Details

Autor(en) / Beteiligte

• Tang, Dezhi
• Chen, Mu
• Han, Yu
• Xiang, Nan
• Ni, Zhonghua

Titel

Asymmetric serpentine microchannel based impedance cytometer enabling consistent transit and accurate characterization of tumor cells and blood cells

Ist Teil von

• Sensors and actuators. B, Chemical, 2021-06, Vol.336, p.129719, Article 129719

Ort / Verlag

Lausanne: Elsevier B.V

Erscheinungsjahr

2021

Link zum Volltext

Quelle

Elsevier ScienceDirect Journals Complete

Beschreibungen/Notizen

• •Serpentine microstructure and elasto-inertial focusing methods were integrated to manipulate cells’ focusing performance.•The manipulation of cells’ transit position improves the accuracy of impendence signals at multiple excitation frequencies.•Using machine learning methods, cell types can be differentiated by accurately detected impedance signatures. A consistent cell transit position is essential for accurately characterize cellular impedance signature. In this paper, we integrated asymmetric serpentine structure and elasto-inertial focusing methods to manipulate cells’ special transit position horizontally and vertically, and minimize the fluctuation of impendence responses at multiple excitation frequencies. The asymmetric serpentine structure is capable to rapidly align randomly distributed particles into a single stream horizontally, and the addition of hyaluronic acid (HA) in the sample fluid enables cells to focus in a single train in a wide range of flow rate. In the experiment section, we evaluated the alignment accuracy of asymmetric serpentine microchannel using standard particles ranging from 7 to 20 μm to mimic tumor cells. Compared with randomly distributed and dually aligned particles, the coefficient of variation (CV) of the impedance of particles is markedly reduced. To further prove this device can differentiate various cells, we tested breast tumor cells (MCF7), non-small lung tumor cells (A549) and white blood cells. Based on their electrical impedance signature including opacity and phase shift, a machine learning algorithm was used to identify cell types. This device is able to differentiate MCF7 cells with an accuracy of 95 % and differentiate A549 cells with an accuracy of 92.3 %. This device is able to efficiently focus tumor cells without sheath flow and accurately detect tumor cells, it has a great potential to discriminate, count and phenotype rare circulating tumor cells in patients’ blood.