Single-cell RNA-sequencing of the brain

Abstract

Single-cell RNA-sequencing (scRNA-seq) is revolutionizing our understanding of the genomic, transcriptomic and epigenomic landscapes of cells within organs. The mammalian brain is composed of a complex network of millions to billions of diverse cells with either highly specialized functions or support functions. With scRNA-seq it is possible to comprehensively dissect the cellular heterogeneity of brain cells, and elucidate their specific functions and state. In this review, we describe the current experimental methods used for scRNA-seq. We also review bioinformatic tools and algorithms for data analyses and discuss critical challenges. Additionally, we summarized recent mouse brain scRNA-seq studies and systematically compared their main experimental approaches, computational tools implemented, and important findings. scRNA-seq has allowed researchers to identify diverse cell subpopulations within many brain regions, pinpointing gene signatures and novel cell markers, as well as addressing functional differences. Due to the complexity of the brain, a great deal of work remains to be accomplished. Defining specific brain cell types and functions is critical for understanding brain function as a whole in development, health, and diseases.

Keywords: Bioinformatic analyses; Brain; Heterogeneity; Single-cell RNA-sequencing.

Fig. 1

Selected relevant scRNA-seq studies revealing brain heterogeneity. Recent high throughput brain scRNA-seq studies indicate that mouse brain is composed of a large diversity of specialized cell subpopulations. Arrows indicate the sample collection region and the number of isolated cells. The numbers to the left represent the quantity of cells belonging to each global cell type. The numbers to the right represent the quantity of subpopulations found within each global cell type. Asterisks indicate cells were enriched for oligodendrocyte-lineage. Brain model schematic obtained from GENSAT (Gene Expression Nervous System Atlas) [120, 125]

Fig. 2

Single-cell widefield representative images acquired by an automated device (C1 Fluidigm chip). a Cell stained with ethidium homodimer-1 (EthD-1, red) labeling unhealthy or dead cells. b Single GFP⁺ cell. c Single GFP⁻ cell. d Capture site containing three cells. e Empty capture site (Figure adapted from [126])

Fig. 3

scRNA-seq quality control and expression estimation flow chart

Fig. 4

Normalization approaches commonly used in scRNA-seq data analyses

Fig. 5

Overview of scRNA-seq downstream analyses