ROCK inhibitor improves survival of cryopreserved serum/feeder-free single human embryonic stem cells
Bibliographic record
Abstract
BACKGROUND: Efficient slow freezing protocols within serum-free and feeder-free culture systems are crucial for the clinical application of human embryonic stem (hES) cells. Frequently, however, hES cells must be cryopreserved as clumps when using conventional slow freezing protocols, leading to lower survival rates during freeze-thaw and limiting their recovery and growth efficiency after thawing, as well as limiting downstream applications that require single cell suspensions. We describe a novel method to increase freeze-thaw survival and proliferation rate of single hES cells in serum-free and feeder-free culture conditions. METHODS: hES cells maintained on Matrigel-coated dishes were dissociated into single cells with Accutase and slow freezing. After thawing at 37 degrees C, cells were cultured in mTeSR medium supplemented with 10 microM of Rho-associated kinase inhibitor Y-27632 for 1 day. RESULTS: The use of Y-27632 and Accutase significantly increases the survival of single hES cells after thawing compared with a control group (P < 0.01). Furthermore, by treatment of hES cell aggregates with EGTA to disrupt cell-cell interaction, we show that Y-27632 treatment does not directly affect hES cell apoptosis. Even in the presence of Y-27632, hES cells deficient in cell-cell interaction undergo apoptosis. Y-27632-treated freeze-thawed hES cells retain typical morphology, stable karyotype, expression of pluripotency markers and the potential to differentiate into derivatives of all three germ layers after long-term culture. CONCLUSIONS: The method described here allows for cryopreservation of single hES cells in serum-free and feeder-free conditions and therefore we believe this method will be ideal for current and future hES cell applications that are targeted towards a therapeutic end-point.
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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) | 0.001 | 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".