An integrated single-nucleus and spatial transcriptomics atlas reveals the molecular landscape of the human hippocampus

Abstract

Cell types in the hippocampus with unique morphology, physiology and connectivity serve specialized functions associated with cognition and mood. These cell types are spatially organized, necessitating molecular profiling strategies that retain cytoarchitectural organization. Here we generated spatially-resolved transcriptomics (SRT) and single-nucleus RNA-sequencing (snRNA-seq) data from anterior human hippocampus in ten adult neurotypical donors. Using non-negative matrix factorization (NMF) and label transfer, we integrated these data by defining gene expression patterns within the snRNA-seq data and then inferring expression in the SRT data. These patterns captured transcriptional variation across neuronal cell types and indicated spatial organization of excitatory and inhibitory postsynaptic specializations. Leveraging the NMF and label transfer approach with rodent datasets, we identified putative patterns of activity-dependent transcription and circuit connectivity in the human SRT dataset. Finally, we characterized the spatial organization of NMF patterns corresponding to pyramidal neurons and identified regionally-specific snRNA-seq clusters of the retrohippocampus, subiculum and presubiculum. To make this molecular atlas widely accessible, raw and processed data are freely available, including through interactive web applications.

Fig. 1. Experimental design to generate paired snRNA-seq and SRT data in the humanHPC.

a, Postmortem human tissue blocks containing the anterior HPC were dissected from ten adult neurotypical brain donors. b, Tissue blocks were cryosectioned for snRNA-seq assays (gold), and placed on Visium slides (Visium-H&E, blue). Tissue sections (n = 2–4, 100 μm cryosections per donor) from all ten donors were collected from the same tissue blocks for measurement with the 10x Genomics Chromium 3′ gene expression platform. For each donor, two samples were generated, one sorted based on PI⁺ (purple) and the second sorted based on PI⁺ and NeuN⁺ (green). Replicate samples were collected from three donors for a total of n = 26 total snRNA-seq libraries. Panels a and b are created with BioRender.com. c, Tissue blocks were scored with a razor blade to demarcate regions of interest and 10 μm tissue sections were collected for measurement with the 10x Genomics Visium-H&E platform. For all ten donors, two to five tissue sections were collected to include the extent of the HPC (n = 36 total capture areas). Dashed outlines indicate the approximate location of score marks, for example, donor, with color indicating capture areas obtained from consecutive sections. Orientation was verified based on the expression of known marker genes, such as SNAP25 (dashed outline color corresponding to the capture area location on the tissue section). d, Canonical marker genes were identified as SVGs using nnSVG. Spots are colored by log₂-normalized counts. e, SRT data were clustered using PRECAST with k = 18, and clusters were annotated (columns) based on expression of known marker genes (rows). Cluster groupings indicated at the top of the heatmap define which clusters contributed to the broad domains of neuron, neuropil, WM and vasc/CSF. Hippocampal region abbreviations are also presented in Supplementary Table 2.

Fig. 2. Spatial domain annotation and DE in the human HPC using SRT data.

a, Integrated and merged spot plot of four Visium capture areas from the same donor (Br3942) with spots colored by the 16 spatial domains annotated from k = 18 PRECAST clusters. CA1.1/CA1.2 were collapsed to CA1 and CA2-4.1/CA2-4.2 were collapsed to CA2–CA4. See Supplementary Table 2 for abbreviations. b, Schematic illustrating pseudobulking approach, which collapses spot-level data to the spatial domain level within each capture area by summing the total UMIs for each group. c, PCA of the pseudobulked samples captures variation across broad spatial domains. Neuron cell body-enriched (greens and light blue (GABA)), neuropil-enriched (grays), WM-enriched (purples) and Vasc/CSF (dark blue). d, Heatmap showing DEG expression (rows) across the spatial domains (columns). Grouping across the top shows broad domain annotations. Spot plots are filled by log₂-normalized counts. Spot borders are colored by a broad domain. e–h, Spot plots demonstrate spatial expression of DEGs—PPFIA2, a known marker for the GCL (e), PRKCG, which is known to be enriched in CA1–CA4 domains (f), APOC1, a known astrocyte cell marker (g) and SFRP2, which was specifically increased in the SLM-subgranular zone (SGZ) domain (h). i, Volcano plots illustrate results from DE analysis for each broad-level domain, with log₂(FC) on the x axis and FDR-adjusted, −log₁₀-transformed P values on the y axis. Genes colored red pass both FDR and log₂(FC) thresholds (FDR-adjusted P < 0.01 and log₂(FC) > 1). Top DEGs for each broad domain grouping are labeled. j, Violin plots of pseudobulk expression of DEGs for neuron-enriched regions (CLSTN3), neuropil-enriched regions (SLC1A3), white matter regions (SHTN1) and vasc/CSF regions (TPM2). Spatial domains are on the x axis and normalized gene expression in log₂(CPM) is on the y axis. Boxes are colored by broad cluster grouping.

Fig. 3. Cell type identification and DE in the human HPC using snRNA-seq.

a, UMAP representation of snRNA-seq data. Individual nuclei are represented as points, colored and labeled according to their cell type. Cell type abbreviations are listed in Supplementary Table 2. b, Left, stacked bar plots of cell types indicated in a showing proportions of nuclei for each donor (columns). Right, stacked bar plots of cell types indicated in a, with columns indicating nuclei grouped by sort strategy (PI⁺ or PI⁺NeuN⁺), and across the overall dataset (all nuclei). c, Violin plots showing log₂-normalized expression (y axis) of select DEGs identified with both spatial domain and n = 60 snRNA-seq cluster DE analyses. Nuclei are grouped based on cell types (x axis) for improved visibility, and the fill color also corresponds to cell type as indicated in a. d, Heatmap showing a selection of DEGs (rows) from snRNA-seq DE analysis across all n = 60 clusters (columns). Heatmap is colored by mean log₂-normalized counts. Cell cluster abbreviations are also presented in Supplementary Table 2. UMAP, uniform manifold approximation and projection; GC, DG granule cell; CA1/ProS; CA1 and prosubiculum; Sub, subiculum; HATA, HPC–amygdala transition area; Amy, amygdala; Thal, thalamus; Cajal, Cajal–Retzius cells; GABA.PENK, PENK positive GABAergic neurons; GABA.MGE, medial ganglionic eminence-derived GABAergic neurons; GABA.LAMP5, LAMP5-positive GABAergic neurons; GABA.CGE, central ganglionic eminence-derived GABAergic neurons; Micro/Macro/T, microglia and macrophages and T cells; Astro, astrocytes; Oligo, oligodendrocytes; OPC, oligodendrocyte progenitor cells; Ependy, ependymal cells; AHi, amygdala-hippocampal region; CXCL14, CXCL14 positive GABAergic neurons; HTR3A, HTR3A positive GABAergic neurons; VIP, VIP positive GABAergic neurons; CRABP1, CRABP1 positive GABAergic neurons; C1QL1, C1QL1 positive GABAergic neurons; PV.FS, PVALB positive fast-spiking GABAergic neurons; SST, SST positive GABAergic neurons; CORT, CORT positive GABAergic neurons; COP, committed oligodendrocyte precursor; CP, choroid plexus tissue; Endo, endothelial cells; PC/SMC, pericytes/smooth muscle cells; VLMC, vascular leptomeningeal cells.

Fig. 4. NMF reveals cell type heterogeneity and biologically relevant pathways in hippocampal subfields compared to RCTD deconvolution results.

For all spot plots of example capture areas from donor Br3942, spot borders are colored by spatial domain (see Supplementary Table 2 for abbreviations). a, RCTD prediction of proportion of oligodendrocytes (Oligo, fill color) per Visium spot. b, Spot plots displaying spot-level weights (fill color) for NMF pattern nmf77. c, GSEA results showed that genes with higher nmf77 weights contributed to the significant enrichment of biological pathways associated with oligodendrocytes. For GSEA results, the x axis shows the ranked gene-level NMF weight. Each vertical black bar indicates the rank of genes for the indicated Reactome gene set. GSEA statistics are presented to the right of the plot. Top, NES; middle, numerator indicates the number of genes present in the experimental gene set, denominator indicates the total number of genes in the Reactome gene set; bottom, FDR-adjusted one-tailed P value (P_adj). d, RCTD prediction of proportion of astrocytes (Astro, fill color) per spot. e, Spot plots displaying spot-level weights (fill color) for NMF pattern nmf81 (specifically elevated in astrocyte snRNA-seq clusters). f, Genes with higher nmf81 weights contributed to the significant enrichment of biological pathways associated with astrocytes. g, Spot plots displaying spot-level weights (fill color) for NMF pattern nmf13 (elevated in neuronal snRNA-seq clusters). h, Spot plots displaying spot-level weights (fill color) for NMF pattern nmf7 (elevated in neuronal snRNA-seq clusters). i, Genes with higher nmf13 weights contributed to the significant enrichment of biological pathways related to neuronal signaling, with increased representation of transcriptional variation highly relevant to excitatory postsynaptic response. j, Genes with higher nmf7 weights contributed to the significant enrichment of biological pathways related to neuronal signaling, with increased representation of transcriptional variation highly relevant to the structure and function of inhibitory synaptic connections. NES, normalized enrichment score.

Fig. 5. NMF captures transcriptional programs relevant to neuronal activity.

a, Select NMF patterns projected onto a mouse snRNA-seq dataset of hippocampal neurons activated by ECS or under control conditions (Sham; y axis, by cell type). The dot color indicates scaled average nuclei NMF weights. Dot size indicates the proportion of y axis group with non-zero pattern weight for the given x axis value. DE analysis was performed on mouse GC nuclei, testing for differences in the expression by activity condition. For all volcano plots (b,c,f,g), the y axis presents the −log₁₀ FDR-adjusted P value and the x axis presents log₂(FC), where negative values indicate greater expression in sham-activated GCs and positive values indicate greater expression in ECS GCs. b,c,f,g, Points are colored for the gene-level NMF weight for nmf91 (b), nmf20 (c), nmf10 (f) and nmf14 (g). d,e, Spot plots isolating the DG GCL spatial domain (green outlined spots) demonstrate the differing spatial organization of nmf10 (d) and nmf14 (e) weights in an example capture area from donor Br3942. Spot fill indicates spot-level NMF pattern weight. h, Left, UMAP plot of all nuclei present in our human snRNA-seq, highlighting the GC clusters. Right, zoomed UMAP plot of only GC nuclei from our human snRNA-seq dataset with color indicating cluster identity. i, UMAP plot of human GC nuclei showing (left) nmf10 nuclei-level weights and (right) log₂-normalized counts of highly-weighted nmf10 gene CHST9. j, UMAP plot of human GC nuclei showing (left) nmf14 nuclei-level weights and (right) log₂-normalized counts of highly-weighted nmf14 gene SORCS3. PS/Sub, prosubiculum and subiculum neurons; L5/Po, layer 5 and polymorphic layer.

Fig. 6. NMF reveals a continuum of pyramidal cell types across the RHP.

All spot plots are filled by the spot-level weights for the indicated NMF pattern, with scales corresponding to the maximum spot-level weight of any spot in the SRT dataset. Spot border color indicates the spatial domain. See Supplementary Table 2 for abbreviations. a, Spot plots of example areas from donor Br3942 highlight the laminar organization of nmf40 (snRNA-seq cluster Sub.1) and nmf54 (snRNA-seq cluster Sub.2), as well as the clear distinction from the CA1 (nmf15). b, Dot plot of NMF weights (dot color) after transfer to mouse single-cell methylation sequencing (snmC-seq) with retroviral tracing (n = 2004 HPC and RHP nuclei). Rows indicate nuclei axonal projection target region. Dot size indicates the number of nuclei with non-zero pattern weights. c, Spot plots of example capture area from donor Br2743 show ENT-specific NMF patterns. d, TSNE plots of pyramidal nuclei from the snRNA-seq dataset colored by the NMF patterns in c. e, Spot plots of example capture area from donor Br2743 show RHP NMF patterns that exhibit spot-level weights distributed across the ENT and subicular complex. f, TSNE plots of pyramidal nuclei from the snRNA-seq dataset colored by the NMF patterns in e. g, Spot plots (for example, donor Br3942) exemplify the anatomical location of nmf17 to the PreS, indicated by the asterisk. h, Violin plots show snRNA-seq log₂-normalized counts (y axis) across HPC and RHP clusters (x axis, reflecting spatially-informed annotations; Supplementary Table 2) for traditional cortical layer markers (SATB2, TLE4 and CUX2), canonical subiculum marker FN1, and new subiculum markers (COL24A1, TOX). i, new DEGs distinguish the superficial, middle and deep subiculum (Sub.1, Sub.2, Sub.3, respectively), and the PreS. Dot size indicates the proportion of nuclei in each cluster (column) with non-zero expression for each gene (row). The dot color indicates average log₂-normalized gene counts. TSNE, t-distributed stochastic neighbor embedding; HPF, hippocampal field; MOB, main olfactory bulb; RSP, retrosplenial cortex; PTLp, posterior parietal cortex; ACA, anterior cingulate cortex; PIR, piriform cortex; STR, striatum; TH, thalamus; AMY, amygdala; HY, hypothalamus; MOp, primary motor cortex; PFC, prefrontal cortex.

Extended Data Fig. 1. Data-driven HPC domain annotations derived from spatial clustering (PRECAST k = 18) across all n = 36 Visium-H&E samples.

Spatial clustering results from PRECAST k = 18 were annotated to HPC spatial domains by examining marker gene expression and anatomical location. From the 18 PRECAST clusters we generated 16 spatial domains by merging clusters that were annotated to the same HPC subfields. Spot color indicates the final spatial domain annotation (legend in bottom right). See Supplementary Table 2 for abbreviations. Capture areas are grouped by donor number.

Extended Data Fig. 2. Spatial domain-level differential expression analysis of spatially-resolved transcriptomics (SRT) data.

Volcano plotting of pseudobulked differential expression (DE) analysis for each spatial domain, with log₂ fold change on the x-axis and FDR adjusted, -log₁₀ transformed p-values on the y-axis. Genes colored red pass both FDR and log₂ fold change thresholds (FDR adjusted p-value < 0.01 and absolute value of log₂ fold change > 1). Top DE genes are labeled. See Supplementary Table 2 for spatial domain abbreviations.

Extended Data Fig. 3. Differentially expressed snRNA-seq genes and correlation with SRT results.

Dot plots for select differentially expressed genes (y-axis) for n = 60 cell types (x-axis). Dot color indicates the cluster average log₂ normalized counts and dot size corresponds to the proportion of cluster nuclei with non-zero expression. Heatmaps are included for (a) glia, (b) retrohippocampus (RHP), (c) amygdala, (d) GABAergic neurons, (e) GABAergic neuron markers with higher expression range (including Cajal–Retzius neurons for RELN expression comparison), (f) hippocampus (HPC), (g) and dentate gyrus granule cells. See Supplementary Table 2 for cluster abbreviations. h, Heatmap of Pearson correlation coefficients (color) showing the relationship between pseudobulk differential expression analysis enrichment model t statistics for the SRT spatial domains (rows) and those of n = 60 snRNAseq clusters (columns).

Extended Data Fig. 4. Spot level deconvolution benchmarking with Visium Spatial Proteogenomics (Visium-SPG).

a, Schematic showing the complexity of cell types in Visium spots. b, Immunofluorescence (IF) images used to train a Classification And Regression Tree (CART) model to identify cell types (indicated by color, legend in image). c, The marker genes detected for selected ‘mid’ cell types (facets) are enriched in Visium-SPG spots from corresponding spatial domains (y-axis). Each data point summarized in boxplot represents an individual spot with the x-axis indicating what proportion of the ‘mid’ cell types marker genes were expressed in each spot. Similar box plots for all ‘mid’ and ‘fine’ cell types are shown in Supplementary Fig. 29. d, Performance of spot-level deconvolution algorithms was benchmarked by comparing to IF-derived assignment of cell type identity in (b). Scatter pie plots (highlighted in inset) illustrate the proportion of cell types predicted in each Visium spot (color legend in b). e, RCTD was applied to the Visium-H&E data, where the orthogonal cell type information is not present. Bar plots illustrate the proportion of spots (x-axis) predicted to each ‘mid’ cell type (fill color) within each spatial domain (y-axis).

Extended Data Fig. 5. Schematic representation of non-negative matrix factorization (NMF).

a, Schematic for how to identify patterns using non-negative matrix factorization (NMF). Specifically, for a given source matrix A with dimensions of i genes and j observations, such as the snRNA-seq data, we use NMF to decompose A into two matrices W, representing the feature-level weights matrix with dimensions of i genes and k NMF patterns, and H, representing the observation-level weights matrix for j observations and k patterns. Gray matrices are known matrices, blue matrices are generated by the analysis step. Panel a is created with BioRender.com. b, Schematic for how we transfer the NMF patterns into other datasets, for example, the SRT data. Specifically, we use the transposed feature-level weights matrix W (learned from the source data, glow) and a target dataset matrix (A’ with dimensions of i’ genes and j’ observations) to obtain a new observation-level weights matrix from the target (H’ with dimensions of k patterns and j’ observations). Therefore, for every observation (spot or nuclei) in the target dataset, we have a weight for every corresponding NMF pattern learned from the source data (snRNA-seq). Gray matrices are known matrices, blue matrices are generated by the analysis step.

Extended Data Fig. 6. Non-negative matrix factorization (NMF) patterns recapitulate transcriptional distinctions by cell type and spatial domain.

a, NMF patterns (x-axis) learned from snRNA-seq log₂ normalized gene expression counts correspond well with snRNA-seq clusters (y-axis). For dot color, the nuclei-level weights for each NMF pattern were scaled and then averaged across the y-axis groups. Many of these patterns mapped to a high number of SRT spots after transfer and can be classified as general NMF patterns and specific NMF patterns based on the abundance of non-zero weighted nuclei across snRNA-seq cell type clusters (dot size). See Supplementary Table 2 for y-axis abbreviations. b, All NMF patterns (points) learned from snRNA-seq data produce non-zero weights for thousands of nuclei (x-axis). c, When these NMF patterns are transferred to SRT data, the number of NMF patterns (points) with few non-zero weighted spots (x-axis) increases. NMF patterns that produced non-zero weights in fewer than 1050 spots (34 patterns, red points) were excluded from downstream analysis, as were the sex-based patterns nmf37 and nmf28 (Supplementary Fig. 36). d, After transfer to SRT data, NMF patterns with non-zero weights in >1050 spots (x-axis) correspond well to spatial domains (y-axis). With one exception (nmf94, discussed in Supplementary Fig. 37), the NMF patterns classified as general or specific in snRNA-seq data (a), likewise, are abundant across multiple spatial domains or specific spatial domains. For dot color, the spot-level weights for each NMF pattern were scaled and then averaged across the y-axis groups. Dot size indicates the proportion of spots belonging to each y-axis group that exhibited non-zero weights for the given NMF pattern (x-axis). See Supplementary Table 2 for y-axis abbreviations.

Extended Data Fig. 7. Non-negative matrix factorization (NMF) identifies donor enrichment due to activity-dependent transcription.

a, Pie charts for snRNA-seq nuclei (top) and SRT spots (bottom) show the proportion of observations with non-zero nmf91 weights originating from each donor. Top: snRNA-seq n = 39,991 total non-zero nmf91 nuclei. Bottom: SRT n = 9,380 total non-zero nmf91 spots; Br3942 (pink) n = 4020 non-zero nmf91 spots; Br6423 (orange) n = 2736 non-zero nmf91 spots. b, Nine of the 50 genes with the highest nmf91 weight (y-axis) are canonical immediate early genes (IEGs, red) that are transcribed after strong stimulus in neurons and non-neuronal cells. Average log₂ normalized gene expression in all nuclei on x-axis, nmf91 gene-level weight on y-axis. c, Pie charts for snRNA-seq nuclei (top) and SRT spots (bottom) show the proportion of observations with non-zero nmf20 weights originating from each donor. Top: snRNA-seq n = 36,679 total non-zero nmf20 nuclei. Bottom: SRT n = 2109 total non-zero nmf20 spots; Br2743 (green) n = 991 non-zero nmf20 spots; Br6423 (orange) n = 565 non-zero nmf20 spots. d, The three genes (red) with the highest nmf20 weights (y-axis) are known regulators of neuronal function in response to activation. Average log₂ normalized gene expression in all nuclei on x-axis, nmf20 gene-level weight on y-axis. e, The log₂ CPM expression (dot color) and abundance (proportion of observations, dot size) of three top-ranked nmf91 genes (x-axis) across donor groups (observations grouped into neurons and non-neurons) indicate consistent enrichment of IEGs in 1–2 donors in both the snRNA-seq data (left) and SRT data (right). Expression of JUN further reflects the more equal non-zero weighting of nmf91 in snRNA-seq nuclei compared to the strong donor enrichment of nmf91 observed in SRT (a). f, The log₂ CPM expression (dot color) and abundance (proportion of observations, dot size) of three top-ranked nmf20 genes (x-axis) across donor groups (observations grouped into neurons and non-neurons) indicate consistent enrichment of SORCS3, HOMER1, and PDE10A in neurons and equal distribution across all donors in snRNA-seq data (left). The increased expression of HOMER1 and SORCS3 in capture areas from two SRT donors reflects the strong donor enrichment of nmf20 observed in SRT (c).

Extended Data Fig. 8. Non-negative matrix factorization (NMF) patterns across annotated cell types in mouse neuron electroconvulsive stimulation (ECS) snRNA-seq data.

a, Following label transfer of NMF patterns to mouse snRNA-seq electroconvulsive stimulation (ECS) dataset with retroviral tracing dataset (n = 15990 nuclei), NMF patterns were removed (red) that mapped to <1000 nuclei (x-axis). b, Dotplot of mouse nuclei-level weights for NMF patterns (x-axis) after filtering, averaged by cluster (y-axis). Dot size indicates the proportion of nuclei in each cluster with non-zero pattern weights and dots are colored by the scaled pattern weight. GC: granule cells, CA2–4: cornu ammonis (CA) regions 2 through 4 (CA2, CA3, CA4), PS/Sub: prosubiculum and subiculum neurons, L5/Po: layer 5 and polymorphic layer. c, Dotplot of nuclei-level weights for NMF patterns (x axis) after further filtering to patterns that were present in >1,050 SRT spots, averaged by cluster and ECS condition (y axis). Dot size indicates the proportion of nuclei in each cluster with non-zero pattern weights and dots are colored by the scaled pattern weight averaged across nuclei in y-axis groups. d, Volcano plot of differential expression results tested on genes with non-zero nmf55 weights. Y-axis of −log₁₀(FDR) values are plotted with a log₁₀ scale. X axis is the log₂ fold change (FC), where negative values indicate greater expression in sham-activated GCs and positive values indicate greater expression in ECS GCs. Points are colored by nmf55 weight. Gene names are shown for genes with nmf55 weight > 0.0025, log₂(FC) > 0.5, and FDR < 0.05. e, Spot plots for example capture area from donor Br3942 are colored by (left) spatial domain and (right) nmf55 weight, demonstrating the ubiquitous presence of nmf55.

Extended Data Fig. 9. Non-negative matrix factorization (NMF) label transfer to mouse single-nucleus methylation sequencing (snmC-seq) dataset; subiculum annotation via binarization of NMF patterns, and elucidation of new deep subiculum cluster snRNA-seq cluster.

a, Following label transfer of NMF patterns to mouse snmC-seq with retroviral tracing dataset (n = 2004 nuclei), hippocampal (HPC)- and retrohippocampal (RHP)-specific patterns were removed that mapped to <45 nuclei (red). X-axis indicates number of nuclei mapping to pattern, y-axis indicates proportion of patterns. b, Verification that NMF patterns (x-axis) identified as HPC- and RHP-specific in our human SRT and human snRNA-seq datasets corresponded with the nuclei collection source (y-axis) for the mouse snmC-seq dataset. Dots are colored by scaled, average pattern weight and dot size indicates the number of mouse nuclei with non-zero pattern weights. ENT: entorhinal cortex, CAa: anterior cornu ammonis, CAp: posterior cornu ammonis, SUB: subiculum. c, t-distributed stochastic neighbor embedding (TSNE) embedding of pyramidal clusters from our human snRNA-seq dataset. Subiculum and deep pyramidal clusters used to characterize nmf65 are labeled and colored. d, nmf65 mapped strongly to a subset of L6b nuclei, so to examine if there were differences in marker expression between these groups, L6b nuclei were thresholded based on nmf65 weights (point color). Each point represents a single nuclei in the L6b cluster, x-axis indicates nmf53 weight (RHP L6; Fig. 6e), y-axis indicates nmf65 weight. e, Dot plot demonstrating the non-overlapping expression of SUB and deep RHP marker genes (y-axis) in snRNA-seq clusters (x-axis). L6.1 exhibits higher expression of SUB markers (orange box) and not deep RHP markers (blue box), while L6b nuclei with high nmf65 weights express both groups and all other L6b nuclei only express deep RHP markers. Bolded y-axis genes are highlighted in (f). Dot size indicates the proportion of nuclei, dot color indicates the average log₂ normalized expression. See Supplementary Table 2 for cluster abbreviations. f, TSNE plots of the expression of select marker genes from (e) that support the reassignment of L6.1 (asterisk) as deep SUB nuclei (Sub.3). Pyramidal nuclei of our human snRNA-seq results are represented by individual points that are colored by the log₂ normalized expression.

Extended Data Fig. 10. Subiculum-focused differential expression analysis in snRNA-seq.

Violin plots depict the log₂ normalized expression (y-axis) across snRNA-seq cell type clusters (x-axis) that were tested. Plots are grouped by the significant cluster: (a) superficial subiculum (Sub.1), (b) middle subiculum (Sub.2), (c) deep subiculum (Sub.3) and (d) presubiculum (PreS).