Beyond bulk: a review of single cell transcriptomics methodologies and applications

[Display omitted] •Technologies such as single-cell RNA sequencing have rapidly progressed in recent years.•Various methods exist for preparing libraries for sequencing, each with its own limitations and advantages.•There are several analytical approaches for analyzing the large amount of data generated by single-cell sequencing experiments.•Single-cell RNA sequencing studies can reveal how distinct cell populations contribute to brain organization and and disease-relevant behaviors. Single-cell RNA sequencing (scRNA-seq) is a promising approach to study the transcriptomes of individual cells in the brain and the central nervous system (CNS). This technology acts as a bridge between neuroscience, computational biology, and systems biology, enabling an unbiased and novel understanding of the cellular composition of the brain and CNS. Gene expression at the single cell resolution is often noisy, sparse, and high-dimensional, creating challenges for computational analysis of such data. In this review, we overview fundamental sample preparation and data analysis processes of scRNA-seq and provide a comparative perspective for analyzing and visualizing these data.