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Record W4212863247 · doi:10.1002/bit.28068

Only a small fraction of cells produce assembled capsids during transfection‐based manufacturing of adeno‐associated virus vectors

2022· article· en· W4212863247 on OpenAlexafffund
Shantoshini Dash, David Sharon, Alaka Mullick, Amine Kamen

Bibliographic record

VenueBiotechnology and Bioengineering · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicVirus-based gene therapy research
Canadian institutionsNational Research Council CanadaMcGill University
FundersNational Research Council Canada
KeywordsTransfectionCapsidAdeno-associated virusFlow cytometryPlasmidVector (molecular biology)TiterVirusRecombinant DNABiologyCell biologyMolecular biologyChemistryVirologyCell cultureGeneGenetics

Abstract

fetched live from OpenAlex

Plasmid transfection of mammalian cells is the dominant platform used to produce adeno-associated virus (AAV) vectors for clinical and research applications. Low yields from this platform currently make it difficult to supply these activities with adequate material. In an effort to better understand the current limitations of transfection-based manufacturing, this study examines what proportion of cells in a model transfection produce appreciable amounts of assembled AAV capsid. Using conformation-specific antibody staining and flow cytometry, we report the surprising result that despite obtaining high transfection efficiencies and nominal vector yields in our model system, only 5%-10% of cells appear to produce measurable levels of assembled AAV capsids. This finding implies that considerable increases in vector titer could be realized through increasing the proportion of productive cells. Furthermore, we suggest that the flow cytometry assay used here to quantify productive cells may be a useful metric for future optimization of transfection-based AAV vector manufacturing platforms.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.701

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.221
Teacher spread0.213 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations23
Published2022
Admission routes2
Has abstractyes

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