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Enregistrement 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 sur OpenAlexaff
Charlie Yu Ming Hsu, Derrick E. Rancourt

Notice bibliographique

RevueMolecular Therapy · 2016
Typearticle
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueAnimal Genetics and Reproduction
Établissements canadiensAlberta Bone and Joint Health InstituteUniversity of Calgary
Organismes subventionnairesnon disponible
Mots-clésTransfectionBiologyMolecular biologyTransgenePlasmidDNACell biologyGeneGenetics

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,082
Score d'incertitude au seuil0,645

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,004
Tête enseignante GPT0,198
Écart entre enseignants0,195 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2016
Routes d'admission1
Résumé présentoui

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