Intelligent image-activated cell sorting 2.0

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 microsco...

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Veröffentlicht in:Lab on a chip 2020-06, Vol.2 (13), p.2263-2273
Hauptverfasser: Isozaki, Akihiro, Mikami, Hideharu, Tezuka, Hiroshi, Matsumura, Hiroki, Huang, Kangrui, Akamine, Marino, Hiramatsu, Kotaro, Iino, Takanori, Ito, Takuro, Karakawa, Hiroshi, Kasai, Yusuke, Li, Yan, Nakagawa, Yuta, Ohnuki, Shinsuke, Ota, Tadataka, Qian, Yong, Sakuma, Shinya, Sekiya, Takeichiro, Shirasaki, Yoshitaka, Suzuki, Nobutake, Tayyabi, Ehsen, Wakamiya, Tsubasa, Xu, Muzhen, Yamagishi, Mai, Yan, Haochen, Yu, Qiang, Yan, Sheng, Yuan, Dan, Zhang, Wei, Zhao, Yaqi, Arai, Fumihito, Campbell, Robert E, Danelon, Christophe, Di Carlo, Dino, Hiraki, Kei, Hoshino, Yu, Hosokawa, Yoichiroh, Inaba, Mary, Nakagawa, Atsuhiro, Ohya, Yoshikazu, Oikawa, Minoru, Uemura, Sotaro, Ozeki, Yasuyuki, Sugimura, Takeaki, Nitta, Nao, Goda, Keisuke
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container_title Lab on a chip
container_volume 2
creator Isozaki, Akihiro
Mikami, Hideharu
Tezuka, Hiroshi
Matsumura, Hiroki
Huang, Kangrui
Akamine, Marino
Hiramatsu, Kotaro
Iino, Takanori
Ito, Takuro
Karakawa, Hiroshi
Kasai, Yusuke
Li, Yan
Nakagawa, Yuta
Ohnuki, Shinsuke
Ota, Tadataka
Qian, Yong
Sakuma, Shinya
Sekiya, Takeichiro
Shirasaki, Yoshitaka
Suzuki, Nobutake
Tayyabi, Ehsen
Wakamiya, Tsubasa
Xu, Muzhen
Yamagishi, Mai
Yan, Haochen
Yu, Qiang
Yan, Sheng
Yuan, Dan
Zhang, Wei
Zhao, Yaqi
Arai, Fumihito
Campbell, Robert E
Danelon, Christophe
Di Carlo, Dino
Hiraki, Kei
Hoshino, Yu
Hosokawa, Yoichiroh
Inaba, Mary
Nakagawa, Atsuhiro
Ohya, Yoshikazu
Oikawa, Minoru
Uemura, Sotaro
Ozeki, Yasuyuki
Sugimura, Takeaki
Nitta, Nao
Goda, Keisuke
description 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. The upgraded version of intelligent image-activated cell sorting (iIACS) has enabled higher-throughput and more sensitive intelligent image-based sorting of single live cells from heterogeneous populations.
doi_str_mv 10.1039/d0lc00080a
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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. 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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. The upgraded version of intelligent image-activated cell sorting (iIACS) has enabled higher-throughput and more sensitive intelligent image-based sorting of single live cells from heterogeneous populations.</description><subject>Artificial neural networks</subject><subject>Biology</subject><subject>Chemical compounds</subject><subject>Decision making</subject><subject>Design specifications</subject><subject>Fluorescence</subject><subject>Freezing</subject><subject>Imaging</subject><subject>Immunology</subject><subject>Machine learning</subject><subject>Microbiology</subject><subject>Microfluidics</subject><subject>Microprocessors</subject><subject>Optical microscopy</subject><subject>Stem cells</subject><issn>1473-0197</issn><issn>1473-0189</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNp9kUtLAzEUhYMotj427pURNyJOvXlNJstSX4WCG12HzExSpkxnapIR_PemtlZw4eoGzpfDuecidIZhhIHKuwqaEgBy0HtoiJmgKeBc7u_eUgzQkfcLAMxZlh-iASWMSyKyIbqdtsE0TT03bUjqpZ6bVJeh_tDBVEkZlcR3LtTtPCEjOEEHVjfenG7nMXp7fHidPKezl6fpZDxLSyZESDUtCBSmzLTAlDErRcyBpcSFJZUBy4WpaCWlIDkHamkEOc2g4JJazLWhx-h647ty3XtvfFDL2q_D6NZ0vVeEQTSM37OIXv1BF13v2pguUjjnEufAInWzoUrXee-MVSsXl3WfCoNad6juYTb57nAc4YutZV8sTbVDf0qLwPkGcL7cqb9HiPrlf7paVZZ-AcZ5fdY</recordid><startdate>20200630</startdate><enddate>20200630</enddate><creator>Isozaki, Akihiro</creator><creator>Mikami, Hideharu</creator><creator>Tezuka, Hiroshi</creator><creator>Matsumura, Hiroki</creator><creator>Huang, Kangrui</creator><creator>Akamine, Marino</creator><creator>Hiramatsu, Kotaro</creator><creator>Iino, Takanori</creator><creator>Ito, Takuro</creator><creator>Karakawa, Hiroshi</creator><creator>Kasai, Yusuke</creator><creator>Li, Yan</creator><creator>Nakagawa, Yuta</creator><creator>Ohnuki, Shinsuke</creator><creator>Ota, Tadataka</creator><creator>Qian, Yong</creator><creator>Sakuma, Shinya</creator><creator>Sekiya, Takeichiro</creator><creator>Shirasaki, Yoshitaka</creator><creator>Suzuki, Nobutake</creator><creator>Tayyabi, Ehsen</creator><creator>Wakamiya, Tsubasa</creator><creator>Xu, Muzhen</creator><creator>Yamagishi, Mai</creator><creator>Yan, Haochen</creator><creator>Yu, Qiang</creator><creator>Yan, Sheng</creator><creator>Yuan, Dan</creator><creator>Zhang, Wei</creator><creator>Zhao, Yaqi</creator><creator>Arai, Fumihito</creator><creator>Campbell, Robert E</creator><creator>Danelon, Christophe</creator><creator>Di Carlo, Dino</creator><creator>Hiraki, Kei</creator><creator>Hoshino, Yu</creator><creator>Hosokawa, Yoichiroh</creator><creator>Inaba, Mary</creator><creator>Nakagawa, Atsuhiro</creator><creator>Ohya, Yoshikazu</creator><creator>Oikawa, Minoru</creator><creator>Uemura, Sotaro</creator><creator>Ozeki, Yasuyuki</creator><creator>Sugimura, Takeaki</creator><creator>Nitta, Nao</creator><creator>Goda, Keisuke</creator><general>Royal Society of Chemistry</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>7TB</scope><scope>7U5</scope><scope>8FD</scope><scope>FR3</scope><scope>L7M</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0002-4145-7264</orcidid><orcidid>https://orcid.org/0000-0002-9004-0799</orcidid><orcidid>https://orcid.org/0000-0003-0391-9832</orcidid><orcidid>https://orcid.org/0000-0001-9628-6979</orcidid><orcidid>https://orcid.org/0000-0002-5472-232X</orcidid><orcidid>https://orcid.org/0000-0001-7297-6713</orcidid><orcidid>https://orcid.org/0000-0003-2553-9005</orcidid><orcidid>https://orcid.org/0000-0002-3808-1630</orcidid><orcidid>https://orcid.org/0000-0002-9093-9016</orcidid><orcidid>https://orcid.org/0000-0003-0604-092X</orcidid><orcidid>https://orcid.org/0000-0002-3306-9077</orcidid><orcidid>https://orcid.org/0000-0002-4449-5899</orcidid><orcidid>https://orcid.org/0000-0002-4364-3247</orcidid><orcidid>https://orcid.org/0000-0002-3744-2799</orcidid><orcidid>https://orcid.org/0000-0001-7433-8379</orcidid><orcidid>https://orcid.org/0000-0002-5550-9483</orcidid><orcidid>https://orcid.org/0000-0003-3638-6956</orcidid><orcidid>https://orcid.org/0000-0001-7873-7246</orcidid><orcidid>https://orcid.org/0000-0001-6302-6038</orcidid><orcidid>https://orcid.org/0000-0003-0767-019X</orcidid><orcidid>https://orcid.org/0000-0002-3463-0786</orcidid></search><sort><creationdate>20200630</creationdate><title>Intelligent image-activated cell sorting 2.0</title><author>Isozaki, Akihiro ; Mikami, Hideharu ; Tezuka, Hiroshi ; Matsumura, Hiroki ; Huang, Kangrui ; Akamine, Marino ; Hiramatsu, Kotaro ; Iino, Takanori ; Ito, Takuro ; Karakawa, Hiroshi ; Kasai, Yusuke ; Li, Yan ; Nakagawa, Yuta ; Ohnuki, Shinsuke ; Ota, Tadataka ; Qian, Yong ; Sakuma, Shinya ; Sekiya, Takeichiro ; Shirasaki, Yoshitaka ; Suzuki, Nobutake ; Tayyabi, Ehsen ; Wakamiya, Tsubasa ; Xu, Muzhen ; Yamagishi, Mai ; Yan, Haochen ; Yu, Qiang ; Yan, Sheng ; Yuan, Dan ; Zhang, Wei ; Zhao, Yaqi ; Arai, Fumihito ; Campbell, Robert E ; Danelon, Christophe ; Di Carlo, Dino ; Hiraki, Kei ; Hoshino, Yu ; Hosokawa, Yoichiroh ; Inaba, Mary ; Nakagawa, Atsuhiro ; Ohya, Yoshikazu ; Oikawa, Minoru ; Uemura, Sotaro ; Ozeki, Yasuyuki ; Sugimura, Takeaki ; Nitta, Nao ; Goda, Keisuke</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c477t-a3b20bec6a71344f974731991bf2de0f57ed3d99728503f3c6a5360b593f15ae3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Artificial neural networks</topic><topic>Biology</topic><topic>Chemical compounds</topic><topic>Decision making</topic><topic>Design specifications</topic><topic>Fluorescence</topic><topic>Freezing</topic><topic>Imaging</topic><topic>Immunology</topic><topic>Machine learning</topic><topic>Microbiology</topic><topic>Microfluidics</topic><topic>Microprocessors</topic><topic>Optical microscopy</topic><topic>Stem cells</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Isozaki, Akihiro</creatorcontrib><creatorcontrib>Mikami, Hideharu</creatorcontrib><creatorcontrib>Tezuka, Hiroshi</creatorcontrib><creatorcontrib>Matsumura, Hiroki</creatorcontrib><creatorcontrib>Huang, Kangrui</creatorcontrib><creatorcontrib>Akamine, Marino</creatorcontrib><creatorcontrib>Hiramatsu, Kotaro</creatorcontrib><creatorcontrib>Iino, Takanori</creatorcontrib><creatorcontrib>Ito, Takuro</creatorcontrib><creatorcontrib>Karakawa, Hiroshi</creatorcontrib><creatorcontrib>Kasai, Yusuke</creatorcontrib><creatorcontrib>Li, Yan</creatorcontrib><creatorcontrib>Nakagawa, Yuta</creatorcontrib><creatorcontrib>Ohnuki, Shinsuke</creatorcontrib><creatorcontrib>Ota, Tadataka</creatorcontrib><creatorcontrib>Qian, Yong</creatorcontrib><creatorcontrib>Sakuma, Shinya</creatorcontrib><creatorcontrib>Sekiya, Takeichiro</creatorcontrib><creatorcontrib>Shirasaki, Yoshitaka</creatorcontrib><creatorcontrib>Suzuki, Nobutake</creatorcontrib><creatorcontrib>Tayyabi, Ehsen</creatorcontrib><creatorcontrib>Wakamiya, Tsubasa</creatorcontrib><creatorcontrib>Xu, Muzhen</creatorcontrib><creatorcontrib>Yamagishi, Mai</creatorcontrib><creatorcontrib>Yan, Haochen</creatorcontrib><creatorcontrib>Yu, Qiang</creatorcontrib><creatorcontrib>Yan, Sheng</creatorcontrib><creatorcontrib>Yuan, Dan</creatorcontrib><creatorcontrib>Zhang, Wei</creatorcontrib><creatorcontrib>Zhao, Yaqi</creatorcontrib><creatorcontrib>Arai, Fumihito</creatorcontrib><creatorcontrib>Campbell, Robert E</creatorcontrib><creatorcontrib>Danelon, Christophe</creatorcontrib><creatorcontrib>Di Carlo, Dino</creatorcontrib><creatorcontrib>Hiraki, Kei</creatorcontrib><creatorcontrib>Hoshino, Yu</creatorcontrib><creatorcontrib>Hosokawa, Yoichiroh</creatorcontrib><creatorcontrib>Inaba, Mary</creatorcontrib><creatorcontrib>Nakagawa, Atsuhiro</creatorcontrib><creatorcontrib>Ohya, Yoshikazu</creatorcontrib><creatorcontrib>Oikawa, Minoru</creatorcontrib><creatorcontrib>Uemura, Sotaro</creatorcontrib><creatorcontrib>Ozeki, Yasuyuki</creatorcontrib><creatorcontrib>Sugimura, Takeaki</creatorcontrib><creatorcontrib>Nitta, Nao</creatorcontrib><creatorcontrib>Goda, Keisuke</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>Electronics &amp; Communications Abstracts</collection><collection>Mechanical &amp; Transportation Engineering Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>MEDLINE - Academic</collection><jtitle>Lab on a chip</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Isozaki, Akihiro</au><au>Mikami, Hideharu</au><au>Tezuka, Hiroshi</au><au>Matsumura, Hiroki</au><au>Huang, Kangrui</au><au>Akamine, Marino</au><au>Hiramatsu, Kotaro</au><au>Iino, Takanori</au><au>Ito, Takuro</au><au>Karakawa, Hiroshi</au><au>Kasai, Yusuke</au><au>Li, Yan</au><au>Nakagawa, Yuta</au><au>Ohnuki, Shinsuke</au><au>Ota, Tadataka</au><au>Qian, Yong</au><au>Sakuma, Shinya</au><au>Sekiya, Takeichiro</au><au>Shirasaki, Yoshitaka</au><au>Suzuki, Nobutake</au><au>Tayyabi, Ehsen</au><au>Wakamiya, Tsubasa</au><au>Xu, Muzhen</au><au>Yamagishi, Mai</au><au>Yan, Haochen</au><au>Yu, Qiang</au><au>Yan, Sheng</au><au>Yuan, Dan</au><au>Zhang, Wei</au><au>Zhao, Yaqi</au><au>Arai, Fumihito</au><au>Campbell, Robert E</au><au>Danelon, Christophe</au><au>Di Carlo, Dino</au><au>Hiraki, Kei</au><au>Hoshino, Yu</au><au>Hosokawa, Yoichiroh</au><au>Inaba, Mary</au><au>Nakagawa, Atsuhiro</au><au>Ohya, Yoshikazu</au><au>Oikawa, Minoru</au><au>Uemura, Sotaro</au><au>Ozeki, Yasuyuki</au><au>Sugimura, Takeaki</au><au>Nitta, Nao</au><au>Goda, Keisuke</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Intelligent image-activated cell sorting 2.0</atitle><jtitle>Lab on a chip</jtitle><addtitle>Lab Chip</addtitle><date>2020-06-30</date><risdate>2020</risdate><volume>2</volume><issue>13</issue><spage>2263</spage><epage>2273</epage><pages>2263-2273</pages><issn>1473-0197</issn><eissn>1473-0189</eissn><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. The upgraded version of intelligent image-activated cell sorting (iIACS) has enabled higher-throughput and more sensitive intelligent image-based sorting of single live cells from heterogeneous populations.</abstract><cop>England</cop><pub>Royal Society of Chemistry</pub><pmid>32459276</pmid><doi>10.1039/d0lc00080a</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0002-4145-7264</orcidid><orcidid>https://orcid.org/0000-0002-9004-0799</orcidid><orcidid>https://orcid.org/0000-0003-0391-9832</orcidid><orcidid>https://orcid.org/0000-0001-9628-6979</orcidid><orcidid>https://orcid.org/0000-0002-5472-232X</orcidid><orcidid>https://orcid.org/0000-0001-7297-6713</orcidid><orcidid>https://orcid.org/0000-0003-2553-9005</orcidid><orcidid>https://orcid.org/0000-0002-3808-1630</orcidid><orcidid>https://orcid.org/0000-0002-9093-9016</orcidid><orcidid>https://orcid.org/0000-0003-0604-092X</orcidid><orcidid>https://orcid.org/0000-0002-3306-9077</orcidid><orcidid>https://orcid.org/0000-0002-4449-5899</orcidid><orcidid>https://orcid.org/0000-0002-4364-3247</orcidid><orcidid>https://orcid.org/0000-0002-3744-2799</orcidid><orcidid>https://orcid.org/0000-0001-7433-8379</orcidid><orcidid>https://orcid.org/0000-0002-5550-9483</orcidid><orcidid>https://orcid.org/0000-0003-3638-6956</orcidid><orcidid>https://orcid.org/0000-0001-7873-7246</orcidid><orcidid>https://orcid.org/0000-0001-6302-6038</orcidid><orcidid>https://orcid.org/0000-0003-0767-019X</orcidid><orcidid>https://orcid.org/0000-0002-3463-0786</orcidid><oa>free_for_read</oa></addata></record>
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source Royal Society Of Chemistry Journals 2008-; Alma/SFX Local Collection
subjects Artificial neural networks
Biology
Chemical compounds
Decision making
Design specifications
Fluorescence
Freezing
Imaging
Immunology
Machine learning
Microbiology
Microfluidics
Microprocessors
Optical microscopy
Stem cells
title Intelligent image-activated cell sorting 2.0
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