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

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.