Computational flow cytometric analysis to detect epidermal subpopulations in human skin

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.

Computational Flow Cytometric Analysis to Detect Epidermal Subpopulations In Human Skin

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.

Computational Flow Cytometric Analysis to Detect Epidermal Subpopulations In Human Skin

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.

Homoeoallelic gene Ncc-tmp of Triticum timopheevii conferring compatibility with the cytoplasm of Aegilops squarrosa in the tetraploid wheat nuclear background

A nuclear gene, Ncc-tmp1A, of Triticum timopheevii is required for the nucleus-cytoplasm (NC) compatibility in tetraploid NC hybrids with the cytoplasm of Aegilops squarrosa. A euploid NC hybrid of T. durum was previously produced by introgressing the gene from chromosome 1A of T. timopheevii. To examine the possible presence of a functional homoeoallele in the G genome of T. timopheevii, segregation of seed viability was studied as a marker phenotype in BC1s involving the two types of NC hybrids, (Ae. squarrosa) - T. timopheevii and (Ae. squarrosa) - T. turgidum. The result of these test crosses suggested that the G genome possesses a functional homoeoallele Ncc-tmp1G. Segregation of two RAPD (random amplified polymorphic DNA) markers that were closely linked to Ncc-tmp1A was further studied among the viable BC1s obtained from a test cross of (Ae. squarrosa) - T. timopheevii × T. turgidum. Some viable BC1 segregants without the markers were obtained, suggesting a limited degree of transmission of chromosome 1G carrying Ncc-tmp1G. However, a similar RAPD analysis of BC1s obtained after backcrosses of reciprocal F1s of T. timopheevii / T. turgidum with T. turgidum showed random marker segregation. Thus, it was concluded that Ncc-tmp1A is not required for compatibility with its own cytoplasm. Southern blot analysis of the euploid NC hybrid using RFLP (restriction fragment length polymorphism) markers on the homoeologous group 1 chromosomes showed that Ncc-tmp1A locates in the centromeric region.Key words: nucleus-cytoplasm (NC) compatibility, Ncc genes, Aegilops squarrosa, Triticum timopheevii, durum wheat.