A unified rodent atlas reveals the cellular complexity and evolutionary divergence of the dorsal vagal complex
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eLife Assessment
This manuscript applies state-of-the-art techniques to define the cellular composition of the dorsal vagal complex in two rodent species (mice and rats). The result is an important resource that substantially advances our understanding of the dorsal vagal complex's role in the regulation of feeding and metabolism while also highlighting key differences between species. While most of the analyses in the manuscript provide convincing insight into the cellular architecture of the dorsal vagal complex, other aspects are incomplete and could be bolstered by additional evidence.
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Abstract
The dorsal vagal complex (DVC) is a region in the brainstem comprised of an intricate network of specialized cells responsible for sensing and propagating many appetite-related cues. Understanding the dynamics controlling appetite requires deeply exploring the cell types and transitory states harbored in this brain site. We generated a multi-species DVC cell atlas using single nuclei RNAseq (sn-RNAseq), by curating and harmonizing mouse and rat data, which includes >180,000 cells and 123 cell identities at 5 granularities of cellular resolution. We report unique DVC features such as Kcnj3 expression in Ca + -permeable astrocytes as well as new cell populations like neurons co-expressing Th and Cck , and a leptin receptor-expressing neuron population in the rat area postrema which is marked by expression of the progenitor marker, Pdgfra . In summary, our findings demonstrate a high degree of complexity within the DVC and provide a valuable tool for the study of this metabolic center.
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eLife Assessment
This manuscript applies state-of-the-art techniques to define the cellular composition of the dorsal vagal complex in two rodent species (mice and rats). The result is an important resource that substantially advances our understanding of the dorsal vagal complex's role in the regulation of feeding and metabolism while also highlighting key differences between species. While most of the analyses in the manuscript provide convincing insight into the cellular architecture of the dorsal vagal complex, other aspects are incomplete and could be bolstered by additional evidence.
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Reviewer #1 (Public review):
Summary:
This paper uses state-of-the-art techniques to define the cellular composition and its complexity in two rodent species (mice and rats). The study is built on available datasets but extends those in a way that future research will be facilitated. The study will be of high impact for the study of metabolic control.
Strengths:
(1) The study is based on experiments that are combined with two exceptional data sets to provide compelling evidence for the cellular composition of the DVC.
(2) The use of two rodent species is very useful.
Weaknesses:
There is no conceptual weakness, the performance of experiments is state-of-the-art, and the discussion of results is appropriate. One minor point that would further strengthen the data is a more distinct analysis of receptors that are characteristic of the …Reviewer #1 (Public review):
Summary:
This paper uses state-of-the-art techniques to define the cellular composition and its complexity in two rodent species (mice and rats). The study is built on available datasets but extends those in a way that future research will be facilitated. The study will be of high impact for the study of metabolic control.
Strengths:
(1) The study is based on experiments that are combined with two exceptional data sets to provide compelling evidence for the cellular composition of the DVC.
(2) The use of two rodent species is very useful.
Weaknesses:
There is no conceptual weakness, the performance of experiments is state-of-the-art, and the discussion of results is appropriate. One minor point that would further strengthen the data is a more distinct analysis of receptors that are characteristic of the different populations of neuronal and non-neuronal cells; this part could be improved. Currently, it is only briefly mentioned, e.g., line 585ff. See also lines 603ff; it is true that the previous studies lack some information about the neurotransmitter profile of cells, but combining all data sets should result in an analysis of the receptors as well, e.g. in the form of an easy-to-read table. -
Reviewer #2 (Public review):
In this manuscript, Hes et al. present a comprehensive multi-species atlas of the dorsal vagal complex (DVC) using single-nucleus RNA sequencing, identifying over 180,000 cells and 123 cell types across five levels of granularity in mice and rats. Intriguingly, the analysis uncovered previously uncharacterized cell populations, including Kcnj3-expressing astrocytes, neurons co-expressing Th and Cck, and a population of leptin receptor-expressing neurons in the rat area postrema, which also express the progenitor marker Pdgfra. These findings suggest species-specific differences in appetite regulation. This study provides a valuable resource for investigating the intricate cellular landscape of the DVC and its role in metabolic control, with potential implications for refining obesity treatments targeting …
Reviewer #2 (Public review):
In this manuscript, Hes et al. present a comprehensive multi-species atlas of the dorsal vagal complex (DVC) using single-nucleus RNA sequencing, identifying over 180,000 cells and 123 cell types across five levels of granularity in mice and rats. Intriguingly, the analysis uncovered previously uncharacterized cell populations, including Kcnj3-expressing astrocytes, neurons co-expressing Th and Cck, and a population of leptin receptor-expressing neurons in the rat area postrema, which also express the progenitor marker Pdgfra. These findings suggest species-specific differences in appetite regulation. This study provides a valuable resource for investigating the intricate cellular landscape of the DVC and its role in metabolic control, with potential implications for refining obesity treatments targeting this hindbrain region.
In line with previous work published by the PI, the topic is of clear scientific relevance, and the data presented in this manuscript are both novel and compelling. Additionally, the manuscript is well-structured, and the conclusions are robust and supported by the data. Overall, this study significantly enhances our understanding of the DVC and sheds light on key differences between rats and mice.
I applaud the authors for the depth of their analysis. However, I have a few major concerns, comments, and suggestions that should be addressed.
(1) If I understand the methodology correctly, mice were fasted overnight and then re-fed for 2 hours before being sacrificed (lines 91-92), which occurred 4 hours after the onset of the light phase (line 111). This means that the re-fed animals had access and consequently consumed food when they typically would not. While I completely recognize that every timepoint has its limitations, the strong influence of the circadian rhythm on the DVC gene expression (highlighted by the work published by Lukasz Chrobok), and the fact that timing of food/eating is a potent Zeitgeber, might have an impact on the analysis and should be mentioned as a potential limitation in the discussion (along with citing Dr Chrobok's work). Could this (i.e., eating during a time when the animals are not "primed by their own circadian clock to eat" potentially explain why the meal-related changes in gene expression were relatively small?
(2) In the Materials and Methods section, LiCl is mentioned as one of the treatment conditions; however, very little corresponding data are presented or discussed. Please include these results and elaborate on the rationale for selecting LiCl over other anorectic compounds.
(3) The number of animals used differs significantly between species, which the authors acknowledge as a limitation in the discussion. Since the authors took advantage of previously published mouse data sets (Ludwig and Dowsett data sets), I wonder if the authors could compare/integrate any rat data set currently available in rats as well to partially address the sample size disparity.
(4) Dividing cells in AP vs NTS vs DMX clusters and analyzing potential species differences would significantly enhance the quality of the manuscript, given the partially diverse functions of these regions. This could be done by leveraging existing published datasets that employed spatial transcriptomics or more classical methodologies (e.g., PMID: 39171288, PMID: 39629676, PMID: 38092916). I would be interested to hear the authors' perspective on the feasibility of such an analysis.
(5) Given the manuscript's focus on feeding and metabolism, I believe a more detailed description and comparison of the transcription profile of known receptors, neurotransmitters, and neuropeptides involved in food intake and energy homeostasis between mice and rats would add value. Adding a curated list of key genes related to feeding regulation would be particularly informative.
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Reviewer #3 (Public review):
Summary:
This manuscript from Cecilia H et al provides a compelling resource for single nuclei RNA sequencing data with an emphasis on facilitating the integration of future data sets across mouse and rat data sets.
Strengths:
There are also several interesting findings that are highlighted, even though without a functional assay the importance remains unclear. However, the manuscript properly addresses where conclusions are speculative.
As with other snRNA seq datasets the manuscript demonstrates convincingly an increased level of complexity, while other neuronal populations like Cck and Th neurons were reproduced. Several recent findings from other groups are well addressed and put into a new context, e.g., DMV expression of AgRP (and Hcrt) was found to result from non-coding sequences, co-localization of …
Reviewer #3 (Public review):
Summary:
This manuscript from Cecilia H et al provides a compelling resource for single nuclei RNA sequencing data with an emphasis on facilitating the integration of future data sets across mouse and rat data sets.
Strengths:
There are also several interesting findings that are highlighted, even though without a functional assay the importance remains unclear. However, the manuscript properly addresses where conclusions are speculative.
As with other snRNA seq datasets the manuscript demonstrates convincingly an increased level of complexity, while other neuronal populations like Cck and Th neurons were reproduced. Several recent findings from other groups are well addressed and put into a new context, e.g., DMV expression of AgRP (and Hcrt) was found to result from non-coding sequences, co-localization of Cck/Th was identified in a small subset. These statements are informative.
The integration of rat data into the mouse data sets is excellent, and the comparison of cellular groups is very detailed, with interesting differences between mouse and rat data.
All data sets are easily accessible and usable on open platforms, this will be an impactful resource for other researchers.Weaknesses:
The data analysis seems incomplete. The title indicates the integration of mouse and rat data into a unified rodent dataset. But the discrepancy of animal numbers (30 mice vs. 2 rats) does not fit well with that focus.
On the other hand, the mouse group is further separated into different treatments to study genetic changes that are associated with distinct energy states of fed/fasting/refeeding responses. Yet, this aspect is not addressed in depth.
While the authors find transcriptional changes in all neuronal and non-neuronal cell types, which is interesting, the verification of known transcriptional changes (e.g., cFos) is unaddressed. cFos is a common gene upregulated with refeeding that was surprisingly not investigated, even though this should be a strong maker of proper meal-induced neuronal activation in the DMV. This is a missed opportunity either to verify the data set or to highlight important limitations if that had been attempted without success.
Additional considerations:
(1) The focus on transmitter classification is highlighted, but surprisingly, the well-accepted distinction of GABAergic neurons by Slc32a1 was not used, instead, Gad1 and Gad2 were used as GABAergic markers. While this may be proper for the DMV, given numerous findings that Gad1/2 are not proper markers for GABAergic neurons and often co-expressed in glutamatergic populations, this confound should have been addressed to make a case if and why they would be proper markers in the DMV.
(2) Figure S3 for anatomical localization of clusters is excellent, but several of the cluster gene names do not have a good signal in the DMV. Specifically, the mixed neurons that do not seem to have clear marker genes. What top markers (top 10?) were used to identify these anatomical locations? At least some examples should be shown for anatomical areas to support Figure S3.
(3) Page 15, lines 410-411: "...could not find clusters sharing all markers with our neuronal classes...". Are the authors trying to say that the DMV has more diverse neurons than other brain sites? It seems not too unusual that the hypothalamus is different from the brainstem. Maybe this could be stated more clearly, and the importance of this could be clarified.
(4) The finding of GIRK1 astrocytes is interesting, but the emphasis that this means these astrocytes are highly/more excitable is confusing. This was not experimentally addressed and should be put into context that astrocyte activation is very different from neuronal activation. This should be better clarified in the results and discussion.
(5) The Pdgfra IHC as verification is great, but images are not very convincing in distinguishing the 2 (mouse) or 3 (rat) classes of cells. Why not compare Pdgfra and HuC/D co-localization by IHC and snRNAseq data (using the genes for HuC/D) in the mouse and in the rat? That would also clarify how specific HuC/D is for DMV neurons, or if it may also be expressed in non-neuronal populations.
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