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Updated: Jun 29, 2025
Mechano-Node-Pore Sensing: A Rapid, Label-Free Platform for Multi-Parameter Single-Cell Viscoelastic Measurements
Published on: December 2, 2022
Microfluidic device featuring micro-constrained channels for multi-parametric assessment of cellular biomechanics and
Yang Heng1, Xinyu Zheng2, Youyuan Xu1
1Robotics and Microsystems Center, School of Mechanical and Electrical Engineering, Soochow University, Suzhou, 215000, China.
Background:
Cellular biomechanics plays a significant role in the regulation of cellular physiological and pathological processes. In recent years, multiple methods have been developed to evaluate cellular biomechanics, such as atomic force microscopy (AFM), micropipette aspiration, and magnetic tweezers. However, most of these methods only focus on a single parameter and cannot automate the process at a high-efficiency level. A novel microfluidic method is necessary to achieve the simultaneous multi-parametric measurement of cellular biomechanics and high-precision cellular mechanical phenotyping at high throughput.
Results:
To tackle the issue concerning the low-throughput and cellular single-parameter evaluation, we designed and fabricated a microfluidic chip featuring multiple micro-constrained channels structure, providing a simultaneous multi-parametric assessment of cellular biomechanics, including elastic modulus, recovery capability, and deformability. We compared the biomechanical properties of normal human gastric mucosal epithelial cells (GES-1) and human gastric cancer cells (AGS and MKN-45) by the chip. Results demonstrated that the elastic modulus of GES-1, AGS, and MKN-45 cells decreased sequentially, which was the opposite of their invasiveness and metastasis potential, suggesting the inverse correlation between cellular elastic modulus and malignancy. Meanwhile, the recovery capability and deformability of GES-1, AGS, and MKN-45 cells increased sequentially, demonstrating the positive correlation between cellular deformability and malignancy. Furthermore, multiple parameters were used to distinguish gastric cancer cells from normal gastric cells via machine learning. An accuracy of over 94.8% for identifying gastric cancer cells was achieved.
Significance:
This study provides a deep insight into the biophysical mechanism of gastric cancer metastasis at the single-cell level and possesses great potential to function as a valuable tool for single-cell analysis, thereby facilitating high-precision and high-throughput discrimination of cellular phenotypes that are not easily discernible through single-marker analysis.
