Combining Spatial Transcriptomics and Machine Learning Unravels Fibroblast-Related Biomarkers in Ulcerative Colitis

Area of Science:

• Gastroenterology
• Immunology
• Molecular Biology

Background:

• Ulcerative colitis (UC) is a chronic inflammatory bowel disease with persistent mucosal inflammation.
• The precise role of fibroblasts in UC pathogenesis, including their contribution to inflammation and fibrosis, requires further elucidation.

Purpose of the Study:

• To investigate the role of fibroblasts in UC pathogenesis.
• To identify potential diagnostic biomarkers and therapeutic targets for UC.

Main Methods:

• Single-cell RNA sequencing and cell-cell communication analysis to profile fibroblast interactions.
• Weighted Gene Co-expression Network Analysis (WGCNA) to identify fibroblast-related gene modules.
• Machine learning models for biomarker prediction, validated by IF, ELISA, and WB assays.
• In vivo studies using a DSS-induced UC mouse model and spatial transcriptomics.

Main Results:

• Single-cell analysis identified fibroblasts as a key cell type in UC.
• WGCNA revealed significant correlation between fibroblast infiltration and specific gene modules enriched in inflammatory pathways.
• Machine learning models identified ANGPTL2 as a highly accurate diagnostic biomarker (AUC 0.917).
• ANGPTL2 was significantly upregulated in UC patient fibroblasts and serum, and in a mouse model of UC.
• Spatial transcriptomics confirmed high ANGPTL2 expression colocalized with fibroblasts in UC tissues.

Conclusions:

• Fibroblasts are central to UC pathogenesis, driving inflammation and fibrosis.
• ANGPTL2 is a promising non-invasive diagnostic biomarker for UC.
• ANGPTL2 represents a potential therapeutic target for ulcerative colitis.