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• W1974595716 abstract "In artificial gene delivery, messenger RNA (mRNA) is an attractive alternative to plasmid DNA (pDNA) since it does not require transfer into the cell nucleus. Here we show that, unlike for pDNA transfection, the delivery statistics and dynamics of mRNA-mediated expression are generic and predictable in terms of mathematical modeling. We measured the single-cell expression time-courses and levels of enhanced green fluorescent protein (eGFP) using time-lapse microscopy and flow cytometry (FC). The single-cell analysis provides direct access to the distribution of onset times, life times and expression rates of mRNA and eGFP. We introduce a two-step stochastic delivery model that reproduces the number distribution of successfully delivered and translated mRNA molecules and thereby the dose–response relation. Our results establish a statistical framework for mRNA transfection and as such should advance the development of RNA carriers and small interfering/micro RNA-based drugs. This team of authors established a statistical framework for mRNA transfection by using a two-step stochastic delivery model that reproduces the number distribution of successfully delivered and translated mRNA molecules and thereby their dose-response relation. This study establishes a nice connection between theory and experimental planning and will aid the cellular delivery of mRNA molecules. EGFP expression following transfection using messenger RNA (mRNA), in contrast to delivery of plasmid DNA (pDNA), exhibits distinct single-cell time courses that are independent of cell type and well described by mathematical modeling. The dose–response relation is reproduced by a two-step stochastic model for the quantal delivery of mRNA packed in lipoplexes." @default.
• W1974595716 created "2016-06-24" @default.
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• W1974595716 date "2014-05-01" @default.
• W1974595716 modified "2023-10-02" @default.
• W1974595716 title "Single-cell mRNA transfection studies: Delivery, kinetics and statistics by numbers" @default.
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• W1974595716 doi "https://doi.org/10.1016/j.nano.2013.11.008" @default.
• W1974595716 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/24333584" @default.
• W1974595716 hasPublicationYear "2014" @default.
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