Single-cell transcriptomics identifies a dampened neutrophil function and accentuated T-cell cytotoxicity in tobacco flavored e-cigarette exposed mouse lungs
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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.
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Abstract
E-cigarettes (e-cigs) are a public health concern for young adults due to their popularity and evidence for increased oxidative stress and immunotoxicity. Yet an extensive study defining the cell-specific immune changes upon exposure to flavored e-cigs remains elusive. To understand 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). scRNA profiles of 71,725 cells were generated from control and treatment groups (n=2/sex/group). A distinct phenotype of Ly6G-neutrophils was identified in lungs exposed to tobacco flavored e-cig aerosol which demonstrated dampened IL-1 mediated and pattern recognition signaling as compared to air controls. Differential gene expression analyses identified dysregulation of T-cell mediated pro-inflammation ( Cct7 , Cct8 ) and stress-response signals ( Neurl3 , Stap1 , Cirbp and Htr2c) in the lungs of mice exposed to e-cig aerosols, with pronounced effects for tobacco flavor. Flow cytometry analyses and cytokine/chemokine assessments within the lungs corroborated the scRNA seq data, demonstrating a significant increase in T-cell percentages and levels of T-cell associated cytokine/chemokines in the lungs of tobacco-flavored aerosol exposed mice. Increased levels of Klra4 and Klra8 expression also suggest an enhanced natural killer (NK) cell activity in this mouse group. Overall, this is a pilot study identifying increase in the percentages of Ly6G-neutrophils that may be responsible for dampened innate immune responses and heightened T-cell cytotoxicity in lungs of tobacco-flavored e-cig aerosol exposed mice. In addition, we provide preliminary evidence for sex-specific changes in the transcriptional landscape of mouse lungs upon exposure to e-cig aerosol, an area that warrants further study.
Article activity feed
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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.
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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 …
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.
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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, …
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.
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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 …
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.
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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 …
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.
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