A practical guide for single-cell transcriptome data analysis in neuroscience

Abstract

Single-cell RNA sequencing (scRNA-seq) has revolutionized our ability to analyze gene expression at the single-cell level, providing unprecedented insights into cellular heterogeneity, rare cell populations, and dynamic cellular processes. In neuroscience, scRNA-seq has enabled the identification of diverse brain cell types, elucidation of developmental pathways, and discovery of mechanisms underlying neurological diseases. This tutorial provides a practical guide to scRNA-seq data analysis in neuroscience, focusing on the essential workflows and theoretical foundations. Key steps covered include quality control, data preprocessing, integration, cell clustering, and differential expression analysis. Using the Seurat R package, the tutorial demonstrates a comparative analysis approach for identifying differentially expressed genes between conditions, emphasizing the biological interpretation of results. By addressing the unique challenges of scRNA-seq data and illustrating methods for robust analysis, this work aims to enhance the reliability and reproducibility of scRNA-seq studies in neuroscience, supporting the exploration of cellular mechanisms and advancing research into brain function and disease.