Micropipette Aspiration of Single Cells for Both Mechanical and Electrical Characterization

• Published in: IEEE transactions on biomedical engineering Vol. 66; no. 11; pp. 3185 - 3191
• Main Authors: Pu, Huayan, Chen, Haige, Sun, Yu, Liu, Na, Yu, Jiasheng, Yang, Yang, Sun, Yi, Peng, Yan, Xie, Shaorong, Luo, Jun, Dong, Liang
• Format: Journal Article
• Language: English
• Published: United States IEEE 01-11-2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Bayes Theorem; Bayesian analysis; Biomechanical Phenomena - physiology; Bladder; Bladder cancer; Cancer; Capacitance; Cell Line, Tumor; Cell Physiological Phenomena - physiology; Classification; Elasticity - physiology; Electrical properties; Equipment Design; Humans; Image Processing, Computer-Assisted - methods; Impedance; Integrated circuit modeling; Mechanical and electrical properties; Mechanical factors; Mechanical properties; Membrane capacitance; Metastases; Micromanipulation - instrumentation; Micromanipulation - methods; micropipette aspiration; Modulus of elasticity; Parameter sensitivity; Physical properties; Single-Cell Analysis - instrumentation; Single-Cell Analysis - methods; specific membrane capacitance; Tumor cell lines; viscoelasticity; Viscosity
• ISSN: 0018-9294; 1558-2531
• DOI: 10.1109/TBME.2019.2901763

Cellular physical properties have been identified to reflect cell states. Existing techniques are able to characterize either mechanical or electrical properties of a cell. This paper presents a micropipette aspiration technique that enables the characterization of both mechanical (instantaneous elastic modulus, equilibrium elastic modulus, and viscosity), and electrical (specific membrane capacitance) properties of the same single cell. Two bladder cancer cell lines (RT4 and T24) with different metastatic potential were used to evaluate the technique. The results showed that high-grade bladder cancer cells (T24, grade III) possess lower viscosity, lower elastic modulus, and larger SMC than the low-grade cancer cells (RT4, grade I). The Naive Bayes classifier was utilized to assess the classification accuracy using single-physical and multi-physical parameters. The classification results confirmed that the use of multi-biophysical parameters resulted in higher accuracy (97.5%), sensitivity (100%), and specificity (95.2%) than the use of a single-physical parameter for distinguishing T24 and RT4 cells.