Abstract 5458: Single-cell metabolic flux analysis defines distinct metabolic programs across tumor-infiltrating immune cells.

Abstract Metabolism strongly influences how immune cells become activated, differentiate, and lose function in the tumor microenvironment. Understanding the metabolic programs that define different tumor-infiltrating immune cell subtypes is important for identifying pathways that could be targeted to improve immune responses in cancer, yet the distinct metabolic features of major immune cell types—including T cells, myeloid cells, NK cells, and B cells—are still not well defined. To address this, we collected multiple tumor-derived scRNA-seq datasets (>500,000 cells across ∼30 immune cell subtypes). Using these datasets, we conducted metabolic flux inference to identify immune cell-specific metabolic states in the tumor microenvironment. scRNA-seq datasets of tumor-infiltrating immune cells were first analyzed using Seurat, where cell type and subtype identities were assigned based on canonical marker genes and confirmed with additional annotation tools. Pseudobulk and meta-cell representations were created to reduce sparsity, and metabolic flux was estimated using our in-house tool MPOCtrL, which infers reaction-level activity from gene expression using curated metabolic gene lists. Dimensionality reduction and clustering were applied to compare metabolic patterns across immune cell types and subtypes. In particular, for T cells, we have discovered distinct metabolic phenotypes for different T cell subtypes. Exhausted T cells showed high serine and glutamate metabolism, low glucose uptake, and reduced β-oxidation, while T follicular helper cells showed opposite trends with low serine and glutamate metabolism but high glucose uptake and elevated glycolysis. Glycolysis was also high in Th1-like cells but reduced in CD4 cytotoxic effector and Tn-like cells. Lactate-associated flux was enriched in Treg and Trm cells, while Tcm and Tn-like cells showed low lactate production. Ketone body metabolism was high in Th17-biased CD4 T cells and low in Trm cells. Fatty acid pathways varied across subsets, with Tn-like cells showing low fatty acid synthesis and Th1-like cells showing high β-oxidation. Pentose phosphate pathway activity remained uniformly low across T-cell subsets. This analysis reveals clear metabolic patterns across immune cell types and subtypes in the tumor microenvironment. By defining these metabolic programs, our work provides a basis for identifying metabolic pathways that could be targeted to change immune cell behavior and improve anti-tumor immunity. Citation Format: Yue Fang, Changlin Wan, Haiqi Zhu, Zheng An, Pengtao Dang, Chi Zhang, Sha Cao. Single-cell metabolic flux analysis defines distinct metabolic programs across tumor-infiltrating immune cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 5458.