Intelligent image-activated cell sorting 2.0

被引：108

作者：

Isozaki, Akihiro ^{[1
,2
]}

Mikami, Hideharu ^{[1
]}

Tezuka, Hiroshi ^{[3
]}

Matsumura, Hiroki ^{[1
]}

Huang, Kangrui ^{[1
]}

Akamine, Marino ^{[1
]}

Hiramatsu, Kotaro ^{[1
]}

Iino, Takanori ^{[4
]}

Ito, Takuro ^{[1
,5
]}

Karakawa, Hiroshi ^{[1
]}

Kasai, Yusuke ^{[6
]}

Li, Yan ^{[7
]}

Nakagawa, Yuta ^{[1
]}

Ohnuki, Shinsuke ^{[8
]}

Ota, Tadataka ^{[1
]}

Qian, Yong ^{[7
]}

Sakuma, Shinya ^{[6
]}

Sekiya, Takeichiro ^{[4
]}

Shirasaki, Yoshitaka ^{[9
]}

Suzuki, Nobutake ^{[9
]}

Tayyabi, Ehsen ^{[10
]}

Wakamiya, Tsubasa ^{[1
]}

Xu, Muzhen ^{[1
]}

Yamagishi, Mai ^{[9
]}

Yan, Haochen ^{[1
]}

Yu, Qiang ^{[4
]}

Yan, Sheng ^{[1
]}

Yuan, Dan ^{[1
]}

Zhang, Wei ^{[7
]}

Zhao, Yaqi ^{[1
]}

Arai, Fumihito ^{[6
]}

Campbell, Robert E. ^{[1
,7
]}

Danelon, Christophe ^{[11
]}

Di Carlo, Dino ^{[1
,12
,13
,14
]}

Hiraki, Kei ^{[1
]}

Hoshino, Yu ^{[15
]}

Hosokawa, Yoichiroh ^{[16
]}

Inaba, Mary ^{[3
]}

Nakagawa, Atsuhiro ^{[17
]}

Ohya, Yoshikazu ^{[8
,18
]}

Oikawa, Minoru ^{[19
]}

Uemura, Sotaro ^{[9
]}

Ozeki, Yasuyuki ^{[4
]}

Sugimura, Takeaki ^{[1
,5
,20
]}

Nitta, Nao ^{[1
,5
,20
]}

Goda, Keisuke ^{[1
,5
,12
,21
]}

机构：

[1] Univ Tokyo, Dept Chem, Tokyo 1130033, Japan

[2] Kanagawa Inst Ind Sci & Technol, 705-1 Shimoimaizumi, Ebina, Kanagawa 2430435, Japan

[3] Univ Tokyo, Dept Creat Informat, Tokyo 1130033, Japan

[4] Univ Tokyo, Dept Elect Engn & Informat Syst, Tokyo 1138656, Japan

[5] Japan Sci & Technol Agcy, Kawaguchi, Saitama 3320012, Japan

[6] Nagoya Univ, Dept Micronano Mech Sci & Engn, Nagoya, Aichi 4648601, Japan

[7] Univ Alberta, Dept Chem, Edmonton, AB T6G 2G2, Canada

[8] Univ Tokyo, Dept Integrated Biosci, 5-1-5 Kashiwanoha, Kashiwa, Chiba 2778562, Japan

[9] Univ Tokyo, Dept Biol Sci, Tokyo 1130033, Japan

[10] Univ Toronto, Dept Chem, Toronto, ON M5S 3H6, Canada

[11] Delft Univ Technol, Dept Bionanosci, van der Maasweg 9, NL-2629 HZ Delft, Netherlands

[12] Univ Calif Los Angeles, Dept Bioengn, Los Angeles, CA 90095 USA

[13] Univ Calif Los Angeles, Dept Mech Engn, Los Angeles, CA 90095 USA

[14] Univ Calif Los Angeles, Calif NanoSyst Inst, Los Angeles, CA 90095 USA

[15] Kyushu Univ, Dept Chem Engn, Fukuoka 8190395, Japan

[16] Nara Inst Sci & Technol, Div Mat Sci, Ikoma 6300192, Japan

[17] Tohoku Univ, Dept Neurosurg, Sendai, Miyagi 9808577, Japan

[18] Natl Inst Adv Ind Sci & Technol, AIST UTokyo Adv Operandomeasurement Technol Open, 5-1-5 Kashiwanoha, Kashiwa, Chiba 2778589, Japan

[19] Kochi Univ, Sci Unit, Nat Sci Cluster, Kochi 7808520, Japan

[20] CYBO, Tokyo 1010022, Japan

[21] Wuhan Univ, Inst Technol Sci, Wuhan 430072, Hubei, Peoples R China

来源：

LAB ON A CHIP | 2020年 / 20卷 / 13期

关键词：

FLOW-CYTOMETRY; IDENTIFICATION; FLUORESCENT; MICROSCOPY;

D O I：

10.1039/d0lc00080a

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

The advent of intelligent image-activated cell sorting (iIACS) has enabled high-throughput intelligent image-based sorting of single live cells from heterogeneous populations. iIACS is an on-chip microfluidic technology that builds on a seamless integration of a high-throughput fluorescence microscope, cell focuser, cell sorter, and deep neural network on a hybrid software-hardware data management architecture, thereby providing the combined merits of optical microscopy, fluorescence-activated cell sorting (FACS), and deep learning. Here we report an iIACS machine that far surpasses the state-of-the-art iIACS machine in system performance in order to expand the range of applications and discoveries enabled by the technology. Specifically, it provides a high throughput of similar to 2000 events per second and a high sensitivity of similar to 50 molecules of equivalent soluble fluorophores (MESFs), both of which are 20 times superior to those achieved in previous reports. This is made possible by employing (i) an image-sensor-based optomechanical flow imaging method known as virtual-freezing fluorescence imaging and (ii) a real-time intelligent image processor on an 8-PC server equipped with 8 multi-core CPUs and GPUs for intelligent decision-making, in order to significantly boost the imaging performance and computational power of the iIACS machine. We characterize the iIACS machine with fluorescent particles and various cell types and show that the performance of the iIACS machine is close to its achievable design specification. Equipped with the improved capabilities, this new generation of the iIACS technology holds promise for diverse applications in immunology, microbiology, stem cell biology, cancer biology, pathology, and synthetic biology.

引用

页码：2263 / 2273

页数：11

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