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Quantifying mechanical opacity as a novel indicator for single-cell phenotyping via integrated dynamic mechanical compression and impedance flow cytometry

Journal 2025 · IF 5.4 · 发表于 2025 年 7 月 4 日
作者: Shan-Shan Li,Chun-Dong Xue,Si-Yu Hu,Yong-Jiang Li,Xu-Qu Hu,Zhuo Yang,Kai-Rong Qin
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摘要

A comprehensive understanding of cellular mechanical heterogeneity is essential for identifying phenotypic variations. Impedance flow cytometry offers a high-throughput, label-free approach to assess single-cell electrical properties, yet current methods focus primarily on undeformed cells and overlook mechanical perturbations that may alter cytoskeletal structure and membrane behavior. Here, we present an integrated system that combines controlled mechanical compression with impedance measurement to quantify mechanical opacity—an electrical metric reflecting membrane permeability under dynamic deformation. This parameter correlates with cytoskeletal integrity and reveals how mechanical stimuli influence electrical responses. Theoretical modeling shows that membrane permittivity and conductivity critically shape frequency-dependent impedance, supporting the use of dual-frequency (500 kHz and 5 MHz) measurements to probe both intra- and extracellular properties. We define a four-parameter feature set (Rsqu, Rsti1, Rsti2, Rrelax) to capture impedance changes during deformation and relaxation, offering a compact and interpretable mechanical signature. Using this system, we demonstrate distinct mechanical opacity profiles among three human cancer cell lines (HeLa, SW1990, BxPC-3), reflecting their inherent biomechanical differences. Fluorescence assays confirm that lower mechanical opacity corresponds to increased membrane permeability, linking electrical measurements to underlying structural changes. Our work establishes mechanical opacity as a dynamic, label-free marker for single-cell mechanics, bridging mechanical stimulation and electrical detection. This approach expands the capability of impedance flow cytometry for applications in cell classification, drug screening, and disease diagnostics.

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