Sex-specific differences in mediobasal hypothalamus in response to nutritional states

Domain: chemistry
Published: 2026-02-19
Editorial outcome: major_revision

Abstract
The arcuate nucleus of the hypothalamus plays a central role in sensing and integrating nutritional, hormonal, and neural signals that regulate feeding, energy homeostasis, growth, and reproduction, all of which show pronounced sex differences. However, the cellular mechanisms underlying these responses remain poorly understood. We performed snRNA-seq of the mediobasal hypothalamus, focusing on the arcuate nucleus, in female and male mice under different nutritional states. Analysis of 42 cell types revealed that Agrp neurons were most sensitive to nutritional changes, dopaminergic neurons showed strong sex-specific differences, and KNDy neurons were highly responsive to both sex and nutrition. Pomc neurons displayed moderate nutritional sensitivity. Most glial populations were stable, although microglia and oligodendrocytes showed moderate variation. Cell–cell communication analysis identified neurotrophic factor signaling as a key pathway regulated by sex and nutrition. This study represents a major effort to comprehensively characterize sex-specific differences in arcuate nucleus response across nutritional conditions.

Introduction
Worldwide obesity has been steadily increasing since the mid 1970s with more than 13% of adults categorized as obese in 20161. In the United States obesity has become an epidemic with greater than 40% of adults categorized as obese in 20202. Obesity increases the risk for many negative health outcomes, including type 2 diabetes, cardiovascular disease, and nonalcoholic fatty liver disease1–3.

Sex differences are evident in metabolic homeostasis. Historically, females experienced higher rates of obesity than males, although this difference may be diminishing in the Western world1,2. Adipose tissue distribution varies between the sexes: females store more subcutaneous fat, while males store more visceral fat. Excessive visceral fat is associated with worse health outcomes4,5, and females are protected from these before menopause6. Interestingly, deletion of estrogen receptor-α (encoded by the Esr1 gene) from the ventromedial hypothalamus (VMH) caused female mice to accumulate visceral fat like male mice7, demonstrating the significant role of specific brain regions in regulating metabolic homeostasis.

The arcuate nucleus of the hypothalamus (ARH) is located beside the third ventricle and above the median eminence (ME), where the blood brain barrier is more permeable8. This positioning enables the ARH to receive peripheral paracrine signals from adipose, liver, and pancreas9,10. Seminal studies by Camus and Roussy followed by Bailey and Bremer established that the infundibulo-tuberal region, including ARH, exerts control over homeostatic feeding, in the early 20th century11. Manipulations of neural circuits in the ARH have demonstrated its critical role in feeding behavior. Chemogenetically activating agouti-related peptide (Agrp) neurons resulted in hyperphagia12, as did optogenetically activating tyrosine hydroxylase (Th)-expressing neurons13. A rapid decrease in feeding was seen when oxytocin receptor-expressing neurons were activated via chemo or optogenetics14. Additionally, a decrease in feeding was observed that took sustained activation of pro-opiomelanocortin (Pomc) neurons via chemogenetics15. The ARH is also home to kisspeptin 1 (Kiss1) neurons responsible for controlling fertility and neurons responsible for controlling growth hormone release16,17.

These findings highlight the remarkable heterogeneity of ARH neurons, both in their transcriptomic profiles and functional diversity. While previous research has explored the ARH’s role in metabolic regulation, most studies have focused on responses to nutritional challenges, with limited attention to sex-specific differences18–20. In this study, we comprehensively investigate sex-specific cellular responses in the mediobasal hypothalamus (MBH) with a focus on the ARH under varying nutritional states, using single-nucleus RNA sequencing (snRNA-seq). This approach allows us to capture the transcriptional landscape at a cellular resolution. We identified pronounced sex differences in Kiss1-Neurokinin B-Dynorphin (KNDy) neurons and moderate differences in dopamine (DA) neurons, highlighting distinct roles these cells may play in male and female energy homeostasis. Additionally, we observed substantial nutritional state-dependent changes in Agrp and KNDy neurons, with Pomc neurons, microglia, and oligodendrocytes showing moderate responses. Our findings offer a perspective on how sex and nutritional state interact at the cellular level within the ARH, paving the way for more targeted and effective interventions for metabolic disorders that account for these critical biological differences.

Results
Single-nucleus transcriptomes for the ARH of female and male mice in different nutritional states

In order to investigate sex differences in nutritional state response, we performed snRNA-seq on the MBH with a focus on ARH tissue from female and male mice that were either fed ad libitum or fasted for 28 h (Fig. 1a, b; Supplementary Fig. 1a, b). In our study, we leveraged mice natural nocturnal behavior, euthanizing them 4 h into their dark cycle when they are most active (Supplementary Fig. 1a). We did this to ensure the ad lib fed mice were in a well fed state and the fasted mice would be in a state of high food craving. To attain a high-quality analysis of sex by nutritional state interactive characteristics, we profiled 36 mice, 18 males, and 18 females (Fig. 1a). After standard preprocessing steps, such as removing ambient RNAs and doublets21–25 (Supplementary Fig. 1c), we got 93,265 high-quality nuclei in total (Fig. 1c). We achieved near equal sequencing depth and feature detection across conditions with around 5700 mean unique molecules (UMIs), and 2900 mean unique genes detected per nucleus with a minimal number of reads assigned to hemoglobin, ribosomal binding proteins, and mitochondrial mRNA (Supplementary Fig. 1d).

Next, we performed principal component analysis (PCA) followed by t-distributed stochastic neighbor embedding (tSNE) dimensionality reduction for visualization. K-nearest neighbors clustering calculations were performed based on the first 30 PCs to identify cell clusters. Our analyses identified 42 distinct cell-types (31 types of neurons and 11 types of glia) (Fig. 1d; Supplementary Figs. 2, 3). We found that nuclei from both sexes under both nutritional states mapped similarly in tSNE space (Fig. 1c), suggesting cell-types do not change across nutritional states or differ between the sexes. We also confirmed that there were no major batch effects (Supplementary Fig. 1e). Overall, 65,014 neurons and 28,251 glia were obtained from the MBH region, including 41,622 neurons representing all neuronal types in ARH (Fig. 1d). To ensure that observed transcriptional differences reflect true biological variation rather than technical artifacts, we performed the RPCA-based integration within the Agrp neurons and KNDy neurons separately across different batches. After integration, the tSNE space embeddings showed well-mixed distributions of cells from different batches (Figs. 2a, 4a, Supplementary Figs. 5b, 12b). In addition, integration of our neuronal dataset with the Campbell et al.18 dataset (neurons only) demonstrated high transcriptomic similarity between datasets (Supplementary Fig. 4a). Most cell types showed strong alignment across both datasets, and only minor differences in cell subtype labeling were observed (Supplementary Fig. 4b). These results support the robustness of our integration approach and validate the accuracy of our cell type annotations for downstream analyses. Our annotation was also aided by annotation from the Allen brain atlas26.

We next examined which cell-types’ transcriptomes differed either across sex or nutritional state. Here we define differential expression (DE) as: greater than 25% nuclei of at least one condition expressed the gene, the difference in expression between the groups larger than 1.25-fold, and the adjusted p-value less than 0.05. We used the model-based analysis of single-cell transcriptomics (MAST) as our statistical test using sample ID as a latent variable26. Six of the 10 most dynamic cell-types were ARH neurons (Fig. 1e), with the other four being Oligodendrocytes, α-Tanycytes, β-Tanycytes and a large cluster MM.01. It was possible that larger cell-type clusters appeared more dynamic (i.e. more DE genes) because they had more statistical power to detect these changes. In order to control this possibility, we performed down-sampling to 200 nuclei per condition per cell-type and repeated our DE analyses. Controlling for cluster size did highlight some smaller clusters as being more dynamic than they initially appeared (e.g. Microglia, DA, and Coch/Slc18a2); however larger clusters that appeared dynamic in absolute terms remained dynamic after controlling for size (e.g. Agrp, and KNDy neurons) (Fig. 1f).

Summary of cell-types that are sensitive to nutritional state in a sex-specific manner

Overall, more nutritionally regulated DE genes (DEGs) were observed in female mice. We saw both male and female mice had the most nutritionally regulated DEGs for the Agrp cluster. Other clusters that saw dynamic responses to nutrition for both sexes were: Pomc, Sst/Unc13c, Lamp5/Npy5r, and Ghrh/Chat neurons. Strikingly, KNDy neurons appeared very sensitive to nutritional state in females but not in males. To a lesser extent DA neurons and related cluster Coch2/Slc18a2 also responded to nutritional state in females but not in males (Fig. 1e). Relatedly, when comparing across sexes within nutritional state, KNDy neurons showed the most sexual dimorphism followed by DA neurons. Of note, Lamp5/Npy5r, Ghrh/Chat, and Coch2/Slc18a2 neurons also showed sexual dimorphism each with numerous DEGs between the sexes (Fig. 1e). Strikingly, almost all sexually regulated DEGs were on autosomes and not on sex chromosomes (X and Y) (Fig. 1e inset). The same patterns hold true after controlling for cluster size (Fig. 1f). Two of the most dynamic glia-types were oligodendrocytes and microglia (Fig. 1f).

Methods

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This manuscript by Bean et al. presents a comprehensive and high-quality single-nucleus RNA sequencing (snRNA-seq) study investigating sex-specific differences in the hypothalamic arcuate nucleus (ARH) in response to fasting. This dataset would be a valuable resource to advance our understanding of how nutritional states are processed differently in male and female brains. There are several suggestions that may help to improve the paper.\nMajor Concerns:\n1. Based on the methodological description, it is unclear whether the dissected brain region is specifically the ARC, especially since the authors identified many cells from the PVp, VMH, MM, and SCN, etc. It is recommended, e.g., in the title, to refer to the region as the medial basal hypothalamus instead.\n2. Relying solely on c-Fos expression to assess the treatment effect is not reliable, particularly given the extremely low level of c-Fos expression. In the dopamine cluster, the number of cells expressing c-Fos is too few, rendering the results unreliable. It is recommended to include additional immediate early genes in the analysis.\n3. The authors provided their justification for the fasting schedule. It remains unclear whether a torpor-like state, which is a completely different metabolic status and also sexually biased, would affect any of their findings. This should be discussed.\nMinor Concerns:\n4. The provided figure legends (e.g., for Fig 1c) are incorrect or misplaced, indicating a need for careful proofreading before submission.\n5. Terms like \"moderate,\" \"major,\" and \"highly sensitive\" are used throughout without consistent quantitative backing. The authors should refer to the actual number of DEGs or effect sizes when using these descriptors.\n6. Statistical methods, corrections, and n should be mentioned for Figures 2b-5b.", "reviewer_2": "- interesting topic\n- this study has major limitations\n1) the gold standard of subclustering arcuate neuronal cell populations in mouse has been given by Campbell et al. The field has recognized that these neuronal subpopulations are functionally distinct. However, the authors did not perform subclustering into the functionally relevant Pomc, Agrp, Kiss1 subclusters. Therefore, the authors' analysis is not reliable, because they compare nutrient and sex on the whole Pomc or Agrp or Kiss1 population level and NOT within the functionally relevant subclusters.\n2) They authors performed RNAscope aiming to validate their findings. However, the sections provided are very distinct anatomically. And thus, they cannot compared to each other. Furthermore, the zooms chosen by the authors, are not at all in corresponding regions.", "reviewer_3": "The authors addressed my major comment regarding the statistical approach of calculating differentially expressed genes by changing the approach to a more sound setup.\nI reread the manuscript and generally think that it has improved throughout. I have the following comments that I think will further help other researchers to use and build on this work:\nExtended fasting duration: Please justify the physiological relevance of the 28-hour fasting paradigm in the Discussion. While prolonged fasting may accentuate certain metabolic responses, it is substantially longer than typical rodent feeding/fasting cycles and may not directly model physiologically relevant states in obesity. A discussion of why this duration was chosen and how it relates to obesity biology would strengthen the manuscript (and helpfully have others researchers consider whether such a long fasting is actually necessary).\nData availability: In the manuscript, you state that 36 mice (18 males, 18 females) were included. However, the GEO entry currently only contains 12 samples. Please ensure that the latest version of the dataset is uploaded so that it matches the study as described in the manuscript.\nCode documentation: The GitHub repository does not currently provide sufficient detail for reproducibility. We recommend incorporating some of the information described in the Methods section into the repository (e.g., preprocessing steps, parameter choices, quality control thresholds), and, importantly, brief descriptions of their code. This would enable other researchers to better reproduce your findings and extend the analyses."}, "author_response": "Dear Editor and Reviewers,\nWe are grateful for the reviewers and editor’s careful evaluations of our last submission.\nWe have now made modifications and additional analyses to the revised manuscript in\nred to address all the new comments. Below we provide point-by-point responses to\neach of the comments/suggestions.\nREVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\nThis manuscript by Bean et al. presents a comprehensive and high-quality single-nucleus RNA\nsequencing (snRNA-seq) study investigating sex-specific differences in the hypothalamic\narcuate nucleus (ARH) in response to fasting. This dataset would be a valuable resource to\nadvance our understanding of how nutritional states are processed differently in male and\nfemale brains. There are several suggestions that may help to improve the paper.\nMajor Concerns:\n1. Based on the methodological description, it is unclear whether the dissected brain region is\nspecifically the ARC, especially since the authors identified many cells from the PVp, VMH, MM,\nand SCN, etc. It is recommended, e.g., in the title, to refer to the region as the medial basal\nhypothalamus instead.\nResponse: We thank the reviewer for this insightful and helpful comment. As described\nin the manuscript, the dissection was centered on the ARH but unavoidably included\nsurrounding areas within the MBH region. In accordance with the reviewer’s suggestion,\nwe have revised the title and the relevant sections of the manuscript to refer to the region\nas the mediobasal hypothalamus (MBH).\n2. Relying solely on c-Fos expression to assess the treatment effect is not reliable, particularly\ngiven the extremely low level of c-Fos expression. In the dopamine cluster, the number of cells\nexpressing c-Fos is too few, rendering the results unreliable. It is recommended to include\nadditional immediate early genes in the analysis.\nsuggestion, we extended our analysis beyond Fos to include a panel of immediate early\ngenes (IEGs) defined by Tullai et al., 2007. We checked the expression of 44 canonical\nIEGs expressed in DA neurons under different sex and nutritional states. As shown in the\nheatmap, expression levels of these IEGs were generally low across all groups. Fos,\nNr4a1, and several other IEGs were detectable in DA neurons, with generally higher\nexpression in the fed condition, particularly in females (please see below and\nSupplementary Fig. 20a). DE analysis further revealed that Fos and Nr4a1 were\nsignificantly upregulated in female compared to male fed states (avg_log2FC = 9.38 and\n5.74, adjusted p < 0.05) (please see below and Supplementary Fig. 20b). These results\nindicate that neuronal activation in DA neurons is not limited to Fos alone but involves\nthe coordinated activation of several IEGs. Notably, Fos showed the most robust and\nconsistent induction among all IEGs, exhibiting both higher expression magnitude and\ngreater sensitivity to sex and nutritional differences. This strong responsiveness\nhighlights Fos as a reliable marker for neuronal activation in our dataset, consistent with\nits established role as a canonical activity-dependent transcription factor. The relevant\nsections of the revised manuscript have also been updated accordingly.\n3. The authors provided their justification for the fasting schedule. It remains unclear whether a\ntorpor-like state, which is a completely different metabolic status and also sexually biased,\nwould affect any of their findings. This should be discussed.\nResponse: We greatly appreciate the reviewer’s insightful suggestion. We have added\nthe discussion (as follows) to acknowledge this limitation.\n“We chose a 28-hour fasting in order to ensure sufficient food deprivation and the\nexpected change of the nutritional state of the mice; in addition, the extra 4 hours\nbeyond the 24-hour period were necessary to allow tissue collection during the dark\ncycle without disrupting animals’ circadian clock. We acknowledge that prolonged\n2\nfasting may cause torpor-like responses and alter gene expression in manners different\nfrom short-term fasting, which warrant future investigations.”\nMinor Concerns:\n4. The provided figure legends (e.g., for Fig 1c) are incorrect or misplaced, indicating a need for\ncareful proofreading before submission.\nResponse: We appreciate the reviewer for pointing this out. We conducted a line-by-line\naudit of all figure legends and panel labels across the main figures and Supplementary\nInformation. We corrected several instances of mismatched or misplaced legends.\n5. Terms like \"moderate,\" \"major,\" and \"highly sensitive\" are used throughout without consistent\nquantitative backing. The authors should refer to the actual number of DEGs or effect sizes\nwhen using these descriptors.\nResponse: We thank the reviewer for this helpful suggestion. In the revised manuscript,\nwe have standardized the use of descriptive terms such as “moderate,” “major,” and\n“highly sensitive” by providing consistent quantitative references. Specifically, we now\naccompany these descriptors with the corresponding number of DEGs and statistical\nsignificance (adjusted p value) to ensure clear and quantitative support for each term.\n6. Statistical methods, corrections, and n should be mentioned for Figures 2b-5b.\nResponse: We thank the reviewer for this helpful comment. In the revised manuscript, we\nhave added detailed descriptions of the statistical methods, multiple comparison\ncorrections, and sample sizes (n) for Figures 2b–5b. Specifically, Fos expression data\nwere visualized as histograms showing mean ± SEM (n = 3 biological replicates per\ncondition) using the ggplot2 package. Statistical significance was determined using the\nWilcoxon rank-sum test implemented in Seurat’s FindMarkers() function, followed by\nbonferroni correction for multiple comparisons. In addition, we have included a detailed\nexplanation of these statistical methods in the Methods section.\nReviewer #2 (Remarks to the Author):\n- interesting topic\n- this study has major limitations\n3\n1) the gold standard of subclustering arcuate neuronal cell populations in mouse has been given\nby Campbell et al. The field has recognized that these neuronal subpopulations are functionally\ndistinct. However, the authors did not perform subclustering into the functionally relevant Pomc,\nAgrp, Kiss1 subclusters. Therefore, the authors' analysis is not reliable, because they compare\nnutrient and sex on the whole Pomc or Agrp or Kiss1 population level and NOT within the\nfunctionally relevant subclusters.\nResponse: We thank the reviewer for this insightful comment. We fully acknowledge that\nCampbell et al. established the gold standard for sub-clustering arcuate neuronal\npopulations and demonstrated that subtypes such as Pomc and Agrp neurons are\nfunctionally heterogeneous. In our previous analysis, we performed integration analysis\nusing the RPCA method in Seurat to align our neuronal data with the Campbell et al.\ndataset. The UMAP plot after integration demonstrates that our data integrates well with\nreference, with most cells showing high consistency in the embedding space (please see\nbelow and refer to Supplementary Fig. 4). Specifically, we also performed separate co-\nembedding analyses for Pomc and Agrp neuron populations with the Campbell reference\ndataset, transferred subcluster-level annotations to our corresponding cells, and\nchecked the expression of marker genes within each sub-cluster. Nutritional and sex\ndependent transcriptional changes were then assessed within each functionally relevant\nsub-cluster with the same method of major cluster (please see below and refer to\nSupplementary Figs. 8, 9, 10, and 11). The updated results and figures confirm that our\nmajor findings remain robust at the sub-cluster level.\n4\nwas reported. Our co-embedding result showed that KNDy neuron from our dataset were\naligned with this Kiss1/Tac2 cluster, indicating good correspondence between the two\ndatasets (please see below and refer to Supplementary Fig. 4). We also checked the\nexpression of known marker genes (Kiss1 and Tac2) of Kiss1/Tac2 cluster, which\nsupported the validity of the co-embedding result (please see below and refer to\nSupplementary Fig. 12d). In our previous analysis, we also checked the expression of\nthese potential marker genes; however, no distinct subclusters defined by these markers\nwere observed (please see below and refer to Supplementary Fig. 12d). The relevant\nsections of the revised manuscript have also been updated accordingly.\n2) They authors performed RNAscope aiming to validate their findings. However, the sections\nprovided are very distinct anatomically. And thus, they cannot compared to each other.\nFurthermore, the zooms chosen by the authors, are not at all in corresponding regions.\nResponse: We thank the reviewer for noting the inconsistencies in our work. We have\nreplaced representative images so that they are more anatomically matched and zoomed\nareas are from more uniform areas. Please see below and refer to Supplementary Figs. 6,\n7, 13 and 14.\n6\n7\n8\n9\nThe authors addressed my major comment regarding the statistical approach of calculating\ndifferentially expressed genes by changing the approach to a more sound setup.\nI reread the manuscript and generally think that it has improved throughout. I have the following\ncomments that I think will further help other researchers to use and build on this work:\nExtended fasting duration: Please justify the physiological relevance of the 28-hour fasting\nparadigm in the Discussion. While prolonged fasting may accentuate certain metabolic\nresponses, it is substantially longer than typical rodent feeding/fasting cycles and may not\ndirectly model physiologically relevant states in obesity. A discussion of why this duration\nwas chosen and how it relates to obesity biology would strengthen the manuscript (and helpfully\nhave others researchers consider whether such a long fasting is actually necessary).\nResponse: We greatly appreciate the reviewer’s insightful suggestion. We have added\nthe discussion (as follows) to acknowledge this limitation.\n“We chose a 28-hour fasting in order to ensure sufficient food deprivation and the\nexpected change of the nutritional state of the mice; in addition, the extra 4 hours\nbeyond the 24-hour period were necessary to allow tissue collection during the dark\ncycle without disrupting animals’ circadian clock. We acknowledge that prolonged\nfasting may cause torpor-like responses and alter gene expression in manners different\nfrom short-term fasting, which warrant future investigations.”\nData availability: In the manuscript, you state that 36 mice (18 males, 18 females) were included.\nHowever, the GEO entry currently only contains 12 samples. Please ensure that the latest\nversion of the dataset is uploaded so that it matches the study as described in the manuscript.\nResponse: We thank the reviewer for carefully noting this point. As clarified in the\nrevised Methods section, a total of 36 mice (18 males and 18 females) were used in this\nstudy. Mice were divided into four experimental conditions: male fed, male fasted, female\nfed, and female fasted (n = 9 mice per condition). Importantly, tissues from three mice\n(within the same condition) were pooled to generate one biological sample, in order to\nminimize inter-individual variability and ensure sufficient RNA yield for snRNA\nsequencing. Thus, the total 36 mice resulted in 12 samples. Please refer to\nSupplementary Fig. 1a for details. These 12 samples are exactly those deposited in the\nGEO database. This clarification has been added to the Methods section of the revised\nmanuscript.\nreproducibility. We recommend incorporating some of the information described in the Methods\nsection into the repository (e.g., preprocessing steps, parameter choices, quality control\nthresholds), and, importantly, brief descriptions of their code. This would enable other\nresearchers to better reproduce your findings and extend the analyses.\nResponse: We thank the reviewer for this valuable comment. We have substantially\nimproved the documentation and reproducibility of our GitHub repository. Specifically,\nwe have now included:\n(1) Detailed descriptions of the preprocessing steps, parameter settings, and quality\ncontrol thresholds used in our analyses.\n(2) Explanations of key scripts and their functions.\nThese updates will greatly enhance the reproducibility and reusability of our analysis\npipeline.\n12"}, {"round": 2, "reviewer_comments": {"reviewer_1": "answer all comments.", "reviewer_2": "- the authors extended their analysis to the sub-cluster level in AgRP and Pomc neurons in agreement with Campbell et al.\nAgRP sub-clusters confirm major findings from the AgRP cluster level\nPomc sub-clusters show a diverse pattern as compared to Pomc cluster level. This needs to be discussed in the manuscript\n- regarding the RNAscope, the sections provided are still not matching", "reviewer_3": "The authors have addressed all of my previous comments satisfactorily, with the exception of the issue regarding code documentation. I appreciate that the authors provide a GitHub repository. However, the repository currently lacks documentation of the individual scripts, with all explanations embedded only in the manuscript Methods section. For reproducibility, it would be preferable for the GitHub repository to include clear descriptions of each script, input/output formats, software versions, and ideally instructions for running the full workflow, or at least the individual scripts.\nIn addition, documentation placed only in the manuscript cannot be updated after publication, whereas the GitHub repository can be actively maintained. Adding script-level explanations directly to the repository would therefore not only make the codebase self-contained and easier for readers to use, but also support ongoing updates, corrections, and extensions of the code. Keeping documentation close to the code is standard best practice and greatly improves long-term usability."}, "author_response": "Dear Editor and Reviewers,\nWe are grateful for the reviewers and editor’s careful evaluations of our last submission.\nWe have now made modifications to the revised manuscript in red to address all the new\ncomments. Below we provide point-by-point responses to each of the\ncomments/suggestions.\nREVIEWER COMMENTS\nReviewer #2 (Remarks to the Author):\n- the authors extended their analysis to the sub-cluster level in AgRP and Pomc neurons in\nagreement with Campbell et al.\nAgRP sub-clusters confirm major findings from the AgRP cluster level\nPomc sub-clusters show a diverse pattern as compared to Pomc cluster level. This needs to be\ndiscussed in the manuscript.\nResponse: We greatly appreciate the reviewer’s insightful suggestion. We have added\ndiscussion (as follows).\n“While cluster-level analysis indicated an overall feeding-associated activation of Pomc\nneurons, sub-cluster–level analysis revealed divergent response patterns across Pomc\nsub-clusters. Pomc/Anxa2 and Pomc/Gipr1 neurons showed minimal Fos induction across\nnutritional states in both sexes, whereas Pomc/Ttr neurons exhibited a pronounced\nfeeding-associated Fos increase selectively in females. Together, these findings suggest\nthat the cluster-level Pomc response reflects the combined activity of heterogeneous\nsubpopulations with distinct sex- and nutrition-dependent profiles. Importantly, this sub-\ncluster–level diversity does not contradict the cluster-level observations but rather\nprovides additional resolution into the cellular complexity underlying POMC neuron\nfunction.”\n- regarding the RNAscope, the sections provided are still not matching.\nResponse: We want to respectfully point out that all the representative images were\naround -1.58 mm to -1.70 mm to the Bregma. A few images had disformed 3rd vehicle due\nto imperfect handling of the sections which may have caused the impression that the\nsections are not matching. More importantly, the quantifications were performed using 4\ndifferent mice per condition with 3-6 sections per mouse between -1.34 and -1.94 mm\nrespective to bregma. This information is now added to the Methods.\nReviewer #3 (Remarks to the Author):\nissue regarding code documentation. I appreciate that the authors provide a GitHub repository.\nHowever, the repository currently lacks documentation of the individual scripts, with all\nexplanations embedded only in the manuscript Methods section. For reproducibility, it would be\npreferable for the GitHub repository to include clear descriptions of each script, input/output\nformats, software versions, and ideally instructions for running the full workflow, or at least the\nindividual scripts.\nIn addition, documentation placed only in the manuscript cannot be updated after publication,\nwhereas the GitHub repository can be actively maintained. Adding script-level explanations\ndirectly to the repository would therefore not only make the codebase self-contained and easier\nfor readers to use, but also support ongoing updates, corrections, and extensions of the code.\nKeeping documentation close to the code is standard best practice and greatly improves long-\nterm usability.\nResponse: We sincerely thank the reviewer for their constructive comments and for\nrecognizing our efforts to address the previous concerns. We fully agree that the GitHub\nrepository should include clear descriptions of each script and instructions for running\nthe full workflow. As suggested, we have substantially expanded the documentation in the\nGitHub repository by adding a comprehensive README file. The README file includes\nclear, script-level descriptions of all analysis scripts, detailed input and output formats,\nsoftware requirements, and instructions for reproducing the analyses starting either from\nraw fastq files or from the processed seurat object (.rds). Each script also contains detailed,\nline-by-line comments explaining the functionality of the code.\n2"}]

Decision letter