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Accurately tracking single-cell movement trajectories in microfluidic cell sorting devices

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  • Jenny Jeong
  • Nicholas J Frohberg
  • Enlu Zhou
  • Todd Sulchek
  • Peng Qiu

Abstract

Microfluidics are routinely used to study cellular properties, including the efficient quantification of single-cell biomechanics and label-free cell sorting based on the biomechanical properties, such as elasticity, viscosity, stiffness, and adhesion. Both quantification and sorting applications require optimal design of the microfluidic devices and mathematical modeling of the interactions between cells, fluid, and the channel of the device. As a first step toward building such a mathematical model, we collected video recordings of cells moving through a ridged microfluidic channel designed to compress and redirect cells according to cell biomechanics. We developed an efficient algorithm that automatically and accurately tracked the cell trajectories in the recordings. We tested the algorithm on recordings of cells with different stiffness, and showed the correlation between cell stiffness and the tracked trajectories. Moreover, the tracking algorithm successfully picked up subtle differences of cell motion when passing through consecutive ridges. The algorithm for accurately tracking cell trajectories paves the way for future efforts of modeling the flow, forces, and dynamics of cell properties in microfluidics applications.

Suggested Citation

  • Jenny Jeong & Nicholas J Frohberg & Enlu Zhou & Todd Sulchek & Peng Qiu, 2018. "Accurately tracking single-cell movement trajectories in microfluidic cell sorting devices," PLOS ONE, Public Library of Science, vol. 13(2), pages 1-16, February.
  • Handle: RePEc:plo:pone00:0192463
    DOI: 10.1371/journal.pone.0192463
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    References listed on IDEAS

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    1. Robert J. Kimmerling & Gregory Lee Szeto & Jennifer W. Li & Alex S. Genshaft & Samuel W. Kazer & Kristofor R. Payer & Jacob de Riba Borrajo & Paul C. Blainey & Darrell J. Irvine & Alex K. Shalek & Sco, 2016. "A microfluidic platform enabling single-cell RNA-seq of multigenerational lineages," Nature Communications, Nature, vol. 7(1), pages 1-7, April.
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    Cited by:

    1. Manibarathi Vaithiyanathan & Nora Safa & Adam T Melvin, 2019. "FluoroCellTrack: An algorithm for automated analysis of high-throughput droplet microfluidic data," PLOS ONE, Public Library of Science, vol. 14(5), pages 1-22, May.

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