The cell type composition of the adult mouse brain revealed by single cell and spatial genomics

Abstract

The function of the mammalian brain relies upon the specification and spatial positioning of diversely specialized cell types. Yet, the molecular identities of the cell types, and their positions within individual anatomical structures, remain incompletely known. To construct a comprehensive atlas of cell types in each brain structure, we paired high-throughput single-nucleus RNA-seq with Slide-seq-a recently developed spatial transcriptomics method with near-cellular resolution-across the entire mouse brain. Integration of these datasets revealed the cell type composition of each neuroanatomical structure. Cell type diversity was found to be remarkably high in the midbrain, hindbrain, and hypothalamus, with most clusters requiring a combination of at least three discrete gene expression markers to uniquely define them. Using these data, we developed a framework for genetically accessing each cell type, comprehensively characterized neuropeptide and neurotransmitter signaling, elucidated region-specific specializations in activity-regulated gene expression, and ascertained the heritability enrichment of neurological and psychiatric phenotypes. These data, available as an online resource (BrainCellData.org) should find diverse applications across neuroscience, including the construction of new genetic tools, and the prioritization of specific cell types and circuits in the study of brain diseases.

Extended Data Figure 1.

Quality control and summary statistics of the snRNA-seq analysis. a, Stacked barplots showing the number of nuclei sampled in each region, for each animal replicate. Female donor IDs contain an “F” in their name, while male donors contain an “M.” Dissectates are colored on the top by their corresponding major brain region. b, Bar plots showing the average number of UMIs per nucleus in each dissectate, colored by main brain region. Error bars indicate standard error of each dissectate’s average number of UMIs. c, Violin plots showing the log10 distribution of the number of UMIs per nucleus in each major cell class. d, Stacked barplots of the number of nuclei sampled in each major mouse brain region, subsetted by individual dissectate. e, Histogram of the maximal proportional representation of individual dissectates in each snRNA-seq cluster. f, Heatmap representing a confusion matrix between clustering of the snRNA-seq data in the current study (x-axis), and published studies (y-axis), for the mouse motor cortex (left) and cerebellum (right). g, Histogram of the log10 number of clusters recovered from each major cell class. h, Plot indicating the probability of sampling 19 very rare populations (prevalence 0.0024%) as a function of the total number of cells profiled in experiment (probability estimated as in methods). Number of high-quality nuclei profiled here (4,388,420) and corresponding probability are indicated.

Extended Data Figure 2.

Quality control and summary statistics of CCF integration and cell type mapping. a, Example images of adjacent Nissl sections aligned to CCF with 2D rigid transformation (wireframe outline) before (left) and after (right) correcting alignment with a 2D diffeomorphism. Red arrows point to example regions with incorrect alignment, and improvement after application of the correction. b, Expression of three highly specific marker genes that label the ventricular lining (Tmem212), dentate gyrus granule layer (Dsp), and layer 6b of isocortex (Ccn2) in Slide-seq (top row). Bottom row shows the positions of individual beads with expression with respect to the boundaries of the expected CCF region (purple). c, Density plot of the distance of each bead expressing each of the three marker genes (or all combined) shown in b across the corresponding Slide-seq sections. The full width half maximum of the density profile is shown. d, Heat map representing the frequency of bead mappings for each glial cell type, across DeepCCF regions.

Extended Data Figure 3.

Extended analyses quantifying neuronal cell type diversity across brain areas. a, Heatmap representing the weighted Jaccard similarity in cell type composition between each of the 12 main brain areas (Methods). b, Cumulative distribution plots of the number of genes needed to label each cell type, within each of the 12 major brain areas. Within each region, each of the sets of individually colored plots denotes an algorithmic run with a different number of nearest neighbors that are tolerated as having the same gene markers (absolute number in parentheses at right). The colored percentages denote the proportion of cell types for which the algorithm was able to find a solution. c, Treemap visualization of the GO hierarchy enriched in the minimum-sized collated gene list after hierarchical reduction (Methods).

Extended Data Figure 4.

Extended analyses of neurotransmitter and neuropeptide usage across the brain. a, Stacked barplots of the number of cell types with confident mappings in each deep CCF region, subsetted by neurotransmitter group. Deep CCF regions are colored on the left by their corresponding major brain region. b, Representative sections showing the spatial localizations of all cell types within three neurotransmitter groups. C, Violin plots of the number of neuropeptides expressed in each cluster, stratified by main brain region. d, Dot plot showing scaled Slide-seq counts per 10,000 of ligand-receptor pairs across main brain regions.

Extended Data Figure 5.

Additional analyses related to activity-related genes. a, Comparison of correlations (right) and quantile of correlation (left) between each gene and both Fos (x-axis) and Junb. Red dots indicate the genes that were selected as candidate ARGs. b, Barplots showing the average counts per 10,000 of the core IEG metagenes (Methods) listed in c. Error bars indicate standard error of average counts per deep CCF region and dashed black lines indicate average counts per main brain region. c, Scaled mean expression of the core IEG metagenes, within each main region, separated by neurotransmitter group. d, Extended heat map showing the correlation with Fos of all candidate ARGs within the seven clustered groups. e, Enrichment analysis (Methods) of each candidate ARG cluster with three established ARG gene sets. Dotted red line indicates an adjusted p-value threshold of 0.05.

Extended Data Figure 6.

Extended analyses of heritability enrichment in murine brain cell types. a, Correlation plot of p-value enrichment scores (FDR-corrected) for each cell type across different scDRS settings: A) default parameters (Methods) B) MAGMA gene z-score > 2.5, C) control gene set size of 2,000, D) control gene set size of 500, E) input expression dataset at the single-cell level (versus pseudocells), F) adjust for cell type proportions. b, Adjusted −log10 p-value enrichment scores for each cell type, grouped and colored by main region, for an extended set of GWAS-measured traits. c, Dot plot of the expression of key cortical pyramidal cell type markers within the eight isocortical clusters that were significantly enriched for schizophrenia heritability.

Figure 1.

Spatially mapping cell types using whole-brain snRNA-seq and Slide-seq datasets. a, Schematic of the experimental and computational workflows for both whole-brain snRNA-seq sampling (top arrows) and Slide-seq sampling (bottom arrows). Top, t-SNE representations of gene expression relationships amongst 1.2 million spatially mapped snRNA-seq profiles (downsampled from 4.3 million) colored by neurotransmitter identity (left) and most common spatially mapped main region (right). b, Ridge plot depicting the spatial distributions of excitatory cortical cell types along the laminar depth of cortex in the Slide-seq dataset. c, Heatmap depicting expression of main neurotransmitter genes (top) and canonical neuronal cell type markers (bottom) across all 1,260 spatially mapped neuronal clusters. d, Heat map showing the spatial distributions of each spatially mapped cluster (rows) within each DeepCCF structure (for complete list, see Supplemental Table 2). Example mapped cell types in other panels are labeled on the heat map. e-h, Example mappings of neuronal cell types throughout the brain plotted in the CCF-aligned Slide-seq data (left) and in t-SNE space (with insets).

Figure 2.

Cell type diversity across regions of the mouse brain. a, Cumulative number of cell types needed to reach 95% of mapped beads in each DeepCCF region (right) and summarized across individual main regions (left). The DeepCCF regions with the largest values are labeled. b, Force-directed graph showing cell type sharing relationships amongst DeepCCF regions. Edges are weighted by the Jaccard overlap between each region (Methods). c, Histogram of the minimum number of genes required to uniquely define each cell type across the nervous system. The algorithm was repeated, tolerating genes to be the same amongst different numbers of nearest cluster neighbors. d, Cumulative distribution plot of minimum number of genes required to distinguish each cell type within each major brain region, grouped by telencephalic regions (left), and non-telencephalic regions (right). e, Optimally-small collated gene set needed to cover a minimal gene list from each cell type, ranked by how many cell types each gene reaches, colored by transcription factor identity (orange) or if it is a currently available Cre line (blue). f, Barplot quantifying the significantly enriched GO terms in the minimum-sized collated gene list after hierarchical reduction (Methods), colored by the absolute number of genes related.

Figure 3.

Neurotransmission and neuropeptide (NP) usage across regions of the mouse brain. a, Top, upset plot of the frequency of neurotransmitter usage by individual snRNA-seq-defined cell types. Bottom, dot plot depicting the spatial distribution of cell types in each of the neurotransmitter groups across major brain areas. b, Point estimates of the fraction of each DeepCCF region composed of mapped inhibitory cell types. Whiskers denote the exact 95% confidence interval of the corresponding binomial distribution. c, Histograms denoting the number of distinct NPs (top) and NP receptors (bottom) expressed in each snRNA-seq-defined neuronal cell type. d, Fraction of all cells expressing each NP (y-axis) in each of the 12 main brain areas. Regions accounting for more than 50% of total expression of that NP are colored and labeled. e, Dot plot depicting the number of cells expressing each NP (left of dotted line) and NP receptor (right of dotted line), within each major cell class.

Figure 4.

Patterns of activity-dependent gene expression across brain regions. a, Force-directed graph of a weighted bipartite network. Nodes comprise two disjoint sets: candidate ARGs (black dots) and sets of neuronal cell types of the same neurotransmitter type, localized to the same brain region (shapes colored by region). Edges are weighted based on the correlation coefficient between a gene node and a region node, and edges with weight < 1.3 are pruned. After pruning, nodes with a degree < 2 are also removed. The sizes of nodes and node labels correlate with the degree of the node, where nodes are labeled only if they share edges of weight ≥ 1.3 with at least six regions. b, Box plots quantifying mean core IEG Slide-seq counts per 10,000, colored by main brain regions. c, Downsampled heatmap of correlation coefficients between Fos and candidate ARGs (columns) across major regions of the brain (rows). Numbers at the top correspond to ARG cluster identities. d, Scatter plot quantifying transcription factor enrichment (p-value < 0.05, FDR-corrected) between excitatory and inhibitory populations. TFs are colored by their cell type enrichment specificity.

Figure 5.

Heritability enrichment for traits studied by GWAS across brain cell types. a, Barplots quantifying significantly enriched (p-value < 0.05, FDR-corrected) cell types for each trait, in non-neurons (gray) and neurons (colored by main region). b, Adjusted −log10 p-value enrichment scores for each cell type, grouped and colored by their main regions, for schizophrenia. Squares and triangles denote excitatory and inhibitory clusters, respectively; glia are shown in gray on the far right of each plot. c, Ridge plots showing the layer distribution of each excitatory cortical cell type found to be enriched for schizophrenia heritability. d, Dot plot of expression of markers of striatal SPN subtype identity, grouped by category (overall cell class identity, pathway identity, matrix versus striosome, and eSPN identity). Five additional genes that are enriched in the schizophrenia-enriched SPN types are also shown. e, Representative sections showing the spatial localizations of three SPN cell types significantly enriched for schizophrenia heritability.