Optimization of muscle progenitor cell isolation techniques for production of cultivated meat
Bibliographic record
Abstract
Traditional meat production has major sustainability and ethical concerns. Cultivated meat helps to address these concerns by reducing the need for mass animal farming. Muscle progenitor cells (MPCs) harvested from skeletal muscle are a promising cell source for cultivated meat. While various protocols have been developed for MPC isolation, which protocol is best suited for the cultivated meat industry requires further investigation. Therefore, the purpose of this thesis was to optimize the MPC isolation technique to produce a pure myogenic cell population and provide the cultivated meat industry with standardized procedures for production. For these proof-of-concept experiments, skeletal muscles harvested from the hindlimb muscles of mice were used. Cells were isolated from the harvested muscle then subjected to one of three protocols for MPC enrichment: pre-plating, ice-cold treatment (ICT), or fluorescence activated cell sorting (FACS). The pre-plating and ICT protocols resulted in impure cell populations with few MPCs after one week in culture. Therefore, FACS using two cell-surface markers, NCAM and CD34, was employed as a more specific method for MPC sorting. CD34+NCAM1- cells grew quickly, however, unwanted cell types remained following FACS. In contrast, CD34+NCAM1+ cells had a consistent small, rounded shape and slow proliferation rate. These cells remained viable in culture for several months and had high Pax7 expression, indicating they were a pure population of myogenic cells. CD34+NCAM1+ cells maintained their capacity to differentiate after culturing for an extended period, demonstrating their potential use for cultivated meat production. The results of this study provide a better understanding of the differences between previously published MPC isolation techniques. Future studies will investigate the potential for CD34+NCAM1+ cells to be grown on a larger scale. These experiments provide insight into MPC populations that may exist in livestock species and will help to streamline the early stages of cultivated meat production.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".