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

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−1 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.

Critical review of single-cell mechanotyping approaches for biomedical applications

“…In doing so we highlight developing approaches that combine low fabrication cost, microfluidic cell handling and the ability to modulate deforming forces, 52,75 in addition to recent developments supporting the potential for machine learning to enhance characterization. [76][77][78][79][80] We also identify directions for future work to improve the throughput, data quality and reliability of single-cell mechanotyping measurements.…”

“…Machine learning techniques for single-cell mechanotyping. The potential for machine learning techniques to advance single-cell mechanotyping technology has recently been proposed, namely as a complimentary data analysis tool utilizing any number of cell deformation techniques, [76][77][78][79]190,191 or as a standalone system capable of estimating cell deformability via associated morphological features, free from any form of mechanical probing. 80 Such machine learning approaches offer a methodology for characterising intrinsic cell stiffness properties, which is otherwise complicated owing to the complex biophysical nature and interplay between cellular components, as well as difficulties in calibration and standardization of mechanotyping load cases.…”

Multiparameter Mechanical Phenotyping for Accurate Cell Identification Using High-Throughput Microfluidic Deformability Cytometry

“…Microfluidic deformability cytometry has been proven to be an effective tool for single-cell mechanical phenotyping, benefiting from the offered advantages of low-cost, high integration, small volume, and great applicability. − The active microfluidic deformability cytometry applies external force fields to induce the deformation of cells, , while the passive method employs hydrodynamic forces for cell deformation without the need of additional equipment. , However, the characterized mechanical properties are largely influenced by cell heterogeneity. Therefore, it is vital to find a suitable mechanical phenotyping method for accurately identifying cell types and determining cell statuses.…”