2020
DOI: 10.1016/j.jaci.2020.03.041
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Single-cell transcriptomics combined with interstitial fluid proteomics defines cell type–specific immune regulation in atopic dermatitis

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Cited by 151 publications
(156 citation statements)
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“…Previous studies have detected expression of IL13 transcripts in human skin from healthy indivdiuals (He et al, 2020; Rojahn et al, 2020), although it was unclear whether it was associated with T cells or ILCs due to the transcriptional similarity of these populations. We interrogated the dataset from one of these studies, which includes scRNA-seq data from skin biopsies and suction blisters of healthy individuals (Rojahn et al, 2020), for expression of IL-13 signalling genes in the immune and non-immune skin cell compartments. Similar to Rojahn et al, we identified several clusters of myeloid cells including monocytes, cDC1s, cDC2s, LCs and a small cluster of CCR7 high /mature DCs ( Figure 6D and S6B-C ).…”
Section: Resultsmentioning
confidence: 99%
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“…Previous studies have detected expression of IL13 transcripts in human skin from healthy indivdiuals (He et al, 2020; Rojahn et al, 2020), although it was unclear whether it was associated with T cells or ILCs due to the transcriptional similarity of these populations. We interrogated the dataset from one of these studies, which includes scRNA-seq data from skin biopsies and suction blisters of healthy individuals (Rojahn et al, 2020), for expression of IL-13 signalling genes in the immune and non-immune skin cell compartments. Similar to Rojahn et al, we identified several clusters of myeloid cells including monocytes, cDC1s, cDC2s, LCs and a small cluster of CCR7 high /mature DCs ( Figure 6D and S6B-C ).…”
Section: Resultsmentioning
confidence: 99%
“…The authors wish to thank Prof Graham Ogg, Oxford University, for sharing raw data of the scRNA-seq analysis of human skin blister (Chen et al, 2020); Dr. Patrick M. Brunner and Dr. Vera Vorstandlechner, Department of Dermatology, Medical University of Vienna, for their advice on single cell QC filtering and data analysis of scRNA-seq data of human skin biopsies and blister cells (GSE153760, Rojahn et al, 2020); Yueqi Wang, Google, for developing the Shiny browser for single cell RNAseq data, and providing debugging help, and all colleagues at the Malaghan Institute of Medical Research for discussion and suggestions. We also thank Dr. Olivier Gasser and the NIH Tetramer Facility for providing the 5-OP-RU-loaded tetramers for MAIT cell identification.…”
Section: Acknowledgementsmentioning
confidence: 99%
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“…Regarding mouse samples, a limited number of proteins (90-100) can be analyzed using this technique. However, up to now, the majority of published reports on PEA have investigated patients with chronic inflammatory skin conditions, but not with systemic inflammatory or autoimmune diseases (69,(71)(72)(73)(74). Based on our own experience, we believe that the PEA technology will increasingly be used to study alterations of proteomic signatures in chronic inflammatory diseases, and thus will significantly contribute to the understanding of disease pathogenesis.…”
Section: Proteomics Approachesmentioning
confidence: 99%
“…For identification of differentially expressed genes (DEGs), normalized count numbers were used, including genes present in the integrated dataset to avoid calculation of batch effects. As keratin and collagen genes were previously found to contaminate skin biopsy datasets and potentially provide a false-positive signal 34 , these genes (COL1A1, COL1A2, COL3A1 and KRT1 KRT5, KRT10, KRT14, KRTDAP) were excluded from DEG calculation in non-fibroblast clusters (collagens) or non-keratinocyte clusters (keratins), respectively. Moreover, genes Gm42418, Gm17056 and Gm26917 caused technical background noise and batch effect in mouse scRNAseq, as described before 35 , and were thus excluded from the dataset.…”
Section: Cell-gene Matrix Preparation and Downstream Analysismentioning
confidence: 99%