Skeletal muscle-derived extracellular vesicles are altered with chronic contractile activity: a dynamic light scattering analysis
Bibliographic record
Abstract
Abstract Extracellular vesicles (EVs) are small lipid bilayer-delimited particles that are secreted by all cells, playing a central role in cellular communication. EVs are released from skeletal muscle during exercise, but the effects of contractile activity on skeletal muscle-derived EVs (Skm-EVs) are poorly understood due to the challenges in distinguishing Skm-EVs derived from exercising muscle in vivo. Using tunable resistive pulse sensing (TRPS), we previously demonstrated that chronic contractile activity (CCA) increased the secretion of Skm-EVs from C2C12 myotubes, while their size and zeta potential remained unchanged. Here, we aimed to determine whether similar results could be obtained using an alternative method of EV characterization, dynamic light scattering (DLS). C2C12 myoblasts were differentiated into myotubes, and electrically paced (3h/day x 4days @14V, C-PACE EM, IonOptix) to mimic chronic exercise in vitro. EVs were isolated from conditioned media of control and stimulated myotubes using differential ultracentrifugation, and characterized based on size and zeta potential. The mean size of EVs from chronically stimulated myotubes (CCA-EVs, 132 nm) was 26% smaller than control (CON-EVs, 178 nm). Size distribution analysis revealed that CCA-EVs were enriched in small EVs (100-150 nm), while CON-EVs were largely composed of 200-250 nm sized vesicles. Additionally, zeta potential was 27% lower in CCA-EVs compared to CON-EVs. Our data indicate that the effect of CCA on facilitating the release of smaller, more stable EVs, is a robust finding, reproducible by multiple methods of EV characterization. Future studies investigating the mechanisms by which CCA influences Skm-EV biogenesis and secretion are warranted.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".