Object-based colocalisation

This ImageJ plug-in is a compilation of co-localization tools. It allows:

-Calculating a set of commonly used co-localization indicators:

Pearson's coefficient Overlap coefficient k1 & k2 coefficients Manders' coefficient Generating commonly used visualizations:

-Cytofluorogram

Having access to more recently published methods:

-Costes' automatic threshold

Li's ICA Costes' randomization Objects based methods (2 methods: distances between centres and centre-particle coincidence)

A workflow template to analyze subcellular structures in fluorescence 2D/3D microscopy images based on a Fiji plugin **Squassh** is described in Rizek et al (2014).

The workflow employs detecting, segmenting, and quantifying subcellular structures. For segmentation, it accounts for the microscope optics and for uneven image background. Further analyses include both colocalization and shape analyses. However, it does not work directly for time-lapse data. A brief summary note can be found here.

The workflow computes cell-based colocalisation of two stainings in 2-D images. Both pixel- and object-based readouts are provided and some pros and cons are discussed. Please read here for more information:

https://github.com/tischi/ImageAnalysisWorkflows/blob/master/CellProfil…

Input data type: 

images

Output data type: 

processed images, numbers, text file, csv files

These two similar KNIME workflow solutions take 3D data stacks to segment the spots first, using local thresholding with subsequent morphological operations in order to remove noise. Colocalization is then defined by overlapping or center point distance between segmented objects. Further filtering such as overlapping ratio or distance range is done through KNIME table processing.

Two different types are available. 

1. colocalization based on overlapping
2. colocalization based on distance between object centers

Sample images: Smapp_Ori files

Chapter 4 in the documentation. 

The Leaf Infection Tools allow to measure the area of leaves, of two stainings in different channels and of the overlap region of the two stainings. 

See: http://dev.mri.cnrs.fr/projects/imagej-macros/wiki/Leaf_Infection_Tools

Test image: http://biii.eu/node/1143