Distinct transcriptomic and epigenomic modalities underpin human memory T cell subsets and their activation potential

Abstract

Human memory T cells (MTC) are poised to rapidly respond to antigen re-exposure. Here, we derived the transcriptional and epigenetic programs of resting and ex vivo activated, circulating CD4+ and CD8+ MTC subsets. A progressive gradient of gene expression from naïve to TCM to TEM is observed, which is accompanied by corresponding changes in chromatin accessibility. Transcriptional changes suggest adaptations of metabolism that are reflected in altered metabolic capacity. Other differences involve regulatory modalities comprised of discrete accessible chromatin patterns, transcription factor binding motif enrichment, and evidence of epigenetic priming. Basic-helix-loop-helix factor motifs for AHR and HIF1A distinguish subsets and predict transcription networks to sense environmental changes. Following stimulation, primed accessible chromatin correlate with an augmentation of MTC gene expression as well as effector transcription factor gene expression. These results identify coordinated epigenetic remodeling, metabolic, and transcriptional changes that enable MTC subsets to ultimately respond to antigen re-encounters more efficiently.

Unraveling the Phenotypic States of Human innate-like T Cells: Comparative Insights with Conventional T Cells and Mouse Models

“…Our analysis, utilizing the previously identified naïve and effector gene modules (GEP3-6; Fig 1K, Supplementary Tables VIII-XII), indicated that each of the investigated cell types could be found within these identified gene programs, albeit with varying proportions for each cell type (Fig 4C, D). To provide further context and understanding of these gene modules, we computed overlap scores and statistically assessed their enrichment with literature-derived signatures (Cano-Gamez et al, 2020; Poon et al, 2023; Rose et al, 2023; Terekhova et al, 2023). Subsequently, we scored the joint signature-GEP interactions in our dataset (Supplementary Fig 9).…”

“…We obtained gene signatures identified from (1) differential expression (DE) analysis from bulk RNAseq between sorted naïve, T cm , T em CD4 + and CD8 + T cell populations by Rose et al . (Rose et al ., 2023); (2) DE genes between cell clusters defined from scRNAseq of naïve and memory CD4 + T cells isolated from PBMCs by Cano-Gamez et al . (Cano-Gamez et al ., 2020); (3) DE genes between cell clusters defined from scRNAseq of blood immune cells by Terekhova et al .…”

“…Proportion of genes in each peripheral GEP (3-6) corresponding to genes in public signature gene lists (Poon et al . (Poon et al ., 2023)), Rose et al (Rose et al ., 2023)), Cano-Gamez et al . (Cano-Gamez et al ., 2020), Terekhova et al .…”

Unveiling the growing significance of metabolism in modulating immune cell function: exploring mechanisms and implications; a review

“…Metabolites derived from glucose metabolism, such as acetyl-CoA and α-ketoglutarate, serve as pivotal substrates for epigenetic modifications and gene expression regulation 40 . These metabolic intermediates influence chromatin accessibility and transcriptional programs, ultimately moulding T-cell differentiation into distinct effector subsets 41 . The comprehensive understanding of the intricate interplay between glucose metabolism and T-cell biology holds therapeutic implications.…”

SifiNet: A robust and accurate method to identify feature gene sets and annotate cells

“…Previous studies used gene chromatin accessibility profiles to supplement genes' expression profiles for defining T cell subpopulations. [85][86][87] . We adopted the same strategy.…”