Our work was structured on four important objectives: 1) develop a neuronal microscope, (2) develop a neuronal cell picking microscope, (3) discover biological signatures at the origin of liver cancer disease, and (4) develop a 3D neuronal microscope.
(1) In the REVEAL project we have contributed to three important evolutionary leaps:
a- computational microscopy, i.e. optics is replaced by algorithms
b- neuronal microscopy, i.e. algorithms are replaced by neural networks
c- an all-neural framework, i.e. physical and biological models can be built upon neural networks, from sample reconstruction to the of study of cell-cell interactions within a large population.
One important achievement of the project is the development of the ‘silico visual cortex’, the interconnection of several neural networks used for image formation and analysis. This software was coupled with a dedicated imaging platform, allowing for time-lapse imaging of cell cultures with four different imaging modalities. Overall, we have demonstrated that several functionalities are now at reach with convolutional neural networks, i.e. image formation, cell analysis, tracking, classification, and prediction. Importantly the framework of deep learning allows fast computation and continuous performance improvements. The ability to conduct live imaging and analysis in quasi real-time (>10.000 cells in few tens of seconds), let us envision the microscope able of decision that will come next.
(2) A concept of a new instrument, called a neuronal cell picking microscope, has been designed and validated with the first proofs-of-concept. It combines a bi-modal microscopy setup with an automated cell picking system, and has been integrated with the silico visual cortex for detection of phenotypic heterogeneity. The system is still on validation to assess its picking performances.
(3) Our stated objective in REVEAL is to validate the capacity of neuronal microscopy to detect and identify the biological heterogeneity that take root in patients at early stages of dysmetabolism in the liver. Under the aegis of this project, we characterized many different types of patient derived cells that grow on a 2D matrix in laboratory culture as well as patient derived normal, or tumoral 3D organoids (mini livers) that reflect either the health or the cancerous states. Moreover, we have developed state-of-the-art murine 2D and 3D models that reflect nearly every stage of liver disease- from early stage metabolic disorders to late stage cancer. These laboratory models have been used to develop both the 2D and the 3D neuronal microscope. We are actively interrogating the molecular signatures of these samples with the aim to provide a state-of-the-art omics signature to the images acquired by the neuronal microscope.
(4) We successfully develop and metrologically validate a 'smart' 3D intensity-only diffraction microscope, specifically designed for large-scale bio-studies of multi-scattering samples. The imaging platform features two imaging arms: a guiding arm for pre-selection of 3D objects and a high-resolution imaging for real time 3D imaging. The microscope has been integrated with a reconstruction software consisting of a multi-slice light scattering model for 3D reconstruction of living multicellular objects. The microscope has been tested and validated on objects of increasing complexity, meaning phase-calibrated phantoms and liver organoids. The platform has been integrated with neural network-based segmentation algorithms for automatic analysis of healthy and tumoral liver organoids.
