Project R-14433

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

Genetic cell engineering has become of increasing interest due to its successful implementation in clinical applications, particularly cell therapies. A crucial step is the transfection of exogenous cargo into living cells. Viral vectors are one of the most widely adopted methods for transfection. However, the associated safety risks, high cost, and limited scalability of the manufacturing process has researchers exploring alternatives. A common method is electroporation (EP); by exposing cells to an electric field, nanometer sized pores are formed in the cell membrane through which exogenous cargo flows into the cell. Poration tolerance depends on the cell type, individual cell characteristics and environmental conditions, and is determined through laborious experimentation. This is especially problematic for time-sensitive applications and when cell availability is limited, both of which hold true for cell therapies. In this project we aim to develop an in-flow EP technology with self-learning capabilities for the optimal transfection of cells. We will achieve this by integrating impedance cytometry modalities at different stages of the EP process. Data obtained from these modalities will enable us to develop data processing algorithms to finely control EP and immediately assess EP performance on the cellular level. The project greatly benefits the pharmaceutical industry and beyond, enabling faster development and manufacturing of new therapeutics.

01 November 2023 - 31 October 2027