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Record W2566277086 · doi:10.1016/s1525-0016(16)33153-7

344. En Route to Non-Viral Genetic Engineering: Kinetics of DNA Uptake and Transgene Expression Following Repeated Transfection with Multiple Episomal Plasmids in Human Primary Fibroblast

2016· article· en· W2566277086 on OpenAlexaff
Charlie Yu Ming Hsu, Derrick E. Rancourt

Bibliographic record

VenueMolecular Therapy · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAnimal Genetics and Reproduction
Canadian institutionsAlberta Bone and Joint Health InstituteUniversity of Calgary
Fundersnot available
KeywordsTransfectionBiologyMolecular biologyTransgenePlasmidDNACell biologyGeneGenetics

Abstract

fetched live from OpenAlex

Non-viral approach to cellular reprogramming or genome editing of mammalian cells often involve co-delivery of multiple types of nucleic acid molecules. Whether the method involves co-transfection with multiple episomal plasmid DNAs, a mixture of mRNA/gRNA oligomers or a combination of both DNA and RNA molecules, the efficacy of these modular approaches hinges upon the efficiency at which all the molecular factors are co-delivered and co-expressed at their optimal stoichiometric ratios. A significant rate-limiting step thus lies in the lack of an efficient co-transfection, in which a subset of the transfected population may be devoid of one, two, or more of the factors required, but the proportion at which these event occur is not clear. Further, because non-viral transfection is a transient process, repeated transfection is often employed to sustain transgene expression, as is often the case in non-viral episomal based cellular reprogramming. However, the effectiveness of these subsequent rounds of transfection in maintaining transgene expression among the transfected cells is presently unclear. In this study, we examined the kinetics of DNA uptake and transgene expression following cationic reagent-mediated non-viral transfection of primary human neonatal foreskin fibroblast with multiple episomal plasmid DNAs. To measure the level of DNA uptake and transgene expression following co-transfection, we employed two fluorescent reporter gene plasmids (eGFP and mtagBFP2) and covalently labeled them with either FITC or Cy5. Cells were transfected using XtremeGENE HP with either one or both of the labeled plasmids. More than 90% of the cells transfected were positive for either FITC or Cy5 plasmid DNA. When the labeled plasmids were mixed at 1:1 ratio or diluted with unlabeled DNA, there was a proportional decrease in the level of fluorescence in the respective fluorescent channel consistent with the relative input ratios between the two labeled DNAs. We also saw a strong correlation in the co-expression of both reporter genes following co-transfection with the majority of the transfected cells dually expressing both GFP and BFP (64%), however, there were subsets of singly transfected cells that express only GFP (8%) or BFP (27%). We next looked at the effectiveness of repeated transfection in enhancing/sustaining transgene expression in transfected cells. In order to distinguish the population of repeatedly transfected ones from new transfection events in subsequent rounds of transfection, we employed the same two reporter gene set-up (eGFP/mTagBFP2) in which cells were transfected with GFP first, followed by a second transfection with BFP a few days later; cells that were repeatedly transfected would then express BFP in addition to GFP. Our result showed that, while the overall transfection efficiency was higher with repeated transfection, to our surprise, the majority of the transfected cells remained GFP+; only a subset of the 40% of the transfected cells were positive for both GFP and BFP (~9% total), with the remaining attributed to newly transfected cells expressing only BFP. Taken together, these data suggest that while cationic reagent can efficiently co-deliver and co-transfect multiple episomal factors, the effectiveness of this modular approach in non-viral genetic engineering may be limited in cases where sustained expression is required due to the majority of the transfected cells being refractory to subsequent rounds of transfection. Addressing these rate-limiting steps should help increase the utility and efficiency of the system.

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 score0.082
Threshold uncertainty score0.645

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 head0.004
GPT teacher head0.198
Teacher spread0.195 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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