Abstract
(English)
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Biomedical screening at single-cell and bioparticles level has the potential to transform clinical diagnostics, but the research and development in this field are scattered in different disciplines: biophotonics, micromanipulation, machine learning, in vitro diagnostics, and clinical regulations are traditionally imparted in separate training programs. NEXTSCREEN aims to train and establish a network of researchers with the expertise required for the development of next-generation screening methods, based on automatic imaging and classification of samples moving along a liquid stream. The researchers have the objectives to reduce the cost and complexity of imaging flow cytometry; empower it with novel contrast mechanisms; build high-resolution automatic microscopes at the diffraction limit and beyond; develop real-time data processing tools able to detect and recognize the samples, circumventing the need for manual annotation. Using these technologies they will characterize blood cells and bioparticles, screening large cellular populations, with the goal to identify and characterize cancer biomarkers, in samples derived from liquid biopsies. The ultimate goal is to initiate the development of diagnostics tools, that could be adopted in clinical settings on a large scale, democratizing the use of automatic screening. The project brings together research groups, small and large companies that are leading the field of imaging flow cytometry, with complementary know-how in high-resolution microscopy, high-precision microfluidics, biotechnologies, and weakly-supervised deep learning, that will facilitate the
development of data-driven cells and bioparticle screening.
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