Dataset Information

Computational flow cytometric analysis to detect epidermal subpopulations in human skin.

ABSTRACT:

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.

SUBMITTER: Zhang L

PROVIDER: S-EPMC7891025 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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Publications

Computational flow cytometric analysis to detect epidermal subpopulations in human skin.

Zhang Lidan L Cen Ying Y Huang Qiaorong Q Li Huifang H Mo Xianming X Meng Wentong W Chen Junjie J

Biomedical engineering online 20210217 1

<h4>Background</h4>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.<h4>Methods</h4>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 metho ...[more]

PMID: 33596908

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Project description:IntroductionScientific literature regarding the characterization of lymphocyte subpopulations of the cecal appendix is sparse, with few precedents limited to immunohistochemical techniques.MethodsWe conducted a prospective pilot study to characterize lymphocyte subpopulations of the cecal appendix in children. Participants were divided into three groups: (1) patients without histological acute appendiceal inflammation, (2) patients with histological uncomplicated acute appendicitis, and (3) patients with histological complicated acute appendicitis (gangrenous, perforated). A fresh sample of the base of the appendix was taken from all patients and a flow cytometric study was performed. Quantitative variables were compared using Kruskal-Wallis test and Mann-Whitney U test.ResultsThis study included 57 patients divided into Group 1 (n = 5), Group 2 (n = 37), and Group 3 (n = 15). Median values (IQR) of the percentage of B-lymphocytes were 67.8 [66.8-68.1] in group 1, 61.15 [53.74-66.4] in group 2, and 52.1 [33-62.02] in group 3 (p = 0.02). Median values (IQR) of the percentage of NK-lymphocytes were 0.26 [0.2-0.3] in group 1, 0.55 [0.37-0.66] in group 2, and 0.84 [0.35-1.45] in group 3 (p = 0.008). Median values (IQR) of the percentage of T-lymphocytes were 31.9 [31.7-33.1] in group 1, 37.68 [32.15-45.69] in group 2, and 46.9 [37.03-67] in group 3 (p = 0.02). Pair comparisons of groups 2 and 3 also showed significant differences in the percentage of B lymphocytes (p = 0.03) and NK-lymphocytes (p = 0.02).ConclusionsSignificant differences in lymphocyte subpopulations were identified according to the histologic grade of the cecal appendix. More specifically, a lower percentage of B-lymphocytes and a higher percentage of T- and NK-lymphocytes were observed in cases of acute appendicitis. These findings must be confirmed and their etiopathogenic, diagnostic, and prognostic implications elucidated in future studies with larger sample sizes.

Project description:Background:Circulating endothelial cells (CECs) and their subpopulations could be potential novel biomarkers for various malignancies. However, reliable enumerable methods are warranted to further improve their clinical utility. This study aimed to optimize a flow cytometric method (FCM) assay for CECs and subpopulations in peripheral blood for patients with solid cancers. Patients and methods:An FCM assay was used to detect and identify CECs. A panel of 60 blood samples, including 44 metastatic cancer patients and 16 healthy controls, were used in this study. Some key issues of CEC enumeration, including sample material and anticoagulant selection, optimal titration of antibodies, lysis/wash procedures of blood sample preparation, conditions of sample storage, sufficient cell events to enhance the signal, fluorescence-minus-one controls instead of isotype controls to reduce background noise, optimal selection of cell surface markers, and evaluating the reproducibility of our method, were integrated and investigated. Wilcoxon and Mann-Whitney U tests were used to determine statistically significant differences. Results:In this validation study, we refined a five-color FCM method to detect CECs and their subpopulations in peripheral blood of patients with solid tumors. Several key technical issues regarding preanalytical elements, FCM data acquisition, and analysis were addressed. Furthermore, we clinically validated the utility of our method. The baseline levels of mature CECs, endothelial progenitor cells, and activated CECs were higher in cancer patients than healthy subjects (P<0.01). However, there was no significant difference in resting CEC levels between healthy subjects and cancer patients (P=0.193). Conclusion:We integrated and comprehensively addressed significant technical issues found in previously published assays and validated the reproducibility and sensitivity of our proposed method. Future work is required to explore the potential of our optimized method in clinical oncologic applications.

Dataset Information

Computational flow cytometric analysis to detect epidermal subpopulations in human skin.

Background

Methods

Results

Conclusion

Publications

Computational flow cytometric analysis to detect epidermal subpopulations in human skin.

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