Induced pluripotent stem cells (iPSCs) greatly facilitate the investigation of human disease mechanisms, the characterization of patient-specific cellular phenotypes, and the development of new, personalized treatments. For brain disorders, iPSC-based disease modelling is particularly advantageous as access to primary tissue is highly restricted. However, optimal study designs with sufficient statistical power were poorly defined.
To address this problem, the research team generated immunocytochemical, electrophysiological, and proteomic data from iPSC-derived neurons of five healthy subjects, analysed variation in these data, and used this information to set up realistic power simulations. These simulations demonstrate that published case-control iPSC studies are generally underpowered.
To reach higher statistical power, isogenic designs, where mutations are generated or corrected within the same genetic background, can be used. However, these designs are limited in their generalizability. Instead, studying multiple isogenic pairs in parallel increases absolute power up to 60% or requires up to 5-fold fewer lines, while allowing generalization of the findings to the larger patient population.
To optimize statistically rigorous iPSC-based studies that will yield robust and replicable results, the research team generated a free web tool that can be used to a priori explore the power of different study designs, using any (pilot) data: https://jessiebrunner.shinyapps.io/App_PowerCurves/