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Issue 23, 2018
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High throughput single cell separation and identification using a self-priming isometric and Equant screw valve-based (SIES) microfluidic chip

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Abstract

The emergence of various single cell separation and identification platforms has greatly promoted the development of single cell research. Among these platforms, microfluidic chip-based strategies occupy a significant position in single cell separation and identification. Here, we proposed a self-priming isometric and Equant screw valve-based microfluidic chip (SIES chip) for high throughput single cell isolation and identification. With several special designs, such as a peripheral water tank to balance negative pressure distribution in a marginal area of the chip, a screw valve to preserve the suction power during the step-by-step sample loading, and multistage branching “T” shape channels to separate cells evenly into the chambers, up to 2000 single cells can be well dispersed and analyzed at the same time using this chip. We applied this chip for the isolation and identification of single A549 cells targeting the activated leukocyte cell adhesion molecule (ALCAM) gene. The results showed that only a small proportion (approximately 5.1%) of A549 cells expressed ALCAM, which can potentially provide a reference for A549 cell reclassification. Besides being inexpensive, user-friendly and portable, our chip can be used in some resource-limited settings and may have a great potential in POC (Point-of-Care) applications.

Graphical abstract: High throughput single cell separation and identification using a self-priming isometric and Equant screw valve-based (SIES) microfluidic chip

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Publication details

The article was received on 31 Jul 2018, accepted on 30 Sep 2018 and first published on 02 Oct 2018


Article type: Paper
DOI: 10.1039/C8AN01464G
Citation: Analyst, 2018,143, 5792-5798
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    High throughput single cell separation and identification using a self-priming isometric and Equant screw valve-based (SIES) microfluidic chip

    J. Hu, Y. Xu, T. Gou, S. Zhou and Y. Mu, Analyst, 2018, 143, 5792
    DOI: 10.1039/C8AN01464G

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