Common and distinct features of cytokine effects on hematopoietic stem and progenitor cells revealed by dose–response surface analysis
Bibliographic record
Abstract
Recent studies have identified thrombopoietin (TPO), flt-3 ligand (FL), Steel factor (SF), and interleukin-11 (IL-11) as cytokines able to stimulate amplification of the most primitive murine hematopoietic cells in vitro. However, dose-response and interaction parameters that predict how to optimize mixtures of these cytokines have not been previously defined. To obtain this information, Sca-1(+)lin(-) and c-kit(+)Sca-1(+)lin(-) adult mouse bone marrow cells were cultured for 10 and 14 days, respectively, in serum-free medium with varying concentrations of these cytokines. Quantitative assays were performed to determine the influences of the cytokine combinations tested on changes in long-term repopulating hematopoietic stem cells (HSCs), in vitro colony-forming cells (CFCs), and total cell numbers. A two-level factorial design was first used to screen the effects of TPO, SF, FL, and IL-11 as well as two different incubation temperatures. IL-11 and SF were found to be the most significant stimulators of murine HSC expansion. More detailed analyses of the effects on c-kit(+)Sca-1(+)lin(-) cells of IL-11, SF, and FL concentrations and their interactions using response surface methodology showed IL-11 to have a maximal stimulatory effect on HSC expansion at 20 ng/mL with higher concentrations being inhibitory. In contrast, not even high concentration saturation of the effects of either SF or FL was observed as the stimulatory effect of both SF and FL increased beyond 300 ng/mL. A negative interaction between SF and FL on HSCs was discovered. Interestingly, a generally similar pattern of cytokine effects was found to influence the 14-day output of CFCs and total cells from the same c-kit(+)Sca-1(+)lin(-) starting cell population. However, compared with HSCs, the cytokine requirements for maximizing the generation of CFCs and total cells were at much lower cytokine doses. From the information provided by the factorial analysis, mathematical models based on Monod kinetics for inhibitory substrates were developed that allow total cell, CFC, and HSC expansion to be predicted as a function of the IL-11, SF, and FL concentrations in terms of more widely recognized parameters. Overall, these methods should also serve as a guide for the future design and testing of other ex vivo stem cell expansion systems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".