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Issue 21, 2017
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Deterministic droplet-based co-encapsulation and pairing of microparticles via active sorting and downstream merging

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Abstract

Co-encapsulation of two distinct particles within microfluidic droplets provides the means to achieve various high-throughput single-cell assays, such as biochemical reactions and cell–cell interactions in small isolated volumes. However, limited by the Poisson statistics, the co-encapsulation rate of the conventional co-flow approach is low even under optimal conditions. Only up to 13.5% of droplets precisely contain a pair of two distinct particles, while the rest, either being empty or encapsulating unpaired particles become wastes. Thus, the low co-encapsulation efficiency makes droplet-based assays impractical in biological applications involving low abundant bioparticles. In this paper, we present a highly promising droplet merging strategy to increase the co-encapsulation efficiency. Our method first enriches droplets exactly encapsulating a single particle via fluorescence or scattering-light activated sorting. Then, two droplets, each with a distinct particle, are precisely one-to-one paired and merged in a novel microwell device. This deterministic approach overcomes the Poisson statistics limitation facing conventional stochastic methods, yielding an up to 90% post-sorting particle capture rate and an overall 88.1% co-encapsulation rate. With its superior single-particle pairing performance, our system provides a promising technological platform to enable highly efficient microdroplet assays.

Graphical abstract: Deterministic droplet-based co-encapsulation and pairing of microparticles via active sorting and downstream merging

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Publication details

The article was received on 17 Jul 2017, accepted on 21 Sep 2017 and first published on 22 Sep 2017


Article type: Paper
DOI: 10.1039/C7LC00745K
Citation: Lab Chip, 2017,17, 3664-3671
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    Deterministic droplet-based co-encapsulation and pairing of microparticles via active sorting and downstream merging

    M. T. Chung, D. Núñez, D. Cai and K. Kurabayashi, Lab Chip, 2017, 17, 3664
    DOI: 10.1039/C7LC00745K

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