A cryopreservation strategy for myoblast storage in paper-based scaffolds for inter-laboratory studies of skeletal muscle health
Bibliographic record
Abstract
Abstract Three-dimensional tissue-engineered models are poised to facilitate understanding of skeletal muscle pathophysiology and identify novel therapeutic agents to improve muscle health. Adopting these culture models within the broader biology community is a challenge as many models involve complex methodologies and significant investments of time and resources to optimize manufacturing protocols. To alleviate this barrier, we developed a protocol with commercially available reagents to cryopreserve myoblasts in a 96-well compatible format that allows tissues to be transferred to users without expertise in 2D or 3D skeletal muscle cell culture. We validate that myoblasts encapsulated in a hydrogel and cryopreserved in paper-based scaffolds maintain cell viability, differentiation, and function via acetylcholine-induced transient calcium responses. Furthermore, we demonstrate successful shipping of myoblasts cryopreserved in paper-based scaffolds to intra-provincial and international collaborators who successfully thawed, cultured, and used the 3D muscle tissues. Finally, we confirm the application of our method to study muscle endogenous repair by seeding freshly isolated skeletal muscle stem cells to cryopreserved then differentiated and injured tissues, demonstrating expected responses to a known stimulator of muscle stem cell self-renewal, p38α/β MAPKi. Altogether, our 3D myoblast cryopreservation protocol offers broadened access of a complex skeletal muscle tissue model to the research community.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".