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Design of transfections: Implementation of design of experiments for cell transfection fine tuning.

ABSTRACT: Transfection is the process by which nucleic acids are introduced into eukaryotic cells. This is fundamental in basic research for studying gene function and modulation of gene expression as well as for many bioprocesses in the manufacturing of clinical-grade recombinant biologics from cells. Transfection efficiency is a critical parameter to increase biologics' productivity; the right protocol has to be identified to ensure high transfection efficiency and therefore high product yield. Design of experiments (DoE) is a mathematical method that has become a key tool in bioprocess development. Based on the DoE method, we developed an operational flow that we called "Design of Transfections" (DoT) for specific transfection modeling and identification of the optimal transfection conditions. As a proof of principle, we applied the DoT workflow to optimize a cell transfection chemical protocol for neural progenitors, using polyethyleneimine (PEI). We simultaneously varied key influencing factors, namely concentration and type of PEI, DNA concentration, and cell density. The transfection efficiency was measured by fluorescence imaging followed by automatic counting of the green fluorescent transfected cells. Taking advantage of the DoT workflow, we developed a new simple, efficient, and economically advantageous PEI transfection protocol through which we were able to obtain a transfection efficiency of 34%.

SUBMITTER: Mancinelli S

PROVIDER: S-EPMC9291525 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature

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Design of transfections: Implementation of design of experiments for cell transfection fine tuning.

Mancinelli Sara S Turcato Andrea A Kisslinger Annamaria A Bongiovanni Antonella A Zazzu Valeria V Lanati Antonella A Liguori Giovanna Lucia GL

Biotechnology and bioengineering 20210901 11

Transfection is the process by which nucleic acids are introduced into eukaryotic cells. This is fundamental in basic research for studying gene function and modulation of gene expression as well as for many bioprocesses in the manufacturing of clinical-grade recombinant biologics from cells. Transfection efficiency is a critical parameter to increase biologics' productivity; the right protocol has to be identified to ensure high transfection efficiency and therefore high product yield. Design o ...[more]

PMID: 34406655

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Project description:BackgroundSingle nucleotide polymorphisms (SNPs) which capture a significant impact on a trait can be identified with genome-wide association studies. High linkage disequilibrium (LD) among SNPs makes it difficult to identify causative variants correctly. Thus, often target regions instead of single SNPs are reported. Sample size has not only a crucial impact on the precision of parameter estimates, it also ensures that a desired level of statistical power can be reached. We study the design of experiments for fine-mapping of signals of a quantitative trait locus in such a target region.MethodsA multi-locus model allows to identify causative variants simultaneously, to state their positions more precisely and to account for existing dependencies. Based on the commonly applied SNP-BLUP approach, we determine the z-score statistic for locally testing non-zero SNP effects and investigate its distribution under the alternative hypothesis. This quantity employs the theoretical instead of observed dependence between SNPs; it can be set up as a function of paternal and maternal LD for any given population structure.ResultsWe simulated multiple paternal half-sib families and considered a target region of 1 Mbp. A bimodal distribution of estimated sample size was observed, particularly if more than two causative variants were assumed. The median of estimates constituted the final proposal of optimal sample size; it was consistently less than sample size estimated from single-SNP investigation which was used as a baseline approach. The second mode pointed to inflated sample sizes and could be explained by blocks of varying linkage phases leading to negative correlations between SNPs. Optimal sample size increased almost linearly with number of signals to be identified but depended much stronger on the assumption on heritability. For instance, three times as many samples were required if heritability was 0.1 compared to 0.3. An R package is provided that comprises all required tools.ConclusionsOur approach incorporates information about the population structure into the design of experiments. Compared to a conventional method, this leads to a reduced estimate of sample size enabling the resource-saving design of future experiments for fine-mapping of candidate variants.

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Design of transfections: Implementation of design of experiments for cell transfection fine tuning.

Publications

Design of transfections: Implementation of design of experiments for cell transfection fine tuning.

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