AAV2/8 Vectors Purified from Culture Medium with a Simple and Rapid Protocol Transduce Murine Liver, Muscle, and Retina Efficiently
Bibliographic record
Abstract
During the production of some adeno-associated virus (AAV) serotypes, a large amount of vectors is found in the medium of producing cells. For their purification, previous protocols used tangential flow filtration (TFF) of the medium followed by iodixanol gradient centrifugation. Taking advantage of the higher purity of the medium than the cell-derived material as the source of AAV, we tested a simple method that combines production of large culture medium volumes containing AAV from cell stacks with medium clarification+TFF without further time-consuming and nonscalable centrifugation. To test this, we selected AAV2/8, which is emerging as a favored serotype for transduction of liver, muscle, and retina and abundantly found in the extracellular medium. We show that yields and in vitro infectivity of AAV2/8 vectors produced from the culture medium using this method are higher than those of vectors purified from the same cell lysate using a conventional CsCl2 gradient ultracentrifugation-based method, although purity appears inferior. In addition, we found that the transduction efficiency of AAV2/8 purified from medium was similar to that of AAV2/8 purified from the same cell lysate in the murine liver, muscle, and retina. Considering that the purification protocol from the medium we describe requires 3 hr as opposed to the 63 hr of a conventional two-round CsCl2-gradient ultracentrifugation+desalting, we conclude that TFF of the medium containing AAV2/8 represents a quick and scalable method to purify research-grade vectors for use in animal models.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".