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<p>The progression from metabolic dysfunction-associated steatotic liver disease (MASLD) to metabolic dysfunction-associated steatohepatitis (MASH) is a critical link leading to cirrhosis and hepatocellular carcinoma. Yet the responsible cellular programs remain unclear. We integrated public single-cell, spatial, and bulk transcriptomic datasets to map microenvironmental remodeling and regulatory networks during MASLD-MASH progression. Among the seven major liver cell types identified, monocytes/macrophages and hepatic stellate cells (HSCs) were significantly enriched and demonstrated spatial co-localization within the context of MASH. We identified a DTNA+distinct macrophage subpopulation that was specifically enriched in MASH. This subpopulation exhibited characteristics consistent with M2 polarization, hypoxia, and enhanced inflammatory signaling. Pseudotime trajectory analysis revealed that this state represents a differentiation pathway originating from Kupffer cells to the DTNA+ state. RUNX2 emerged as the key transcriptional regulator. Cell communication analysis demonstrated that DTNA+ macrophages potentially interact with activated HSCs via the RUNX2-PLG-PARD3 axis, contributing to the exacerbation of liver fibrosis. Finally, ensemble machine learning models (mean AUC = 0.839), identified DTNA as the optimal predictive biomarker for distinguishing MASLD from MASH. This study highlight DTNA+ macrophages and the RUNX2–PLG–PARD3 axis as candidate mechanisms and targets for non-invasive diagnosis and therapy in MASH.</p>

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Integrating multi-omics and machine learning systematically deciphers cellular heterogeneity and fibrotic regulatory networks in the progression from MASLD to MASH

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月16日

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  • Weiheng Wen,
  • Zenghui Liu,
  • Wenliang Tan,
  • Yingzheng Tan,
  • Wei Li,
  • Jian Wan,
  • Hongsai Hu,
  • Zhengwu Jiang,
  • Xing Tang,
  • Jing Yang,
  • Jiao Xiao,
  • Xiongjin Tan,
  • Xun Chen,
  • Peili Wu,
  • Yukun Li

摘要

The progression from metabolic dysfunction-associated steatotic liver disease (MASLD) to metabolic dysfunction-associated steatohepatitis (MASH) is a critical link leading to cirrhosis and hepatocellular carcinoma. Yet the responsible cellular programs remain unclear. We integrated public single-cell, spatial, and bulk transcriptomic datasets to map microenvironmental remodeling and regulatory networks during MASLD-MASH progression. Among the seven major liver cell types identified, monocytes/macrophages and hepatic stellate cells (HSCs) were significantly enriched and demonstrated spatial co-localization within the context of MASH. We identified a DTNA+distinct macrophage subpopulation that was specifically enriched in MASH. This subpopulation exhibited characteristics consistent with M2 polarization, hypoxia, and enhanced inflammatory signaling. Pseudotime trajectory analysis revealed that this state represents a differentiation pathway originating from Kupffer cells to the DTNA+ state. RUNX2 emerged as the key transcriptional regulator. Cell communication analysis demonstrated that DTNA+ macrophages potentially interact with activated HSCs via the RUNX2-PLG-PARD3 axis, contributing to the exacerbation of liver fibrosis. Finally, ensemble machine learning models (mean AUC = 0.839), identified DTNA as the optimal predictive biomarker for distinguishing MASLD from MASH. This study highlight DTNA+ macrophages and the RUNX2–PLG–PARD3 axis as candidate mechanisms and targets for non-invasive diagnosis and therapy in MASH.