analysis and conclusions.

    While we agree in part with the concern raised here, we wish to point out that there are limitations to every experiment. In our opinion, an omics study is not necessarily aimed to find the changes at transcript level with absolute certainty, rather to identify probable cell and gene targets to validate with subsequent work. We never claim that our findings are absolute outcomes but rather add the limitation of sample number and need for further research at every step. The strength of this work is to be the first study of its kind looking at changes in the lung cell population at single cell level upon e-cig aerosol exposure. This study has provided us with interesting gene and cell targets that we are now validating with future work. We still strongly believe that a dataset like this is a useful resource for a wider audience to allow efficient study designs and hence it is befitting to be published and discussed amongst our peers.

    The data in Figure 7A is confusing, if this is a comparison to air, then why does air vs air not equal 1? Even if this was the comparison to the average of air between males and females, then this doesn't explain why CCL12 is >1 in both. Is this z-score instead? Regardless the data is difficult to interpret in this format.

    We thank the reviewer for pointing this out. We realize that the data might be difficult to understand due to scaling of the color codes for the heatmap. We will change the graphical representation and include actual number for fold change in our revised manuscript to allow easy interpretation of these results.

    Individual n was not shown for almost all experiments - e.g. Figure 1D - what is this representative of? Figure 2D - is this bulk-grouped data for all cells and all mice? The heatmaps are also pooled from 2n and don't show the variability.

    While we have included a pictorial representation of the n number in Figure 1A and mentioned n number in the Figure legends for each figure, we understand that it maybe difficult to navigate. We will attempt to address this in a better manner in the revised manuscript.

    However, with respect to the second comment we would like to differ from the reviewer’s opinion. Each scRNA seq data had 2 samples – one for male and another for female which has been clearly shown in the current figures. The pooling of cells as mentioned in the comment happened at the stage of preparation of cell suspension from each sex/group at the start of the sequencing. We do not have any means to show the variability amongst pooled samples, which we acknowledge as a shortcoming of our work. So, in terms of representation of the heatmaps and data analyses we have included all the needed information to uphold transparency of our study design and data visualization for each figure and would like to stick to the current representations.

    Reviewer #3 (Public review):

    This work aims to establish cell-type specific changes in gene expression upon exposure to different flavors of commercial e-cigarette aerosols compared to control or vehicle. Kaur et al. conclude that immune cells are most affected, with the greatest dysregulation found in myeloid cells exposed to tobacco-flavored e-cigs and lymphoid cells exposed to fruit-flavored e-cigs. The up-and-down-regulated genes are heavily associated with innate immune response. The authors suggest that a Ly6G-deficient subset of neutrophils is found to be increased in abundance for the treatment groups, while gene expression remains consistent, which could indicate impaired function. Increased expression of CD4+ and CD8+ T cells along with their associated markers for proliferation and cytotoxicity is thought to be a result of activation following this decline in neutrophil-mediated immune response.

    Strengths:

    (1) Single-cell sequencing data can be very valuable in identifying potential health risks and clinical pathologies of lung conditions associated with e-cigarettes considering they are still relatively new.

    (2) Not many studies have been performed on cell-type specific differential gene expression following exposure to e-cig aerosols.

    (3) The assays performed address several factors of e-cig exposure such as metal concentration in the liquid and condensate, coil composition, cotinine/nicotine levels in serum and the product itself, cell types affected, which genes are up- or down-regulated and what pathways they control.

    (4) Considerations were made to ensure clinical relevance such as selecting mice whose ages corresponded with human adolescents so that the data collected was relevant.

    We thank the reviewer for identifying the key strengths of our work and listing it in a concise and well-rounded fashion.

    Weaknesses:

    The exposure period of 1 hour a day for 5 days is not representative of chronic use and this time point may be too short to see a full response in all cell types. The experimental design is not well-supported based on the literature available for similar mouse models.

    This study was not designed to study the effects of chronic exposures on lung tissues. We were interested in delineating the effect of acute exposures for which the proposed study design was chosen. Previous work by our group has performed similar exposures and has been well received by the community. We understand that chronic exposures will be interesting to look at, however that was not the purpose of this pilot study. We will now explicitly mention this aspect in the revised manuscript.

    Several claims lack supporting evidence or use data that is not statistically significant. In particular, there were no statistical analyses to compare results across sex, so conclusions stating there is a sex bias for things like Ly6G+ neutrophil percentage by condition are observational.

    We thank the reviewer for this observation, and we will include the necessary validations and details of the sex-based statistical analyses in the revised version of this manuscript.

    Statistical analyses lack rigor and are not always displayed with the most appropriate graphical representation.

    We thank the reviewer and will include all the necessary statistical details with more details in the revised manuscript.

    Overall, the paper and its discussion are relatively limited and do not delve into the significance of the findings or how they fit into the bigger picture of the field.

    We are in process of performing a few validatory experiments and intend to include few other pieces of data to this manuscript to add to the overall merit of our findings. However as pointed out by the reviewer themselves the strength of this work is in the first ever scRNA seq analyses of mouse exposed to differently flavored e-cig aerosols in vivo. We also show cell-specific differential gene expression and address some of the major queries made around e-cig research including release of metals on a day-to-day basis from the same coil. The limited sample number make it difficult to draw solid conclusions from this work, which has been discussed as a shortcoming. However the major strength of this work is not in identifying specific trends but rather to explore the possible cell and gene targets to expand the study for longer (chronic) exposures with a larger sample group.

    The manuscript lacks validation of findings in tissue by other methods such as staining.

    We are conducting some studies and will include the validatory experiments and staining in the revised manuscript to support our findings.

    This paper provides a foundation for follow-up experiments that take a closer look at the effects of e-cig exposure on innate immunity. There is still room to elaborate on the differential gene expression within and between various cell types.

    We thank the reviewer for this observation. The cell numbers for some cell clusters (especially epithelial cells) were too low. So, though we have performed the differential gene expression analyses on all the cell clusters, we refrained from discussing it in the manuscript to avoid over interpretation of our results. Only clusters with high enough (~1000) cells per sex per group were used to plot the heatmaps. We will also include the cell numbers for each cell type in the revisions to allow better interpretation of our data. Furthermore, the raw data from this study will be freely available to the public upon publication of this manuscript. This would enable the interested readers to access the raw data and study the cell types of interest in detail based on their study requirements. This data will be a useful resource for all in this community to inform and design future studies.

  18. Version published to 10.7554/elife.106380.1 on eLife
  19. Version published to 10.1101/2025.02.17.638715 on bioRxiv