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Review
. 2021 Jan 6;109(1):11-26.
doi: 10.1016/j.neuron.2020.12.010.

Single-Cell Sequencing of Brain Cell Transcriptomes and Epigenomes

Ethan J Armand  1 , Junhao Li  1 , Fangming Xie  2 , Chongyuan Luo  3 , Eran A Mukamel  4
Affiliations
Review

Single-Cell Sequencing of Brain Cell Transcriptomes and Epigenomes

Ethan J Armand et al. Neuron. .

Abstract

Single-cell sequencing technologies, including transcriptomic and epigenomic assays, are transforming our understanding of the cellular building blocks of neural circuits. By directly measuring multiple molecular signatures in thousands to millions of individual cells, single-cell sequencing methods can comprehensively characterize the diversity of brain cell types. These measurements uncover gene regulatory mechanisms that shape cellular identity and provide insight into developmental and evolutionary relationships between brain cell populations. Single-cell sequencing data can aid the design of tools for targeted functional studies of brain circuit components, linking molecular signatures with anatomy, connectivity, morphology, and physiology. Here, we discuss the fundamental principles of single-cell transcriptome and epigenome sequencing, integrative computational analysis of the data, and key applications in neuroscience.

Keywords: ATAC-seq; DNA methylation; cell state; cell type; epigenome; multi-omics; open chromatin; single-cell sequencing; spatial transcriptomics; transcriptome.

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Conflict of interest statement

Declaration of Interests: The authors declare no competing interests.

Figures

Figure 1.
Figure 1.
Single cell sequencing modalities for neuroscience. (A) Transcriptome and epigenome signatures that can be assayed by single cell sequencing, alongside spatial context, connectivity, and physiology. (B) Comparison of single-cell molecular assays in terms of the number of cells (scope, x axis) and number of unique molecules characterized per cell (depth, y axis). Below: spatial resolution for imaging and spatial transcriptomics. Shaded boxes show the range of values achieved in recent single-cell studies of brain cells. The estimated number of cells in an adult human brain (Azevedo et al., 2009) or mouse brain (Erö et al., 2018), the typical size of a neuronal cell body (Zhang et al., 2020a), and the estimated number of mRNA molecules in a mammalian cell (Shapiro et al., 2013) are indicated for comparison.
Figure 2.
Figure 2.
Single cell transcriptomics applications in neuroscience. (A) The mouse gene Tac1, encoding the neuropeptide precursor Tachykinin-1 which is a specific marker of a subset of MGE-derived GABAergic neurons in cortex (Yao et al., 2020a), has five isoforms with different promoters, exon usage, and end sites (black gene models). Schematic tracks illustrate full-length RNA-sequencing (red) or 3’-end tagging (blue). (B) Hierarchical clustering of cell types in human medial temporal gyrus adapted from (Hodge et al., 2019), focusing on medial ganglionic eminence (MGE)-derived GABAergic interneurons. Branches from other cell classes truncated to (double-slash). CGE: caudal ganglionic eminence. (C) A two-dimensional embedding of 2,260 single nucleus transcriptomes computed using Uniform Manifold Approximation and Projection (UMAP) (McInnes et al., 2018). Each point represents one cell, and the arrangement of cells is an approximate representation of their degree of similarity (nearby points) or difference (distant points). Points are colored according to the cell type clusters from (B) (arrows). Rare subpopulations of SST CHODL and Chandelier cells are circled. (D) Violin plots show expression of marker genes in MGE-derived interneurons.
Figure 3.
Figure 3.
Single cell epigenomics. (A) 2-dimensional embedding of MGE-derived inhibitory neurons profiled by DNA methylation (snmC-seq, (Luo et al., 2017)) and open chromatin (snATAC-seq, 10x Genomics). (B) Genome browser tracks showing pseudo-bulk signals pooled from many single cells of the same cell type (PV-expressing inhibitory interneurons from mouse cortex) around the marker gene Lhx6, showing open chromatin (10x Genomics), CG and non-CG DNA methylation (Luo et al., 2017). (C) 3D chromatin conformation from sn-m3C-seq of human frontal cortex VIP cells (Lee et al., 2019). Heatmap shows the number of contacts detected between pairs of genomic bins; red triangular blocks correspond to domains of frequent chromatin interactions. (D) Illustration of enhancer-gene interaction, in which a 3D chromatin loop brings an enhancer in physical proximity with a gene promoter. (E) Enhancer-gene interaction inferred from the correlation of chromatin accessibility across cell types. (F) Schematic illustration of partitioned heritability analysis. Top: Genome-wide association studies (GWAS) provide a statistical measure of genetic association with a disease (y-axis; higher points are more significant) for individual genetic variants (x-axis: genomic coordinate). Red dots/shaded regions are loci passing a stringent threshold for genome-wide significance (p<5e-8). Bottom: Gene expression or accessible chromatin profiles in three cell types. Right: Enrichment of active gene expression or accessible chromatin near significant disease-associated variants indicates a potential vulnerability specifically in cell type 1.
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