Computational flow cytometric analysis to detect epidermal subpopulations in human skin

BioMedical Engineering OnLine

Junjie Chen¹  Lidan Zhang¹  Ying Cen¹  Qiaorong Huang²  Xianming Mo²  Huifang Li²  Wentong Meng² 

[1] Department of Burn and Plastic Surgery, West China Hospital, Sichuan University, 610041, Chengdu, Sichuan, China;Laboratory of Stem Cell Biology, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, 610041, Chengdu, Sichuan, China;

关键词: Flow cytometry;    Heterogeneity;    Human epidermis;    Machine learning;   

DOI  :  10.1186/s12938-021-00858-8

来源: Springer

【 摘 要 】

BackgroundThe detection and dissection of epidermal subgroups could lead to an improved understanding of skin homeostasis and wound healing. Flow cytometric analysis provides an effective method to detect the surface markers of epidermal cells while producing high-dimensional data files.MethodsA 9-color flow cytometric panel was optimized to reveal the heterogeneous subgroups in the epidermis of human skin. The subsets of epidermal cells were characterized using automated methods based on dimensional reduction approaches (viSNE) and clustering with Spanning-tree Progression Analysis of Density-normalized Events (SPADE).ResultsThe manual analysis revealed differences in epidermal distribution between body sites based on a series biaxial gating starting with the expression of CD49f and CD29. The computational analysis divided the whole epidermal cell population into 25 clusters according to the surface marker phenotype with SPADE. This automatic analysis delineated the differences between body sites. The consistency of the results was confirmed with PhenoGraph.ConclusionA multicolor flow cytometry panel with a streamlined computational analysis pipeline is a feasible approach to delineate the heterogeneity of the epidermis in human skin.

【 授权许可】

CC BY