A spatial and projection-based transcriptomic atlas of paraventricular hypothalamic cell types

A spatial and projection-based transcriptomic atlas of paraventricular hypothalamic cell types | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Resource A spatial and projection-based transcriptomic atlas of paraventricular hypothalamic cell types Jon Resch, Yuxi Li, Trevor Butler, Stefano Nardone, Christopher Jacobs, and 14 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7895391/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The paraventricular hypothalamus (PVH) controls many behavioral and physiologic processes, including appetite, social behavior, autonomic outflow, and pituitary hormone secretion. However, molecular markers for centrally-projecting PVH neuron populations remain largely undefined, and a complete census of PVH cell types has not been established. Therefore, we performed extensive single-cell/nucleus RNA sequencing to catalog PVH neuron subtypes and multiplexed error-robust fluorescence in situ hybridization (MERFISH) to map them spatially. Our spatial transcriptomic atlas resolves 26 Sim1+ and 29 GABAergic neuron populations from the PVH and surrounding areas, revealing multiple subtypes not described previously and distinct transcriptional programs between neuroendocrine and centrally-projecting neurons. Additionally, projection-based profiling determined neuronal subtypes that project to the parabrachial region (PB) and spinal cord, helping to identify PVH populations that regulate satiety and sympathetic nervous system activity, respectively. Notably, activation of PB-projecting PVH neurons expressing bombesin-like receptor 3 (Brs3) reduces food intake and silencing them causes obesity. Together, this atlas contributes high-resolution PVH spatial and circuit-based gene expression profiles, representing a valuable resource for the field of homeostasis. Biological sciences/Neuroscience/Genetics of the nervous system Biological sciences/Neuroscience/Feeding behaviour/Hypothalamus Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction The paraventricular hypothalamus (PVH) is among the most functionally diverse and anatomically complex regions of the brain. Essential for maintaining homeostasis, the PVH integrates information about the internal state and external environment, and accordingly adapts endocrine, autonomic, and behavioral outputs 1-3 . PVH neurons are typically classified based on cytoarchitectural subdivisions, projections, and neuroendocrine hormone expression 1-9 . PVH parvicellular neuron projections to the median eminence release hormones into the hypophyseal portal system that then cause release of anterior pituitary hormones to regulate the stress response, thyroid function, and growth, whereas PVH magnocellular neuron projections to the posterior pituitary release vasopressin and oxytocin directly into the systemic circulation 1,3 . Centrally-projecting PVH neurons, on the other hand, are a highly heterogeneous and poorly defined class of PVH neurons innervating regions of the hypothalamus, midbrain, hindbrain, and spinal cord to mediate autonomic and behavioral responses 10 . Despite their importance, the molecular and functional diversity of centrally-projecting PVH neurons remains unresolved. Among their many functions, the centrally-projecting PVH neurons are well-known for regulating energy balance. PVH neurons that express the melanocortin 4 receptor ( Mc4r ) are crucial for body weight control as their activation reduces food intake, while loss of function causes hyperphagia and obesity 11-15 . Notably, several other PVH neurons have been reported to decrease food intake 15-21 , including prodynorphin ( Pdyn )-expressing neurons, which, like PVH Mc4r neurons, regulate feeding behavior via projections to the parabrachial region (PB). These two populations are distinct, however, because their simultaneous inhibition causes additive effects on hyperphagia and obesity 12,21 . In contrast, the PVH oppositely regulates feeding behavior via neurons expressing thyrotropin-releasing hormone ( Trh ) and pituitary adenylate cyclase-activating peptide ( Adcyap1 ) that induce hunger through activation of agouti-related peptide (AgRP) neurons in the arcuate nucleus (ARC) 22 – highlighting the complexity of appetite regulation by the PVH. Besides appetite, the PVH also controls energy expenditure through nitric oxide synthase 1 ( Nos1 ) - and brain-derived neurotrophic factor ( Bdnf )-expressing neuron projections to the spinal cord that drive sympathetic nervous system output to adipose tissue 19,23,24 . That said, because these previously described genetic markers are expressed across multiple PVH neuron subpopulations, the exact transcriptional identity of energy balance-regulating neurons remains unclear, and the lack of precise markers limits our ability to study their regulation and function selectively. Recent studies characterizing PVH neurons at the molecular level represent an important step towards understanding the diversity of cell types present 25-27 . However, the power of these studies has been limited by sample size and the inability to resolve their spatial organization. Moreover, large-scale single-cell and spatial transcriptomic studies of the entire mouse brain 28-31 or hypothalamus 32 lack detailed analysis of PVH neuron subtypes, leaving significant gaps in our understanding of the molecular heterogeneity of PVH neurons. To address these limitations, we employed single-cell/nucleus RNA sequencing (sc/snRNA-seq) and multiplexed error-robust fluorescence in situ hybridization (MERFISH) to generate a comprehensive spatial transcriptomic atlas of the PVH at single-cell resolution. This approach uncovered novel marker genes for neuroendocrine populations and revealed molecular signatures of previously unidentified centrally-projecting PVH neurons. Further, we sequenced spinal cord- and PB-projecting PVH neurons to identify marker genes for neurons controlling sympathetic nervous system activity and feeding behavior, respectively. Leveraging this information, we show that stimulation of bombesin-like receptor 3 ( Brs3 )-expressing PVH neuron projections to the PB reduces food intake, PVH Brs3 neurons are downstream of AgRP neurons, and their silencing promotes weight gain. This atlas serves as a foundational resource for understanding the molecular architecture of the PVH and lays the groundwork for future investigations into the functional roles of its diverse neuronal populations. Results Molecular profiling of the paraventricular hypothalamus. To classify PVH cell types based on their genome-wide expression patterns, we performed single-cell RNA-seq using Drop-seq 33 , and single-nucleus RNA-seq using DroNc-seq 34 and the 10X Chromium platform on adult male and female mice ( Extended Data Fig. 1a,b ). For each approach, we micro-dissected the PVH region from Sim1 -Cre 14 ::L10-GFP 22 or wild-type mice ( Fig. 1a ). After droplet generation, library preparation, sequencing, data pre-processing, and quality control, downstream sc/snRNA-seq analyses were performed using Seurat version 5 35,36 , integrating by sequencing run (“batch”), to generate an atlas of 42,948 cells/nuclei from the PVH and immediately surrounding regions. Cell type clusters were visualized with uniform manifold approximation and projection (UMAP) and annotated using canonical cell type marker genes previously reported in the literature, revealing nearly 80% neurons, with the remaining cells forming distinct populations of non-neuronal/glial cells ( Extended Data Fig. 1c-e; Supplementary Table 1 ). We next examined the effects of sc/snRNA-seq technology and sex on cell clustering. While gene and cell type detection differed somewhat between the droplet-based sc/snRNA-seq methods, cells/nuclei from both sexes and all technologies were represented in all clusters ( Extended Data Fig. 1f-i ). To gain specific insight into PVH neuron diversity, we next reclustered 33,644 neuronal cells/nuclei, which produced a UMAP with clusters predominantly segregated into inhibitory neurons expressing the vesicular GABA transporter ( Slc32a1 ; VGAT) and excitatory neurons expressing the vesicular glutamate transporter 2 ( Slc17a6 ; VGLUT2) ( Extended Data Fig. 2a-d,g,h; Supplementary Table 2 ). We also observed further segregation of excitatory neurons into those expressing the PVH marker gene Sim1 or the thalamic marker gene Tcf7l2 ( Extended Data Fig. 2b,e,f,I,j ). Histological assessment confirmed that Slc32a1 is expressed primarily in areas surrounding the PVH 37,38 , Sim1 is predominantly expressed within the PVH 39 , and Tcf7l2 expression is constrained to thalamus dorsal to the PVH 31,40 . We subsequently reclustered glutamatergic and GABAergic neurons separately, resulting in 22 excitatory clusters from 18,920 glutamatergic cells/nuclei ( Extended Data Fig. 2k,l; Supplementary Table 3 ) and 28 inhibitory populations from 13,075 GABAergic cells/nuclei surrounding the PVH ( Extended Data Fig. 2m,n; Supplementary Table 4 ). Finally, to specifically investigate PVH neuron gene expression profiles, we reclustered only neurons from Sim1 -positive populations. At this point, we also sought to take advantage of publicly available data. To do so, we examined PVH-assigned cells from the “HypoMap” study, an integrated reference atlas of the entire mouse hypothalamus ( Extended Data Fig. 3a-f; Supplementary Table 5 ) 32 . However, after integrating 5,119 putative PVH neurons expressing Sim1 from HypoMap with our study, we observed discrepancies between the data sets ( Extended Data Fig. 3g-i; Supplementary Table 6 ). Notably, seven Sim1 + clusters were comprised almost entirely of neurons from this study ( Extended Data Fig. 3j ), and a large proportion of HypoMap neurons express markers for “peri-PVH” neurons, including Cabp7 , Onecut3 41 , and Gsc 42 ( Extended Data Fig. 3k ). These results suggest that there is inadequate representation of PVH neuron subtypes within the HypoMap study 32 . Thus, we instead integrated Sim1 + PVH neurons from the Allen Brain Cell (ABC) Atlas 29 , resulting in 9,301 Sim1 + neurons from this study and 7,297 from the ABC Atlas. Analysis after integration identified 20 distinct clusters, each consisting of cells from both studies that we annotated based on the expression of one or more marker genes ( Fig. 1b,c; Extended Data Fig. 3l-n; Supplementary Table 7 ). This final sc/snRNA-seq atlas, comprising 16,598 Sim1 + neurons, greatly surpasses the number of cells previously available from single-cell transcriptomic studies of the PVH, and also provides detailed molecular markers for PVH neuron populations. Unique transcriptional profiles of PVH neuroendocrine populations revealed by sc/snRNA-seq The PVH is home to parvicellular and magnocellular neuroendocrine neurons that are defined by the synthesis and release of one of five well-known hormones, which include corticotropin-releasing hormone ( Crh ), thyrotropin-releasing hormone ( Trh ), somatostatin ( Sst ), arginine vasopressin ( Avp ), and oxytocin ( Oxt ) 1 . In this study, we identified distinct Sim1 + neuronal clusters that are enriched for these genes annotated as Seq_S1.Crh-Scgn, Seq_S2.Trh-Satb2 , Seq_S3.Sst-Vgll3, Seq_S4.Sst-Rxfp2, Seq_S5.Oxt-Rxfp3, and Seq_S6.Avp-Pla2r1 ( Fig.1d-i ), and hypothesized that these clusters represent the PVH neuroendocrine populations. However, since these pituitary-regulating hormone genes are expressed across multiple PVH neuron clusters, albeit at lower levels, we sought to confirm our neuroendocrine cluster classifications. To label median eminence- and posterior pituitary-projecting PVH neurons, C57BL/6J mice received intraperitoneal (ip) injections of the retrograde tracer Fluoro-Gold, which labels neurons that project outside the blood-brain barrier when administered systemically ( Fig. 1j ) 1,43,44 . We then performed co-labeling studies for each putative neuroendocrine cluster using fluorescence in situ hybridization (FISH) to demonstrate co-expression of neuroendocrine hormones with novel marker genes determined by sc/snRNA-seq, followed by immunofluorescence for Fluoro-Gold. Of note, sc/snRNAseq identified two putative PVH neuroendocrine populations that express Sst , Seq_S3.Sst-Vgll3 and Seq_S4.Sst-Rxfp2, the significance of which is unknown as each expresses the growth hormone receptor ( Ghr ), likely to facilitate negative feedback 45 . To assess the neuroendocrine identity of these PVH Sst neuron clusters, we performed FISH for Col12a1, taking advantage of its enrichment in both clusters ( Fig. 1d ). Other gene pairs tested were Crh-Scgn , Trh-Nfix , Oxt-Rxfp3 , and Avp-Pla2r1 . In all cases, greater than 80% of neurons co-expressing a neuroendocrine peptide and its corresponding marker gene were also positive for Fluoro-Gold ( Fig. 1k-p ). This is consistent with prior reports of Crh and Scgn co-expression in neuroendocrine neurons controlling the hypothalamic-pituitary-adrenal axis 25,27 . Furthermore, Fluoro-Gold negative neurons expressing Crh , Trh , Sst , Avp , and Oxt rarely co-expressed the corresponding neuroendocrine marker gene determined by sc/snRNA-seq ( Fig. 1p ). These findings confirm our neuroendocrine classifications and demonstrate that the intersection of neuroendocrine marker gene pairs identified by sc/snRNA-seq enables approaches for gaining selective genetic access to pituitary-regulating PVH neuron populations. Given that neuroendocrine neurons share a common projection target and release large amounts of neuropeptide hormones into the circulation, we next assessed whether we could identify a shared transcriptional program that differentiates them from centrally-projecting PVH populations. Marker gene analysis revealed a sharp division in transcriptional profiles ( Extended Data Fig. 4a,b; Supplmentary Tables 8,9) , identifying genes that distinguish neuroendocrine populations (e.g., Creb3l2 ; Extended Data Fig. 4c,e ) and centrally-projecting neurons (e.g., Ntng1 ; Extended Data Fig. 4d,e ) . To further characterize these transcriptional differences, we performed Gene Ontology (GO) enrichment analysis on genes upregulated in PVH neuroendocrine and centrally-projecting populations ( Extended Data Fig. 4f,g; Supplementary Tables 10,11 ). We found neuroendocrine neurons are most significantly enriched for genes related to ribosomal function and translation, which may be crucial for the synthesis of large quantities of neuropeptides. In contrast, centrally-projecting neurons were strongly enriched for genes related to the formation and regulation of synapses. These findings suggest differences in the signaling machinery of neuroendocrine versus non-neuroendocrine neuron populations. Additional marker gene analysis comparing median eminence-projecting and posterior pituitary-projecting neuroendocrine subtypes also demonstrated transcriptional differences, highlighting Agtr1a as a marker for median eminence-projecting (parvicelluar) neurons and Plekhg1 as a marker for posterior pituitary-projecting (magnocellular) neurons ( Extended Data Fig. 4h-l; Supplementary Tables 12,13 ). GO enrichment analysis revealed that the top pathways for median eminence-projecting populations are related to ion channel activity ( Extended Data Fig. 4m; Supplementary Table 14 ). Meanwhile, posterior pituitary-projecting populations again showed enrichment for ribosomal function and translation-related pathways, which likely are critical for supporting direct secretion of large quantities of AVP and OXT into the systemic circulation to regulate distant target organs ( Extended Data Fig. 4n; Supplementary Table 15) 1 . Spatial transcriptomic profiling of the PVH with MERFISH. Droplet-based sc/snRNA-seq technologies are powerful tools for identifying and characterizing cell type diversity. However, they require tissue dissociation, preventing the retention of spatial information, and may fail to detect functionally important genes expressed at low levels. Therefore, we used MERSCOPE 29,46 , an imaging-based MERFISH platform capable of detecting low-abundance transcripts with single-molecule sensitivity 46-48 , to resolve the spatial organization of the PVH and surrounding regions. We assayed the spatial distribution of 503 genes specifically curated for the PVH region, comprised of top differentially expressed genes identified in our sc/snRNA-seq analyses, canonical marker genes for neuronal and glial populations, and functionally relevant genes selected from the literature ( Supplementary Tables 16,17 ). In total, we imaged 41 coronal sections across six mice. Brain sections were collected at intervals of approximately 100 μm along the rostral-caudal axis of the PVH, ranging from approximately 0.4 mm to 1.2 mm caudal to bregma according to the Franklin-Paxinos atlas 49 . After imaging, individual cells were segmented using Cellpose 2.0 50 and filtered to remove cells with low transcript counts ( Extended Data Fig. 5a ). Then, for each coronal slice, we systematically defined the region of interest (ROI) covering the PVH and peri-PVH and subset the data to retain only cells within these regions ( Fig. 2a; Supplementary Table 18 ). After subsetting for the ROI, we were able to perform cell type clustering on 155,546 spatially resolved cells. Our initial all-cell MERFISH clustering comprised eight major cell types, approximately 65% of which were classified as neurons ( Extended Data Fig. 5b-d; Supplementary Table 19 ). Importantly, each MERFISH slide contributed proportionally to all major cell type clusters, with no sex-dependent batch effects on clustering observed after data integration, demonstrating the technical replicability of the MERFISH assay across multiple trials ( Extended Data Fig. 5e,f ). Importantly, plotting our MERFISH spatial data using polygons color-coded by major cell type, with neurons divided into excitatory and inhibitory populations, recapitulates the known cellular organization in this region of the hypothalamus ( Extended Data Fig. 5g,h ). Specifically, polygons of excitatory neurons are organized in the distinct triangular distribution of the PVH, while inhibitory neurons surround the PVH. Non-neuronal cell types do not show a particular spatial organization, except for a distinct layer of polygons classified as ependymal cells that line the third ventricle and enrichment of oligodendrocytes in the fornix. Following initial all-cell MERFISH analysis, we performed subclustering of excitatory ( Slc17a6 + ) and inhibitory ( Slc32a1 + ) neurons as we did for sc/snRNA-seq data. Excitatory neurons were further divided based on Sim1 expression, and the three major neuron types, Slc17a6 + / Sim1 + , Slc17a6 + / Sim1 - ( Extended Data Fig. 5i,j; Supplementary Table 20 ), and Slc32a1 + , were reclustered. To characterize the anatomical location of MERFISH cell types, we next performed spatial domain analysis on all neuron subpopulations using the SpaDo package in R 51 . This computational method integrates gene expression and spatial proximity information from multiple slices, allowing for unbiased anatomical categorization of neurons, which can be used to link the molecular profiles from MERFISH cell types to previously described neuroanatomical PVH subdivisions 1 . Twenty-nine domains were identified distributed across “Rostral” (R1-R11; -0.4 to -0.6 mm from bregma), “Intermediate” (M1-M9; -0.7 to -0.9 mm from bregma), and “Caudal” (C1-C9; -1.0 to -1.2 mm from bregma) regions ( Extended Data Fig. 6a,b; Supplementary Table 21 ). Finally, the majority of spatial domains show neuron subtype enrichment, with domains R4, R5, M1, M2, M9, C4, and C7 primarily encompassing Slc17a6 + / Sim1 + neurons ( Extended Data Fig. 6c; Supplementary Table 22 ). Spatial distribution of Sim1 + MERFISH clusters. MERFISH cell clustering of 24,132 Sim1 -expressing neurons resulted in the identification of 26 glutamatergic ( Slc17a6 + ) clusters that we annotated according to the expression of one or more marker genes ( Fig. 2b,c; Supplementary Table 23 ). Importantly, plotting Sim1 expression and Sim1 + MERFISH clusters confirms the expected spatial enrichment within the PVH ( Fig.2d; Extended Data Fig. 6d ) 31,39 . Next, we performed canonical correlation analysis (CCA) to examine the transcriptional similarity between MERFISH-defined and sc/snRNA-seq-defined Sim1 + clusters 36 . CCA identified strong correspondence between cells belonging to MERFISH Sim1 + clusters and those from Sim1 + sc/snRNA-seq ( Fig. 2e; Supplementary Table 24 ). There are, however, a few instances where multiple MERFISH Sim1 + clusters map to a single sc/snRNA-seq cluster. For example, all MERFISH clusters enriched for Onecut3 , including MF_S17.Onecut3-Frem3, MF_S18.Onecut3-Pvalb, and MF_S19.Onecut3-Hmcn1 ( Extended Data Fig. 6e ), map to the Seq_S15.Onecut3 cluster. We hypothesize that this is due to the improved gene detection with MERFISH, which increased our resolution of neurons enriched for Onecut3 expression and produced multiple clusters upon analysis. Overall, there is a general correspondence between Sim1 + MERFISH and sc/snRNA-seq clusters, enabling the inference of genome-wide expression levels for spatially-resolved neuron populations in the PVH region. We next evaluated the spatial location of Sim1 + clusters from rostral to caudal ( Fig. 2f ). Using the multi-slice spatial domain analysis performed on all neurons above ( Extended Data Fig. 6a-c ), we delineated PVH and “peri-PVH” Sim1 + neuron compartments ( Fig. 3a-b; Extended Data Fig. 8a,c ), and PVH neurons were further partitioned into “Rostral,” “Rostal-Intermediate,” Caudal-Intermediate,” and “Caudal” spatial groups ( Fig. 3c-j ). The Rostral PVH clusters include MF_S3.Sst-Rxfp2, MF_S4.Sst-Vgll3, MF_S10.Npy2r-Tll2, and MF_S15.Sim2-Crhr2, which are primarily located in spatial domains R1 and R5, approximating respectively, the anterior (PVa) and anterior periventricular (PVHpv) parts of the PVH ( Fig. 3a-c,g ) 1 . As expected, Sst + neurons are concentrated in the PVHpv, while MF_S10.Npy2r-Tll2, and MF_S15.Sim2-Crhr2 are located in the PVa. Of interest, single-minded 2 ( Sim2 ), a homolog of Sim1 , marks the MF_S15.Sim2-Crhr2 cluster ( Extended Data Fig. 7a,b,d ). While Sim1 expression is required for the development of the PVH 23 , disruption of Sim2 expression causes reductions in the density of Trh + and Sst + neurons 52 . Sim2 is primarily expressed by two distinct Sim1 + clusters, one of which is the aforementioned MF_S15.Sim2-Crhr2 cluster that also expresses corticotropin-releasing hormone receptor 2 ( Crhr2 ) ( Extended Data Fig. 7c-e ). The other is MF_S18.Onecut3-Pvalb, which is located in the caudal ventrolateral Peri-PVH region ( Extended Data Fig. 7d; Extended Data Fig. 8b ). Notably, PVH Sim2 neurons are not labeled by systemic Fluoro-Gold injection and the MF_S15.Sim2-Crhr2 cluster expresses both Trh and Adcyap1 ( Extended Data Fig. 7d,f ), suggesting that they are the previously described excitatory afferents to ARC Agrp neurons that drive feeding 22,53 . MF_S15.Sim2-Crhr2 neurons are also enriched for known drivers of synaptic plasticity, including Bdnf 54 and cerebellin-2 ( Cbln2 ; Extended Data Fig. 7d ) 55,56 , which is consistent with increased excitatory synapses formed between the PVH and ARC Agrp neurons after fasting 53,57 . Indeed, our recent study has revealed that PVH Sim2 neurons play an important role in hunger regulation 58 . On the other hand, the MF_S10.Npy2r-Tll2 cluster is marked by neuropeptide Y (NPY) Y2 receptor ( Npy2r ) and tolloid-like protein 2 ( Tll2 ) ( Fig. 2c and Fig. 3c,g ), but does not express other NPY receptors. Given thatthe orexigenic effects of NPY in the PVH 59 are mediated by NPY1R and NPY5R 60 , we speculate MF_S10.Npy2r-Tll2 neurons may be modulated by caloric deficit, but do not regulate food intake. The Rostral-Intermediate group consists of four MERFISH clusters that correspond to neuroendocrine populations ( Fig. 2e ), MF_S1.Crh-Scgn, MF_S2.Trh-Satb2, MF_S5.Avp-Pla2r1, and MF_S6.Oxt-Rxfp3 ( Fig. 3d,h ). All clusters are primarily located in spatial domain M2, but MF_S6.Oxt-Rxfp3 also has a substantial number of neurons located in spatial domain R5, corresponding to the anterior magnocellular part of the PVH (PVHam) ( Fig. 3a,b ) 1 . Of note, spatial domain analysis did not differentiate parvicelluar and magnocelluar neuroendocrine subtypes previously defined in rats 2,7 . This may be because spatial domain analysis with SpaDo does not incorporate cytoarchitecture; however, parvicellular and magnocellular cells are also difficult to distinguish with Nissl staining alone in mouse 1 . The Caudal-Intermediate PVH group is comprised of MF_S7.Esr2-Inhbb, MF_S8.Esr2-Ret, MF_S9.Npr3-Radx, MF_S13.Pde3a-Tmem215, and MF_S14.Brs3 clusters located primarily in spatial domain M9, which closely corresponds to the ventral zone of the medial parvicellular (PVHmpv) part of the PVH ( Fig. 3a,b,e,i ) 1 . Many clusters in this group are marked by genes for hormone and neuropeptide receptors, such as estrogen receptor 2 ( Esr2 ) and natriuretic peptide receptor 3 ( Npr3 ), which have been reported to regulate stress responses and blood pressure 61-65 . Esr2 is enriched in two distinct clusters, MF_S7.Esr2-Inhbb and MF_S8.Esr2-Ret ( Extended Data Fig. 7g-i) , while Npr3 is primarily expressed by MF_S9.Npr3-Radx neurons located in the intermediate and caudal PVH, which exhibit minimal co-labeling with systemically injected Fluoro-Gold ( Extended Data Fig. 7j-m ). Notably, MF_S14.Brs3 is marked by specific expression of bombesin-like receptor subtype 3 ( Brs3 ), an important gene for body weight regulation and metabolism ( Fig. 2C and Fig. 3e,i ) 66 . Consistent with this, PVH Brs3 neurons exhibit increased Fos expression following refeeding 67,68 , and chemogenetic manipulation of their activity bidirectionally regulates food intake 67 , similar to PVH Mc4r and PVH Pdyn neurons. Thus, based on prior work, PVH Brs3 neurons are of interest for the future study of satiety regulation. The Caudal PVH group comprises the MF_S11.Aox3, MF_S12.Grp, and MF_S26.Npnt clusters located in spatial domains C4 and C7, which are comparable to the lateral parvicellular (PVHlp) and forniceal (PVHf) parts of the PVH ( Fig. 3a,b,f,j ). Of interest, MF_S12.Grp cluster is marked by specific expression of gastrin-releasing peptide ( Grp ; Fig. 2c and Extended Data Fig. 3f,j ), which is decreased in the PVH following fasting and increased by melanocortin signaling, raising the possibility that these neurons may regulate energy balance 69 . Finally, there are 10 Sim1 + neuronal clusters in the Peri-PVH group ( Extended Data Fig. 8a-c ). While Peri-PVH clusters express Sim1 , they are located adjacent to the PVH in separate spatial domains (R2, R4, R9, M1, M6, and C6) and have distinct transcriptional characteristics. With the exception of neurons expressing urocortin 3 ( Ucn3 ), neuron subtypes in this region are largely of unknown function, and include MF_S16.Ucn3, MF_S17.Onecut3-Frem3, MF_S18.Onecut3-Pvalb, MF_S19.Onecut3-Hmcn1, MF_S20.Gsc-Serpinb1b, MF_S21.Gsc-Nms, MF_S22.Gsc-Nmbr, MF_S23.Ebf2-Hpgd, MF_S24.Ebf2-Hmcn2, and MF_S25.Ebf2-Pou6f2. Consistent with our spatial characterization of these Peri-PVH groups ( Extended Data Fig. 8a,b) , previous studies have identified Onecut3 - and Gsc -expressing neurons to be located laterally and ventrally to the PVH 41,42 . Moreover, Ucn3 -expressing neurons are a relatively small population of peri-PVH neurons that extend laterally from the PVH towards the fornix in spatial domain M6 ( Extended Data Fig. 8a-c) and are involved in stress and parenting behaviors 70,71 . Transcriptional similarity of mouse and human PVH neurons. Previous characterization of PVH neuron populations in human samples has primarily focused on neuroendocrine subtypes 72-74 . To ascertain whether the PVH neuron populations identified in our transcriptomic study resemble those in the human PVH, we performed a comparative analysis between our mouse sc/snRNA-seq atlas and human brain snRNA-seq data. To achieve this, first, we retrieved all cells from dissections containing the PVH from two publicly available human studies 75,76 and clustered them using Seurat 5. Next, as we did for mouse sc/snRNA-seq clustering of PVH neurons, we subset the data to only include SIM1 + clusters and reclustered the remaining 3,432 SIM1 + nuclei, resulting in 21 distinct SIM1 + neuronal clusters ( Extended Data Fig. 8d,e; Supplementary Table 25 ). To estimate the transcriptomic similarity between human and mouse PVH neurons, we performed CCA comparing Sim1 / SIM1 -positive clusters, which also allowed us to provide the analogous mouse MERFISH cluster identifiers. Strikingly, we observed a high degree of transcriptional correlation across species, with notable similarity between humans and mice for neuroendocrine hormone-, Sim2-, and Ucn3 -expressing neuron populations ( Extended Data Fig. 8f; Supplementary Table 26 ). MERFISH atlas of peri-PVH GABAergic neurons As noted above, the PVH is surrounded by GABAergic ( Slc32a1 + ) neurons, some of which have been shown to project locally into the PVH 38 and are proposed to regulate the HPA axis 37,77 . Specific analysis of GABAergic MERFISH populations included 53,294 neurons that clustered into 29 distinct populations. We labeled each cluster according to the expression of one or more marker genes identified through differential gene expression analysis ( Fig. 4a,b; Supplementary Table 27 ). Next, we performed CCA between MERFISH and sc/snRNA-seq GABAergic neuron clusters to assess transcriptomic agreement between technologies, and this analysis demonstrated a high degree of similarity ( Extended Data Fig. 9a; Supplementary Table 28 ). Finally, we plotted the spatial distribution of the GABAergic MERFISH clusters along the rostral-to-caudal axis, grouping clusters according to spatial domains into “Rostral,” “Intermediate,” or “Caudal” categories ( Fig. 4c-g; Extended Data Fig. 9b ). Rostral GABAergic neurons include MF_i1.Nms, MF_i4.Dach2, MF_i5. Fezf2, MF_i6.Eya1, MF_i8.Gldn, MF_i9.Piezo2, MF_i10.Egr3, MF_i12.Grp, MF_i14.Rfx4, MF_i15.Sntb1, MF_i16.Fshr, MF_i21.Pax6-Vgll3, and MF_i22.Pax6-Otx2 ( Fig. 4d-e ). Of these, MF_i1.Nms and MF_i12.Grp represent neurons located in the suprachiasmatic nucleus (SCN; Fig. 4e ). Rostral GABAergic neurons also identify subparaventricular zone (SPZ) neuron populations that have been difficult to target previously. Of interest, the SPZ is the major output of the SCN 78 , and SPZ clusters include MF_i5. Fezf2, MF_i6.Eya1, and MF_i14.Rfx4. Intermediate GABAergic clusters include MF_i17.Ano1, MF_i18.Rxfp1, MF_i19.Gdnf, MF_i20.Ndnf, MF_i26.Pmfbp1-Prdm8, and MF_i27.Pmfbp1-Pde11a neurons residing ventral and lateral to the PVH in the anterior hypothalamic area (AHA), and the MF_i23.Pax6-Pdgfd cluster located dorsal to the PVH ( Fig. 4f ). Finally, the Caudal GABAergic neuron subtypes include MF_i2.Corin and MF_i29.Th-Prph located in the periventricular hypothalamus, the latter of which expresses Th , Ddc , Slc18a2 , and Slc6a3 , suggesting they release dopamine in addition to GABA ( Fig. 4g ). Remaining Caudal clusters include MF_i3.Otp, MF_i7.St18 , MF_i11.Ror1, MF_i13.Hcrtr2, MF_i24.Pmfbp1_Nostrin, MF_i25.Pmfbp1-Etv1, and MF_i28.Th-Lhx8 clusters located in the posterior AHA ( Fig. 4g ). Together, MERFISH analysis offers the first comprehensive molecular characterization of peri-PVH GABAergic neurons. Targeted transcriptomic profiling of spinal cord-projecting PVH neurons. Numerous studies have demonstrated that PVH neurons project to the spinal cord 2,4,5,8,9,19,79-84 , many of which are thought to activate sympathetic preganglionic neurons in the intermediolateral cell column to regulate cardiometabolic physiology 19,23,24,80,85-88 . Spinal cord-projecting PVH neurons have been sequenced previously 89,90 ; however, prior studies did not profile PVH neurons that project to the thoracic spinal cord, where most sympathetic preganglionic neurons are located, and they did not provide molecular markers that differentiate spinal cord-projecting neurons from other PVH neuron subtypes. Therefore, we profiled PVH neurons that project to the thoracic spinal cord and mapped them onto our Sim1 + sc/snRNA-seq reference atlas. H2B-TRAP mice 91 were injected with retrograde AAV-Cre into the thoracic (~T2-T4) spinal cord to selectively label the nuclei of spinal cord-projecting PVH neurons with mCherry for subsequent fluorescence-activated nuclei sorting (FANS; Fig. 5a ). After sequencing and clustering, we merged the thoracic spinal cord-projecting Sim1 + neuron data with Sim1 + neurons present in previously published spinal cord-projecting datasets 89,90 . Subsequently, we classified the spinal cord-projecting cells based on our Sim1 + sc/snRNA-seq reference atlas and projected them onto the reference UMAP using the MapQuery function in Seurat 5. Results showed agreement across all studies, suggesting that spinal cord-projecting PVH neurons share transcriptional similarities regardless of the spinal level to which they project, with most clustering within one of three populations: Seq_S10_Npsr1-Npnt (13.6%), Seq_S11_Esr2-Abcc9 (35.1%), or Seq_S12_Npr3-Radx (45.4%) ( Fig. 5b, Supplementary Table 29 ). Based on Sim1 + MERFISH to sc/snRNA-seq CCA mapping, the corresponding MERFISH clusters for spinal cord-projecting populations are MF_S7.Esr2-Inhbb, MF_S8.Esr2-Ret, MF_S9.Npr3-Radx, and MF_S26.Npnt ( Fig. 5c ). To confirm the molecular identity of spinal cord-projecting PVH neurons, we injected the retrograde tracer, Fluoro-Gold, into the thoracic spinal cord and subsequently performed FISH for Esr2 , Npr3, or Neuropeptide S receptor 1 ( Npsr1) ( Fig. 5d,e ). Our histological analysis revealed colocalization of Fluoro-Gold with the mRNA of all three marker genes we assayed. Notably, the colocalization of Esr2 and Npr3 with Fluoro-Gold was predominantly observed in the intermediate and caudal regions of the PVH ( Fig. 5f,g ), which is consistent with the spatial patterning of these genes identified by MERFISH ( Extended Data Fig. 7i,l ). Likewise, a separate population of Fluoro-Gold-labeled neurons in the caudal PVH was also found to be positive for Npsr1 mRNA ( Fig. 5h ), matching the pattern identified by MERFISH ( Fig. 3j ). Together, these data support that there are three predominant and transcriptionally distinct spinal cord-projecting PVH neuron populations that are likely involved in sympathetic regulation. However, the functional role of each specific spinal cord-projecting PVH population is not known and is an important area of future study. Detection of satiety marker genes in Sim1 + neurons with MERFISH PVH regulation of feeding behavior has been studied extensively, yet the precise PVH neurons mediating satiety are still unknown. Further, several marker genes expressed by PVH neurons have been proposed to be involved in satiety regulation, but the relationship among these genes is unresolved. Given the limited number of centrally-projecting PVH neurons and the low expression of many satiety-associated genes, prior studies have lacked the sample size and/or sensitivity to reliably characterize the expression of satiety genes in different PVH neuron populations. Therefore, since MERFISH has increased sensitivity over droplet-based sc/snRNA-seq methods 48 , we examined our Sim1 + MERFISH atlas to assess the expression patterns of genes associated with satiety. To begin, we analyzed expression of Mc4r as MC4R signaling in the PVH is necessary and sufficient for satiety and body weight regulation 12-15,21 . Mc4r is expressed by several Sim1 + neuron populations and highly correlated with expression of Npy1r , as expected, given its role in feeding behavior ( Fig. 6a-c ) 92-94 . Expression of Mc4r and Npy1r is widespread throughout the PVH, with an enrichment in the Caudal-Intermediate region between bregma levels -0.7 mm to -1.0 mm ( Fig. 6h,i ). Despite Mc4r being expressed by multiple PVH neuron subtypes, three clusters display the strongest enrichment, MF_S2.Trh-Satb2, MF_S11.Aox3, and MF_S14.Brs3 ( Fig. 6a,d-g ). These marker genes, Satb2 , Aox3 , and Brs3 , have limited spatial distributions, often enriched within areas of high Mc4r and Npy1r expression ( Fig. 6j-l ). MF_S2.Trh-Satb2 neurons have the highest expression of Mc4r and represent PVH Trh neurons that project to the median eminence ( Fig. 1c,d,f ) to control the hypothalamic-pituitary-thyroid axis, which is consistent with MC4R and NPYregulation of thyroid hormone release during fasting 95 . The next highest Mc4r -expressing clusters are MF_S11.Aox3 and MF_S14.Brs3, both of which project centrally as they are not labeled by systemic Fluoro-Gold injection ( Extended Data Fig. 10a,b ). MF_S11.Aox3 represents a novel population of centrally-projecting PVH neurons with unknown function(s), while PVH Brs3 neurons regulate feeding behavior, as noted above 67 . In support of an interaction between Brs3 and Mc4r , conditional knockout of Brs3 from Mc4r -expressing neurons produces obesity 96 . Other genes used to investigate PVH satiety-regulating populations, including Calcr 16 , Glp1r 15 , Irs4 17 , Ntrk2 18 , Nos1 19 , and Pdyn 21 , are expressed widely across different PVH neuron subtypes ( Extended Data Fig. 10c) . Among them, Calcr and Glp1r have the most restricted expression patterns but are expressed by neuroendocrine and centrally-projecting populations. With regard to identifying candidate PVH satiety neurons within our atlas, three clusters express the majority of the satiety genes above (i.e., Calcr , Glp1r , Irs4 , Ntrk2 , Nos1 , and Pdyn ), MF_S8.Esr2-Ret, MF_S13.Pde3a-Tmem215, and MF_S14.Brs3 ( Extended Data Fig. 10c ). As noted before, MF_S14.Brs3 neurons are enriched for Mc4r expression and may represent Mc4r -expressing satiety neurons. MF_S8.Esr2-Ret and MF_S13.Pde3a-Tmem215 neurons, on the other hand, express little Mc4r but co-express Glp1r and Pdyn ( Extended Data Fig. 10c-g ). Given that PVH Pdyn and PVH Glp1r neurons are key regulators of satiety and body weight 15,21,97 , and PVH Mc4r and PVH Pdyn neurons are distinct satiety-regulating populations 21 , MF_S8.Esr2-Ret and MF_S13.Pde3a-Tmem215 neurons are candidates to be the Pdyn -expressing PVH satiety neurons. Targeted transcriptomic profiling of PVH Sim1 + neurons that project to the parabrachial region. PVH neurons promote satiety through direct excitatory projections to the PB. PVH Mc4r neurons elicit robust glutamatergic synaptic responses in downstream neurons located in the lateral parabrachial nucleus (LPBN) 12 , whereas PVH Pdyn neurons preferentially do so in neurons found in the nearby pre-locus coeruleus (pLC) 21,98 , despite each satiety population projecting to both regions. That said, Mc4r and Pdyn are expressed by multiple PVH neuron subtypes, as noted above, and specific molecular markers for PB-projecting PVH neurons have not been identified. Hence, the precise PVH neurons that regulate satiety are unknown. To elucidate the specific PVH populations that project to the PB, we performed targeted snRNA-seq similar to spinal cord-projecting neuron profiling above. Retrograde Cre virus was injected bilaterally into the PB, targeting the LPBN and adjacent pLC, to selectively label the nuclei of PB-projecting PVH neurons with mCherry. Next, PB-projecting nuclei were isolated, collected via FANS, and sequenced ( Fig. 7a ). After clustering, PB-projecting Sim1 + neurons were classified based on our Sim1 + PVH sc/snRNA-seq atlas and projected onto the reference UMAP ( Fig. 7b, Supplementary Table 30 ). Our results show that most of the PB-projecting PVH neurons cluster with one of the following populations: Seq_S11.Esr2-Abcc9 (32.4%), Seq_S12.Npr3-Radx (21.2%), Seq_S15.Brs3 (14.7%), Seq_S16.Pde3a-Tmem215 (7.9%), or Seq_S17.Sfta3-ps (16.9%). Of interest, Mc4r- and Npy1r -enriched MF_S14.Brs3 neurons correspond to the Seq_S7.Brs3 cluster based on our Sim1 + MERFISH to sc/snRNA-seq CCA mapping ( Fig. 7c ). To confirm that PVH Brs3 neurons express Mc4r and project to the PB, we injected the retrograde tracer cholera toxin subunit B (CTB) into the PB and Cre-dependent AAV-EGFP-L10a into the PVH of Mc4r -2A-Cre mice 12,99 . Subsequently, we performed FISH to detect Brs3 expression in the PVH. Histological analysis revealed triple-labeling of fluorescent signals from Brs3 FISH, Mc4r -positive neurons labeled with EGFP, and PB-projecting PVH neurons labeled with CTB ( Fig. 7d,e ). Collectively, these findings support the hypothesis that PVH Brs3 neurons regulate satiety. PVH Brs3 neurons regulate feeding via projections to the PB. Given the importance of PVH Mc4r neurons to energy balance and prior studies demonstrating PVH Brs3 neuron inhibition increases food intake 67 , we next asked whether PVH Brs3 neurons are necessary for body weight regulation. To test this, we silenced PVH Brs3 neurons by bilaterally injecting an AAV driving Cre-dependent expression of tetanus toxin light chain (TeTxLC) or GFP as control into the PVH of Brs3 -IRES-Cre mice 100 . Additionally, we injected a cohort of wild-type mice with Cre-dependent AAV-TeTxLC as another control group. Body weights were measured weekly, and after six weeks, Brs3 -IRES-Cre mice receiving TeTxLC gained significantly more body weight compared to both control groups ( Fig. 7f ). This finding demonstrates that PVH Brs3 neurons regulate body weight by preventing weight gain. PVH Mc4r neurons are directly inhibited by ARC Agrp neurons to induce hunger 12,21 . Since PVH Brs3 neurons express Mc4r , project to the PB, and have been implicated in feeding behavior regulation, we next tested whether they receive synaptic input from ARC Agrp neurons 12,21 . ARC Agrp à PVH Brs3 neuron connectivity was assessed by channelrhodopsin-2 (ChR2)-assisted circuit mapping (CRACM) using Brs3 -IRES-Cre:: Npy -IRES-Flp 101 mice as Npy and Agrp are co-expressed in the ARC 102 . Cre-dependent AAV-mCherry was injected into the PVH to visualize Brs3 -expressing neurons for ex vivo brain slice electrophysiology recordings, and Flp-dependent AAV-ChR2-eYFP was injected into the ARC to drive ChR2 expression in NPY/AgRP neurons. Light-evoked inhibitory postsynaptic currents (IPSCs) were detected in 8 out of 14 PVH Brs3 neuron recordings ( Fig. 7g ), indicating ARC Agrp neurons are monosynaptically connected to many PVH Brs3 neurons – further supporting their role in satiety regulation. Having established that PVH Brs3 neurons receive input from ARC Agrp neurons, we next asked if PVH Brs3 projections to the PB are sufficient to reduce food intake using in vivo optogenetics. Brs3 -IRES-Cre mice were injected with either Cre-dependent AAV-ChR2 or AAV-mCherry into the PVH, and optical fibers were implanted bilaterally above the PB. Photostimulation of ChR2-expressing PVH Brs3 à PB terminals at the onset of the dark cycle significantly reduced food intake ( Fig. 7h ), which is consistent with the effects observed after chemogenetic activation of the entire PVH Brs3 population 67 and photostimulation of PVH Mc4r neuron projections to the PB 12 . No reduction in food intake was observed in the mCherry control group after photostimulation. Together, these data establish PVH Brs3 neurons as a precise neuronal subtype mediating satiety via projections to the PB. Discussion We leveraged single-cell and spatial transcriptomics technologies to develop a high-resolution, spatially resolved atlas of the mouse PVH region. Extensive transcriptional profiling enabled a detailed analysis of the molecular diversity among PVH cell types, highlighting stark differences between neuroendocrine and centrally-projecting PVH neurons. Using the marker gene profiles revealed by sc/snRNA-seq, we then performed MERFISH on the PVH region from multiple male and female mice, yielding spatial transcriptomic information from 41 coronal sections spanning bregma levels -0.4 mm to -1.2 mm, and including more than 150,000 cells. Specific analysis of Sim1 + neurons identified by MERFISH revealed 26 transcriptionally distinct populations, six of which were neuroendocrine, expressing peptide hormones along with secondary markers that exhibited highly specific expression patterns. Spatial domain analysis of MERFISH data further designated Sim1 + neurons as PVH or Peri-PVH, and segregated PVH neurons into Rostral, Rostral-Intermediate, Caudal-Intermediate, or Caudal groups. Analysis of the similarity between Sim1 + neuron populations identified by MERFISH and sc/snRNA-seq demonstrated remarkably high correspondence for neuron subtypes located within the PVH. Our study also cataloged 29 GABAergic neuron subtypes that surround the PVH, highlighting the significant heterogeneity of this region and providing a means for gaining selective genetic access to these neuron populations for future investigation. Noted above, our atlas provides molecular markers capable of distinguishing neuroendocrine and centrally-projecting PVH neurons that express the same neuropeptide hormone gene, which is of great interest for PVH Crh and PVH Oxt neurons that control stress-related and social behaviors 103-105 . For instance, PVH Crh neuroendocrine neurons (Seq_S1.Crh-Scgn) express Scgn , whereas centrally-projectingPVH neurons expressing Crh include Seq_S12.Npr3-Radx, Seq_S13.Npy2r-Tll2, Seq_S14_Aox3, and Seq_S15_Brs3 clusters, all of which lack Scgn expression. These cluster-specific genetic markers make it possible for future functional studies to selectively target PVH Crh and PVH Oxt neuron subtypes and link them to distinct physiological and behavioral phenotypes. However, it remains unclear which centrally-mediated behaviors are driven by collateral projections from neuroendocrine populations to other hypothalamic sites 106-109 versus those resulting from distinct centrally-projecting PVH Crh and PVH Oxt neurons. In addition to our spatially resolved atlas, we conducted targeted snRNA-seq of spinal cord- and PB-projecting PVH neurons to elucidate the neuronal populations involved in regulating sympathetic nervous system activity and feeding behavior, respectively. Prior studies have identified several marker genes for spinal cord-projecting PVH neurons, including Avp , Oxt 4,8,110-112 , Bdnf 23 , Mc4r 12 , Nos1 , Sim1 19 , Erbb4 , Otp , Pcsk5 , Prlr , and Zeb2 89 , but none of these genes are unique to a single PVH neuron type. Further, prior sc/snRNA-seq studies only profiled cervical- and lumbar-projecting neurons in the brain 90 . Given our interest in the regulation of the sympathetic nervous system, we sequenced PVH neurons that project to the thoracic cord, where preganglionic neurons are primarily located. Our results for thoracic-projecting neurons aligned well with publicly available data as all spinal cord-projecting PVH neurons predominantly mapped to Seq_S11.Esr2-Abcc9, Seq_S12.Npr3-Radx, and Seq_S10.Npsr1-Npnt clusters of our Sim1 + sc/snRNA-seq reference atlas. The functional roles of these neuron populations remain unknown, but pharmacological manipulation of ESR2 and NPR3 activity in the PVH has been shown to reduce blood pressure 62,63,65 . Of interest, it has long been recognized that a small number of PVH Avp and PVH Oxt neurons project to the spinal cord; however, none of the sequenced spinal cord-projecting PVH neurons mapped to neuroendocrine Seq_S5.Oxt-Rxfp3 or Seq_S6.Avp-Pla2r1 clusters. This is consistent with neuroanatomical tracing studies showing pituitary-projecting PVH neurons do not collateralize to the brainstem and spinal cord 1,5 . Therefore, spinal cord-projecting PVH Avp and PVH Oxt neurons likely belong to the centrally-projecting Seq_S11.Esr2-Abcc9 population, which is positive for both Avp and Oxt . We sequenced PB-projecting PVH neurons to ascertain their cell type identities because PVH Mc4r and PVH Pdyn satiety neuronsrepresent distinct PB-projecting populations, andmultiple neuron subtypes express Mc4r and Pdyn 12,21 . The majority of Sim1 + PB-projecting neurons mapped to five clusters, including two spinal cord-projecting sc/snRNA-seq clusters, Seq_S11.Esr2-Abcc9 and Seq_S12.Npr3-Radx. This may represent similarities in transcriptomes between PB- and spinal cord-projecting PVH neurons or that some PVH neurons collateralize between these two regions. However, we cannot rule out that retrograde AAV injections were taken up by spinal cord-projecting fibers passing through the PB, which has been observed with some retrograde tracers 113 . PB-projecting neurons did map to three clusters that spinal cord-projecting PVH neurons did not, including Seq_S15.Brs3, Seq_S16.Pde3a-Tmem215, and Seq_S17.Sfta3-ps. Taking advantage of the enhanced gene detection capability of MERFISH, we were able to identify the PVH neurons with the highest expression of Mc4r and compare them with those identified as PB-projecting. Notably, the MF_S14.Brs3 cluster is among the highest expressors of Mc4r and corresponds to the PB-projecting cluster Seq_S15.Brs3. This information, in conjunction with prior work demonstrating that chemogenetic activation of PVH Brs3 neurons reduces food intake and inhibition does the opposite 67 , inspired us to further examine their role in energy balance. We show that 1) chronic PVH Brs3 neuron silencing causes significant weight gain, 2) they receive direct GABAergic input from hunger-driving ARC Agrp neurons, and 3) stimulation of PVH Brs3 neuron projections to the PB reduces food intake. These results are all consistent with PVH Brs3 neurons representing Mc4r + satiety neurons, yet the effects on food intake that we and others observed were smaller compared to manipulating all PVH Mc4r neurons 12,67 . Thus, there may be multiple PVH Mc4r neuron populations that control food intake. With regard to pinpointing the specific cluster containing PVH Pdyn satiety neurons 21 , MF_S8.Esr2-Ret and MF_S13.Pde3a-Tmem215 neurons correspond to PB-projecting PVH neurons that express Pdyn and Glp1r but lack Mc4r . However, additional studies are required to test whether PB-projecting MF_S8.Esr2-Ret and/or MF_S13.Pde3a-Tmem215 neurons control satiety. This atlas of the PVH serves as a versatile resource to support future studies of PVH organization and function. It also has several advantages over prior work 25,26 , including a vastly increased sample size, both unbiased and circuit-based molecular profiling, and the ability to resolve spatial information with MERFISH using a gene panel curated for the PVH and surrounding regions. To facilitate accessibility for the scientific community, we uploaded our analyzed sc/snRNA-seq and MERFISH data to the Broad Single Cell Portal (https://singlecell.broadinstitute.org/single_cell/study/SCP2858), an open-access, web-based tool for exploring single-cell genomics data – thus, providing a valuable resource for the field of homeostasis. Methods Mice: All animal care and experimental procedures were approved by the Institutional Animal Care and Use Committees at Beth Israel Deaconess Medical Center and the University of Iowa. Prior to the start of experiments, mice were housed in a temperature- and humidity-controlled room with a 12-hr light-dark cycle and maintained on standard diet (Inotiv 7913) unless stated otherwise. C57BL/6J background wild-type mice were used for the majority of single-cell and single-nucleus RNA sequencing experiments as well as MERFISH experiments. In some cases, Sim1 -Cre (JAX006395 14 ), Sim1 -Cre::R26-LSL-EGFP-L10a 22 , or H2B-TRAP mice (JAX029789 91 ) were used for single-cell and single-nucleus RNA sequencing studies to guide dissections and sample collection with FANS. Behavior experiments were completed with Brs3 -IRES-Cre mice (JAX030540 100 ), which were crossed to Npy -IRES-Flp (JAX030211 101 ) mice for CRACM experiments. Additionally, Slc17a6 -IRES-Cre (JAX028863 114,115 )::R26-LSL-EGFP-L10a 22 , Slc32a1 -IRES-Cre (JAX028862 114 )::R26-LSL-EGFP-L10a, Mc4r -2a-Cre (JAX030759 12 ), and C57BL/6J wild-type mice were used for histological experiments. Single-cell/nucleus RNA sequencing tissue collection, library preparation, and sequencing: C57BL/6J, Sim1 -Cre, or Sim1 -Cre::R26-LSL-EGFP-L10a mice aged 6-12 weeks were sacrificed between 9 am – 12 pm by rapid decapitation immediately after removal from the home cage. Brains were extracted and chilled in DMEM/F12 media slush. Next, brains were placed ventral side up in a chilled stainless steel brain matrix (Roboz Surgical Instrument Co.: SA-2165), and 1 mm coronal sections of the hypothalamus were collected. The PVH was then micro-dissected under a fluorescent stereoscope. For each sample preparation, 4-10 male or female mice were pooled. Sample and library preparation was performedas described previously for Drop-seq 116 and DroNc-seq 46 with minor modifications. One of three DroNc-seq samples was prepared from fasted C57BL/6J mice. For 10X Chromium v3 sequencing runs, samples and library preps were prepared as described previously for incorporation with fluorescence-activated nuclei sorting (FANS) with minor modifications 117,118 . In addition, subsets of 10X Chromium v3 samples from Sim1 -Cre mice injected with AAVDJ-hSyn-H2B-mCherry (Boston Children’s Hospital Viral Core) 119 or H2B-TRAP mice injected with AAVrg-hSyn-Cre, for projection-specific snRNA-seq experiments described below, were incubated with hashtag oligos for 15 minutes for eventual multiplexing prior to FANS enrichment based on nuclear mCherry. Multiplexed Sim1 -Cre samples were obtained from mice that were ad libitum fed, fasted, or refed for 60 minutes before sacrifice. Libraries were sequenced on an Illumina NextSeq 500 or Illumina NovaSeq 6000 at a minimum read depth of 20,000 reads per cell/nucleus. Hashtag oligo libraries were sequenced to a minimum read depth of either 1,000 or 5,000 reads/nucleus and processed into count matrices using either the Cumulus Tool on Feature Barcoding (https://github.com/lilab-bcb/cumulus_feature_barcoding) or kallisto | bustools (https://www.kallistobus.tools/). For Drop-seq and DroNc-seq data, raw sequencing reads were processed using the Drop-seq tools pipeline 46,116 . Barcodes with base quality <10 were removed, and 5’ and 3’ ends of reads were trimmed to remove TSO and poly(A) tails, respectively. Reads were then aligned to the GRCm38 reference genome using STAR v2.7.7. Feature-barcode matrices were then generated by summing detected unique molecular identifiers (UMIs) for each barcode with errors corrected at a hamming distance of 1. For 10X Chromium v3 libraries, 10X Genomics Cell Ranger was used to map reads to the GRCm38 reference genome and generate feature-barcode matrices. Single-cell/nucleus RNA sequencing Quality Control: For all sequencing data regardless of technology, CellBender (v0.2.2) was used to identify and filter out reads captured from ambient RNA and random barcode swapping 120 . Subsequently, data from Drop-seq, DroNc-seq, and 10X Chromium v3 sequencing runs were loaded into an RStudio environment (R v 4.4.1) and processed through a custom Seurat-based analysis pipeline run in Seurat v5.0.1.9001 35 . First, we applied additional filtering to remove cells/nuclei with fewer than 250 unique genes. DroNc-seq data were then filtered to exclude nuclei with total UMI count outside the range of 1,000 to 10,000, while Drop-seq and 10X chromium-v3 data were filtered to exclude cells/nuclei with total UMI count outside the range of 1,000 to 25,000. Additionally, PercentFeatureSet () was used to calculate mitochondrial gene expression, and cells/nuclei from all datasets were removed if they had a mitochondrial gene expression rate of greater than 10%. Finally, all cells/nuclei with a ratio of log 10 (unique genes)/log 10 (unique molecules) less than 0.8 were removed. After quality control filtering was complete, all data were merged into a single Seurat object for integrated analysis. Single-cell/nucleus RNA sequencing and data integration: For integrated analysis, 11 batches of sequencing runs were merged into a single Seurat object (Drop-seq = 7 batches, DroNc-seq = 2 batches, and 10X Chromium-v3 = 2 batches) followed by joining of the “RNA” assay layers using JoinLayers(). Raw counts were log-normalized, using Seurat NormalizeData() , and cell cycle scoring for S phase and G2/M was computed using the Seurat CellCycleScoring() function 121 . Subsequently, given that stress readily activates PVH neurons, particularly PVH Crh neurons controlling the HPA axis, the AddModuleScore() function was used to measure the expression level of a set of 19 primary rapidly responding activity-dependent genes to compute a “cellular activation score” based on this transcriptional signature for each cell 122 . Next, layers were split by sequencing run (“batch”), and FindVariableFeatures() was used to select the top 5,000 highly variable genes. Data were then scaled with ScaleData() , while regressing out the following covariates: mitochondrial gene percentage, cell-cycle scores, and cellular activation score. Principal component analysis (PCA) was performed with the RunPCA () function. Following calculation of principal components, integration of layers was carried out using IntegrateLayers() with reciprocal principal component analysis (RPCA)-based integration 36 . After integration, we used the top 30 principal components for clustering and dimensionality reduction using the Seurat FindNeighbors() , FindClusters(), and RunUMAP() functions. To identify marker genes for each cluster, we re-joined layers using JoinLayers() and ran FindAllMarkers() for differential gene expression analysis (DGEA) using the non-parametric Wilcoxon Rank Sum test . Differentially expressed genes were defined as those with > 0.2 average log 2 fold change and a Bonferroni-corrected p-value less than 0.01. Marker gene analysis guided identification of doublets/multiplets, which were classified as clusters that expressed high levels of more than one canonical cell type marker genes (e.g., clusters expressing marker genes for both neurons and astrocytes) and were removed. In addition, clusters comprised of doublets/multiplets and/or “low quality” metrics, including mitochondrial gene enrichment or absence of cell type-defining markers indicating low complexity, were removed. This process was repeated at several levels of analysis, beginning with all cells, then after subclustering for neurons, GABAergic neurons, glutamatergic neurons, and Sim1 -expressing neurons. Integration of Sim1-expressing clusters with publicly available HypoMap and Allen Brain Cell Atlas data: To integrate Sim1 + PVH sc/snRNA-seq data from our study with publicly available sequencing data from the murine PVH, we downloaded data from HypoMap, an integrated atlas of mouse hypothalamus 32 . Using the provided anatomical annotations with the Seurat object, we subset for and clustered only cells/nuclei annotated as “paraventricular hypothalamic nucleus” using the pipeline described for this study. Notably, during clustering, we curated the HypoMap data for Sim1 -expressing cells/nuclei, filtering out any clusters marked by specific expression of GABAergic or thalamic marker genes (i.e., Slc32a1 and Tcf7l2 ). We then merged and integrated the HypoMap PVH Sim1 + neurons with PVH Sim1 + sc/snRNA-seq data collected in this study using our Seurat-based analysis workflow. However, after integration, inconsistencies were observed across datasets. We then instead integrated publicly available PVH Sim1 + scRNA-seq data from the whole mouse brain Allen Brain Cell (ABC) Atlas with Sim1 + PVH sc/snRNA-seq data from this study 29 . To specifically access PVH cells from the ABC Atlas, we first downloaded two H5 AnnData expression matrices (WMB-10Xv2-Hy-raw.h5ad and WMB-10Xv3-Hy-raw.h5ad) containing all cells collected from hypothalamic dissections and sequenced using either 10X Chromium v2 or 10X Chromium v3 chemistry. Subsequently, we used the Convert() and LoadH5Seurat() functions toload the ABC Atlas data into a Seurat object and used the published taxonomic classifications to select for data from the PVH region. The ABC Atlas assigned anatomical annotation was used to specifically select clusters that spatially mapped to either the PVH (“PVH”) or the anterior portion of the periventricular area (“PVa”). Subsequently, we further filtered our selection only to keep glutamatergic clusters using the ABC Atlas assigned neurotransmitter type label, keeping clusters annotated as either “Glut” or “Glut-GABA”. We then removed cells with a mitochondrial gene expression rate greater than 10% and clustered the data in Seurat version 5. For clustering, ABC Atlas data were processed as described above with minor modifications. Notably, the 10X chemistry (i.e., v2 and v3) were each treated as a “batch” for integrated analysis. After clustering, any identified “low quality” or doublet/multiplet clusters were removed as described above. Finally, we merged and integrated the ABC Atlas Sim1 + neurons with the PVH Sim1 + sc/snRNA-seq data from this study following the workflow described above. Analysis of neuroendocrine neuron transcriptional profiles: DGEA was run using Seurat FindMarkers() on the different neuron classes: centrally-projecting, neuroendocrine, median eminence-projecting, and posterior pituitary-projecting neurons. Differentially expressed genes were defined as having > 0.2 average log 2 fold change and a Bonferroni-corrected p-value <0.01. Next, the clusterProfiler package was used to perform Gene Ontology (GO) enrichment analysis of genes differentially expressed by centrally-projecting, neuroendocrine, median eminence-projecting, and posterior pituitary-projecting neuronal classes 123 . Specifically, compareCluster() was used to perform “enrichGO” analysis, which executes an over-representation analysis 124 for all GO ontology categories (i.e., biological process, cellular component, and molecular function) with Bonferroni correction for multiple comparisons at an alpha value of 0.05. Single-nucleus RNA sequencing of projection -specific PVH neuron populations: We sequenced projection-specific PVH neurons using either 10X Chromium v3 or Smart-Seq2 (“sNuc-seq”) 125,126 technologies. For 10X Chromium v3 experiments, H2B-TRAP mice received bilateral stereotaxic injections of AAVrg-hSyn-Cre (Addgene #105553) into either the upper thoracic spinal cord or the parabrachial region. sNuc-seq samples were prepared by bilaterally injecting C57BL/6J mice with AAVDJ-hSyn-H2B-mCherry into the PVH and AAVrg-CAG-GFP-Cre (Boston Children’s Hospital Viral Core) or HSV-hEf1a-mCherry-IRES-Cre (Mass General Brigham Gene Delivery Technology Core; Dr. Rachael Neve) into the PB. Two weeks post-surgery, animals were sacrificed, and tissue was collected as described above. Samples processed with 10X Chromium v3 were completed as described in Schwalbe et al. 117 , while sNuc-seq was performed as described in Tao et al. 119 . Spinal cord-projecting data consists of two 10X Chromium v3 sequencing runs, while the parabrachial-projecting data consists of two runs of 10X Chromium v3 and two sNuc-Seq sequencing runs. In addition, we downloaded two publicly available spinal cord-projecting datasets (GEO accession numbers GSE247594 and GSE212409) 89,90 and accordingly classified these data using our Sim1 + PVH sc/snRNA-seq reference atlas. Briefly, we calculated the percentage of mitochondrial gene expression using Seurat’s PercentFeatureSet () to identify and remove any cells/nuclei with a mitochondrial gene expression rate greater >10%. Cells/nuclei with fewer than 1000 UMIs were also removed from further analysis. Subsequently, we clustered all parabrachial- and spinal cord-projecting data using the analysis pipeline described above and filtered the data to only retain Sim1 -expressing clusters. To classify each cell, we proceeded to use FindTransferAnchors () to project our mouse Sim1 + sc/snRNA-seq reference atlas PCA structure onto the parabrachial- and spinal cord-projecting data to identify paired anchor cells across datasets. We then used the identified anchors and the MapQuery () function to map parabrachial- and spinal cord-projecting data into our mouse Sim1 + sc/snRNA-seq reference atlas UMAP space. Analysis of human PVH single-nucleus RNA sequencing data: Two published datasets contain snRNA-seq data from the hypothalamus of adult humans 75,76 . From Siletti et al., 2023 75 , we downloaded a Seurat object containing data from dissections encompassing the medial preoptic region of the hypothalamus, supraoptic region of the hypothalamus, and paraventricular nucleus of the hypothalamus. We then filtered the data for neurons with > 1,000 UMIs and < 10% mitochondrial gene expression and retained SIM1 + clusters for further analysis. After filtering for SIM1 + neurons, the data included samples from one 60-year-old female and one 50-year-old male. We also downloaded a Seurat object from Tadross et al., 2025 76 , containing data from the entire adult human hypothalamus, which was filtered as above and contributed data from two females, aged 63 and 94 years, and four males, aged 83, 88, 91, and 94. After analyzing the integrated human SIM1 + data, we used a text file (“gene_ortologs.gz”) available from NCBI (https://ftp.ncbi.nlm.nih.gov/gene/DATA/) to identify all gene homologs present in both the human SIM1 + object and our mouse Sim1 + sc/snRNA-seq atlas. We then completed a canonical correlation analysis (CCA) to assess the transcriptional similarity of each cluster between the human and mouse atlases by using Seurat’s FindTransferAnchors () and TransferData () functions. MERFISH gene panel selection : A gene panel of 503 genes ( Supplementary Table 16 ) was curated specifically for the PVH and surrounding regions based on differentially expressed genes identified in sc/snRNA-seq experiments ( Supplementary Tables 1-4,7 ), canonical marker genes for neurons and non-neuronal cells, and functionally important genes described in the scientific literature. After gene selection, Vizgen manufactured the custom “MERFISH 500 Gene Panel” (Vizgen: 20300008), comprised of probes targeting a minimum of 30 regions per gene (except for Avp and Oxt ) and using a 25-bit binary code readout for gene assignment after combinatorial single molecule FISH (smFISH). Furthermore, 50 “blanks” comprising non-encoding scrambled sequences were included in the gene panel as negative controls ( Supplementary Table 17 ). Three of the 503 genes, Avp , Oxt , and Sst , were assigned to the “sequential panel” to avoid optical overcrowding artifacts due to high abundance of expression. Genes in the sequential panel are detected using unique probes identified by their direct fluorescent signal in distinct imaging rounds occurring after combinatorial smFISH imaging. MERFISH tissue collection and sample preparation : MERFISH experiments were conducted according to Vizgen MERSCOPE protocols for fresh frozen tissue using six C57BL/6J mice, comprised of four males and two females, aged 8-10 weeks. Sacrifice and brain extraction was done as described for sc/snRNA-seq studies above. Brains were then positioned ventral side up in a chilled stainless steel brain matrix and sliced into 3-mm thick coronal slices that included the PVH region. Subsequently, the coronal slices were placed anterior side up and trimmed dorsally, removing tissue above the lateral septum, and laterally to remove cortex and much of the striatum. PVH tissue blocks were then embedded in a square mold (S22, Kisker Biotech) with Tissue-Tek® O.C.T. Compound (Sakura) and stored at −80°C until sectioning. Tissue blocks were placed in a cryostat (Epredia CryoStar NX50 HD Cryostat) and incubated at -20°C for 1 hour prior to sectioning coronally at 10 µm thickness. We mounted 4-10 sections from each brain at ~100 µm intervals onto warm MERSCOPE slides (Vizgen: 20400001), beginning at approximately bregma level -0.4 mm and continuing to -1.2 mm according to the Franklin-Paxinos atlas 49 . After sectioning, MERFISH slides were placed face-up in a 60 mm petri dish (VWR, 25382-687) and left at room temperature for 5 minutes. Next, slides were incubated in freshly made 4% paraformaldehyde (PFA; Electron Microscopy Sciences: 15714-S) in RNase-free phosphate-buffered saline (PBS; Thermo Fisher Scientific: AM9625) for 15 minutes at room temperature. Slides were then washed three times for five minutes each with PBS at room temperature and treated with freshly made 70% ethanol for tissue permeabilization and storage for a minimum of 24 hours at 4°C in parafilm-sealed 60 mm dishes. MERFISH probe hybridization and imaging : Slides were taken out of 4°C and washed with Sample Preparation Wash Buffer (Vizgen: 20300001) for five minutes at room temperature, followed by incubation in Formamide Wash Buffer (Vizgen: 20300002) for 30 minutes at 37°C. Subsequently, our custom 503 gene MERSCOPE panel for the PVH was applied to the slides with a parafilm coverslip and incubated at 37°C for 36-42 hours. Slides were then washed twice with Formamide Wash Buffer for 30 minutes each at 47°C. To gel-embed tissue samples on slides, a mix composed of Gel Embedding Premix (Vizgen: 20300004), ammonium persulfate (APS; Sigma: 09913-100G), and TEMED (Sigma: T7024-25ML) was prepared and applied to the tissue. A circular Gel Coverslip (Vizgen: 30200004), treated with RNaseZap, 70% ethanol, and Gel Slick Solution, was then placed on the slide over the gel embedding solution. Gel embedding solution was allowed to solidify for 90 minutes, after which the coverslip was removed. The sample was then incubated at 37°C in Clearing Solution, comprised of Protease K (New England Biolabs: P8107S) and Clearing Premix (Vizgen: 20300003), for a minimum of 24 hours and up to five days prior to imaging. On the day of imaging, the slides were washed twice with Sample Preparation Wash Buffer at room temperature and treated with DAPI and PolyT Staining Reagent (Vizgen: 20300021) for 15 minutes on a rocker. The slides were then washed with Formamide Wash Buffer for 15 minutes, followed by a final wash with Sample Prep Wash Buffer. To begin the imaging process, an individual slide was assembled into the MERSCOPE Flow Chamber and inserted into the instrument, along with a MERSCOPE 500 Gene Imaging Cartridge (Vizgen: 20300019). After defining the regions of interest on the slide within the Vizgen MERSCOPE Instrument Software, we started the fully automated instrument run. The MERSCOPE Instrument Software automatically processed the raw images to generate spatial genomics data ready for downstream analysis. Although MERFISH was successful, Slides 3 and 6 underwent unsuccessful Vizgen MERSCOPE protein staining, and these protein staining results were excluded from downstream analyses. MERFISH image analysis and cell segmentation : After image acquisition, the data were initially processed by Vizgen MERSCOPE Instrument Software, before custom cell segmentation was performed with the deep learning algorithm, Cellpose 2.0 50 , using DAPI and PolyT-stained images as training files. First, we uploaded a field of view from one PVH section (Slide 3, bregma level -0.8) as an initial training image. Next, we employed the generalizable ‘cyto2’ model in Cellpose 2.0 with a diameter parameter of 123.73 pixels to initially segment various cell types in the PVH and surrounding regions. Manual annotations were then adjusted by correcting misidentified cells and adding cells missed by the automated ‘cyto2’ model. This process was repeated for 10 fields of view, and the new set of 10 human-processed images were used to optimize the training of our custom Cellpose 2.0 segmentation model. This enhanced model was then utilized to segment cells in all Z planes across 41 coronal sections using the Vizgen Post-processing Tool (VPT). All regions underwent 7-layer segmentation, except for the section corresponding to bregma level -0.7 mm on Slide 2, which underwent segmentation with 6 layers of DAPI and PolyT images due to the loss of the DAPI image from layer 3 during data transfer. Four output files were generated for each coronal section: 1) cellpose2_micron_to_mosaic.parquat (cell boundaries file); 2) cell_by_gene.csv (cell by gene matrix)l; 3) detected_transcripts.csv (cartesian coordinates of each transcript); and 4) cell_metadata.csv (cell morphology characteristics). MERFISH sequential gene panel preprocessing : Due to high-expression levels within the PVH, Avp , Oxt , and Sst expression was assayed with a non-combinatorial sequential gene panel as noted above. Using the VPT sum_signal command on data segmented by Cellpose 2.0, we generated summed fluorescent values for Avp , Oxt , and Sst for each cell in our MERFISH study. We then performed a volume-based normalization of the fluorescent signals using a modified version of previously published methods 127 . Specifically, we first took the High_pass fluorescent values for Avp , Oxt , and Sst for each cell and divided each value by the cell’s volume to yield volume-normalized fluorescence values. Subsequently, we subtracted the respective median volume-normalized fluorescence value for Avp , Oxt , and Sst from all cells and set any negative values to 0. Finally, we divided our median-subtracted, volume-normalized fluorescence value by 1,000 and appended the resulting values for Avp , Oxt , and Sst expression to the cell_by_gene matrix. MERFISH data analysis: VPT output files were loaded as Seurat objects in an R Studio environment (R v 4.4.1) (Seurat v5.0.1.9001) using the Seurat LoadVizgen() function. Data from all 41 sections were then merged into one MERFISH Seurat object. Next, we defined the region of interest (ROI) for each section by selecting the rectangular area 200 µm dorsal, 1000 µm ventral, and 700 µm lateral to the top of the third ventricle. The unique IDs for all cells within each ROI detected in z -plane three were exported to a .csv file using the Vizgen MERSCOPE Visualizer. The merged MERFISH Seurat object was then subset to retain only cells within our defined ROIs. Subsequently, all cells with less than 15 gene counts were removed, and the remaining cells were analyzed with the Seurat-based pipeline described above, with minor modifications. Notably, i) during FindVariableFeatures() , clip. range was set to “(-10, 10)”, according to Seurat recommendations for analyzing FISH-based counts, ii) no covariates were regressed during scaling of variable features, and iii) PCA was conducted with only the combinatorial smFISH features, excluding mCherry. As with sc/snRNA-seq, the merged Seurat MERFISH object was split by ROI (“Slide_ID”) after running PCA, and we subsequently performed a reciprocal principal component analysis (RPCA)-based integration 36 with Seurat IntegrateLayers() to correct for any batch effects. After integration, multiplet clusters driven by inaccurate cell segmentation were removed, and the post-integration steps in our pipeline were repeated until no multiplet clusters were observed. For differential gene expression analysis, we joined layers and ran FindAllMarkers () using the non-parametric Wilcoxon Rank Sum test . Differentially expressed genes were defined as those > 0.2 average log 2 fold change and a Bonferroni-corrected p-value < 0.01. The post-integration pipeline was run for all levels of subclustering, beginning with all cells, followed by analysis of Slc17a6 + / Sim1 + , Slc17a6 + / Sim1 - , and Slc32a1 + populations. MERFISH Spatial Domain Analysis: After cell-type clustering with Seurat, we performed a multi-slice spatial domain detection analysis using the R package SpaDo 51 . Due to computational processing limitations, the initial analysis was limited to data from three animals (two male and one female), which had the most extensive rostral-to-caudal coverage of the PVH region and included 25 out of the total 41 tissue slices of the MERFISH analysis. Specifically, we selected slices spanning bregma levels -0.4 mm to -1.2 mm from Slides 3, 4, and 5. Spatial domain analysis was performed by using the SpatialCellTypeDistribution_multiple () function to calculate the Spatially Adjacent Cell type Embedding (SPACE) for the MERFISH data. SPACE is calculated via a k-nearest neighbor analysis that identifies a cell’s local niche, which is then integrated with its cell-type annotation derived from the Seurat analysis. Once SPACE was computed, we used the DistributionDistance () function to assess similarities between local niches, quantified by Jensen-Shannon divergence (JSD). Subsequently, the DomainHclust () function was used with ‘auto_resolution’ set to 1, to derive spatial domains across all included cells and tissue sections. We then imported the calculated spatial domain information into Seurat as metadata to facilitate figure generation. Finally, to allow visualization of spatial domains across all tissue slices, we leveraged the results from this initial analysis to perform reference-based spatial domain annotation of the remaining 16 tissue slices. To accomplish this, we used the SpatialReference() and SpatialQuery() functions to assign spatial domain annotations to a query dataset based on JSD-distance between the SPACE of each cell in the query dataset and the SPACE centroid for each domain in the reference dataset ( Extended Data Fig. 6a ). Stereotaxic injections and optic fiber implantation: Mice aged 6-10 weeks were deeply anesthetized by intraperitoneal injection of a ketamine/xylazine cocktail (100 mg/kg ketamine; 10 mg/kg xylazine). Next, the surgical area was shaved and sterilized prior to placing the mouse into a stereotactic frame (David Kopf model 940). For spinal cord injections, a midline incision was made above the interscapular region. Vertebrae were visualized by blunt dissection, and T2 was used to identify the injection site location between T2 and T3. The dorsal part of one vertebra was removed with forceps, allowing access to the spinal cord for injection. Injections were made ± 0.4 mm lateral to the midline by lowering a pulled glass pipette containing adeno-associated virus (AAV) or retrograde tracer (Fluoro-Gold or cholera toxin subunit B) into the spinal cord and using an air pressure injection system controlled by a Grass S48 stimulator to control injection speed 128 . Spinal cord injections began at -0.9 mm ventral to the surface of the spinal cord, and AAV/tracer continued to be injected while slowly raising the glass pipette to -0.2 mm. At the completion of each injection, the pipette was left in place for five minutes before removal. This process was then repeated on the contralateral side. To close the incision, the muscle layer was sutured with absorbable sutures (MedVet International: JORG22419), and the skin was sutured with non-absorbable sutures (MedVet International: MV-8661-V). For brain injections, a midline incision was made to expose the skull. At the site of injection, a small hole was drilled, and a pulled glass micropipette containing AAV or retrograde tracer was lowered to the desired injection site depth before infusions commenced using the air pressure injection system described above. Stereotactic coordinates for brain injections were as follows (from bregma): PVH, posterior -0.85, lateral ± 0.2, and ventral -4.9; PB, posterior -5.25, lateral ±1.35, and ventral -3.4; ARC, posterior -1.45, lateral ± 0.3, ventral -6.1. After an injection was completed, the pipette was left in place for five minutes before removal, and this process was repeated for other injection sites. After the injections were completed, the incision was closed using veterinary tissue adhesive (3M Vetbond). For optic fiber implantation, small holes were drilled and 200 µm core fiber optic cannulae with ceramic ferrules (RWD Life Science) were lowered into the PB (posterior, -5.25, lateral ±1.5, and ventral -3.1 from bregma). To secure the cannula, a mixture of dental acrylic and adhesive (dental cement) was then applied to cover the bottom of the ceramic ferrule and the entire exposed area of the skull, anchoring the fiber optic cannulae to the skull. Once the cement had hardened, a non-absorbable suture was placed at the back of the incision to tighten the skin around the cement. After removing the mouse from the stereotaxic frame, the cannula was capped to prevent debris from entering. After surgery, mice were injected with Meloxicam subcutaneously at a dose of 4mg/kg and placed on a 37°C heating pad until recovered. AAV and retrograde tracer injections: For projection-specific sequencing experiments, AAVrg-hSyn-Cre (Addgene: 105553) or HSV-hEf1a-mCherry-IRES-Cre (Mass General Brigham Gene Delivery Technology Core; Dr. Rachael Neve) was injected into the thoracic spinal cord (200 nl/side) or parabrachial region (100 nl/side) of H2B-TRAP mice. Spinal cord retrograde tracing histology was performed by injecting wild-type mice with Fluoro-Gold (FG; Fluorochrome) into the thoracic spinal cord (200 nl/side). For Cre-dependent EGFP-L10a expression in PVH Mc4r neurons, Mc4r -2a-Cre mice received injections of AAV5-EF1a-FLEX-EGFP-L10a (Addgene: 98747) into the PVH (100 nl/side). These same mice received cholera toxin subunit B (CTB; List Biological Laboratories: 104) injections into the PB (50 nl/side). AAVDJ-hSyn-DIO-EGFP-TeTxLC (ETH Zurich Viral Vector Facility: v322-5) or AAV8-hSyn-DIO-EGFP (control virus; Addgene: 50457) was used for chronic Cre-dependent neuronal silencing experiments via injections into the PVH of Brs3 -IRES-Cre or wild-type mice (15 nl/side). For CRACM electrophysiology experiments, Cre-dependent AAV8-hSyn-DIO-mCherry (Addgene: 50459) was injected into the PVH (50 nl/side) and Flp-dependent AAV5-EF1a-fDIO-ChR2-eYFP (UNC viral vector core: 172055) was injected into the ARC (200 nl/side) of Brs3 -IRES-Cre:: Npy -IRES-Flp mice. In vivo optogenetic terminal stimulation experiments were done by injecting Cre-dependent AAV9-EF1a-DIO-ChR2-eYFP (Addgene: 20298) or AAV9-EF1a-DIO-ChR2-mCherry (Addgene: 20297) into the PVH (15 nl/side) of Brs3 -IRES-Cre mice. Control virus for the optogenetic terminal stimulation experiments was Cre-dependent AAV8-hSyn-DIO-mCherry. Of note, one round of snRNA-seq with 10X Chromium was done by injecting the PVH (50 nl/side) of Sim1 -Cre mice with AAVDJ-hSyn-H2B-mCherry (Boston Children’s Hospital Viral Core) 119 and collecting mCherry-positive nuclei 117,118 . Also, the male mouse used for MERFISH Slide 3 was injected with AAVrg-hSyn-mCherry into the spinal cord (200 nl/side), and the male mouse used for MERFISH Slide 6 was injected with AAVrg-hSyn-mCherry into the parabrachial region (50 nl/side). After stereotactic injections, experiments were initiated three weeks post-surgery for all AAVs to allow for suitable expression levels. FG and CTB were injected 3–7 days before sacrifice to enable retrograde transport. All stereotaxic injection sites were validated by post hoc immunofluorescence. All “misses” or "partial" hits, as determined by fluorescent expression in the target cells, were excluded from data analysis. RNAscope fluorescent in situ hybridization and immunofluorescence : RNAscope Multiplex Fluorescent Reagent Kit V2 (Advanced Cell Diagnostics: 323100) was used to perform in situ hybridization of mRNA in the PVH. For neuroendocrine PVH neuron labeling paired with FISH, adult mice aged 8-12 weeks were injected intraperitoneally with Fluoro-Gold (Fluorochrome; 30 mg/kg) one week prior to lethal injection of ketamine/xylazine (150mg/kg ketamine + 15 mg/kg xylazine) and transcardial perfusion with RNase-free PBS and 10% phosphate-buffered formalin (Fisher: SF100-20). Brains were then extracted and post-fixed in 10% phosphate-buffered formalin overnight, followed by consecutive overnight incubations in 10%, 20%, and 30% RNase-free sucrose solution in PBS. Coronal brain sections were then sliced at 30 µm using a freezing microtome, briefly washed in RNase-free 0.5% Triton X-100 (Sigma Aldrich) in PBS, mounted onto Superfrost™ Plus slides, and stored at -80˚C until ready for FISH. RNAscope was completed according to the manufacturer’s protocol. First, the slides were removed from the freezer and washed with sterile PBS, followed by a 30-minute incubation at 60°C. The slides were then fixed again with 10% phosphate-buffered formalin for 15 minutes at 4°C, followed by dehydration in 50%, 70%, and 100% ethanol solutions. Hydrogen Peroxide was then added to slides for 10 minutes at room temperature. After washing twice with PBS, a hydrophobic barrier surrounding the tissue sections was drawn on the slide (ImmEdge™: H-4000), and the slide was treated with Protease III for 30 minutes. Slides were next hybridized with RNAscope probes targeting mRNA for genes of interest for two hours at 40°C, including Aox3 (Mm-Aox3: 836451-C1), Avp (Mm-Avp: 401391-C3), Brs3 (Mm-Brs3: 454111-C1 or C3), Col12a1 (Mm-Col12a1: 312631-C2), Crh (Mm-Crh: 316091-C1), Esr2 (Mm-Esr2: 316121-C3), Nfix (Mm-Nfix: 522331-C2), Npr3 (Mm-Npr3: 502991-C2), Npsr1 (Mm-Npsr1: 317501-C1), Oxt (Mm-Oxt: 493171-C2), Pla2r1 (Mm-Pla2r1-No-XHs: 854581-C1), Rxfp3 (Mm-Rxfp3: 439381-C1), Scgn (Mm-Scgn: 482721-C2), Sim2 (Mm-Sim2: 1108911-C1), Sst (Mm-Sst: 404631-C1), or Trh (Mm-Trh, 436811-C1).After hybridization, slides underwent three amplification steps at 40°C (AMP1-FL and AMP2-FL for 30 minutes each, AMP3-FL for 15 minutes), followed by probe-specific HRP amplification and Opal dye (Akoya Biosciences) incubations at 40°C for visualization. After the Opal dye step, HRP blocker was applied, and this process was repeated until all probes were developed. After completing RNAscope slides were washed three times with PBS and incubated overnight at 4°C with primary antibody prepared in blocking solution made with PBS, 0.4% Triton X-100, and 3% normal donkey serum. The primary antibodies used include rabbit anti-Fluoro-Gold (1:300; Fluorochrome), goat anti-cholera toxin subunit B (1:300: List Biological Laboratories: 703), and rabbit anti-GFP (1:1,000; Thermo Fisher Scientific: A-11122). The next day, slides were washed five times with PBS, and incubated for two hours at room temperature in the appropriate Alex Fluor-conjugated donkey secondary antibody (1:1,000; Thermo Fisher Scientific) prepared in blocking solution. Finally, slides were washed again three times with PBS before coverslipping with VECTASHIELD mounting media with DAPI (Vector Laboratories: H-1900-10). Slides were imaged at 10X magnification with an Olympus Slideview VS200 slide-scanning microscope or at 20X magnification with a Leica Stellaris 5 confocal microscope. Quantification of PVH neuroendocrine neurons : For each neuroendocrine subtype, 12 images (for Crh , Trh and Sst ) and 8 images (for Avp and Oxt ) covering rostral to caudal PVH, were exported using QuPath 129 from the RNAscope and ip Fluoro-Gold labeling experiments ( Fig. 1k-p ), which consisted of three channels: Fluoro-Gold, the neuroendocrine hormone of interest, and the novel marker gene for the corresponding neuroendocrine subtype identified by sc/snRNA-seq. Neuroendocrine peptide gene-positive cells were identified using the Cellpose2 model (“cyto2”), with manual adjustments made for any misidentified or missed cells. The selected cell masks were saved and imported into Fiji (ImageJ) 130 , where the multi-point tool further facilitated counting of neurons expressing neuroendocrine marker gene pairs ( Crh-Scgn , Trh-Nfix , Sst-Col12a1 , Avp-Pla2r1 , and Oxt-Rxfp3 ) and whether they were labeled by Fluoro-Gold. The percentage of FG-positive neurons for each neuroendocrine marker gene pair was then calculated. The same method is applied to count FG-negative neurons that expressed neuroendocrine peptide genes (FG-negative Crh , Trh , Sst , Avp and Oxt ) and whether they co-expressed the associated neuroendocrine marker gene identified by sc/snRNA-seq. Histological analysis of Cre -reporters and immunofluorescent experiments: At the conclusion of experiments involving Cre-reporter expression, retrograde tracer injections, and AAV injections, brain/spinal cord histology was performed. For Cre-reporter histology, R26-LSL-EGFP-L10a reporter mice were crossed with Slc17a6 (VGLUT2)-IRES-Cre, Slc32a1 (VGAT)-IRES-Cre, and Sim1 -Cre mice. Adult mice were lethally anesthetized and transcardially perfused as above. Brains were then extracted and postfixed overnight in 10% phosphate-buffered formalin. Brains were then sliced coronally at 40 µm and mounted directly onto glass slides. For experiments requiring immunofluorescence, floating sections were washed in PBS prior to incubation overnight at room temperature in primary antibody solution as described above. All primary antibodies used are described above, except rat anti-mCherry (1:3,000; Thermo Fisher Scientific: M11217). The next day, sections were washed and incubated with Alex Fluor-conjugated donkey secondary antibody as above. Subsequently, tissue was washed, mounted onto slides, and coverslipped with VECTASHIELD mounting media with DAPI. Slides were imaged at 10X magnification with an Olympus Slideview VS200 slide-scanning microscope. Body weight measurements after PVH Brs3 neuron silencing: To begin bodyweight studies, initial body weights were recorded for littermate Brs3 -IRES-Cre and wild-type mice, and mice were then divided into the stereotactic surgery groups described above (AAVDJ-hSyn-DIO-EGFP-TeTxLC or AAV8-hSyn-DIO-EGFP). Subsequently, mice remained group-housed for the duration of the experiment. Body weights were recorded during the light cycle between 10:00 AM and 12:00 PM every 7 days for a total of 6 weeks. At the end of the study, mice were transcardially perfused as above for histological analysis of AAV expression in the PVH. Mice without bilateral expression of GFP were removed from the analysis. Channelrhodopsin- 2 (ChR2)-assisted circuit mapping (CRACM): Brs3 -IRES-Cre:: Npy -IRES-Flp mice underwent stereotactic surgery at 5-7 weeks old as described above, and CRACM experiments were completed at 8-10 weeks-old as described previously 131 . Briefly, mice were anesthetized with isoflurane, decapitated, and brains were rapidly extracted and submerged in ice-cold choline-based cutting solution saturated with carbogen (95% O 2, 5% CO 2 ). For slice preparation, brains were sliced at 300 µM coronally with a vibrotome (Campden 7000smz-2) and kept in cutting solution at 34°C for 10 min. Next, slices were transferred to artificial cerebrospinal fluid (aCSF) for at least 45 min at room temperature. After recovery, an individual coronal slice containing the PVH region was placed in a recording chamber where it was continuously superfused with aCSF and viewed under a microscope (SliceScope Pro 1000, Scientifica). PVH Brs3 neurons were fluorescently labeled by Cre-dependent AAV-mCherry, and Flp-dependent AAV-ChR2-eYFP drove ChR2 expression in ARC Npy/Agrp neurons. Open-tip resistances for patch pipettes were 3-5 MW and were backfilled with CsCl internal solution. To assess connectivity between ARC Npy/Agrp à PVH Brs3 neurons, whole-cell voltage clamp recordings from PVH Brs3 neurons were done while photostimulating ChR2-expressing terminals from ARC Npy/Agrp neurons. To evoke IPSCs with light, four 470 nm light pulses of 2 ms duration were administered one second apart during the first four seconds of a ten second protocol that was repeated 30 times. Blue light was applied via wide-field exposure through the 40X objective with an LED (Cool LED pE-100). The light output was controlled by a programmable pulse stimulator (Master 8, A.M.P.I.) and pClamp 10.5 software (Axon Instruments). Light-evoked IPSCs were isolated via glutamate receptor antagonism with 1 mM kynurenate, and short latency (≤ 6 ms) responses upon light stimulation were considered to be light-driven. Food Intake measurements after PVH Brs3 neuron à PB optogenetic stimulation: We assayed dark-cycle food intake while optogenetically stimulating PVH Brs3 neuron projections to the parabrachial region. Brs3 -IRES-Cre mice underwent stereotactic surgery for AAV injections and optic fiber implants as described above. Prior to beginning optogenetics studies, mice were allowed to recover for at least three weeks and were acclimated to tethering to patch cords and single housing. On experimental days, patch cords were bilaterally attached to optic fibers over the PB two hours before the onset of dark, and food was removed. Food was returned at the onset of dark and intake was then measured every hour for the first three hours of the dark cycle. Trials consisted of a baseline light-off tests, followed by light-stimulation experimental trials on the following day. Photostimulation was delivered with square wave pulses of 473 nm blue light, delivered at ~8-10 mW of power measured at the fiber tip, with 20 Hz stimulation (10ms pulses; 2 seconds on, 3 seconds off). LabView software and a National Instruments NIDAQ board were used to control our stimulation protocol. Statistical analysis Statistical analyses for sc/snRNA-seq and MERFISH were performed in R, as described above. All other analyses were conducted using GraphPad Prism (v10.3.0), with the specific statistical tests for each experiment indicated in the figure legends. No statistical methods were used to predetermine sample size, and randomization and/or blinding were not applied for sc/snRNA-seq or MERFISH experiments. Randomization was applied for body weight and food intake experiments. For the body weight study, a two-tailed two-way repeated measures ANOVA with virus and time as factors was performed, followed by Tukey’s post hoc multiple comparisons test. For body weight gain measurements, a two-tailed one-way ANOVA followed by Tukey’s post hoc test was used. For the optogenetic feeding behavior assay, a two-tailed two-way repeated measures ANOVA with virus and laser as factors was performed, followed by Sidak’s post hoc multiple comparisons test. All results are presented as mean ± SEM. Statistical significance was defined as P < 0.05, with asterisks indicating significance levels: *P < 0.05, **P < 0.01, and ****P <0.0001. Declarations Materials availability This study did not generate any new and unique reagents. Data availability Mouse sc/snRNA-seq data from this study have been deposited into the NCBI Gene Expression Omnibus (GEO) with accession number GSE303256. Allen Brain Cell Atlas mouse sc/snRNA-seq data were downloaded from the following locations: https://allen-brain-cell-atlas.s3.us-west-2.amazonaws.com/index.html#expression_matrices/WMB-10Xv2/20230630/ (10Xv2), and https://allen-brain-cell-atlas.s3.us-west-2.amazonaws.com/index.html#expression_matrices/WMB-10Xv3/20230630/ (10Xv3). Spinal cord-projecting snRNA-seq data not generated in this study were obtained by accessing publicly available datasets within the GEO database: GSE247594 89 and GSE212409 90 . Human snRNA-seq data were obtained by downloading publicly available datasets, including the Siletti et al. 75 dataset from the Human Brain Cell Atlas Repository (https://datasets.cellxgene.cziscience.com/5e399d37-23d3-4673-8761-9f443c1fdc14.rds) and the Tadross et al. 76 dataset from the University of Cambridge Apollo Repository (https://www.repository.cam.ac.uk/items/cad1c61a-e4e5-4443-ad11-92e4f48b3861). Mouse MERFISH data from this study have been deposited in the Iowa Research Online (IRO) repository with record accession number 9984403060302771. All Seurat objects have been deposited to Zenodo at: https://zenodo.org/records/15983704. Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request. Code availability All custom code has been deposited to Zenodo at: https://zenodo.org/records/15983704. Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request. Acknowledgements We would like to thank Drs. Mark Andermann, Joel Geerling, and Clifford Saper as well as the Lowell, Tsai, and Resch laboratories for helpful discussions; Alysia Berns, Jia Yu, and Yanfang Li for technical support; The BNORC Functional Genomics and Bioinformatics Core (P30DK046200) and the Iowa Institute for Human Genetics Genomics Division (IIHG, RRID: SCR_023422) for helpful discussions and technical assistance with sc/snRNA-seq; Zachary Niziolek and the Bauer Core Facility at Harvard University, the BIDMC Flow Cytometry Core, and Heath Vignes, Michael Shey, and Thomas Kaufman of the Flow Cytometry Facility at the University of Iowa Carver College of Medicine for helpful discussions and technical support; The ICCB-Longwood Screening Facility of Harvard Medical School for assistance with the snRNA-seq experiments; Dr. Sayak Mitter and Vizgen support for technical assistance with the MERSCOPE platform; Mara Jendro and Li-Chun (Queena) Lin for their assistance with MERSCOPE experiments within the Iowa NeuroBank Core in the Iowa Neuroscience Institute at the University of Iowa Carver College of Medicine. This research was funded by the following NIH grants to L.T.T.: R01DK128406; to B.B.L.: R01DK075632, R01DK134427, and R01DK096010; to J.M.R.: R00HL144923; to M.C.M.: F31HL170784; and T.C.B. and M.C.M. were supported by a pharmacological sciences predoctoral training grant T32GM144636. Additional funding to J.M.R. came from the American Heart Association (AHA 935362) and from a University of Iowa Fraternal Order of Eagles Diabetes Research Center Pilot and Feasibility Catalyst Grant. Y.L. was supported by a predoctoral fellowship from the American Heart Association (AHA 25PRE1372983). A.M.D. was supported by a postdoctoral fellowship from the Charles A. King Trust. Author contributions Conceptualization, Y.L., T.C.B., J.N.C., L.T.T., B.B.L., and J.M.R. Data curation, Y.L., T.C.B., C.L.J, L.T.T., and J.M.R. Formal analysis, T.C.B., S.N., C.L.J., S.J.W., J.N.C., and L.T.T. (sc/snRNA-seq); Y.L., T.C.B., S.N., and J.M.R. (MERFISH). Funding acquisition, Y.L., L.T.T., B.B.L., and J.M.R. Investigation, T.C.B., S.N., L.W., D.P., A.W., H.S., J.N.C., L.T.T. (sc/snRNA-seq); Y.L., and J.M.R. (MERFISH); Y.L., M.C.M., J.T., and E.D.L. (FISH/IF); A.M.D., J.C.M., Z.Y., and J.M.R (electrophysiology and behavioral experiments). Methodology, Y.L., T.C.B., S.N., C.L.J., J.N.C., L.T.T., B.B.L., and J.M.R. Project administration, L.T.T., B.B.L., and J.M.R. Resources, L.T.T., B.B.L., and J.M.R. Software, Y.L., T.C.B., S.N., C.L.J., L.T.T., and J.M.R. Supervision, L.T.T., B.B.L., and J.M.R. Validation, T.C.B., S.N., C.L.J., S.J.W., J.N.C., and L.T.T. (sc/snRNA-seq); Y.L., T.C.B., S.N., and J.M.R. (MERFISH). Visualization, Y.L., T.C.B., and J.M.R. Writing – original draft , Y.L., T.C.B., L.T.T., B.B.L., and J.M.R. All authors have reviewed and approved the final version of the manuscript. Competing interests The authors declare no competing interests. References Biag, J., Huang, Y., Gou, L., Hintiryan, H., Askarinam, A., Hahn, J.D., Toga, A.W., and Dong, H.W. (2012). 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Nature 620 , 154-162. 10.1038/s41586-023-06358-0. Additional Declarations There is NO Competing Interest. Supplementary Files Lietal.Reportingsummary.pdf Reporting summary SupplementaryDataTables.xlsx Supplementary Tables EDFig1.jpg EDFig2.jpg EDFig3.jpg EDFig4.jpg EDFig5.jpg EDFig6.jpg EDFig7.jpg EDFig8.jpg EDFig9.jpg EDFig10.jpg Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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05:03:28","extension":"jpg","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":714659,"visible":true,"origin":"","legend":"","description":"","filename":"EDFig9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7895391/v1/bf0a937629bc78db5d092fbf.jpg"},{"id":93989106,"identity":"a0a20e50-acba-46c6-a752-3ec9f1c8fca7","added_by":"auto","created_at":"2025-10-21 05:03:28","extension":"jpg","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":1468378,"visible":true,"origin":"","legend":"","description":"","filename":"EDFig10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7895391/v1/4184dfaccd8e05e40714cc33.jpg"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"A spatial and projection-based transcriptomic atlas of paraventricular hypothalamic cell types","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe paraventricular hypothalamus (PVH) is among the most functionally diverse and anatomically complex regions of the brain. Essential for maintaining homeostasis, the PVH integrates information about the internal state and external environment, and accordingly adapts endocrine, autonomic, and behavioral outputs\u003csup\u003e1-3\u003c/sup\u003e. PVH neurons are typically classified based on cytoarchitectural subdivisions, projections, and neuroendocrine hormone expression\u003csup\u003e1-9\u003c/sup\u003e. PVH parvicellular neuron projections to the median eminence release hormones into the hypophyseal portal system that then cause release of anterior pituitary hormones to regulate the stress response, thyroid function, and growth, whereas PVH magnocellular neuron projections to the posterior pituitary release vasopressin and oxytocin directly into the systemic circulation\u003csup\u003e1,3\u003c/sup\u003e. Centrally-projecting PVH neurons, on the other hand, are a highly heterogeneous and poorly defined class of PVH neurons innervating regions of the hypothalamus, midbrain, hindbrain, and spinal cord to mediate autonomic and behavioral responses\u003csup\u003e10\u003c/sup\u003e. Despite their importance, the molecular and functional diversity of centrally-projecting PVH neurons remains unresolved.\u003c/p\u003e\n\u003cp\u003eAmong their many functions, the centrally-projecting PVH neurons are well-known for regulating energy balance. PVH neurons that express the melanocortin 4 receptor (\u003cem\u003eMc4r\u003c/em\u003e) are crucial for body weight control as their activation reduces food intake, while loss of function causes hyperphagia and obesity\u003csup\u003e11-15\u003c/sup\u003e. Notably, several other PVH neurons have been reported to decrease food intake\u003cem\u003e\u003csup\u003e15-21\u003c/sup\u003e\u003c/em\u003e, including prodynorphin (\u003cem\u003ePdyn\u003c/em\u003e)-expressing neurons, which, like PVH\u003cem\u003e\u003csup\u003eMc4r\u003c/sup\u003e\u003c/em\u003e neurons, regulate feeding behavior via projections to the parabrachial region (PB). These two populations are distinct, however, because their simultaneous inhibition causes additive effects on hyperphagia and obesity\u003csup\u003e12,21\u003c/sup\u003e. In contrast,\u0026nbsp;the PVH oppositely regulates feeding behavior via neurons expressing thyrotropin-releasing hormone (\u003cem\u003eTrh\u003c/em\u003e) and pituitary adenylate cyclase-activating peptide (\u003cem\u003eAdcyap1\u003c/em\u003e) that induce hunger through activation of agouti-related peptide (AgRP) neurons in the arcuate nucleus (ARC)\u003csup\u003e22\u003c/sup\u003e –\u0026nbsp;highlighting the complexity of appetite regulation by the PVH.\u0026nbsp;Besides appetite, the PVH also controls energy expenditure through nitric oxide synthase 1 (\u003cem\u003eNos1\u003c/em\u003e)\u003cem\u003e-\u003c/em\u003e and brain-derived neurotrophic factor (\u003cem\u003eBdnf\u003c/em\u003e)-expressing neuron projections to the spinal cord that drive sympathetic nervous system output to adipose tissue\u003csup\u003e19,23,24\u003c/sup\u003e. That said, because these previously described genetic markers are expressed across multiple PVH neuron subpopulations, the\u0026nbsp;exact transcriptional identity of energy balance-regulating neurons remains unclear, and the lack of precise markers limits our ability to study their regulation and function selectively.\u003c/p\u003e\n\u003cp\u003eRecent studies characterizing PVH neurons at the molecular level represent an important step towards understanding the diversity of cell types present\u003csup\u003e25-27\u003c/sup\u003e. However, the power of these studies has been limited by sample size and the inability to resolve their spatial organization. Moreover, large-scale single-cell and spatial transcriptomic studies of the entire mouse brain\u003csup\u003e28-31\u003c/sup\u003e or hypothalamus\u003csup\u003e32\u003c/sup\u003e lack detailed analysis of PVH neuron subtypes, leaving significant gaps in our understanding of the molecular heterogeneity of PVH neurons. To address these limitations, we employed single-cell/nucleus RNA sequencing (sc/snRNA-seq) and multiplexed error-robust fluorescence \u003cem\u003ein situ\u003c/em\u003e hybridization (MERFISH) to generate a comprehensive spatial transcriptomic atlas of the PVH at single-cell resolution. This approach uncovered novel marker genes for neuroendocrine populations and revealed molecular signatures of previously unidentified centrally-projecting PVH neurons. Further, we sequenced spinal cord- and PB-projecting PVH neurons to identify marker genes for neurons controlling sympathetic nervous system activity and feeding behavior, respectively. Leveraging this information, we show that stimulation of bombesin-like receptor 3 (\u003cem\u003eBrs3\u003c/em\u003e)-expressing PVH neuron projections to the PB reduces food intake, PVH\u003cem\u003e\u003csup\u003eBrs3\u003c/sup\u003e\u003c/em\u003e neurons are downstream of AgRP neurons, and their silencing promotes weight gain. This atlas serves as a foundational resource for understanding the molecular architecture of the PVH and lays the groundwork for future investigations into the functional roles of its diverse neuronal populations.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eMolecular profiling of the paraventricular hypothalamus.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo classify PVH cell types based on their genome-wide expression patterns, we performed single-cell RNA-seq using Drop-seq\u003csup\u003e33\u003c/sup\u003e, and single-nucleus RNA-seq using DroNc-seq\u003csup\u003e34\u003c/sup\u003e and the 10X Chromium platform on adult male and female mice (\u003cstrong\u003eExtended Data\u003c/strong\u003e \u003cstrong\u003eFig. 1a,b\u003c/strong\u003e). For each approach, we micro-dissected the PVH region from \u003cem\u003eSim1\u003c/em\u003e-Cre\u003csup\u003e14\u003c/sup\u003e::L10-GFP\u003csup\u003e22\u003c/sup\u003e or wild-type mice (\u003cstrong\u003eFig. 1a\u003c/strong\u003e). After droplet generation, library preparation, sequencing, data pre-processing, and quality control, downstream sc/snRNA-seq analyses were performed using Seurat version 5\u003csup\u003e35,36\u003c/sup\u003e, integrating by sequencing run (“batch”), to generate an atlas of 42,948 cells/nuclei from the PVH and immediately surrounding regions. Cell type clusters were visualized with uniform manifold approximation and projection (UMAP) and annotated using canonical cell type marker genes previously reported in the literature, revealing nearly 80% neurons, with the remaining cells forming distinct populations of non-neuronal/glial cells (\u003cstrong\u003eExtended Data\u003c/strong\u003e \u003cstrong\u003eFig. 1c-e; Supplementary Table 1\u003c/strong\u003e). We next examined the effects of sc/snRNA-seq technology and sex on cell clustering. While gene and cell type detection differed somewhat between the droplet-based sc/snRNA-seq methods, cells/nuclei from both sexes and all technologies were represented in all clusters (\u003cstrong\u003eExtended Data\u003c/strong\u003e \u003cstrong\u003eFig. 1f-i\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eTo gain specific insight into PVH neuron diversity, we next reclustered 33,644 neuronal cells/nuclei, which produced a UMAP with clusters predominantly segregated into inhibitory neurons expressing the vesicular GABA transporter (\u003cem\u003eSlc32a1\u003c/em\u003e; VGAT) and excitatory neurons expressing the vesicular glutamate transporter 2 (\u003cem\u003eSlc17a6\u003c/em\u003e; VGLUT2) (\u003cstrong\u003eExtended Data\u003c/strong\u003e \u003cstrong\u003eFig. 2a-d,g,h; Supplementary Table 2\u003c/strong\u003e). We also observed further segregation of excitatory neurons into those expressing the PVH marker gene \u003cem\u003eSim1\u0026nbsp;\u003c/em\u003eor the thalamic marker gene \u003cem\u003eTcf7l2\u003c/em\u003e (\u003cstrong\u003eExtended Data\u003c/strong\u003e \u003cstrong\u003eFig. 2b,e,f,I,j\u003c/strong\u003e). Histological assessment confirmed that \u003cem\u003eSlc32a1\u003c/em\u003e is expressed primarily in areas surrounding the PVH\u003csup\u003e37,38\u003c/sup\u003e, \u003cem\u003eSim1\u003c/em\u003e is predominantly expressed within the PVH\u003csup\u003e39\u003c/sup\u003e, and \u003cem\u003eTcf7l2\u003c/em\u003e expression is constrained to thalamus dorsal to the PVH\u003csup\u003e31,40\u003c/sup\u003e. We subsequently reclustered glutamatergic and GABAergic neurons separately, resulting in 22 excitatory clusters from 18,920 glutamatergic cells/nuclei (\u003cstrong\u003eExtended Data\u003c/strong\u003e \u003cstrong\u003eFig. 2k,l; Supplementary Table 3\u003c/strong\u003e) and 28 inhibitory populations from 13,075 GABAergic cells/nuclei surrounding the PVH (\u003cstrong\u003eExtended Data\u003c/strong\u003e \u003cstrong\u003eFig. 2m,n; Supplementary Table 4\u003c/strong\u003e). Finally, to specifically investigate PVH neuron gene expression profiles, we reclustered only neurons from \u003cem\u003eSim1\u003c/em\u003e-positive populations. At this point, we also sought to take advantage of publicly available data. To do so, we examined PVH-assigned cells from the “HypoMap” study, an integrated reference atlas of the entire mouse hypothalamus (\u003cstrong\u003eExtended Data\u003c/strong\u003e \u003cstrong\u003eFig. 3a-f; Supplementary Table 5\u003c/strong\u003e)\u003csup\u003e32\u003c/sup\u003e. However, after integrating 5,119 putative PVH neurons expressing \u003cem\u003eSim1\u003c/em\u003e from HypoMap with our study, we observed discrepancies between the data sets (\u003cstrong\u003eExtended Data\u003c/strong\u003e \u003cstrong\u003eFig. 3g-i; Supplementary Table 6\u003c/strong\u003e). Notably, seven \u003cem\u003eSim1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e clusters were comprised almost entirely of neurons from this study (\u003cstrong\u003eExtended Data\u003c/strong\u003e \u003cstrong\u003eFig. 3j\u003c/strong\u003e), and a large proportion of HypoMap neurons express markers for “peri-PVH” neurons, including \u003cem\u003eCabp7\u003c/em\u003e, \u003cem\u003eOnecut3\u003c/em\u003e\u003csup\u003e41\u003c/sup\u003e, and \u003cem\u003eGsc\u003c/em\u003e\u003cem\u003e\u003csup\u003e42\u003c/sup\u003e\u003c/em\u003e (\u003cstrong\u003eExtended Data\u003c/strong\u003e \u003cstrong\u003eFig. 3k\u003c/strong\u003e). These results suggest that there is inadequate representation of PVH neuron subtypes within the HypoMap study\u003csup\u003e32\u003c/sup\u003e. Thus, we instead integrated \u003cem\u003eSim1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e PVH neurons from the Allen Brain Cell (ABC) Atlas\u003csup\u003e29\u003c/sup\u003e, resulting in 9,301\u003cem\u003eSim1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e neurons from this study and 7,297 from the ABC Atlas. Analysis after integration identified 20 distinct clusters, each consisting of cells from both studies that we annotated based on the expression of one or more marker genes (\u003cstrong\u003eFig. 1b,c; Extended Data\u003c/strong\u003e \u003cstrong\u003eFig. 3l-n; Supplementary Table 7\u003c/strong\u003e). This final sc/snRNA-seq atlas, comprising 16,598 \u003cem\u003eSim1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e neurons, greatly surpasses the number of cells previously available from single-cell transcriptomic studies of the PVH, and also provides detailed molecular markers for PVH neuron populations.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUnique transcriptional profiles of PVH neuroendocrine populations revealed by sc/snRNA-seq\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe PVH is home to parvicellular and magnocellular neuroendocrine neurons that are defined by the synthesis and release of one of five well-known hormones, which include corticotropin-releasing hormone (\u003cem\u003eCrh\u003c/em\u003e), thyrotropin-releasing hormone (\u003cem\u003eTrh\u003c/em\u003e), somatostatin (\u003cem\u003eSst\u003c/em\u003e), arginine vasopressin (\u003cem\u003eAvp\u003c/em\u003e), and oxytocin (\u003cem\u003eOxt\u003c/em\u003e)\u003csup\u003e1\u003c/sup\u003e. In this study, we identified distinct \u003cem\u003eSim1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e neuronal clusters that are enriched for these genes annotated as Seq_S1.Crh-Scgn, Seq_S2.Trh-Satb2 , Seq_S3.Sst-Vgll3, Seq_S4.Sst-Rxfp2, Seq_S5.Oxt-Rxfp3, and Seq_S6.Avp-Pla2r1 (\u003cstrong\u003eFig.1d-i\u003c/strong\u003e), and hypothesized that these clusters represent the PVH neuroendocrine populations. However, since these pituitary-regulating hormone genes are expressed across multiple PVH neuron clusters, albeit at lower levels, we sought to confirm our neuroendocrine cluster classifications. To label median eminence- and posterior pituitary-projecting PVH neurons, C57BL/6J mice received intraperitoneal (ip) injections of the retrograde tracer Fluoro-Gold, which labels neurons that project outside the blood-brain barrier when administered systemically (\u003cstrong\u003eFig. 1j\u003c/strong\u003e)\u003csup\u003e1,43,44\u003c/sup\u003e. We then performed co-labeling studies for each putative neuroendocrine cluster using fluorescence \u003cem\u003ein situ\u003c/em\u003e hybridization (FISH) to demonstrate co-expression of neuroendocrine hormones with novel marker genes determined by sc/snRNA-seq, followed by immunofluorescence for Fluoro-Gold. Of note, sc/snRNAseq identified two putative PVH neuroendocrine populations that express \u003cem\u003eSst\u003c/em\u003e, Seq_S3.Sst-Vgll3 and Seq_S4.Sst-Rxfp2, the significance of which is unknown as each expresses the growth hormone receptor (\u003cem\u003eGhr\u003c/em\u003e), likely to facilitate negative feedback\u003csup\u003e45\u003c/sup\u003e. To assess the neuroendocrine identity of these PVH\u003cem\u003e\u003csup\u003eSst\u003c/sup\u003e\u003c/em\u003e neuron clusters, we performed FISH for \u003cem\u003eCol12a1,\u003c/em\u003e taking advantage of its enrichment in both clusters (\u003cstrong\u003eFig. 1d\u003c/strong\u003e). Other gene pairs tested were \u003cem\u003eCrh-Scgn\u003c/em\u003e, \u003cem\u003eTrh-Nfix\u003c/em\u003e, \u003cem\u003eOxt-Rxfp3\u003c/em\u003e, and \u003cem\u003eAvp-Pla2r1\u003c/em\u003e. In all cases, greater than 80% of neurons co-expressing a neuroendocrine peptide and its corresponding marker gene were also positive for Fluoro-Gold (\u003cstrong\u003eFig. 1k-p\u003c/strong\u003e). This is consistent with prior reports of \u003cem\u003eCrh\u003c/em\u003e and \u003cem\u003eScgn\u003c/em\u003e co-expression in neuroendocrine neurons controlling the hypothalamic-pituitary-adrenal axis\u003csup\u003e25,27\u003c/sup\u003e. Furthermore, Fluoro-Gold negative neurons expressing \u003cem\u003eCrh\u003c/em\u003e, \u003cem\u003eTrh\u003c/em\u003e, \u003cem\u003eSst\u003c/em\u003e, \u003cem\u003eAvp\u003c/em\u003e, and \u003cem\u003eOxt\u003c/em\u003e rarely co-expressed the corresponding neuroendocrine marker gene determined by sc/snRNA-seq (\u003cstrong\u003eFig. 1p\u003c/strong\u003e). These findings confirm our neuroendocrine classifications and demonstrate that the intersection of neuroendocrine marker gene pairs identified by sc/snRNA-seq enables approaches for gaining selective genetic access to pituitary-regulating PVH neuron populations.\u003c/p\u003e\n\u003cp\u003eGiven that neuroendocrine neurons share a common projection target and release large amounts of neuropeptide hormones into the circulation, we next assessed whether we could identify a shared transcriptional program that differentiates them from centrally-projecting PVH populations. Marker gene analysis revealed a sharp division in transcriptional profiles (\u003cstrong\u003eExtended Data\u003c/strong\u003e \u003cstrong\u003eFig. 4a,b; Supplmentary\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eTables 8,9)\u003c/strong\u003e, identifying\u0026nbsp;genes that distinguish\u0026nbsp;neuroendocrine populations (e.g., \u003cem\u003eCreb3l2\u003c/em\u003e;\u003cstrong\u003eExtended Data\u003c/strong\u003e \u003cstrong\u003eFig. 4c,e\u003c/strong\u003e) and centrally-projecting neurons (e.g., \u003cem\u003eNtng1\u003c/em\u003e; \u003cstrong\u003eExtended Data\u003c/strong\u003e \u003cstrong\u003eFig. 4d,e\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e. To further characterize these transcriptional differences, we performed Gene Ontology (GO) enrichment analysis on genes upregulated in PVH neuroendocrine and centrally-projecting populations (\u003cstrong\u003eExtended Data\u003c/strong\u003e \u003cstrong\u003eFig. 4f,g; Supplementary Tables 10,11\u003c/strong\u003e).\u0026nbsp;We found\u0026nbsp;neuroendocrine neurons are most significantly enriched for genes related to ribosomal function and translation, which may be crucial for the synthesis of large quantities of neuropeptides. In contrast, centrally-projecting neurons were strongly enriched for genes related to the formation and regulation of synapses. These findings suggest differences in the signaling machinery of neuroendocrine versus non-neuroendocrine neuron populations. Additional marker gene analysis comparing\u0026nbsp;median eminence-projecting and posterior pituitary-projecting neuroendocrine subtypes also demonstrated transcriptional differences, highlighting \u003cem\u003eAgtr1a\u003c/em\u003e as a marker for median eminence-projecting (parvicelluar) neurons and \u003cem\u003ePlekhg1\u003c/em\u003e as a marker for posterior pituitary-projecting (magnocellular) neurons (\u003cstrong\u003eExtended Data\u003c/strong\u003e \u003cstrong\u003eFig. 4h-l; Supplementary Tables 12,13\u003c/strong\u003e). GO enrichment analysis revealed that\u0026nbsp;the top pathways for median eminence-projecting populations are related to ion channel activity (\u003cstrong\u003eExtended Data\u003c/strong\u003e \u003cstrong\u003eFig. 4m; Supplementary Table 14\u003c/strong\u003e). Meanwhile, posterior pituitary-projecting populations again showed enrichment for ribosomal function and translation-related pathways, which likely are critical for supporting direct secretion of large quantities of AVP and OXT into the systemic circulation to regulate distant target organs (\u003cstrong\u003eExtended Data\u003c/strong\u003e \u003cstrong\u003eFig. 4n; Supplementary Table 15)\u003c/strong\u003e\u003csup\u003e1\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSpatial transcriptomic profiling of the PVH with MERFISH.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDroplet-based sc/snRNA-seq technologies are powerful tools for identifying and characterizing cell type diversity. However, they require tissue dissociation, preventing the retention of spatial information, and may fail to detect functionally important genes expressed at low levels. Therefore, we used MERSCOPE\u003csup\u003e29,46\u003c/sup\u003e, an imaging-based MERFISH platform capable of detecting low-abundance transcripts with single-molecule sensitivity\u003csup\u003e46-48\u003c/sup\u003e, to resolve the spatial organization of the PVH and surrounding regions. We assayed the spatial distribution of 503 genes specifically curated for the PVH region, comprised of top differentially expressed genes identified in our sc/snRNA-seq analyses, canonical marker genes for neuronal and glial populations, and functionally relevant genes selected from the literature (\u003cstrong\u003eSupplementary\u003c/strong\u003e \u003cstrong\u003eTables 16,17\u003c/strong\u003e). In total, we imaged 41 coronal sections across six mice. Brain sections were collected at intervals of approximately 100 μm along the rostral-caudal axis of the PVH, ranging from approximately 0.4 mm to 1.2 mm caudal to bregma according to the Franklin-Paxinos atlas\u003csup\u003e49\u003c/sup\u003e. After imaging, individual cells were segmented using Cellpose 2.0\u003csup\u003e50\u003c/sup\u003e and filtered to remove cells with low transcript counts (\u003cstrong\u003eExtended Data\u003c/strong\u003e \u003cstrong\u003eFig. 5a\u003c/strong\u003e). Then, for each coronal slice, we systematically defined the region of interest (ROI) covering the PVH and peri-PVH and subset the data to retain only cells within these regions (\u003cstrong\u003eFig. 2a; Supplementary Table 18\u003c/strong\u003e). After subsetting for the ROI, we were able to perform cell type clustering on 155,546 spatially resolved cells. Our initial all-cell MERFISH clustering comprised eight major cell types, approximately 65% of which were classified as neurons (\u003cstrong\u003eExtended Data\u003c/strong\u003e \u003cstrong\u003eFig. 5b-d; Supplementary Table 19\u003c/strong\u003e). Importantly, each MERFISH slide contributed proportionally to all major cell type clusters, with no sex-dependent batch effects on clustering observed after data integration, demonstrating the technical replicability of the MERFISH assay across multiple trials (\u003cstrong\u003eExtended Data\u003c/strong\u003e \u003cstrong\u003eFig. 5e,f\u003c/strong\u003e). Importantly, plotting our MERFISH spatial data using polygons color-coded by major cell type, with neurons divided into excitatory and inhibitory populations, recapitulates the known cellular organization in this region of the hypothalamus (\u003cstrong\u003eExtended Data\u003c/strong\u003e \u003cstrong\u003eFig. 5g,h\u003c/strong\u003e). Specifically, polygons of excitatory neurons are organized in the distinct triangular distribution of the PVH, while inhibitory neurons surround the PVH. Non-neuronal cell types do not show a particular spatial organization, except for a distinct layer of polygons classified as ependymal cells that line the third ventricle and enrichment of oligodendrocytes in the fornix.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFollowing initial all-cell MERFISH analysis, we performed subclustering of excitatory (\u003cem\u003eSlc17a6\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e) and inhibitory (\u003cem\u003eSlc32a1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e) neurons as we did for sc/snRNA-seq data. Excitatory neurons were further divided based on \u003cem\u003eSim1\u003c/em\u003e expression, and the three major neuron types, \u003cem\u003eSlc17a6\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e/\u003cem\u003eSim1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e, \u003cem\u003eSlc17a6\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e/\u003cem\u003eSim1\u003c/em\u003e\u003csup\u003e-\u003c/sup\u003e (\u003cstrong\u003eExtended Data\u003c/strong\u003e \u003cstrong\u003eFig. 5i,j; Supplementary Table 20\u003c/strong\u003e), and \u003cem\u003eSlc32a1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e, were reclustered. To characterize the anatomical location of MERFISH cell types, we next performed spatial domain analysis on all neuron subpopulations using the SpaDo package in R\u003csup\u003e51\u003c/sup\u003e. This computational method integrates gene expression and spatial proximity information from multiple slices, allowing for unbiased anatomical categorization of neurons, which can be used to link the molecular profiles from MERFISH cell types to previously described neuroanatomical PVH subdivisions\u003csup\u003e1\u003c/sup\u003e. Twenty-nine domains were identified distributed across “Rostral” (R1-R11; -0.4 to -0.6 mm from bregma), “Intermediate” (M1-M9; -0.7 to -0.9 mm from bregma), and “Caudal” (C1-C9; -1.0 to -1.2 mm from bregma) regions (\u003cstrong\u003eExtended Data\u003c/strong\u003e \u003cstrong\u003eFig. 6a,b; Supplementary Table 21\u003c/strong\u003e). Finally, the majority of spatial domains show neuron subtype enrichment, with domains R4, R5, M1, M2, M9, C4, and C7 primarily encompassing \u003cem\u003eSlc17a6\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e/\u003cem\u003eSim1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e neurons (\u003cstrong\u003eExtended Data\u003c/strong\u003e \u003cstrong\u003eFig. 6c; Supplementary Table 22\u003c/strong\u003e). \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSpatial distribution of \u003cem\u003eSim1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e MERFISH clusters.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMERFISH cell clustering of 24,132 \u003cem\u003eSim1\u003c/em\u003e-expressing neurons resulted in the identification of 26 glutamatergic (\u003cem\u003eSlc17a6\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e) clusters that we annotated according to the expression of one or more marker genes (\u003cstrong\u003eFig. 2b,c; Supplementary Table 23\u003c/strong\u003e). Importantly, plotting \u003cem\u003eSim1\u003c/em\u003e expression and \u003cem\u003eSim1\u003c/em\u003e\u003csup\u003e+\u0026nbsp;\u003c/sup\u003eMERFISH clusters confirms the expected spatial enrichment within the PVH (\u003cstrong\u003eFig.2d; Extended Data\u003c/strong\u003e \u003cstrong\u003eFig. 6d\u003c/strong\u003e)\u003csup\u003e31,39\u003c/sup\u003e. Next, we performed canonical correlation analysis (CCA) to examine the transcriptional similarity between MERFISH-defined and sc/snRNA-seq-defined \u003cem\u003eSim1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e clusters\u003csup\u003e36\u003c/sup\u003e. CCA identified strong correspondence between cells belonging to MERFISH \u003cem\u003eSim1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e clusters and those from \u003cem\u003eSim1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e sc/snRNA-seq (\u003cstrong\u003eFig. 2e; Supplementary Table 24\u003c/strong\u003e). There are, however, a few instances where multiple MERFISH \u003cem\u003eSim1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e clusters map to a single sc/snRNA-seq cluster. For example, all MERFISH clusters enriched for \u003cem\u003eOnecut3\u003c/em\u003e, including MF_S17.Onecut3-Frem3, MF_S18.Onecut3-Pvalb, and MF_S19.Onecut3-Hmcn1 (\u003cstrong\u003eExtended Data\u003c/strong\u003e \u003cstrong\u003eFig. 6e\u003c/strong\u003e), map to the Seq_S15.Onecut3 cluster. We hypothesize that this is due to the improved gene detection with MERFISH, which increased our resolution of neurons enriched for \u003cem\u003eOnecut3\u003c/em\u003e expression and produced multiple clusters upon analysis. Overall, there is a general correspondence between \u003cem\u003eSim1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e MERFISH and sc/snRNA-seq clusters, enabling the inference of genome-wide expression levels for spatially-resolved neuron populations in the PVH region. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe next evaluated the spatial location of \u003cem\u003eSim1\u003c/em\u003e\u003csup\u003e+\u0026nbsp;\u003c/sup\u003eclusters from rostral to caudal (\u003cstrong\u003eFig. 2f\u003c/strong\u003e). Using the multi-slice spatial domain analysis performed on all neurons above (\u003cstrong\u003eExtended Data Fig. 6a-c\u003c/strong\u003e), we delineated PVH and “peri-PVH” \u003cem\u003eSim1\u003c/em\u003e\u003csup\u003e+\u0026nbsp;\u003c/sup\u003eneuron compartments (\u003cstrong\u003eFig. 3a-b; Extended Data\u003c/strong\u003e \u003cstrong\u003eFig. 8a,c\u003c/strong\u003e), and PVH neurons were further partitioned into “Rostral,” “Rostal-Intermediate,” Caudal-Intermediate,” and “Caudal” spatial groups (\u003cstrong\u003eFig. 3c-j\u003c/strong\u003e). The Rostral PVH clusters include MF_S3.Sst-Rxfp2, MF_S4.Sst-Vgll3, MF_S10.Npy2r-Tll2, and MF_S15.Sim2-Crhr2, which are primarily located in spatial domains R1 and R5, approximating respectively, the anterior (PVa) and anterior periventricular (PVHpv) parts of the PVH (\u003cstrong\u003eFig. 3a-c,g\u003c/strong\u003e)\u003csup\u003e1\u003c/sup\u003e. As expected, \u003cem\u003eSst\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e neurons are concentrated in the PVHpv, while MF_S10.Npy2r-Tll2, and MF_S15.Sim2-Crhr2 are located in the PVa. Of interest, single-minded 2 (\u003cem\u003eSim2\u003c/em\u003e), a homolog of \u003cem\u003eSim1\u003c/em\u003e, marks the MF_S15.Sim2-Crhr2 cluster (\u003cstrong\u003eExtended Data\u003c/strong\u003e \u003cstrong\u003eFig. 7a,b,d\u003c/strong\u003e\u003cem\u003e).\u003c/em\u003e While \u003cem\u003eSim1\u003c/em\u003e expression is required for the development of the PVH\u003csup\u003e23\u003c/sup\u003e, disruption of \u003cem\u003eSim2\u003c/em\u003e expression causes reductions in the density of\u003cem\u003e\u0026nbsp;Trh\u003csup\u003e+\u003c/sup\u003e\u003c/em\u003e and \u003cem\u003eSst\u003csup\u003e+\u003c/sup\u003e\u003c/em\u003e neurons\u003csup\u003e52\u003c/sup\u003e. \u003cem\u003eSim2\u003c/em\u003e is primarily expressed by two distinct \u003cem\u003eSim1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e clusters, one of which is the aforementioned MF_S15.Sim2-Crhr2 cluster that also expresses corticotropin-releasing hormone receptor 2 (\u003cem\u003eCrhr2\u003c/em\u003e) (\u003cstrong\u003eExtended Data\u003c/strong\u003e \u003cstrong\u003eFig. 7c-e\u003c/strong\u003e). The other is MF_S18.Onecut3-Pvalb, which is located in the caudal ventrolateral Peri-PVH region (\u003cstrong\u003eExtended Data Fig. 7d; Extended Data\u003c/strong\u003e \u003cstrong\u003eFig. 8b\u003c/strong\u003e). Notably, PVH\u003cem\u003e\u003csup\u003eSim2\u003c/sup\u003e\u003c/em\u003e neurons are not labeled by systemic Fluoro-Gold injection and the MF_S15.Sim2-Crhr2 cluster expresses both \u003cem\u003eTrh\u003c/em\u003e and \u003cem\u003eAdcyap1\u003c/em\u003e (\u003cstrong\u003eExtended Data\u003c/strong\u003e \u003cstrong\u003eFig. 7d,f\u003c/strong\u003e), suggesting that they are the previously described excitatory afferents to ARC\u003cem\u003e\u003csup\u003eAgrp\u003c/sup\u003e\u003c/em\u003e neurons that drive feeding\u003csup\u003e22,53\u003c/sup\u003e. MF_S15.Sim2-Crhr2 neurons are also enriched for known drivers of synaptic plasticity, including \u003cem\u003eBdnf\u0026nbsp;\u003c/em\u003e\u003csup\u003e54\u003c/sup\u003e and cerebellin-2 (\u003cem\u003eCbln2\u003c/em\u003e; \u003cstrong\u003eExtended Data Fig. 7d\u003c/strong\u003e)\u003csup\u003e55,56\u003c/sup\u003e, which is consistent with increased excitatory synapses formed between the PVH and ARC\u003cem\u003e\u003csup\u003eAgrp\u003c/sup\u003e\u003c/em\u003e neurons after fasting\u003csup\u003e53,57\u003c/sup\u003e. Indeed, our recent study has revealed that PVH\u003cem\u003e\u003csup\u003eSim2\u003c/sup\u003e\u003c/em\u003e neurons play an important role in hunger regulation\u003csup\u003e58\u003c/sup\u003e. On the other hand, the MF_S10.Npy2r-Tll2 cluster is marked by neuropeptide Y (NPY) Y2 receptor (\u003cem\u003eNpy2r\u003c/em\u003e) and tolloid-like protein 2 (\u003cem\u003eTll2\u003c/em\u003e) (\u003cstrong\u003eFig. 2c and Fig. 3c,g\u003c/strong\u003e), but does not express other NPY receptors. Given thatthe orexigenic effects of NPY in the PVH\u003csup\u003e59\u003c/sup\u003e are mediated by NPY1R and NPY5R\u003csup\u003e60\u003c/sup\u003e, we speculate MF_S10.Npy2r-Tll2 neurons may be modulated by caloric deficit, but do not regulate food intake.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe Rostral-Intermediate group consists of four MERFISH clusters that correspond to neuroendocrine populations (\u003cstrong\u003eFig. 2e\u003c/strong\u003e), MF_S1.Crh-Scgn, MF_S2.Trh-Satb2, MF_S5.Avp-Pla2r1, and MF_S6.Oxt-Rxfp3 (\u003cstrong\u003eFig. 3d,h\u003c/strong\u003e). All clusters are primarily located in spatial domain M2, but MF_S6.Oxt-Rxfp3 also has a substantial number of neurons located in spatial domain R5, corresponding to the anterior magnocellular part of the PVH (PVHam) (\u003cstrong\u003eFig. 3a,b\u003c/strong\u003e)\u003csup\u003e1\u003c/sup\u003e. Of note, spatial domain analysis did not differentiate parvicelluar and magnocelluar neuroendocrine subtypes previously defined in rats\u003csup\u003e2,7\u003c/sup\u003e. This may be because spatial domain analysis with SpaDo does not incorporate cytoarchitecture; however, parvicellular and magnocellular cells are also difficult to distinguish with Nissl staining alone in mouse\u003csup\u003e1\u003c/sup\u003e. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe Caudal-Intermediate PVH group is comprised of MF_S7.Esr2-Inhbb, MF_S8.Esr2-Ret, MF_S9.Npr3-Radx, MF_S13.Pde3a-Tmem215, and MF_S14.Brs3 clusters located primarily in spatial domain M9, which closely corresponds to the ventral zone of the medial parvicellular (PVHmpv) part of the PVH (\u003cstrong\u003eFig. 3a,b,e,i\u003c/strong\u003e)\u003csup\u003e1\u003c/sup\u003e. Many clusters in this group are marked by genes for hormone and neuropeptide receptors, such as estrogen receptor 2 (\u003cem\u003eEsr2\u003c/em\u003e) and natriuretic peptide receptor 3 (\u003cem\u003eNpr3\u003c/em\u003e), which have been reported to regulate stress responses and blood pressure\u003csup\u003e61-65\u003c/sup\u003e. \u003cem\u003eEsr2\u003c/em\u003e is enriched in two distinct clusters, MF_S7.Esr2-Inhbb and MF_S8.Esr2-Ret (\u003cstrong\u003eExtended Data\u003c/strong\u003e \u003cstrong\u003eFig. 7g-i)\u003c/strong\u003e, while \u003cem\u003eNpr3\u003c/em\u003e is primarily expressed by MF_S9.Npr3-Radx neurons located in the intermediate and caudal PVH, which exhibit minimal co-labeling with systemically injected Fluoro-Gold (\u003cstrong\u003eExtended Data\u003c/strong\u003e \u003cstrong\u003eFig. 7j-m\u003c/strong\u003e). Notably, MF_S14.Brs3 is marked by specific expression of bombesin-like receptor subtype 3 (\u003cem\u003eBrs3\u003c/em\u003e), an important gene for body weight regulation and metabolism (\u003cstrong\u003eFig. 2C and Fig. 3e,i\u003c/strong\u003e)\u003csup\u003e66\u003c/sup\u003e. Consistent with this, PVH\u003cem\u003e\u003csup\u003eBrs3\u003c/sup\u003e\u003c/em\u003e neurons exhibit increased Fos expression following refeeding\u003csup\u003e67,68\u003c/sup\u003e, and chemogenetic manipulation of their activity bidirectionally regulates food intake\u003csup\u003e67\u003c/sup\u003e, similar to PVH\u003cem\u003e\u003csup\u003eMc4r\u003c/sup\u003e\u003c/em\u003e and PVH\u003cem\u003e\u003csup\u003ePdyn\u003c/sup\u003e\u003c/em\u003e neurons. Thus, based on prior work, PVH\u003cem\u003e\u003csup\u003eBrs3\u003c/sup\u003e\u003c/em\u003e neurons are of interest for the future study of satiety regulation. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe Caudal PVH group comprises the MF_S11.Aox3, MF_S12.Grp, and MF_S26.Npnt clusters located in spatial domains C4 and C7, which are comparable to the lateral parvicellular (PVHlp) and forniceal (PVHf) parts of the PVH (\u003cstrong\u003eFig. 3a,b,f,j\u003c/strong\u003e). Of interest, MF_S12.Grp cluster is marked by specific expression of gastrin-releasing peptide (\u003cem\u003eGrp\u003c/em\u003e; \u003cstrong\u003eFig. 2c and Extended Data\u003c/strong\u003e \u003cstrong\u003eFig. 3f,j\u003c/strong\u003e), which is decreased in the PVH following fasting and increased by melanocortin signaling, raising the possibility that these neurons may regulate energy balance\u003csup\u003e69\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eFinally, there are 10 \u003cem\u003eSim1\u003csup\u003e+\u003c/sup\u003e\u003c/em\u003e neuronal clusters in the Peri-PVH group (\u003cstrong\u003eExtended Data\u003c/strong\u003e \u003cstrong\u003eFig. 8a-c\u003c/strong\u003e). While Peri-PVH clusters express \u003cem\u003eSim1\u003c/em\u003e, they are located adjacent to the PVH in separate spatial domains (R2, R4, R9, M1, M6, and C6) and have distinct transcriptional characteristics. With the exception of neurons expressing urocortin 3 (\u003cem\u003eUcn3\u003c/em\u003e), neuron subtypes in this region are largely of unknown function, and include MF_S16.Ucn3, MF_S17.Onecut3-Frem3, MF_S18.Onecut3-Pvalb, MF_S19.Onecut3-Hmcn1, MF_S20.Gsc-Serpinb1b, MF_S21.Gsc-Nms, MF_S22.Gsc-Nmbr, \u0026nbsp;MF_S23.Ebf2-Hpgd, MF_S24.Ebf2-Hmcn2, and MF_S25.Ebf2-Pou6f2. Consistent with our spatial characterization of these Peri-PVH groups (\u003cstrong\u003eExtended Data\u003c/strong\u003e \u003cstrong\u003eFig. 8a,b)\u003c/strong\u003e, previous studies have identified \u003cem\u003eOnecut3\u003c/em\u003e- and \u003cem\u003eGsc\u003c/em\u003e-expressing neurons to be located laterally and ventrally to the PVH\u003csup\u003e41,42\u003c/sup\u003e. Moreover, \u003cem\u003eUcn3\u003c/em\u003e-expressing neurons are a relatively small population of peri-PVH neurons that extend laterally from the PVH towards the fornix in spatial domain M6 (\u003cstrong\u003eExtended Data\u003c/strong\u003e \u003cstrong\u003eFig. 8a-c)\u003c/strong\u003e and are involved in stress and parenting behaviors\u003csup\u003e70,71\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTranscriptional similarity of mouse and human PVH neurons.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrevious characterization of PVH neuron populations in human samples has primarily focused on neuroendocrine subtypes\u003csup\u003e72-74\u003c/sup\u003e. To ascertain whether the PVH neuron populations identified in our transcriptomic study resemble those in the human PVH, we performed a comparative analysis between our mouse sc/snRNA-seq atlas and human brain snRNA-seq data. To achieve this, first, we retrieved all cells from dissections containing the PVH from two publicly available human studies\u003csup\u003e75,76\u003c/sup\u003e and clustered them using Seurat 5. Next, as we did for mouse sc/snRNA-seq clustering of PVH neurons, we subset the data to only include \u003cem\u003eSIM1\u003c/em\u003e\u003csup\u003e+\u0026nbsp;\u003c/sup\u003eclusters and reclustered the remaining 3,432 \u003cem\u003eSIM1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e nuclei, resulting in 21 distinct \u003cem\u003eSIM1\u003c/em\u003e\u003csup\u003e+\u0026nbsp;\u003c/sup\u003eneuronal clusters (\u003cstrong\u003eExtended Data\u003c/strong\u003e \u003cstrong\u003eFig. 8d,e; Supplementary Table 25\u003c/strong\u003e). To estimate the transcriptomic similarity between human and mouse PVH neurons, we performed CCA comparing \u003cem\u003eSim1\u003c/em\u003e/\u003cem\u003eSIM1\u003c/em\u003e-positive clusters, which also allowed us to provide the analogous mouse MERFISH cluster identifiers. Strikingly, we observed a high degree of transcriptional correlation across species, with notable similarity between humans and mice for neuroendocrine hormone-, \u003cem\u003eSim2-,\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;Ucn3\u003c/em\u003e-expressing neuron populations (\u003cstrong\u003eExtended Data\u003c/strong\u003e \u003cstrong\u003eFig. 8f; Supplementary Table 26\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMERFISH atlas of peri-PVH GABAergic neurons\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs noted above, the PVH is surrounded by GABAergic (\u003cem\u003eSlc32a1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e) neurons, some of which have been shown to project locally into the PVH\u003csup\u003e38\u003c/sup\u003e and are proposed to regulate the HPA axis\u003csup\u003e37,77\u003c/sup\u003e. Specific analysis of GABAergic MERFISH populations included 53,294 neurons that clustered into 29 distinct populations. We labeled each cluster according to the expression of one or more marker genes identified through differential gene expression analysis (\u003cstrong\u003eFig. 4a,b; Supplementary Table 27\u003c/strong\u003e). Next, we performed CCA between MERFISH and sc/snRNA-seq GABAergic neuron clusters to assess transcriptomic agreement between technologies, and this analysis demonstrated a high degree of similarity (\u003cstrong\u003eExtended Data\u003c/strong\u003e \u003cstrong\u003eFig. 9a; Supplementary Table 28\u003c/strong\u003e). Finally, we plotted the spatial distribution of the GABAergic MERFISH clusters along the rostral-to-caudal axis, grouping clusters according to spatial domains into “Rostral,” “Intermediate,” or “Caudal” categories (\u003cstrong\u003eFig. 4c-g;\u003c/strong\u003e \u003cstrong\u003eExtended Data\u003c/strong\u003e \u003cstrong\u003eFig. 9b\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRostral GABAergic neurons include MF_i1.Nms, MF_i4.Dach2, MF_i5. Fezf2, MF_i6.Eya1, MF_i8.Gldn, MF_i9.Piezo2, MF_i10.Egr3, MF_i12.Grp, MF_i14.Rfx4, MF_i15.Sntb1, MF_i16.Fshr, MF_i21.Pax6-Vgll3, and MF_i22.Pax6-Otx2 (\u003cstrong\u003eFig.\u003c/strong\u003e \u003cstrong\u003e4d-e\u003c/strong\u003e). Of these, MF_i1.Nms and MF_i12.Grp represent neurons located in the suprachiasmatic nucleus (SCN; \u003cstrong\u003eFig. 4e\u003c/strong\u003e).\u0026nbsp;Rostral GABAergic neurons also identify\u0026nbsp;subparaventricular zone (SPZ)\u0026nbsp;neuron populations that have been difficult to target previously.\u0026nbsp;Of interest, the SPZ is the major output of the SCN\u003csup\u003e78\u003c/sup\u003e, and SPZ clusters include MF_i5. Fezf2,\u0026nbsp;MF_i6.Eya1, and MF_i14.Rfx4. Intermediate GABAergic clusters include MF_i17.Ano1, MF_i18.Rxfp1, MF_i19.Gdnf, MF_i20.Ndnf, MF_i26.Pmfbp1-Prdm8, and MF_i27.Pmfbp1-Pde11a neurons residing ventral and lateral to the PVH in the anterior hypothalamic area (AHA), and the MF_i23.Pax6-Pdgfd cluster located dorsal to the PVH (\u003cstrong\u003eFig.\u003c/strong\u003e \u003cstrong\u003e4f\u003c/strong\u003e). Finally, the\u0026nbsp;Caudal GABAergic neuron subtypes include MF_i2.Corin and MF_i29.Th-Prph located in the periventricular hypothalamus, the latter\u0026nbsp;of which expresses \u003cem\u003eTh\u003c/em\u003e, \u003cem\u003eDdc\u003c/em\u003e, \u003cem\u003eSlc18a2\u003c/em\u003e, and \u003cem\u003eSlc6a3\u003c/em\u003e, suggesting they release dopamine in addition to GABA (\u003cstrong\u003eFig.\u003c/strong\u003e \u003cstrong\u003e4g\u003c/strong\u003e). Remaining Caudal clusters include\u0026nbsp;MF_i3.Otp, MF_i7.St18 , MF_i11.Ror1, MF_i13.Hcrtr2, MF_i24.Pmfbp1_Nostrin, MF_i25.Pmfbp1-Etv1, and \u0026nbsp;MF_i28.Th-Lhx8 clusters located in the posterior AHA (\u003cstrong\u003eFig.\u003c/strong\u003e \u003cstrong\u003e4g\u003c/strong\u003e).\u0026nbsp;Together, MERFISH analysis offers the first comprehensive molecular characterization of peri-PVH GABAergic neurons.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTargeted transcriptomic profiling of spinal cord-projecting PVH neurons.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNumerous studies have demonstrated that PVH neurons project to the spinal cord\u003csup\u003e2,4,5,8,9,19,79-84\u003c/sup\u003e, many of which are thought to activate sympathetic preganglionic neurons in the intermediolateral cell column to regulate cardiometabolic physiology\u003csup\u003e19,23,24,80,85-88\u003c/sup\u003e. Spinal cord-projecting PVH neurons have been sequenced previously\u003csup\u003e89,90\u003c/sup\u003e; however, prior studies did not profile PVH neurons that project to the thoracic spinal cord, where most sympathetic preganglionic neurons are located, and they did not provide molecular markers that differentiate spinal cord-projecting neurons from other PVH neuron subtypes. Therefore, we profiled PVH neurons that project to the thoracic spinal cord and mapped them onto our \u003cem\u003eSim1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e sc/snRNA-seq reference atlas. H2B-TRAP mice\u003csup\u003e91\u003c/sup\u003e were injected with retrograde AAV-Cre into the thoracic (~T2-T4) spinal cord to selectively label the nuclei of spinal cord-projecting PVH neurons with mCherry for subsequent fluorescence-activated nuclei sorting (FANS; \u003cstrong\u003eFig. 5a\u003c/strong\u003e). After sequencing and clustering, we merged the thoracic spinal cord-projecting \u003cem\u003eSim1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e neuron data with \u003cem\u003eSim1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e neurons present in previously published spinal cord-projecting datasets\u003csup\u003e89,90\u003c/sup\u003e. Subsequently, we classified the spinal cord-projecting cells based on our \u003cem\u003eSim1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e sc/snRNA-seq reference atlas and projected them onto the reference UMAP using the MapQuery function in Seurat 5. Results showed agreement across all studies, suggesting that spinal cord-projecting PVH neurons share transcriptional similarities regardless of the spinal level to which they project, with most clustering within one of three populations: Seq_S10_Npsr1-Npnt (13.6%), Seq_S11_Esr2-Abcc9 (35.1%), or Seq_S12_Npr3-Radx (45.4%) (\u003cstrong\u003eFig. 5b, Supplementary Table 29\u003c/strong\u003e). Based on \u003cem\u003eSim1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e MERFISH to sc/snRNA-seq CCA mapping, the corresponding MERFISH clusters for spinal cord-projecting populations are MF_S7.Esr2-Inhbb, MF_S8.Esr2-Ret, MF_S9.Npr3-Radx, and MF_S26.Npnt (\u003cstrong\u003eFig. 5c\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eTo confirm the molecular identity of spinal cord-projecting PVH neurons, we injected the retrograde tracer, Fluoro-Gold, into the thoracic spinal cord and subsequently performed FISH for \u003cem\u003eEsr2\u003c/em\u003e, \u003cem\u003eNpr3,\u0026nbsp;\u003c/em\u003eor Neuropeptide S receptor 1 (\u003cem\u003eNpsr1)\u003c/em\u003e (\u003cstrong\u003eFig. 5d,e\u003c/strong\u003e). Our histological analysis revealed colocalization of Fluoro-Gold with the mRNA of all three marker genes we assayed. Notably, the colocalization of \u003cem\u003eEsr2\u003c/em\u003e and \u003cem\u003eNpr3\u003c/em\u003e with Fluoro-Gold was predominantly observed in the intermediate and caudal regions of the PVH (\u003cstrong\u003eFig. 5f,g\u003c/strong\u003e), which is consistent with the spatial patterning of these genes identified by MERFISH (\u003cstrong\u003eExtended Data\u003c/strong\u003e \u003cstrong\u003eFig. 7i,l\u003c/strong\u003e). Likewise, a separate population of Fluoro-Gold-labeled neurons in the caudal PVH was also found to be positive for \u003cem\u003eNpsr1\u003c/em\u003e mRNA (\u003cstrong\u003eFig. 5h\u003c/strong\u003e), matching the pattern identified by MERFISH (\u003cstrong\u003eFig. 3j\u003c/strong\u003e). Together, these data support that there are three predominant and transcriptionally distinct spinal cord-projecting PVH neuron populations that are likely involved in sympathetic regulation. However, the functional role of each specific spinal cord-projecting PVH population is not known and is an important area of future study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDetection of satiety marker genes in \u003cem\u003eSim1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;neurons with MERFISH\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePVH regulation of feeding behavior has been studied extensively, yet the precise PVH neurons mediating satiety are still unknown. Further, several marker genes expressed by PVH neurons have been proposed to be involved in satiety regulation, but the relationship among these genes is unresolved. Given the limited number of centrally-projecting PVH neurons and the low expression of many satiety-associated genes, prior studies have lacked the sample size and/or sensitivity to reliably characterize the expression of satiety genes in different PVH neuron populations. Therefore, since MERFISH has increased sensitivity over droplet-based sc/snRNA-seq methods\u003csup\u003e48\u003c/sup\u003e, we examined our \u003cem\u003eSim1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e MERFISH atlas to assess the expression patterns of genes associated with satiety. To begin, we analyzed expression of \u003cem\u003eMc4r\u0026nbsp;\u003c/em\u003eas MC4R signaling in the PVH is necessary and sufficient for satiety and body weight regulation\u003csup\u003e12-15,21\u003c/sup\u003e. \u003cem\u003eMc4r\u003c/em\u003e is expressed by several \u003cem\u003eSim1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e neuron populations and highly correlated with expression of \u003cem\u003eNpy1r\u003c/em\u003e, as expected, given its role in feeding behavior (\u003cstrong\u003eFig. 6a-c\u003c/strong\u003e)\u003csup\u003e92-94\u003c/sup\u003e. Expression of \u003cem\u003eMc4r\u003c/em\u003e and \u003cem\u003eNpy1r\u003c/em\u003e is widespread throughout the PVH, with an enrichment in the Caudal-Intermediate region between bregma levels -0.7 mm to -1.0 mm (\u003cstrong\u003eFig. 6h,i\u003c/strong\u003e). Despite \u003cem\u003eMc4r\u003c/em\u003e being expressed by multiple PVH neuron subtypes, three clusters display the strongest enrichment, MF_S2.Trh-Satb2, MF_S11.Aox3, and MF_S14.Brs3 (\u003cstrong\u003eFig. 6a,d-g\u003c/strong\u003e). These marker genes, \u003cem\u003eSatb2\u003c/em\u003e, \u003cem\u003eAox3\u003c/em\u003e, and \u003cem\u003eBrs3\u003c/em\u003e, have limited spatial distributions, often enriched within areas of high \u003cem\u003eMc4r\u003c/em\u003e and \u003cem\u003eNpy1r\u003c/em\u003e expression (\u003cstrong\u003eFig. 6j-l\u003c/strong\u003e). MF_S2.Trh-Satb2 neurons have the highest expression of \u003cem\u003eMc4r\u003c/em\u003e and represent PVH\u003cem\u003e\u003csup\u003eTrh\u003c/sup\u003e\u003c/em\u003e neurons that project to the median eminence (\u003cstrong\u003eFig. 1c,d,f\u003c/strong\u003e) to control the hypothalamic-pituitary-thyroid axis, which is consistent with MC4R and NPYregulation of thyroid hormone release during fasting\u003csup\u003e95\u003c/sup\u003e. The next highest \u003cem\u003eMc4r\u003c/em\u003e-expressing clusters are MF_S11.Aox3 and MF_S14.Brs3, both of which project centrally as they are not labeled by systemic Fluoro-Gold injection (\u003cstrong\u003eExtended Data\u003c/strong\u003e \u003cstrong\u003eFig. 10a,b\u003c/strong\u003e). MF_S11.Aox3 represents a novel population of centrally-projecting PVH neurons with unknown function(s), while PVH\u003cem\u003e\u003csup\u003eBrs3\u003c/sup\u003e\u003c/em\u003e neurons regulate feeding behavior, as noted above\u003csup\u003e67\u003c/sup\u003e. In support of an interaction between \u003cem\u003eBrs3\u0026nbsp;\u003c/em\u003eand \u003cem\u003eMc4r\u003c/em\u003e, conditional knockout of \u003cem\u003eBrs3\u003c/em\u003e from \u003cem\u003eMc4r\u003c/em\u003e-expressing neurons produces obesity\u003csup\u003e96\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOther genes used to investigate PVH satiety-regulating populations, including \u003cem\u003eCalcr\u003c/em\u003e\u003csup\u003e16\u003c/sup\u003e, \u003cem\u003eGlp1r\u003c/em\u003e\u003csup\u003e15\u003c/sup\u003e, \u003cem\u003eIrs4\u003c/em\u003e\u003csup\u003e17\u003c/sup\u003e, \u003cem\u003eNtrk2\u003c/em\u003e\u003csup\u003e18\u003c/sup\u003e, \u003cem\u003eNos1\u003c/em\u003e\u003csup\u003e19\u003c/sup\u003e, \u003cem\u003eand Pdyn\u003c/em\u003e\u003csup\u003e21\u003c/sup\u003e, are expressed widely across different PVH neuron subtypes (\u003cstrong\u003eExtended Data\u003c/strong\u003e \u003cstrong\u003eFig. 10c)\u003c/strong\u003e. Among them, \u003cem\u003eCalcr\u003c/em\u003e and \u003cem\u003eGlp1r\u003c/em\u003e have the most restricted expression patterns but are expressed by neuroendocrine and centrally-projecting populations. With regard to identifying candidate PVH satiety neurons within our atlas, three clusters express the majority of the satiety genes above (i.e., \u003cem\u003eCalcr\u003c/em\u003e, \u0026nbsp;\u003cem\u003eGlp1r\u003c/em\u003e, \u003cem\u003eIrs4\u003c/em\u003e, \u003cem\u003eNtrk2\u003c/em\u003e, \u003cem\u003eNos1\u003c/em\u003e, and\u003cem\u003e\u0026nbsp;Pdyn\u003c/em\u003e), MF_S8.Esr2-Ret, MF_S13.Pde3a-Tmem215, and MF_S14.Brs3 (\u003cstrong\u003eExtended Data\u003c/strong\u003e \u003cstrong\u003eFig. 10c\u003c/strong\u003e). As noted before, MF_S14.Brs3 neurons are enriched for \u003cem\u003eMc4r\u003c/em\u003e expression and may represent \u003cem\u003eMc4r\u003c/em\u003e-expressing satiety neurons. MF_S8.Esr2-Ret and MF_S13.Pde3a-Tmem215 neurons, on the other hand, express little \u003cem\u003eMc4r\u003c/em\u003e but co-express \u003cem\u003eGlp1r\u003c/em\u003e and \u003cem\u003ePdyn\u0026nbsp;\u003c/em\u003e(\u003cstrong\u003eExtended Data\u003c/strong\u003e \u003cstrong\u003eFig. 10c-g\u003c/strong\u003e). Given that PVH\u003cem\u003e\u003csup\u003ePdyn\u003c/sup\u003e\u003c/em\u003e and PVH\u003cem\u003e\u003csup\u003eGlp1r\u003c/sup\u003e\u003c/em\u003e neurons are key regulators of satiety and body weight\u003csup\u003e15,21,97\u003c/sup\u003e, and PVH\u003cem\u003e\u003csup\u003eMc4r\u003c/sup\u003e\u003c/em\u003e and PVH\u003cem\u003e\u003csup\u003ePdyn\u003c/sup\u003e\u003c/em\u003e neurons are distinct satiety-regulating populations\u003csup\u003e21\u003c/sup\u003e, MF_S8.Esr2-Ret and MF_S13.Pde3a-Tmem215 neurons are candidates to be the \u003cem\u003ePdyn\u003c/em\u003e-expressing PVH satiety neurons.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eTargeted\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;transcriptomic profiling of PVH \u003cem\u003eSim1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;neurons that project to the parabrachial region.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePVH neurons promote satiety through direct excitatory projections to the PB. PVH\u003cem\u003e\u003csup\u003eMc4r\u003c/sup\u003e\u003c/em\u003e neurons elicit robust glutamatergic synaptic responses in downstream neurons located in the lateral parabrachial nucleus (LPBN)\u003csup\u003e12\u003c/sup\u003e, whereas PVH\u003cem\u003e\u003csup\u003ePdyn\u003c/sup\u003e\u003c/em\u003e neurons preferentially do so in neurons found in the nearby pre-locus coeruleus (pLC)\u003csup\u003e21,98\u003c/sup\u003e, despite each satiety population projecting to both regions. That said, \u003cem\u003eMc4r\u003c/em\u003e and \u003cem\u003ePdyn\u003c/em\u003e are expressed by multiple PVH neuron subtypes, as noted above, and specific molecular markers for PB-projecting PVH neurons have not been identified. Hence, the precise PVH neurons that regulate satiety are unknown. To elucidate the specific PVH populations that project to the PB, we performed targeted snRNA-seq similar to spinal cord-projecting neuron profiling above. Retrograde Cre virus was injected bilaterally into the PB, targeting the LPBN and adjacent pLC, to selectively label the nuclei of PB-projecting PVH neurons with mCherry. Next, PB-projecting nuclei were isolated, collected via FANS, and sequenced (\u003cstrong\u003eFig. 7a\u003c/strong\u003e). After clustering, PB-projecting \u003cem\u003eSim1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e neurons were classified based on our \u003cem\u003eSim1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e PVH sc/snRNA-seq atlas and projected onto the reference UMAP (\u003cstrong\u003eFig. 7b, Supplementary Table 30\u003c/strong\u003e). Our results show that most of the PB-projecting PVH neurons cluster with one of the following populations: Seq_S11.Esr2-Abcc9 (32.4%), Seq_S12.Npr3-Radx (21.2%), Seq_S15.Brs3 (14.7%), Seq_S16.Pde3a-Tmem215 (7.9%), or Seq_S17.Sfta3-ps (16.9%). Of interest, \u003cem\u003eMc4r-\u003c/em\u003e and \u003cem\u003eNpy1r\u003c/em\u003e-enriched MF_S14.Brs3 neurons correspond to the Seq_S7.Brs3 cluster based on our \u003cem\u003eSim1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e MERFISH to sc/snRNA-seq CCA mapping (\u003cstrong\u003eFig. 7c\u003c/strong\u003e). To confirm that PVH\u003cem\u003e\u003csup\u003eBrs3\u003c/sup\u003e\u003c/em\u003e neurons express \u003cem\u003eMc4r\u003c/em\u003e and project to the PB, we injected the retrograde tracer cholera toxin subunit B (CTB) into the PB and Cre-dependent AAV-EGFP-L10a into the PVH of \u003cem\u003eMc4r\u003c/em\u003e-2A-Cre mice\u003csup\u003e12,99\u003c/sup\u003e. Subsequently, we performed FISH to detect \u003cem\u003eBrs3\u003c/em\u003e expression in the PVH. Histological analysis revealed triple-labeling of fluorescent signals from \u003cem\u003eBrs3\u003c/em\u003e FISH, \u003cem\u003eMc4r\u003c/em\u003e-positive neurons labeled with EGFP, and PB-projecting PVH neurons labeled with CTB (\u003cstrong\u003eFig. 7d,e\u003c/strong\u003e). Collectively, these findings support the hypothesis that PVH\u003cem\u003e\u003csup\u003eBrs3\u003c/sup\u003e\u003c/em\u003e neurons regulate satiety. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePVH\u003cem\u003e\u003csup\u003eBrs3\u003c/sup\u003e\u003c/em\u003e neurons regulate feeding via projections to the PB.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGiven the importance of PVH\u003cem\u003e\u003csup\u003eMc4r\u003c/sup\u003e\u003c/em\u003e neurons to energy balance and prior studies demonstrating PVH\u003cem\u003e\u003csup\u003eBrs3\u003c/sup\u003e\u003c/em\u003e neuron inhibition increases food intake\u003csup\u003e67\u003c/sup\u003e, we next asked whether PVH\u003cem\u003e\u003csup\u003eBrs3\u003c/sup\u003e\u003c/em\u003e neurons are necessary for body weight regulation. To test this, we silenced PVH\u003cem\u003e\u003csup\u003eBrs3\u003c/sup\u003e\u003c/em\u003e neurons by bilaterally injecting an AAV driving Cre-dependent expression of tetanus toxin light chain (TeTxLC) or GFP as control into the PVH of \u003cem\u003eBrs3\u003c/em\u003e-IRES-Cre mice\u003csup\u003e100\u003c/sup\u003e. Additionally, we injected a cohort of wild-type mice with Cre-dependent AAV-TeTxLC as another control group. Body weights were measured weekly, and after six weeks, \u003cem\u003eBrs3\u003c/em\u003e-IRES-Cre mice receiving TeTxLC gained significantly more body weight compared to both control groups (\u003cstrong\u003eFig. 7f\u003c/strong\u003e). This finding demonstrates that PVH\u003cem\u003e\u003csup\u003eBrs3\u003c/sup\u003e\u003c/em\u003e neurons regulate body weight by preventing weight gain.\u003c/p\u003e\n\u003cp\u003ePVH\u003cem\u003e\u003csup\u003eMc4r\u003c/sup\u003e\u003c/em\u003e neurons are directly inhibited by ARC\u003cem\u003e\u003csup\u003eAgrp\u003c/sup\u003e\u003c/em\u003e neurons to induce hunger\u003csup\u003e12,21\u003c/sup\u003e. Since PVH\u003cem\u003e\u003csup\u003eBrs3\u003c/sup\u003e\u003c/em\u003e neurons express \u003cem\u003eMc4r\u003c/em\u003e, project to the PB, and have been implicated in feeding behavior regulation, we next tested whether they receive synaptic input from ARC\u003cem\u003e\u003csup\u003eAgrp\u003c/sup\u003e\u003c/em\u003e neurons\u003csup\u003e12,21\u003c/sup\u003e. ARC\u003cem\u003e\u003csup\u003eAgrp\u003c/sup\u003e\u003c/em\u003e à\u0026nbsp;PVH\u003cem\u003e\u003csup\u003eBrs3\u0026nbsp;\u003c/sup\u003e\u003c/em\u003eneuron connectivity was assessed by channelrhodopsin-2 (ChR2)-assisted circuit mapping (CRACM) using \u003cem\u003eBrs3\u003c/em\u003e-IRES-Cre::\u003cem\u003eNpy\u003c/em\u003e-IRES-Flp\u003csup\u003e101\u003c/sup\u003e mice as \u003cem\u003eNpy\u003c/em\u003e and \u003cem\u003eAgrp\u003c/em\u003e are co-expressed in the ARC\u003csup\u003e102\u003c/sup\u003e. Cre-dependent AAV-mCherry was injected into the PVH to visualize \u003cem\u003eBrs3\u003c/em\u003e-expressing neurons for \u003cem\u003eex vivo\u003c/em\u003e brain slice electrophysiology recordings, and Flp-dependent AAV-ChR2-eYFP was injected into the ARC to drive ChR2 expression in NPY/AgRP neurons. Light-evoked inhibitory postsynaptic currents (IPSCs) were detected in 8 out of 14 PVH\u003cem\u003e\u003csup\u003eBrs3\u003c/sup\u003e\u003c/em\u003e neuron recordings (\u003cstrong\u003eFig. 7g\u003c/strong\u003e), indicating ARC\u003cem\u003e\u003csup\u003eAgrp\u003c/sup\u003e\u003c/em\u003e neurons are monosynaptically connected to many PVH\u003cem\u003e\u003csup\u003eBrs3\u003c/sup\u003e\u003c/em\u003e neurons – further supporting their role in satiety regulation. Having established that PVH\u003cem\u003e\u003csup\u003eBrs3\u003c/sup\u003e\u003c/em\u003e neurons receive input from ARC\u003cem\u003e\u003csup\u003eAgrp\u003c/sup\u003e\u003c/em\u003e neurons, we next asked if PVH\u003cem\u003e\u003csup\u003eBrs3\u003c/sup\u003e\u003c/em\u003e projections to the PB are sufficient to reduce food intake using\u003cem\u003e\u0026nbsp;in vivo\u003c/em\u003e optogenetics. \u003cem\u003eBrs3\u003c/em\u003e-IRES-Cre mice were injected with either Cre-dependent AAV-ChR2 or AAV-mCherry into the PVH, and optical fibers were implanted bilaterally above the PB. Photostimulation of ChR2-expressing PVH\u003cem\u003e\u003csup\u003eBrs3\u003c/sup\u003e\u003c/em\u003e à\u0026nbsp;PB terminals at the onset of the dark cycle significantly reduced food intake (\u003cstrong\u003eFig. 7h\u003c/strong\u003e), which is consistent with the effects observed after chemogenetic activation of the entire PVH\u003cem\u003e\u003csup\u003eBrs3\u003c/sup\u003e\u003c/em\u003e population\u003csup\u003e67\u003c/sup\u003e and photostimulation of PVH\u003cem\u003e\u003csup\u003eMc4r\u003c/sup\u003e\u003c/em\u003e neuron projections to the PB\u003csup\u003e12\u003c/sup\u003e. No reduction in food intake was observed in the mCherry control group after photostimulation. Together, these data establish PVH\u003cem\u003e\u003csup\u003eBrs3\u003c/sup\u003e\u003c/em\u003e neurons as a precise neuronal subtype mediating satiety via projections to the PB.\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe leveraged single-cell and spatial transcriptomics technologies to develop a high-resolution, spatially resolved atlas of the mouse PVH region. Extensive transcriptional profiling enabled a detailed analysis of the molecular diversity among PVH cell types, highlighting stark differences between neuroendocrine and centrally-projecting PVH neurons. Using the marker gene profiles revealed by sc/snRNA-seq, we then performed MERFISH on the PVH region from multiple male and female mice, yielding spatial transcriptomic information from 41 coronal sections spanning bregma levels -0.4 mm to -1.2 mm, and including more than 150,000 cells. Specific analysis of \u003cem\u003eSim1\u003csup\u003e+\u003c/sup\u003e\u003c/em\u003e neurons identified by MERFISH revealed 26 transcriptionally distinct populations, six of which were neuroendocrine, expressing peptide hormones along with secondary markers that exhibited highly specific expression patterns. Spatial domain analysis of MERFISH data further designated \u003cem\u003eSim1\u003csup\u003e+\u003c/sup\u003e\u003c/em\u003e neurons as PVH or Peri-PVH, and segregated PVH neurons into Rostral, Rostral-Intermediate, Caudal-Intermediate, or Caudal groups. Analysis of the similarity between \u003cem\u003eSim1\u003csup\u003e+\u003c/sup\u003e\u003c/em\u003e neuron populations identified by MERFISH and sc/snRNA-seq demonstrated remarkably high correspondence for neuron subtypes located within the PVH. Our study also cataloged 29 GABAergic neuron subtypes that surround the PVH, highlighting the significant heterogeneity of this region and providing a means for gaining selective genetic access to these neuron populations for future investigation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNoted above, our atlas provides molecular markers capable of distinguishing neuroendocrine and centrally-projecting PVH neurons that express the same neuropeptide hormone gene, which is of great interest for PVH\u003cem\u003e\u003csup\u003eCrh\u003c/sup\u003e\u003c/em\u003e and PVH\u003cem\u003e\u003csup\u003eOxt\u0026nbsp;\u003c/sup\u003e\u003c/em\u003eneurons that control stress-related and social behaviors\u003csup\u003e103-105\u003c/sup\u003e. For instance, PVH\u003cem\u003e\u003csup\u003eCrh\u003c/sup\u003e\u003c/em\u003e neuroendocrine neurons (Seq_S1.Crh-Scgn) express \u003cem\u003eScgn\u003c/em\u003e, whereas centrally-projectingPVH neurons expressing \u003cem\u003eCrh\u003c/em\u003e include Seq_S12.Npr3-Radx, Seq_S13.Npy2r-Tll2, Seq_S14_Aox3, and Seq_S15_Brs3 clusters, all of which lack \u003cem\u003eScgn\u003c/em\u003e expression. These cluster-specific genetic markers make it possible for future functional studies to selectively target PVH\u003cem\u003e\u003csup\u003eCrh\u003c/sup\u003e\u003c/em\u003e and PVH\u003cem\u003e\u003csup\u003eOxt\u003c/sup\u003e\u003c/em\u003e neuron subtypes and link them to distinct physiological and behavioral phenotypes. However, it remains unclear which centrally-mediated behaviors are driven by collateral projections from neuroendocrine populations to other hypothalamic sites\u003csup\u003e106-109\u003c/sup\u003e versus those resulting from distinct centrally-projecting PVH\u003cem\u003e\u003csup\u003eCrh\u003c/sup\u003e\u003c/em\u003e and PVH\u003cem\u003e\u003csup\u003eOxt\u0026nbsp;\u003c/sup\u003e\u003c/em\u003eneurons.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn addition to our spatially resolved atlas, we conducted targeted snRNA-seq of spinal cord- and PB-projecting PVH neurons to elucidate the neuronal populations involved in regulating sympathetic nervous system activity and feeding behavior, respectively. Prior studies have identified several marker genes for spinal cord-projecting PVH neurons, including \u003cem\u003eAvp\u003c/em\u003e, \u003cem\u003eOxt\u003c/em\u003e\u003csup\u003e4,8,110-112\u003c/sup\u003e, \u003cem\u003eBdnf\u0026nbsp;\u003c/em\u003e\u003csup\u003e23\u003c/sup\u003e, \u003cem\u003eMc4r\u003c/em\u003e\u003csup\u003e12\u003c/sup\u003e\u003cem\u003e, Nos1\u003c/em\u003e, \u003cem\u003eSim1\u003c/em\u003e\u003csup\u003e19\u003c/sup\u003e, \u003cem\u003eErbb4\u003c/em\u003e, \u003cem\u003eOtp\u003c/em\u003e, \u003cem\u003ePcsk5\u003c/em\u003e, \u003cem\u003ePrlr\u003c/em\u003e, and \u003cem\u003eZeb2\u003c/em\u003e\u003csup\u003e89\u003c/sup\u003e, but none of these genes are unique to a single PVH neuron type. Further, prior sc/snRNA-seq studies only profiled cervical- and lumbar-projecting neurons in the brain\u003csup\u003e90\u003c/sup\u003e. Given our interest in the regulation of the sympathetic nervous system, we sequenced PVH neurons that project to the thoracic cord, where preganglionic neurons are primarily located. Our results for thoracic-projecting neurons aligned well with publicly available data as all spinal cord-projecting PVH neurons predominantly mapped to Seq_S11.Esr2-Abcc9, Seq_S12.Npr3-Radx, and Seq_S10.Npsr1-Npnt clusters of our \u003cem\u003eSim1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e sc/snRNA-seq reference atlas. The functional roles of these neuron populations remain unknown, but pharmacological manipulation of ESR2 and NPR3 activity in the PVH has been shown to reduce blood pressure\u003csup\u003e62,63,65\u003c/sup\u003e. Of interest, it has long been recognized that a small number of PVH\u003cem\u003e\u003csup\u003eAvp\u003c/sup\u003e\u003c/em\u003e and PVH\u003cem\u003e\u003csup\u003eOxt\u003c/sup\u003e\u003c/em\u003e neurons project to the spinal cord; however, none of the sequenced spinal cord-projecting PVH neurons mapped to neuroendocrine Seq_S5.Oxt-Rxfp3 or Seq_S6.Avp-Pla2r1 clusters. This is consistent with neuroanatomical tracing studies showing pituitary-projecting PVH neurons do not collateralize to the brainstem and spinal cord\u003csup\u003e1,5\u003c/sup\u003e. Therefore, spinal cord-projecting PVH\u003cem\u003e\u003csup\u003eAvp\u003c/sup\u003e\u003c/em\u003e and PVH\u003cem\u003e\u003csup\u003eOxt\u003c/sup\u003e\u003c/em\u003e neurons likely belong to the centrally-projecting Seq_S11.Esr2-Abcc9 population, which is positive for both \u003cem\u003eAvp\u003c/em\u003e and \u003cem\u003eOxt\u003c/em\u003e. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe sequenced PB-projecting PVH neurons to ascertain their cell type identities because PVH\u003cem\u003e\u003csup\u003eMc4r\u003c/sup\u003e\u003c/em\u003e and PVH\u003cem\u003e\u003csup\u003ePdyn\u003c/sup\u003e\u003c/em\u003e satiety neuronsrepresent distinct PB-projecting populations, andmultiple neuron subtypes express\u003cem\u003e\u0026nbsp;Mc4r\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;Pdyn\u003c/em\u003e\u003csup\u003e12,21\u003c/sup\u003e. The majority of \u003cem\u003eSim1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e PB-projecting neurons mapped to five clusters, including two spinal cord-projecting sc/snRNA-seq clusters, Seq_S11.Esr2-Abcc9 and Seq_S12.Npr3-Radx. This may represent similarities in transcriptomes between PB- and spinal cord-projecting PVH neurons or that some PVH neurons collateralize between these two regions. However, we cannot rule out that\u0026nbsp;retrograde AAV injections\u0026nbsp;were taken up by spinal cord-projecting fibers passing through the PB, which has been observed with some retrograde tracers\u003csup\u003e113\u003c/sup\u003e. PB-projecting neurons did map to three clusters that spinal cord-projecting PVH neurons did not, including Seq_S15.Brs3, Seq_S16.Pde3a-Tmem215, and Seq_S17.Sfta3-ps.\u0026nbsp;Taking advantage of the enhanced gene detection capability of MERFISH, we were able to identify the PVH neurons with the highest expression of \u003cem\u003eMc4r\u003c/em\u003e and compare them with those identified as PB-projecting. Notably, the\u0026nbsp;MF_S14.Brs3\u0026nbsp;cluster is among the highest expressors of \u003cem\u003eMc4r\u003c/em\u003e and corresponds to the PB-projecting cluster\u0026nbsp;Seq_S15.Brs3. This information, in conjunction with prior work demonstrating that\u0026nbsp;chemogenetic activation of PVH\u003cem\u003e\u003csup\u003eBrs3\u003c/sup\u003e\u003c/em\u003e neurons reduces food intake and inhibition does the opposite\u003csup\u003e67\u003c/sup\u003e,\u0026nbsp;inspired us to further\u0026nbsp;examine their role in energy balance. We show that 1) chronic\u0026nbsp;PVH\u003cem\u003e\u003csup\u003eBrs3\u003c/sup\u003e\u003c/em\u003e neuron silencing causes significant weight gain, 2) they receive direct GABAergic input from hunger-driving ARC\u003cem\u003e\u003csup\u003eAgrp\u003c/sup\u003e\u003c/em\u003e neurons, and 3) stimulation of PVH\u003cem\u003e\u003csup\u003eBrs3\u003c/sup\u003e\u003c/em\u003e neuron projections to the PB reduces food intake. These results are all consistent with PVH\u003cem\u003e\u003csup\u003eBrs3\u003c/sup\u003e\u003c/em\u003e neurons representing \u003cem\u003eMc4r\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e satiety neurons, yet the effects on food intake that we and others observed were smaller compared to manipulating all PVH\u003cem\u003e\u003csup\u003eMc4r\u003c/sup\u003e\u003c/em\u003e neurons\u003csup\u003e12,67\u003c/sup\u003e. Thus, there may be multiple PVH\u003cem\u003e\u003csup\u003eMc4r\u003c/sup\u003e\u0026nbsp;\u003c/em\u003eneuron populations that control food intake. With regard to pinpointing the specific cluster containing\u0026nbsp;PVH\u003cem\u003e\u003csup\u003ePdyn\u003c/sup\u003e\u003c/em\u003e satiety neurons\u003csup\u003e21\u003c/sup\u003e, MF_S8.Esr2-Ret and MF_S13.Pde3a-Tmem215 neurons correspond to PB-projecting PVH neurons that express \u003cem\u003ePdyn\u003c/em\u003e and \u003cem\u003eGlp1r\u003c/em\u003e but lack \u003cem\u003eMc4r\u003c/em\u003e. However, additional studies are required to test whether PB-projecting MF_S8.Esr2-Ret and/or MF_S13.Pde3a-Tmem215 neurons control satiety.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis atlas of the PVH serves as a versatile resource to support future studies of PVH organization and function. It also has several advantages over prior work\u003csup\u003e25,26\u003c/sup\u003e, including a vastly increased sample size, both unbiased and circuit-based molecular profiling, and the ability to resolve spatial information with MERFISH using a gene panel curated for the PVH and surrounding regions. To facilitate accessibility for the scientific community, we uploaded our analyzed sc/snRNA-seq and MERFISH data to the Broad Single Cell Portal (https://singlecell.broadinstitute.org/single_cell/study/SCP2858), an open-access, web-based tool for exploring single-cell genomics data \u0026ndash; thus, providing a valuable resource for the field of homeostasis.\u0026nbsp;\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cu\u003eMice:\u003c/u\u003e All animal care and experimental procedures were approved by the Institutional Animal Care and Use Committees at Beth Israel Deaconess Medical Center and the University of Iowa. Prior to the start of experiments, mice were housed in a temperature- and humidity-controlled room with a 12-hr light-dark cycle and maintained on standard diet (Inotiv 7913) unless stated otherwise. C57BL/6J background wild-type mice were used for the majority of single-cell and single-nucleus RNA sequencing experiments as well as MERFISH experiments. In some cases, \u003cem\u003eSim1\u003c/em\u003e-Cre (JAX006395\u003csup\u003e14\u003c/sup\u003e), \u003cem\u003eSim1\u003c/em\u003e-Cre::R26-LSL-EGFP-L10a\u003csup\u003e22\u003c/sup\u003e, or H2B-TRAP mice (JAX029789\u003csup\u003e91\u003c/sup\u003e) were used for single-cell and single-nucleus RNA sequencing studies to guide dissections and sample collection with FANS. Behavior experiments were completed with \u003cem\u003eBrs3\u003c/em\u003e-IRES-Cre mice (JAX030540\u003csup\u003e100\u003c/sup\u003e), which were crossed to \u003cem\u003eNpy\u003c/em\u003e-IRES-Flp (JAX030211\u003csup\u003e101\u003c/sup\u003e) mice for CRACM experiments. Additionally, \u003cem\u003eSlc17a6\u003c/em\u003e-IRES-Cre (JAX028863\u003csup\u003e114,115\u003c/sup\u003e)::R26-LSL-EGFP-L10a\u003csup\u003e22\u003c/sup\u003e, \u003cem\u003eSlc32a1\u003c/em\u003e-IRES-Cre (JAX028862\u003csup\u003e114\u003c/sup\u003e)::R26-LSL-EGFP-L10a, \u003cem\u003eMc4r\u003c/em\u003e-2a-Cre (JAX030759\u003csup\u003e12\u003c/sup\u003e), and C57BL/6J wild-type mice were used for histological experiments. \u003c/p\u003e\n\u003cp\u003e\u003cu\u003eSingle-cell/nucleus RNA sequencing tissue collection, library preparation, and sequencing:\u003c/u\u003e C57BL/6J, \u003cem\u003eSim1\u003c/em\u003e-Cre, or \u003cem\u003eSim1\u003c/em\u003e-Cre::R26-LSL-EGFP-L10a mice aged 6-12 weeks were sacrificed between 9 am – 12 pm by rapid decapitation immediately after removal from the home cage. Brains were extracted and chilled in DMEM/F12 media slush. Next, brains were placed ventral side up in a chilled stainless steel brain matrix (Roboz Surgical Instrument Co.: SA-2165), and 1 mm coronal sections of the hypothalamus were collected. The PVH was then micro-dissected under a fluorescent stereoscope. For each sample preparation, 4-10 male or female mice were pooled. Sample and library preparation was performedas described previously for Drop-seq\u003csup\u003e116\u003c/sup\u003e and DroNc-seq\u003csup\u003e46\u003c/sup\u003e with minor modifications. One of three DroNc-seq samples was prepared from fasted C57BL/6J mice. For 10X Chromium v3 sequencing runs, samples and library preps were prepared as described previously for incorporation with fluorescence-activated nuclei sorting (FANS) with minor modifications\u003csup\u003e117,118\u003c/sup\u003e. In addition, subsets of 10X Chromium v3 samples from \u003cem\u003eSim1\u003c/em\u003e-Cre mice injected with AAVDJ-hSyn-H2B-mCherry (Boston Children’s Hospital Viral Core)\u003csup\u003e119\u003c/sup\u003e or H2B-TRAP mice injected with AAVrg-hSyn-Cre, for projection-specific snRNA-seq experiments described below, were incubated with hashtag oligos for 15 minutes for eventual multiplexing prior to FANS enrichment based on nuclear mCherry. Multiplexed \u003cem\u003eSim1\u003c/em\u003e-Cre samples were obtained from mice that were \u003cem\u003ead libitum\u003c/em\u003e fed, fasted, or refed for 60 minutes before sacrifice. Libraries were sequenced on an Illumina NextSeq 500 or Illumina NovaSeq 6000 at a minimum read depth of 20,000 reads per cell/nucleus. Hashtag oligo libraries were sequenced to a minimum read depth of either 1,000 or 5,000 reads/nucleus and processed into count matrices using either the Cumulus Tool on Feature Barcoding (https://github.com/lilab-bcb/cumulus_feature_barcoding) or kallisto | bustools (https://www.kallistobus.tools/). For Drop-seq and DroNc-seq data, raw sequencing reads were processed using the Drop-seq tools pipeline\u003csup\u003e46,116\u003c/sup\u003e. Barcodes with base quality \u0026lt;10 were removed, and 5’ and 3’ ends of reads were trimmed to remove TSO and poly(A) tails, respectively. Reads were then aligned to the GRCm38 reference genome using STAR v2.7.7. Feature-barcode matrices were then generated by summing detected unique molecular identifiers (UMIs) for each barcode with errors corrected at a hamming distance of 1. For 10X Chromium v3 libraries, 10X Genomics Cell Ranger was used to map reads to the GRCm38 reference genome and generate feature-barcode matrices. \u003c/p\u003e\n\u003cp\u003e\u003cu\u003eSingle-cell/nucleus RNA sequencing Quality Control:\u003c/u\u003e For all sequencing data regardless of technology, CellBender (v0.2.2) was used to identify and filter out reads captured from ambient RNA and random barcode swapping\u003csup\u003e120\u003c/sup\u003e. Subsequently, data from Drop-seq, DroNc-seq, and 10X Chromium v3 sequencing runs were loaded into an RStudio environment (R v 4.4.1) and processed through a custom Seurat-based analysis pipeline run in Seurat v5.0.1.9001\u003csup\u003e35\u003c/sup\u003e. First, we applied additional filtering to remove cells/nuclei with fewer than 250 unique genes. DroNc-seq data were then filtered to exclude nuclei with total UMI count outside the range of 1,000 to 10,000, while Drop-seq and 10X chromium-v3 data were filtered to exclude cells/nuclei with total UMI count outside the range of 1,000 to 25,000. Additionally, \u003cem\u003ePercentFeatureSet\u003c/em\u003e() was used to calculate mitochondrial gene expression, and cells/nuclei from all datasets were removed if they had a mitochondrial gene expression rate of greater than 10%. Finally, all cells/nuclei with a ratio of log\u003csub\u003e10\u003c/sub\u003e(unique genes)/log\u003csub\u003e10\u003c/sub\u003e(unique molecules) less than 0.8 were removed. After quality control filtering was complete, all data were merged into a single Seurat object for integrated analysis. \u003c/p\u003e\n\u003cp\u003e\u003cu\u003eSingle-cell/nucleus RNA sequencing and data integration:\u003c/u\u003e For integrated analysis, 11 batches of sequencing runs were merged into a single Seurat object (Drop-seq = 7 batches, DroNc-seq = 2 batches, and 10X Chromium-v3 = 2 batches) followed by joining of the “RNA” assay layers using JoinLayers(). Raw counts were log-normalized, using Seurat \u003cem\u003eNormalizeData()\u003c/em\u003e, and cell cycle scoring for S phase and G2/M was computed using the Seurat \u003cem\u003eCellCycleScoring() \u003c/em\u003efunction\u003csup\u003e121\u003c/sup\u003e. Subsequently, given that stress readily activates PVH neurons, particularly PVH\u003cem\u003e\u003csup\u003eCrh\u003c/sup\u003e\u003c/em\u003e neurons controlling the HPA axis, the \u003cem\u003eAddModuleScore()\u003c/em\u003e function was used to measure the expression level of a set of 19 primary rapidly responding activity-dependent genes to compute a “cellular activation score” based on this transcriptional signature for each cell\u003csup\u003e122\u003c/sup\u003e. Next, layers were split by sequencing run (“batch”), and \u003cem\u003eFindVariableFeatures()\u003c/em\u003e was used to select the top 5,000 highly variable genes. Data were then scaled with \u003cem\u003eScaleData()\u003c/em\u003e, while regressing out the following covariates: mitochondrial gene percentage, cell-cycle scores, and cellular activation score. Principal component analysis (PCA) was performed with the \u003cem\u003eRunPCA\u003c/em\u003e() function. Following calculation of principal components, integration of layers was carried out using \u003cem\u003eIntegrateLayers() \u003c/em\u003ewith reciprocal principal component analysis (RPCA)-based integration\u003csup\u003e36\u003c/sup\u003e\u003cem\u003e. \u003c/em\u003eAfter integration, we used the top 30 principal components for clustering and dimensionality reduction using the Seurat \u003cem\u003eFindNeighbors()\u003c/em\u003e, \u003cem\u003eFindClusters(), \u003c/em\u003eand\u003cem\u003e RunUMAP()\u003c/em\u003e functions. To identify marker genes for each cluster, we re-joined layers using \u003cem\u003eJoinLayers() \u003c/em\u003eand ran \u003cem\u003eFindAllMarkers()\u003c/em\u003e for differential gene expression analysis (DGEA) using the non-parametric \u003cem\u003eWilcoxon Rank Sum test\u003c/em\u003e. Differentially expressed genes were defined as those with \u0026gt; 0.2 average log\u003csub\u003e2\u003c/sub\u003e fold change and a \u003cem\u003eBonferroni-corrected\u003c/em\u003e p-value less than 0.01. Marker gene analysis guided identification of doublets/multiplets, which were classified as clusters that expressed high levels of more than one canonical cell type marker genes (e.g., clusters expressing marker genes for both neurons and astrocytes) and were removed. In addition, clusters comprised of doublets/multiplets and/or “low quality” metrics, including mitochondrial gene enrichment or absence of cell type-defining markers indicating low complexity, were removed. This process was repeated at several levels of analysis, beginning with all cells, then after subclustering for neurons, GABAergic neurons, glutamatergic neurons, and \u003cem\u003eSim1\u003c/em\u003e-expressing neurons. \u003c/p\u003e\n\u003cp\u003e\u003cu\u003eIntegration of Sim1-expressing clusters with publicly available HypoMap and Allen Brain Cell Atlas data:\u003c/u\u003e To integrate \u003cem\u003eSim1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e PVH sc/snRNA-seq data from our study with publicly available sequencing data from the murine PVH, we downloaded data from HypoMap, an integrated atlas of mouse hypothalamus\u003csup\u003e32\u003c/sup\u003e. Using the provided anatomical annotations with the Seurat object, we subset for and clustered only cells/nuclei annotated as “paraventricular hypothalamic nucleus” using the pipeline described for this study. Notably, during clustering, we curated the HypoMap data for \u003cem\u003eSim1\u003c/em\u003e-expressing cells/nuclei, filtering out any clusters marked by specific expression of GABAergic or thalamic marker genes (i.e., \u003cem\u003eSlc32a1\u003c/em\u003e and \u003cem\u003eTcf7l2\u003c/em\u003e). We then merged and integrated the HypoMap PVH \u003cem\u003eSim1\u003c/em\u003e\u003csup\u003e+ \u003c/sup\u003eneurons with PVH \u003cem\u003eSim1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e sc/snRNA-seq data collected in this study using our Seurat-based analysis workflow. However, after integration, inconsistencies were observed across datasets. We then instead integrated publicly available PVH \u003cem\u003eSim1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e scRNA-seq data from the whole mouse brain Allen Brain Cell (ABC) Atlas with \u003cem\u003eSim1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e PVH sc/snRNA-seq data from this study\u003csup\u003e29\u003c/sup\u003e. To specifically access PVH cells from the ABC Atlas, we first downloaded two H5 AnnData expression matrices (WMB-10Xv2-Hy-raw.h5ad and WMB-10Xv3-Hy-raw.h5ad) containing all cells collected from hypothalamic dissections and sequenced using either 10X Chromium v2 or 10X Chromium v3 chemistry. Subsequently, we used the \u003cem\u003eConvert()\u003c/em\u003e and \u003cem\u003eLoadH5Seurat() \u003c/em\u003efunctions toload the ABC Atlas data into a Seurat object and used the published taxonomic classifications to select for data from the PVH region. The ABC Atlas assigned anatomical annotation was used to specifically select clusters that spatially mapped to either the PVH (“PVH”) or the anterior portion of the periventricular area (“PVa”). Subsequently, we further filtered our selection only to keep glutamatergic clusters using the ABC Atlas assigned neurotransmitter type label, keeping clusters annotated as either “Glut” or “Glut-GABA”. We then removed cells with a mitochondrial gene expression rate greater than 10% and clustered the data in Seurat version 5. For clustering, ABC Atlas data were processed as described above with minor modifications. Notably, the 10X chemistry (i.e., v2 and v3) were each treated as a “batch” for integrated analysis. After clustering, any identified “low quality” or doublet/multiplet clusters were removed as described above. Finally, we merged and integrated the ABC Atlas \u003cem\u003eSim1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e neurons with the PVH \u003cem\u003eSim1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e sc/snRNA-seq data from this study following the workflow described above.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eAnalysis of neuroendocrine neuron transcriptional profiles:\u003c/u\u003e DGEA was run using Seurat \u003cem\u003eFindMarkers()\u003c/em\u003e on the different neuron classes: centrally-projecting, neuroendocrine, median eminence-projecting, and posterior pituitary-projecting neurons. Differentially expressed genes were defined as having \u0026gt; 0.2 average log\u003csub\u003e2\u003c/sub\u003e fold change and a \u003cem\u003eBonferroni-corrected\u003c/em\u003e p-value \u0026lt;0.01. Next, the clusterProfiler package was used to perform Gene Ontology (GO) enrichment analysis of genes differentially expressed by centrally-projecting, neuroendocrine, median eminence-projecting, and posterior pituitary-projecting neuronal classes\u003csup\u003e123\u003c/sup\u003e. Specifically, \u003cem\u003ecompareCluster() \u003c/em\u003ewas used to perform “enrichGO” analysis, which executes an over-representation analysis\u003csup\u003e124\u003c/sup\u003e for all GO ontology categories (i.e., biological process, cellular component, and molecular function) with \u003cem\u003eBonferroni correction\u003c/em\u003e for multiple comparisons at an alpha value of 0.05. \u003c/p\u003e\n\u003cp\u003e\u003cu\u003eSingle-nucleus RNA sequencing of projection\u003c/u\u003e\u003cu\u003e-specific PVH neuron populations:\u003c/u\u003e We sequenced projection-specific PVH neurons using either 10X Chromium v3 or Smart-Seq2 (“sNuc-seq”)\u003csup\u003e125,126\u003c/sup\u003e technologies. For 10X Chromium v3 experiments, H2B-TRAP mice received bilateral stereotaxic injections of AAVrg-hSyn-Cre (Addgene #105553) into either the upper thoracic spinal cord or the parabrachial region. sNuc-seq samples were prepared by bilaterally injecting C57BL/6J mice with AAVDJ-hSyn-H2B-mCherry into the PVH and AAVrg-CAG-GFP-Cre (Boston Children’s Hospital Viral Core) or HSV-hEf1a-mCherry-IRES-Cre (Mass General Brigham Gene Delivery Technology Core; Dr. Rachael Neve) into the PB. Two weeks post-surgery, animals were sacrificed, and tissue was collected as described above. Samples processed with 10X Chromium v3 were completed as described in Schwalbe \u003cem\u003eet al.\u003c/em\u003e\u003cem\u003e\u003csup\u003e117\u003c/sup\u003e\u003c/em\u003e, while sNuc-seq was performed as described in Tao \u003cem\u003eet al.\u003c/em\u003e\u003csup\u003e119\u003c/sup\u003e. Spinal cord-projecting data consists of two 10X Chromium v3 sequencing runs, while the parabrachial-projecting data consists of two runs of 10X Chromium v3 and two sNuc-Seq sequencing runs. In addition, we downloaded two publicly available spinal cord-projecting datasets (GEO accession numbers GSE247594 and GSE212409)\u003csup\u003e89,90\u003c/sup\u003e and accordingly classified these data using our \u003cem\u003eSim1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e PVH sc/snRNA-seq reference atlas. Briefly, we calculated the percentage of mitochondrial gene expression using Seurat’s \u003cem\u003ePercentFeatureSet\u003c/em\u003e() to identify and remove any cells/nuclei with a mitochondrial gene expression rate greater \u0026gt;10%. Cells/nuclei with fewer than 1000 UMIs were also removed from further analysis. Subsequently, we clustered all parabrachial- and spinal cord-projecting data using the analysis pipeline described above and filtered the data to only retain \u003cem\u003eSim1\u003c/em\u003e-expressing clusters. To classify each cell, we proceeded to use \u003cem\u003eFindTransferAnchors\u003c/em\u003e() to project our mouse \u003cem\u003eSim1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e sc/snRNA-seq reference atlas PCA structure onto the parabrachial- and spinal cord-projecting data to identify paired anchor cells across datasets. We then used the identified anchors and the \u003cem\u003eMapQuery\u003c/em\u003e() function to map parabrachial- and spinal cord-projecting data into our mouse \u003cem\u003eSim1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e sc/snRNA-seq reference atlas UMAP space. \u003c/p\u003e\n\u003cp\u003e\u003cu\u003eAnalysis of human PVH single-nucleus RNA sequencing data:\u003c/u\u003e Two published datasets contain snRNA-seq data from the hypothalamus of adult humans\u003csup\u003e75,76\u003c/sup\u003e. From Siletti et al., 2023\u003csup\u003e75\u003c/sup\u003e, we downloaded a Seurat object containing data from dissections encompassing the medial preoptic region of the hypothalamus, supraoptic region of the hypothalamus, and paraventricular nucleus of the hypothalamus. We then filtered the data for neurons with \u0026gt; 1,000 UMIs and \u0026lt; 10% mitochondrial gene expression and retained \u003cem\u003eSIM1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e clusters for further analysis. After filtering for \u003cem\u003eSIM1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e neurons, the data included samples from one 60-year-old female and one 50-year-old male. We also downloaded a Seurat object from Tadross et al., 2025\u003csup\u003e76\u003c/sup\u003e, containing data from the entire adult human hypothalamus, which was filtered as above and contributed data from two females, aged 63 and 94 years, and four males, aged 83, 88, 91, and 94. After analyzing the integrated human \u003cem\u003eSIM1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e data, we used a text file (“gene_ortologs.gz”) available from NCBI (https://ftp.ncbi.nlm.nih.gov/gene/DATA/) to identify all gene homologs present in both the human \u003cem\u003eSIM1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e object and our mouse \u003cem\u003eSim1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e sc/snRNA-seq atlas. We then completed a canonical correlation analysis (CCA) to assess the transcriptional similarity of each cluster between the human and mouse atlases by using Seurat’s \u003cem\u003eFindTransferAnchors\u003c/em\u003e() and \u003cem\u003eTransferData\u003c/em\u003e() functions. \u003c/p\u003e\n\u003cp\u003e\u003cu\u003eMERFISH gene panel selection\u003c/u\u003e: A gene panel of 503 genes (\u003cstrong\u003eSupplementary\u003c/strong\u003e \u003cstrong\u003eTable 16\u003c/strong\u003e) was curated specifically for the PVH and surrounding regions based on differentially expressed genes identified in sc/snRNA-seq experiments (\u003cstrong\u003eSupplementary\u003c/strong\u003e \u003cstrong\u003eTables 1-4,7\u003c/strong\u003e), canonical marker genes for neurons and non-neuronal cells, and functionally important genes described in the scientific literature. After gene selection, Vizgen manufactured the custom “MERFISH 500 Gene Panel” (Vizgen: 20300008), comprised of probes targeting a minimum of 30 regions per gene (except for \u003cem\u003eAvp\u003c/em\u003e and \u003cem\u003eOxt\u003c/em\u003e) and using a 25-bit binary code readout for gene assignment after combinatorial single molecule FISH (smFISH). Furthermore, 50 “blanks” comprising non-encoding scrambled sequences were included in the gene panel as negative controls (\u003cstrong\u003eSupplementary Table 17\u003c/strong\u003e). Three of the 503 genes, \u003cem\u003eAvp\u003c/em\u003e, \u003cem\u003eOxt\u003c/em\u003e, and \u003cem\u003eSst\u003c/em\u003e, were assigned to the “sequential panel” to avoid optical overcrowding artifacts due to high abundance of expression. Genes in the sequential panel are detected using unique probes identified by their direct fluorescent signal in distinct imaging rounds occurring after combinatorial smFISH imaging. \u003c/p\u003e\n\u003cp\u003e\u003cu\u003eMERFISH tissue collection and sample preparation\u003c/u\u003e\u003cu\u003e:\u003c/u\u003e MERFISH experiments were conducted according to Vizgen MERSCOPE protocols for fresh frozen tissue using six C57BL/6J mice, comprised of four males and two females, aged 8-10 weeks. Sacrifice and brain extraction was done as described for sc/snRNA-seq studies above. Brains were then positioned ventral side up in a chilled stainless steel brain matrix and sliced into 3-mm thick coronal slices that included the PVH region. Subsequently, the coronal slices were placed anterior side up and trimmed dorsally, removing tissue above the lateral septum, and laterally to remove cortex and much of the striatum. PVH tissue blocks were then embedded in a square mold (S22, Kisker Biotech) with Tissue-Tek® O.C.T. Compound (Sakura) and stored at −80°C until sectioning. Tissue blocks were placed in a cryostat (Epredia CryoStar NX50 HD Cryostat) and incubated at -20°C for 1 hour prior to sectioning coronally at 10 µm thickness. We mounted 4-10 sections from each brain at ~100 µm intervals onto warm MERSCOPE slides (Vizgen: 20400001), beginning at approximately bregma level -0.4 mm and continuing to -1.2 mm according to the Franklin-Paxinos atlas\u003csup\u003e49\u003c/sup\u003e. After sectioning, MERFISH slides were placed face-up in a 60 mm petri dish (VWR, 25382-687) and left at room temperature for 5 minutes. Next, slides were incubated in freshly made 4% paraformaldehyde (PFA; Electron Microscopy Sciences: 15714-S) in RNase-free phosphate-buffered saline (PBS; Thermo Fisher Scientific: AM9625) for 15 minutes at room temperature. Slides were then washed three times for five minutes each with PBS at room temperature and treated with freshly made 70% ethanol for tissue permeabilization and storage for a minimum of 24 hours at 4°C in parafilm-sealed 60 mm dishes.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eMERFISH probe hybridization and imaging\u003c/u\u003e: Slides were taken out of 4°C and washed with Sample Preparation Wash Buffer (Vizgen: 20300001) for five minutes at room temperature, followed by incubation in Formamide Wash Buffer (Vizgen: 20300002) for 30 minutes at 37°C. Subsequently, our custom 503 gene MERSCOPE panel for the PVH was applied to the slides with a parafilm coverslip and incubated at 37°C for 36-42 hours. Slides were then washed twice with Formamide Wash Buffer for 30 minutes each at 47°C. To gel-embed tissue samples on slides, a mix composed of Gel Embedding Premix (Vizgen: 20300004), ammonium persulfate (APS; Sigma: 09913-100G), and TEMED (Sigma: T7024-25ML) was prepared and applied to the tissue. A circular Gel Coverslip (Vizgen: 30200004), treated with RNaseZap, 70% ethanol, and Gel Slick Solution, was then placed on the slide over the gel embedding solution. Gel embedding solution was allowed to solidify for 90 minutes, after which the coverslip was removed. The sample was then incubated at 37°C in Clearing Solution, comprised of Protease K (New England Biolabs: P8107S) and Clearing Premix (Vizgen: 20300003), for a minimum of 24 hours and up to five days prior to imaging. \u003c/p\u003e\n\u003cp\u003eOn the day of imaging, the slides were washed twice with Sample Preparation Wash Buffer at room temperature and treated with DAPI and PolyT Staining Reagent (Vizgen: 20300021) for 15 minutes on a rocker. The slides were then washed with Formamide Wash Buffer for 15 minutes, followed by a final wash with Sample Prep Wash Buffer. To begin the imaging process, an individual slide was assembled into the MERSCOPE Flow Chamber and inserted into the instrument, along with a MERSCOPE 500 Gene Imaging Cartridge (Vizgen: 20300019). After defining the regions of interest on the slide within the Vizgen MERSCOPE Instrument Software, we started the fully automated instrument run. The MERSCOPE Instrument Software automatically processed the raw images to generate spatial genomics data ready for downstream analysis. Although MERFISH was successful, Slides 3 and 6 underwent unsuccessful Vizgen MERSCOPE protein staining, and these protein staining results were excluded from downstream analyses.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eMERFISH \u003c/u\u003e\u003cu\u003eimage analysis and cell segmentation\u003c/u\u003e\u003cu\u003e:\u003c/u\u003e After image acquisition, the data were initially processed by Vizgen MERSCOPE Instrument Software, before custom cell segmentation was performed with the deep learning algorithm, Cellpose 2.0\u003csup\u003e50\u003c/sup\u003e, using DAPI and PolyT-stained images as training files. First, we uploaded a field of view from one PVH section (Slide 3, bregma level -0.8) as an initial training image. Next, we employed the generalizable ‘cyto2’ model in Cellpose 2.0 with a diameter parameter of 123.73 pixels to initially segment various cell types in the PVH and surrounding regions. Manual annotations were then adjusted by correcting misidentified cells and adding cells missed by the automated ‘cyto2’ model. This process was repeated for 10 fields of view, and the new set of 10 human-processed images were used to optimize the training of our custom Cellpose 2.0 segmentation model. This enhanced model was then utilized to segment cells in all Z planes across 41 coronal sections using the Vizgen Post-processing Tool (VPT). All regions underwent 7-layer segmentation, except for the section corresponding to bregma level -0.7 mm on Slide 2, which underwent segmentation with 6 layers of DAPI and PolyT images due to the loss of the DAPI image from layer 3 during data transfer. Four output files were generated for each coronal section: 1) cellpose2_micron_to_mosaic.parquat (cell boundaries file); 2) cell_by_gene.csv (cell by gene matrix)l; 3) detected_transcripts.csv (cartesian coordinates of each transcript); and 4) cell_metadata.csv (cell morphology characteristics).\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eMERFISH sequential gene panel preprocessing\u003c/u\u003e: Due to high-expression levels within the PVH, \u003cem\u003eAvp\u003c/em\u003e, \u003cem\u003eOxt\u003c/em\u003e, and \u003cem\u003eSst\u003c/em\u003e expression was assayed with a non-combinatorial sequential gene panel as noted above. Using the VPT sum_signal command on data segmented by Cellpose 2.0, we generated summed fluorescent values for \u003cem\u003eAvp\u003c/em\u003e, \u003cem\u003eOxt\u003c/em\u003e, and \u003cem\u003eSst \u003c/em\u003efor each cell in our MERFISH study. We then performed a volume-based normalization of the fluorescent signals using a modified version of previously published methods\u003csup\u003e127\u003c/sup\u003e. Specifically, we first took the High_pass fluorescent values for \u003cem\u003eAvp\u003c/em\u003e, \u003cem\u003eOxt\u003c/em\u003e, and \u003cem\u003eSst \u003c/em\u003efor each cell and divided each value by the cell’s volume to yield volume-normalized fluorescence values. Subsequently, we subtracted the respective median volume-normalized fluorescence value for \u003cem\u003eAvp\u003c/em\u003e, \u003cem\u003eOxt\u003c/em\u003e, and \u003cem\u003eSst\u003c/em\u003e from all cells and set any negative values to 0. Finally, we divided our median-subtracted, volume-normalized fluorescence value by 1,000 and appended the resulting values for \u003cem\u003eAvp\u003c/em\u003e, \u003cem\u003eOxt\u003c/em\u003e, and \u003cem\u003eSst\u003c/em\u003e expression to the cell_by_gene matrix. \u003c/p\u003e\n\u003cp\u003e\u003cu\u003eMERFISH data analysis:\u003c/u\u003e VPT output files were loaded as Seurat objects in an R Studio environment (R v 4.4.1) (Seurat v5.0.1.9001) using the Seurat \u003cem\u003eLoadVizgen()\u003c/em\u003e function. Data from all 41 sections were then merged into one MERFISH Seurat object. Next, we defined the region of interest (ROI) for each section by selecting the rectangular area 200 µm dorsal, 1000 µm ventral, and 700 µm lateral to the top of the third ventricle. The unique IDs for all cells within each ROI detected in \u003cem\u003ez\u003c/em\u003e-plane three were exported to a .csv file using the Vizgen MERSCOPE Visualizer. The merged MERFISH Seurat object was then subset to retain only cells within our defined ROIs. Subsequently, all cells with less than 15 gene counts were removed, and the remaining cells were analyzed with the Seurat-based pipeline described above, with minor modifications. Notably, \u003cem\u003ei)\u003c/em\u003e during \u003cem\u003eFindVariableFeatures()\u003c/em\u003e, clip. range was set to “(-10, 10)”, according to Seurat recommendations for analyzing FISH-based counts, \u003cem\u003eii) \u003c/em\u003eno covariates were regressed during scaling of variable features, and \u003cem\u003eiii)\u003c/em\u003e PCA was conducted with only the combinatorial smFISH features, excluding mCherry. As with sc/snRNA-seq, the merged Seurat MERFISH object was split by ROI (“Slide_ID”) after running PCA, and we subsequently performed a reciprocal principal component analysis (RPCA)-based integration\u003csup\u003e36\u003c/sup\u003e with Seurat \u003cem\u003eIntegrateLayers()\u003c/em\u003e to correct for any batch effects. After integration, multiplet clusters driven by inaccurate cell segmentation were removed, and the post-integration steps in our pipeline were repeated until no multiplet clusters were observed. For differential gene expression analysis, we joined layers and ran \u003cem\u003eFindAllMarkers\u003c/em\u003e() using the non-parametric \u003cem\u003eWilcoxon Rank Sum test\u003c/em\u003e. Differentially expressed genes were defined as those \u0026gt; 0.2 average log\u003csub\u003e2\u003c/sub\u003e fold change and a \u003cem\u003eBonferroni-corrected\u003c/em\u003e p-value \u0026lt; 0.01. The post-integration pipeline was run for all levels of subclustering, beginning with all cells, followed by analysis of \u003cem\u003eSlc17a6\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e/\u003cem\u003eSim1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e, \u003cem\u003eSlc17a6\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e/\u003cem\u003eSim1\u003c/em\u003e\u003csup\u003e-\u003c/sup\u003e, and \u003cem\u003eSlc32a1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e populations.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eMERFISH Spatial Domain Analysis:\u003c/u\u003e After cell-type clustering with Seurat, we performed a multi-slice spatial domain detection analysis using the R package SpaDo\u003csup\u003e51\u003c/sup\u003e. Due to computational processing limitations, the initial analysis was limited to data from three animals (two male and one female), which had the most extensive rostral-to-caudal coverage of the PVH region and included 25 out of the total 41 tissue slices of the MERFISH analysis. Specifically, we selected slices spanning bregma levels -0.4 mm to -1.2 mm from Slides 3, 4, and 5. Spatial domain analysis was performed by using the \u003cem\u003eSpatialCellTypeDistribution_multiple\u003c/em\u003e() function to calculate the Spatially Adjacent Cell type Embedding (SPACE) for the MERFISH data. SPACE is calculated via a k-nearest neighbor analysis that identifies a cell’s local niche, which is then integrated with its cell-type annotation derived from the Seurat analysis. Once SPACE was computed, we used the \u003cem\u003eDistributionDistance\u003c/em\u003e() function to assess similarities between local niches, quantified by Jensen-Shannon divergence (JSD). Subsequently, the \u003cem\u003eDomainHclust\u003c/em\u003e() function was used with ‘auto_resolution’ set to 1, to derive spatial domains across all included cells and tissue sections. We then imported the calculated spatial domain information into Seurat as metadata to facilitate figure generation. Finally, to allow visualization of spatial domains across all tissue slices, we leveraged the results from this initial analysis to perform reference-based spatial domain annotation of the remaining 16 tissue slices. To accomplish this, we used the \u003cem\u003eSpatialReference()\u003c/em\u003e and \u003cem\u003eSpatialQuery() \u003c/em\u003efunctions to assign spatial domain annotations to a query dataset based on JSD-distance between the SPACE of each cell in the query dataset and the SPACE centroid for each domain in the reference dataset (\u003cstrong\u003eExtended Data Fig. 6a\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eStereotaxic injections and optic fiber implantation:\u003c/u\u003e Mice aged 6-10 weeks were deeply anesthetized by intraperitoneal injection of a ketamine/xylazine cocktail (100 mg/kg ketamine; 10 mg/kg xylazine). Next, the surgical area was shaved and sterilized prior to placing the mouse into a stereotactic frame (David Kopf model 940). For spinal cord injections, a midline incision was made above the interscapular region. Vertebrae were visualized by blunt dissection, and T2 was used to identify the injection site location between T2 and T3. The dorsal part of one vertebra was removed with forceps, allowing access to the spinal cord for injection. Injections were made ± 0.4 mm lateral to the midline by lowering a pulled glass pipette containing adeno-associated virus (AAV) or retrograde tracer (Fluoro-Gold or cholera toxin subunit B) into the spinal cord and using an air pressure injection system controlled by a Grass S48 stimulator to control injection speed\u003csup\u003e128\u003c/sup\u003e. Spinal cord injections began at -0.9 mm ventral to the surface of the spinal cord, and AAV/tracer continued to be injected while slowly raising the glass pipette to -0.2 mm. At the completion of each injection, the pipette was left in place for five minutes before removal. This process was then repeated on the contralateral side. To close the incision, the muscle layer was sutured with absorbable sutures (MedVet International: JORG22419), and the skin was sutured with non-absorbable sutures (MedVet International: MV-8661-V). For brain injections, a midline incision was made to expose the skull. At the site of injection, a small hole was drilled, and a pulled glass micropipette containing AAV or retrograde tracer was lowered to the desired injection site depth before infusions commenced using the air pressure injection system described above. Stereotactic coordinates for brain injections were as follows (from bregma): PVH, posterior -0.85, lateral ± 0.2, and ventral -4.9; PB, posterior -5.25, lateral ±1.35, and ventral -3.4; ARC, posterior -1.45, lateral ± 0.3, ventral -6.1. After an injection was completed, the pipette was left in place for five minutes before removal, and this process was repeated for other injection sites. After the injections were completed, the incision was closed using veterinary tissue adhesive (3M Vetbond). For optic fiber implantation, small holes were drilled and 200 µm core fiber optic cannulae with ceramic ferrules (RWD Life Science) were lowered into the PB (posterior, -5.25, lateral ±1.5, and ventral -3.1 from bregma). To secure the cannula, a mixture of dental acrylic and adhesive (dental cement) was then applied to cover the bottom of the ceramic ferrule and the entire exposed area of the skull, anchoring the fiber optic cannulae to the skull. Once the cement had hardened, a non-absorbable suture was placed at the back of the incision to tighten the skin around the cement. After removing the mouse from the stereotaxic frame, the cannula was capped to prevent debris from entering. After surgery, mice were injected with Meloxicam subcutaneously at a dose of 4mg/kg and placed on a 37°C heating pad until recovered. \u003c/p\u003e\n\u003cp\u003e\u003cu\u003eAAV and retrograde tracer injections:\u003c/u\u003e For projection-specific sequencing experiments, AAVrg-hSyn-Cre (Addgene: 105553) or HSV-hEf1a-mCherry-IRES-Cre (Mass General Brigham Gene Delivery Technology Core; Dr. Rachael Neve) was injected into the thoracic spinal cord (200 nl/side) or parabrachial region (100 nl/side) of H2B-TRAP mice. Spinal cord retrograde tracing histology was performed by injecting wild-type mice with Fluoro-Gold (FG; Fluorochrome) into the thoracic spinal cord (200 nl/side). For Cre-dependent EGFP-L10a expression in PVH\u003cem\u003e\u003csup\u003eMc4r\u003c/sup\u003e\u003c/em\u003e neurons, \u003cem\u003eMc4r\u003c/em\u003e-2a-Cre mice received injections of AAV5-EF1a-FLEX-EGFP-L10a (Addgene: 98747) into the PVH (100 nl/side). These same mice received cholera toxin subunit B (CTB; List Biological Laboratories: 104) injections into the PB (50 nl/side). AAVDJ-hSyn-DIO-EGFP-TeTxLC (ETH Zurich Viral Vector Facility: v322-5) or AAV8-hSyn-DIO-EGFP (control virus; Addgene: 50457) was used for chronic Cre-dependent neuronal silencing experiments via injections into the PVH of \u003cem\u003eBrs3\u003c/em\u003e-IRES-Cre or wild-type mice (15 nl/side). For CRACM electrophysiology experiments, Cre-dependent AAV8-hSyn-DIO-mCherry (Addgene: 50459) was injected into the PVH (50 nl/side) and Flp-dependent AAV5-EF1a-fDIO-ChR2-eYFP (UNC viral vector core: 172055) was injected into the ARC (200 nl/side) of \u003cem\u003eBrs3\u003c/em\u003e-IRES-Cre::\u003cem\u003eNpy\u003c/em\u003e-IRES-Flp mice. \u003cem\u003eIn vivo\u003c/em\u003e optogenetic terminal stimulation experiments were done by injecting Cre-dependent AAV9-EF1a-DIO-ChR2-eYFP (Addgene: 20298) or AAV9-EF1a-DIO-ChR2-mCherry (Addgene: 20297) into the PVH (15 nl/side) of \u003cem\u003eBrs3\u003c/em\u003e-IRES-Cre mice. Control virus for the optogenetic terminal stimulation experiments was Cre-dependent AAV8-hSyn-DIO-mCherry. Of note, one round of snRNA-seq with 10X Chromium was done by injecting the PVH (50 nl/side) of \u003cem\u003eSim1\u003c/em\u003e-Cre mice with AAVDJ-hSyn-H2B-mCherry (Boston Children’s Hospital Viral Core)\u003csup\u003e119\u003c/sup\u003e and collecting mCherry-positive nuclei\u003csup\u003e117,118\u003c/sup\u003e. Also, the male mouse used for MERFISH Slide 3 was injected with AAVrg-hSyn-mCherry into the spinal cord (200 nl/side), and the male mouse used for MERFISH Slide 6 was injected with AAVrg-hSyn-mCherry into the parabrachial region (50 nl/side). After stereotactic injections, experiments were initiated three weeks post-surgery for all AAVs to allow for suitable expression levels. FG and CTB were injected 3–7 days before sacrifice to enable retrograde transport. All stereotaxic injection sites were validated by post hoc immunofluorescence. All “misses” or \"partial\" hits, as determined by fluorescent expression in the target cells, were excluded from data analysis.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eRNAscope fluorescent \u003cem\u003ein situ\u003c/em\u003e hybridization and immunofluorescence\u003c/u\u003e\u003cu\u003e:\u003c/u\u003e RNAscope Multiplex Fluorescent Reagent Kit V2 (Advanced Cell Diagnostics: 323100) was used to perform \u003cem\u003ein situ\u003c/em\u003e hybridization of mRNA in the PVH. For neuroendocrine PVH neuron labeling paired with FISH, adult mice aged 8-12 weeks were injected intraperitoneally with Fluoro-Gold (Fluorochrome; 30 mg/kg) one week prior to lethal injection of ketamine/xylazine (150mg/kg ketamine + 15 mg/kg xylazine) and transcardial perfusion with RNase-free PBS and 10% phosphate-buffered formalin (Fisher: SF100-20). Brains were then extracted and post-fixed in 10% phosphate-buffered formalin overnight, followed by consecutive overnight incubations in 10%, 20%, and 30% RNase-free sucrose solution in PBS. Coronal brain sections were then sliced at 30 µm using a freezing microtome, briefly washed in RNase-free 0.5% Triton X-100 (Sigma Aldrich) in PBS, mounted onto Superfrost™ Plus slides, and stored at -80˚C until ready for FISH. RNAscope was completed according to the manufacturer’s protocol. First, the slides were removed from the freezer and washed with sterile PBS, followed by a 30-minute incubation at 60°C. The slides were then fixed again with 10% phosphate-buffered formalin for 15 minutes at 4°C, followed by dehydration in 50%, 70%, and 100% ethanol solutions. Hydrogen Peroxide was then added to slides for 10 minutes at room temperature. After washing twice with PBS, a hydrophobic barrier surrounding the tissue sections was drawn on the slide (ImmEdge™: H-4000), and the slide was treated with Protease III for 30 minutes. Slides were next hybridized with RNAscope probes targeting mRNA for genes of interest for two hours at 40°C, including \u003cem\u003eAox3 \u003c/em\u003e(Mm-Aox3: 836451-C1), \u003cem\u003eAvp \u003c/em\u003e(Mm-Avp: 401391-C3), \u003cem\u003eBrs3\u003c/em\u003e (Mm-Brs3: 454111-C1 or C3), \u003cem\u003eCol12a1 \u003c/em\u003e(Mm-Col12a1: 312631-C2),\u003cem\u003e Crh \u003c/em\u003e(Mm-Crh: 316091-C1), \u003cem\u003eEsr2\u003c/em\u003e (Mm-Esr2: 316121-C3), \u003cem\u003eNfix \u003c/em\u003e(Mm-Nfix: 522331-C2), \u003cem\u003eNpr3\u003c/em\u003e (Mm-Npr3: 502991-C2), \u003cem\u003eNpsr1\u003c/em\u003e (Mm-Npsr1: 317501-C1), \u003cem\u003eOxt \u003c/em\u003e(Mm-Oxt: 493171-C2), \u003cem\u003ePla2r1 \u003c/em\u003e(Mm-Pla2r1-No-XHs: 854581-C1), \u003cem\u003eRxfp3 \u003c/em\u003e(Mm-Rxfp3: 439381-C1), \u003cem\u003eScgn \u003c/em\u003e(Mm-Scgn: 482721-C2), \u003cem\u003eSim2 \u003c/em\u003e(Mm-Sim2: 1108911-C1), \u003cem\u003eSst \u003c/em\u003e(Mm-Sst: 404631-C1), or \u003cem\u003eTrh \u003c/em\u003e(Mm-Trh, 436811-C1).After hybridization, slides underwent three amplification steps at 40°C (AMP1-FL and AMP2-FL for 30 minutes each, AMP3-FL for 15 minutes), followed by probe-specific HRP amplification and Opal dye (Akoya Biosciences) incubations at 40°C for visualization. After the Opal dye step, HRP blocker was applied, and this process was repeated until all probes were developed. \u003c/p\u003e\n\u003cp\u003eAfter completing RNAscope slides were washed three times with PBS and incubated overnight at 4°C with primary antibody prepared in blocking solution made with PBS, 0.4% Triton X-100, and 3% normal donkey serum. The primary antibodies used include rabbit anti-Fluoro-Gold (1:300; Fluorochrome), goat anti-cholera toxin subunit B (1:300: List Biological Laboratories: 703), and rabbit anti-GFP (1:1,000; Thermo Fisher Scientific: A-11122). The next day, slides were washed five times with PBS, and incubated for two hours at room temperature in the appropriate Alex Fluor-conjugated donkey secondary antibody (1:1,000; Thermo Fisher Scientific) prepared in blocking solution. Finally, slides were washed again three times with PBS before coverslipping with VECTASHIELD mounting media with DAPI (Vector Laboratories: H-1900-10). Slides were imaged at 10X magnification with an Olympus Slideview VS200 slide-scanning microscope or at 20X magnification with a Leica Stellaris 5 confocal microscope.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eQuantification of PVH neuroendocrine neurons\u003c/u\u003e: For each neuroendocrine subtype, 12 images (for \u003cem\u003eCrh\u003c/em\u003e, \u003cem\u003eTrh\u003c/em\u003e and \u003cem\u003eSst\u003c/em\u003e) and 8 images (for \u003cem\u003eAvp\u003c/em\u003e and \u003cem\u003eOxt\u003c/em\u003e) covering rostral to caudal PVH, were exported using QuPath\u003csup\u003e129\u003c/sup\u003e from the RNAscope and ip Fluoro-Gold labeling experiments (\u003cstrong\u003eFig. 1k-p\u003c/strong\u003e), which consisted of three channels: Fluoro-Gold, the neuroendocrine hormone of interest, and the novel marker gene for the corresponding neuroendocrine subtype identified by sc/snRNA-seq. Neuroendocrine peptide gene-positive cells were identified using the Cellpose2 model (“cyto2”), with manual adjustments made for any misidentified or missed cells. The selected cell masks were saved and imported into Fiji (ImageJ)\u003csup\u003e130\u003c/sup\u003e, where the multi-point tool further facilitated counting of neurons expressing neuroendocrine marker gene pairs (\u003cem\u003eCrh-Scgn\u003c/em\u003e, \u003cem\u003eTrh-Nfix\u003c/em\u003e, \u003cem\u003eSst-Col12a1\u003c/em\u003e, \u003cem\u003eAvp-Pla2r1\u003c/em\u003e, and \u003cem\u003eOxt-Rxfp3\u003c/em\u003e) and whether they were labeled by Fluoro-Gold. The percentage of FG-positive neurons for each neuroendocrine marker gene pair was then calculated. The same method is applied to count FG-negative neurons that expressed neuroendocrine peptide genes (FG-negative \u003cem\u003eCrh\u003c/em\u003e, \u003cem\u003eTrh\u003c/em\u003e, \u003cem\u003eSst\u003c/em\u003e, \u003cem\u003eAvp\u003c/em\u003e and \u003cem\u003eOxt\u003c/em\u003e ) and whether they co-expressed the associated neuroendocrine marker gene identified by sc/snRNA-seq.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eHistological analysis of Cre\u003c/u\u003e\u003cu\u003e-reporters and immunofluorescent experiments: \u003c/u\u003eAt the conclusion of experiments involving Cre-reporter expression, retrograde tracer injections, and AAV injections, brain/spinal cord histology was performed. For Cre-reporter histology, R26-LSL-EGFP-L10a reporter mice were crossed with \u003cem\u003eSlc17a6\u003c/em\u003e (VGLUT2)-IRES-Cre, \u003cem\u003eSlc32a1\u003c/em\u003e (VGAT)-IRES-Cre, and \u003cem\u003eSim1\u003c/em\u003e-Cre mice. Adult mice were lethally anesthetized and transcardially perfused as above. Brains were then extracted and postfixed overnight in 10% phosphate-buffered formalin. Brains were then sliced coronally at 40 µm and mounted directly onto glass slides. For experiments requiring immunofluorescence, floating sections were washed in PBS prior to incubation overnight at room temperature in primary antibody solution as described above. All primary antibodies used are described above, except rat anti-mCherry (1:3,000; Thermo Fisher Scientific: M11217). The next day, sections were washed and incubated with Alex Fluor-conjugated donkey secondary antibody as above. Subsequently, tissue was washed, mounted onto slides, and coverslipped with VECTASHIELD mounting media with DAPI. Slides were imaged at 10X magnification with an Olympus Slideview VS200 slide-scanning microscope. \u003c/p\u003e\n\u003cp\u003e\u003cu\u003eBody weight measurements after PVH\u003cem\u003e\u003csup\u003eBrs3\u003c/sup\u003e\u003c/em\u003e neuron silencing:\u003c/u\u003e To begin bodyweight studies, initial body weights were recorded for littermate \u003cem\u003eBrs3\u003c/em\u003e-IRES-Cre and wild-type mice, and mice were then divided into the stereotactic surgery groups described above (AAVDJ-hSyn-DIO-EGFP-TeTxLC or AAV8-hSyn-DIO-EGFP). Subsequently, mice remained group-housed for the duration of the experiment. Body weights were recorded during the light cycle between 10:00 AM and 12:00 PM every 7 days for a total of 6 weeks. At the end of the study, mice were transcardially perfused as above for histological analysis of AAV expression in the PVH. Mice without bilateral expression of GFP were removed from the analysis.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eChannelrhodopsin-\u003c/u\u003e\u003cu\u003e2 (ChR2)-assisted circuit mapping (CRACM):\u003c/u\u003e \u003cem\u003eBrs3\u003c/em\u003e-IRES-Cre::\u003cem\u003eNpy\u003c/em\u003e-IRES-Flp mice underwent stereotactic surgery at 5-7 weeks old as described above, and CRACM experiments were completed at 8-10 weeks-old as described previously\u003csup\u003e131\u003c/sup\u003e. Briefly, mice were anesthetized with isoflurane, decapitated, and brains were rapidly extracted and submerged in ice-cold choline-based cutting solution saturated with carbogen (95% O\u003csub\u003e2, \u003c/sub\u003e5% CO\u003csub\u003e2\u003c/sub\u003e). For slice preparation, brains were sliced at 300 µM coronally with a vibrotome (Campden 7000smz-2) and kept in cutting solution at 34°C for 10 min. Next, slices were transferred to artificial cerebrospinal fluid (aCSF) for at least 45 min at room temperature. After recovery, an individual coronal slice containing the PVH region was placed in a recording chamber where it was continuously superfused with aCSF and viewed under a microscope (SliceScope Pro 1000, Scientifica). PVH\u003cem\u003e\u003csup\u003eBrs3\u003c/sup\u003e\u003c/em\u003e neurons were fluorescently labeled by Cre-dependent AAV-mCherry, and Flp-dependent AAV-ChR2-eYFP drove ChR2 expression in ARC\u003cem\u003e\u003csup\u003eNpy/Agrp\u003c/sup\u003e\u003c/em\u003e neurons. Open-tip resistances for patch pipettes were 3-5 MW and were backfilled with CsCl internal solution. To assess connectivity between ARC\u003cem\u003e\u003csup\u003eNpy/Agrp\u003c/sup\u003e\u003c/em\u003e à PVH\u003cem\u003e\u003csup\u003eBrs3\u003c/sup\u003e\u003c/em\u003e neurons, whole-cell voltage clamp recordings from PVH\u003cem\u003e\u003csup\u003eBrs3\u003c/sup\u003e\u003c/em\u003e neurons were done while photostimulating ChR2-expressing terminals from ARC\u003cem\u003e\u003csup\u003eNpy/Agrp\u003c/sup\u003e\u003c/em\u003e neurons. To evoke IPSCs with light, four 470 nm light pulses of 2 ms duration were administered one second apart during the first four seconds of a ten second protocol that was repeated 30 times. Blue light was applied via wide-field exposure through the 40X objective with an LED (Cool LED pE-100). The light output was controlled by a programmable pulse stimulator (Master 8, A.M.P.I.) and pClamp 10.5 software (Axon Instruments). Light-evoked IPSCs were isolated via glutamate receptor antagonism with 1 mM kynurenate, and short latency (≤ 6 ms) responses upon light stimulation were considered to be light-driven. \u003c/p\u003e\n\u003cp\u003e\u003cu\u003eFood Intake measurements after PVH\u003cem\u003e\u003csup\u003eBrs3\u003c/sup\u003e\u003c/em\u003e neuron \u003c/u\u003e\u003cu\u003eà PB optogenetic stimulation:\u003c/u\u003e We assayed dark-cycle food intake while optogenetically stimulating PVH\u003cem\u003e\u003csup\u003eBrs3\u003c/sup\u003e\u003c/em\u003e neuron projections to the parabrachial region. \u003cem\u003eBrs3\u003c/em\u003e-IRES-Cre mice underwent stereotactic surgery for AAV injections and optic fiber implants as described above. Prior to beginning optogenetics studies, mice were allowed to recover for at least three weeks and were acclimated to tethering to patch cords and single housing. On experimental days, patch cords were bilaterally attached to optic fibers over the PB two hours before the onset of dark, and food was removed. Food was returned at the onset of dark and intake was then measured every hour for the first three hours of the dark cycle. Trials consisted of a baseline light-off tests, followed by light-stimulation experimental trials on the following day. Photostimulation was delivered with square wave pulses of 473 nm blue light, delivered at ~8-10 mW of power measured at the fiber tip, with 20 Hz stimulation (10ms pulses; 2 seconds on, 3 seconds off). LabView software and a National Instruments NIDAQ board were used to control our stimulation protocol. \u003c/p\u003e\n\u003cp\u003e\u003cu\u003eStatistical analysis\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eStatistical analyses for sc/snRNA-seq and MERFISH were performed in R, as described above. All other analyses were conducted using GraphPad Prism (v10.3.0), with the specific statistical tests for each experiment indicated in the figure legends. No statistical methods were used to predetermine sample size, and randomization and/or blinding were not applied for sc/snRNA-seq or MERFISH experiments. Randomization was applied for body weight and food intake experiments. For the body weight study, a two-tailed two-way repeated measures ANOVA with virus and time as factors was performed, followed by Tukey’s post hoc multiple comparisons test. For body weight gain measurements, a two-tailed one-way ANOVA followed by Tukey’s post hoc test was used. For the optogenetic feeding behavior assay, a two-tailed two-way repeated measures ANOVA with virus and laser as factors was performed, followed by Sidak’s post hoc multiple comparisons test. All results are presented as mean ± SEM. Statistical significance was defined as P \u0026lt; 0.05, with asterisks indicating significance levels: *P \u0026lt; 0.05, **P \u0026lt; 0.01, and ****P \u0026lt;0.0001.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eMaterials availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study did not generate any new and unique reagents.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMouse sc/snRNA-seq data from this study have been deposited into the NCBI Gene Expression Omnibus (GEO) with accession number GSE303256. Allen Brain Cell Atlas mouse sc/snRNA-seq data were downloaded from the following locations: https://allen-brain-cell-atlas.s3.us-west-2.amazonaws.com/index.html#expression_matrices/WMB-10Xv2/20230630/ (10Xv2), and https://allen-brain-cell-atlas.s3.us-west-2.amazonaws.com/index.html#expression_matrices/WMB-10Xv3/20230630/ (10Xv3). Spinal cord-projecting snRNA-seq data not generated in this study were obtained by accessing publicly available datasets within the GEO database: GSE247594\u003csup\u003e89\u003c/sup\u003e and GSE212409\u003csup\u003e90\u003c/sup\u003e. Human snRNA-seq data were obtained by downloading publicly available datasets, including the Siletti et al.\u003csup\u003e75\u003c/sup\u003e dataset from the Human Brain Cell Atlas Repository (https://datasets.cellxgene.cziscience.com/5e399d37-23d3-4673-8761-9f443c1fdc14.rds) and the Tadross et al.\u003csup\u003e76\u003c/sup\u003e dataset from the University of Cambridge Apollo Repository (https://www.repository.cam.ac.uk/items/cad1c61a-e4e5-4443-ad11-92e4f48b3861). Mouse MERFISH data from this study have been deposited in the Iowa Research Online (IRO) repository with record accession number 9984403060302771. All Seurat objects have been deposited to Zenodo at: https://zenodo.org/records/15983704. Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll custom code has been deposited to Zenodo at: https://zenodo.org/records/15983704. Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank Drs. Mark Andermann, Joel Geerling, and Clifford Saper as well as the Lowell, Tsai, and Resch laboratories for helpful discussions; Alysia Berns, Jia Yu, and Yanfang Li for technical support; \u0026nbsp;The BNORC Functional Genomics and Bioinformatics Core (P30DK046200) and the Iowa Institute for Human Genetics Genomics Division (IIHG, RRID: SCR_023422) for helpful discussions and technical assistance with sc/snRNA-seq; Zachary Niziolek and the Bauer Core Facility at Harvard University, the BIDMC Flow Cytometry Core, and Heath Vignes, Michael Shey, and Thomas Kaufman of the Flow Cytometry Facility at the University of Iowa Carver College of Medicine for helpful discussions and technical support; The ICCB-Longwood Screening Facility of Harvard Medical School for assistance with the snRNA-seq experiments; Dr. Sayak Mitter and Vizgen support for technical assistance with the MERSCOPE platform; Mara Jendro and Li-Chun (Queena) Lin for their assistance with MERSCOPE experiments within the Iowa NeuroBank Core in the Iowa Neuroscience Institute at the University of Iowa Carver College of Medicine. This research was funded by the following NIH grants to L.T.T.: R01DK128406; to B.B.L.: R01DK075632, R01DK134427, and R01DK096010; to J.M.R.: R00HL144923; to M.C.M.: F31HL170784; and T.C.B. and M.C.M. were supported by a pharmacological sciences predoctoral training grant T32GM144636. Additional funding to J.M.R. came from the American Heart Association (AHA 935362) and from a University of Iowa Fraternal Order of Eagles Diabetes Research Center Pilot and Feasibility Catalyst Grant. Y.L. was supported by a predoctoral fellowship from the American Heart Association (AHA 25PRE1372983). A.M.D. was supported by a postdoctoral fellowship from the Charles A. King Trust.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, Y.L., T.C.B., J.N.C., L.T.T., B.B.L., and J.M.R. Data curation, Y.L., T.C.B., C.L.J, L.T.T., and J.M.R. Formal analysis, T.C.B., S.N., C.L.J., S.J.W., J.N.C., and L.T.T. (sc/snRNA-seq); Y.L., T.C.B., S.N., and J.M.R. (MERFISH). Funding acquisition, Y.L., L.T.T., B.B.L., and J.M.R. Investigation, T.C.B., S.N., L.W., D.P., A.W., H.S., J.N.C., L.T.T. (sc/snRNA-seq); Y.L., and J.M.R. (MERFISH); Y.L., M.C.M., J.T., and E.D.L. (FISH/IF); A.M.D., J.C.M., Z.Y., and J.M.R (electrophysiology and behavioral experiments). Methodology, Y.L., T.C.B., S.N., C.L.J., J.N.C., L.T.T., B.B.L., and J.M.R. Project administration, L.T.T., B.B.L., and J.M.R. Resources, L.T.T., B.B.L., and J.M.R. Software, Y.L., T.C.B., S.N., C.L.J., L.T.T., and J.M.R. Supervision, L.T.T., B.B.L., and J.M.R. Validation, T.C.B., S.N., C.L.J., S.J.W., J.N.C., and L.T.T. (sc/snRNA-seq); Y.L., T.C.B., S.N., and J.M.R. (MERFISH). Visualization, Y.L., T.C.B., and J.M.R. Writing – original draft , Y.L., T.C.B., L.T.T., B.B.L., and J.M.R. 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Nature \u003cem\u003e620\u003c/em\u003e, 154-162. 10.1038/s41586-023-06358-0.\u003cstrong\u003e\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7895391/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7895391/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The paraventricular hypothalamus (PVH) controls many behavioral and physiologic processes, including appetite, social behavior, autonomic outflow, and pituitary hormone secretion. However, molecular markers for centrally-projecting PVH neuron populations remain largely undefined, and a complete census of PVH cell types has not been established. Therefore, we performed extensive single-cell/nucleus RNA sequencing to catalog PVH neuron subtypes and multiplexed error-robust fluorescence in situ hybridization (MERFISH) to map them spatially. Our spatial transcriptomic atlas resolves 26 Sim1+ and 29 GABAergic neuron populations from the PVH and surrounding areas, revealing multiple subtypes not described previously and distinct transcriptional programs between neuroendocrine and centrally-projecting neurons. Additionally, projection-based profiling determined neuronal subtypes that project to the parabrachial region (PB) and spinal cord, helping to identify PVH populations that regulate satiety and sympathetic nervous system activity, respectively. Notably, activation of PB-projecting PVH neurons expressing bombesin-like receptor 3 (Brs3) reduces food intake and silencing them causes obesity. Together, this atlas contributes high-resolution PVH spatial and circuit-based gene expression profiles, representing a valuable resource for the field of homeostasis.","manuscriptTitle":"A spatial and projection-based transcriptomic atlas of paraventricular hypothalamic cell types","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-21 05:03:23","doi":"10.21203/rs.3.rs-7895391/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"13c1a220-c711-4617-8e15-bdcf3ef5f089","owner":[],"postedDate":"October 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":56565825,"name":"Biological sciences/Neuroscience/Genetics of the nervous system"},{"id":56565826,"name":"Biological sciences/Neuroscience/Feeding behaviour/Hypothalamus"}],"tags":[],"updatedAt":"2025-10-27T14:43:16+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-21 05:03:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7895391","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7895391","identity":"rs-7895391","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}