Intelligent image-activated cell sorting 2.0

Akihiro Isozaki; Hideharu Mikami; Hiroshi Tezuka; Hiroki Matsumura; Kangrui Huang; Marino Akamine; Kotaro Hiramatsu; Takanori Iino; Takuro Ito; Hiroshi Karakawa; Yusuke Kasai; Yan Li; Yuta Nakagawa; Shinsuke Ohnuki; Tadataka Ota; Yong Qian; Shinya Sakuma; Takeichiro Sekiya; Yoshitaka Shirasaki; Nobutake Suzuki; Ehsen Tayyabi; Tsubasa Wakamiya; Muzhen Xu; Mai Yamagishi; Haochen Yan; Qiang Yu; Sheng Yan; Dan Yuan; Wei Zhang; Yaqi Zhao; Fumihito Arai; Robert E. Campbell; Christophe Danelon; Dino Di Carlo; Kei Hiraki; Yu Hoshino; Yoichiroh Hosokawa; Mary Inaba; Atsuhiro Nakagawa; Yoshikazu Ohya; Minoru Oikawa; Sotaro Uemura; Yasuyuki Ozeki; Takeaki Sugimura; Nao Nitta; Keisuke Goda

doi:10.1039/D0LC00080A

Intelligent image-activated cell sorting 2.0†

Akihiro Isozaki,

^ab Hideharu Mikami,

^a Hiroshi Tezuka,^c Hiroki Matsumura,

^a Kangrui Huang,

^a Marino Akamine,^a Kotaro Hiramatsu,

^a Takanori Iino,

^d Takuro Ito,^ae Hiroshi Karakawa,^a Yusuke Kasai,^f Yan Li,^g Yuta Nakagawa,

^a Shinsuke Ohnuki,^h Tadataka Ota,^a Yong Qian,^g Shinya Sakuma,

^f Takeichiro Sekiya,^d Yoshitaka Shirasaki,

ⁱ Nobutake Suzuki,ⁱ Ehsen Tayyabi,^j Tsubasa Wakamiya,^a Muzhen Xu,

^a Mai Yamagishi,

ⁱ Haochen Yan,

^a Qiang Yu,^d Sheng Yan,

^a Dan Yuan,

^a Wei Zhang,^g Yaqi Zhao,

^a Fumihito Arai,^f Robert E. Campbell,

^ag Christophe Danelon,^k Dino Di Carlo,^almn Kei Hiraki,^a Yu Hoshino,

^o Yoichiroh Hosokawa,^p Mary Inaba,^c Atsuhiro Nakagawa,^q Yoshikazu Ohya,^hr Minoru Oikawa,^s Sotaro Uemura,ⁱ Yasuyuki Ozeki,

^d Takeaki Sugimura,

^aet Nao Nitta

^aet and Keisuke Goda

*^aelu

Author affiliations

* Corresponding authors

^a Department of Chemistry, The University of Tokyo, Tokyo 113-0033, Japan
E-mail: goda@chem.s.u-tokyo.ac.jp

^b Kanagawa Institute of Industrial Science and Technology, 705-1 Shimoimaizumi, Ebina, Kanagawa, Japan

^c Department of Creative Informatics, The University of Tokyo, Tokyo 113-0033, Japan

^d Department of Electrical Engineering and Information Systems, The University of Tokyo, Tokyo 113-8656, Japan

^e Japan Science and Technology Agency, Kawaguchi 332-0012, Japan

^f Department of Micro-Nano Mechanical Science and Engineering, Nagoya University, Nagoya 464-8601, Japan

^g Department of Chemistry, University of Alberta, Edmonton T6G 2G2, Canada

^h Department of Integrated Biosciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8562, Japan

ⁱ Department of Biological Sciences, The University of Tokyo, Tokyo 113-0033, Japan

^j Department of Chemistry, University of Toronto, Ontario M5S 3H6, Canada

^k Department of Bionanoscience, Delft University of Technology, van der Maasweg 9, Delft 2629 HZ, The Netherlands

^l Department of Bioengineering, University of California, Los Angeles, California 90095, USA

^m Department of Mechanical Engineering, University of California, Los Angeles, California 90095, USA

ⁿ California NanoSystems Institute, University of California, Los Angeles, California 90095, USA

^o Department of Chemical Engineering, Kyushu University, Fukuoka 819-0395, Japan

^p Division of Materials Science, Nara Institute of Science and Technology, Ikoma 630-0192, Japan

^q Department of Neurosurgery, Tohoku University, Sendai 980-8577, Japan

^r AIST-UTokyo Advanced Operando-Measurement Technology Open Innovation Laboratory (OPERANDO-OIL), National Institute of Advanced Industrial Science and Technology (AIST), 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8589, Japan

^s Natural Sciences Cluster, Sciences Unit, Kochi University, Kochi 780-8520, Japan

^t CYBO, Tokyo 101-0022, Japan

^u Institute of Technological Sciences, Wuhan University, Hubei 430072, China

Abstract

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 ∼2000 events per second and a high sensitivity of ∼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.

This article is part of the themed collection: Single Cell Analysis

Lab on a Chip

Intelligent image-activated cell sorting 2.0†

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