An integrated transcriptomic and epigenomic atlas of mouse primary motor cortex cell types

Abstract

Single cell transcriptomics has transformed the characterization of brain cell identity by providing quantitative molecular signatures for large, unbiased samples of brain cell populations. With the proliferation of taxonomies based on individual datasets, a major challenge is to integrate and validate results toward defining biologically meaningful cell types. We used a battery of single-cell transcriptome and epigenome measurements generated by the BRAIN Initiative Cell Census Network (BICCN) to comprehensively assess the molecular signatures of cell types in the mouse primary motor cortex (MOp). We further developed computational and statistical methods to integrate these multimodal data and quantitatively validate the reproducibility of the cell types. The reference atlas, based on more than 600,000 high quality single-cell or -nucleus samples assayed by six molecular modalities, is a comprehensive molecular account of the diverse neuronal and non-neuronal cell types in MOp.Collectively, our study indicates that the mouse primary motor cortex contains over 55 neuronal cell types that are highly replicable across analysis methods, sequencing technologies, and modalities. We find many concordant multimodal markers for each cell type, as well as thousands of genes and gene regulatory elements with discrepant transcriptomic and epigenomic signatures. These data highlight the complex molecular regulation of brain cell types and will directly enable design of reagents to target specific MOp cell types for functional analysis. IntroductionNeural circuits are characterized by extraordinary diversity of their cellular components 1,2 . Single-cell molecular assays, especially transcriptomic measurements by RNA-Seq, have accelerated the discovery and characterization of cell types across brain regions and in diverse species. Recent advances include single-cell transcriptome datasets with >10 5 individual cells, identifying hundreds of neuronal and non-neuronal cell types across the mouse nervous system 3-5 . As the number of profiled cells grows into the millions, a key question is whether these data will converge toward a comprehensive and coherent taxonomy of cell types with broad utility for organizing knowledge of brain cells and their function. Data from different modalities, including transcriptomic and epigenomic data, must be cross-referenced and integrated to establish robust and consistent cell type classifications.Although a comprehensive atlas should incorporate anatomical and physiological information, the high throughput of single cell sequencing assays makes integration of molecular data a particularly urgent challenge. A rigorous and reproducible consensus molecular atlas of brain cell types would drive progress across modalities, including obtaining functional information.Single cell sequencing technologies can measure multiple molecular signatures of cell identity. The core molecular identity of a cell is largely established during development and maintained by a combination of gene regulatory proteins...

Isoform cell type specificity in the mouse primary motor cortex

“…To identify isoform markers of cell types, we first sought to visualize our SMART-Seq data using gene derived cluster labels from the BICCN analysis 17 . Rather than layering cluster labels on cells mapped to 2-D with an unsupervised dimensionality reduction technique such as t-SNE 18 or UMAP 19 , we projected cells with neighborhood component analysis (NCA).…”

“…The software used to generate the results and figures of the paper is available at https://github.com/pachterlab/BYVSTZP_2020 . The single-cell RNA-seq data used in this study was generated as part of the BICCN consortium 17 . The 10xv3 and SMART-Seq data can be downloaded from http://data.nemoarchive.org/biccn/lab/zeng/transcriptome/scell/ .…”

“…Gene count matrices were made by using the --genecounts flag and TCC matrices were made by omitting it. The mouse transcriptome reference used was GRCm38.p3 (mm10) RefSeq annotation gff file retrieved from NCBI on 18 January 2016 ( https://www.ncbi.nlm.nih.gov/genome/annotation_euk/all/ ), for consistency with the reference used by the BICCN consortium 17 .…”

“…Isoform and gene count matrices were generated for the Smart-seq2 data using the kallisto pseudo command. Cluster assignments were associated with cells using cluster labels generated by the BICCN consortium 17 . The labels are organized in a hierarchy of three levels: classes, subclasses and clusters.…”

“…By leveraging the isoform resolution of SMART-Seq, we are able to identify isoform-specific markers for dozens of cell types characterized by the BICCN consortium 17 , and find isoform switches between cell types that are masked in standard analyses. Our work thus highlights another tradeoff in the race to identify cell types.…”