Quantitative non-invasive cell characterisation and discrimination based on multispectral autofluorescence features
Automated and unbiased methods of non-invasive cell monitoring able to deal with complex biological heterogeneity are fundamentally important for biology and medicine. Label-free cell imaging provides information about endogenous autofluorescent metabolites, enzymes and cofactors in cells. However e...
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| Veröffentlicht in: | Scientific reports 2016-03, Vol.6 (1), p.23453-23453, Article 23453 |
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| creator | Gosnell, Martin E. Anwer, Ayad G. Mahbub, Saabah B. Menon Perinchery, Sandeep Inglis, David W. Adhikary, Partho P. Jazayeri, Jalal A. Cahill, Michael A. Saad, Sonia Pollock, Carol A. Sutton-McDowall, Melanie L. Thompson, Jeremy G. Goldys, Ewa M. |
| description | Automated and unbiased methods of non-invasive cell monitoring able to deal with complex biological heterogeneity are fundamentally important for biology and medicine. Label-free cell imaging provides information about endogenous autofluorescent metabolites, enzymes and cofactors in cells. However extracting high content information from autofluorescence imaging has been hitherto impossible. Here, we quantitatively characterise cell populations in different tissue types, live or fixed, by using novel image processing and a simple multispectral upgrade of a wide-field fluorescence microscope. Our optimal discrimination approach enables statistical hypothesis testing and intuitive visualisations where previously undetectable differences become clearly apparent. Label-free classifications are validated by the analysis of Classification Determinant (CD) antigen expression. The versatility of our method is illustrated by detecting genetic mutations in cancer, non-invasive monitoring of CD90 expression, label-free tracking of stem cell differentiation, identifying stem cell subpopulations with varying functional characteristics, tissue diagnostics in diabetes and assessing the condition of preimplantation embryos. |
| doi_str_mv | 10.1038/srep23453 |
| format | Article |
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Label-free cell imaging provides information about endogenous autofluorescent metabolites, enzymes and cofactors in cells. However extracting high content information from autofluorescence imaging has been hitherto impossible. Here, we quantitatively characterise cell populations in different tissue types, live or fixed, by using novel image processing and a simple multispectral upgrade of a wide-field fluorescence microscope. Our optimal discrimination approach enables statistical hypothesis testing and intuitive visualisations where previously undetectable differences become clearly apparent. Label-free classifications are validated by the analysis of Classification Determinant (CD) antigen expression. The versatility of our method is illustrated by detecting genetic mutations in cancer, non-invasive monitoring of CD90 expression, label-free tracking of stem cell differentiation, identifying stem cell subpopulations with varying functional characteristics, tissue diagnostics in diabetes and assessing the condition of preimplantation embryos.</description><identifier>ISSN: 2045-2322</identifier><identifier>EISSN: 2045-2322</identifier><identifier>DOI: 10.1038/srep23453</identifier><identifier>PMID: 27029742</identifier><language>eng</language><publisher>London: Nature Publishing Group UK</publisher><subject>631/57 ; 631/57/2267 ; Animals ; Blastocyst - metabolism ; Blastocyst - ultrastructure ; Cancer ; CD90 antigen ; Cell Differentiation ; Cell Line, Tumor ; Cell Tracking - instrumentation ; Cell Tracking - methods ; Cofactors ; Diabetes mellitus ; Diabetes Mellitus, Experimental - genetics ; Diabetes Mellitus, Experimental - metabolism ; Diabetes Mellitus, Experimental - pathology ; Embryos ; Enzymes ; Gene Expression ; Gene Expression Regulation ; Heterogeneity ; Humanities and Social Sciences ; Humans ; Image processing ; Image Processing, Computer-Assisted - statistics & numerical data ; Membrane Proteins - genetics ; Membrane Proteins - metabolism ; Metabolites ; Mice ; multidisciplinary ; Mutation ; Optical Imaging - methods ; Optical Imaging - statistics & numerical data ; Pancreatic Neoplasms - genetics ; Pancreatic Neoplasms - metabolism ; Pancreatic Neoplasms - ultrastructure ; Receptors, Progesterone - genetics ; Receptors, Progesterone - metabolism ; Science ; Stem cells ; Stem Cells - cytology ; Stem Cells - metabolism ; Subpopulations ; Thy-1 Antigens - genetics ; Thy-1 Antigens - metabolism</subject><ispartof>Scientific reports, 2016-03, Vol.6 (1), p.23453-23453, Article 23453</ispartof><rights>The Author(s) 2016</rights><rights>Copyright Nature Publishing Group Mar 2016</rights><rights>Copyright © 2016, Macmillan Publishers Limited 2016 Macmillan Publishers Limited</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c438t-4d4c704a67468da1cd05f2144b8113ea168dcb25784e0d8d226908a4e4670f863</citedby><cites>FETCH-LOGICAL-c438t-4d4c704a67468da1cd05f2144b8113ea168dcb25784e0d8d226908a4e4670f863</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4814840/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4814840/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,27901,27902,41096,42165,51551,53766,53768</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/27029742$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Gosnell, Martin E.</creatorcontrib><creatorcontrib>Anwer, Ayad G.</creatorcontrib><creatorcontrib>Mahbub, Saabah B.</creatorcontrib><creatorcontrib>Menon Perinchery, Sandeep</creatorcontrib><creatorcontrib>Inglis, David W.</creatorcontrib><creatorcontrib>Adhikary, Partho P.</creatorcontrib><creatorcontrib>Jazayeri, Jalal A.</creatorcontrib><creatorcontrib>Cahill, Michael A.</creatorcontrib><creatorcontrib>Saad, Sonia</creatorcontrib><creatorcontrib>Pollock, Carol A.</creatorcontrib><creatorcontrib>Sutton-McDowall, Melanie L.</creatorcontrib><creatorcontrib>Thompson, Jeremy G.</creatorcontrib><creatorcontrib>Goldys, Ewa M.</creatorcontrib><title>Quantitative non-invasive cell characterisation and discrimination based on multispectral autofluorescence features</title><title>Scientific reports</title><addtitle>Sci Rep</addtitle><addtitle>Sci Rep</addtitle><description>Automated and unbiased methods of non-invasive cell monitoring able to deal with complex biological heterogeneity are fundamentally important for biology and medicine. Label-free cell imaging provides information about endogenous autofluorescent metabolites, enzymes and cofactors in cells. However extracting high content information from autofluorescence imaging has been hitherto impossible. Here, we quantitatively characterise cell populations in different tissue types, live or fixed, by using novel image processing and a simple multispectral upgrade of a wide-field fluorescence microscope. Our optimal discrimination approach enables statistical hypothesis testing and intuitive visualisations where previously undetectable differences become clearly apparent. Label-free classifications are validated by the analysis of Classification Determinant (CD) antigen expression. 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statistics & numerical data</subject><subject>Membrane Proteins - genetics</subject><subject>Membrane Proteins - metabolism</subject><subject>Metabolites</subject><subject>Mice</subject><subject>multidisciplinary</subject><subject>Mutation</subject><subject>Optical Imaging - methods</subject><subject>Optical Imaging - statistics & numerical data</subject><subject>Pancreatic Neoplasms - genetics</subject><subject>Pancreatic Neoplasms - metabolism</subject><subject>Pancreatic Neoplasms - ultrastructure</subject><subject>Receptors, Progesterone - genetics</subject><subject>Receptors, Progesterone - metabolism</subject><subject>Science</subject><subject>Stem cells</subject><subject>Stem Cells - cytology</subject><subject>Stem Cells - metabolism</subject><subject>Subpopulations</subject><subject>Thy-1 Antigens - genetics</subject><subject>Thy-1 Antigens - metabolism</subject><issn>2045-2322</issn><issn>2045-2322</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><sourceid>C6C</sourceid><sourceid>EIF</sourceid><sourceid>BENPR</sourceid><recordid>eNplkVtrFDEUx4NYbGn74BeQAV9UmJrbTDIvQineoFAEfQ5nkzNtymyy5rLgtzfL1mXVvORcfvzzPzmEvGT0ilGh3-eEGy7kIJ6RM07l0HPB-fOj-JRc5vxI2xn4JNn0gpxyRfmkJD8j-VuFUHyB4rfYhRh6H7aQd4nFZensAySwBZPPDYmhg-A657NNfu3DvrSCjK5rwbouxecN2pJg6aCWOC81JswWg8VuRii1ZRfkZIYl4-XTfU5-fPr4_eZLf3v3-evN9W1vpdCll05aRSWMSo7aAbOODjNnUq40YwKBtapd8UFpidRpx_k4UQ0S5ajorEdxTj7sdTd1tUbXTOx8mU2zDumXieDN353gH8x93BqpmdSSNoE3TwIp_qyYi1m3ydu3QMBYs2FKqUlrznRDX_-DPsaaQhvPMD3pUSshpka93VM2xdwWNx_MMGp22zSHbTb21bH7A_lndw14twdya4V7TEdP_qf2G6yFrBY</recordid><startdate>20160331</startdate><enddate>20160331</enddate><creator>Gosnell, Martin E.</creator><creator>Anwer, Ayad G.</creator><creator>Mahbub, Saabah B.</creator><creator>Menon Perinchery, Sandeep</creator><creator>Inglis, David W.</creator><creator>Adhikary, Partho P.</creator><creator>Jazayeri, Jalal A.</creator><creator>Cahill, Michael A.</creator><creator>Saad, Sonia</creator><creator>Pollock, Carol A.</creator><creator>Sutton-McDowall, Melanie L.</creator><creator>Thompson, Jeremy G.</creator><creator>Goldys, Ewa M.</creator><general>Nature Publishing Group UK</general><general>Nature Publishing Group</general><scope>C6C</scope><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>3V.</scope><scope>7X7</scope><scope>7XB</scope><scope>88A</scope><scope>88E</scope><scope>88I</scope><scope>8FE</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M2P</scope><scope>M7P</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>Q9U</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20160331</creationdate><title>Quantitative non-invasive cell characterisation and discrimination based on multispectral autofluorescence features</title><author>Gosnell, Martin E. ; Anwer, Ayad G. ; Mahbub, Saabah B. ; Menon Perinchery, Sandeep ; Inglis, David W. ; Adhikary, Partho P. ; Jazayeri, Jalal A. ; Cahill, Michael A. ; Saad, Sonia ; Pollock, Carol A. ; Sutton-McDowall, Melanie L. ; Thompson, Jeremy G. ; Goldys, Ewa M.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c438t-4d4c704a67468da1cd05f2144b8113ea168dcb25784e0d8d226908a4e4670f863</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>631/57</topic><topic>631/57/2267</topic><topic>Animals</topic><topic>Blastocyst - 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Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Scientific reports</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Gosnell, Martin E.</au><au>Anwer, Ayad G.</au><au>Mahbub, Saabah B.</au><au>Menon Perinchery, Sandeep</au><au>Inglis, David W.</au><au>Adhikary, Partho P.</au><au>Jazayeri, Jalal A.</au><au>Cahill, Michael A.</au><au>Saad, Sonia</au><au>Pollock, Carol A.</au><au>Sutton-McDowall, Melanie L.</au><au>Thompson, Jeremy G.</au><au>Goldys, Ewa M.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Quantitative non-invasive cell characterisation and discrimination based on multispectral autofluorescence features</atitle><jtitle>Scientific reports</jtitle><stitle>Sci Rep</stitle><addtitle>Sci Rep</addtitle><date>2016-03-31</date><risdate>2016</risdate><volume>6</volume><issue>1</issue><spage>23453</spage><epage>23453</epage><pages>23453-23453</pages><artnum>23453</artnum><issn>2045-2322</issn><eissn>2045-2322</eissn><abstract>Automated and unbiased methods of non-invasive cell monitoring able to deal with complex biological heterogeneity are fundamentally important for biology and medicine. Label-free cell imaging provides information about endogenous autofluorescent metabolites, enzymes and cofactors in cells. However extracting high content information from autofluorescence imaging has been hitherto impossible. Here, we quantitatively characterise cell populations in different tissue types, live or fixed, by using novel image processing and a simple multispectral upgrade of a wide-field fluorescence microscope. Our optimal discrimination approach enables statistical hypothesis testing and intuitive visualisations where previously undetectable differences become clearly apparent. Label-free classifications are validated by the analysis of Classification Determinant (CD) antigen expression. The versatility of our method is illustrated by detecting genetic mutations in cancer, non-invasive monitoring of CD90 expression, label-free tracking of stem cell differentiation, identifying stem cell subpopulations with varying functional characteristics, tissue diagnostics in diabetes and assessing the condition of preimplantation embryos.</abstract><cop>London</cop><pub>Nature Publishing Group UK</pub><pmid>27029742</pmid><doi>10.1038/srep23453</doi><tpages>1</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | 631/57 631/57/2267 Animals Blastocyst - metabolism Blastocyst - ultrastructure Cancer CD90 antigen Cell Differentiation Cell Line, Tumor Cell Tracking - instrumentation Cell Tracking - methods Cofactors Diabetes mellitus Diabetes Mellitus, Experimental - genetics Diabetes Mellitus, Experimental - metabolism Diabetes Mellitus, Experimental - pathology Embryos Enzymes Gene Expression Gene Expression Regulation Heterogeneity Humanities and Social Sciences Humans Image processing Image Processing, Computer-Assisted - statistics & numerical data Membrane Proteins - genetics Membrane Proteins - metabolism Metabolites Mice multidisciplinary Mutation Optical Imaging - methods Optical Imaging - statistics & numerical data Pancreatic Neoplasms - genetics Pancreatic Neoplasms - metabolism Pancreatic Neoplasms - ultrastructure Receptors, Progesterone - genetics Receptors, Progesterone - metabolism Science Stem cells Stem Cells - cytology Stem Cells - metabolism Subpopulations Thy-1 Antigens - genetics Thy-1 Antigens - metabolism |
| title | Quantitative non-invasive cell characterisation and discrimination based on multispectral autofluorescence features |
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