Microfluidic Impedance Cytometer with Inertial Focusing and Liquid Electrodes for High-Throughput Cell Counting and Discrimination.
Wenlai TangDezhi TangZhonghua NiNan XiangHong YiPublished in: Analytical chemistry (2017)
In this paper, we present a novel impedance microcytometer integrated with inertial focusing and liquid electrode techniques for high-throughput cell counting and discrimination. The inertial prefocusing unit orders cells into a determinate train to reduce the possibility of cell adhesions and ensure that only one cell passes through detection region at a time, which improves the accuracy of downstream detection. The liquid electrodes are constructed by inserting Ag/AgCl wires into the electrode chambers filled with flowing highly conductive electrolyte solutions, which have a high detection sensitivity while requiring a simple fabrication process. The effects of main sample flow rate, feed flow rate in electrode chambers, and feed solution type on measured impedance signals are experimentally explored. On the basis of the optimized system, we establish a linear relationship between the amplitude of impedance peaks and the volume of size-calibrated particles and achieve a high detection throughput of ∼5000 cells/s. Finally, using the calibrated microcytometer, we further investigate the size distributions of human breast tumor cells (MCF-7 cells) and leukocytes (white blood cells (WBCs)) and set a threshold amplitude to successfully distinguish the MCF-7 cells spiked in WBCs. Our impedance microcytometer may provide a potential tool for label-free cell enumeration and identification.
Keyphrases
- induced apoptosis
- single cell
- label free
- high throughput
- cell cycle arrest
- cell therapy
- ionic liquid
- mass spectrometry
- stem cells
- cell death
- endothelial cells
- signaling pathway
- computed tomography
- breast cancer cells
- magnetic resonance
- risk assessment
- solid state
- carbon nanotubes
- gold nanoparticles
- real time pcr
- reduced graphene oxide
- resting state
- pi k akt
- human health
- neural network