Predictive Modeling of Non-Viral Gene Transfer

TL;DR

This study combines experimental single-cell analysis and a stochastic mathematical model to understand and predict gene expression variability in non-viral gene transfer, revealing insights into plasmid delivery and expression noise.

Contribution

It introduces a simple stochastic model of transfection that accurately predicts expression distributions and cotransfection ratios, advancing understanding of non-viral gene delivery mechanisms.

Findings

Poisson-like expression distributions observed for both transfection agents

Multiple plasmids are delivered in correlated complexes

The model predicts transfection efficiency and expression noise

Abstract

In non-viral gene delivery, the variance of transgenic expression stems from the low number of plasmids successfully transferred. Here, we experimentally determine Lipofectamine- and PEI-mediated exogenous gene expression distributions from single cell time-lapse analysis. Broad Poisson-like distributions of steady state expression are observed for both transfection agents, when used with synchronized cell lines. At the same time, co-transfection analysis with YFP- and CFP-coding plasmids shows that multiple plasmids are simultaneously expressed, suggesting that plasmids are delivered in correlated units (complexes). We present a mathematical model of transfection, where a stochastic, two-step process is assumed, with the first being the low-probability entry step of complexes into the nucleus, followed by the subsequent release and activation of a small number of plasmids from a delivered complex. This conceptually simple model consistently predicts the observed fraction of transfected cells, the cotransfection ratio and the expression level distribution. It yields the number of efficient plasmids per complex and elucidates the origin of the associated noise, consequently providing a platform for evaluating and improving non-viral vectors.