JACoPx – an ImageJ plugin
ImageJ (http://imagej.nih.gov/ij/index.html) is an open-source, Java-based image analysis program developed at the National Institutes of Health . It works with multiple operating systems (Windows, Mac OS, OS X, Unix-based systems) and its open architecture allows for extensions using custom Java plugins and macros. ImageJ is one of the most widely used image processing systems with applications in biological and medical sciences including analysis of microscopy, pathology and radiology images . JACoP (Just Another Colocalization Plugin) is an ImageJ plugin that provides a variety of co-localization measures including Pearson’s coefficient, Overlap coefficient, Manders’coefficient and Costes’ automated threshold. We have extended JACoP to now include RWC coefficients. This extended plugin (JACoPx) provides an option along with the default measures to additionally calculate RWC coefficients. Since JACoP is already widely used, and provides an extensive collection of co-localization measures, we reasoned that implementing RWC into the same plugin will enable users to use the same familiar tool to also extract RWC coefficients, and thus also be able to easily compare these measures against other coefficients. JACoPx can be easily installed using the jacopx_.jar file provided through our website.
To install JACoPx:
Download the associate jar file (jacopx_.jar) to the plugins folder within the ImageJ installation directory;
Restart ImageJ. JACoPx should now be available for use under plugins tab.
MeasureCorrelationx – a CellProfiler module
CellProfiler (http://cellprofiler.org) is a Python-based, open source high-throughput image analysis system developed at the Broad Institute of MIT and Harvard . CellProfiler is available for Mac OS X, Windows and Linux operating systems. CellProfiler has a modular design enabling users to choose the image processing routines specific to their assays. CellProfiler is a highly ranked cell image analysis tool that provides an interface to build analysis pipelines using the image processing modules as building blocks . CellProfiler is designed for high-throughput analysis where quantitative phenotypic measurements can be extracted from thousands of images automatically. One of the measurement modules available in CellProfiler, called ‘MeasureCorrelation’, provides the Pearson’s co-localization coefficient for a pair of images. We have extended this module to include Overlap, Mander’s coefficient, Costes’ automated threshold and RWC coefficients. This extended module (MeasureCorrelationx) now enable users to extract a variety of co-localization measures with relative ease.
To install MeasureCorrelationx:
Download the associated Python script (MeasureCorrelationx.py) to a folder;
Point the “CellProfiler plugins directory” within the preferences option to this folder;
Restart CellProfiler. MeasureCorrelationx should now be available under the measurement modules list.
RWC_Co-localization.script – an Acapella script for Columbus
Columbus Image Data Storage and Analysis System (http://www.perkinelmer.com/pages/020/cellularimaging/products/columbus.xhtml) is a proprietary web enabled system for storage and analysis of image data developed by Perkin Elmer. Columbus is a modular system similar to CellProfiler allowing users to build custom analysis pipelines and perform high-throughput analysis of very large image data sets. Modules are developed using Acapella scripts (Evotec Technologies GmbH), and Tony J. Collins (MacBioPhotonics, McMaster University, Canada) and the Andrews Lab (Sunnybrook Research Institute, Toronto, ON, Canada) have provided a suite of co-localization procedures (MBF_ColocalisationCoefficientsb03.proc) written in the Acapella scripting language. We have extended this procedure to create an Acapella script (RWC_Co-localization.script) that implements the RWC algorithm, such that this script can also be used as an independent ‘assay’ within the Columbus system. The script works with 3 channel input images and the user can select the two channels between which the co-localization coefficients wish to be calculated. The script allows for segmentation and detection of cell nuclei and cytoplasmic areas using the various inbuilt detection options, and then extracts co-localization coefficients on the objects identified. This script can be downloaded and directly imported as an assay in a ‘ready-to-use’ format.