Optimization of a Cytokine Cocktail for the Expansion of Cord Blood (CB) CD34+ Cells into Megakaryocytes (MK) Progenitors towards the Ex Vivo Production of Platelets.
Bibliographic record
Abstract
Abstract Platelet transfusion remains to this date the only therapy to restore safe platelet levels in thrombocytopenic patients. There are currently no substitutes for blood-derived platelets but the development of culture processes for HSC has raised the possibility of producing MK-progenitors and platelets ex vivo. The objective of this study is to optimize a cytokine cocktail for the expansion of MK-progenitors from CB CD34+ cells to enhance ex vivo platelet production. The cytokine selection process would be based on the assessment of the individual and synergistic effects of the cytokines tested. In short, CB CD34+ cells were first expanded for 6 days with TPO, SCF and different combinations of other cytokines. The expansion of various hematopoietic populations (CD34, CD41, GPA, CD15) and MK-progenitors were determined by FACS and CFU-MK assay respectively at day 6. The capacity of the expanded cells to produce platelets was also measured at day-14. Statistical analysis of these responses revealed the individual and interactive effects of cytokines tested by two-level factorial designs in the first six days of culture. To ascertain the magnitude of these, the calculated effects (E) were compared to the mean response (MR), which is the average of all conditions tested. FL, IL-3, IL-6, IL-9 and IL-11, cytokines commonly used to expand progenitors and MK cells, were first tested with SCF and TPO present at 1 and 30 ng/ml. IL-9, FL and IL-6 were best for the expansion of day-6 CD41+ MK, with E of 1.3, 1.1 and 0.6 respectively (MR=25, P<0.003). FL, IL-9 and IL-3 showed a similar quantitative capacity to expand CFU-MK progenitors (MR=57, E = 2.2–2.9, P<0.006), though a strong negative synergy between these 3 was apparent (−1.7 E). The use of all 5 cytokines was clearly disadvantageous since it led to non-MK expansion. Analysis of day-14 results indicated that FL and IL-9 strongly stimulated platelet production (E of 68 and 53, MR=391, P<0.003), whereas IL-11 was highly detrimental due to its individual effect (−30 E) and negative interaction with IL-9 (−33 E, P<0.03). Numerous other interactions were also revealed, including a strong negative interaction between FL and IL-3 on platelet production (−53 E, P<0.003). Based on these, IL-3 and IL-11 were eliminated. Next, a series of factorial screens were done to test the effect of SCF concentration levels and to select the cytokine(s) for the final cocktail. As expected, increasing SCF concentration (50 ng/ml) strongly promoted cell expansion (P<0.0001), though this did not increase day-6 MK and CFU-MK (P=0.2), but rather led to the expansion of erythrocytes (27 E, MR=69, P<0.0001) resulting in a reduction of 25 and 10% in mature CD42+ MK (P<0.02) and platelets produced. In contrast to FL, IL-6 and IL-9 also favoured erythrocyte expansion under elevated SCF concentration (individual and synergistic effects with SCF, P<0.05). In summary, strong cytokine interactions with important impacts on the differentiation fate of CD34+ cells were identified. Notably, the concentration level of SCF had profound influences on the individual and interactive effects of other cytokines. Our results demonstrate redundant and distinctive properties for IL-9 and IL-6, both stimulating megakaryopoiesis but at distinct stages of maturation. A dose-response surface methodology is presently used to find the concentrations of FL, IL-9, TPO and SCF that will optimize the production of MK-progenitors and platelets.
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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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".