Multi-omics integration reveals convergent extracellular matrix remodelling and lipid metabolic reprogramming as central axes of adipocyte differentiation from mouse embryonic stem cells.
Al-Sayegh M., Khalili M., Alzaabi M., Sultana M., Ali L., Ali M.
Animal Study, published in Adipocyte (2026) — summary generated from the PubMed abstract.
Evidence from laboratory and animal studies provides groundwork for understanding mechanisms and potential before human studies continue.
- Level A · Stronger Clinical Evidence
- Level B · Emerging clinical evidence with positive signals
- Level C · Early human research exploring benefits
- Level D · Scientific groundwork from lab and animal studies
- Emerging · Emerging topic under active research
This page is generated from the PubMed record. The Thai description is an automated summary of bibliographic fields and the abstract, not a full translation, and is not medical advice.
- Study type
- Animal Study
- Journal
- Adipocyte (2026)
- Country
- United States
- Reported sample size
- —
- Source database
- PubMed
- PMID
- 42228580
- PMCID
- PMC13232875
- DOI
- 10.1080/21623945.2026.2680625
Abstract (original English)
Adipogenesis from mouse embryonic stem cells (mESCs) offers a tractable model for dissecting early adipocyte commitment, yet the mechanisms coordinating this transition across multiple biological layers remain incompletely understood. Here we present the first simultaneous five-layer multi-omics characterization of mESC-derived adipocyte differentiation, integrating transcriptomics, proteomics, secretomics, lipidomics, and metabolomics from matched adipogenic (Pos) and non-differentiating (Neg) cell populations at day 30. Applying Multi-Omics Factor Analysis (MOFA+), we identified a dominant shared latent axis that perfectly segregated Pos from Neg cells across all five views. Layer-specific functional enrichment converged on two principal biological axes: ECM remodeling - encompassing collagens, laminins, thrombospondins, and lysyl oxidases - and lipid metabolic reprogramming, with phospholipid and glycerolipid metabolic processes dominating the lipidomics/metabolomics layer. Ensemble Machine Learning Feature Ranking (EMFR) identified a secreted factor (Scpep1; importance score 0.90) as the top-ranked discriminatory feature. Network analysis revealed indirect ECM-lipid connectivity mediated by four bridging nodes (Plod1, Thbs2, Plg, Pmp22) through a hub subnetwork of phospholipid-metabolizing enzymes. Targeted qPCR validation of six candidate regulators (Itga5, Igfbp6, Pik3cg,
What this study does not prove
- • This study does not prove SVF is an approved treatment or a replacement for standard care.
- • This is preclinical work; animal or laboratory results cannot be applied to humans.
Evidence level
Evidence from laboratory and animal studies provides groundwork for understanding mechanisms and potential before human studies continue.
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