Optimization of the nutritional environment for differentiation of human‐induced pluripotent stem cells using design of experiments—A proof of concept

Abstract

The utilization of human-induced pluripotent stem cells (hiPSCs) in cell therapy has a tremendous potential but faces many practical challenges, including costs associated with cell culture media and growth factors. There is an immediate need to establish an optimized culture platform to direct the differentiation of hiPSCs into germ layers in a defined nutritional microenvironment to generate cost-effective and robust therapeu-

Clever Experimental Designs: Shortcuts for Better iPSC Differentiation

“…Emerging metabolome analysis and online monitoring of medium compositions along the differentiation process should offer hints for further multifactorial screening. The significant contributions of basal media composition on PSC lineage commitment [ 50 ] suggest that further addition or removal of energy sources can improve the quality and/or yield of PSC and PSC-derived materials.…”

“…Mixed-level orthogonal arrays were used to screen cytokines for choroidal endothelium differentiation [ 46 ] and optimize additive doses for four distinct types of endodermal cell induction [ 47 ]. RSM was used to optimize hydrogel peptide concentrations for neural progenitor maturation [ 48 ], optimize the ECM composition for cardiomyocyte differentiation [ 49 ], and screen nutrient compositions driving trilineage specification from hiPSCs based on the response surface models for endodermal, mesodermal, and ectodermal protein expressions [ 50 ].…”

Quality Management Tools on the Stage: Old but New Allies for Rigor and Standardization of Extracellular Vesicle Studies

“…To overcome this limitation, a Design of Experiments (DoE) statistical approach can be applied to multivariable processes to identify the optimal combination of factors and model their interaction, leading to major benefits in both product performance as well as management of resources ( Mandenius and Brundin, 2008 ; Montgomery, 2012 ; Weissman and Anderson, 2015 ; Lipsitz et al, 2016 ; Myers et al, 2016 ; Politis et al, 2017 ; Grangeia et al, 2020 ). In recent years, the DoE approach is emerging in many fields of scientific research, including cell biology, biochemistry and nanotechnologies ( Mancinelli et al, 2015 , 2021 ; Durakovic, 2017 ; Toms et al, 2017 ; Lanati, 2018 ; Narenderan et al, 2019 ; Papaneophytou, 2019 ; Xu et al, 2020 ; Esteban et al, 2021 ; Papaneophytou et al, 2021 ; Tavares Luiz et al, 2021 ). In the EV context, DoE has been successfully implemented to model the effect of different factors, such as time, EV dose, EV type (exosomes or microvesicles) on different parameters related to EV uptake and cargo delivery, fundamental for therapeutic application ( Xu et al, 2020 ; Dave et al, 2021 ).…”

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

“…DoE has been widely used to maximize yields and improve processes at an early stage, leading to major benefits in both product performance as well as management of resources (Grangeia et al, 2020 ; Mandenius & Brundin, 2008 ; Politis et al, 2017 ; Weissman & Anderson, 2015 ). In recent years, the use of the DoE approach as an alternative to the traditional OFAT method is definitely increasing in many fields of scientific research, including cell biology, biochemistry, and nanotechnologies, to identify the main factors controlling the scientific process and for modeling their effects (Bollin et al, 2011 ; Durakovic, 2017 ; Esteban et al, 2021 ; Lanati, 2018 ; Mancinelli et al, 2015 ; Narenderan et al, 2019 ; Papaneophytou et al, 2021 ; Papaneophytou, 2019 ; Tavares Luiz et al, 2021 ; Toms et al, 2017 ; Xu et al, 2020 ). In this scenario, we have been committed for many years in developing, applying, and validating models that can be useful for the management or research activities, helping in standardizing, and optimizing processes, thus improving data reliability and reproducibility (Bongiovanni et al, 2015 ; Digilio et al, 2016 ; Liguori & Kisslinger, 2020 ; Mancinelli et al, 2015 ; Mascia et al, 2020 ).…”