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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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 |
format | Article |
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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><identifier>ISSN: 1473-0197</identifier><identifier>EISSN: 1473-0189</identifier><identifier>DOI: 10.1039/d0lc00080a</identifier><identifier>PMID: 32459276</identifier><language>eng</language><publisher>England: Royal Society of Chemistry</publisher><subject>Artificial neural networks ; Biology ; Chemical compounds ; Decision making ; Design specifications ; Fluorescence ; Freezing ; Imaging ; Immunology ; Machine learning ; Microbiology ; Microfluidics ; Microprocessors ; Optical microscopy ; Stem cells</subject><ispartof>Lab on a chip, 2020-06, Vol.2 (13), p.2263-2273</ispartof><rights>Copyright Royal Society of Chemistry 2020</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c477t-a3b20bec6a71344f974731991bf2de0f57ed3d99728503f3c6a5360b593f15ae3</citedby><cites>FETCH-LOGICAL-c477t-a3b20bec6a71344f974731991bf2de0f57ed3d99728503f3c6a5360b593f15ae3</cites><orcidid>0000-0002-4145-7264 ; 0000-0002-9004-0799 ; 0000-0003-0391-9832 ; 0000-0001-9628-6979 ; 0000-0002-5472-232X ; 0000-0001-7297-6713 ; 0000-0003-2553-9005 ; 0000-0002-3808-1630 ; 0000-0002-9093-9016 ; 0000-0003-0604-092X ; 0000-0002-3306-9077 ; 0000-0002-4449-5899 ; 0000-0002-4364-3247 ; 0000-0002-3744-2799 ; 0000-0001-7433-8379 ; 0000-0002-5550-9483 ; 0000-0003-3638-6956 ; 0000-0001-7873-7246 ; 0000-0001-6302-6038 ; 0000-0003-0767-019X ; 0000-0002-3463-0786</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27901,27902</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32459276$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><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><title>Intelligent image-activated cell sorting 2.0</title><title>Lab on a chip</title><addtitle>Lab Chip</addtitle><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.</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 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Keisuke</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Mechanical & 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> |
fulltext | fulltext |
identifier | ISSN: 1473-0197 |
ispartof | Lab on a chip, 2020-06, Vol.2 (13), p.2263-2273 |
issn | 1473-0197 1473-0189 |
language | eng |
recordid | cdi_pubmed_primary_32459276 |
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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