A Novel Method for Generating Xeno-Free Human Feeder Cells for Human Embryonic Stem Cell Culture
Bibliographic record
Abstract
Long-term cultures of human embryonic stem (hES) cells require a feeder layer for maintaining cells in an undifferentiated state and increasing karyotype stability. In routine hES cell culture, mouse embryonic fibroblast (MEF) feeders and animal component-containing media (FBS or serum replacement) are commonly used. However, the use of animal materials increases the risk of transmitting pathogens to hES cells and therefore is not optimal for use in cultures intended for human transplantation. There are other limitations with conventional feeder cells, such as MEFs, which have a short lifespan and can only be propagated five to six passages before senescing. Several groups have investigated maintaining existing hES cell lines and deriving new hES cell lines on human feeder layers. However, almost all of these human source feeder cells employed in previous studies were derived and cultured in animal component conditions. Even though one group previously reported the derivation and culture of human foreskin fibroblasts (HFFs) in human serum-containing medium, this medium is not optimal because HFFs routinely undergo senescence after 10 passages when cultured in human serum. In this study we have developed a completely animal-free method to derive HFFs from primary tissues. We demonstrate that animal-free (AF) HFFs do not enter senescence within 55 passages when cultured in animal-free conditions. This methodology offers alternative and completely animal-free conditions for hES cell culture, thus maintaining hES cell morphology, pluripotency, karyotype stability, and expression of pluripotency markers. Moreover, no difference in hES cell maintenance was observed when they were cultured on AF-HFFs of different passage number or independent derivations.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".