Exosome Classification by Pattern Analysis of Surface-Enhanced Raman Spectroscopy Data for Lung Cancer Diagnosis
Owing to the role of exosome as a cargo for intercellular communication, especially in cancer metastasis, the evidence has been consistently accumulated that exosomes can be used as a noninvasive indicator of cancer. Consequently, several studies applying exosome have been proposed for cancer diagno...
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| Veröffentlicht in: | Analytical chemistry (Washington) 2017-06, Vol.89 (12), p.6695-6701 |
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| container_title | Analytical chemistry (Washington) |
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| creator | Park, Jaena Hwang, Miyeon Choi, ByeongHyeon Jeong, Hyesun Jung, Jik-han Kim, Hyun Koo Hong, Sunghoi Park, Ji-ho Choi, Yeonho |
| description | Owing to the role of exosome as a cargo for intercellular communication, especially in cancer metastasis, the evidence has been consistently accumulated that exosomes can be used as a noninvasive indicator of cancer. Consequently, several studies applying exosome have been proposed for cancer diagnostic methods such as ELISA assay. However, it has been still challenging to get reliable results due to the requirement of a labeling process and high concentration of exosome. Here, we demonstrate a label-free and highly sensitive classification method of exosome by combining surface-enhanced Raman scattering (SERS) and statistical pattern analysis. Unlike the conventional method to read different peak positions and amplitudes of a spectrum, whole SERS spectra of exosomes were analyzed by principal component analysis (PCA). By employing this pattern analysis, lung cancer cell derived exosomes were clearly distinguished from normal cell derived exosomes by 95.3% sensitivity and 97.3% specificity. Moreover, by analyzing the PCA result, we could suggest that this difference was induced by 11 different points in SERS signals from lung cancer cell derived exosomes. This result paved the way for new real-time diagnosis and classification of lung cancer by using exosome as a cancer marker. |
| doi_str_mv | 10.1021/acs.analchem.7b00911 |
| format | Article |
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Consequently, several studies applying exosome have been proposed for cancer diagnostic methods such as ELISA assay. However, it has been still challenging to get reliable results due to the requirement of a labeling process and high concentration of exosome. Here, we demonstrate a label-free and highly sensitive classification method of exosome by combining surface-enhanced Raman scattering (SERS) and statistical pattern analysis. Unlike the conventional method to read different peak positions and amplitudes of a spectrum, whole SERS spectra of exosomes were analyzed by principal component analysis (PCA). By employing this pattern analysis, lung cancer cell derived exosomes were clearly distinguished from normal cell derived exosomes by 95.3% sensitivity and 97.3% specificity. Moreover, by analyzing the PCA result, we could suggest that this difference was induced by 11 different points in SERS signals from lung cancer cell derived exosomes. This result paved the way for new real-time diagnosis and classification of lung cancer by using exosome as a cancer marker.</description><identifier>ISSN: 0003-2700</identifier><identifier>EISSN: 1520-6882</identifier><identifier>DOI: 10.1021/acs.analchem.7b00911</identifier><identifier>PMID: 28541032</identifier><language>eng</language><publisher>United States: American Chemical Society</publisher><subject>Analytical chemistry ; Biomarkers, Tumor - analysis ; Cancer ; Cargo ; Cell signaling ; Chemistry ; Classification ; Diagnosis ; Diagnostic systems ; Enzyme-linked immunosorbent assay ; Exosomes ; Exosomes - chemistry ; Exosomes - pathology ; Humans ; Lung cancer ; Lung Neoplasms - diagnosis ; Medical diagnosis ; Metastases ; Metastasis ; Pattern analysis ; Principal Component Analysis ; Principal components analysis ; Raman spectra ; Raman spectroscopy ; Scattering ; Sensitivity analysis ; Spectroscopic analysis ; Spectroscopy ; Spectrum Analysis, Raman ; Surface Properties</subject><ispartof>Analytical chemistry (Washington), 2017-06, Vol.89 (12), p.6695-6701</ispartof><rights>Copyright © 2017 American Chemical Society</rights><rights>Copyright American Chemical Society Jun 20, 2017</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-a442t-765da8f2a1a383822aac522414ca04db8ca24d7f091b809dd47847e8d7aa09ea3</citedby><cites>FETCH-LOGICAL-a442t-765da8f2a1a383822aac522414ca04db8ca24d7f091b809dd47847e8d7aa09ea3</cites><orcidid>0000-0002-0721-0428 ; 0000-0003-2018-3599</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/acs.analchem.7b00911$$EPDF$$P50$$Gacs$$H</linktopdf><linktohtml>$$Uhttps://pubs.acs.org/doi/10.1021/acs.analchem.7b00911$$EHTML$$P50$$Gacs$$H</linktohtml><link.rule.ids>314,776,780,2752,27053,27901,27902,56713,56763</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/28541032$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Park, Jaena</creatorcontrib><creatorcontrib>Hwang, Miyeon</creatorcontrib><creatorcontrib>Choi, ByeongHyeon</creatorcontrib><creatorcontrib>Jeong, Hyesun</creatorcontrib><creatorcontrib>Jung, Jik-han</creatorcontrib><creatorcontrib>Kim, Hyun Koo</creatorcontrib><creatorcontrib>Hong, Sunghoi</creatorcontrib><creatorcontrib>Park, Ji-ho</creatorcontrib><creatorcontrib>Choi, Yeonho</creatorcontrib><title>Exosome Classification by Pattern Analysis of Surface-Enhanced Raman Spectroscopy Data for Lung Cancer Diagnosis</title><title>Analytical chemistry (Washington)</title><addtitle>Anal. Chem</addtitle><description>Owing to the role of exosome as a cargo for intercellular communication, especially in cancer metastasis, the evidence has been consistently accumulated that exosomes can be used as a noninvasive indicator of cancer. Consequently, several studies applying exosome have been proposed for cancer diagnostic methods such as ELISA assay. However, it has been still challenging to get reliable results due to the requirement of a labeling process and high concentration of exosome. Here, we demonstrate a label-free and highly sensitive classification method of exosome by combining surface-enhanced Raman scattering (SERS) and statistical pattern analysis. Unlike the conventional method to read different peak positions and amplitudes of a spectrum, whole SERS spectra of exosomes were analyzed by principal component analysis (PCA). By employing this pattern analysis, lung cancer cell derived exosomes were clearly distinguished from normal cell derived exosomes by 95.3% sensitivity and 97.3% specificity. Moreover, by analyzing the PCA result, we could suggest that this difference was induced by 11 different points in SERS signals from lung cancer cell derived exosomes. This result paved the way for new real-time diagnosis and classification of lung cancer by using exosome as a cancer marker.</description><subject>Analytical chemistry</subject><subject>Biomarkers, Tumor - analysis</subject><subject>Cancer</subject><subject>Cargo</subject><subject>Cell signaling</subject><subject>Chemistry</subject><subject>Classification</subject><subject>Diagnosis</subject><subject>Diagnostic systems</subject><subject>Enzyme-linked immunosorbent assay</subject><subject>Exosomes</subject><subject>Exosomes - chemistry</subject><subject>Exosomes - pathology</subject><subject>Humans</subject><subject>Lung cancer</subject><subject>Lung Neoplasms - diagnosis</subject><subject>Medical diagnosis</subject><subject>Metastases</subject><subject>Metastasis</subject><subject>Pattern analysis</subject><subject>Principal Component Analysis</subject><subject>Principal components analysis</subject><subject>Raman spectra</subject><subject>Raman spectroscopy</subject><subject>Scattering</subject><subject>Sensitivity analysis</subject><subject>Spectroscopic analysis</subject><subject>Spectroscopy</subject><subject>Spectrum Analysis, Raman</subject><subject>Surface Properties</subject><issn>0003-2700</issn><issn>1520-6882</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9kUFP3DAQhS1EVRbaf4CQJS5cssw43th7RMu2VFqJCug5mjgOBCV2aicS--_r1S5U6qGnuXzvjZ4-xs4R5ggCr8nEOTnqzIvt56oCWCIesRkuBGSF1uKYzQAgz4QCOGGnMb4CIAIWn9mJ0AuJkIsZG9ZvPvre8lVHMbZNa2hsvePVlv-kcbTB8Zv0ZBvbyH3DH6fQkLHZ2r2QM7bmD9ST44-DNWPw0fhhy29pJN74wDeTe-arHRf4bUvPzqeWL-xTQ120Xw_3jP36tn5a3WWb--8_VjebjKQUY6aKRU26EYSU61wLQWQWQkiUhkDWlTYkZK2atLrSsKxrqbRUVteKCJaW8jN2te8dgv892TiWfRuN7Tpy1k-xxCUIqQXmRUIv_0Ff_RTS6h2FqlAotEyU3FMmDY3BNuUQ2p7CtkQod0bKZKR8N1IejKTYxaF8qnpbf4TeFSQA9sAu_vfx_zr_AMKKmq0</recordid><startdate>20170620</startdate><enddate>20170620</enddate><creator>Park, Jaena</creator><creator>Hwang, Miyeon</creator><creator>Choi, ByeongHyeon</creator><creator>Jeong, Hyesun</creator><creator>Jung, Jik-han</creator><creator>Kim, Hyun Koo</creator><creator>Hong, Sunghoi</creator><creator>Park, Ji-ho</creator><creator>Choi, Yeonho</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>7QF</scope><scope>7QO</scope><scope>7QQ</scope><scope>7SC</scope><scope>7SE</scope><scope>7SP</scope><scope>7SR</scope><scope>7TA</scope><scope>7TB</scope><scope>7TM</scope><scope>7U5</scope><scope>7U7</scope><scope>7U9</scope><scope>8BQ</scope><scope>8FD</scope><scope>C1K</scope><scope>F28</scope><scope>FR3</scope><scope>H8D</scope><scope>H8G</scope><scope>H94</scope><scope>JG9</scope><scope>JQ2</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>P64</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0002-0721-0428</orcidid><orcidid>https://orcid.org/0000-0003-2018-3599</orcidid></search><sort><creationdate>20170620</creationdate><title>Exosome Classification by Pattern Analysis of Surface-Enhanced Raman Spectroscopy Data for Lung Cancer Diagnosis</title><author>Park, Jaena ; Hwang, Miyeon ; Choi, ByeongHyeon ; Jeong, Hyesun ; Jung, Jik-han ; Kim, Hyun Koo ; Hong, Sunghoi ; Park, Ji-ho ; Choi, Yeonho</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a442t-765da8f2a1a383822aac522414ca04db8ca24d7f091b809dd47847e8d7aa09ea3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Analytical chemistry</topic><topic>Biomarkers, Tumor - analysis</topic><topic>Cancer</topic><topic>Cargo</topic><topic>Cell signaling</topic><topic>Chemistry</topic><topic>Classification</topic><topic>Diagnosis</topic><topic>Diagnostic systems</topic><topic>Enzyme-linked immunosorbent assay</topic><topic>Exosomes</topic><topic>Exosomes - chemistry</topic><topic>Exosomes - pathology</topic><topic>Humans</topic><topic>Lung cancer</topic><topic>Lung Neoplasms - diagnosis</topic><topic>Medical diagnosis</topic><topic>Metastases</topic><topic>Metastasis</topic><topic>Pattern analysis</topic><topic>Principal Component Analysis</topic><topic>Principal components analysis</topic><topic>Raman spectra</topic><topic>Raman spectroscopy</topic><topic>Scattering</topic><topic>Sensitivity analysis</topic><topic>Spectroscopic analysis</topic><topic>Spectroscopy</topic><topic>Spectrum Analysis, Raman</topic><topic>Surface Properties</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Park, Jaena</creatorcontrib><creatorcontrib>Hwang, Miyeon</creatorcontrib><creatorcontrib>Choi, ByeongHyeon</creatorcontrib><creatorcontrib>Jeong, Hyesun</creatorcontrib><creatorcontrib>Jung, Jik-han</creatorcontrib><creatorcontrib>Kim, Hyun Koo</creatorcontrib><creatorcontrib>Hong, Sunghoi</creatorcontrib><creatorcontrib>Park, Ji-ho</creatorcontrib><creatorcontrib>Choi, Yeonho</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Aluminium Industry Abstracts</collection><collection>Biotechnology Research Abstracts</collection><collection>Ceramic Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Corrosion Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Materials Business File</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>Toxicology Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Copper Technical Reference Library</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Analytical chemistry (Washington)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Park, Jaena</au><au>Hwang, Miyeon</au><au>Choi, ByeongHyeon</au><au>Jeong, Hyesun</au><au>Jung, Jik-han</au><au>Kim, Hyun Koo</au><au>Hong, Sunghoi</au><au>Park, Ji-ho</au><au>Choi, Yeonho</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Exosome Classification by Pattern Analysis of Surface-Enhanced Raman Spectroscopy Data for Lung Cancer Diagnosis</atitle><jtitle>Analytical chemistry (Washington)</jtitle><addtitle>Anal. Chem</addtitle><date>2017-06-20</date><risdate>2017</risdate><volume>89</volume><issue>12</issue><spage>6695</spage><epage>6701</epage><pages>6695-6701</pages><issn>0003-2700</issn><eissn>1520-6882</eissn><abstract>Owing to the role of exosome as a cargo for intercellular communication, especially in cancer metastasis, the evidence has been consistently accumulated that exosomes can be used as a noninvasive indicator of cancer. Consequently, several studies applying exosome have been proposed for cancer diagnostic methods such as ELISA assay. However, it has been still challenging to get reliable results due to the requirement of a labeling process and high concentration of exosome. Here, we demonstrate a label-free and highly sensitive classification method of exosome by combining surface-enhanced Raman scattering (SERS) and statistical pattern analysis. Unlike the conventional method to read different peak positions and amplitudes of a spectrum, whole SERS spectra of exosomes were analyzed by principal component analysis (PCA). By employing this pattern analysis, lung cancer cell derived exosomes were clearly distinguished from normal cell derived exosomes by 95.3% sensitivity and 97.3% specificity. Moreover, by analyzing the PCA result, we could suggest that this difference was induced by 11 different points in SERS signals from lung cancer cell derived exosomes. This result paved the way for new real-time diagnosis and classification of lung cancer by using exosome as a cancer marker.</abstract><cop>United States</cop><pub>American Chemical Society</pub><pmid>28541032</pmid><doi>10.1021/acs.analchem.7b00911</doi><tpages>7</tpages><orcidid>https://orcid.org/0000-0002-0721-0428</orcidid><orcidid>https://orcid.org/0000-0003-2018-3599</orcidid></addata></record> |
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| subjects | Analytical chemistry Biomarkers, Tumor - analysis Cancer Cargo Cell signaling Chemistry Classification Diagnosis Diagnostic systems Enzyme-linked immunosorbent assay Exosomes Exosomes - chemistry Exosomes - pathology Humans Lung cancer Lung Neoplasms - diagnosis Medical diagnosis Metastases Metastasis Pattern analysis Principal Component Analysis Principal components analysis Raman spectra Raman spectroscopy Scattering Sensitivity analysis Spectroscopic analysis Spectroscopy Spectrum Analysis, Raman Surface Properties |
| title | Exosome Classification by Pattern Analysis of Surface-Enhanced Raman Spectroscopy Data for Lung Cancer Diagnosis |
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