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

Hoque, Md Tamjidul and Windus, Louisa C. E. ORCID: https://orcid.org/0000-0002-7186-459X and Lovitt, Carrie J. and Avery, Vicky M. (2013) PCaAnalyser: A 2D-Image Analysis Based Module for Effective Determination of Prostate Cancer Progression in 3D Culture. PLoS One, 8 (11):e79865. pp. 1-13.

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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.

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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Faculty/School / Institute/Centre: No Faculty
Faculty/School / Institute/Centre: No Faculty
Date Deposited: 26 Oct 2020 23:55
Last Modified: 27 Oct 2020 05:27
Uncontrolled Keywords: Algorithms; Cell Line, Tumor; Humans; Image Processing, Computer-Assisted; Immunohistochemistry; Male; Prostatic Neoplasms; Signal-To-Noise Ratio; Spheroids, Cellular
Fields of Research (2008): 06 Biological Sciences > 0601 Biochemistry and Cell Biology > 060199 Biochemistry and Cell Biology not elsewhere classified
06 Biological Sciences > 0601 Biochemistry and Cell Biology > 060102 Bioinformatics
Fields of Research (2020): 31 BIOLOGICAL SCIENCES > 3101 Biochemistry and cell biology > 310199 Biochemistry and cell biology not elsewhere classified
31 BIOLOGICAL SCIENCES > 3102 Bioinformatics and computational biology > 310299 Bioinformatics and computational biology not elsewhere classified
Socio-Economic Objectives (2008): E Expanding Knowledge > 97 Expanding Knowledge > 970106 Expanding Knowledge in the Biological Sciences
Identification Number or DOI: https://doi.org/10.1371/journal.pone.0079865
URI: http://eprints.usq.edu.au/id/eprint/39986

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