Improved expansion of human bone marrow-derived mesenchymal stem cells in microcarrier-based suspension culture
Bibliographic record
Abstract
Human bone marrow-derived mesenchymal stem cells (hBM-MSCs) have potential clinical utility in the treatment of a multitude of ailments and diseases, due to their relative ease of isolation from patients and their capacity to form many cell types. However, hBM-MSCs are sparse, and can only be isolated in very small quantities, thereby hindering the development of clinical therapies. The use of microcarrier-based stirred suspension bioreactors to expand stem cell populations offers an approach to overcome this problem. Starting with standard culture protocols commonly reported in the literature, we have successfully developed new protocols that allow for improved expansion of hBM-MSCs in stirred suspension bioreactors using CultiSpher-S microcarriers. Cell attachment was facilitated by using intermittent bioreactor agitation, removing fetal bovine serum, modifying the stirring speed and manipulating the medium pH. By manipulating these parameters, we enhanced the cell attachment efficiency in the first 8 h post-inoculation from 18% (standard protocol) to 72% (improved protocol). Following microcarrier attachment, agitation rate was found to impact cell growth kinetics, whereas feeding had no significant effect. By serially subculturing hBM-MSCs using the new suspension bioreactor protocols, we managed to obtain cell fold increases of 10³ within 30 days, which was superior to the 200-fold increase obtained using the standard protocol. The cells were found to retain their defining characteristics after several passages in suspension. This new bioprocess represents a more efficient approach for generating large numbers of hBM-MSCs in culture, which in turn should facilitate the development of new stem cell-based therapies.
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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) | 0.001 | 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) | 0.001 | 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".