Automated

WASH, Exo84, and cortactin spot detection and codistribution analysis To detect endosomes, an automatic Otsu threshold is applied to the Gaussian-filtered MT1-MMP–positive endosome image (= 1.5 pixels for the sample image). Statistics about each endosome are then saved, for example random positioning of spots can be compared to actual positioning. For each endosome, WASH and Exo84 (or WASH and cortactin) spots are searched for in a neighboring of x pixels in their respective channel. Their number and position are saved per endosome (**see the macro in Text file S2 downloadable from here**).

From the position of WASH and Exo84 (or WASH and cortactin) spots around each endosomes, each WASH spot is paired with its closest Exo84 (or cortactin) spot neighbor, optimized over all spots around this endosome.

This allowed measuring of the distribution of distance between WASH-Exo84 (or WASH-cortactin) spots (**for the co-distribution analysis, see matlab scripts in Zip file S3 downloadable).

Simple macro to separates blobs.

• Load the ImageJ sample image "Blobs"
• Run the plugin Morphological Segmentation
• Display the overlaid

This macro depends on "Morphological Segmentation" component of the MorphoLibJ library, which should be installed via update sites.

This macro was designed to measure the size of the scratch wound in a wound scratch assay. It uses an edge-detection and thresholding technique.

It will batch process all images in a directory. Images captured by time-lapse should be compiled into stacks using a tool similar to "Metamorph nd & ROI files importer (nd stack builder)" by Fabrice P. Cordelières. Images to be analyzed should be placed in one directory (Source Directory). A second directory should be created to save results files and images (Destination Directory). Setting correct Lower and Upper thresholds is important to obtain a good result. Two macros are available, one using edge detection, the second one using background subtraction.

The workflow contains a Matlab package (plusTipTracker) for segmentation and tracking of microtubule tips, based on fluorescence time-lapse movies from microtubule tip markers such as EB-GFP. The tracking model accounts for the specific movement characteristics of microtubules Moreover, scripts for secondary analysis of detected microtubule paths are provided.

plusTipTracker is part of u-track 2.0 package. The workflow is described in the reference. 

Python-bioformats is a Python wrapper for Bio-Formats, a standalone Java library for reading and writing life sciences image file formats. Bio-Formats is capable of parsing both pixels and metadata for a large number of formats, as well as writing to several formats. Python-bioformats uses the python-javabridge to start a Java virtual machine from Python and interact with it. Python-bioformats was developed for and is used by the cell image analysis software CellProfiler (cellprofiler.org). PyPI record: https://pypi.python.org/pypi/python-bioformats Documentation: http://pythonhosted.org/python-bioformats/ GitHub repository: https://github.com/CellProfiler/python-bioformats Report bugs here: https://github.com/CellProfiler/python-bioformats/issues python-bioformats is licensed under the GPL license to be compatible with the copy of Bio-Formats that is distributed with the package, but is compatible with a BSD license if loci_tools.jar is replaced with SCIFIO jars. See the accompanying file LICENSE for details.