Fluorescent Sensor Array for Highly Efficient Microbial Lysate Identification through Competitive Interactions
Optical cross-reactive sensor arrays have recently been proven to be a powerful tool for high-throughput bioanalytes identification. Nevertheless, identification and classification of microbes, especially using microbial lysates as the analytes, still is a great challenge due to their complex compos...
Gespeichert in:
Veröffentlicht in: | ACS sensors 2018-11, Vol.3 (11), p.2218-2222 |
---|---|
Hauptverfasser: | , , , , , , , , , |
Format: | Artikel |
Sprache: | eng |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 2222 |
---|---|
container_issue | 11 |
container_start_page | 2218 |
container_title | ACS sensors |
container_volume | 3 |
creator | Shen, Jianlei Hu, Rong Zhou, Taotao Wang, Zhiming Zhang, Yiru Li, Shiwu Gui, Chen Jiang, Meijuan Qin, Anjun Tang, Ben Zhong |
description | Optical cross-reactive sensor arrays have recently been proven to be a powerful tool for high-throughput bioanalytes identification. Nevertheless, identification and classification of microbes, especially using microbial lysates as the analytes, still is a great challenge due to their complex composition. Herein, we achieve this goal by using luminogens featuring aggregation-induced emission characteristics (AIEgens) and graphene oxide (GO) to construct a microbial lysate responsive fluorescent sensor array. The combination of AIEgen with GO not only reduces the background signal but also induces the competition interactions among AIEgen, microbial lysates, and GO, which highly improves the discrimination ability of the sensor array. As a result, six microbes, including two fungi, two Gram-positive bacteria, and two Gram-negative bacteria are precisely identified. Thus, this work provides a new way to design safer and simpler sensor arrays for the discrimination of complex analytes. |
doi_str_mv | 10.1021/acssensors.8b00650 |
format | Article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2126901389</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2126901389</sourcerecordid><originalsourceid>FETCH-LOGICAL-a342t-a8a79a16f57d35972881f78e5b04aef5b6f3813601d37c92d8bf5bebc26ce90a3</originalsourceid><addsrcrecordid>eNp9kEtPAjEUhRujEYL8ARdmlm7APubRLgkBIcG4UNdNp9NCycwU247J_HuL4GPl6t7c-52TnAPALYJTBDF6ENJ71Xrr_JSWEOYZvABDTAo2ITlLL__sAzD2fg8hRFmOMwqvwYBAkkGWsiFol3VnnfJStSF5-TJMZs6JPtFxW5ntru6ThdZGmiPxZKSzpRF1sum9CCpZV_Fs4lsEY9sk7JzttrtkbpuDCiaYj4i0QTkhj39_A660qL0an-cIvC0Xr_PVZPP8uJ7PNhNBUhwmgoqCCZTrrKhIxgpMKdIFVVkJU6F0VuaaUERyiCpSSIYrWsajKiXOpWJQkBG4P_kenH3vlA-8MTFjXYtW2c5zjHDOICKURRSf0JjMe6c0PzjTCNdzBPmxav5bNT9XHUV3Z_-ubFT1I_kuNgLTExDFfG8718a4_zl-AqeCjwI</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2126901389</pqid></control><display><type>article</type><title>Fluorescent Sensor Array for Highly Efficient Microbial Lysate Identification through Competitive Interactions</title><source>ACS Publications</source><creator>Shen, Jianlei ; Hu, Rong ; Zhou, Taotao ; Wang, Zhiming ; Zhang, Yiru ; Li, Shiwu ; Gui, Chen ; Jiang, Meijuan ; Qin, Anjun ; Tang, Ben Zhong</creator><creatorcontrib>Shen, Jianlei ; Hu, Rong ; Zhou, Taotao ; Wang, Zhiming ; Zhang, Yiru ; Li, Shiwu ; Gui, Chen ; Jiang, Meijuan ; Qin, Anjun ; Tang, Ben Zhong</creatorcontrib><description>Optical cross-reactive sensor arrays have recently been proven to be a powerful tool for high-throughput bioanalytes identification. Nevertheless, identification and classification of microbes, especially using microbial lysates as the analytes, still is a great challenge due to their complex composition. Herein, we achieve this goal by using luminogens featuring aggregation-induced emission characteristics (AIEgens) and graphene oxide (GO) to construct a microbial lysate responsive fluorescent sensor array. The combination of AIEgen with GO not only reduces the background signal but also induces the competition interactions among AIEgen, microbial lysates, and GO, which highly improves the discrimination ability of the sensor array. As a result, six microbes, including two fungi, two Gram-positive bacteria, and two Gram-negative bacteria are precisely identified. Thus, this work provides a new way to design safer and simpler sensor arrays for the discrimination of complex analytes.</description><identifier>ISSN: 2379-3694</identifier><identifier>EISSN: 2379-3694</identifier><identifier>DOI: 10.1021/acssensors.8b00650</identifier><identifier>PMID: 30350949</identifier><language>eng</language><publisher>United States: American Chemical Society</publisher><ispartof>ACS sensors, 2018-11, Vol.3 (11), p.2218-2222</ispartof><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-a342t-a8a79a16f57d35972881f78e5b04aef5b6f3813601d37c92d8bf5bebc26ce90a3</citedby><cites>FETCH-LOGICAL-a342t-a8a79a16f57d35972881f78e5b04aef5b6f3813601d37c92d8bf5bebc26ce90a3</cites><orcidid>0000-0002-7918-7538 ; 0000-0002-3047-3285 ; 0000-0002-0293-964X ; 0000-0001-7158-1808</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/acssensors.8b00650$$EPDF$$P50$$Gacs$$H</linktopdf><linktohtml>$$Uhttps://pubs.acs.org/doi/10.1021/acssensors.8b00650$$EHTML$$P50$$Gacs$$H</linktohtml><link.rule.ids>314,777,781,2752,27057,27905,27906,56719,56769</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/30350949$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Shen, Jianlei</creatorcontrib><creatorcontrib>Hu, Rong</creatorcontrib><creatorcontrib>Zhou, Taotao</creatorcontrib><creatorcontrib>Wang, Zhiming</creatorcontrib><creatorcontrib>Zhang, Yiru</creatorcontrib><creatorcontrib>Li, Shiwu</creatorcontrib><creatorcontrib>Gui, Chen</creatorcontrib><creatorcontrib>Jiang, Meijuan</creatorcontrib><creatorcontrib>Qin, Anjun</creatorcontrib><creatorcontrib>Tang, Ben Zhong</creatorcontrib><title>Fluorescent Sensor Array for Highly Efficient Microbial Lysate Identification through Competitive Interactions</title><title>ACS sensors</title><addtitle>ACS Sens</addtitle><description>Optical cross-reactive sensor arrays have recently been proven to be a powerful tool for high-throughput bioanalytes identification. Nevertheless, identification and classification of microbes, especially using microbial lysates as the analytes, still is a great challenge due to their complex composition. Herein, we achieve this goal by using luminogens featuring aggregation-induced emission characteristics (AIEgens) and graphene oxide (GO) to construct a microbial lysate responsive fluorescent sensor array. The combination of AIEgen with GO not only reduces the background signal but also induces the competition interactions among AIEgen, microbial lysates, and GO, which highly improves the discrimination ability of the sensor array. As a result, six microbes, including two fungi, two Gram-positive bacteria, and two Gram-negative bacteria are precisely identified. Thus, this work provides a new way to design safer and simpler sensor arrays for the discrimination of complex analytes.</description><issn>2379-3694</issn><issn>2379-3694</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><recordid>eNp9kEtPAjEUhRujEYL8ARdmlm7APubRLgkBIcG4UNdNp9NCycwU247J_HuL4GPl6t7c-52TnAPALYJTBDF6ENJ71Xrr_JSWEOYZvABDTAo2ITlLL__sAzD2fg8hRFmOMwqvwYBAkkGWsiFol3VnnfJStSF5-TJMZs6JPtFxW5ntru6ThdZGmiPxZKSzpRF1sum9CCpZV_Fs4lsEY9sk7JzttrtkbpuDCiaYj4i0QTkhj39_A660qL0an-cIvC0Xr_PVZPP8uJ7PNhNBUhwmgoqCCZTrrKhIxgpMKdIFVVkJU6F0VuaaUERyiCpSSIYrWsajKiXOpWJQkBG4P_kenH3vlA-8MTFjXYtW2c5zjHDOICKURRSf0JjMe6c0PzjTCNdzBPmxav5bNT9XHUV3Z_-ubFT1I_kuNgLTExDFfG8718a4_zl-AqeCjwI</recordid><startdate>20181126</startdate><enddate>20181126</enddate><creator>Shen, Jianlei</creator><creator>Hu, Rong</creator><creator>Zhou, Taotao</creator><creator>Wang, Zhiming</creator><creator>Zhang, Yiru</creator><creator>Li, Shiwu</creator><creator>Gui, Chen</creator><creator>Jiang, Meijuan</creator><creator>Qin, Anjun</creator><creator>Tang, Ben Zhong</creator><general>American Chemical Society</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0002-7918-7538</orcidid><orcidid>https://orcid.org/0000-0002-3047-3285</orcidid><orcidid>https://orcid.org/0000-0002-0293-964X</orcidid><orcidid>https://orcid.org/0000-0001-7158-1808</orcidid></search><sort><creationdate>20181126</creationdate><title>Fluorescent Sensor Array for Highly Efficient Microbial Lysate Identification through Competitive Interactions</title><author>Shen, Jianlei ; Hu, Rong ; Zhou, Taotao ; Wang, Zhiming ; Zhang, Yiru ; Li, Shiwu ; Gui, Chen ; Jiang, Meijuan ; Qin, Anjun ; Tang, Ben Zhong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a342t-a8a79a16f57d35972881f78e5b04aef5b6f3813601d37c92d8bf5bebc26ce90a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Shen, Jianlei</creatorcontrib><creatorcontrib>Hu, Rong</creatorcontrib><creatorcontrib>Zhou, Taotao</creatorcontrib><creatorcontrib>Wang, Zhiming</creatorcontrib><creatorcontrib>Zhang, Yiru</creatorcontrib><creatorcontrib>Li, Shiwu</creatorcontrib><creatorcontrib>Gui, Chen</creatorcontrib><creatorcontrib>Jiang, Meijuan</creatorcontrib><creatorcontrib>Qin, Anjun</creatorcontrib><creatorcontrib>Tang, Ben Zhong</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>ACS sensors</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Shen, Jianlei</au><au>Hu, Rong</au><au>Zhou, Taotao</au><au>Wang, Zhiming</au><au>Zhang, Yiru</au><au>Li, Shiwu</au><au>Gui, Chen</au><au>Jiang, Meijuan</au><au>Qin, Anjun</au><au>Tang, Ben Zhong</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Fluorescent Sensor Array for Highly Efficient Microbial Lysate Identification through Competitive Interactions</atitle><jtitle>ACS sensors</jtitle><addtitle>ACS Sens</addtitle><date>2018-11-26</date><risdate>2018</risdate><volume>3</volume><issue>11</issue><spage>2218</spage><epage>2222</epage><pages>2218-2222</pages><issn>2379-3694</issn><eissn>2379-3694</eissn><abstract>Optical cross-reactive sensor arrays have recently been proven to be a powerful tool for high-throughput bioanalytes identification. Nevertheless, identification and classification of microbes, especially using microbial lysates as the analytes, still is a great challenge due to their complex composition. Herein, we achieve this goal by using luminogens featuring aggregation-induced emission characteristics (AIEgens) and graphene oxide (GO) to construct a microbial lysate responsive fluorescent sensor array. The combination of AIEgen with GO not only reduces the background signal but also induces the competition interactions among AIEgen, microbial lysates, and GO, which highly improves the discrimination ability of the sensor array. As a result, six microbes, including two fungi, two Gram-positive bacteria, and two Gram-negative bacteria are precisely identified. Thus, this work provides a new way to design safer and simpler sensor arrays for the discrimination of complex analytes.</abstract><cop>United States</cop><pub>American Chemical Society</pub><pmid>30350949</pmid><doi>10.1021/acssensors.8b00650</doi><tpages>5</tpages><orcidid>https://orcid.org/0000-0002-7918-7538</orcidid><orcidid>https://orcid.org/0000-0002-3047-3285</orcidid><orcidid>https://orcid.org/0000-0002-0293-964X</orcidid><orcidid>https://orcid.org/0000-0001-7158-1808</orcidid></addata></record> |
fulltext | fulltext |
identifier | ISSN: 2379-3694 |
ispartof | ACS sensors, 2018-11, Vol.3 (11), p.2218-2222 |
issn | 2379-3694 2379-3694 |
language | eng |
recordid | cdi_proquest_miscellaneous_2126901389 |
source | ACS Publications |
title | Fluorescent Sensor Array for Highly Efficient Microbial Lysate Identification through Competitive Interactions |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-18T14%3A11%3A22IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Fluorescent%20Sensor%20Array%20for%20Highly%20Efficient%20Microbial%20Lysate%20Identification%20through%20Competitive%20Interactions&rft.jtitle=ACS%20sensors&rft.au=Shen,%20Jianlei&rft.date=2018-11-26&rft.volume=3&rft.issue=11&rft.spage=2218&rft.epage=2222&rft.pages=2218-2222&rft.issn=2379-3694&rft.eissn=2379-3694&rft_id=info:doi/10.1021/acssensors.8b00650&rft_dat=%3Cproquest_cross%3E2126901389%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2126901389&rft_id=info:pmid/30350949&rfr_iscdi=true |