Elucidation of Biological Networks across Complex Diseases Using Single-Cell Omics. Issue 12 (December 2020)

• Record Type: Journal Article
• Authors: Li, Yang; Ma, Anjun; Mathé, Ewy A.; Li, Lang; Liu, Bingqiang; Ma, Qin
• Is Part Of: Trends in genetics. Volume 36:Issue 12(2020)
• Page Start: 951
• Page End: 966
• Publication Date: 2020-12
• Subjects: single-cell multimodal omics -- complex diseases -- heterogeneous regulatory landscape -- biological networks -- integrative methods; Genetics -- Periodicals; 576.5
• DOI: 10.1016/j.tig.2020.08.004
• Languages: English
• ISSNs: 0168-9525

Abstract

Abstract : 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. Highlights: Advances in single-cell sequencing technologies open a window to understanding the heterogeneous regulatory landscape (HRL) encoded in complex diseases, by inferring various biological networks. The development of single-cell multimodal omics (scMulti-omics) technologies combined with scMulti-omics integration tools provides multimodal measurements of HRL. Application of HRL to complex diseases presents opportunities and poses challenges. Among the remaining challenges, establishing a robust benchmarking pipeline is paramount. In support of the integration of diverse single-cell modalities, bulk and single-cell omics, the deep learning and artificial intelligence (AI) omics become the major trends.