High-throughput analysis of the morphology and mechanics of tip growing cells using a microrobotic platform

We present a microrobotic platform that combines MEMS-based capacitive force sensing technology, a dual-stage positioning system and a real-time control and acquisition architecture with computer vision automation to manipulate and mechanically characterize growing plant cells. The topography accuracy of the system, using a silicon wafer sample is measured to be 28 nm(1σ, 200Hz). With an SI-traceable stiffness reference we estimate the accuracy of the RT-CFM to be 3.49%. The target locations are selected from an interactive image of the workspace, and the sensing tip is positioned at each location using visual servoing techniques. Topography and stiffness maps were successfully obtained on growing pollen tubes. With the proposed system, cells can be mechanically stimulated at high speeds and with high precision while the intracellular components are visualized using confocal imaging. The system offers a versatile solution for dexterous and high-throughput characterization of biological specimen.