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Cell Type-Specific Biomarkers of Systemic Sclerosis Disease Severity Capture Cell-Intrinsic and Cell-Extrinsic
Jacob S Berkowitz1, Tracy Tabib2, Hanxi Xiao1
1Center for Systems Immunology, Departments of Immunology and Computational & Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania.
Objective:
Systemic sclerosis (SSc) is a multifactorial autoimmune fibrotic disorder involving complex rewiring of cell-intrinsic and cell-extrinsic signaling coexpression networks involving a range of cell types. However, the rewired circuits as well as corresponding cell-cell interactions remain poorly understood. To address this, we used a predictive machine learning framework to analyze single-cell RNA-sequencing data from 24 SSc patients across the severity spectrum as quantified by the modified Rodnan skin score (MRSS).
Methods:
We used a least absolute shrinkage and selection operator (LASSO)-based predictive machine learning approach on the single-cell RNA-sequencing data set to identify predictive biomarkers of SSc severity, both across and within cell types. The use of L1 regularization helps prevent overfitting on high-dimensional data. Correlation network analyses were coupled to the LASSO model to identify cell-intrinsic and cell-extrinsic co-correlates of the identified biomarkers of SSc severity.
Results:
We found that the uncovered cell type-specific predictive biomarkers of MRSS included previously implicated genes in fibroblast and myeloid cell subsets (e.g., SFPR2+ fibroblasts and monocytes), as well as novel gene biomarkers of MRSS, especially in keratinocytes. Correlation network analyses revealed novel cross-talk between immune pathways and implicated keratinocytes in addition to fibroblast and myeloid cells as key cell types involved in SSc pathogenesis. We then validated the uncovered association of key gene expression and protein markers in keratinocytes, KRT6A and S100A8, with SSc skin disease severity.
Conclusion:
Our global systems analyses reveal previously uncharacterized cell-intrinsic and cell-extrinsic signaling coexpression networks underlying SSc severity that involve keratinocytes, myeloid cells, and fibroblasts.
Insights
Machine learning identified novel gene biomarkers in skin cells, fibroblasts, and myeloid cells that predict systemic sclerosis (SSc) severity. This reveals new cell communication pathways in SSc pathogenesis.
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.
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