Show simple item record

dc.contributor.authorHoque, Md Tamjidul
dc.contributor.authorWindus, Louisa CE
dc.contributor.authorLovitt, Carrie J
dc.contributor.authorAvery, Vicky M
dc.date.accessioned2017-05-03T14:32:59Z
dc.date.available2017-05-03T14:32:59Z
dc.date.issued2013
dc.date.modified2014-03-30T22:20:24Z
dc.identifier.issn1932-6203
dc.identifier.doi10.1371/journal.pone.0079865
dc.identifier.urihttp://hdl.handle.net/10072/57444
dc.description.abstractThree-dimensional (3D) in vitro cell based assays for Prostate Cancer (PCa) research are rapidly becoming the preferred alternative to that of conventional 2D monolayer cultures. 3D assays more precisely mimic the microenvironment found in vivo, and thus are ideally suited to evaluate compounds and their suitability for progression in the drug discovery pipeline. To achieve the desired high throughput needed for most screening programs, automated quantification of 3D cultures is required. Towards this end, this paper reports on the development of a prototype analysis module for an automated high-content-analysis (HCA) system, which allows for accurate and fast investigation of in vitro 3D cell culture models for PCa. The Java based program, which we have named PCaAnalyser, uses novel algorithms that allow accurate and rapid quantitation of protein expression in 3D cell culture. As currently configured, the PCaAnalyser can quantify a range of biological parameters including: nuclei-count, nuclei-spheroid membership prediction, various function based classification of peripheral and non-peripheral areas to measure expression of biomarkers and protein constituents known to be associated with PCa progression, as well as defining segregate cellular-objects effectively for a range of signal-to-noise ratios. In addition, PCaAnalyser architecture is highly flexible, operating as a single independent analysis, as well as in batch mode; essential for High-Throughput-Screening (HTS). Utilising the PCaAnalyser, accurate and rapid analysis in an automated high throughput manner is provided, and reproducible analysis of the distribution and intensity of well-established markers associated with PCa progression in a range of metastatic PCa cell-lines (DU145 and PC3) in a 3D model demonstrated.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.format.extent4784514 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglish
dc.language.isoeng
dc.publisherPublic Library of Science
dc.publisher.placeUnited States
dc.relation.ispartofstudentpublicationN
dc.relation.ispartofpagefrome79865-1
dc.relation.ispartofpagetoe79865-13
dc.relation.ispartofissue11
dc.relation.ispartofjournalPloS One
dc.relation.ispartofvolume8
dc.rights.retentionY
dc.subject.fieldofresearchMedicinal and biomolecular chemistry not elsewhere classified
dc.subject.fieldofresearchcode340499
dc.titlePCaAnalyser: A 2D-Image Analysis Based Module for Effective Determination of Prostate Cancer Progression in 3D Culture
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
dcterms.licensehttp://www.plos.org/journals/license.html
gro.facultyGriffith Sciences, School of Information and Communication Technology
gro.rights.copyright© 2013 Hoque et al. This is an Open Access article distributed under the terms of the Creative Commons Attribution License CCAL. (http://www.plos.org/journals/license.html)
gro.date.issued2013
gro.hasfulltextFull Text
gro.griffith.authorAvery, Vicky M.


Files in this item

This item appears in the following Collection(s)

  • Journal articles
    Contains articles published by Griffith authors in scholarly journals.

Show simple item record