Prater Katherine E, Lin Kevin Z
Department of Neurology, School of Medicine, University of Washington, Seattle, Washington, USA.
Department of Biostatistics, University of Washington, Seattle, Washington, USA.
Glia. 2025 Mar;73(3):451-473. doi: 10.1002/glia.24633. Epub 2024 Nov 19.
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 are 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.
单细胞转录组学、表观基因组学以及其他以单细胞分辨率应用的“组学”技术能够显著推动关于神经胶质生物学的假说及认识。“组学”技术正在揭示大量且数量不断增加的新神经胶质细胞亚型,这些亚型由其基因表达谱所定义。这些亚型对于理解神经胶质细胞功能、细胞间通讯以及神经胶质细胞在稳态与诸如神经疾病等状况之间的特异性变化具有重要意义。对于许多人而言,关于如何分析、解读和理解这些大型数据集的培训一直是通过阅读和理解生物统计学等其他领域的文献来进行的。在此,我们为神经胶质生物学家提供一份关于单细胞RNA测序数据集的实验设计与分析的入门指南。我们的目标是进一步理解为何要对数据集做出相关决策,并增强生物学家解读和评判自己及他人工作的能力。我们回顾单细胞分析所涉及的步骤,重点关注决策点以及针对神经胶质细胞的特别注意事项。本入门指南的目标是确保单细胞“组学”实验继续以严谨且可重复的方式推动神经胶质生物学的发展。