Supplementation‐dependent differences in the rates of embryonic stem cell self‐renewal, differentiation, and apoptosis
Bibliographic record
Abstract
Although it is known that leukemia inhibitory factor (LIF) supports the derivation and expansion of murine embryonic stem (ES) cells, it is unclear whether this is due to inhibitory effects of LIF on ES cell differentiation or stimulatory effects on ES cell survival and proliferation. Using an ES cell line transgenic for green fluorescent protein (GFP) expression under control of the Oct4 promoter, we were able to simultaneously track the responses of live Oct4-GFP-positive (ES) and -negative (differentiated) fractions to LIF, serum, and other growth factors. Our findings show that, in addition to inhibiting differentiation of undifferentiated cells, the administration of LIF resulted in a distinct dose-dependent survival and proliferation advantage, thus enabling the long-term propagation of undifferentiated cells. Competitive responses from the differentiated cell fraction could only be elicited upon addition of serum, fibroblast growth factor-4 (FGF-4), or insulin-like growth factor-1 (IGF-1). The growth factors did not induce additional differentiation of ES cells, but rather they significantly improved the proliferation of already differentiated cells. Our analyses show that, by adjusting culture conditions, including the type and amount of growth factors or cytokines present, the frequency of media exchange, and the presence or absence of serum, we could selectively and specifically alter the survival, proliferation, and differentiation dynamics of the two subpopulations, and thus effectively control population outputs. Our findings therefore have important applications in engineering stem cell culture systems to predictably generate desired stem cells or their derivatives for various regenerative 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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".