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Sequentially addressable dielectrophoretic array for high-throughput sorting of large-volume biological compartments
Author(s) -
Akihiro Isozaki,
Yuta Nakagawa,
Mun Hong Loo,
Yosuke Shibata,
Naoki Tanaka,
D. L. Setyaningrum,
Jee-Woong Park,
Yoshitaka Shirasaki,
Hideharu Mikami,
Dou Huang,
Hoilun Tsoi,
Carson T. Riche,
Taku Ota,
Hiromi Miwa,
Y. Kanda,
Takuro Ito,
Koji Yamada,
Osamu Iwata,
Kengo Suzuki,
Shinsuke Ohnuki,
Yoshikazu Ohya,
Yuichi Kato,
Tomohisa Hasunuma,
Souta Matsusaka,
Mai Yamagishi,
Masayuki Yazawa,
Sotaro Uemura,
Kazuya Nagasawa,
Hiroshi Watarai,
Dino Di Carlo,
Keisuke Goda
Publication year - 2020
Publication title -
science advances
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.928
H-Index - 146
ISSN - 2375-2548
DOI - 10.1126/sciadv.aba6712
Subject(s) - microfluidics , sorting , throughput , nanotechnology , computer science , dielectrophoresis , electrode array , lab on a chip , limiting , biological system , materials science , chemistry , electrode , biology , engineering , wireless , mechanical engineering , telecommunications , programming language
Droplet microfluidics has become a powerful tool in precision medicine, green biotechnology, and cell therapy for single-cell analysis and selection by virtue of its ability to effectively confine cells. However, there remains a fundamental trade-off between droplet volume and sorting throughput, limiting the advantages of droplet microfluidics to small droplets (<10 pl) that are incompatible with long-term maintenance and growth of most cells. We present a sequentially addressable dielectrophoretic array (SADA) sorter to overcome this problem. The SADA sorter uses an on-chip array of electrodes activated and deactivated in a sequence synchronized to the speed and position of a passing target droplet to deliver an accumulated dielectrophoretic force and gently pull it in the direction of sorting in a high-speed flow. We use it to demonstrate large-droplet sorting with ~20-fold higher throughputs than conventional techniques and apply it to long-term single-cell analysis of based on their growth rate.

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