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

By Arpiar Saunders, Evan Z. Macosko, Alec Wysoker, Melissa Goldman, Fenna M. Krienen, Heather de Rivera, Elizabeth Bien, Matthew Baum, Shuyu Wang, A. Goeva and 5 more. 2018

1,389 citations recorded by the source corpus.

Summary The mammalian brain is composed of diverse, specialized cell populations. To systematically ascertain and learn from these cellular specializations, we used Drop-seq to profile RNA expression in 690,000 individual cells, sampled from nine regions of the adult mouse brain. We identified 565 transcriptionally distinct groups of cells using computational approaches developed to distinguish biological from technical signals. Cross-region analysis of these 565 cell populations revealed features of brain organization, including a gene-expression module for synthesizing axonal and presynaptic components, patterns in the co-deployment of voltage-gated ion channels, functional distinctions among the cells of the vasculature and specialization of glutamatergic neurons across cortical regions. Systematic neuronal classifications for two complex basal ganglia nuclei and the striatum revealed a rare population of spiny projection neurons. This adult mouse brain cell atlas, accessible through interactive online software (DropViz), serves as a reference for development, disease, and evolution.

Subfields: Biophysics, Developmental Neuroscience, Molecular Biology

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Citations in the corpus

Current best practices in single‐cell RNA‐seq analysis: a tutorial (2019, background): “Reference webtools such as www.mousebrain.org (Zeisel et al , 2018) or http://dropviz.org/ (Saunders et al , 2018) allow users to visualize the expression of dataset marker genes in the reference dataset to facilitate cell-identity annotation.”
A molecular cell atlas of the human lung from single cell RNA sequencing (2019, background): “The 58 identified cell populations included 15 epithelial (clusters 1-15), 9 endothelial (16-24), 9 stromal (25-33), and 25 immune (34-58) populations, greater than the number of classical cell types in each compartment (11 epithelial, 5 endothelial, 7 stromal, 20 immune, and 2 neuronal cell types) ”
Probabilistic harmonization and annotation of single‐cell transcriptomics data with deep generative models (2019, positive): “We harmonized two data-sets from mouse (Saunders et al , 2018) and human (Welch et al , 2019) Substantia Niagra after mapping homologous genes using the Mouse Genome Informatics Web Site (Bult et al , 2019).”
scDeepSort: a pre-trained cell-type annotation method for single-cell transcriptomics using deep learning with a weighted graph neural network (2021, methodology): “Moreover, on three more challenging single-cell atlases (33–35), scDeepSort also accurately predicts the main cell type (Supplementary Figure S3) for most cells except the partial neurons in the Loom dataset (35).”
Spatially Informed Cell Type Deconvolution for Spatial Transcriptomics (2022, methodology): “We used the hippocampus scRNA-seq dataset by Drop-seq 23,59 for deconvoluting both datasets (Supplementary Table 2).”
Efficient integration of heterogeneous single-cell transcriptomes using Scanorama (2019, methodology): “…demonstrate the scalability of our method by applying Scanorama to integrate 1,095,538 cells from two large-scale single-cell transcriptomic studies of the central nervous system (CNS) in mouse, 26,27 including samples taken from the mouse spinal cord and from different regions of the mouse brain.”