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3 results on '"Alexandre Labedade"'

1. Deciphering tumour tissue organization by 3D electron microscopy and machine learning

Author

Baudouin Denis de Senneville, Fatma Zohra Khoubai, Marc Bevilacqua, Alexandre Labedade, Kathleen Flosseau, Christophe Chardot, Sophie Branchereau, Jean Ripoche, Stefano Cairo, Etienne Gontier, and Christophe F. Grosset

Subjects
Biology (General), QH301-705.5
Abstract

de Senneville et al. demonstrate an integrated workflow combining 3D imaging, manual and machine learning-based semi-automatic segmentation, mathematics and infographics to study the spatial organization of patient-derived hepatoblastoma xenograft tissues. Their approach potentially assists investigations of this childhood liver tumour and other types of tumour tissues.

Published
2021
Full Text
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2. Deciphering Tumour Tissue Organization by 3D Electron Microscopy and machine learning

Author

Etienne Gontier, Jean Ripoche, Kathleen Flosseau, Sophie Branchereau, Stefano Cairo, Alexandre Labedade, Marc Bevilacqua, Baudouin Denis de Senneville, Christophe Grosset, Christophe Chardot, Fatma Zohra Khoubai, Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Modélisation Mathématique pour l'Oncologie (MONC), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Institut Bergonié [Bordeaux], UNICANCER-UNICANCER-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Biothérapies des maladies génétiques et cancers, Université Bordeaux Segalen - Bordeaux 2-Institut National de la Santé et de la Recherche Médicale (INSERM), Bordeaux Imaging Center (BIC), Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut François Magendie-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS), Chercheur indépendant, XenTech [Evry], CHU Necker - Enfants Malades [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), AP-HP Hôpital Bicêtre (Le Kremlin-Bicêtre), Université de Bordeaux (UB), This work was supported by the charity Eva pour la Vie, La Fondation ARC pour la Recherche sur le Cancer (contract N° PJA 20191209631), La Région Nouvelle-Aquitaine, La Fondation Groupama pour la Santé and Groupama Centre-Atlantique. Microscopy Imaging was performed at the Bordeaux Imaging Centre, which is a member of the FranceBioImaging national infrastructure (ANR-10-INBS-04)., ANR-10-INBS-0004,France-BioImaging,Développment d'une infrastructure française distribuée coordonnée(2010), Bioingénierie tissulaire (BIOTIS), Université de Bordeaux (UB)-Institut National de la Santé et de la Recherche Médicale (INSERM), Istituto di Ricerca Pediatrica [Padova, Italy] (IRP), Hôpital Bicêtre, Istituto di Ricerca Pediatrica Città della Speranza, Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Institut Bergonié [Bordeaux], Université de Bordeaux (UB)-Institut François Magendie-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Université Paris-Saclay, Institut National de la Santé et de la Recherche Médicale (INSERM), PlaFRIM (https://www.plafrim.fr), and Grosset, Christophe

Subjects
[SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, Medicine (miscellaneous), Pilot Projects, [MATH] Mathematics [math], Mitochondrion, computer.software_genre, Machine Learning, Tumour tissue, 0302 clinical medicine, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], Image Processing, Computer-Assisted, Biology (General), [MATH]Mathematics [math], Child, Cancer, 0303 health sciences, mathematics, Liver Neoplasms, medicine.anatomical_structure, 030220 oncology & carcinogenesis, Cancer, hepatoblastoma, patient-derived xenograft, 3D imaging, serial blockface scanning electron microscopy, nanotomy, mathematics, General Agricultural and Biological Sciences, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Hepatoblastoma, 3d electron microscopy, patient-derived xenograft, QH301-705.5, [SDV.CAN]Life Sciences [q-bio]/Cancer, Biology, Machine learning, General Biochemistry, Genetics and Molecular Biology, Article, 03 medical and health sciences, nanotomy, [SDV.CAN] Life Sciences [q-bio]/Cancer, 3D imaging, Organelle, Electron microscopy, medicine, Humans, 030304 developmental biology, [SDV.MHEP.PED]Life Sciences [q-bio]/Human health and pathology/Pediatrics, business.industry, [SDV.MHEP.HEG]Life Sciences [q-bio]/Human health and pathology/Hépatology and Gastroenterology, hepatoblastoma, medicine.disease, [STAT.ML] Statistics [stat]/Machine Learning [stat.ML], [SDV.IB.IMA] Life Sciences [q-bio]/Bioengineering/Imaging, Cytoplasm, Microscopy, Electron, Scanning, serial blockface scanning electron microscopy, Ultrastructure, Cancer imaging, Artificial intelligence, serial block-face scanning electron microscopy, business, Nucleus, computer
Abstract

Despite recent progress in the characterization of tumour components, the tri-dimensional (3D) organization of this pathological tissue and the parameters determining its internal architecture remain elusive. Here, we analysed the spatial organization of patient-derived xenograft tissues generated from hepatoblastoma, the most frequent childhood liver tumour, by serial block-face scanning electron microscopy using an integrated workflow combining 3D imaging, manual and machine learning-based semi-automatic segmentations, mathematics and infographics. By digitally reconstituting an entire hepatoblastoma sample with a blood capillary, a bile canaliculus-like structure, hundreds of tumour cells and their main organelles (e.g. cytoplasm, nucleus, mitochondria), we report unique 3D ultrastructural data about the organization of tumour tissue. We found that the size of hepatoblastoma cells correlates with the size of their nucleus, cytoplasm and mitochondrial mass. We also found anatomical connections between the blood capillary and the planar alignment and size of tumour cells in their 3D milieu. Finally, a set of tumour cells polarized in the direction of a hot spot corresponding to a bile canaliculus-like structure. In conclusion, this pilot study allowed the identification of bioarchitectural parameters that shape the internal and spatial organization of tumours, thus paving the way for future investigations in the emerging onconanotomy field., de Senneville et al. demonstrate an integrated workflow combining 3D imaging, manual and machine learning-based semi-automatic segmentation, mathematics and infographics to study the spatial organization of patient-derived hepatoblastoma xenograft tissues. Their approach potentially assists investigations of this childhood liver tumour and other types of tumour tissues.

Published
2021

3. Deciphering tumour tissue organization by 3D electron microscopy and machine learning.

Author

de Senneville BD, Khoubai FZ, Bevilacqua M, Labedade A, Flosseau K, Chardot C, Branchereau S, Ripoche J, Cairo S, Gontier E, and Grosset CF

Subjects
Child, Humans, Pilot Projects, Hepatoblastoma ultrastructure, Image Processing, Computer-Assisted, Liver Neoplasms ultrastructure, Machine Learning, Microscopy, Electron, Scanning
Abstract

Despite recent progress in the characterization of tumour components, the tri-dimensional (3D) organization of this pathological tissue and the parameters determining its internal architecture remain elusive. Here, we analysed the spatial organization of patient-derived xenograft tissues generated from hepatoblastoma, the most frequent childhood liver tumour, by serial block-face scanning electron microscopy using an integrated workflow combining 3D imaging, manual and machine learning-based semi-automatic segmentations, mathematics and infographics. By digitally reconstituting an entire hepatoblastoma sample with a blood capillary, a bile canaliculus-like structure, hundreds of tumour cells and their main organelles (e.g. cytoplasm, nucleus, mitochondria), we report unique 3D ultrastructural data about the organization of tumour tissue. We found that the size of hepatoblastoma cells correlates with the size of their nucleus, cytoplasm and mitochondrial mass. We also found anatomical connections between the blood capillary and the planar alignment and size of tumour cells in their 3D milieu. Finally, a set of tumour cells polarized in the direction of a hot spot corresponding to a bile canaliculus-like structure. In conclusion, this pilot study allowed the identification of bioarchitectural parameters that shape the internal and spatial organization of tumours, thus paving the way for future investigations in the emerging onconanotomy field., (© 2021. The Author(s).)

Published
2021
Full Text
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