On-chip characterization of cell mechanics assisted by external physical fields and artificial intelligence

TY - JOUR

T1 - On-chip characterization of cell mechanics assisted by external physical fields and artificial intelligence

AU - Ge, Jingjin

AU - Bai, Chenhao

AU - Chen, Zhuo

AU - Fukuda, Toshio

AU - Arai, Tatsuo

AU - Liu, Xiaoming

N1 - Publisher Copyright: This journal is © The Royal Society of Chemistry, 2026

PY - 2026/3/3

Y1 - 2026/3/3

N2 - Traditional microfluidic chips for single-cell mechanical characterization face challenges such as cell aggregation and low throughput, limiting their clinical applicability. While fluid-driven methods such as constricted extrusion, pipette aspiration, and shear-induced or stretch-induced deformation have demonstrated laboratory success, they require improvements in accuracy and scalability. To overcome these limitations, integration of external physical fields, including acoustic, optical, electrical, and magnetic, enables non-contact, high-throughput cell operations and analysis. Acoustic waves and magnetic fields provide precise control over cell deformation, optical tweezers enable contact-free trapping, and electric fields facilitate dielectrophoretic manipulation. These techniques improve measurement sensitivity and throughput, making them more suitable for clinical applications, but also increase follow-up processing time. Artificial intelligence (AI) further enhances microfluidic automation across all these methodologies by enabling real-time image processing, parameter optimization, and data analysis to shorten processing time. This review particularly explores how AI is poised to solve fundamental, long-standing problems in cell mechanics that are intractable for conventional methods. Future microfluidic systems will integrate multiple physical fields controlled with AI, improving precision and scalability. The convergence of microfluidics, external fields, and AI is expected to revolutionize single-cell mechanobiology, advancing both fundamental research and clinical applications.

AB - Traditional microfluidic chips for single-cell mechanical characterization face challenges such as cell aggregation and low throughput, limiting their clinical applicability. While fluid-driven methods such as constricted extrusion, pipette aspiration, and shear-induced or stretch-induced deformation have demonstrated laboratory success, they require improvements in accuracy and scalability. To overcome these limitations, integration of external physical fields, including acoustic, optical, electrical, and magnetic, enables non-contact, high-throughput cell operations and analysis. Acoustic waves and magnetic fields provide precise control over cell deformation, optical tweezers enable contact-free trapping, and electric fields facilitate dielectrophoretic manipulation. These techniques improve measurement sensitivity and throughput, making them more suitable for clinical applications, but also increase follow-up processing time. Artificial intelligence (AI) further enhances microfluidic automation across all these methodologies by enabling real-time image processing, parameter optimization, and data analysis to shorten processing time. This review particularly explores how AI is poised to solve fundamental, long-standing problems in cell mechanics that are intractable for conventional methods. Future microfluidic systems will integrate multiple physical fields controlled with AI, improving precision and scalability. The convergence of microfluidics, external fields, and AI is expected to revolutionize single-cell mechanobiology, advancing both fundamental research and clinical applications.

UR - https://www.scopus.com/pages/publications/105027282729

U2 - 10.1039/d5lc00855g

DO - 10.1039/d5lc00855g

M3 - Review article

AN - SCOPUS:105027282729

SN - 1473-0197

VL - 26

SP - 1528

EP - 1546

JO - Lab on a Chip

JF - Lab on a Chip

IS - 5

ER -