CD46 Null Packaging Cell Line Improves Measles Lentiviral Vector Production and Gene Delivery to Hematopoietic Stem and Progenitor Cells
Bibliographic record
Abstract
Lentiviral vectors (LVs) pseudotyped with the measles virus hemagglutinin (H) and fusion (F) glycoproteins have been reported to more efficiently transduce hematopoietic stem and progenitor cells (HSPCs) compared with vesicular stomatitis virus glycoprotein (VSV-G) pseudotyped LVs. However, a limit to H/F LV use is the low titer of produced vector. Here we show that measles receptor (CD46) expression on H/F transfected HEK293T vector-producing cells caused adjacent cell membrane fusion, resulting in multinucleate syncytia formation and death prior to peak vector production, leading to contaminating cell membranes that co-purified with LV. H/F LVs produced in CD46 null HEK293T cells, generated by CRISPR/Cas9-mediated knockout of CD46, produced 2-fold higher titer vector compared with LVs produced in CD46 + HEK293T cells. This resulted in approximately 2- to 3-fold higher transduction of HSPCs while significantly reducing target cell cytotoxicity caused by producer cell contaminates. Improved H/F LV entry into HSPCs and distinct entry mechanisms compared with VSV-G LV were also observed by confocal microscopy. Given that vector production is a major source of cost and variability in clinical trials of gene therapy, we propose that the use of CD46 null packaging cells may help to address these challenges.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".