Computational flow cytometric analysis to detect epidermal subpopulations in human skin

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. A 9-color flow cytome...

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Bibliographic Details
Published in:Biomedical engineering online 2021-02, Vol.20 (1), p.22-22, Article 22
Main Authors: Zhang, Lidan, Cen, Ying, Huang, Qiaorong, Li, Huifang, Mo, Xianming, Meng, Wentong, Chen, Junjie
Format: Article
Language:English
Subjects:
Abdomen
Algorithms
Automation
CD29 antigen
Cluster Analysis
Clustering
Color
Composition
Computational biology
Computer applications
Computer Simulation
Connective tissue cells
Epidermis
Epidermis - physiology
Flow cytometry
Flow Cytometry - methods
Gating
Hair
Heterogeneity
Homeostasis
Human epidermis
Humans
Identification and classification
Keratin
Machine Learning
Markers
Medical examination
Methods
Pattern Recognition, Automated
Phenotype
Phenotypes
Population
Production methods
Skin
Skin - cytology
Software
Stem cells
Subgroups
Subpopulations
Surface markers
Thorax
Wound healing
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Summary: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. 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). 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. 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.
ISSN:1475-925X
1475-925X
DOI:10.1186/s12938-021-00858-8