Single cell transcriptomics of vomeronasal neuroepithelium reveals a differential endoplasmic reticulum environment amongst neuronal subtypes

Reviewer #1 (Public Review):

Devakinandan and colleagues present a manuscript analyzing single-cell RNA-sequencing data from the mouse vomeronasal organ. The main advances in this manuscript are to identify and verify the differential expression of genes that distinguish apical and basal vomeronasal neurons. The authors also identify the enriched expression of ER-related genes in Gnao1 neurons, which they verify with in situ hybridizations and immunostaining, and also explore via electron microscopy. Finally, the results of this manuscript are presented in an online R shiny app. Overall, these data are a useful resource to the community. I have a few concerns about the manuscript, which I've listed below.

General Concerns:

(1) The authors mention that they were unable to identify the cells in cluster 13. This cluster looks similar to the "secretory VSN" subtype described in a recent preprint from C. Ron Yu's lab (10.1101/2024.02.22.581574). The authors could try comparing or integrating their data with this dataset (or that in Katreddi et al. 2022) to see if this is a common cell type across datasets (or arises from a specific type of cell doublets). In situ hybridizations for some of the marker genes for this cluster could also highlight where in the VNO these cells reside.

(2) I found the UMAPs for the neurons somewhat difficult to interpret. Unlike Katreddi et al. 2022 or Hills et al. 2024, it's tricky to follow the developmental trajectories of the cells in the UMAP space. Perhaps the authors could try re-embedding the data using gene sets that don't include the receptors? It would also be interesting to see if the neuron clusters still cluster by receptor-type even when the receptors are excluded from the gene sets used for clustering. Plots relating the original clusters to the neuronal clusters, or dot plots showing marker gene expression for the neuronal clusters might both be useful. For example, right now it's difficult to interpret clusters like n8-13.

**Reviewer #2 (Public Review):
**
Summary:

The study focuses on the vomeronasal organ, the peripheral chemosensory organ of the accessory olfactory system, by employing single-cell transcriptomics. The author analyzed the mouse vomeronasal organ, identifying diverse cell types through their unique gene expression patterns. Developmental gene expression analysis revealed that two classes of sensory neurons diverge in their maturation from common progenitors, marked by specific transient and persistent transcription factors. A comparative study between major neuronal subtypes, which differ in their G-protein sensory receptor families and G-protein subunits (Gnai2 and Gnao1, respectively), highlighted a higher expression of endoplasmic reticulum (ER) associated genes in Gnao1 neurons. Moreover, distinct differences in ER content and ultrastructure suggest some intriguing roles of ER in Gnao1-positive vomeronasal neurons. This work is likely to provide useful data for the community and is conceptually novel with the unique role of ER in a subset of vomeronasal neurons. This reviewer has some minor concerns and some suggestions to improve the manuscript.

Strengths:

(1) The study identified diverse cell types based on unique gene expression patterns, using single-cell transcriptomic.

(2) The analysis suggests that two classes of sensory neurons diverge during maturation from common progenitors, characterized by specific transient and persistent transcription factors.

(3) A comparative study highlighted differences in Gnai2- and Gnao1-positive sensory neurons.

(4) Higher expression of endoplasmic reticulum (ER) associated genes in Gnao1 neurons.

(5) Distinct differences in ER content and ultrastructure suggest unique roles of ER in Gnao1-positive vomeronasal neurons.

(6) The research provides conceptually novel on the unique role of ER in a subset of vomeronasal neurons, offering valuable insights to the community.

Weaknesses:

(1) The connection between observations from sc RNA-seq and EM is unclear.

(2) The lack of quantification for the ER phenotype is a concern.

Reviewer #3 (Public Review):

Summary:

In this manuscript, Devakinandan and colleagues have undertaken a thorough characterization of the cell types of the mouse vomeronasal organ, focusing on the vomeronasal sensory neurons (VSNs). VSNs are known to arise from a common pool of progenitors that differentiate into two distinct populations characterized by the expression of either the G protein subunit Gnao1 or Gnai2. Using single-cell RNA sequencing followed by unsupervised clustering of the transcriptome data, the authors identified three Gnai2+ VSN subtypes and a single Gnao1+ VSN type. To study VSN developmental trajectories, Devakinandan and colleagues took advantage of the constant renewal of the neuronal VSN pool, which allowed them to harvest all maturation states. All neurons were re-clustered and a pseudotime analysis was performed. The analysis revealed the emergence of two pools of Gap43+ clusters from a common lineage, which differentiate into many subclusters of mature Gnao1+ and Gnai2+ VSNs. By comparing the transcriptomes of these two pools of immature VSNs, the authors identified a number of differentially expressed transcription factors in addition to known markers. Next, by comparing the transcriptomes of mature Gnao1+ and Gnai2+ VSNs, the authors report the enrichment of ER-related genes in Gnao1+ VSNs. Using electron microscopy, they found that this enrichment was associated with specific ER morphology in Gnao1+ neurons. Finally, the authors characterized chemosensory receptor expression and co-expression (as well as H2-Mv proteins) in mature VSNs, which recapitulated known patterns.

Strengths:

The data presented here provide new and interesting perspectives on the distinguishing features between Gnao1+ and Gnai2+ VSNs. These features include newly identified markers, such as transcription factors, as well as an unsuspected ER-related peculiarity in Gnao1+ neurons, consisting of a hypertrophic ER and an enrichment in ER-related genes. In addition, the authors provide a comprehensive picture of specific co-expression patterns of V2R chemoreceptors and H2-Mv genes.

Importantly, the authors provide a browser (scVNOexplorer) for anyone to explore the data, including gene expression and co-expression, number and proportion of cells, with a variety of graphical tools (violin plots, feature plots, dot plots, ...).

Weaknesses:

The study still requires refined analyses of the data and rigorous quantification to support the main claims.

The method description for filtering and clustering single-cell RNA-sequencing data is incomplete. The Seurat package has many available pipelines for single-cell RNA-seq analysis, with a significant impact on the output data. How did the authors pre-process and normalize the data? Was the pipeline used with default settings? What batch correction method was applied to the data to mitigate possible sampling or technical effects? Moreover, the authors do not describe how cell and gene filtering was performed. The data in Figure 7-Supplement 3 show that one-sixth of the V1Rs do not express any chemoreceptor, while over a hundred cells express more than one chemoreceptor. Do these cells have unusually high or low numbers of genes or counts? To exclude the possibility of a technical artifact in these observations, the authors should describe how they dealt with putative doublet cells or debris. Surprisingly, some clusters are characterized by the expression of specific chemoreceptors (VRs). Have these been used for clustering? If so, clustering should be repeated after excluding these receptors.

The identification of the VSN types should be consistent across the different analyses and validated. The data presented in Figure 1 lists four mature VSN types, whereas the re-clustering of neurons presented in Figure 3 leads to a different subdivision. At present, it remains unclear whether these clusters reflect the biology of the system or are due to over-clustering of the data, and therefore correspond to either noise or arbitrary splitting of continua. Clusters should be merged if they do not correspond to discrete categories of cells, and correspondence should be established between the different clustering analyses. To validate the detected clusters as cell types, markers characteristic of each of these populations can be evaluated by ISH or IHC.

There is a lack of quantification of imaging data, which provides little support for the ER-related main claim. Quantification of co-expression and statistics on labeling intensity or coverage would greatly strengthen the conclusions and the title of the paper.

Author response:

eLife assessment

This valuable study uses single-cell transcriptomics to explore the mouse vomeronasal organ and represents an advance that enhances our understanding of neural diversity within this sensory system. Findings suggest a unique endoplasmic reticulum (ER) structure in Gnao1 neurons and allow for the synthesis of a developmental trajectory from stem cells to mature vomeronasal sensory neurons. Convincing methods, data, and analyses broadly support the claims, although experiments supporting the main ER-related claim are incomplete and lack quantification of co-expression and statistics on labeling intensity or coverage. Adding these data would greatly strengthen the conclusions of the paper.

Public Reviews:

Reviewer #1 (Public Review):

Devakinandan and colleagues present a manuscript analyzing single-cell RNA-sequencing data from the mouse vomeronasal organ. The main advances in this manuscript are to identify and verify the differential expression of genes that distinguish apical and basal vomeronasal neurons. The authors also identify the enriched expression of ER-related genes in Gnao1 neurons, which they verify with in situ hybridizations and immunostaining, and also explore via electron microscopy. Finally, the results of this manuscript are presented in an online R shiny app. Overall, these data are a useful resource to the community. I have a few concerns about the manuscript, which I've listed below.

General Concerns:

(1) The authors mention that they were unable to identify the cells in cluster 13. This cluster looks similar to the "secretory VSN" subtype described in a recent preprint from C. Ron Yu's lab (10.1101/2024.02.22.581574). The authors could try comparing or integrating their data with this dataset (or that in Katreddi et al. 2022) to see if this is a common cell type across datasets (or arises from a specific type of cell doublets). In situ hybridizations for some of the marker genes for this cluster could also highlight where in the VNO these cells reside.

Cluster13 (Obp2a+) cells identified in our study have similar gene expression markers to those identified with the “putative secretory” cells in Hills et al. manuscript. At the time this manuscript was available publicly, our publication was already finalized and communicated. We welcome the suggestion to integrate data, which we will attempt and address in our revision.

(2) I found the UMAPs for the neurons somewhat difficult to interpret. Unlike Katreddi et al. 2022 or Hills et al. 2024, it's tricky to follow the developmental trajectories of the cells in the UMAP space. Perhaps the authors could try re-embedding the data using gene sets that don't include the receptors? It would also be interesting to see if the neuron clusters still cluster by receptor-type even when the receptors are excluded from the gene sets used for clustering. Plots relating the original clusters to the neuronal clusters, or dot plots showing marker gene expression for the neuronal clusters might both be useful. For example, right now it's difficult to interpret clusters like n8-13.

We will represent the UMAPs to make the developmental trajectory clearer. How neuron clusters are affected by the presence or exclusion of receptors is an interesting question that we will address in our revision, along with showing markers of each neuronal cluster, as suggested by the reviewer.

Reviewer #2 (Public Review):

Summary:

The study focuses on the vomeronasal organ, the peripheral chemosensory organ of the accessory olfactory system, by employing single-cell transcriptomics. The author analyzed the mouse vomeronasal organ, identifying diverse cell types through their unique gene expression patterns. Developmental gene expression analysis revealed that two classes of sensory neurons diverge in their maturation from common progenitors, marked by specific transient and persistent transcription factors. A comparative study between major neuronal subtypes, which differ in their G-protein sensory receptor families and G-protein subunits (Gnai2 and Gnao1, respectively), highlighted a higher expression of endoplasmic reticulum (ER) associated genes in Gnao1 neurons. Moreover, distinct differences in ER content and ultrastructure suggest some intriguing roles of ER in Gnao1-positive vomeronasal neurons. This work is likely to provide useful data for the community and is conceptually novel with the unique role of ER in a subset of vomeronasal neurons. This reviewer has some minor concerns and some suggestions to improve the manuscript.

Strengths:

(1) The study identified diverse cell types based on unique gene expression patterns, using single-cell transcriptomic.

(2) The analysis suggests that two classes of sensory neurons diverge during maturation from common progenitors, characterized by specific transient and persistent transcription factors.

(3) A comparative study highlighted differences in Gnai2- and Gnao1-positive sensory neurons.

(4) Higher expression of endoplasmic reticulum (ER) associated genes in Gnao1 neurons.

(5) Distinct differences in ER content and ultrastructure suggest unique roles of ER in Gnao1-positive vomeronasal neurons.

(6) The research provides conceptually novel on the unique role of ER in a subset of vomeronasal neurons, offering valuable insights to the community.

Weaknesses:

(1) The connection between observations from sc RNA-seq and EM is unclear.

(2) The lack of quantification for the ER phenotype is a concern.

We would like to point out that the connection between scRNA-seq and EM was made in our experiments that investigated the localization of ER proteins via IHC (in Figure 5). The intriguing observation that the levels of a number of ER luminal and membrane proteins were higher in Gnao1 compared to Gnai2 neurons, led us to hypothesize a differential ER content or ultrastructure, which was verified by EM. The quantification of ER phenotype would definitely strengthen our observations, which we will add in our revised manuscript.

Reviewer #3 (Public Review):

Summary:

In this manuscript, Devakinandan and colleagues have undertaken a thorough characterization of the cell types of the mouse vomeronasal organ, focusing on the vomeronasal sensory neurons (VSNs). VSNs are known to arise from a common pool of progenitors that differentiate into two distinct populations characterized by the expression of either the G protein subunit Gnao1 or Gnai2. Using single-cell RNA sequencing followed by unsupervised clustering of the transcriptome data, the authors identified three Gnai2+ VSN subtypes and a single Gnao1+ VSN type. To study VSN developmental trajectories, Devakinandan and colleagues took advantage of the constant renewal of the neuronal VSN pool, which allowed them to harvest all maturation states. All neurons were re-clustered and a pseudotime analysis was performed. The analysis revealed the emergence of two pools of Gap43+ clusters from a common lineage, which differentiate into many subclusters of mature Gnao1+ and Gnai2+ VSNs. By comparing the transcriptomes of these two pools of immature VSNs, the authors identified a number of differentially expressed transcription factors in addition to known markers. Next, by comparing the transcriptomes of mature Gnao1+ and Gnai2+ VSNs, the authors report the enrichment of ER-related genes in Gnao1+ VSNs. Using electron microscopy, they found that this enrichment was associated with specific ER morphology in Gnao1+ neurons. Finally, the authors characterized chemosensory receptor expression and co-expression (as well as H2-Mv proteins) in mature VSNs, which recapitulated known patterns.

Strengths:

The data presented here provide new and interesting perspectives on the distinguishing features between Gnao1+ and Gnai2+ VSNs. These features include newly identified markers, such as transcription factors, as well as an unsuspected ER-related peculiarity in Gnao1+ neurons, consisting of a hypertrophic ER and an enrichment in ER-related genes. In addition, the authors provide a comprehensive picture of specific co-expression patterns of V2R chemoreceptors and H2-Mv genes.

Importantly, the authors provide a browser (scVNOexplorer) for anyone to explore the data, including gene expression and co-expression, number and proportion of cells, with a variety of graphical tools (violin plots, feature plots, dot plots, ...).

Weaknesses:

The study still requires refined analyses of the data and rigorous quantification to support the main claims.

The method description for filtering and clustering single-cell RNA-sequencing data is incomplete. The Seurat package has many available pipelines for single-cell RNA-seq analysis, with a significant impact on the output data. How did the authors pre-process and normalize the data? Was the pipeline used with default settings? What batch correction method was applied to the data to mitigate possible sampling or technical effects? Moreover, the authors do not describe how cell and gene filtering was performed.

The data in Figure 7-Supplement 3 show that one-sixth of the V1Rs do not express any chemoreceptor, while over a hundred cells express more than one chemoreceptor. Do these cells have unusually high or low numbers of genes or counts? To exclude the possibility of a technical artifact in these observations, the authors should describe how they dealt with putative doublet cells or debris.

Surprisingly, some clusters are characterized by the expression of specific chemoreceptors (VRs). Have these been used for clustering? If so, clustering should be repeated after excluding these receptors.

The identification of the VSN types should be consistent across the different analyses and validated. The data presented in Figure 1 lists four mature VSN types, whereas the re-clustering of neurons presented in Figure 3 leads to a different subdivision. At present, it remains unclear whether these clusters reflect the biology of the system or are due to over-clustering of the data, and therefore correspond to either noise or arbitrary splitting of continua. Clusters should be merged if they do not correspond to discrete categories of cells, and correspondence should be established between the different clustering analyses. To validate the detected clusters as cell types, markers characteristic of each of these populations can be evaluated by ISH or IHC.

There is a lack of quantification of imaging data, which provides little support for the ER-related main claim. Quantification of co-expression and statistics on labeling intensity or coverage would greatly strengthen the conclusions and the title of the paper.

scRNA-seq data analysis methods: We agree with the reviewer and will elaborate on the various criterion, parameters and methods in our revision. As described above, our revised manuscript will include analysis of how inclusion / exclusion of VRs affects cell clusters, as well as quantification of the ER phenotype. We will address the reviewer’s concern of over-clustering.

We think that the cells expressing zero as well as two V1Rs are real and cannot be attributed to debris or doublets for the following reasons:

a) Cells expressing no V1Rs are not necessarily debris because they express other neuronal markers at the same level as cells that express one or two V1Rs. Higher expression threshold values used in our analysis may have somewhat increased the proportion of cells with zero V1Rs. We will modify figure 7-supplement 3c to add another group showing Gnai2 level in cells expressing zero V1Rs.

b) Cells co-expressing V1R genes: We listed the frequency of cells co-expressing V1R gene combinations in Supplementary table - 8. Among 134 cells that express two V1Rs, 44 cells express Vmn1r85+Vmn1r86, 21 express Vmn1r184+Vmn1r185, 13 express Vmn1r56+Vmn1r57, 6 express Vmn1r168+Vmn1r177, and so on. Doublets generally are a random combination of two cells. Here, each specific co-expression combination represents multiple cells and is highly unlikely by random chance. Some of the co-expression combinations were identified earlier and verified experimentally in Lee et al., 2019 and Hills et. al. Furthermore, Figure-7 supplement 3c shows that the level of Gnai2 expression is comparable across cells expressing one or two V1Rs. If the V1R expressing cells are doublets, we expect the level of Gnai2 to be higher, as compared to cells expressing single V1R. We will elaborate on this in our revised manuscript.