QuimP software for quantifying cellular morphodynamics

Over the last twenty years live cell microscopy has made enormous progress in visualising dynamic processes inside living cells. To learn how specific cellular functions are normally regulated or affected by disease requires mapping cellular dynamics in a quantitative manner. This is a difficult task, because cells are highly deformable and can adopt complex shapes. Therefore we cannot use landmarks to map corresponding regions within one cell over time, or, what is even more difficult, aggregate data from multiple cells. To date no general solutions to this problem exist. We have pioneered mapping the regulatory dynamics of the actin cytoskeleton in cells. Assembly of actin into dense networks of filaments adjacent to the cell membrane drives cellular shape changes and migration, as needed for example when immune cells chase bacterial intruders.

To this end we have developed QuimP (Quantitative Imaging of Membrane Proteins) image analysis software. The main tasks QuimP performs are 1) automated tracing of cell outlines in image time series, 2) matching corresponding regions on the cell boundary at subsequent time points, 3) extracting spatial distributions of fluorescently labelled constituents of the membrane or the cell cortex. The results can be analysed in many different ways. A large number of global parameters such as cell speed, directionality of movement, elongation and many more can be easily computed. Detailed spatio-temporal maps can be generated to perform statistical analyses of measurements such as fluorescence or membrane curvature, and ask how they are related. A main feature is that maps can be processed to automatically identify particular events, for example the formation of actin rich protrusions driving cell motility. These events can then serve as landmarks and multiple events from many cells with different molecular labels can be combined to obtain a detailed picture of the underlying regulatory dynamics.

QuimP has been used by us, and groups in the UK and worldwide to study different aspects of cell motility. Users have closely informed its development. It has contributed to a number of important discoveries, for example that in metastatic breast cancers cells membrane protrusions and retractions are highly synchronized both in space and in time, which can explain why these cells move more efficiently than non-metastatic cells. These results suggest the possibility to use QuimP for example to assay the invasiveness of cancer cells.

Because of its origin QuimP is currently regarded as a highly specialised tool for the cell motility research community. However, its ability to map spatio-temporal cellular dynamics at the membrane and in the cell cortex, makes QuimP an obvious choice for studying a host of other problems. These include in particular cellular responses to external stimuli, which are transmitted through receptors in the cell membrane. The molecular signalling machinery that is triggered by the activation of membrane receptors is in large parts closely associated with the membrane and therefore easily accessible through QuimP. In a recent example QuimP has been used to study how binding of chemical signals to membrane receptors results in their subsequent internalisation, important to prevent overly prolonged cell stimulation.

We here propose to enhance the usability of QuimP and make it accessible to a broader user group. This requires changes to the user interface and improving documentation, better handling of large-scale image data, and will benefit from integration of our most recent developments in other areas, which concern cell detection and 3D cell surface reconstruction.

Live cell fluorescence microscopy is the method of choice for studying cellular dynamics. Our QuimP image analysis software addresses the difficult problem of extracting patterns of fluorescence in an automated and quantitative manner. It consists of a set of plugins for the popular image analysis package ImageJ, and was first released in 2002. Originally, QuimP has been developed to correlate cortical fluorescence distributions of the actin-myosin system and its regulators with local cell shape changes. Since then others have used it to investigate many different aspects of cell motility, in a wide range of cell types, or to screen motility mutants. QuimP provides a unique method for linking the evolution of specific membrane regions over time, which makes it possible to extract particular dynamic events, for example actin rich protrusions, in an automated way. Elaborate spatial statistics can be performed on multiple events gathered from multiple cells expressing different fluorescent reporters.

The main goals of this proposal are software enhancement and maturation, and introducing QuimP to a wider user community, aiming at researchers working on the spatial organisation of membrane receptor signalling, particularly GPCR signalling, a major drug target. The latter demands better support for working with large-scale screening data. We will therefore implement seamless connectivity with the OMERO image database system, the open source standard for high-throughput imaging data. Furthermore, we will enhance the automated recognition of cells with more complex cell boundaries, and under different imaging conditions, enhance graphical user interfaces and error handling, support the user community by regular QuimP updates, improved documentation, and by facilitating exchange between users and developers, and amongst users, through online tools. In addition we will integrate our latest developments for reconstructing and tracking cell surfaces in 3D.

The resource we propose addresses three strategic priority areas, which are quantitative, data-driven biology, systems approaches to the biosciences and technology development for the biosciences. The extraction of meaningful, quantitative data from live-cell microscopy image time-series is a major bottleneck. Our QuimP software for analysing cellular morphodynamics is one of very few solutions that offers state of the art computational analysis of cellular dynamics, but does not require users to have expert-knowledge in digital image processing.

QuimP has already contributed to high quality research, new knowledge and scientific advancement as evidenced by a number of publications where it has been employed. More generally, the work contributes to the UK's economic competitiveness by supporting the bioscience research community with key enabling technology in form of software. QuimP enables biologists to become more quantitative, resulting in deeper theoretical analysis of complex biological image datasets, and greater understanding of biological systems as a whole. Therefore the proposed resource directly enhances the users' research capacity, knowledge and skills in the aforementioned strategic priority areas. We specifically take on one of the big challenges in our high-technology age, namely that of extracting information from complex "big data". These emerging technologies will eventually create, and are creating already, new industries and jobs that demand people with high-level multidisciplinary skills and experience. By training users in our technology we directly contribute to equipping biologist with the appropriate knowledge in managing, analysing and sharing of large and complex imaging data. Individual researchers greatly benefit by having free and unrestricted access to our technology, with multiplicative effects for Research Councils and other funders of biomedical research.

At the same time the commercial private sector can integrate our technology into their products, or start building services around our resources, for example by adding specific functionality which customers would have to pay for. There are many prominent examples where open-source technology is employed by companies in this way. A particular area where we envisage this to take place is large-scale drug screening. For example, using QuimP it was shown that in metastatic breast cancers cells membrane protrusions and retractions are highly synchronized both in space and in time, which can explain why these cells move more efficiently than non-metastatic cells. Results like these might lead to the development of QuimP based assays of the invasiveness of cancer cells and novel drug screens. Therefore the technology we develop bears direct relevance for health and well-being.

The PDRA who will develop QuimP further will be immersed in a multidisciplinary environment and develop not only skills in computational image analysis, use of image database and software design, but also in setting up efficient tools for communicating with users of the technology. These are skills that are highly sought after in a number of employment sectors, such as IT, pharma and academia.