High-dimensional profiling clusters asthma severity by lymphoid and non-lymphoid status.
Journal:
Cell reports
PMID:
33852838
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 CD206FcεRICD127IL-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.
Authors
Keywords
Adaptive Immunity
Adrenal Cortex Hormones
Anti-Asthmatic Agents
Asthma
Bronchoalveolar Lavage Fluid
Case-Control Studies
CD4-Positive T-Lymphocytes
CD8-Positive T-Lymphocytes
Gene Expression Profiling
Gene Expression Regulation
Humans
Immunity, Innate
Immunologic Memory
Interferon-gamma
Interleukin-10
Interleukin-7
Machine Learning
Macrophages
Proteomics
Receptors, IgE
Severity of Illness Index
Signal Transduction