NMR metabolomics of plant and yeast-based hydrolysates for cell culture media applications — A comprehensive assessment
Bibliographic record
Abstract
Cultivated meat products, generated by growing isolated skeletal muscle and fat tissue, offer the promise of a more sustainable and ethical alternative to traditional meat production. However, with cell culture media used to grow the cells accounting for 55-95% of the overall production cost, achieving true sustainability requires significant media optimization. One means of dealing with these high costs is the use of low-cost complex additives such as hydrolysates to provide a wide range of nutrients, from small molecules (metabolites) to growth factors and peptides. Despite their potential, most hydrolysate products remain poorly characterized and many are thought to suffer from persistent issues of high batch-to-batch variability. Although there have been a number of isolated efforts to determine metabolic profiles for a handful of hydrolysate products, we present the first attempt at a more comprehensive metabolomic characterization of nine different products (four plant and five yeast-based) from two to four different lots each. NMR analysis identified 90 unique metabolites, with only 15 metabolites common to all hydrolysate products (including eight of the nine essential amino acids), and 16 metabolites found in only a single hydrolysate product. The different hydrolysate products were found to have substantial differences in metabolite concentrations (as a fraction of overall mass), ranging from a high of 43% in yeast extract to a low of 14% in soy hydrolysates. The proportion of various metabolites also varied between products, with carbohydrate concentrations particularly high in soy hydrolysates and nucleosides more prominent in two of the yeast products. Overall, yeast extract generally had higher metabolite concentrations than all the other products, whereas both yeast extract and cotton had the largest variety of metabolites. A direct calculation of batch-to-batch variability revealed although there are significant differences between lots, these are largely driven by a relatively small fraction of compounds. This report will hopefully serve as a useful starting point for a more nuanced consideration of hydrolysate products in cell culture media optimization, both in the context of cultivated meat and beyond.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".