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

Ulcerative colitis (UC) is a chronic inflammatory bowel disease characterized by persistent mucosal inflammation. While fibroblasts contribute to tissue repair and fibrosis, their role in UC pathogenesis remains incompletely understood. Single-cell RNA sequencing (GSE114374, GSE231993) and cell-cell communication analysis were performed to profile fibroblast interactions. Weighted Gene Co-expression Network Analysis (WGCNA) identified fibroblast-related modules. Machine learning models predicted diagnostic biomarkers. IF, ELISA and WB assays were used to validate the expression of ANGPTL2 in UC samples. In vivo validation using a DSS-induced UC mouse model evaluated ANGPTL2 expression and therapeutic response to GDC-0152. Spatial transcriptomics (GSE189184) was used to analyze tissue regionalization, cell-type spatial distribution, and the localized expression of ANGPTL2 and its colocalization with fibroblasts. Single-cell analysis identified fibroblasts as a key cell type in UC. WGCNA revealed a strong correlation between the turquoise module and fibroblast infiltration, with genes in this module enriched in pathways such as cytokine-cytokine receptor interaction and TNF signaling. Machine learning models identified PHLDA1 and ANGPTL2 as potential biomarkers with high diagnostic accuracy (AUC 0.935 and 0.917, respectively). Furthermore, exprimental analyses confirmed that ANGPTL2 was significantly upregulated in fibroblasts from UC patients, serum ELISA experiments demonstrated that ANGPTL2 was also significantly elevated in the serum of UC patients, supporting its potential as a non-invasive diagnostic indicator. In vivo experiments confirmed ANGPTL2 upregulation in DSS-treated mice, reversed by GDC-0152. Spatial transcriptomics partitioned UC tissue into 13 distinct regions, revealing prominent fibroblast distribution and markedly high ANGPTL2 expression with significant spatial colocalization with fibroblasts. Fibroblasts are central to UC pathogenesis, contributing to inflammation and fibrosis. ANGPTL2 is a promising diagnostic biomarker and potential therapeutic target in UC.

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Publication Details

Journal
Inflammation
Published
2026-02-10
DOI
https://doi.org/10.1007/s10753-025-02387-1
Primary Topic
Lipid metabolism and disorders
Type
article
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0.00
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Combining Spatial Transcriptomics and Machine Learning Unravels Fibroblast-Related Biomarkers in Ulcerative Colitis

Kuo Kang, Changhao Huang, Shalong Wang, Zhiwei Wu et al.
Inflammation
Lipid metabolism and disorders
article

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

Kuo Kang, Changhao Huang, Shalong Wang, Zhiwei Wu, Xuanxuan Li
article en

Abstract

Ulcerative colitis (UC) is a chronic inflammatory bowel disease characterized by persistent mucosal inflammation. While fibroblasts contribute to tissue repair and fibrosis, their role in UC pathogenesis remains incompletely understood. Single-cell RNA sequencing (GSE114374, GSE231993) and cell-cell communication analysis were performed to profile fibroblast interactions. Weighted Gene Co-expression Network Analysis (WGCNA) identified fibroblast-related modules. Machine learning models predicted diagnostic biomarkers. IF, ELISA and WB assays were used to validate the expression of ANGPTL2 in UC samples. In vivo validation using a DSS-induced UC mouse model evaluated ANGPTL2 expression and therapeutic response to GDC-0152. Spatial transcriptomics (GSE189184) was used to analyze tissue regionalization, cell-type spatial distribution, and the localized expression of ANGPTL2 and its colocalization with fibroblasts. Single-cell analysis identified fibroblasts as a key cell type in UC. WGCNA revealed a strong correlation between the turquoise module and fibroblast infiltration, with genes in this module enriched in pathways such as cytokine-cytokine receptor interaction and TNF signaling. Machine learning models identified PHLDA1 and ANGPTL2 as potential biomarkers with high diagnostic accuracy (AUC 0.935 and 0.917, respectively). Furthermore, exprimental analyses confirmed that ANGPTL2 was significantly upregulated in fibroblasts from UC patients, serum ELISA experiments demonstrated that ANGPTL2 was also significantly elevated in the serum of UC patients, supporting its potential as a non-invasive diagnostic indicator. In vivo experiments confirmed ANGPTL2 upregulation in DSS-treated mice, reversed by GDC-0152. Spatial transcriptomics partitioned UC tissue into 13 distinct regions, revealing prominent fibroblast distribution and markedly high ANGPTL2 expression with significant spatial colocalization with fibroblasts. Fibroblasts are central to UC pathogenesis, contributing to inflammation and fibrosis. ANGPTL2 is a promising diagnostic biomarker and potential therapeutic target in UC.

InflammationVol. 49(1)
Central South University (CN), Hunan Cancer Hospital (CN), Second Xiangya Hospital of Central South University (CN), Xiangya Hospital Central South University (CN)
Openalex Percentile: Top 63%
Lipid metabolism and disorders
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