Recently both academic institutions and the pharmaceutical industry have identified 3 dimensional (3D) cellular model systems as one of the most important areas of future development in drug discovery programs and cell-based screening. High-content screening (HCS) platforms with large-scale automation and data analysis are extensively used in such programs. Automated image processing is a critical and rate-limiting step in HCS workflows that can be resolved by computational approaches. However, this has currently only been approached in a 2D manner and would benefit hugely from the ability to extend into 3D analyses.
I plan to develop a 3D image processing-statistical framework with emphasis on 3D segmentation that can be highly useful for numerous researchers working in biomedical and molecular biological fields. This project is based on my previous expertise in developing a high-content analysis pipeline around 2D image segmentation. Any current areas of research that use traditional microscopic high-throughput analyses have the potential to benefit from this tool. These include microscopic model organisms and mammalian cell research. This software will not be limited to fluorescence microscopy, but can also be applied to other platforms such as electron microscopy and in vivo imaging of animals. I will expand and characterize the means by which 3D image segmentation enables the analysis of these models in large-scale.
Continuing my previous research, one interesting application is the study of autophagy in MCF-10A mammary epithelial cells that is an easy and tractable 3D model system used in drug screening for the pathogenesis of epithelial tumours.
In summary, the development of 3D imaging tools will move the field of high-content analysis forward and open a new dimension to HCS methodologies that I hope will find its way into standard protocols. The successful completion of this project will enable me to become a leading expert in this field within Europe.
Fields of science
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