Isolation of small extracellular vesicles from regenerating muscle tissue using tangential flow filtration and size exclusion chromatography
Bibliographic record
Abstract
We have recently made the strikingly discovery that upon a muscle injury, Wnt7a is upregulated and secreted from new regenerating myofibers on the surface of exosomes to elicit its myogenerative response distally. Despite recent advances in extracellular vesicle (EVs) isolation from diverse tissues, there is still a lack of specific methodology to purify EVs from muscle tissue. To eliminate contamination with non-EV secreted proteins and cytoplasmic fragments, which are typically found when using classical methodology, such as ultracentrifugation, we adapted a protocol combining Tangential Flow Filtration (TFF) and Size Exclusion Chromatography (SEC). We found that this approach allows simultaneous purification of Wnt7a, bound to EVs (retentate fraction) and free non-EV Wnt7a (permeate fraction). Here we described this optimized protocol designed to specifically isolate EVs from hind limb muscle explants, without cross-contamination with other sources of non-EV bounded proteins. The first step of the protocol is to remove large EVs with sequential centrifugation. Extracellular vesicles are then concentrated and washed in exchange buffer by TFF. Lastly, SEC is performed to remove any soluble protein traces remaining after TFF. Overall, this procedure can be used to isolate EVs from conditioned media or biofluid that contains EVs derived from any cell type or tissue, improving reproducibility, efficiency, and purity of EVs preparations. Our purification protocol results in high purity EVs that maintain structural integrity and thus fully compatible with in vitro and in vivo bioactivity and analytic assays.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".