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
AUTHORS (17)
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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