Abstract
Design of Experiments (DoE) statistical methodology permits the simultaneous evaluation of the effects of different factors on experimental performance and the analysis of their interactions in order to identify their optimal combinations. Compared to classical approaches based on changing only one factor at a time (OFAT), DoE facilitates the exploration of a broader range of parameters combinations, as well as providing the possibility to select a limited number of combinations covering the whole frame. The advantage of DoE is to maximise the amount of information provided and to save both time and money. DoE has been primarily used in industry to maximise process robustness, but recently it has also been applied in biomedical research to different types of multivariable analyses, from determination of the best cell media composition to the optimisation of entire multi-step laboratory protocols such as cell transfection.
Our case study is the optimisation of a transfection protocol for neural progenitor cell lines. These cells are very hard to transfect and are refractory to lipidic reagents, so we decided to set-up a protocol based on the non-lipidic Poliethylenimine (PEI) reagent. However, the effect of PEI toxicity on cells has to be correctly evaluated in the experimental design, since it can affect output computation. For this reason, we decided to apply DoE methodology to investigate the effect of PEI, both concentration and type, on cell viability and its interaction with other factors, such as DNA and cell density. The statistics-based DoE approach allowed us to express analytically the neural cell viability dependence on PEI amount/cell and efficiently identify the dose levels of PEI suitable for transfection experiments.
The authors “Sara Mancinelli”, “Valeria Zazzu” and “Andrea Turcato” contributed equally to this work.
The authors “Antonella Lanati” and “Giovanna L. Liguori” share senior co-authorship.
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Acknowledgments
We would like to thank Umberto di Porzio and Giancarlo Bellenchi (Institute of Genetics and Biophysics ‘Adriano Buzzati-Traverso’) for helpful suggestions and for providing us with the mes-c-myc A1 cells, Salvatore Pulcrano and Valerio Piscopo (Institute of Genetics and Biophysics ‘Adriano Buzzati-Traverso’) for providing A1 cell culturing protocols, Genesia Manganelli and Emilia Giorgio (Institute of Genetics and Biophysics ‘Adriano Buzzati-Traverso’) for helping set up the preliminary assays, and Teresa Nutile (Institute of Genetics and Biophysics ‘Adriano Buzzati-Traverso’) for her statistical support. Portions of information contained in this work are printed with permission of Minitab Inc. All such material remains the exclusive property and copyright of Minitab Inc. All rights reserved. We also thank Richard E. Burket for editing and English revision.
This work was supported by grants from the Ministero dell’Economia (Ministry of Economics and Finance in Italy, CNR FaReBio di Qualità, qPMO Project), the Ministero Istruzione Università Ricerca (Medical Research in Italy RBNE08LN4P_002) and ‘Fondazione con il Sud’ (2011-PDR-13) to G.L.L.
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Mancinelli, S. et al. (2015). Applying Design of Experiments Methodology to PEI Toxicity Assay on Neural Progenitor Cells. In: Zazzu, V., Ferraro, M., Guarracino, M. (eds) Mathematical Models in Biology. Springer, Cham. https://doi.org/10.1007/978-3-319-23497-7_4
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DOI: https://doi.org/10.1007/978-3-319-23497-7_4
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