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Deterministic droplet codingviaacoustofluidics
Author(s) -
Peiran Zhang,
Wei Wang,
Hai Fu,
Joseph Rich,
Xingyu Su,
Hunter Bachman,
Jianping Xia,
Jinxin Zhang,
Shuaiguo Zhao,
Jia Zhou,
Tony Jun Huang
Publication year - 2020
Publication title -
lab on a chip
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.064
H-Index - 210
eISSN - 1473-0197
pISSN - 1473-0189
DOI - 10.1039/d0lc00538j
Subject(s) - coding (social sciences) , encoding (memory) , sequence (biology) , computer science , biology , genetics , mathematics , artificial intelligence , statistics
Droplet microfluidics has become an indispensable tool for biomedical research and lab-on-a-chip applications owing to its unprecedented throughput, precision, and cost-effectiveness. Although droplets can be generated and screened in a high-throughput manner, the inability to label the inordinate amounts of droplets hinders identifying the individual droplets after generation. Herein, we demonstrate an acoustofluidic platform that enables on-demand, real-time dispensing, and deterministic coding of droplets based on their volumes. By dynamically splitting the aqueous flow using an oil jet triggered by focused traveling surface acoustic waves, a sequence of droplets with deterministic volumes can be continuously dispensed at a throughput of 100 Hz. These sequences encode barcoding information through the combination of various droplet lengths. As a proof-of-concept, we encoded droplet sequences into end-to-end packages (e.g., a series of 50 droplets), which consisted of an address barcode with binary volumetric combinations and a sample package with consistent volumes for hosting analytes. This acoustofluidics-based, deterministic droplet coding technique enables the tagging of droplets with high capacity and high error-tolerance, and can potentially benefit various applications involving single cell phenotyping and multiplexed screening.

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