Effective ex vivo expansion of hematopoietic stem cells using osteoblast‐differentiated mesenchymal stem cells is CXCL12 dependent
Bibliographic record
Abstract
Effective ex vivo expansion of hematopoietic stem cells (HSCs) is a prerequisite for HSC transplantation. Growth and maintenance of HSC is dependent on cytokine and niche factors. We investigated whether mesenchymal stem cells (MSCs) or osteogenic cytokine-differentiated MSCs play a role in HSC expansion. We used the human HM3.B10 (B10) MSC cell line and the osteoblast-differentiated B10 (Ost-B10) as a feeder layer and examined ex vivo expansion of CD34(+)CD38(-) HSCs obtained from peripheral blood (PB) and cord blood (CB) with or without several growth cytokines. Both undifferentiated B10 and Ost-B10 cells exhibited similar effects on total HSC expansion; however, Ost-B10 demonstrated a higher potency in CD34(+)CD38(-) cell-specific proliferation in the presence of cytokines compared to undifferentiated B10 HSCs. Colony-forming cell assay and long-term culture initiating cell assay revealed that Ost-B10 displayed multipotent differentiation and enabled long-term ex vivo culture of HSCs. We next examined the relationship between HSC expansion and the presence of various chemokines. CXCL4 and CXCL12 expression were increased in Ost-B10 cells compared with the B10 cells. CD34(+)CD38(-) cells were significantly increased with CXCL12, but not CXCL4 treatment. siRNA inhibition of CXCL12 decreased CXCL12 secretion in both B10 and Ost-B10, whereas expansion of CD34(+)CD38(-) cells was decreased in Ost-B10 alone. These results demonstrated that ex vivo expansion of HSCs may be highly effective through osteoblast-differentiated MSCs acting as a feeder layer, and likely operates through the CXCL12 chemokines signaling pathway.
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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 | 0.001 |
| 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".