Smart Transfection: In-flow electroporation with self-learning capabilities using integrated impedance cytometry modalities

Abstract

Intracellular delivery of exogenous cargo is an essential step in many cell engineering applications. The limitations and stringent safety requirements of viral methods have researchers looking at physical methods. However, the optimization remains to be a time-consuming procedure, making the development a long and costly process. Here we combine in-flow electroporation and real-time data from impedance cytometry modalities together with machine learning techniques to enable a dynamic optimization process. We envision this device to not only reduce optimization times, but also enable real time in-situ monitoring of the in-flow electroporation process on an individual cell level.