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Separation of malignant tumor cells (MTCs) from large background cells in untreated malignant pleural and peritoneal effusions (MPPEs) is critical for improving the sensitivity and efficiency of cytological diagnosis. Herein, we proposed a cascaded elasto-inertial cell separation (CEICS) device integrating an interfacial elasto-inertial microfluidic channel with a symmetric contraction expansion array (CEA) channel for pretreatment-free, high-recovery-ratio, and high-purity separation of MTCs from clinical MPPEs. First, the effects of flow-rate ratio, cell concentration, and cell size on separation performances in two single-stage channels were investigated. Then, the performances of the integrated CEICS device were characterized using blood cells spiked with three different tumor cells (MCF-7, MDA-MB-231, and A549 cells) at a high total throughput of 240 μL min−1. An average recovery ratio of ∼95% and an average purity of ∼61% for the three tumor cells were achieved. Finally, we successfully applied the CEICS device for the pretreatment-free separation of MTCs from clinical MPPEs of different cancers. Our CEICS device may provide a preparation tool for improving the sensitivity and efficiency of cytological examination. [ABSTRACT FROM AUTHOR]

Cell mechanical properties provide a label-free marker for indicating cell states and disease processes. Although microfluidic deformability cytometry has demonstrated great potential and successes in mechanical phenotyping in recent years, its universal applicability for characterizing multiple sizes of cells using a single device has not been realized. Herein, we propose high-throughput adjustable deformability cytometry integrated with three-dimensional (3D) elasto-inertial focusing and a virtual fluidic channel. By properly adjusting the flow ratio of the sample and sheath, the virtual fluidic channel in a wide solid channel can generate a strong shear force in the normal direction of the flow velocity and simultaneously squeeze cells from both sides to induce significant cell deformation. The combination of elasto-inertial focusing and a virtual fluidic channel provides a great hydrodynamic symmetrical force for inducing significant and homogeneous cell deformation. In addition, our deformability cytometry system not only achieves rapid and precise cell deformation, but also allows the adjustable detection of multiple sizes of cells at a high throughput of up to 3000 cells per second. The mini-bilateral segmentation network (mini-BiSeNet) was developed to identify cells and extract features quickly. The classification of different cell populations (A549, MCF-7, MDA-MB-231, and WBCs) was carried out based on the cell size and deformation. By applying deep learning to cell classification, a high accuracy reaching approximately 90% was achieved. We also revealed the potential of our deformability cytometry for characterizing pleural effusions. The flexibility of our deformability cytometry holds promise for the mechanical phenotyping and detection of various biological samples. [ABSTRACT FROM AUTHOR]

The differences in the cross-sectional positions of cells in the detection area have a severe negative impact on achieving accurate characterization of the impedance spectra of cells. Herein, we proposed a three-dimensional (3D) inertial focusing based impedance cytometer integrating sheath fluid compression and inertial focusing for the high-accuracy electrical characterization and identification of tumor cells. First, we studied the effects of the particle initial position and the sheath fluid compression on particle focusing. Then, the relationship of the particle height and the signal-to-noise ratio (SNR) of the impedance signal was explored. The results showed that efficient single-line focusing of 7-20 μm particles close to the electrodes was achieved and impedance signals with a high SNR and a low coefficient of variation (CV) were obtained. Finally, the electrical properties of three types of tumor cells (A549, MDA-MB-231, and UM-UC-3 cells) were accurately characterized. Machine learning algorithms were implemented to accurately identify tumor cells based on the amplitude and phase opacities at multiple frequencies. Compared with traditional two-dimensional (2D) inertial focusing, the identification accuracy of A549, MDA-MB-231, and UM-UC-3 cells using our 3D inertial focusing increased by 57.5%, 36.4% and 36.6%, respectively. The impedance cytometer enables the detection of cells with a wide size range without causing clogging and obtains high SNR signals, improving applicability to different complex biological samples and cell identification accuracy.