计算生物学
生物
转录组
免疫系统
工作流程
基因组学
系统生物学
核糖核酸
神经炎症
质量细胞仪
单细胞分析
细胞
生物信息学
神经科学
计算机科学
基因
基因表达
遗传学
基因组
免疫学
表型
炎症
数据库
作者
Isabelle Scheyltjens,Hannah Van Hove,Karen De Vlaminck,Daliya Kancheva,Jonathan Bastos,Mónica Vara‐Pérez,Ana Rita Pombo Antunes,Liesbet Martens,Charlotte L. Scott,Jo A. Van Ginderachter,Yvan Saeys,Martin Guilliams,Niels Vandamme,Kiavash Movahedi
出处
期刊:Nature Protocols
[Springer Nature]
日期:2022-08-05
卷期号:17 (10): 2354-2388
被引量:18
标识
DOI:10.1038/s41596-022-00716-4
摘要
Brain–immune cross-talk and neuroinflammation critically shape brain physiology in health and disease. A detailed understanding of the brain immune landscape is essential for developing new treatments for neurological disorders. Single-cell technologies offer an unbiased assessment of the heterogeneity, dynamics and functions of immune cells. Here we provide a protocol that outlines all the steps involved in performing single-cell multi-omic analysis of the brain immune compartment. This includes a step-by-step description on how to microdissect the border regions of the mouse brain, together with dissociation protocols tailored to each of these tissues. These combine a high yield with minimal dissociation-induced gene expression changes. Next, we outline the steps involved for high-dimensional flow cytometry and droplet-based single-cell RNA sequencing via the 10x Genomics platform, which can be combined with cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) and offers a higher throughput than plate-based methods. Importantly, we detail how to implement CITE-seq with large antibody panels to obtain unbiased protein-expression screening coupled to transcriptome analysis. Finally, we describe the main steps involved in the analysis and interpretation of the data. This optimized workflow allows for a detailed assessment of immune cell heterogeneity and activation in the whole brain or specific border regions, at RNA and protein level. The wet lab workflow can be completed by properly trained researchers (with basic proficiency in cell and molecular biology) and takes between 6 and 11 h, depending on the chosen procedures. The computational analysis requires a background in bioinformatics and programming in R.
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