All the single cells: single-cell transcriptomics/epigenomics experimental design and analysis considerations for glial biologists

TL;DR

This paper provides a comprehensive primer for glial biologists on designing and analyzing single-cell transcriptomics and epigenomics experiments, aiming to improve rigor and interpretation in glial research.

Contribution

It offers tailored guidance on experimental design and analysis decisions specific to glial cell studies using single-cell 'omics technologies.

Findings

Clarifies key decision points in single-cell data analysis.

Highlights considerations specific to glial cell biology.

Aims to improve rigor and reproducibility in glial 'omics studies.

Abstract

Single-cell transcriptomics, epigenomics, and other 'omics applied at single-cell resolution can significantly advance hypotheses and understanding of glial biology. Omics technologies are revealing a large and growing number of new glial cell subtypes, defined by their gene expression profile. These subtypes have significant implications for understanding glial cell function, cell-cell communications, and glia-specific changes between homeostasis and conditions such as neurological disease. For many, the training in how to analyze, interpret, and understand these large datasets has been through reading and understanding literature from other fields like biostatistics. Here, we provide a primer for glial biologists on experimental design and analysis of single-cell RNA-seq datasets. Our goal is to further the understanding of why decisions might be made about datasets and to enhance biologists' ability to interpret and critique their work and the work of others. We review the steps involved in single-cell analysis with a focus on decision points and particular notes for glia. The goal of this primer is to ensure that single-cell 'omics experiments continue to advance glial biology in a rigorous and replicable way.