Differential Marker Expression between Keratinocyte Stem Cells and Their Progeny Generated from a Single Colony

The stemness in keratinocyte stem cells (KSCs) is determined by their gene expression patterns. KSCs are crucial in maintaining epidermal homeostasis and wound repair and are widely used candidates for therapeutic applications. Although several studies have reported their positive identifiers, unique biomarkers for KSCs remain elusive. Here, we aim to identify potential candidate stem cell markers. Human epidermal keratinocytes (HEKs) from neonatal foreskin tissues were isolated and cultured. Single-cell clonal analysis identified and characterized three types of cells: KSCs (holoclones), transient amplifying cells (TACs; meroclones), and differentiated cells (DSCs; paraclones). The clonogenic potential of KSCs demonstrated the highest proliferation potential of KSCs, followed by TACs and DSCs, respectively. Whole-transcriptome analysis using microarray technology unraveled the molecular signatures of these cells. These results were validated by quantitative real-time polymerase chain reaction and flow cytometry analysis. A total of 301 signature upregulated and 149 downregulated differentially expressed genes (DEGs) were identified in the KSCs, compared to TACs and DSCs. Furthermore, DEG analyses revealed new sets of genes related to cell proliferation, cell adhesion, surface makers, and regulatory factors. In conclusion, this study provides a useful source of information for the identification of potential SC-specific candidate markers.

Automated collective motion analysis validates human keratinocyte stem cell cultures

Identification and quality assurance of stem cells cultured in heterogeneous cell populations are indispensable for successful stem cell therapy. Here we present an image-processing pipeline for automated identification and quality assessment of human keratinocyte stem cells. When cultivated under appropriate conditions, human epidermal keratinocyte stem cells give rise to colonies and exhibit higher locomotive capacity as well as significant proliferative potential. Image processing and kernel density estimation were used to automatically extract the area of keratinocyte colonies from phase-contrast images of cultures containing feeder cells. The DeepFlow algorithm was then used to calculate locomotion speed of the colony area by analyzing serial images. This image-processing pipeline successfully identified keratinocyte stem cell colonies by measuring cell locomotion speed, and also assessed the effect of oligotrophic culture conditions and chemical inhibitors on keratinocyte behavior. Therefore, this study provides automated procedures for image-based quality control of stem cell cultures and high-throughput screening of small molecules targeting stem cells.