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Dysfunctional CD8(+) T cells, which have defective production of antitumor effectors, represent a major mediator of immunosuppression in the tumor microenvironment. Here, we showed that SUSD2 was a negative regulator of CD8(+) T cell antitumor function. Susd2(−/−) effector CD8(+) T cells showed enhanced production of antitumor molecules, which consequently blunted tumor growth in multiple syngeneic mouse tumor models. Through a quantitative mass spectrometry assay, we found that SUSD2 interacted with interleukin-2 receptor α (IL-2Rα) through sushi domain-dependent protein interactions and that this interaction suppressed the binding of IL-2, an essential cytokine for the effector functions of CD8(+) T cells, to IL-2Rα. SUSD2 was not expressed on regulatory CD4(+) T cells and did not affect the inhibitory function of these cells. Adoptive transfer of Susd2(−/−) chimeric antigen receptor T cells induced a robust antitumor response in mice, highlighting the potential of SUSD2 as an immunotherapy target for cancer.

Single-cell multi-omics (scMulti-omics) allows the quantification of multiple modalities simultaneously to capture the intricacy of complex molecular mechanisms and cellular heterogeneity. Existing tools cannot effectively infer the active biological networks in diverse cell types and the response of these networks to external stimuli. Here we present DeepMAPS for biological network inference from scMulti-omics. It models scMulti-omics in a heterogeneous graph and learns relations among cells and genes within both local and global contexts in a robust manner using a multi-head graph transformer. Benchmarking results indicate DeepMAPS performs better than existing tools in cell clustering and biological network construction. It also showcases competitive capability in deriving cell-type-specific biological networks in lung tumor leukocyte CITE-seq data and matched diffuse small lymphocytic lymphoma scRNA-seq and scATAC-seq data. In addition, we deploy a DeepMAPS webserver equipped with multiple functionalities and visualizations to improve the usability and reproducibility of scMulti-omics data analysis.

Drug screening data from massive bulk gene expression databases can be analyzed to determine the optimal clinical application of cancer drugs. The growing amount of single-cell RNA sequencing (scRNA-seq) data also provides insights into improving therapeutic effectiveness by helping to study the heterogeneity of drug responses for cancer cell subpopulations. Developing computational approaches to predict and interpret cancer drug response in single-cell data collected from clinical samples can be very useful. We propose scDEAL, a deep transfer learning framework for cancer drug response prediction at the single-cell level by integrating large-scale bulk cell-line data. The highlight in scDEAL involves harmonizing drug-related bulk RNA-seq data with scRNA-seq data and transferring the model trained on bulk RNA-seq data to predict drug responses in scRNA-seq. Another feature of scDEAL is the integrated gradient feature interpretation to infer the signature genes of drug resistance mechanisms. We benchmark scDEAL on six scRNA-seq datasets and demonstrate its model interpretability via three case studies focusing on drug response label prediction, gene signature identification, and pseudotime analysis. We believe that scDEAL could help study cell reprogramming, drug selection, and repurposing for improving therapeutic efficacy.