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. 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. 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.

中文翻译：

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具有微约束通道的微流体装置，用于细胞生物力学的多参数评估和胃细胞的高精度机械表型分析

细胞生物力学在细胞生理和病理过程的调节中发挥着重要作用。近年来，已经开发了多种评估细胞生物力学的方法，例如原子力显微镜（AFM）、微移液器抽吸和磁力镊子。然而，这些方法大多数只关注单个参数，无法高效地实现过程自动化。需要一种新颖的微流控方法来实现高通量细胞生物力学的同时多参数测量和高精度细胞机械表型分析。针对低通量和细胞单参数评估的问题，我们设计并制造了一种具有多个微约束通道结构的微流控芯片，提供细胞生物力学的同时多参数评估，包括弹性模量、恢复能力和变形能力。我们通过芯片比较了正常人胃粘膜上皮细胞（GES-1）和人胃癌细胞（AGS和MKN-45）的生物力学特性。结果表明，GES-1、AGS和MKN-45细胞的弹性模量依次降低，与其侵袭性和转移潜力相反，表明细胞弹性模量与恶性肿瘤之间呈负相关。同时，GES-1、AGS和MKN-45细胞的恢复能力和变形能力依次增加，表明细胞变形能力与恶性肿瘤之间呈正相关。此外，通过机器学习使用多个参数来区分胃癌细胞和正常胃细胞。胃癌细胞识别准确率达到94.8%以上。该研究在单细胞水平上深入了解胃癌转移的生物物理机制，并具有作为单细胞分析的有价值工具的巨大潜力，从而促进细胞表型的高精度和高通量区分。通过单标记分析不容易辨别。