Numerical optimization of microfluidic vortex shedding for genome editing human primary T cells using machine learning

Microfluidic vortex shedding () can rapidly deliver mRNA to human T cells with high yield and minimal perturbation. However, the mechanistic underpinning of as an intracellular delivery method remains undefined with no optimization framework. Herein, we evaluated a series of devices containing various splitter plates to attenuate vortex shedding and understand the contribution of force and frequency on expression efficiency and cell viability. We selected and applied a design to knockout the expression of the endogenous T cell receptor of T cells via delivery of Cas9-RNP. 255 samples were characterized across more than 150 parameters and machine learning was used to identify the 11 most predictive parameters for expression efficiency and cell viability. These results demonstrate the utility of for genome editing of human T cells with CRISPR-Cas9 and provide a robust framework to optimize for various constructs, cell types and protocols.