Computational flow cytometric analysis to detect epidermal subpopulations in human skin

Abstract

Background The 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. Methods A 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). Results The 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. Conclusion A multicolor flow cytometry panel with a streamlined computational analysis pipeline is a feasible approach to delineate the heterogeneity of the epidermis in human skin.

Development of a Spectral Flow Cytometry Analysis Pipeline for High-Dimensional Immune Cell Characterization

“…17,18 Conventional flow cytometry gating strategies examine two markers at a time to subset cells. 19 These subsets can then be drilled down to examine two more markers until all markers have been analyzed. If a flow cytometry panel has only six antibodies, there are only six possible unique gating sequences possible.…”