QuimP

Description

Summary

QuimP is software for tracking cellular shape changes and dynamic distributions of fluorescent reporters at the cell membrane. QuimP's unique selling point is the possibility to aggregate data from many cells in form of spatio-temporal maps of dynamic events, independently of cell size and shape. QuimP has been successfully applied to address a wide range of problems related to cell movement in many different cell types. 

Introduction

In transmembrane signalling the cell membrane plays a fundamental role in localising intracellular signalling components to specific sites of action, for example to reorganise the actomyosin cortex during cell polarisation and locomotion. The localisation of different components can be directly or indirectly visualised using fluorescence microscopy, for high-throughput screening commonly in 2D. A quantitative understanding demands segmentation and tracking of whole cells and fluorescence signals associated with the moving cell boundary, for example those associated with actin polymerisation at the cell front of locomoting cells. As regards segmentation, a wide range of methods can be used (threshold based, region growing, active contours or level sets) to obtain closed cell contours, which then are used to sample fluorescence adjacent to the cell edge in a straightforward manner. The most critical step however is cell edge tracking, which links points on contours at time t to corresponding points at t+1. Optical flow methods have been employed, but usually fail to meet the requirement that total fluorescence must not change. QuimP uses a method (ECMM, electrostatic contour migration method (Tyson et al., 2010) which has been shown to outperform traditional level set methods. ECMM minimises the sum of path lengths connecting all pairs of points, equivalent to minimising the energy required for cell deformation. The original segmentation based on an active contour method and outline tracking algorithms have been described in (Dormann et al., 2002; Tyson et al., 2010; Tyson et al., 2014).

Screenshot

Wound healing assay: analysis in CellProfiler

Description

In this example, cells are grown as a tissue monolayer. Rather than identifying individual cells, this pipeline quantifies the area occupied by the tissue sample.

 

Download package also contains example images. 

has topic

Wound healing assay: analysis in ImageJ

Description

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.

WIS-PhagoTracker

Description

WIS-PhagoTracker is a software application for quantitative analysis of high throughput cell migration assay. The cell migration assay is based on a modified Phagokinetic tracks procedure, in which motile cells "leave their tracks" on a specialized surface. These tracks are visualized using a screening microscope.

WIS-PhagoTracker enables morphometric analysis of such tracks. It uses multiscale segmentation algorithm for fine detection of tracks and cells boundaries.

Following the segmentation step, it quantifies various morphometric parameters for each track, such as track area, perimeter, major and minor axis and solidity. All these measures are calculated for each track in each well of a well plate and saved for further statistical analysis WIS-PhagoTracker supports all the analysis phases starting from preprocessing, finding tracks of selected wells or a whole plate, through viewing the results and manually rejecting tracks to statistical analysis of the results. It also supports batch processing of several plates, and analysis of single image files. A user interface enables the user to modify the relevant parameters of the process, according to specific image's requirements.

Results are exported into Excel readable files.