Single-cell transcriptomics combined with interstitial fluid proteomics defines cell type–specific immune regulation in atopic dermatitis

Rojahn, Thomas B.; Vorstandlechner, Vera; Krausgruber, Thomas; Bauer, Wolfgang; Alkon, Natalia; Bangert, Christine; Thaler, Felix M.; Sadeghyar, Farzaneh; Fortelny, Nikolaus; Gernedl, Victoria; Rindler, Katharina; Elbe‐Bürger, Adelheid; Bock, Christoph; Mildner, Michael; Brunner, Patrick M.

doi:10.1016/j.jaci.2020.03.041

Cited by 123 publications

(124 citation statements)

References 77 publications

(84 reference 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%

“…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%

“…In order to compare with the microarray data, gene expression for each group was summarised as the mean SCT expression across all cells within the group. Human skin blister cells and biopsy from healthy volunteers (Rojahn et al, 2020) were downloaded from GEO (GSE153760) and processed using Seurat v3.2.0 (Stuart et al, 2019), following the v3.2 "Integration and Label Transfer" vignette. Cells were pre-filtered to include only cells with at least 500 features and less than 10000 counts, as well as restricting mitochondrial fraction to between 0.5 and 15 percent.…”

Section: Human Dc2 Transcriptomic Data Integrationmentioning

confidence: 99%

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The skin environment controls local dendritic cell differentiation and function through innate IL-13

Mayer

Lamiable

Hilligan

et al. 2021

Preprint

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The signals driving the adaptation of type-2 dendritic cells (DC2s) to diverse peripheral environments are not well understood. We show that the development of CD11blow migratory DC2s, a DC2 population unique to the dermis, requires STAT6- and KLF4-dependent IL-13 signaling, whereas DC2s in lung and small intestine are STAT6-independent. Dermal IL-13 is mostly derived from innate lymphoid cells expressing a resting ICOS+ KLRG1-ST2-phenotype. Analysis of public datasets indicates that human skin DC2s also express an IL-4/IL-13 gene signature compared to blood or spleen, suggesting a similar developmental pathway in mice and humans. In the absence of IL-13 signaling, dermal DC2s are stable in number but remain CD11bhi and show defective activation in response to allergen with diminished ability to support IL-4+ GATA3+ Th development, whereas anti-fungal IL-17+ RORγt+ responses are increased. Thus, steady-state IL-13 fosters a non-inflammatory and pro-allergic environment in healthy skin via conditioning of local DC2s.

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Section: Resultsmentioning

confidence: 99%

Section: Acknowledgementsmentioning

confidence: 99%

Section: Human Dc2 Transcriptomic Data Integrationmentioning

confidence: 99%

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The skin environment controls local dendritic cell differentiation and function through innate IL-13

Mayer

Lamiable

Hilligan

et al. 2021

Preprint

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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%

Phenotyping of Adaptive Immune Responses in Inflammatory Diseases

et al. 2020

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Immunophenotyping on the molecular and cellular level is a central aspect for characterization of patients with inflammatory diseases, both to better understand disease etiopathogenesis and based on this to develop diagnostic and prognostic biomarkers which allow patient stratification and tailor-made treatment strategies. Technology-driven developments have considerably expanded the range of analysis tools. Especially the analysis of adaptive immune responses, often regarded as central though mostly poorly characterized disease drivers, is a major focus of personalized medicine. The identification of the disease-relevant antigens and characterization of corresponding antigen-specific lymphocytes in individual patients benefits significantly from recent developments in cytometry by sequencing and proteomics. The aim of this workshop was to identify the important developments for state-of-the-art immunophenotyping for clinical application and precision medicine. We focused here on recent key developments in analysis of antigen-specific lymphocytes, sequencing, and proteomics approaches, their relevance in precision medicine and the discussion of the major challenges and opportunities for the future.

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“…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%

Single cell sequencing identifies serine proteases as regulators of myofibroblast differentiation

Vorstandlechner¹,

Laggner²,

Copic³

et al. 2020

Preprint

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Despite recent advances in understanding skin scarring, mechanisms triggering hypertrophic scar formation are still poorly understood. In the present study we performed single-cell sequencing of mature human hypertrophic scars and developing scars in mice. Compared to normal skin, we found significant differences in gene expression in most cell types present in scar tissue. Fibroblasts (FBs) showed the most prominent alterations in gene expression, displaying a distinct fibrotic signature. By comparing genes upregulated in murine FBs during scar development with genes highly expressed in mature human hypertrophic scars, we identified a group of serine proteases, tentatively involved in scar formation. Two of them, dipeptidyl-peptidase 4 (DPP4) and urokinase (PLAU), were further analyzed in functional assays, revealing a role in TGFβ1-mediated myofibroblast differentiation and over-production of components of the extracellular matrix (ECM) without interfering with the canonical TGFbeta1-signaling pathway. In this study, we delineate the genetic landscape of hypertrophic scars and present new insights into mechanisms involved in hypertrophic scar formation. Our data suggest the use of serine protease inhibitors for the treatment of skin fibrosis.

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Single-cell transcriptomics combined with interstitial fluid proteomics defines cell type–specific immune regulation in atopic dermatitis

Cited by 123 publications

References 77 publications

The skin environment controls local dendritic cell differentiation and function through innate IL-13

The skin environment controls local dendritic cell differentiation and function through innate IL-13

Phenotyping of Adaptive Immune Responses in Inflammatory Diseases

Single cell sequencing identifies serine proteases as regulators of myofibroblast differentiation

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