MI2025 – Yirong Dai

Title:
Identification of Antigen-Specific T-Cells via scRNA-seq Analysis for TCR-T Cell Therapy

Abstract:
TCR-T cell therapy holds great promise for solid tumour treatment, but its feasibility relies critically on identifying antigen-specific T cell receptors (TCRs) capable of recognizing cancer cells. This typically involves characterising tumour-infiltrating lymphocytes (TILs); however, the majority of TILs are bystander cells, making the identification of antigen-specific tumour-reactive T cells a laborious and costly “needle in a haystack” problem. Hence, we aim to establish and validate an antigen-agnostic, gene-signature based approach to rapidly identify antigen-specific T cells based solely on their transcriptional profiles. We stimulated peripheral blood mononuclear cells (PBMCs) in vitro with peptide pools derived from SARS-CoV-2 (Spike), Influenza A (IAV; HA, NA, MP1, MP2, NP), and EBV (EBNA1, LMP1, LMP2A, BZLF1) and sorted total activated T cells, containing both TCR- and bystander- activated populations, for single-cell RNA (scRNA-seq) and T cell receptor sequencing (TCR-seq). In parallel, PBMCs were stained with the corresponding MHC Class I multimers to identify known CoV-2/IAV/EBV- specific TCR clonotypes. By integrating both datasets, we classified peptide-activated T cells, of known specificity defined by multimers, as TCR-activated when stimulated by matching peptide pools, and as bystander-activated when stimulated by non-matching peptide pools. We subsequently applied a TCR activation–associated gene signature derived from a public dataset to these two populations and observed an increase in gene signature module scores in TCR-activated T cells, but not in bystander or unstimulated T cells, validating the signature’s ability to robustly distinguish TCR-activated T cells from bystanders. Using this validated signature, we selected putative antigen-specific T cells directly from the scRNA-seq data of peptide-activated T cells. Future studies will functionally validate these candidates and extend this approach to rapidly identify tumor-reactive TILs in solid tumors.

Biography:
Yirong is a PhD student in Prof. Antonio Bertoletti’s laboratory in the Emerging Infectious Diseases (EID) Programme at Duke-NUS Medical School. Her research focuses on leveraging single-cell RNA sequencing (scRNA-seq) and T cell receptor sequencing (TCR-seq) to distinguish antigen-specific T cells from bystander cells.