Molecular, spatial, and functional single-cell profiling of the hypothalamic preoptic region

scRNA-seq of the preoptic region We dissected a rostral part of the mouse hypothalamus containing the preoptic region ( Fig. 1A ), i.e. the medial preoptic area (MPOA) and surrounding nuclei (~2.5 mm x 2.5 mm x 1.1 mm, Bregma +0.5 to −0.6) from adult female and male brains, and dissociated the tissue using a custom protocol that improved cell survival and capture ( fig. S1 ). We collected scRNA-seq profiles from 31,299 cells across 3 replicates of each sex using droplet-based scRNA-seq ( 27 – 29 ). We used unsupervised, graph-based, community detection methods ( 28 , 30 , 31 ) modified by us ( fig. S2 ) to cluster cells ( 29 ). This led to the delineation of major cell classes including inhibitory and excitatory neurons, microglia, astrocytes, immature oligodendrocytes (newly formed oligodendrocytes and oligodendrocyte progenitor cells), mature oligodendrocytes, ependymal cells, endothelial cells, fibroblasts, macrophages, and mural cells, as well as subdivisions within these cell classes ( Fig. 1B , Table S1 ). Further clustering of inhibitory neurons (15,042 cells) and excitatory neurons (3,511 cells) separately revealed 43 and 23 subpopulations, respectively ( Figs. 1B , S3A , B ; Tables S1 , S2 ). Hereafter, we denote excitatory and inhibitory neuronal clusters as e1, e2, …, and i1, i2, …, respectively. We also provide specific names for these clusters based on marker genes ( Fig. 1C , D and see figs. S4 , S5 for displays emphasizing neuropeptide expression) ( 29 ). While the majority of the identified clusters expressed either excitatory or inhibitory neuronal markers, we observed expression of the GABA synthetic genes Gad1 and Gad2 in many excitatory neuronal clusters classified on the basis of expression of Vglut2 (Slc17a6), with Gad2 expression being particularly widespread ( fig. S3C ). By contrast, very few Slc17a6-positive clusters expressed the GABA transporter gene Vgat (Slc32a1). These data suggest that Slc17a6 and Slc32a1 are better discriminators for excitatory versus inhibitory neurons, corroborating evidence from other brain areas ( 32 ). Interestingly, cells in two neuronal clusters originally designated as inhibitory and one originally designated as excitatory co-expressed Slc17a6 (or Slc17a8, vGlut3) and Slc32a1. These cells were unlikely to be a clustering artifact because individual cells co-expressed both markers, nor did they correspond to doublets ( 29 ), hence they potentially represent hybrid neurons capable of GABA/Glutamate co-release, as characterized in the hypothalamus and a few other brain regions ( 32 – 34 ). We denote these clusters as h1, h2 and h3 ( Figs. 1C , D , S3C ). To determine the gene categories that best discriminate neuronal clusters, we examined the top 5 most differentially expressed genes in each cluster and observed enrichment for neuropeptides and molecules involved in neuromodulator production and transport, as well as for transcription factors, but not for neuromodulator (neuropeptide and hormone) receptors. Quantitative analyses of enrichment profiles of these three gene classes among differentially expressed genes further support this notion ( Figs. 1E , S6 ; Table S3 ). Neuromodulator receptors did discriminate some clusters (for example Npr1, Rxfp1, Brs3, Drd1; Figs. 1C , D , S4 , S5 ). However, on average, neuromodulator receptors were expressed more widely and at lower levels than neuromodulators and transcription factors, limiting their use as potential markers for functional studies. Notably, most clusters were discriminated by combinations of genes, rather than by single markers. Hierarchical tree analyses ( 29 ) showed that inhibitory neuronal clusters expressing a common neuromodulator were often grouped together on the tree, for example clusters expressing Avp, Gal, Crh, Tac1 and Sst ( Fig. 1C ), suggesting potential functional or developmental commonality among them. By contrast, neuromodulators largely failed to group excitatory neuronal clusters ( Fig. 1D ). Instead, predicted locations of individual clusters on the basis of spatial expression patterns of their marker genes observed in the Allen Brain Atlas ( 35 ) and our own in situ hybridization data ( fig. S7 ) suggest that excitatory clusters tended to be grouped on the tree by anatomical structures or nuclei ( Fig. 1D ). For example, markers of clusters e4, e2, e21, h3, and e17 defined a node in the tree located in the PVN and adjacent nuclei (MPN, PaAP, BAC, and BNST), markers of node-sharing clusters e13 and e7 placed these populations in the MnPO/AvPe/VMPO region, while markers of e12, e6, e5, and e1 placed these cells in the MPN/MPA region ( Fig. 1D ) (See Table S4 for full names of the nuclei described in this work). We thus hypothesize that excitatory neuron types tend to be spatially segregated in distinct anatomical structures of the preoptic region, in a manner similar to the spatial segregation of different types of excitatory neurons in various layers of the cortex ( 36 ).

Next, we performed MERFISH measurements of the preoptic region (1.8 mm x 1.8 mm x 0.6 mm, Bregma +0.26 to −0.34), within the area characterized by scRNA-seq, targeting a set of 155 genes ( Fig. 3A ; Table S6 ) ( 29 ). These genes were comprised of two groups: 1) 85 preselected genes that were either known markers for major cell classes or relevant to neuronal functions of the hypothalamus, such as neuropeptides and neuromodulator receptors; 2) 70 additional genes that were identified by scRNA-seq as neuronal cluster markers but not already included in the 85 preselected genes. Among these 155 genes, 135 genes were imaged using combinatorial smFISH with an error-robust barcoding scheme, as demonstrated previously for MERFISH ( 20 , 26 , 40 ). The remaining 20 genes were relatively short and/or expressed at high levels, challenging for combinatorial smFISH detection, and hence were measured in sequential rounds of multi-color FISH following the combinatorial run. The sexually dimorphic expression previously reported for eleven genes ( 41 , 42 ) was confirmed here ( fig. S8 ). We sectioned the preoptic region into 60 evenly spaced slices along the anterior-posterior axis and performed 3D MERFISH imaging on every fifth slice ( 29 ). Individual RNA molecules were clearly detected and identified ( fig. S9 ) and individual cells were segmented based on DAPI and total mRNA staining ( fig. S10 ) ( 29 ). In total, we profiled >400,000 cells from 3–4 replicates in naïve male and female animals, as well as >500,000 additional cells from 3–5 replicates of animals subjected to behavioral stimuli ( 29 ). MERFISH expression data showed high reproducibility between replicates ( fig. S11A ), good correlation with bulk RNA-seq data of the preoptic region ( 43 ) ( fig. S11B ), and a low false-detection rate ( fig. S11C ). For the targeted genes, MERFISH detected on average 6 – 8 fold more transcript copies per cell than scRNA-seq ( fig. S12A – D ), underscoring the high sensitivity of MERFISH. An unsupervised, community-detection-based clustering approach similar to that applied to scRNA-seq data was used to identify transcriptionally distinct cell populations in MERFISH data ( Fig. 3B , C ; Table S7 ) ( 29 ). MERFISH identified all major cell classes ( Fig. 3B , C ), except for macrophages and fibroblasts potentially because the corresponding marker genes were not included in the MERFISH gene library. The expression profiles of cell classes measured by MERFISH were strongly correlated with those determined by scRNA-seq ( Fig. 3D ). However, the relative abundance of cells in various cell classes differed in the two datasets ( fig. S12E ). In particular, astrocytes, endothelial cells and ependymal cells were depleted in our scRNA-seq data, presumably due to cell loss during tissue dissociation. MERFISH also provided a direct measurement of the spatial distribution of major cell classes. As expected, mature oligodendrocytes were enriched in the anterior commissure and the fornix—major myelinated fiber tracts of the rostral hypothalamus, whereas immature oligodendrocytes, astrocytes, microglia, and endothelial cells were dispersed throughout ( Fig. 3E , F ). Ependymal cells formed a single layer lining the more caudal aspects of the third ventricle, and mural cells were organized in vermiform structures resembling blood vessels ( Fig. 3E , F ). Notably, inhibitory and excitatory neurons exhibited distinct distributions ( Fig. 3E , F ). Inhibitory neurons, the more abundant neuronal type in the preoptic region, were widely dispersed across this region but enriched in specific posterior nuclei, including the BNST and MPN. By contrast, excitatory neurons were specifically enriched in a few nuclei anteriorly, but became more dispersed posteriorly, and, in agreement with previous reports ( 44 ), were depleted in the posterior BNST.

Next, we examined the spatial distributions of individual neuronal clusters ( Figs. 5A , S17 , S18 ) within the framework of major anatomically defined nuclei of the preoptic region as depicted in Fig. 5B ( 45 ). About 30% of the MERFISH clusters were enriched primarily in a single nucleus (pink shading, Fig. 5C ), for example cluster I-5 primarily in the VLPO and cluster E-9 in the PVA ( Fig. 5A - C ), while approximately half of the clusters were distributed over a few (two to four), often physically contiguous nuclei (unshaded clusters, Fig. 5C )—for instance cluster I-3 in the BNST-mv, PaAP, PS and SHy and cluster I-12 in the StHy, MPA, and MPN ( Fig. 5A - C ). This anatomical dispersion may reflect a similar function of the same cell type across distinct nuclei, or a developmental relationship of spatially distinct cells. By contrast, a small fraction of the neuronal clusters were dispersed and not enriched in any given nucleus, e.g. I-21 and E-22 ( Fig. 5A ). While most nuclei were populated by both excitatory and inhibitory neurons, the PVA and BAC only contained excitatory clusters ( Fig. 5C , green shaded row) and the BNST-p and BNST-mv contained only inhibitory clusters ( Fig. 5C , blue shaded rows), consistent with previous observations of high expression of Slc17a6 and Slc32a1 in these regions, respectively ( 35 , 44 ). Neuronal clusters of the preoptic region appeared highly intermixed, with multiple clusters occupying any given nucleus. To quantify the degree of intermixing, we calculated the neighborhood composition for each neuron. This analysis showed that each neighborhood contained multiple clusters and was typically not dominated by a single cell population ( Fig. 5D , E ). These direct spatial measurements allowed us to provide an anatomy-based taxonomy for the identified neuronal clusters, except for the dispersed clusters, which we named based on marker genes ( Table S9 ). The putative correspondence between these MERFISH clusters and scRNA-seq clusters allowed us to further assess the spatial locations of scRNA-seq clusters and compare them with our earlier predictions shown in Fig. 1C , D . In nearly all cases, the predicted locations matched or partially overlapped those of the corresponding MERFISH clusters ( Table S9 ), lending additional support to our earlier observations on the spatial relationship between transcriptionally similar clusters ( Fig. 1C , D ). However, we cautiously note that the predicted scRNA-seq cluster locations represent rough approximations due to the relatively low resolution of available in situ hybridization data and suffer from occasional ambiguity in the spatial patterns of some marker genes. Moreover, due to the remaining heterogeneity in some of the identified scRNA-seq and MERFISH clusters, the putative correspondence may only represent similarity between subsets of cells within these clusters.

Our MERFISH data also partitioned a number of previously reported single cell types, for example Gal-expressing and Adcyap1-expressing neurons, into multiple distinct cell populations ( Fig. 7A ). We observed ten Gal-enriched MERFISH clusters, several of which were scattered across multiple nuclei, such as cluster I-14:MPA/MPN/StHy ( Fig. 7A - C ). I-14 was strongly activated during parenting, as shown below, revealing that molecularly and functionally defined cell types can spread across multiple nuclei. Similarly, MERFISH identified 14 clusters enriched in Adcyap1 and Bdnf ( Fig. 7A , D , E ), previously designated as markers for warm-sensitive neurons ( 8 ). Yet only one of these clusters, E-3: AvPe/Pe/VMPO/VLPO, displayed the established spatial location of warm-sensitive cells ( 8 ) ( Fig. 7E ). Indeed, upon heat stress ( 29 ), a high level of the IEG cFos was expressed in cells in the region covered by E-3, and Sncg, a marker gene of E-3’s corresponding scRNA-seq cluster e13 ( Table S9 ), was highly enriched in these cFos-positive cells ( Fig. 7F ). Interestingly, E-3 expressed the leptin and prolactin receptors (Lepr and Prlr; Fig. 7D ), suggesting a mechanism whereby metabolic and reproductive states may modulate thermoregulation. Indeed, preoptic cells receiving projections from Arcuate Nucleus Kiss1 cells were recently implicated in the regulation of hormonally induced hot flashes via the activation of the receptor Tacr3 ( 58 ). Notably, E-3 and e13 expressed both Kiss1 receptor and Tacr3, implicating the warm-sensitive cluster in the generation of hot flashes.

Here, we combined the power of scRNA-seq and MERFISH to create a spatially resolved and functionally aware cell atlas of the preoptic region of the mouse hypothalamus. These methods identified major cell classes and neuronal subpopulations with correlated gene expression profiles, providing cross validations for both methods. Moreover, the two methods are complementary: scRNA-seq measured more genes than MERFISH and helped define marker genes for MERFISH; whereas MERFISH provided spatial context of cells at high resolution, as well as more accurate detection and quantification of weakly expressed genes, including functionally important genes such as neuropeptide and hormone receptors. As a result, the combined data provided a more complete picture of the transcriptional diversity and spatial organization of individual cells in the preoptic region. We observed a remarkable diversity of neurons in this region, comprising ~70 different neuronal clusters. Transcription factors and cell surface markers have been observed as markers of neuronal identity in the mouse spinal cord ( 65 ) and cortex ( 66 ) and the Drosophila olfactory system ( 67 ). In the mouse preoptic region, genes discriminating neuronal clusters were enriched for neuropeptides and molecules involved in neuromodulator synthesis and transport, and for transcription factors. By contrast, neuromodulator receptors were weaker discriminators of these neuronal populations or were expressed at low levels, providing a useful note of caution in using these genes for targeted functional studies in the preoptic region. Many of these neuronal populations were defined by a combination of multiple genes, indicating that genetic intersectional approaches will be most useful in the functional interrogation of specific cell types ( 67 ). The list of cell populations identified in this work substantially expands and further defines previously reported cell types in this region. We observed specific clusters enriched in genes previously identified as markers of functionally important preoptic cells (for example, cells involved in parenting, aggression, thermoregulation, sleep, thirst), such as Galanin, Th, Adcyap1, Nts, Crh, Tac1, Cck, Agtr1a, Nos1, and Aromatase ( Tables S5 , S9 ). Our data also resolved many of the previously described singular cell types into multiple cell populations. We observed good correlation between our data and recent scRNA-seq analyses of the whole mouse hypothalamus ( 68 ) and of the whole mouse brain ( 69 ). In the latter study, which we compared to ours in more detail because it had a larger whole hypothalamic dataset, 15 neuronal clusters were identified from ~2,000 profiled hypothalamic neurons ( fig. S22 ) ( 69 ). However, because we used ~10 times more neuronal scRNA-seq profiles to characterize about 1/5 of the whole hypothalamus (the preoptic region), we were able to analyze the preoptic region with a greater depth and thus gain finer delineation of cell populations ( fig. S22B ). The neuronal populations that we uncovered in the preoptic region also largely differed from cell clusters described in scRNA-seq studies of other hypothalamic areas ( 33 , 70 ), perhaps suggesting a molecular and cellular distinctiveness of this brain area. MERFISH further allowed us to map the spatial organization of cell populations. Structural features of the hypothalamus are not as visibly apparent as in laminated parts of the brain, and hypothalamic nuclei have largely been defined by subtle differences in neuronal density together with specific connectivity and functional roles ( 1 ). However, the differences versus similarities in the cell type composition and function of distinct nuclei in the hypothalamus remains unclear ( 71 ). MERFISH allowed us to examine the organization of distinct cell populations within individual hypothalamic nuclei, providing a framework to explore the molecular basis of their anatomical segregation. Notably, the spatial organizations of neuronal clusters were diverse: many of the neuronal clusters were each primarily enriched in one or a few nuclei whereas several clusters were substantially more dispersed. Moreover, individual nuclei were composed of multiple neuronal clusters. We also observed specific topographical organizations that can support defined modes of function: for instance, the spatial proximity of aromatase- and Esr1-expressing cells may support paracrine estrogen signaling. Interestingly, although aromatase- and Esr1-enriched cell populations regulate sex hormone production and signaling, rarely any of them appeared to be exclusively expressed in either sex, consistent with behavioral evidence that males and females are capable of exhibiting behaviors typical of the opposite sex ( 5 , 72 ). Lastly, the ability of MERFISH to interrogate intact tissue allowed us to include activity-dependent IEGs in our measurements, allowing the identification of neuronal populations activated by specific behaviors.