Microfluidic Digital Twin for Enhanced Single-Cell Analysis

Advancing single-cell analysis requires tools that not only enable precise experimental measurements but also offer predictive capabilities to guide device optimization and expand experimental possibilities. This study addresses this need by developing a digital twin framework for mechano-node-pore sensing (mechano-NPS), a high-throughput microfluidic platform for single-cell analysis. By creating a virtual replica that integrates models of fluid dynamics and cellular behavior, the digital twin serves as a critical tool for both device development and hypothesis exploration. The foundation of the digital twin was established by accurately modeling the fluid dynamics within the mechano-NPS device, with simulations at various inlet pressures verified against analytical solutions. To ensure biological relevance, cellular models were rigorously tested to replicate key behaviors within the platform. The digital twin’s performance was validated against experimental data, focusing on cell velocity and whole cell deformation index (wCDI). While variances in cell velocity highlighted systematic biases, the strong agreement of simulated wCDI with experimental results underscores the digital twin’s reliability. This framework not only demonstrates the potential to enhance the mechano-NPS platform but also exemplifies how digital twins can transform experimental approaches in cellular biology.

Duke Scholars

Author Amanda Randles Biomedical Engineering