Single-cell transcriptomics applied to emigrating cells from psoriasis elucidate pathogenic vs. regulatory immune cell subsets
Background: In previous human skin single-cell data, inflammatory cells constituted only a small fraction of the overall cell population, such that functional subsets were difficult to ascertain. Objective: Our aims were to overcome the aforesaid limitation by applying single-cell transcriptomics to emigrating cells from skin and elucidate ex vivo gene expression profiles of pathogenic versus regulatory immune cell subsets in the skin of individuals with psoriasis. Methods: We harvested emigrating cells from human psoriasis skin after incubation in culture medium without enzyme digestion or cell sorting and analyzed cells with single-cell RNA sequencing and flow cytometry simultaneously. Results: Unsupervised clustering of harvested cells from psoriasis skin and control skin identified natural killer cells, T-cell subsets, dendritic cell subsets, melanocytes, and keratinocytes in different layers. Comparison between psoriasis cells and control cells within each cluster revealed that (1) cutaneous type 17 T cells display highly differing transcriptome profiles depending on IL-17A versus IL-17F expression and IFN-γ versus IL-10 expression; (2) semimature dendritic cells are regulatory dendritic cells with high IL-10 expression, but a subset of semimature dendritic cells expresses IL-23A and IL-36G in psoriasis; and (3) CCL27-CCR10 interaction is potentially impaired in psoriasis because of decreased CCL27 expression in basal keratinocytes. Conclusion: We propose that single-cell transcriptomics applied to emigrating cells from human skin provides an innovative study platform to compare gene expression profiles of heterogenous immune cells in various inflammatory skin diseases. Keywords: Psoriasis; T cells; dendritic cells; emigrating cells; keratinocytes; single-cell RNA sequencing.
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基因水平:PCR Array、RT-PCR、PCR、单细胞测序
蛋白水平:MSD、Luminex、CBA、Elispot、Antibody Array、ELISA、Sengenics
细胞水平:细胞染色、细胞分选、细胞培养、细胞功能
组织水平:空间多组学、多重荧光免疫组化、免疫组化、免疫荧光
数据分析:流式数据分析、组化数据分析、多因子数据分析
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