Efficient Lentiviral Transduction and Improved Engraftment of Human Bone Marrow Mesenchymal Cells
Bibliographic record
Abstract
Human bone marrow (BM) mesenchymal stem/progenitor cells are potentially attractive targets for ex vivo gene therapy. The potential of lentiviral vectors for transducing BM mesenchymal cells was examined using a self-inactivating vector that expressed the green fluorescent protein (GFP) from an internal cytomegalovirus (CMV) promoter. This vector was compared with oncoretroviral vectors expressing GFP from the CMV promoter or a modified long-terminal repeat that had been optimized for long-term expression in stem cells. The percentage of GFP-positive cells was consistently higher following lentiviral versus oncoretroviral transduction, consistent with increased GFP mRNA levels and increased gene transfer efficiency measured by polymerase chain reaction and Southern blot analysis. In vitro GFP and FVIII expression lasted for several months post-transduction, although expression slowly declined. The transduced cells retained their stem/progenitor cell properties since they were still capable of differentiating along adipogenic and osteogenic lineages in vitro while maintaining high GFP and FVIII expression levels. Implantation of lentivirally transduced human BM mesenchymal cells using collagen scaffolds into immunodeficient mice resulted in efficient engraftment of gene-engineered cells and long-term transgene expression in vivo. These biocompatible BM mesenchymal implants represent a reversible, safe, and versatile protein delivery approach because they can be retrieved in the event of an unexpected adverse reaction or when expression of the protein of interest is no longer required. In conclusion, efficient gene delivery with lentiviral vectors in conjunction with the use of bioengineered reversible scaffolds improves the therapeutic prospects of this novel approach for gene therapy, protein delivery, or tissue engineering.
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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 | 0.001 |
| 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".