Optical tissue clearing and machine learning can precisely characterize extravasation and blood vessel architecture in brain tumors

Autor: Andreas Kjaer, Petra Hamerlik, Casper Hempel, Johann Mar Gudbergsson, Thomas Hartig Braunstein, Thomas Lars Andresen, Frederikke P. Fliedner, Anders Elias Hansen, Elisabeth Anne Adanma Obara, Kasper Bendix Johnsen, Serhii Kostrikov
Rok vydání: 2021
Předmět:
0301 basic medicine
Medicine (miscellaneous)
computer.software_genre
DISEASE
Machine Learning
Mice
0302 clinical medicine
NANOPARTICLES
Biology (General)
Microscopy
Tissue clearing
Brain Neoplasms
Optical Imaging
Extravasation
Perfusion
medicine.anatomical_structure
030220 oncology & carcinogenesis
GLIOMA
Female
General Agricultural and Biological Sciences
CYCLING HYPOXIA
Blood vessel
Clearance
RESECTION
QH301-705.5
BEVACIZUMAB
Brain tumor
GLIOBLASTOMA
Machine learning
Article
General Biochemistry
Genetics and Molecular Biology
TARGETED DRUG-DELIVERY
03 medical and health sciences
Cell Line
Tumor
medicine
Animals
Humans
business.industry
Nanobiotechnology
QUANTIFICATION
Drug accumulation
medicine.disease
CNS cancer
030104 developmental biology
Preclinical research
VISUALIZATION
Cancer imaging
Artificial intelligence
Glioblastoma
business
computer
Ex vivo
Extravasation of Diagnostic and Therapeutic Materials
Zdroj: Communications Biology, Vol 4, Iss 1, Pp 1-16 (2021)
Kostrikov, S, Johnsen, K B, Braunstein, T H, Gudbergsson, J M, Fliedner, F P, Obara, E A A, Hamerlik, P, Hansen, A E, Kjaer, A, Hempel, C & Andresen, T L 2021, ' Optical tissue clearing and machine learning can precisely characterize extravasation and blood vessel architecture in brain tumors ', Communications Biology, vol. 4, no. 1, 815 . https://doi.org/10.1038/s42003-021-02275-y
Communications Biology
ISSN: 2399-3642
Popis: Precise methods for quantifying drug accumulation in brain tissue are currently very limited, challenging the development of new therapeutics for brain disorders. Transcardial perfusion is instrumental for removing the intravascular fraction of an injected compound, thereby allowing for ex vivo assessment of extravasation into the brain. However, pathological remodeling of tissue microenvironment can affect the efficiency of transcardial perfusion, which has been largely overlooked. We show that, in contrast to healthy vasculature, transcardial perfusion cannot remove an injected compound from the tumor vasculature to a sufficient extent leading to considerable overestimation of compound extravasation. We demonstrate that 3D deep imaging of optically cleared tumor samples overcomes this limitation. We developed two machine learning-based semi-automated image analysis workflows, which provide detailed quantitative characterization of compound extravasation patterns as well as tumor angioarchitecture in large three-dimensional datasets from optically cleared samples. This methodology provides a precise and comprehensive analysis of extravasation in brain tumors and allows for correlation of extravasation patterns with specific features of the heterogeneous brain tumor vasculature.
Kostrikov et al. report a deficiency of transcardial perfusion in brain tumor vasculature, which leads to exaggeration of drug extravasation measurements. They then demonstrate how optical tissue clearing can help to overcome this limitation and provide two machine learning-based image analysis workflows enabling detailed quantitative characterization of compound extravasation patterns as well as tumor angioarchitecture in large three-dimensional datasets.
Databáze: OpenAIRE
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