Insights Into Systemic Sclerosis from Gene Expression Profiling.

Abstract

Purpose of review: The emergence of genomic data science stands poised to revolutionize our molecular understanding of the heterogeneity of complex diseases including systemic autoimmune diseases. In systemic sclerosis (SSc), bulk and single-cell transcriptomics have provided a new lens into the heterogeneity of this complex condition, both in terms of molecular heterogeneity, treatment response, and cell types important for the disease.

Recent findings: Transcriptomics has revealed reproducible patterns of gene expression among SSc patients. These conserved patterns of gene expression provide insights into SSc etiology, and evidence suggests that these groups may have important implications for treatment decisions by targeting specific patients. Integration and analyses of publicly available data are providing new insights into the disease. Single-cell technologies are illuminating cell types that may be important in pathogenesis. The disease trajectory for SSc remains difficult to predict, but the interactions between adaptive and innate immune cells with tissue-resident stromal cells may play an important role.

Summary: The heterogeneity in SSc can be broken down and quantified using molecular methods that range from bulk analysis to single cells. Further study of cellular and molecular dynamics in end-target tissues is likely to result in better disease management through personalized, data-driven treatment decisions.