Optimization of a Cytokine Cocktail for the Expansion of Cord Blood CD34+ Cells into Megakaryocytes Progenitors.
Bibliographic record
Abstract
Abstract Platelet transfusion remains the only therapy available to restore safe platelet levels in thrombocytopenic patients following stem cell transplantation. However, the development of improved cytokine cocktail for the pre-expansion of megakaryocyte progenitors (Mk-p) to support the transplantation may reduce the demand for platelet transfusions. Toward this, we previously reported the results of two-level factorial design screens of 6 cytokines (SCF, FL, IL-3, IL-6, IL-9 and IL-11) with TPO which identified TPO, SCF, FL and IL-9 as the best combination for the expansion of cord blood (CB) CD34+ cells into Mk-p (ASH abstract#1673, 2006). The objective of this study was to find the optimal concentration levels of these cytokines and validate the newly optimized cocktails. In brief, CB CD34+ cells were first expanded for 6 days in various conditions specified by a central composite design (CCD). The expansion of various hematopoietic populations and Mk-p were determined by FACS and CFU-Mk assay respectively, while the capacity of the expanded cells to produce Mk and platelets ex vivo was also measured at day-14 using the cytokine cocktail BS1 (optimized for the production of CB Mk). Statistical analysis of these responses revealed the synergistic effects of the cytokines and provided mathematical models for the expansion of Mk-p and the day-14 final Mk production. The CCD results demonstrated that SCF and TPO were the principal cytokines for the expansion of Mk and CFU-Mk at day-6. This was evident by their positive individual effect and their strong positive synergistic effect (+25% on CFU-Mk, P<0.02). Importantly, a strong negative interaction between TPO and IL-9 on the expansion of CFU-Mk was also revealed (−39%, p<0.001), as well as a strong positive interaction between FL and SCF on the expansion of CD34+ cells (+50%, P=0.05). Under the CCD settings FL and IL-9 effects on CFU-Mk expansion did not reach statistical significance, though the CFU-Mk model (R2=0.95) suggested that IL-9 should be used at the lowest concentration while FL could have a positive effect when used within a certain range. The next experiments were done to validate or complement the CCD-based models. The positive effect of SCF on CFU-Mk was confirmed. In regards to IL-9, we confirmed the model prediction that under optimum SCF and TPO concentration (determined by the response surface map), that removal of IL-9 was beneficial to the expansion of CFU-Mk (1.4–1.5- fold increase), while the addition of FL was found to slightly increase the expansion of CFU-Mk (1.2-fold). Finally, the CCD models were used to optimized 2 new cocktails; one specific for the expansion of Mk-p (ocMk-p) and one for the production of Mk at day-14 (ocMk). The yields and efficiencies of each cocktail was compared to the control cocktails TGS and BS1; TGS is currently used in clinical trial for the expansion of progenitors prior transplantation (TPO, G-CSF and SCF at 100 ng/ml), whereas BS1 (TPO,SCF, IL-6, IL-9) is known to favor good CFU-Mk expansion and excellent Mk production. Preliminary experiments have shown that ocMk-p increased the expansion of CFU-Mk up to 1.4 ±0.2 fold (range +1.2–1.9, n=3) compared to TGS while using 85% less cytokines, and that ocMk increased final Mk (P=0.01) and platelets (P<0.05) yields by 1.4- and 1.7-fold respectively compared to BS1 (n=8). In conclusion, two new cocktails were optimized using a dose-response surface methodology which allow maximal expansion while using significantly less cytokines.
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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 | 0.000 |
| Bibliometrics | 0.001 | 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".