Semi-automated quantification of living cells with internalized nanostructures

Handle URI:
http://hdl.handle.net/10754/593682
Title:
Semi-automated quantification of living cells with internalized nanostructures
Authors:
Margineanu, Michael B. ( 0000-0002-9836-1802 ) ; Julfakyan, Khachatur ( 0000-0003-2482-0911 ) ; Sommer, Christoph; Perez, Jose E. ( 0000-0002-2206-0034 ) ; Contreras, Maria F. ( 0000-0001-6239-5325 ) ; Khashab, Niveen M. ( 0000-0003-2728-0666 ) ; Kosel, Jürgen ( 0000-0002-8998-8275 ) ; Ravasi, Timothy ( 0000-0002-9950-465X )
Abstract:
Background Nanostructures fabricated by different methods have become increasingly important for various applications in biology and medicine, such as agents for medical imaging or cancer therapy. In order to understand their interaction with living cells and their internalization kinetics, several attempts have been made in tagging them. Although methods have been developed to measure the number of nanostructures internalized by the cells, there are only few approaches aimed to measure the number of cells that internalize the nanostructures, and they are usually limited to fixed-cell studies. Flow cytometry can be used for live-cell assays on large populations of cells, however it is a single time point measurement, and does not include any information about cell morphology. To date many of the observations made on internalization events are limited to few time points and cells. Results In this study, we present a method for quantifying cells with internalized magnetic nanowires (NWs). A machine learning-based computational framework, CellCognition, is adapted and used to classify cells with internalized and no internalized NWs, labeled with the fluorogenic pH-dependent dye pHrodo™ Red, and subsequently to determine the percentage of cells with internalized NWs at different time points. In a “proof-of-concept”, we performed a study on human colon carcinoma HCT 116 cells and human epithelial cervical cancer HeLa cells interacting with iron (Fe) and nickel (Ni) NWs. Conclusions This study reports a novel method for the quantification of cells that internalize a specific type of nanostructures. This approach is suitable for high-throughput and real-time data analysis and has the potential to be used to study the interaction of different types of nanostructures in live-cell assays.
KAUST Department:
Biological and Environmental Sciences and Engineering (BESE) Division; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Physical Sciences and Engineering (PSE) Division
Citation:
Semi-automated quantification of living cells with internalized nanostructures 2016, 14 (1) Journal of Nanobiotechnology
Publisher:
Springer Science + Business Media
Journal:
Journal of Nanobiotechnology
Issue Date:
15-Jan-2016
DOI:
10.1186/s12951-015-0153-x
Type:
Article
ISSN:
1477-3155
Additional Links:
http://www.jnanobiotechnology.com/content/14/1/4
Appears in Collections:
Articles; Physical Sciences and Engineering (PSE) Division; Biological and Environmental Sciences and Engineering (BESE) Division; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorMargineanu, Michael B.en
dc.contributor.authorJulfakyan, Khachaturen
dc.contributor.authorSommer, Christophen
dc.contributor.authorPerez, Jose E.en
dc.contributor.authorContreras, Maria F.en
dc.contributor.authorKhashab, Niveen M.en
dc.contributor.authorKosel, Jürgenen
dc.contributor.authorRavasi, Timothyen
dc.date.accessioned2016-01-18T08:17:41Zen
dc.date.available2016-01-18T08:17:41Zen
dc.date.issued2016-01-15en
dc.identifier.citationSemi-automated quantification of living cells with internalized nanostructures 2016, 14 (1) Journal of Nanobiotechnologyen
dc.identifier.issn1477-3155en
dc.identifier.doi10.1186/s12951-015-0153-xen
dc.identifier.urihttp://hdl.handle.net/10754/593682en
dc.description.abstractBackground Nanostructures fabricated by different methods have become increasingly important for various applications in biology and medicine, such as agents for medical imaging or cancer therapy. In order to understand their interaction with living cells and their internalization kinetics, several attempts have been made in tagging them. Although methods have been developed to measure the number of nanostructures internalized by the cells, there are only few approaches aimed to measure the number of cells that internalize the nanostructures, and they are usually limited to fixed-cell studies. Flow cytometry can be used for live-cell assays on large populations of cells, however it is a single time point measurement, and does not include any information about cell morphology. To date many of the observations made on internalization events are limited to few time points and cells. Results In this study, we present a method for quantifying cells with internalized magnetic nanowires (NWs). A machine learning-based computational framework, CellCognition, is adapted and used to classify cells with internalized and no internalized NWs, labeled with the fluorogenic pH-dependent dye pHrodo™ Red, and subsequently to determine the percentage of cells with internalized NWs at different time points. In a “proof-of-concept”, we performed a study on human colon carcinoma HCT 116 cells and human epithelial cervical cancer HeLa cells interacting with iron (Fe) and nickel (Ni) NWs. Conclusions This study reports a novel method for the quantification of cells that internalize a specific type of nanostructures. This approach is suitable for high-throughput and real-time data analysis and has the potential to be used to study the interaction of different types of nanostructures in live-cell assays.en
dc.language.isoenen
dc.publisherSpringer Science + Business Mediaen
dc.relation.urlhttp://www.jnanobiotechnology.com/content/14/1/4en
dc.rightsOpen AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.en
dc.subjectNanomedicineen
dc.subjectNanoparticleen
dc.subjectNWsen
dc.subjectMagneticen
dc.subjectQuantificationen
dc.subjectLive-cell imagingen
dc.subjectMachine learningen
dc.subjectComputational methodsen
dc.titleSemi-automated quantification of living cells with internalized nanostructuresen
dc.typeArticleen
dc.contributor.departmentBiological and Environmental Sciences and Engineering (BESE) Divisionen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentPhysical Sciences and Engineering (PSE) Divisionen
dc.identifier.journalJournal of Nanobiotechnologyen
dc.eprint.versionPublisher's Version/PDFen
dc.contributor.institutionInstitute of Molecular Biotechnology of the Austrian Academy of Sciences (IMBA), Dr. Bohr-Gasse 3, Vienna 1030, Austriaen
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)en
kaust.authorMargineanu, Michael B.en
kaust.authorJulfakyan, Khachaturen
kaust.authorPerez, Jose E.en
kaust.authorContreras, Maria F.en
kaust.authorKhashab, Niveen M.en
kaust.authorKosel, Jürgenen
kaust.authorRavasi, Timothyen
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