Applying Design of Experiments Methodology to PEI Toxicity Assay on Neural Progenitor Cells

Sara Mancinelli, Valeria Zazzu, Andrea Turcato, Giuseppina Lacerra, Filomena Anna Digilio, Anna Mascia, Marta Di Carlo, Anna Maria Cirafici, Antonella Bongiovanni, Gianni Colotti, Annamaria Kisslinger, Antonella Lanati and Giovanna L. Liguori ()
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Sara Mancinelli: Institute of Genetics and Biophysics “A. Buzzati Traverso” (IGB), CNR
Valeria Zazzu: Institute of Genetics and Biophysics “A. Buzzati Traverso” (IGB), CNR
Andrea Turcato: Valore Qualità
Giuseppina Lacerra: Institute of Genetics and Biophysics “A. Buzzati Traverso” (IGB), CNR
Filomena Anna Digilio: Institute of Biosciences and Bioresources (IBBR), CNR
Anna Mascia: Institute of Experimental Endocrinology and Oncology “G. Salvatore” (IEOS), CNR
Marta Di Carlo: Institute of Biomedicine and Molecular Immunology “A. Monroy” (IBIM), CNR
Anna Maria Cirafici: Institute of Experimental Endocrinology and Oncology “G. Salvatore” (IEOS), CNR
Antonella Bongiovanni: Institute of Biomedicine and Molecular Immunology “A. Monroy” (IBIM), CNR
Gianni Colotti: CNR, Sapienza University, Department of Biochemical Sciences, Institute of Molecular Biology and Pathology (IBPM)
Annamaria Kisslinger: Institute of Experimental Endocrinology and Oncology “G. Salvatore” (IEOS), CNR
Antonella Lanati: Valore Qualità
Giovanna L. Liguori: Institute of Genetics and Biophysics “A. Buzzati Traverso” (IGB), CNR

A chapter in Mathematical Models in Biology, 2015, pp 45-63 from Springer

Abstract: 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.

Keywords: Design of experiments; PEI toxicity; Neural cell transfection; Factorial analysis (search for similar items in EconPapers)
Date: 2015
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DOI: 10.1007/978-3-319-23497-7_4

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