Machine-Learning-Assisted Microfluidic Nanoplasmonic Digital Immunoassay for Cytokine Storm Profiling in COVID-19 Patients
Cytokine storm, known as an exaggerated hyperactive immune response characterized by elevated release of cytokines, has been described as a feature associated with life-threatening complications in COVID-19 patients. A critical evaluation of a cytokine storm and its mechanistic linkage to COVID-19 r...
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Veröffentlicht in: | ACS nano 2021-11, Vol.15 (11), p.18023-18036 |
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creator | Gao, Zhuangqiang Song, Yujing Hsiao, Te Yi He, Jiacheng Wang, Chuanyu Shen, Jialiang MacLachlan, Alana Dai, Siyuan Singer, Benjamin H Kurabayashi, Katsuo Chen, Pengyu |
description | Cytokine storm, known as an exaggerated hyperactive immune response characterized by elevated release of cytokines, has been described as a feature associated with life-threatening complications in COVID-19 patients. A critical evaluation of a cytokine storm and its mechanistic linkage to COVID-19 requires innovative immunoassay technology capable of rapid, sensitive, selective detection of multiple cytokines across a wide dynamic range at high-throughput. In this study, we report a machine-learning-assisted microfluidic nanoplasmonic digital immunoassay to meet the rising demand for cytokine storm monitoring in COVID-19 patients. Specifically, the assay was carried out using a facile one-step sandwich immunoassay format with three notable features: (i) a microfluidic microarray patterning technique for high-throughput, multiantibody-arrayed biosensing chip fabrication; (ii) an ultrasensitive nanoplasmonic digital imaging technology utilizing 100 nm silver nanocubes (AgNCs) for signal transduction; (iii) a rapid and accurate machine-learning-based image processing method for digital signal analysis. The developed immunoassay allows simultaneous detection of six cytokines in a single run with wide working ranges of 1–10,000 pg mL–1 and ultralow detection limits down to 0.46–1.36 pg mL–1 using a minimum of 3 μL serum samples. The whole chip can afford a 6-plex assay of 8 different samples with 6 repeats in each sample for a total of 288 sensing spots in less than 100 min. The image processing method enhanced by convolutional neural network (CNN) dramatically shortens the processing time ∼6,000 fold with a much simpler procedure while maintaining high statistical accuracy compared to the conventional manual counting approach. The immunoassay was validated by the gold-standard enzyme-linked immunosorbent assay (ELISA) and utilized for serum cytokine profiling of COVID-19 positive patients. Our results demonstrate the nanoplasmonic digital immunoassay as a promising practical tool for comprehensive characterization of cytokine storm in patients that holds great promise as an intelligent immunoassay for next generation immune monitoring. |
doi_str_mv | 10.1021/acsnano.1c06623 |
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A critical evaluation of a cytokine storm and its mechanistic linkage to COVID-19 requires innovative immunoassay technology capable of rapid, sensitive, selective detection of multiple cytokines across a wide dynamic range at high-throughput. In this study, we report a machine-learning-assisted microfluidic nanoplasmonic digital immunoassay to meet the rising demand for cytokine storm monitoring in COVID-19 patients. Specifically, the assay was carried out using a facile one-step sandwich immunoassay format with three notable features: (i) a microfluidic microarray patterning technique for high-throughput, multiantibody-arrayed biosensing chip fabrication; (ii) an ultrasensitive nanoplasmonic digital imaging technology utilizing 100 nm silver nanocubes (AgNCs) for signal transduction; (iii) a rapid and accurate machine-learning-based image processing method for digital signal analysis. The developed immunoassay allows simultaneous detection of six cytokines in a single run with wide working ranges of 1–10,000 pg mL–1 and ultralow detection limits down to 0.46–1.36 pg mL–1 using a minimum of 3 μL serum samples. The whole chip can afford a 6-plex assay of 8 different samples with 6 repeats in each sample for a total of 288 sensing spots in less than 100 min. The image processing method enhanced by convolutional neural network (CNN) dramatically shortens the processing time ∼6,000 fold with a much simpler procedure while maintaining high statistical accuracy compared to the conventional manual counting approach. The immunoassay was validated by the gold-standard enzyme-linked immunosorbent assay (ELISA) and utilized for serum cytokine profiling of COVID-19 positive patients. Our results demonstrate the nanoplasmonic digital immunoassay as a promising practical tool for comprehensive characterization of cytokine storm in patients that holds great promise as an intelligent immunoassay for next generation immune monitoring.</description><identifier>ISSN: 1936-0851</identifier><identifier>ISSN: 1936-086X</identifier><identifier>EISSN: 1936-086X</identifier><identifier>DOI: 10.1021/acsnano.1c06623</identifier><identifier>PMID: 34714639</identifier><language>eng</language><publisher>United States: American Chemical Society</publisher><subject>COVID-19 - diagnosis ; Cytokine Release Syndrome - diagnosis ; Cytokines - analysis ; Humans ; Immunoassay - methods ; Machine Learning ; Microfluidics</subject><ispartof>ACS nano, 2021-11, Vol.15 (11), p.18023-18036</ispartof><rights>2021 American Chemical Society</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-a429t-4bc8022cf1a6062c9bbea8d02c4d6529296876b0d7a9b872ba5c23d699e041053</citedby><cites>FETCH-LOGICAL-a429t-4bc8022cf1a6062c9bbea8d02c4d6529296876b0d7a9b872ba5c23d699e041053</cites><orcidid>0000-0002-4721-6920 ; 0000-0001-7259-7182 ; 0000-0003-3380-872X ; 0000-0001-9097-1799 ; 0000-0002-9613-3590</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://pubs.acs.org/doi/pdf/10.1021/acsnano.1c06623$$EPDF$$P50$$Gacs$$H</linktopdf><linktohtml>$$Uhttps://pubs.acs.org/doi/10.1021/acsnano.1c06623$$EHTML$$P50$$Gacs$$H</linktohtml><link.rule.ids>230,314,780,784,885,2756,27067,27915,27916,56729,56779</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/34714639$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Gao, Zhuangqiang</creatorcontrib><creatorcontrib>Song, Yujing</creatorcontrib><creatorcontrib>Hsiao, Te Yi</creatorcontrib><creatorcontrib>He, Jiacheng</creatorcontrib><creatorcontrib>Wang, Chuanyu</creatorcontrib><creatorcontrib>Shen, Jialiang</creatorcontrib><creatorcontrib>MacLachlan, Alana</creatorcontrib><creatorcontrib>Dai, Siyuan</creatorcontrib><creatorcontrib>Singer, Benjamin H</creatorcontrib><creatorcontrib>Kurabayashi, Katsuo</creatorcontrib><creatorcontrib>Chen, Pengyu</creatorcontrib><title>Machine-Learning-Assisted Microfluidic Nanoplasmonic Digital Immunoassay for Cytokine Storm Profiling in COVID-19 Patients</title><title>ACS nano</title><addtitle>ACS Nano</addtitle><description>Cytokine storm, known as an exaggerated hyperactive immune response characterized by elevated release of cytokines, has been described as a feature associated with life-threatening complications in COVID-19 patients. 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The developed immunoassay allows simultaneous detection of six cytokines in a single run with wide working ranges of 1–10,000 pg mL–1 and ultralow detection limits down to 0.46–1.36 pg mL–1 using a minimum of 3 μL serum samples. The whole chip can afford a 6-plex assay of 8 different samples with 6 repeats in each sample for a total of 288 sensing spots in less than 100 min. The image processing method enhanced by convolutional neural network (CNN) dramatically shortens the processing time ∼6,000 fold with a much simpler procedure while maintaining high statistical accuracy compared to the conventional manual counting approach. The immunoassay was validated by the gold-standard enzyme-linked immunosorbent assay (ELISA) and utilized for serum cytokine profiling of COVID-19 positive patients. Our results demonstrate the nanoplasmonic digital immunoassay as a promising practical tool for comprehensive characterization of cytokine storm in patients that holds great promise as an intelligent immunoassay for next generation immune monitoring.</description><subject>COVID-19 - diagnosis</subject><subject>Cytokine Release Syndrome - diagnosis</subject><subject>Cytokines - analysis</subject><subject>Humans</subject><subject>Immunoassay - methods</subject><subject>Machine Learning</subject><subject>Microfluidics</subject><issn>1936-0851</issn><issn>1936-086X</issn><issn>1936-086X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp1kUtP3DAUhS1UxLNrdpWXlVDAj8SJN5XQQMtIw0MCKnbWjeMMpok92Eml4dfXaIYRXbCyLZ_zHfsehI4oOaGE0VPQ0YHzJ1QTIRjfQntUcpGRSjx-2ewLuov2Y3wmpCirUuygXZ6XNBdc7qHXK9BP1plsZiA46-bZWYw2DqbBV1YH33ajbazG1yll0UHsvUunczu3A3R42vej8xAjLHHrA54sB_8n0fDd4EOPb5PfdgmKrcOTm9_T84xKfAuDNW6Ih2i7hS6ar-v1AD38vLifXGazm1_Tydksg5zJIctrXRHGdEtBEMG0rGsDVUOYzhtRMMmkSJ-qSVOCrKuS1VBoxhshpSE5JQU_QD9W3MVY96bRKTtApxbB9hCWyoNV_984-6Tm_q-qirLkJU-A72tA8C-jiYPqbdSm68AZP0bFCkkoF0y8ZZ2upGl0MQbTbmIoUW-NqXVjat1Ycnz7-LqN_r2iJDheCZJTPfsxuDSsT3H_AFmRpDM</recordid><startdate>20211123</startdate><enddate>20211123</enddate><creator>Gao, Zhuangqiang</creator><creator>Song, Yujing</creator><creator>Hsiao, Te Yi</creator><creator>He, Jiacheng</creator><creator>Wang, Chuanyu</creator><creator>Shen, Jialiang</creator><creator>MacLachlan, Alana</creator><creator>Dai, Siyuan</creator><creator>Singer, Benjamin H</creator><creator>Kurabayashi, Katsuo</creator><creator>Chen, Pengyu</creator><general>American Chemical Society</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-4721-6920</orcidid><orcidid>https://orcid.org/0000-0001-7259-7182</orcidid><orcidid>https://orcid.org/0000-0003-3380-872X</orcidid><orcidid>https://orcid.org/0000-0001-9097-1799</orcidid><orcidid>https://orcid.org/0000-0002-9613-3590</orcidid></search><sort><creationdate>20211123</creationdate><title>Machine-Learning-Assisted Microfluidic Nanoplasmonic Digital Immunoassay for Cytokine Storm Profiling in COVID-19 Patients</title><author>Gao, Zhuangqiang ; Song, Yujing ; Hsiao, Te Yi ; He, Jiacheng ; Wang, Chuanyu ; Shen, Jialiang ; MacLachlan, Alana ; Dai, Siyuan ; Singer, Benjamin H ; Kurabayashi, Katsuo ; Chen, Pengyu</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a429t-4bc8022cf1a6062c9bbea8d02c4d6529296876b0d7a9b872ba5c23d699e041053</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>COVID-19 - diagnosis</topic><topic>Cytokine Release Syndrome - diagnosis</topic><topic>Cytokines - analysis</topic><topic>Humans</topic><topic>Immunoassay - methods</topic><topic>Machine Learning</topic><topic>Microfluidics</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Gao, Zhuangqiang</creatorcontrib><creatorcontrib>Song, Yujing</creatorcontrib><creatorcontrib>Hsiao, Te Yi</creatorcontrib><creatorcontrib>He, Jiacheng</creatorcontrib><creatorcontrib>Wang, Chuanyu</creatorcontrib><creatorcontrib>Shen, Jialiang</creatorcontrib><creatorcontrib>MacLachlan, Alana</creatorcontrib><creatorcontrib>Dai, Siyuan</creatorcontrib><creatorcontrib>Singer, Benjamin H</creatorcontrib><creatorcontrib>Kurabayashi, Katsuo</creatorcontrib><creatorcontrib>Chen, Pengyu</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>ACS nano</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Gao, Zhuangqiang</au><au>Song, Yujing</au><au>Hsiao, Te Yi</au><au>He, Jiacheng</au><au>Wang, Chuanyu</au><au>Shen, Jialiang</au><au>MacLachlan, Alana</au><au>Dai, Siyuan</au><au>Singer, Benjamin H</au><au>Kurabayashi, Katsuo</au><au>Chen, Pengyu</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Machine-Learning-Assisted Microfluidic Nanoplasmonic Digital Immunoassay for Cytokine Storm Profiling in COVID-19 Patients</atitle><jtitle>ACS nano</jtitle><addtitle>ACS Nano</addtitle><date>2021-11-23</date><risdate>2021</risdate><volume>15</volume><issue>11</issue><spage>18023</spage><epage>18036</epage><pages>18023-18036</pages><issn>1936-0851</issn><issn>1936-086X</issn><eissn>1936-086X</eissn><abstract>Cytokine storm, known as an exaggerated hyperactive immune response characterized by elevated release of cytokines, has been described as a feature associated with life-threatening complications in COVID-19 patients. A critical evaluation of a cytokine storm and its mechanistic linkage to COVID-19 requires innovative immunoassay technology capable of rapid, sensitive, selective detection of multiple cytokines across a wide dynamic range at high-throughput. In this study, we report a machine-learning-assisted microfluidic nanoplasmonic digital immunoassay to meet the rising demand for cytokine storm monitoring in COVID-19 patients. Specifically, the assay was carried out using a facile one-step sandwich immunoassay format with three notable features: (i) a microfluidic microarray patterning technique for high-throughput, multiantibody-arrayed biosensing chip fabrication; (ii) an ultrasensitive nanoplasmonic digital imaging technology utilizing 100 nm silver nanocubes (AgNCs) for signal transduction; (iii) a rapid and accurate machine-learning-based image processing method for digital signal analysis. The developed immunoassay allows simultaneous detection of six cytokines in a single run with wide working ranges of 1–10,000 pg mL–1 and ultralow detection limits down to 0.46–1.36 pg mL–1 using a minimum of 3 μL serum samples. The whole chip can afford a 6-plex assay of 8 different samples with 6 repeats in each sample for a total of 288 sensing spots in less than 100 min. The image processing method enhanced by convolutional neural network (CNN) dramatically shortens the processing time ∼6,000 fold with a much simpler procedure while maintaining high statistical accuracy compared to the conventional manual counting approach. The immunoassay was validated by the gold-standard enzyme-linked immunosorbent assay (ELISA) and utilized for serum cytokine profiling of COVID-19 positive patients. Our results demonstrate the nanoplasmonic digital immunoassay as a promising practical tool for comprehensive characterization of cytokine storm in patients that holds great promise as an intelligent immunoassay for next generation immune monitoring.</abstract><cop>United States</cop><pub>American Chemical Society</pub><pmid>34714639</pmid><doi>10.1021/acsnano.1c06623</doi><tpages>14</tpages><orcidid>https://orcid.org/0000-0002-4721-6920</orcidid><orcidid>https://orcid.org/0000-0001-7259-7182</orcidid><orcidid>https://orcid.org/0000-0003-3380-872X</orcidid><orcidid>https://orcid.org/0000-0001-9097-1799</orcidid><orcidid>https://orcid.org/0000-0002-9613-3590</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | COVID-19 - diagnosis Cytokine Release Syndrome - diagnosis Cytokines - analysis Humans Immunoassay - methods Machine Learning Microfluidics |
title | Machine-Learning-Assisted Microfluidic Nanoplasmonic Digital Immunoassay for Cytokine Storm Profiling in COVID-19 Patients |
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