Cell Type-Specific Biomarkers of Systemic Sclerosis Disease Severity Capture Cell-Intrinsic and Cell-Extrinsic...

Area of Science:

• Immunology
• Genomics
• Computational Biology

Background:

• Systemic sclerosis (SSc) is a complex autoimmune fibrotic disease characterized by intricate signaling networks within and between cells.
• The precise cell-cell interactions and rewiring of signaling pathways in SSc remain incompletely understood.
• Understanding these molecular mechanisms is crucial for developing targeted therapies.

Purpose of the Study:

• To identify predictive biomarkers of SSc severity using a machine learning approach.
• To uncover novel cell-intrinsic and cell-extrinsic signaling networks involved in SSc pathogenesis.
• To investigate the role of specific cell types, including keratinocytes, in disease progression.

Main Methods:

• Analysis of single-cell RNA-sequencing data from 24 SSc patients with varying disease severity (modified Rodnan skin score, MRSS).
• Application of a least absolute shrinkage and selection operator (LASSO)-based predictive machine learning model to identify SSc severity biomarkers.
• Correlation network analyses to map cell-intrinsic and cell-extrinsic interactions associated with identified biomarkers.

Main Results:

• Identification of cell type-specific predictive biomarkers for MRSS, including known genes in fibroblasts and monocytes, and novel genes in keratinocytes.
• Discovery of previously uncharacterized cross-talk between immune pathways and keratinocytes.
• Validation of key gene expression and protein markers (KRT6A, S100A8) in keratinocytes correlating with SSc skin severity.

Conclusions:

• Global systems analysis reveals novel cell-intrinsic and cell-extrinsic signaling networks underlying SSc severity.
• Keratinocytes, myeloid cells, and fibroblasts are identified as key cell types involved in SSc pathogenesis.
• The findings provide new insights into the molecular basis of SSc and potential therapeutic targets.