Computational flow cytometric analysis to detect epidermal subpopulations in human skin

Area of Science:

• Dermatology
• Immunology
• Cell Biology

Background:

• Understanding epidermal heterogeneity is crucial for skin homeostasis and wound healing.
• Flow cytometry is a powerful tool for analyzing epidermal cell surface markers and high-dimensional data.

Purpose of the Study:

• To develop and validate a multicolor flow cytometry panel for dissecting epidermal subgroups in human skin.
• To characterize epidermal cell heterogeneity using computational analysis methods.

Main Methods:

• Optimized a 9-color flow cytometry panel for human skin epidermis.
• Employed automated computational methods, including viSNE and Spanning-tree Progression Analysis of Density-normalized Events (SPADE), for cell subset characterization.
• Confirmed results using PhenoGraph for consistency.

Main Results:

• Manual analysis identified differences in epidermal cell distribution across body sites based on CD49f and CD29 expression.
• Computational analysis with SPADE delineated 25 distinct epidermal cell clusters based on surface marker phenotypes.
• Automated analysis successfully highlighted inter-body site variations in epidermal composition.

Conclusions:

• A multicolor flow cytometry panel combined with a streamlined computational pipeline effectively delineates epidermal heterogeneity in human skin.
• This approach provides a feasible method for detailed analysis of skin cell populations.