Integrating single-cell RNA sequencing with multi-omics to decode disease microenvironments

Cellular heterogeneity underpins the complexity of human development and disease, as cells with the same genome exhibit distinct transcriptomic profiles that define their identity, state, and function. Traditional bulk RNA sequencing obscures this heterogeneity by averaging gene expression across mixed cell populations, limiting its ability to resolve rare or disease-associated cell types. Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful technology enabling high-resolution transcriptomic profiling of thousands of individual cells in a single run. Widely adopted platforms, such as the 10x Genomics Chromium system, have accelerated large-scale single-cell studies through their scalability and robust barcoding strategies. Recent advances integrating scRNA-seq with multi-omics approaches, including epigenomics, spatial transcriptomics, and temporal profiling, have further enhanced our understanding of cellular interactions and disease mechanisms. In parallel, artificial intelligence-driven methods, including deep learning and graph-based models, have improved data denoising, clustering, and cell-type annotation. Despite these advances, technical noise, dropout events, and computational challenges remain. This review highlights the integration of single-cell RNA sequencing with multi-omics to decode disease microenvironments.