Single-Cell and Spatial Multiomics: Applications for Diseases

Abstract

Complex and dynamic networks of molecules are involved in human diseases. Single-cell and spatial multiomics approaches have created new avenues for understanding the pathogenesis and diagnosis of diseases. Cell connections and characteristics in diseases may be examined more thoroughly by integration single-cell and spatial multiomics. In this paper, we first reviewed the single-cell and spatial multiomics approaches. Subsequently, the use of single-cell and spatial multiomics to comprehend the mechanisms of human diseases, such as cancer (head and neck squamous cell carcinoma), neurodegenerative diseases, and aging, was discussed. Furthermore, we outline how deep learning approaches are now being applied to single-cell and spatial multiomics data analysis in an effort to better define the pathogenic alterations upstream and the downstream molecular effects of diseases. Particularly, single-cell and spatial multiomics are being utilized to help guide treatment plans, evaluate risks, and determine how they can affect precision medicine. Despite the relative youth of the field, the development of single-cell coupled with spatial multiomics promises to provide a powerful tool for elucidating the pathogenesis of diseases.

Keywords

deep learning / multiomics / precision medicine / single-cell omics / spatial omics

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