PCaAnalyser: A 2D-Image Analysis Based Module for Effective Determination of Prostate Cancer Progression in 3D Culture

Article


Hoque, Md Tamjidul, Windus, Louisa C. E., Lovitt, Carrie J. and Avery, Vicky. 2013. "PCaAnalyser: A 2D-Image Analysis Based Module for Effective Determination of Prostate Cancer Progression in 3D Culture." PLoS One. 8 (11), pp. 1-13. https://doi.org/10.1371/journal.pone.0079865
Article Title

PCaAnalyser: A 2D-Image Analysis Based Module for Effective Determination of Prostate Cancer Progression in 3D Culture

ERA Journal ID39745
Article CategoryArticle
AuthorsHoque, Md Tamjidul (Author), Windus, Louisa C. E. (Author), Lovitt, Carrie J. (Author) and Avery, Vicky (Author)
Journal TitlePLoS One
Journal Citation8 (11), pp. 1-13
Article Numbere79865
Number of Pages13
Year2013
PublisherPublic Library of Science (PLoS)
Place of PublicationUnited States
ISSN1932-6203
Digital Object Identifier (DOI)https://doi.org/10.1371/journal.pone.0079865
Web Address (URL)https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0079865
Abstract

Three-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.

KeywordsAlgorithms; Cell Line, Tumor; Humans; Image Processing, Computer-Assisted; Immunohistochemistry; Male; Prostatic Neoplasms; Signal-To-Noise Ratio; Spheroids, Cellular
ANZSRC Field of Research 2020310199. Biochemistry and cell biology not elsewhere classified
310299. Bioinformatics and computational biology not elsewhere classified
Open access urlhttps://journals.plos.org/plosone/article?id=10.1371/journal.pone.0079865
Institution of OriginUniversity of Southern Queensland
Byline AffiliationsUniversity of New Orleans, United States
Griffith University
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