Synergy between erythropoietin and stem cell factor during erythropoiesis can be quantitatively described without co‐signaling effects
Bibliographic record
Abstract
Synergistic interactions between cytokines underlie developmental processes fundamental to tissue and cellular engineering. However, a mechanistic understanding of the cell-specific and population-mediated effects is often lacking. In this study, we have investigated the synergistic generation of erythroid cells in response to erythropoietin (EPO) and stem cell factor (SCF). We have used a quantitative approach to determine if the effects of EPO and SCF superpose in a supra-additive fashion on the cell proliferation rate or on the death rate, suggesting a contribution from a joint cytokine effect (co-signaling). Primary mouse bone marrow hematopoietic cells and the stem cell-like FDCP-mix cell line were used to investigate the effects of EPO and SCF (individually or in combination) on erythroid output. Carboxyfluorescein diacetate succinimidyl ester (CFSE)-based cell-division tracking and mathematical modeling were used to measure cell type-specific proliferation and death rates. We observed a significant synergistic effect of EPO and SCF on the net generation of benzidine positive (erythroid) colony-forming cells, CD71++ (early erythroblasts) cells and TER-119+ (late erythroblasts and reticulocytes) cells in culture. When the observed increases in cell number were decomposed into proliferation and death rates, the cytokines were shown to act independently at different stages of erythroid development; SCF promoted the early proliferation of primitive cells, while EPO primarily promoted the survival of differentiating erythroid progenitor cells. Our analysis demonstrates that EPO and SCF have distinct and predominantly sequential effects on erythroid differentiation. This study emphasizes the necessity to separate proliferation rates from death rates to understand apparent cytokine synergies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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 teacher head, 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".