Abstract
Photoporation is an up-and-coming technology for the gentle and efficient transfection of cells. Inherent to the application of photoporation is the optimization of several process parameters, such as laser fluence and sensitizing particle concentration, which is typically done one factor at a time (OFAT). However, this approach is tedious and runs the risk of missing a global optimum. Therefore, in this study, we explored whether response surface methodology (RSM) would allow for more efficient optimization of the photoporation procedure. As a case study, FITC-dextran molecules of 500 kDa were delivered to RAW264.7 mouse macrophage-like cells, making use of polydopamine nanoparticles (PDNPs) as photoporation sensitizers. Parameters that were varied to obtain an optimal delivery yield were PDNP size, PDNP concentration and laser fluence. Two established RSM designs were compared: the central composite design and the Box-Behnken design. Model fitting was followed by statistical assessment, validation, and response surface analysis. Both designs successfully identified a delivery yield optimum five- to eight-fold more efficiently than when using OFAT methodology while revealing a strong dependence on PDNP size within the design space. In conclusion, RSM proves to be a valuable approach to efficiently optimize photoporation conditions for a particular cell type.