Progress in single-cell multimodal sequencing and multi-omics data integration.

With the rapid advance of single-cell sequencing technology, cell heterogeneity in various biological processes was dissected at different omics levels. However, single-cell mono-omics results in fragmentation of information and could not provide complete cell states. In the past several years, a variety of single-cell multimodal omics technologies have been developed to jointly profile multiple molecular modalities, including genome, transcriptome, epigenome, and proteome, from the same single cell. With the availability of single-cell multimodal omics data, we can simultaneously investigate the effects of genomic mutation or epigenetic modification on transcription and translation, and reveal the potential mechanisms underlying disease pathogenesis. Driven by the massive single-cell omics data, the integration method of single-cell multi-omics data has rapidly developed. Integration of the massive multi-omics single-cell data in public databases in the future will make it possible to construct a cell atlas of multi-omics, enabling us to comprehensively understand cell state and gene regulation at single-cell resolution. In this review, we summarized the experimental methods for single-cell multimodal omics data and computational methods for multi-omics data integration. We also discussed the future development of this field.

[Modern research on Chinese medicine based on single-cell omics: technologies and strategies].

As one of the most advanced technologies, single-cell omics technology develops rapidly in recent years. Based on different technical strategies, it enables unbiased and high-throughput access to multiple omics information at single-cell resolution. So far, single-cell omics technology, by virtue of its great powder in resolving tissue heterogeneity, has become a revolutionary tool to deeply understand the functional structure of tissues, reveal complex disease processes, and elucidate drug mechanisms of action. In view of the technical challenges in deconstructing the complexity of Chinese medicine and clarifying the modern scientific connotation of traditional Chinese medicine(TCM) theory, single-cell omics technology has huge application potential in the discovery of pharmacodynamic substances, construction of action networks, and elucidation of integrated regulatory mechanisms, which brings new opportunities for modern research in TCM. The present study briefly introduced three representative single-cell omics technologies, i.e., single-cell transcriptome sequencing, spatial transcriptomics, and single-cell multimodal omics, and their main application patterns. On this basis, an outlook was proposed on the strategies and applications for modern research in TCM using single-cell omics technology.

Elucidation of Biological Networks across Complex Diseases Using Single-Cell Omics.

Single-cell multimodal omics (scMulti-omics) technologies have made it possible to trace cellular lineages during differentiation and to identify new cell types in heterogeneous cell populations. The derived information is especially promising for computing cell-type-specific biological networks encoded in complex diseases and improving our understanding of the underlying gene regulatory mechanisms. The integration of these networks could, therefore, give rise to a heterogeneous regulatory landscape (HRL) in support of disease diagnosis and drug therapeutics. In this review, we provide an overview of this field and pay particular attention to how diverse biological networks can be inferred in a specific cell type based on integrative methods. Then, we discuss how HRL can advance our understanding of regulatory mechanisms underlying complex diseases and aid in the prediction of prognosis and therapeutic responses. Finally, we outline challenges and future trends that will be central to bringing the field of HRL in complex diseases forward.