Periodic Reporting for period 2 - REVEAL (Neuronal microscopy for cell behavioural examination and manipulation)
Reporting period: 2022-07-01 to 2023-12-31
Hepatocellular carcinoma (HCC) is the most common liver cancer, accounting for 90% of all liver cancers, and the second most frequent cause of cancer-related deaths worldwide. One of the main hallmark of HCC is biological heterogeneity at the level of single cell morphologies and genetic fingerprints. These variations are often too subtle to detect or are extremely rare events, highlighting the need for new biophotonics tools capable to decipher the biomechanism at the origin of these modifications. In the REVEAL project, we propose to couple live cell microscopy and omics analysis to obtain evidence of the dynamic processes at the origin of HCC. By introducing the concept of neuronal microscopy, we are building a novel technique relying heavily on neural networks able to perceive, interpret, conjecture, infer, anticipate and act. In a proof of principle, a ‘cell picking neuronal microscope’ will detect and collect different subtypes of liver cells. Cells that are due to become tumoral will be collected and analyzed with integrated omics techniques. While live cell imaging provides information about the past of the cell, the molecular analysis reflects the present biological state of the cell and the prediction algorithm would then suggest the future state. The framework we propose is disruptive - we imagine a future where live cell microscopy and biomolecular analysis will form a continuum to generate a comprehensive biological timestamp for any cell of interest.
Our work is structured on 4 main objectives: 1) develop a 2D 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. The Consortium is a strong partnership of leading European research centres as well as companies, universities and medical centres: CEA-Leti, ENS-Lyon, Iprasense, Policlinico Milano, University of Munich, Sartorius, and Politechnika Warszawska.
(1) The development of neuronal microscopy is part of the ongoing evolution of cell microscopy. One important achievement of the project is the development of the ‘silico visual cortex’, the interconnection of several neural networks used for e.g. image formation, cell analysis, cell classification, and fate prediction. 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, cell tracking, cell classification, and cell prediction. Importantly the framework of deep learning allows (i) fast computation and (ii) 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 and action that will come next.
(2) A concept of a new instrument, called a neuronal cell picking microscope, has been designed and is now under fabrication. It combines a bi-modal microscopy setup with an automated cell picking system. To achieve single cell picking, the live-imaging acquisition and the cell picking module must match to within one micrometer, therefore a linear path measurement system has been developed. The integration of the ‘silico visual cortex’ to this new automation will deliver the microscope able of cell fate prediction and decision making that we envision.
(3) The bio-team of the project is focusing on hepatocellular carcinoma (HCC), the more predominant form of liver cancer. Our stated objective in REVEAL is to validate the capacity of neuronal microscopy to detect and identify the biological heterogeneity that takes root in patients on early stages of dysmetabolism in the liver, far before they experience steatohepatitis, fibrosis, liver adenomas or cancer. Under the aegis of this project, we have now 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 state in laboratory conditions. Moreover, we have developed state-of-the-art murine 2D cellular and 3D mini livers that reflect nearly every stage of liver disease- from early stage metabolic disorders to late stage cancer. As a first step, these laboratory models have been used to develop both the 2D and the 3D neuronal microscope, proving that our laboratory models do capture the heterogeneity seen in the clinical patient-derived samples. We are actively interrogating the molecular signatures of these samples to provide a state-of-the-art omics signature to the images acquired by the neuronal microscope.
(4) The main components of the 3D neuronal microscope has been successfully achieved, including: i) the fabrication and installation of the 3D imaging system and ii) the development of the multi-slice light scattering model for 3D reconstruction of living multicellular liver organoids. The coupling of the large surface scanning 3D microscope and the 3D silico visual cortex created the first 3D neuronal microscope. The microscope has been tested and validated on objects of increasing complexity, meaning phase-calibrated phantoms and liver organoids. Phantoms mimicking real biological 3D models (i.e. healthy and tumor-like organoids) were manufactured with 2-photon polimerization 3D-printer, and their reference measurements performed on metrologically calibrated optical diffraction tomography system.
(1) The development of neuronal microscopy is part of the ongoing evolution of cell microscopy. One important achievement of the project is the development of the ‘silico visual cortex’, the interconnection of several neural networks used for e.g. image formation, cell analysis, cell classification, and fate prediction. 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, cell tracking, cell classification, and cell prediction. Importantly the framework of deep learning allows (i) fast computation and (ii) 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 and action that will come next.
(2) A concept of a new instrument, called a neuronal cell picking microscope, has been designed and is now under fabrication. It combines a bi-modal microscopy setup with an automated cell picking system. To achieve single cell picking, the live-imaging acquisition and the cell picking module must match to within one micrometer, therefore a linear path measurement system has been developed. The integration of the ‘silico visual cortex’ to this new automation will deliver the microscope able of cell fate prediction and decision making that we envision.
(3) The bio-team of the project is focusing on hepatocellular carcinoma (HCC), the more predominant form of liver cancer. Our stated objective in REVEAL is to validate the capacity of neuronal microscopy to detect and identify the biological heterogeneity that takes root in patients on early stages of dysmetabolism in the liver, far before they experience steatohepatitis, fibrosis, liver adenomas or cancer. Under the aegis of this project, we have now 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 state in laboratory conditions. Moreover, we have developed state-of-the-art murine 2D cellular and 3D mini livers that reflect nearly every stage of liver disease- from early stage metabolic disorders to late stage cancer. As a first step, these laboratory models have been used to develop both the 2D and the 3D neuronal microscope, proving that our laboratory models do capture the heterogeneity seen in the clinical patient-derived samples. We are actively interrogating the molecular signatures of these samples to provide a state-of-the-art omics signature to the images acquired by the neuronal microscope.
(4) The main components of the 3D neuronal microscope has been successfully achieved, including: i) the fabrication and installation of the 3D imaging system and ii) the development of the multi-slice light scattering model for 3D reconstruction of living multicellular liver organoids. The coupling of the large surface scanning 3D microscope and the 3D silico visual cortex created the first 3D neuronal microscope. The microscope has been tested and validated on objects of increasing complexity, meaning phase-calibrated phantoms and liver organoids. Phantoms mimicking real biological 3D models (i.e. healthy and tumor-like organoids) were manufactured with 2-photon polimerization 3D-printer, and their reference measurements performed on metrologically calibrated optical diffraction tomography system.
Several advancements beyond the state-of-the-art have been made to improve the silico visual cortex algorithms. A novel pipeline that performs single-cell tracking analysis and feature quantification was developed, as well as a first software pipeline to detect heterogeneity in cell culture. This framework takes as input the results from the analysisand outputs a class label for each cell of the time-lapse. Advancements have also been made on the prediction of pathological changes on single cell behavior, developing a novel unsupervised method, StArDusTS, for automatic detection of cell anomalies.
Politechnika Warszawska has pioneered the methodology to design and print 3D microstructures as novel targets for phase imaging. Importantly, the team conceived a new pipeline to obtain phase-calibrated objects that perfectly mimic the real structure of biological cells and 3D organoids. In REVEAL, we are actively using these phantoms for characterization of the phase systems and algorithms developed in the context of 2D and 3D microscopy for cell imaging and analysis. The approach appears to be the state-of-the-art for metrological analysis in the field of quantitative phase imaging for biomedical applications.
The coupling of the large surface scanning 3D microscope and the 3D silico visual cortex created the first 3D neuronal microscope. It has been tested in operative environment with different liver organoid models and primary tissue samples of patients undergoing cholecystectomy or hepatic resection for hepatocellular carcinoma.
Overall, the scope of this project is to confirm that a new methodology is at reach: spatio-temporal omics. This goes beyond the state-of-the art of spatial-omics and will let us study in a near future the dynamic processes at the origin of liver cancer disease.
Politechnika Warszawska has pioneered the methodology to design and print 3D microstructures as novel targets for phase imaging. Importantly, the team conceived a new pipeline to obtain phase-calibrated objects that perfectly mimic the real structure of biological cells and 3D organoids. In REVEAL, we are actively using these phantoms for characterization of the phase systems and algorithms developed in the context of 2D and 3D microscopy for cell imaging and analysis. The approach appears to be the state-of-the-art for metrological analysis in the field of quantitative phase imaging for biomedical applications.
The coupling of the large surface scanning 3D microscope and the 3D silico visual cortex created the first 3D neuronal microscope. It has been tested in operative environment with different liver organoid models and primary tissue samples of patients undergoing cholecystectomy or hepatic resection for hepatocellular carcinoma.
Overall, the scope of this project is to confirm that a new methodology is at reach: spatio-temporal omics. This goes beyond the state-of-the art of spatial-omics and will let us study in a near future the dynamic processes at the origin of liver cancer disease.