Unique and shared transcriptomic signatures underlying localized scleroderma pathogenesis identified using interpretable machine learning
Aaron BI Rosen, Anwesha Sanyal, Theresa Hutchins, Giffin Werner, Jacob S. Berkowitz, Tracy Tabib, Robert Lafyatis, Heidi Jacobe, Jishnu Das, Kathryn S. Torok
Aaron BI Rosen, Anwesha Sanyal, Theresa Hutchins, Giffin Werner, Jacob S. Berkowitz, Tracy Tabib, Robert Lafyatis, Heidi Jacobe, Jishnu Das, Kathryn S. Torok
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Research Article Immunology

Unique and shared transcriptomic signatures underlying localized scleroderma pathogenesis identified using interpretable machine learning

Abstract

Using transcriptomic profiling at single-cell resolution, we investigated cell-intrinsic and cell-extrinsic signatures associated with pathogenesis and inflammation-driven fibrosis in both adult and pediatric patients with localized scleroderma (LS). We performed single-cell RNA-Seq on adult and pediatric patients with LS and healthy controls. We then analyzed the single-cell RNA-Seq data using an interpretable factor analysis machine learning framework, significant latent factor interaction discovery and exploration (SLIDE), which moves beyond predictive biomarkers to infer latent factors underlying LS pathophysiology. SLIDE is a recently developed latent factor regression-based framework that comes with rigorous statistical guarantees regarding identifiability of the latent factors, corresponding inference, and FDR control. We found distinct differences in the characteristics and complexity in the molecular signatures between adult and pediatric LS. SLIDE identified cell type–specific determinants of LS associated with age and severity and revealed insights into signaling mechanisms shared between LS and systemic sclerosis (SSc), as well as differences in onset of the disease in the pediatric compared with adult population. Our analyses recapitulate known drivers of LS pathology and identify cellular signaling modules that stratify LS subtypes and define a shared signaling axis with SSc.

Authors

Aaron BI Rosen, Anwesha Sanyal, Theresa Hutchins, Giffin Werner, Jacob S. Berkowitz, Tracy Tabib, Robert Lafyatis, Heidi Jacobe, Jishnu Das, Kathryn S. Torok

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Figure 3

Cell-specific expression modules in adult LS resemble expression in SSc.

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Cell-specific expression modules in adult LS resemble expression in SSc....
(A) SLIDE model performance measured by AUC using k-fold cross-validation. Significance assessed using permutation testing (negative control). Asterisks indicate Wilcoxon P < 0.0001. (B) Latent factors trained to classify adult patients with LS can classify patients with SSc in cross-prediction. Cross-prediction AUCs for classifying each group from healthy controls. (C) Levels of significant latent factor 4 in adult patients with LS and healthy controls. Asterisks indicate Wilcoxon P < 0.001. (D) Correlation network representation of significant latent factor 4. Purple and green edges indicate positive and negative correlations, respectively. Triangles indicate genes with higher expression in LS, and squares indicate genes with higher expression in control. (E) Levels of significant latent factor 5 in adult patients with LS and healthy controls. (F) Correlation network representation of significant latent factor 5. Box plots show the interquartile range, median (line), and minimum and maximum (whiskers).