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      Single-cell analysis reveals innate lymphoid cell lineage infidelity in atopic dermatitis

      research-article
      , MSc a , , , , MD a , , , PhD b , , BSc c , , MD, PhD a , , MSc a , a , , MD a , , MD d , , MD, MSc a , , PhD b , e , , MD, PhD c , f , g , , MD a
      The Journal of Allergy and Clinical Immunology
      Mosby
      Innate lymphoid cells, atopic dermatitis, human skin, single-cell RNA sequencing, AD, Atopic dermatitis, AHR, Aryl hydrocarbon receptor, GATA3, GATA binding protein 3, HC, Healthy control, ILC, Innate lymphoid cell, ILC1, Group 1 innate lymphoid cell, ILC2, Group 2 innate lymphoid cell, ILC3, Group 3 innate lymphoid cell, NHS, Normal human skin, PB, Peripheral blood, PE, Phycoerythrin, RORA, RAR related orphan receptor A, RORC, RAR related orphan receptor C, scRNA-seq, Single-cell RNA sequencing, TSLP, Thymic stromal lymphopoietin

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          Abstract

          Background

          Although ample knowledge exists about phenotype and function of cutaneous T lymphocytes, much less is known about the lymphocytic components of the skin’s innate immune system.

          Objective

          To better understand the biologic role of cutaneous innate lymphoid cells (ILCs), we investigated their phenotypic and molecular features under physiologic (normal human skin [NHS]) and pathologic (lesional skin of patients with atopic dermatitis [AD]) conditions.

          Methods

          Skin punch biopsies and reduction sheets as well as blood specimens were obtained from either patients with AD or healthy individuals. Cell and/or tissue samples were analyzed by flow cytometry, immunohistochemistry, and single-cell RNA sequencing or subjected to in vitro/ ex vivo culture.

          Results

          Notwithstanding substantial quantitative differences between NHS and AD skin, we found that the vast majority of cutaneous ILCs belong to the CRTH2 + subset and reside in the upper skin layers. Single-cell RNA sequencing of cutaneous ILC-enriched cell samples confirmed the predominance of biologically heterogeneous group 2 ILCs and, for the first time, demonstrated considerable ILC lineage infidelity (coexpression of genes typical of either type 2 [ GATA3 and IL13] or type 3/17 [ RORC, IL22, and IL26] immunity within individual cells) in lesional AD skin, and to a much lesser extent, in NHS. Similar events were demonstrated in ILCs from skin explant cultures and in vitro expanded ILCs from the peripheral blood.

          Conclusion

          These findings support the concept that instead of being a stable entity with well-defined components, the skin immune system consists of a network of highly flexible cellular players that are capable of adjusting their function to the needs and challenges of the environment.

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          Most cited references69

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          Fast, sensitive, and accurate integration of single cell data with Harmony

          The emerging diversity of single cell RNAseq datasets allows for the full transcriptional characterization of cell types across a wide variety of biological and clinical conditions. However, it is challenging to analyze them together, particularly when datasets are assayed with different technologies. Here, real biological differences are interspersed with technical differences. We present Harmony, an algorithm that projects cells into a shared embedding in which cells group by cell type rather than dataset-specific conditions. Harmony simultaneously accounts for multiple experimental and biological factors. In six analyses, we demonstrate the superior performance of Harmony to previously published algorithms. We show that Harmony requires dramatically fewer computational resources. It is the only currently available algorithm that makes the integration of ~106 cells feasible on a personal computer. We apply Harmony to PBMCs from datasets with large experimental differences, 5 studies of pancreatic islet cells, mouse embryogenesis datasets, and cross-modality spatial integration.
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            SCANPY : large-scale single-cell gene expression data analysis

            Scanpy is a scalable toolkit for analyzing single-cell gene expression data. It includes methods for preprocessing, visualization, clustering, pseudotime and trajectory inference, differential expression testing, and simulation of gene regulatory networks. Its Python-based implementation efficiently deals with data sets of more than one million cells (https://github.com/theislab/Scanpy). Along with Scanpy, we present AnnData, a generic class for handling annotated data matrices (https://github.com/theislab/anndata).
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              • Record: found
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              Massively parallel digital transcriptional profiling of single cells

              Characterizing the transcriptome of individual cells is fundamental to understanding complex biological systems. We describe a droplet-based system that enables 3′ mRNA counting of tens of thousands of single cells per sample. Cell encapsulation, of up to 8 samples at a time, takes place in ∼6 min, with ∼50% cell capture efficiency. To demonstrate the system's technical performance, we collected transcriptome data from ∼250k single cells across 29 samples. We validated the sensitivity of the system and its ability to detect rare populations using cell lines and synthetic RNAs. We profiled 68k peripheral blood mononuclear cells to demonstrate the system's ability to characterize large immune populations. Finally, we used sequence variation in the transcriptome data to determine host and donor chimerism at single-cell resolution from bone marrow mononuclear cells isolated from transplant patients.
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                Author and article information

                Contributors
                Journal
                J Allergy Clin Immunol
                J Allergy Clin Immunol
                The Journal of Allergy and Clinical Immunology
                Mosby
                0091-6749
                1097-6825
                1 February 2022
                February 2022
                : 149
                : 2
                : 624-639
                Affiliations
                [a ]Department of Dermatology, Medical University of Vienna, Vienna, Austria
                [b ]Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
                [c ]Institute of Cellular Medicine, Newcastle University, Newcastle upon Tyne, United Kingdom
                [d ]Department of Surgery, Division of Plastic and Reconstructive Surgery, Medical University of Vienna, Vienna, Austria
                [e ]Center for Medical Statistics, Informatics, and Intelligent Systems, Institute of Artificial Intelligence and Decision Support, Medical University of Vienna, Vienna, Austria
                [f ]Department of Dermatology and NIHR Newcastle Biomedical Research Centre, Newcastle Hospitals NHS Foundation Trust, Newcastle upon Tyne, United Kingdom
                [g ]Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom
                Author notes
                []Corresponding author: Natalia Alkon, MSc, Department of Dermatology, Medical University of Vienna, General Hospital - AKH, Waehringer Guertel 18-20, A-1090, Vienna, Austria. natalia.alkon@ 123456meduniwien.ac.at
                [∗]

                These authors contributed equally to this work.

                Article
                S0091-6749(21)01198-2
                10.1016/j.jaci.2021.07.025
                9130781
                34363841
                f5e3fb0d-17eb-475a-81ac-a3315012aa93
                © 2021 The Authors

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 13 November 2020
                : 23 June 2021
                : 13 July 2021
                Categories
                Atopic Dermatitis and Inflammatory Skin Disease

                Immunology
                innate lymphoid cells,atopic dermatitis,human skin,single-cell rna sequencing,ad, atopic dermatitis,ahr, aryl hydrocarbon receptor,gata3, gata binding protein 3,hc, healthy control,ilc, innate lymphoid cell,ilc1, group 1 innate lymphoid cell,ilc2, group 2 innate lymphoid cell,ilc3, group 3 innate lymphoid cell,nhs, normal human skin,pb, peripheral blood,pe, phycoerythrin,rora, rar related orphan receptor a,rorc, rar related orphan receptor c,scrna-seq, single-cell rna sequencing,tslp, thymic stromal lymphopoietin

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