Single-cell manipulation is essential for investigating cellular heterogeneity and enabling downstream assays, such as single-cell proteomics and drug screening. Acoustofluidic techniques, particularly needle-based acoustic tweezer, have emerged as a gentle, label-free, and cost-effective alternative for contactless cell control. However, existing methods suffer from limited precision and automation due to acoustic streaming-induced particle instability around the microneedle, hindering their translation into biomedical applications. Here, we present a robotic acoustofluidic platform that integrates a needle-based acoustic tweezer with a YOLOv8-powered real-time machine vision module for automated single-cell picking and placement. We systematically characterized the effects of excitation voltage and frequency modulation on dynamic manipulation stability. By characterizing the dynamic relationship between particle rotation and driving voltage, we identified optimal operating regimes for stable control and implemented a regulation strategy that dynamically adjusts acoustic excitation to mitigate streaming-induced disturbances. Furthermore, we utilized real-time target recognition and closed-loop feedback to precisely guide the picking and placement processes under dynamic rotational states. The platform achieved automated positioning of diverse targets (10-100 μm), including microparticles, single cells, and cell spheroids. Additionally, the system's capability for dual-particle co-placement was verified. Subsequent single-cell proteomic analysis confirms that our platform maintains cellular structural integrity and viability throughout the process. This low-cost, versatile single-cell platform provides a scalable solution for automated single-cell workflows, facilitating advanced biomedical applications such as single-cell protein immunoblotting, point-of-care diagnostics, combinatorial screening, and advanced material synthesis.
Li et al. (2026) studied this question.