Workflow

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. 

This simple KNIME workflow solution tracks 2D objects/cells in time series. After a few intensity based preprocessing steps, objects/cells are segmented first, then it uses Fiji TrackMate LAP method for the tracking task.

Documentation starts from p23 of the linked PDF. 

Example Image: mitocheck_small.tif (2.9M)

These two KNIME workflow solutions are similar: first one detects nuclei and spots inside the nuclei without taking care of surrounding regions, i.e. mitochondria. The second one provides the full solution including spots in mitochondria.

see section 2.4 for KNIME workflow. Section 2.3 is also available, using Fiji. 

Sample image: hela-cells.tif (674k x 3)

The article describes how a FRAP experiment can be conducted and subsequently analyzed. This includes steps in ImageJ and subsequent normalization of the intensity data.

This is a qualitative analysis, and curve fitting is done using Excel. 

Requires "Template matching and Slice alignment plugin"

OMERO is an image database application consisting of a server and several clients, the most important of which are the web client and _Insight_ java application. Metadata are extracted from images that have been imported (either using the Insight client, or directly from the filesystem), and this is accessible for search. A standardised hierarchy of _Project > Dataset > Image_ in which image thumbnails can be viewed, combined with group membership, tagging, and attachment of results and other files gives a powerful framework for organising scientific image data. Images can also be analysed server-side or client-side within the base OMERO application or one of its many extensions. OMERO has APIs for extension in multiple languages: java, python, C++ and MATLAB; and such extensions have easy access to the image data and metadata in the database.