Size-Exclusion Chromatography Purification of High-Titer Vesicular Stomatitis Virus G Glycoprotein-Pseudotyped Retrovectors for Cell and Gene Therapy Applications
Bibliographic record
Abstract
Vesicular stomatitis virus G glycoprotein (VSV-G)-pseudotyped replication-defective retroviral particles are pantropic and amenable to concentration to high titer by ultracentrifugation. These features have allowed development of effective retroviral transduction protocols for stem cells in vitro as well as for tissue engineering in vivo. However, retroparticle ultracentrifugation protocols will also copellet cellular and subcellular debris released from retroviral producer cell lines during vector manufacture. We have analyzed concentrated vector preparations by chromatography and have found that a significant amount of genomic DNA released from producer cells coconcentrates with retroviral particles. In an effort to generate high-purity retroparticle preparations, devoid of subcellular contaminants and contaminating genomic DNA, we have developed a process using size-exclusion chromatography combined with host cell nucleic acid digestion and concentration by ultrafiltration. The procedure allowed for a final recovery of 19 +/- 0.4% infectious viral particles from unfractionated starting material, with an average retroparticle concentration of 7.7 x 10(7) +/- 1.5 x 10(6)/ml. The intact virus is of high purity, >90% as determined by anion-exchange high-performance liquid chromatography. Retroparticle structure appeared intact as determined by negative stain electron microscopy and purified virus was functional and allowed for efficient transduction of primary human bone marrow stromal cells in vitro. In conclusion, we have developed a VSV-G retrovector purification process that can be applied to large-scale retroviral production ideal for cell and gene therapy applications.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".