Multiplexed Single-cell Metabolic Profiles Organize the Spectrum of Human Cytotoxic T Cells
Hartmann, F. J.; Mrdjen, D.; McCaffrey, E.; Glass, D. R.; Greenwald, N. F.; Bharadwaj, A.; Khair, Z.; Baranski, A.; Baskar, R.; Angelo, M.; Bendall, S. C.
Show abstract
Cellular metabolism regulates immune cell activation, differentiation and effector functions to the extent that its perturbation can augment immune responses. However, the analytical technologies available to study cellular metabolism lack single-cell resolution, obscuring metabolic heterogeneity and its connection to immune phenotype and function. To that end, we utilized high-dimensional, antibody-based technologies to simultaneously quantify the single-cell metabolic regulome in combination with phenotypic identity. Mass cytometry (CyTOF)-based application of this approach to early human T cell activation enabled the comprehensive reconstruction of the coordinated metabolic remodeling of naive CD8+ T cells and aligned with conventional bulk assays for glycolysis and oxidative phosphorylation. Extending this analysis to a variety of tissue-resident immune cells revealed tissue-restricted metabolic states of human cytotoxic T cells, including metabolically repressed subsets that expressed CD39 and PD1 and that were enriched in colorectal carcinoma versus healthy adjacent tissue. Finally, combining this approach with multiplexed ion beam imaging by time-of-flight (MIBI-TOF) demonstrated the existence of spatially enriched metabolic neighborhoods, independent of cell identity and additionally revealed exclusion of metabolically repressed cytotoxic T cell states from the tumor-immune boundary in human colorectal carcinoma. Overall, we provide an approach that permits the robust approximation of metabolic states in individual cells along with multimodal analysis of cell identity and functional characteristics that can be applied to human clinical samples to study cellular metabolism how it may be perturbed to affect immunological outcomes.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Projecting single-cell transcriptomics data onto a reference T cell atlas to interpret immune responses 97%
- Time-, tissue- and treatment-associated heterogeneity in tumour-residing migratory DCs 96%
- APMAT analysis reveals the association between CD8 T cell receptors, cognate antigen, and T cell phenotype and persistence 96%
Similar papers in this journal
- Spatial analysis of human lung cancer reveals organized immune hubs enriched for stem-like CD8 T cells and associated with immunotherapy response 96%
- Restriction of innate Tγδ17 cell plasticity by an AP-1 regulatory axis 96%
- NFAT5 induction by the tumor microenvironment enforces CD8 T cell exhaustion 95%
Similar papers in this journal
- Universal recording of cell-cell contacts in vivo for interaction-based transcriptomics 96%
- A human DNA methylation atlas reveals principles of cell type-specific methylation and identifies thousands of cell type-specific regulatory elements 96%
- Distinguishing features of Long COVID identified through immune profiling 96%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.