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. 2018 Aug 1;34(15):2695-2697.
doi: 10.1093/bioinformatics/bty169.

QuimP: analyzing transmembrane signalling in highly deformable cells

Piotr Baniukiewicz  1 , Sharon Collier  1 , Till Bretschneider  1
Affiliations

QuimP: analyzing transmembrane signalling in highly deformable cells

Piotr Baniukiewicz et al. Bioinformatics. .

Abstract

Summary: Transmembrane signalling plays important physiological roles, with G protein-coupled cell surface receptors being particularly important therapeutic targets. Fluorescent proteins are widely used to study signalling, but analyses of image time series can be challenging, in particular when cells change shape. QuimP software semi-automatically tracks spatio-temporal patterns of fluorescence at the cell membrane at high spatial resolution. This makes it a unique tool for studying transmembrane signalling, particularly during cell migration in immune or cancer cells for example.

Availability and implementation: QuimP (http://warwick.ac.uk/quimp) is a set of Java plugins for Fiji/ImageJ (http://fiji.sc) installable through the Fiji Updater (http://warwick.ac.uk/quimp/wiki-pages/installation). It is compatible with Mac, Windows and Unix operating systems, requiring version >1.45 of ImageJ and Java 8. QuimP is released as open source (https://github.com/CellDynamics/QuimP) under an academic licence.

Supplementary information: Supplementary data are available at Bioinformatics online.

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Figures

Fig. 1.
Fig. 1.
QuimP GUI for analyzing cell motility. (a) QuimP toolbar, with tools arranged in the order of a typical workflow. Upper row: Open image time series, and main data analysis plugins (BOA: cell segmentation, ECMM: contour tracking, ANA: sampling of cortical fluorescence, QA: detailed quantitative analysis and visualization in the form of spatial-temporal maps, PA: protrusion analysis (experimental, working Matlab routines are provided)). Bottom row: Pre- and post-processing plugins (DIC: DIC image reconstruction, RW: customized random walk segmentation, Mask: Cell outline to mask converter). (b) BOA segmentation window with novel feature of external contour filters. (c) Interface for the new random walk segmentation module. (d) New BOA plugin that integrates random walk and active contour segmentation. (e) Conversion tool to export csv files. (f) Exemplary results from the QA module: cell outlines, fluorescence map, convexity map
See this image and copyright information in PMC

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