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.
