Measurement of generation‐dependent proliferation rates and death rates during mouse erythroid progenitor cell differentiation
Bibliographic record
Abstract
Herein, we describe an experimental and computational approach to perform quantitative carboxyfluorescein diacetate succinimidyl ester (CFSE) cell-division tracking in cultures of primary colony-forming unit-erythroid (CFU-E) cells, a hematopoietic progenitor cell type, which is an important target for the treatment of blood disorders and for the manufacture of red blood cells. CFSE labeling of CFU-Es isolated from mouse fetal livers was performed to examine the effects of stem cell factor (SCF) and erythropoietin (EPO) in culture. We used a dynamic model of proliferation based on the Smith-Martin representation of the cell cycle to extract proliferation rates and death rates from CFSE time-series. However, we found that to accurately represent the cell population dynamics in differentiation cultures of CFU-Es, it was necessary to develop a model with generation-specific rate parameters. The generation-specific rates of proliferation and death were extracted for six generations (G(0) -G(5) ) and they revealed that, although SCF alone or EPO alone supported similar total cell outputs in culture, stimulation with EPO resulted in significantly higher proliferation rates from G(2) to G(5) and higher death rates in G(2) , G(3) , and G(5) compared with SCF. In addition, proliferation rates tended to increase from G(1) to G(5) in cultures supplemented with EPO and EPO + SCF, while they remained lower and more constant across generations with SCF. The results are consistent with the notion that SCF promotes CFU-E self-renewal while EPO promotes CFU-E differentiation in culture.
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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) | 0.000 | 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".