Shaoran Zhang

Cell-surface sialoglycans overexpressed on cancer cells suppress activation of tumor-infiltrating immune cells (TIICs) and thus represent promising targets for cancer immunotherapy. However, the functional roles of sialylation in TIICs remain incompletely understood. Here we developed a strategy for single-cell correlative analysis of α2,3- and α2,6-linked sialoglycans and transcriptomes to determine the correlations of cell-surface sialylation with gene expression of the same cells in a linkage-specific manner by single-cell RNA sequencing. We found that α2,3- and α2,6-sialoglycans were distinctively regulated across specific TIIC subpopulations in murine tumors. Notably, downregulation of α2,6-sialoglycans was identified as a cell-surface marker for intratumoral tumor-specific antigen-reactive CD8⁺ T cells with high avidity. Mechanistically, high-avidity (but not low-avidity) activation of murine CD8⁺ T cells within the tumor suppressed β-galactoside α2,6-sialyltransferase 1 expression, which downregulates α2,6-sialylation and exposes galectin-1 ligands. Functionally, galectin-1 binding promoted T cell apoptosis and immune evasion in cancer. This work demonstrates the functional significance and therapeutic potential of specific sialoglycans on TIICs.

Proximity labeling has emerged as a powerful strategy for interrogating cell-cell interactions. However, the nanometer-scale labeling radius impedes the use of current methods for indirect cell communications and makes recording cell spatial organization in tissue samples difficult. Here, we develop quinone methide–assisted identification of cell spatial organization (QMID), a chemical strategy with the labeling radius matching the cell dimension. The activating enzyme is installed on the surface of bait cells, which produces QM electrophiles that can diffuse across micrometers and label proximal prey cells independent of cell-cell contacts. In cell coculture, QMID reveals gene expression of macrophages that are regulated by spatial proximity to tumor cells. Furthermore, QMID enables labeling and isolation of proximal cells of CD4 ⁺ and CD8 ⁺ T cells in the mouse spleen, and subsequent single-cell RNA sequencing uncovers distinctive cell populations and gene expression patterns within the immune niches of specific T cell subtypes. QMID should facilitate dissecting cell spatial organization in various tissues.

Single‐cell RNA sequencing (scRNA‐seq) has emerged as a powerful tool for profiling gene expression of distinct cell populations at the single‐cell level. However, the information of the positions of cells within the multicellular samples is missing in scRNA‐seq datasets. To overcome this limitation, we herein develop OpTAG (optical cell tagging) as a new chemical platform for attaching functional tags onto cell surfaces in a spatially resolved manner. With OpTAG, we establish OpTAG‐seq, which enables spatially resolved scRNA‐seq. We apply OpTAG‐seq to investigate the spatially defined transcriptional program in migrating cancer cells and identified a list of genes that are potential regulators for cancer cell migration and invasion. OpTAG‐seq provides a convenient method for mapping cellular heterogeneity with spatial information within multicellular biological systems.

Single‐cell RNA sequencing (scRNA‐seq) has emerged as a powerful tool for profiling gene expression of distinct cell populations at the single‐cell level. However, the information of the positions of cells within the multicellular samples is missing in scRNA‐seq datasets. To overcome this limitation, we herein develop OpTAG (optical cell tagging) as a new chemical platform for attaching functional tags onto cell surfaces in a spatially resolved manner. With OpTAG, we establish OpTAG‐seq, which enables spatially resolved scRNA‐seq. We apply OpTAG‐seq to investigate the spatially defined transcriptional program in migrating cancer cells and identified a list of genes that are potential regulators for cancer cell migration and invasion. OpTAG‐seq provides a convenient method for mapping cellular heterogeneity with spatial information within multicellular biological systems.