High-dimensional profiling clusters asthma severity by lymphoid and non-lymphoid status

severe asthma CD4-Positive T-Lymphocytes Proteomics 0301 basic medicine QH301-705.5 Adaptive Immunity CD8-Positive T-Lymphocytes Article Machine Learning Interferon-gamma 03 medical and health sciences Adrenal Cortex Hormones Humans Anti-Asthmatic Agents Biology (General) BAL Receptors, IgE Gene Expression Profiling Interleukin-7 Macrophages multi-omics Asthma Immunity, Innate Interleukin-10 3. Good health Gene Expression Regulation Case-Control Studies CyTOF immune RNA-seq Bronchoalveolar Lavage Fluid Immunologic Memory
DOI: 10.1016/j.celrep.2021.108974 Publication Date: 2021-04-15T14:23:11Z
ABSTRACT
Clinical definitions of asthma fail to capture the heterogeneity of immune dysfunction in severe, treatment-refractory disease. Applying mass cytometry and machine learning to bronchoalveolar lavage (BAL) cells, we find that corticosteroid-resistant asthma patients cluster largely into two groups: one enriched in interleukin (IL)-4+ innate immune cells and another dominated by interferon (IFN)-γ+ T cells, including tissue-resident memory cells. In contrast, BAL cells of a healthier population are enriched in IL-10+ macrophages. To better understand cellular mediators of severe asthma, we developed the Immune Cell Linkage through Exploratory Matrices (ICLite) algorithm to perform deconvolution of bulk RNA sequencing of mixed-cell populations. Signatures of mitosis and IL-7 signaling in CD206-FcεRI+CD127+IL-4+ innate cells in one patient group, contrasting with adaptive immune response in T cells in the other, are preserved across technologies. Transcriptional signatures uncovered by ICLite identify T-cell-high and T-cell-poor severe asthma patients in an independent cohort, suggesting broad applicability of our findings.
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