"Comprehensive multi-omics of age-respective plasma and matrix-bound extracellular vesicles identifies anti-fibrotic miRNAs validated on a heart-on-a-chip".

Young tissue-derived extracellular vesicles (TEVs) and plasma-derived extracellular vesicles (PEVs) showed notable overlap in miRNA cargo, but aged EVs from the two sources differed substantially from each other.

• This was the first direct comparison of extracellular vesicles obtained from young and aged patients to identify therapeutic or disease-associated agents.
• The study directly compared vesicles isolated from heart tissue matrix (TEVs) or plasma (PEVs).
• Aged EVs showed differential aging-related changes between TEVs and PEVs, indicating compartment-specific aging responses.
• Young TEVs and PEVs showed notable overlap of miRNA cargo while aged counterparts diverged substantially.

TEVs were uniquely enriched in miRNAs with cardioprotective effects, with 45 potential therapeutic agents identified in the analysis.

• TEVs overall were uniquely enriched in miRNAs which directly or indirectly demonstrate cardioprotective effects.
• 45 potential therapeutic agents were identified in the TEV analysis.
• These cardioprotective miRNAs were enriched in TEVs compared to PEVs.

Both PEV and TEV populations showed increased predisposition to disease with aging, but through different molecular mechanisms.

• Changes in PEV cargo were largely correlated with chronic systemic inflammation.
• Changes in TEV cargo were more related to cardiac homeostasis and local inflammation.
• The differential mechanisms indicate that aging affects circulating and tissue-resident extracellular vesicle populations through distinct pathways.

Seventeen protein targets were identified that were unique to TEVs and highly correlated with aging and the onset of cardiovascular disease.

• These 17 protein targets were unique to TEVs and not found in the PEV population.
• The targets were highly correlated with aging and cardiovascular disease onset.
• These targets were identified through non-machine learning analysis of the multi-omics data.
• With further study, this biomarker set may serve as a potential indicator of cardiac health and age measurable from PEVs.

Machine learning analysis implicated several new miRNA and protein targets, independently corroborating several targets identified by non-machine learning approaches.

• Machine learning techniques were applied to further analyze the multi-omics dataset.
• The machine learning analysis independently suggested several of the targets identified by non-machine learning analysis.
• The targets identified correlated with aging-related changes in TEVs.
• The convergence of machine learning and traditional analysis strengthened confidence in the identified biomarker candidates.

Young-enriched miRNAs encapsulated in lipid nanoparticles successfully prevented cell death and cardiac fibrosis in disease-like conditions modeled on a heart-on-a-chip device.

• This was the first study in literature to test the efficacy of a miRNA-based therapeutic encapsulated in lipid nanoparticles in an organ-on-a-chip device.
• The microfluidic heart-on-a-chip modeled both acute and chronic fibrosis conditions.
• Several proposed 'young-enriched' therapeutic agents were validated in the heart-on-a-chip model.
• The validated miRNAs could successfully prevent cell death and cardiac fibrosis in disease-like conditions.