Large-Scale Expansion of Mammary Epithelial Stem Cell Aggregates in Suspension Bioreactors
Bibliographic record
Abstract
Mutations in the pathways regulating mammary epithelial stem cell (MESC) self-renewal and differentiation are currently hypothesized to result in uncontrolled cell division and, in turn, breast tumor formation. Although research is aggressively being pursued to understand how such pathways result in breast cancer formation, current studies have been greatly limited by MESC scarcity. To address this issue, this study has successfully developed large-scale expansion protocols for MESC through the subculture of murine mammary epithelial tissue aggregates, called mammospheres, in suspension bioreactors. Growth kinetics of mammospheres cultured in 125 mL suspension bioreactors and T-flasks were found to be comparable, achieving cell densities of 3.10 x 10(5) and 2.75 x 10(5) cells/mL, respectively. This corresponded to a 4-fold expansion over 8 days. Yields were also found to be strongly affected by liquid shear forces, where high agitation rates reduced overall cell numbers. Bioreactor cultures were scaled up to 1000 mL operating volumes, resulting in the production of 4.21 x 10(8) total cells (5.6-fold expansion) from a single passage. Furthermore, intermittent replacement of culture medium with fresh medium dramatically improved maximum cell densities, resulting in an 11-fold expansion, thereby enabling the generation of stem cells in quantities sufficient for standard biochemical and genetic analyses. After being cultured in suspension bioreactors for several passages, analysis by flow cytometry of Ki-67 revealed that 85% of the population was composed of proliferating cells. The successful development of expansion protocols for MESC aggregates in suspension bioreactors makes available experimental avenues that were not previously accessible for breast cancer research, thereby facilitating future investigations into elucidating the role of MESCs in breast cancer tumorigenesis.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".