Engineering Murine Adipocytes and Skeletal Muscle Cells in Meat-like Constructs Using Self-Assembled Layer-by-Layer Biofabrication: A Platform for Development of Cultivated Meat
Bibliographic record
Abstract
Global meat consumption has been growing on a per capita basis over the past 20 years resulting in ever-increasing devotion of resources in the form of arable land and potable water to animal husbandry which is unsustainable and inefficient. One approach to meet this insatiable demand is to use biofabrication methods used in tissue engineering in order to make skeletal muscle tissue-like constructs known as cultivated meat to be used as a food source. Here, we demonstrate the use of a scaffold-free biofabrication method that forms cell sheets composed of murine adipocytes and skeletal muscle cells and assembles these sheets in parallel to create a 3D meat-like construct without the use of any exogenous materials. This layer-by-layer self-assembly and stacking process is fast (4 days of culture to form sheets and few hours for assembly) and scalable (stable sheets with diameters >3 cm are formed). Tissues formed with only muscle cells were equivalent to lean meat with comparable protein and fat contents (lean beef had 1.5 and 0.9 times protein and fat, respectively, as our constructs) and incorporating adipocyte cells in different ratios to myoblasts and/or treatment with different media cocktails resulted in a 5% (low fat meat) to 35% (high fat meat) increase in the fat content. Not only such constructs can be used as cultivated meat, they can also be used as skeletal muscle models.
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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) | 0.000 | 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".