Muscle stem cell intramuscular delivery within hyaluronan methylcellulose improves engraftment efficiency and dispersion
Bibliographic record
Abstract
Abstract Adult skeletal muscle tissue harbors the capacity for self-repair due to the presence of tissue resident muscle stem cells (MuSCs). Advances in the area of prospective MuSC isolation demonstrated the potential of cell transplantation therapy as a regenerative medicine strategy to restore strength and long-term regenerative capacity to aged, injured, or diseased skeletal muscle tissue. However, cell loss during ejection, limits to post-injection proliferation, and poor donor cell dispersion distal to the injection site are amongst hurdles to overcome to maximize MuSC transplant impact. Here, we assess a physical blend of hyaluronan and methylcellulose (HAMC) as a bioactive, shear thinning hydrogel cell delivery system to improve MuSC transplantation efficiency. Using in vivo transplantation studies, we found that the HAMC delivery system results in a >45% increase in the number of donor-derived fibers as compared to saline delivery. Furthermore, we observed a significant improvement in donor fiber dispersion when transplanted MuSCs were delivered in the HAMC hydrogel. Studies to assess primary myoblast and MuSC viability in HAMC culture revealed no differences compared to the media control even when the cells were first ejected through a syringe and needle or exposed to regenerating skeletal muscle extract to mimic the transplantation procedure. However, when we quantified absolute numbers, we found that more cells pass through the syringe and needle when delivered in HAMC. Culture in HAMC also increased the proportion of MuSCs in cell cycle, via a CD44-independent mechanism. An effect on myoblast proliferation was not observed, suggesting a hierarchical effect. Finally, a series of transplant studies indicated that HAMC delivery does not influence passive cell clearance or alter the host immune response, but instead may serve to support in vivo expansion by delaying differentiation following transplant. Therefore, we conclude that MuSC engraftment efficacy is improved by delivering the therapeutic cell population within HAMC.
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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".