Accurate Titration of Infectious AAV Particles Requires Measurement of Biologically Active Vector Genomes and Suitable Controls
Bibliographic record
Abstract
Although the clinical use of recombinant adeno-associated virus (rAAV) vectors is constantly increasing, the development of suitable quality control methods is still needed for accurate vector characterization. Among the quality criteria, the titration of infectious particles is critical to determine vector efficacy. Different methods have been developed for the measurement of rAAV infectivity in vitro , based on detection of vector genome replication in trans -complementing cells infected with adenovirus, detection of transgene expression in permissive cells, or simply detection of intracellular vector genomes following the infection of indicator cells. In the present study, we have compared these methods for the titration of infectious rAAV8 vector particles, and, to assess their ability to discriminate infectious and non-infectious rAAV serotype 8 particles, we have generated a VP1-defective AAV8-GFP vector. Since VP1 is required to enter the cell nucleus, the lack of VP1 should drastically reduce the infectivity of rAAV particles. The AAV8 reference standard material was used as a positive control. Our results demonstrated that methods based on measurement of rAAV biological activity (i.e., vector genome replication or transgene expression) were able to accurately discriminate infectious versus non-infectious particles, whereas methods simply measuring intracellular vector genomes were not. Several cell fractionation protocols were tested in an attempt to specifically measure vector genomes that had reached the nucleus, but genomes from wild-type and VP1-defective AAV8 particles were equally detected in the nuclear fraction by qPCR. These data highlight the importance of using suitable controls, including a negative control, for the development of biological assays such as infectious unit titration.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".