The Application of Single-Cell RNA Sequencing in Studies of Autoimmune Diseases: a Comprehensive Review.

Complex composition is one of the most important features of the immune system, involving many types of organs, tissues, cells, and molecules that perform immune functions. The normal function of each component of the immune system is the guarantee for maintaining the relatively stable immune function of the body. When the self-immune tolerance mechanism of the body is unregulated or destroyed, the immune system reacts to autoantigens, resulting in damage to self-tissues and organs or an immunopathological state with abnormal functions. Autoimmune diseases are diverse, and their pathogenesis is complicated. Various immune cells and their interactions play significant roles in the occurrence and development of diseases. The solution to heterogeneity of immune cells is the basic science and translational understanding of how genes and the environment interact to induce disease so that we can develop personalized medicine, a goal that has to this point eluded scientists. Single-cell RNA sequencing (scRNA-Seq) refers to a new technique allowing high-throughput sequencing analysis of the whole transcriptome to reveal the gene expression status of individual cells. It has emerged as an indispensable tool in the field of life science research, and can help identify the complex mechanism of cell heterogeneity, discover new cell subsets, and help uncover the molecular mechanisms of pathogenesis, the evolution of disorders, and drug resistance. This information can provide us with new strategies for diagnosis and prognostic evaluation, as well as monitoring treatment responses. In this review, we summarize the crucial experimental procedures used for single-cell RNA sequencing, and the current applications of this technique to study autoimmune diseases are described in detail. This technique will be widely used in more in-depth studies of autoimmune diseases and will contribute to the diagnosis and therapies of these disorders.