A statistical framework for determination of minimal plasmid copy number required for transgene expression in mammalian cells.

Journal Article (Journal Article)

Plasmid DNA (pDNA) has been widely used for non-viral gene delivery. After pDNA molecules enter a mammalian cell, they may be trapped in subcellular structures or degraded by nucleases. Only a fraction of them can function as templates for transcription in the nucleus. Thus, an important question is, what is the minimal amount of pDNA molecules that need to be delivered into a cell for transgene expression? At present, it is technically a challenge to experimentally answer the question. To this end, we developed a statistical framework to establish the relationship between two experimentally quantifiable factors - average copy number of pDNA per cell among a group of cells after transfection and percent of the cells with transgene expression. The framework was applied to the analysis of electrotransfection under different experimental conditions in vitro. We experimentally varied the average copy number per cell and the electrotransfection efficiency through changes in extracellular pDNA dose, electric field strength, and pulse number. The experimental data could be explained or predicted quantitatively by the statistical framework. Based on the data and the framework, we could predict that the minimal number of pDNA molecules in the nucleus for transgene expression was on the order of 10. Although the prediction was dependent on the cell and experimental conditions used in the study, the framework may be generally applied to analysis of non-viral gene delivery.

Full Text

Duke Authors

Cited Authors

  • Wang, L; Chang, C-C; Sylvers, J; Yuan, F

Published Date

  • April 2021

Published In

Volume / Issue

  • 138 /

Start / End Page

  • 107731 -

PubMed ID

  • 33434786

Pubmed Central ID

  • 33434786

Electronic International Standard Serial Number (EISSN)

  • 1878-562X

International Standard Serial Number (ISSN)

  • 1567-5394

Digital Object Identifier (DOI)

  • 10.1016/j.bioelechem.2020.107731

Language

  • eng