Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain.

Single-cell transcriptional profiling of the adult mouse brain using Drop-seq and identification of transcriptional programs with independent component analysis(A) Sagittal schematic illustrating profiled brain regions and numbers of cells sampled (anatomical detail in Data S1). (B) Workflow for semi-supervised Independent Components Analysis (ICA)-based signal extraction and clustering (STAR Methods). In stage 1, the DGE matrix is clustered into cell classes (Figure S1) using ICA (“global clustering”). In stage 2 (“subclustering”), the process is repeated for each individual cluster from stage 1. In stage 2, however, the resulting ICs are curated as “technical” or “biological” with only “biological” ICs used as input for subclustering. (Figure S2) (C) tSNE plots for frontal cortex global clustering (left) and two representative subclusterings, GABAergic interneurons (cluster 1) and glutamatergic layer 2/3 and a subset of layer 5 neurons (cluster 6). (D) Examples of heterogeneous “Biological” ICs from frontal cortex cluster 6, representing a cell state (top, IC 16), cell type (middle, IC 22), and spatial anatomical signal (bottom, IC 29). For each example, a cell-loading tSNE plot, gene loading plot, and in situ hybridization experiment (Allen Mouse Brain Atlas, “Allen”) for a top-loading gene are shown from left to right. IC 16 corresponds to the immediate early gene signal. The IC 22 signal originates from layer 5a glutamatergic neurons, as suggested by Deptor expression. IC 29 represents a spatial signal, evidenced by a medial to lateral gradient of Lypd1. (E) Correspondence between heterogeneous transcriptional signals (biological ICs) and subclusters identified by modularity-based clustering (STAR Methods). Cell loadings for Biological ICs from frontal cortex cluster 6 and the resulting n=5 subclusters identified. Alternative subcluster solutions are shown in Figure S2K.

Comprehensive description of transcriptional diversity within non-neurons as illustrated by cell classes of the vasculature.(A) Number of Biological ICs identified during curation for each non-neuronal cell class. All non-neuronal ICs are shown in Data S4. (B) Vasculature cell classes. (C-E) Subcluster assignments and examples of two biological ICs for each vasculature cell class. Subclusters (color-coded), IC cell-loadings, and gene expression values displayed on tSNE plots. Left, subcluster assignments. Middle, IC cell- and gene-loadings. For each IC, the top ten loading genes are listed. Right, expression plots for individual genes. For Mural Cell IC 19, the bottom loading gene Acta2 is shown in purple. (F) Dot plots illustrating fractional representation of cells from each region contributing to fibroblast-like and endothelial subclusters. Other non-neuronal cell classes are shown Data S4H.

A prevalent expression program in neurons related to axon structure and presynaptic function(A) Hierarchical clustering of pairwise Pearson correlations of gene-loading scores for biological ICs from 45 neuronal subclustering analyses. Right, enlargement of boxed region. Correlation blocks correspond to the immediate early gene (“IEG”) transcriptional state, thalamus-specific ICs (“TH”), or “Neurofilament” ICs, which are contributed from different regions and driven by genes that encode neurofilament subunits and other proteins involved in Ca2+ handling, vesicle exocytosis, and membrane excitability. (B) The Neurofilament transcriptional signal (IC 17) in frontal cortex Sst+/Pvalb+ interneurons (Cluster 2). Left, IC 17 cell-loadings displayed on subcluster tSNE plot. Right, gene-loading plot, with the top 20 genes shown. (C) Color-coded subcluster identities for frontal cortex cluster 2. N=10 subclusters were based on n=9 biological ICs. The graded loading of IC 17 is discretized into subclusters 2–8, 2–7, and 2–9. (D) Single gene expression plots. (E) Comparison of Neurofilament (Syt2, Pvalb, and Nefm) and control gene (Gabra4) single-cell transcript counts across Pvalb+ subclusters from Drop-seq. Transcript means were compared with a one-way Anova. Asterisk, P < 0.05; n.s P > 0.05. Tukey Honest Significance Difference Test. (F-G) Neurofilament gene and control gene in situ transcript count experiments within Pvalb+ frontal cortex cells using smFISH. Left, example single confocal planes. Right, quantification of transcript densities. Pvalb+ cells were split into n=3 groups based on Syt2 levels (low, medium, and high) mimicking subclusters 2–9, 2–7, and 2–8. Differences in transcript densities were statistically tested as in (E). Longer arrows indicate higher Pvalb expression. (F) Experiment 1, Pvalb, Syt2, and Nefm. (G) Experiment 2, Pvalb, Syt2, and Gabra4 (control).(H) The Neurofilament IC is observed in flash-frozen nuclei from frontal cortex. The Neurofilament (IC 25) cell-loading signal distribution across the Sst+/Pvalb+ interneuron subcluster. Left, cell-loadings displayed on subcluster tSNE plot. Right, gene-loading plots with top 20 genes are shown.

Inferring ion channel gene-gene co-expression relationships across hundreds of brain cell types and states(A-B) Nicotinic acetylcholine receptor (nAChR) subunit co-expression correlations across 565 brain cell populations. (A) Hierarchical clustering of pairwise correlations of n=16 nAChR subunit genes (color-coded by family). (B) Scatterplots of subunit expression (log10 scale). (C-E) Correlation structure among voltage-gated (VG) Na and K channels measured from 323 neuronal populations. (C) Hierarchical clustering of pairwise expression correlations. The VGK (n=17) and VGNA (n=1) alpha subunit families are color-coded and labeled. The correlation block containing channels known to control firing rate is shown with an arrow. (D-E) Select pairwise subunit expression correlations. Neuronal populations known to exhibit fast firing rates are shown in red (Figure S3D). Slc6a8 and Hcn2 were frequently correlated with the alpha subunit genes that putatively encode firing rate (Figure S3D). See also Figure S3.

Excitatory glutamatergic neurons underlie regional specialization in cortex.(A) Relative contributions of frontal (FC) vs posterior (PC) cortex cells to biological ICs in six separate cell-class analyses. IC Skew is 1 if only FC cells contribute and 0 if only PC cells contribute; equal contribution is 0.5 (dotted line). (B-D) Subcluster analyses illustrate stronger regionalization for excitatory neurons than other cell classes across cortical regions. (B) Subcluster tSNE plots for six cell classes. Cells are color-coded by region (left) and subcluster (right). Total numbers of subclusters are shown. (C) Representation of FC vs PC cells within subclusters. Dot size denotes fractional representation; asterisks denote significant FC vs PC difference (> 3:1 compositional skew and P < 0.05, Barnard’s test, STAR Methods). (D) Top left, tSNE plot of excitatory neurons color-coded by region. Top right, expression of Sccpdh and Whrn, genes enriched in subclusters disproportionately composed of FC or PC cells, respectively (Figure S5). Bottom, ISH (Allen). High expression, long arrow; Medium expression, short arrow. (E) FC-PC expression differences within cell populations. Barplot shows the number of differentially expressed genes between FC and PC cells within each subcluster (> 2-fold change, P < .05, Bonferroni corrected). See also Figure S4 and Figure S5.

Transcription-based identification of known and novel neuron type distinctions within the basal ganglia.(A-E) Globus pallidus externus (GPe). (F-J) Substantia nigra reticulata (SNr). (K-O) Dopaminergic vs acetylcholinergic neuromodulatory neuron populations. (A) tSNE plot of color-coded global clusters (n=11) for GP/NB dataset. Clusters 1, 2, and 3 are neuronal. (B) Subclusters within cluster 2. Black subclusters correspond to those of GP/NB. (C) Subclusters color-coded by candidate anatomical regions, inferred by ISH expression patterns of selective marker genes (Figure S6) and consistent with dissections (Data S1). Ventral pallidum (VP), substantia innominata (SI), striatum (STR), lateral olfactory tract (LOT), rostral entopeduncular nucleus (EP) and the thalamic reticular nucleus (TRN). (D) Dot plot illustrating the expression patterns of neurotransmitter genes, neuron type markers from the literature and novel markers identified here. (E) ISH experiments (Allen) illustrating expression within the GPe and/or VP (sagittal sections). Dotted line approximate boundaries. (F) tSNE plot of color-coded global clusters (n=14) for substantia nigra/VTA. Clusters 1, 3, and 4 are neuronal. (G) Subcluster structure within cluster 3. Black subclusters correspond to those of SNr. (H) Candidate anatomical regions inferred by ISH (Figure S6). Ventral tagmental area (VTA), red nucleus (RN), supramammillary nucleus (SuM), thalamus (TH), and deep mesencephalic nucleus (DpMe). (I) Dot plot as in (D). Genes for neurotransmitters, current SNr markers, and novel markers identified here. (J) ISH experiments (Allen) illustrating expression within the SNr (sagittal sections). (K) Subclusters within Th+/Ddc+ dopaminergic cluster 3 from the SN/VTA dataset. (L-M) Example cluster 3 ICs that encode spatial signals within the SNc/VTA. (L) IC cell loadings displayed on tSNE plot. (K). IC gene-loadings. Top ten genes shown at right.(L) ISH experiments (sagittal sections) for Lpl (IC 10, top) and Aldh1a1 (IC 12, bottom). IC 10 identifies the dorsal VTA, while IC 12 identifies the ventral VTA and SNc (Allen). (O-P) Minimal heterogeneity identified within Chat+/Slc5a7+ cholinergic cluster 1 from the GP/NB dataset. (O) Plot of IC 4 cell-loadings. Based on IC 4, cells are assigned as subcluster 1–1 or 1–2. (P) IC 4 gene-loading plot. Top ten loading genes suggest a Neurofilament-type signal (Figure 3).

Eccentric spiny projection neurons represent a third axis of SPN diversity(A) tSNE plot of color-coded global clusters (n=15) for striatum dataset. Clusters 10, 11, and 13 are presumed SPNs. (B) Expression plot of pan-SPN marker Ppp1r1b, direct pathway SPN (dSPN) marker Drd1, and indirect pathway SPN (iSPN) marker Adora2a. Ppp1r1b+ cells within Cluster 13 are eccentric SPNs (eSPN). (C) Mean expression comparisons between SPN populations (log-normal scale). Left, cluster 10 vs cluster 11 (iSPN vs dSPNs). Right, cluster 13 vs clusters 10 and 11 (eSPNs vs d/iSPNs). Differentially expressed genes (fold ratio > 2 and P < 10−100 by binomTest (Robinson et al., 2010), STAR Methods) are shown with dark dots and totals listed above. Red arrow indicates selective expression in eSPNs. (D) Expression plot of n=4 genes (Casz1, Otof, Cacng5, and Pcdh8) enriched in eSPNs vs d/iSPNs (red arrow in C). Across all global clusters, genes are highly enriched in cluster 13 (red arrows). (E-F) eSPNs are anatomically dispersed throughout the striatum. (E) Single confocal planes from smFISH experiments validating co-expression of pan-SPN (Ppp1r1b) and highly-selective eSPN markers (Cacng5, Otof, and Casz1) in dorsal striatum. Top, Ppp1r1b, Cacng5, and Otof. Bottom, Ppp1r1b, Cacng5, and Casz1. Arrowhead indicates triple-positive cells. (F) Locations of triple positive Ppp1r1b, Cacng5, and Otof cells on a schematic of coronal striatum. D, dorsal; V, ventral; L, lateral; M, medial. (G) Color-coded subclusters from cluster 13. Subclusters 13–1, 13–2, 13–3, 13–4, and 13–5 correspond to eSPNs (83% of cells, black labels). The identity of other subclusters (17% of cells, gray labels) is described in Figure S7. (H) Expression plot of pan-SPN (Ppp1r1b), pan-eSPN (Otof), dSPN (Drd1), iSPN (Adora2a), subcluster 13–5 (Th, Npffr1) markers. (I-J) Single confocal planes from smFISH experiments validating co-expression of markers in dorsal striatum. Arrowhead indicates triple-positive cells. (I) Co-expression of Otof with Adora2a and Drd1. (J) Co-expression of subcluster 13–5 markers. Triple-positive cells in dorsal striatum are indicated with white arrowheads. Top, Adora2a, Th, Otof. Bottom, Adora2a, Th, Npffr1.