Serum-Free Culture of Human Mesenchymal Stem Cell Aggregates in Suspension Bioreactors for Tissue Engineering Applications
Bibliographic record
Abstract
Mesenchymal stem cells (MSCs) have the capacity to differentiate towards bone, fat, and cartilage lineages. The most widely used culture and differentiation protocols for MSCs are currently limited by their use of serum-containing media and small-scale static culture vessels. Suspension bioreactors have multiple advantages over static culture vessels (e.g., scalability, control, and mechanical forces). This study sought to compare the formation and culture of 3D aggregates of human synovial fluid MSCs within suspension bioreactors and static microwell plates. It also sought to elucidate the benefits of these techniques in terms of productivity, cell number, and ability to generate aggregates containing extracellular matrix deposition. MSCs in serum-free medium were either (1) inoculated as single cells into suspension bioreactors, (2) aggregated using static microwell plates prior to being inoculated in the bioreactor environment, or (3) aggregated using microwell plates and kept in the static environment. Preformed aggregates that were size-controlled at inoculation had a greater tendency to form large, irregular super aggregates after a few days of suspension culture. The single MSCs inoculated into suspension bioreactors formed a more uniform population of smaller aggregates after a definite culture period of 8 days. Both techniques showed initial deposition of extracellular matrix within the aggregates. When the relationship between aggregate size and ECM deposition was investigated in static culture, midsized aggregates (100-300 cells/aggregate) were found to most consistently maximize sGAG and collagen productivity. Thus, this study presents a 3D tissue culture method, which avoids the clinical drawbacks of serum-containing medium that can easily be scaled for tissue culture applications.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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".