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Single-cell RNA sequencing reveals induction of distinct trained-immunity programs in human monocytes
Bowen Zhang, … , Mihai G. Netea, Yang Li
Bowen Zhang, … , Mihai G. Netea, Yang Li
Published February 8, 2022
Citation Information: J Clin Invest. 2022;132(7):e147719. https://doi.org/10.1172/JCI147719.
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Research Article Immunology Infectious disease

Single-cell RNA sequencing reveals induction of distinct trained-immunity programs in human monocytes

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Abstract

Trained immunity refers to the long-lasting memory traits of innate immunity. Recent studies have shown that trained immunity is orchestrated by sustained changes in epigenetic marks and metabolic pathways, leading to an altered transcriptional response to a second challenge. However, the potential heterogeneity of trained-immunity induction in innate immune cells has not been explored. In this study, we demonstrate cellular transcriptional programs in response to 4 different inducers of trained immunity in monocyte populations at single-cell resolution. Specifically, we identified 3 monocyte subpopulations upon the induction of trained immunity, and replicated these findings in an in vivo study. In addition, we found gene signatures consistent with these functional programs in patients with ulcerative colitis, sepsis, and COVID-19, suggesting the impact of trained-immunity programs in immune-mediated diseases.

Authors

Bowen Zhang, Simone J.C.F.M Moorlag, Jorge Dominguez-Andres, Özlem Bulut, Gizem Kilic, Zhaoli Liu, Reinout van Crevel, Cheng-Jian Xu, Leo A.B. Joosten, Mihai G. Netea, Yang Li

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Figure 4

Subgroups of trained cells reveal diverse trained-immunity phenotypes.

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Subgroups of trained cells reveal diverse trained-immunity phenotypes.
(...
(A) Heatmap showing log(fold change) of 6 marker genes (rows) in trained macrophages relative to the average expression in control macrophages (columns). Red and blue colors correspond to upregulation and downregulation, respectively. (B) KEGG enrichment of training response (TR) genes (comparing trained conditions with RPMI controls) in each subgroup of trained cells. (C) Annotation of the subgroups of trained cells in UMAP plots. (D) Comparison of the cell frequency of subgroups between trained tissues. M-MONO, monocytes trained in the absence of lymphocytes; M-PBMC, monocytes trained in the presence of lymphocytes. Dirichlet’s regression model was applied to test the differences in cell frequency between groups; P values are shown on the box-and-whisker plot. (E) UMAP of cellular trajectories inferred by Monocle 3 with trained subgroups or original clusters. (F) UMAP and violin plot of pseudo-time state of trained cells estimated by Monocle 3. P values from Wilcoxon’s rank-sum test are shown on the violin plot. (G) Integrated UMAP of cells from the initial and replicate in vitro experiments showing the distribution of cells sampled at different time points. (H) Violin plots showing AUCell-based scores (R/AUCell package) of trained-immunity signatures from MCI and MC subgroups in trained cells and nontrained controls sampled from the replicate experiment. The lines in the violin plots represent the median of the AUC scores and the 0.25 and 0.75 quantiles, and colors represent the average scores centered on zero. Wilcoxon’s rank-sum test was applied to ascertain whether the AUC scores in trained cells were larger than in nontrained controls. T1, 4 hours after training (or RPMI) stimulation; pre-T2, 5 days after training (or RPMI) stimulation and before LPS restimulation; T2, 4 hours after LPS restimulation. P values are shown at the top in D, F, and H.

Copyright © 2025 American Society for Clinical Investigation
ISSN: 0021-9738 (print), 1558-8238 (online)

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