@article{Cieslik:2026:10.1039/d5lc00738k,
author = {Cieslik, J-P and Xia, X and Salehi-Reyhani, A},
doi = {10.1039/d5lc00738k},
journal = {Lab Chip},
title = {MaGIC-OT: an AI-guided optical tweezers platform for autonomous single-cell isolation in microfluidic devices.},
url = {http://dx.doi.org/10.1039/d5lc00738k},
year = {2026}
}

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TY  - JOUR
AB  - Automating the isolation of rare cells such as circulating tumour cells (CTCs) within crowded microfluidic environments remains a bottleneck in liquid biopsy workflows. Optical tweezers offer contact-free, selective manipulation but traditionally rely on expert operators. We present MaGIC-OT (machine-guided isolation of cells using optical tweezers), a platform that integrates classical path planning and deep reinforcement learning (DRL) to automate single-cell manipulation inside a microfluidic chip. We built a high-fidelity simulation to train and benchmark control policies and show that cooperative, human-in-the-loop training improves DRL performance. Trained agents outperform expert users in speed and isolation success in silico, and we demonstrate proof-of-concept isolation of a cancer cell from a spiked blood sample on-chip. MaGIC-OT provides a flexible framework for intelligent optical manipulation, aligning microfluidic device design with autonomous control strategies and offering a pathway toward high-purity, label-free single-cell workflows.
AU  - Cieslik,J-P
AU  - Xia,X
AU  - Salehi-Reyhani,A
DO  - 10.1039/d5lc00738k
PY  - 2026///
TI  - MaGIC-OT: an AI-guided optical tweezers platform for autonomous single-cell isolation in microfluidic devices.
T2  - Lab Chip
UR  - http://dx.doi.org/10.1039/d5lc00738k
UR  - https://www.ncbi.nlm.nih.gov/pubmed/41677178
ER  - 

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