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CellsBox: a modular system for automated cell imaging experiments

Project description

Integrating cell biology experiments in a box

Results from cell biology experiments are inherently noisy due to cell-to-cell variability in biological processes. To address this issue, the EU-funded CellsBox project will develop an integrated cell culture and imaging unit that provides information at the single-cell level. The idea is to offer a systematic and reproducible system for conducting cell biology experiments that can provide real-time information. The automated CellsBox system will also be able to accommodate deep learning and AI technologies in the analysis, further advancing the types of experiments that can be undertaken. Implementation of this disruptive technology will assist cellular investigation at an unprecedented level.


"Based on research from ERC CoG HydroSync where we developed both hardware and software approaches to cell imaging, we are in the position to develop translationally a cell culture and imaging unit, with integrated modular hardware and analysis software allowing automated robust performance. This promises to not just significantly bring down the costs for a broad spectrum of cell biology and single cell imaging experiments. It will also make these experiments more reproducible, systematic and easily accessible in standardised fashion. Perhaps most important of all, by designing in an integrated (whilst modular architecture) system all the components of the experiment (optics, mechanics, cell environment and fluids control, cell sample chambers, analysis), we make it possible to rapidly feedback information on the state of the sample into actions by any of the other modules, thus allowing a new space of experimental design. This automation in running the experimental stage of cell biology, microbiology, infectious disease models, early embryo developmental work, etc (i.e. any of the many situations where one aims to follow the properties of individual cells) also aligns to the current revolution triggered by machine and deep learning approaches. We can imagine a day when integrated experimental ""CellsBoxes"" perform, in a hypothesis-driven optimised and tireless fashion, a battery of experiments that today would simply be inconceivable. This project addresses one of the experimental bottlenecks that still make too much of biological and medical research subject to bias and poor reproducibility, and change the nature of point-of-care cell tissue analysis. It will be disruptive in the current landscape of optical cell imaging, a market > $1bn globally."



Net EU contribution
€ 149 931,00
Trinity lane the old schools
CB2 1TN Cambridge
United Kingdom

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East of England East Anglia Cambridgeshire CC
Activity type
Higher or Secondary Education Establishments
Other funding
€ 0,00

Beneficiaries (1)