The use of single-cell multi-omics in immuno-oncology

Abstract

Single-cell multi-omics (scMulti-omics) has brought transformative insights into immuno-oncology, demonstrating success in describing novel immune subsets and defining important regulators of antitumor immunity. Here, we give examples of how scMulti-omics has been used in specific tumor studies and discuss how this may develop in the future.

Fig. 1. scMulti-omics profiling and application examples in immuno-oncology.

a An overview of various scMulti-omics data types. Sequencing techniques including single-cell DNA sequencing (scDNA-seq) for DNA sequence profiling, single-cell RNA sequencing (scRNA-seq) for gene expression profiling, Single-cell sequencing assay for transposase-accessible chromatin sequencing (scATAC-seq) for chromatin accessibility profiling, single-cell high-throughput chromosome conformation sequencing (scHiC-seq) for chromatin architecture organization, single-cell cleavage under targets and release using nuclease (scCUN&RUN) for histone modification profiling, single-cell antibody-derived tag sequencing (scADT-seq) for protein abundance profiling, single-cell T cell or B cell receptor sequencing (scT/BCR-seq) for receptor repertoire (the recombination of the variable (V), diversity (D), and joining (J) genes of T/B cell receptors) diversity and clonality profiling, and single-cell methylation sequencing (scMethyl-seq) for DNA methylation status profiling. b–d scMulti-omics enabled immuno-oncology research.

Fig. 2. Deep learning modeling and wet-lab validations for scMulti-omics data.

a Heterogeneous graphs and deep learning models can enable sophisticated biological network inference from scMulti-omics data. b Wet-lab experimental validations bridge scMulti-omics predictive findings with phenotype changes.