Paired snRNA-seq and scRNA-seq analysis of MASLD patients to identify early-stage markers for disease progression

Background and Aims: Metabolic dysfunction–associated steatotic liver disease (MASLD) is a leading cause of chronic liver disease worldwide. Progression from simple metabolic dysfunction–associated steatotic liver (MASL) without necro-inflammation to metabolic dysfunction–associated steatohepatitis (MASH) triggers fibrosis, leading to liver-related morbidity and mortality. Early recognition of MASH is imperative to enable appropriate interventions aimed at preventing liver damage. Thus, this study aimed to elucidate molecular mechanisms driving MASLD progression and identify early-stage transcriptomic signatures by analyzing paired liver tissue and peripheral blood mononuclear cells (PBMCs). Methods: We collected 16 paired liver and PBMC samples from 8 histologically confirmed patients with MASLD. Liver tissue was obtained by needle biopsy for single-nucleus RNA sequencing, and PBMCs underwent single-cell and bulk RNA sequencing. PBMC–liver interactions were examined to identify cross-tissue signaling, and machine learning was applied to derive transcriptomic signatures predictive of fibrosis stage. Results: Hepatocyte transcriptomic profiling revealed distinct MASH-associated alterations, including downregulated fatty acid metabolism, upregulated immune activation pathways, and changes in tissue remodeling. PBMC analysis identified shifts in immune populations, with increased aTregs and chronic CD4+ T cell activation. Liver–PBMC interaction analysis highlighted enhanced HSC–natural killer cell signaling in MASH, linking immune responses to fibrosis progression. Machine learning identified liver-derived and PBMC-derived transcriptomic signatures that robustly distinguished mild (F0–F2) from advanced (F3–F4) fibrosis (AUC=0.93), suggesting their potential for early diagnostic stratification. Conclusions: Significant molecular and immune alterations occur in disease progression of MASLD to MASH, reflecting both localized hepatic changes and systemic immune dysregulation. The identified transcriptomic signatures provide a promising tool for fibrosis prediction and monitoring, underscoring the need to target early disease mechanisms for improved diagnosis and therapeutic strategies.