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Denis de Senneville, B.

Publications and source records attributed to Denis de Senneville, B..

2 recordsLinked to original sources

Multi-scale Assessment of Brain Blood Volume and Perfusion in the APP/PS1 Mouse Model of Amyloidosis

Vascular dysfunction is increasingly recognized to play a role in the development of Alzheimers disease (AD). The relation between vascular dysfunction and the neuropathological amyloid {beta} accumulation characteristic for AD is however unclear. The limited resolution of in vivo imaging techniques, the intricate 3D structure of the microvasculature and the different co-occurring types of amyloid {beta} accumulation in patients hamper studying this relation in patients. Here, we therefore employed the APP/PS1 mouse model, which develops parenchymal amyloid {beta} plaques, to study the effect of parenchymal amyloid {beta} plaques on the structure and function of the vasculature. Blood vessels and amyloid {beta} plaques were fluorescently labeled in vivo with lectin-DyLight594 and methoxy XO4, respectively, in APP/PS1 mice at old age. The brain tissue was cleared post-mortem with the CUBIC clearing protocol, which allowed structural imaging at microscopic resolution of the vessels and plaques in a large 3D volume. Segmentation of the vasculature enabled mapping of the microvascular Cerebral Blood Volume (mCBV), which ranged from 2 % to 5 % in the white matter and the thalamus, respectively. No mCBV differences were observed between APP/PS1 mice and wild type (WT) control mice. The effect of the amyloid {beta} plaques on vascular function was studied in vivo by measuring Cerebral Blood Flow (CBF) and Arterial Transit Time (ATT) with Arterial Spin Labeling (ASL) MRI. Similar to the mCBV findings, no differences were observed in CBF or ATT between APP/PS1 and control mice, indicating that brain vascular morphology and function in this mouse model are preserved in the presence of amyloid {beta} plaques.

neuroscience↗

Deciphering Tumour Tissue Organization by 3D Electron Microscopy and machine learning

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 tumoral tissue. We found that the size of hepatoblastoma cells correlates with the size of their nucleus, cytoplasm and mitochondrial mass. We also discovered that the blood capillary controls 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 new investigations in an emerging field that we call onconanotomy.

cancer biology↗