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惯性多力变形细胞术用于高通量、高精度和高适用性的肿瘤细胞力学分析。

Inertial Multi-Force Deformability Cytometry for High-Throughput, High-Accuracy, and High-Applicability Tumor Cell Mechanotyping.

机构信息

School of Mechanical Engineering, and Jiangsu Key Laboratory for Design and Manufacture of Micro-Nano Biomedical Instruments, Southeast University, Nanjing, 211189, China.

出版信息

Small. 2024 Feb;20(7):e2303962. doi: 10.1002/smll.202303962. Epub 2023 Oct 3.

Abstract

Previous on-chip technologies for characterizing the cellular mechanical properties often suffer from a low throughput and limited sensitivity. Herein, an inertial multi-force deformability cytometry (IMFDC) is developed for high-throughput, high-accuracy, and high-applicability tumor cell mechanotyping. Three different deformations, including shear deformations and stretch deformations under different forces, are integrated with the IMFDC. The 3D inertial focusing of cells enables the cells to deform by an identical fluid flow, and 10 parameters, such as cell area, perimeter, deformability, roundness, and rectangle deformability, are obtained in three deformations. The IMFDC is able to evaluate the deformability of different cells that are sensitive to different forces on a single chip, demonstrating the high applicability of the IMFDC in analyzing different cell lines. In identifying cell types, the three deformations exhibit different mechanical responses to cells with different sizes and deformability. A discrimination accuracy of ≈93% for both MDA-MB-231 and MCF-10A cells and a throughput of ≈500 cells s can be achieved using the multiple-parameters-based machine learning model. Finally, the mechanical properties of metastatic tumor cells in pleural and peritoneal effusions are characterized, enabling the practical application of the IMFDC in clinical cancer diagnosis.

摘要

先前用于表征细胞机械特性的片上技术通常存在通量低和灵敏度有限的问题。在此,开发了一种用于高通量、高精度和高适用性的肿瘤细胞机械分型的惯性多力变形细胞术(IMFDC)。该技术整合了三种不同的变形,包括在不同力下的剪切变形和拉伸变形。细胞的 3D 惯性聚焦使细胞能够通过相同的流体流动变形,并且在三种变形中获得了 10 个参数,如细胞面积、周长、变形性、圆形度和矩形变形性。IMFDC 能够在单个芯片上评估对不同力敏感的不同细胞的变形性,证明了 IMFDC 在分析不同细胞系方面的高适用性。在识别细胞类型时,三种变形对不同大小和变形性的细胞表现出不同的力学响应。使用基于多参数的机器学习模型,可以实现对 MDA-MB-231 和 MCF-10A 细胞的≈93%的区分准确率和约 500 个细胞/秒的通量。最后,对胸腔和腹腔积液中的转移性肿瘤细胞的机械特性进行了表征,使 IMFDC 能够在临床癌症诊断中得到实际应用。

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