Biosensors & bioelectronicsOct 9, 2026

Metabolic chemical patterns predict aging and muscle cell development in immature muscle cells

Longevity & Aging Weekly Brief ↗PubMed ↗DOI ↗

Updated Oct 10, 2026

Abstract

A non-destructive electrochemical platform predicts myogenic potential in senescent myoblasts.

Mitochondrial dysfunction is linked to myoblast senescence, impairing muscle regeneration.
The developed platform identifies a significant bioenergetic deficit in undifferentiated senescent myoblasts.
Real-time monitoring showed that these cells struggle to shift from glycolysis to oxidative phosphorylation during differentiation.
The electrochemical signal correlates with key senescence regulators, suggesting a link between metabolic state and cell differentiation.
The platform can also assess the effects of treatments like metformin on metabolic recovery, indicating its potential for senotherapeutic screening.

Simplified

The functional decline of aging skeletal muscle is primarily driven by myoblast senescence, a process that severely impairs regenerative capacity. While mitochondrial dysfunction is a hallmark of this state, traditional senescence assays rely on static, late-stage markers requiring destructive cell processing, making them unsuitable for real-time monitoring. To bridge this diagnostic gap, we developed a non-destructive electrochemical (EC) platform utilizing a Matrigel-functionalized gold nanostructure electrode to predict the myogenic potential of senescent myoblasts in their undifferentiated state. The platform resolved a pronounced bioenergetic deficit in undifferentiated senescent cells and, during myogenic induction, tracked in real-time their failure to execute the metabolic shift from glycolysis to oxidative phosphorylation (OXPHOS). Notably, the basal EC signal correlated with the senescence regulators p53 and p21 and predicted subsequent myogenic competence, linking metabolic state to differentiation fate. Furthermore, we validated the platform's utility for senotherapeutic screening by quantifying the biphasic metabolic recovery induced by metformin, distinguishing effective recovery range from supra-pharmacological toxicity. This study establishes EC metabolic profiling as a rapid, label-free, and predictive framework for assessing stem cell quality and accelerating the discovery of anti-aging interventions.

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Metabolic chemical patterns predict aging and muscle cell development in immature muscle cells

Abstract

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