Computational Identification of Migrating T cells in Spatial Transcriptomics Data

T cells are the central players in antitumor immunity, and effective tumor killing depends on their ability to infiltrate into the tumor microenvironment (TME) while maintaining normal cytotoxicity. However, late-stage tumors develop immunosuppressive mechanisms that impede T cell movement and induce exhaustion. Investigating T cell migration in human tumors in vivo could provide novel insights into tumor immune escape, although it remains a challenging task. In this study, we developed ReMiTT, a computational method that leverages spatial transcriptomics data to track T cell migration patterns within tumor tissue. Applying ReMiTT to ovarian tumor samples, we identified key genes and pathways that enriched on algorithm-identified T cell migration trails, including leukocyte chemotaxis, cell-cell adhesion, and ECM remodeling. We also characterized the phenotypes of T cells on the migrating trails, suggesting that regulatory T cells may accompany cytotoxic T cells during migration. Our findings provide a novel approach to studying T cell migration and interactions within the TME, offering new insights into tumor-immune dynamics.