Epidermal stem cell lineages

Oinam, Lalhaba; Changarathil, Gopakumar; Ngo, Yen Xuan; Yanagisawa, Hiromi; Sada, Aiko

doi:10.1016/bs.asn.2019.04.003

“…The tip of the rete ridge suggests the enrichment of epidermal stem cells in rete ridges with high expression of keratin 15(K15) and CD 117 [7,10,11]. Once the differentiation begins, basal cells become detached from the basement membrane, reorganize adhesive junctions and cytoskeleton, case expression of the basal markers and start terminal differentiation programs with expression of differentiation markers, including K1 and K10, early-stage differentiation marker, Loricrin and Filaggrin, late-stage differentiation marker [3,5,6]. CD24 is a small and highly glycosylated protein expressing in the suprabasal layer and stratum spinosum, rarely in stratum granulosum [7].…”

Section: Introductionmentioning

confidence: 99%

“…The epidermis regenerates itself throughout life constantly as the body's most dynamic tissues [3]. The epidermal stem cell populations are lineage restricted within their own respective territory during homeostasis with the capacity to differentiate into all epidermal lineages [4,5]. The basal cell layer of IFE remains in contact with the dermis through integrin-based adhesion and hemidesmosomes and express basal marker proteins, such as keratin 5 (K5) and K14 [3,6].…”

Section: Introductionmentioning

confidence: 99%

Computational Flow Cytometric Analysis to Detect Epidermal Subpopulations In Human Skin

Zhang

¹

,

Cen

²

,

Huang

³

et al. 2021

Preprint

0

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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.

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“…The tip of the rete ridge suggests the enrichment of epidermal stem cells in rete ridges with high expression of keratin 15(K15) and CD 117 [7,10,11]. Once the differentiation begins, basal cells become detached from the basement membrane, reorganize adhesive junctions and cytoskeleton, case expression of the basal markers and start terminal differentiation programs with expression of differentiation markers, including K1 and K10, early-stage differentiation marker, Loricrin and Filaggrin, late-stage differentiation marker [3,5,6]. CD24 is a small and highly glycosylated protein expressing in the supbasal layer and stratum spinosum, rarely in stratum granulosum [7].…”

Section: Introductionmentioning

confidence: 99%

Computational Flow Cytometric Analysis to Detect Epidermal Subpopulations In Human Skin

Zhang

¹

,

Cen

²

,

Huang

³

et al. 2020

Preprint

0

View full text Add to dashboard Cite

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.

show abstract

Computational flow cytometric analysis to detect epidermal subpopulations in human skin

Zhang

¹

,

Cen

²

,

Huang

³

et al. 2021

BioMed Eng OnLine

3

0

View full text Add to dashboard Cite

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.

show abstract

Epidermal stem cell lineages

Cited by 3 publications

References 189 publications

Computational Flow Cytometric Analysis to Detect Epidermal Subpopulations In Human Skin

Computational Flow Cytometric Analysis to Detect Epidermal Subpopulations In Human Skin

Computational Flow Cytometric Analysis to Detect Epidermal Subpopulations In Human Skin

Computational flow cytometric analysis to detect epidermal subpopulations in human skin

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