生物
流式细胞术
质量细胞仪
功能(生物学)
计算生物学
恒河猴
巨噬细胞
猕猴
细胞生物学
免疫学
神经科学
体外
表型
遗传学
基因
作者
Martin Guilliams,Charles‐Antoine Dutertre,Charlotte L. Scott,Naomi McGovern,Dorine Sichien,Svetoslav Chakarov,Sofie Van Gassen,Jinmiao Chen,Michael Poidinger,Sofie De Prijck,Simon J. Tavernier,Ivy Low,Sergio Erdal Irac,Citra Nurfarah Zaini Mattar,Hermi Sumatoh,Gillian Low,John Kit Chung Tam,Dedrick Kok Hong Chan,Ker‐Kan Tan,Tony Lim Kiat Hon
出处
期刊:Immunity
[Cell Press]
日期:2016-09-01
卷期号:45 (3): 669-684
被引量:722
标识
DOI:10.1016/j.immuni.2016.08.015
摘要
Dendritic cells (DCs) are professional antigen-presenting cells that hold great therapeutic potential. Multiple DC subsets have been described, and it remains challenging to align them across tissues and species to analyze their function in the absence of macrophage contamination. Here, we provide and validate a universal toolbox for the automated identification of DCs through unsupervised analysis of conventional flow cytometry and mass cytometry data obtained from multiple mouse, macaque, and human tissues. The use of a minimal set of lineage-imprinted markers was sufficient to subdivide DCs into conventional type 1 (cDC1s), conventional type 2 (cDC2s), and plasmacytoid DCs (pDCs) across tissues and species. This way, a large number of additional markers can still be used to further characterize the heterogeneity of DCs across tissues and during inflammation. This framework represents the way forward to a universal, high-throughput, and standardized analysis of DC populations from mutant mice and human patients.
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