Cécile Pohar𝟏,𝟐, Yousr Rekik𝟏, Benoit Gallet𝟑, Emmanuelle Tillet𝟒, Agnès Castan𝟒, Mireille Chevallet𝟏, Nicola Vigano𝟐, Pierre−Henri Jouneau𝟐, Aurélien Deniaud𝟏 1 Univ. Grenoble Alpes – CEA – CNRS, Laboratoire de Chimie et Biologie des Métaux, Grenoble, France; 2 Univ. Grenoble Alpes, CEA, IRIG, Laboratoire Modélisation et Exploration des Matériaux, 38000 Grenoble, France; 3Institut de Biologie Structurale, CEA, CNRS, Univ. Grenoble Alpes, 71 avenue des Martyrs, F-38042 Grenoble, France; 4 Univ. Grenoble Alpes - CEA – CNRS, Laboratoire de Biologie du Cancer et de l’Infection, Grenoble, France 1 Liver Architecture : Structural unit : hepatic lobule formed by hepatocyte cords Sinusoids : fine capillaries connecting the hepatic artery and portal vein to the central vein LSEC : complex structure, wide regions + thin regions with fenestrae LSEC fenestration : bidirectional flow of small molecules like an ultrafiltration system. Figure 1 : Liver architecture 3D electron microscopy (3D-EM) enables to obtain the ultrastructure of biological samples. Liver Sinusoidal Endothelial Cell fenestration (LSEC) forms an interface that allows the transport of essential molecules between blood and hepatocytes. LSEC fenestration can be characterised by Focused Ion Beam - Scanning Electron Microscope (FIB-SEM) at nanometer resolution. Analysing how Bone Morphogenetic Protein 9 (BMP9) affects the structure of LSEC is essential to understand the role of BMP9. Context and Objectives : Analysis of Liver Sinusoidal Endothelial Cell fenestration from 3D electron microscopy data Contact : cecile.pohar@cea.fr / cecile.pohar@gmail.com Methods : 2 • Minimum voxel size ~ 3 x 3 x 3 nm𝟑 • Thickness of a FIB slice down to 3 nm • Area selected : tens of microns Results : 3 3D Electron Microscopy Figure 2 : Microscope FIB-SEM Zeiss crossbeam XB 550 Image analysis : Semantic segmentation Deep Learning : nnU-Net Figure 3 : LSEC segmentation Ilastik software • Workflow Carving • Watershed method group of supervoxels • Workflow Pixel Classification • Random Forest Classifier pixels features • nnU-Net : Convolutional neural network (CNN) architecture Model tested on data from Medical Segmentation Decathlon Train from a labeled set of Ground Truth Optimising training nnU-Net : Python code in progress Figure 4 : Hepatic sinusoid segmentation mask : Ground Truth ACKNOWLEDGEMENTS – This research is part of the LabEx GRAL. This project received funding from the Chemistry Biology Health (CBH) Graduate School of University Grenoble Alpes (ANR-17-EURE-0003). This work was also supported by the CEA DRF-Impulsion grant FIB-Bio and the Région Auvergne Rhône-Alpes. This project is supported by GRAL. This work uses the Nanocaracterisation platforms (PFNC) @ Minatec and also the ME platform of the Institute of Structural Biology (IBS). The EM facility is supported by the Auvergne Rhône-Alpes Region, the Fondation Recherche Medicale (FRM). I would like to thank Met&Or team for fruitful discussions. 4 Conclusion Perspectives • 3D Tissue environment • FIB-SEM acquisitions pixel size : 64nm𝟑and 1000nm𝟑 • Complexity of LSEC segmentation : difficult shape FIB-SEM acquisitions 2D image-stack registration 3D reconstruction Quantitative data : LSEC fenestration • Diameter, number, porosity of LSEC fenestration • Diameter between 50 and 300 nm • Defenestration liver diseases • Quantitative measurements of LSEC fenestration : method to be automated • Hypothesis to be confirmed : BMP9 maintains LSEC fenestration • Ability of the deep learning model nnU-Net to adapt to a new task nnU-Net : a deep learning-based segmentation method • Train the model on part of the sub-volume select a random region of the sub-stack by programming in Python • LSEC 3D Ultrastructure study LSEC fenestration closer to reality • Enhance our understanding on LSEC architecture in vivo Data volume : around 60 Go software limits large dataset size for segmentation software • nnU-Net generation of segmenta