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

• Published in: Communications biology Vol. 4; no. 1; pp. 815 - 16
• Main Authors: Kostrikov, Serhii, Johnsen, Kasper B., Braunstein, Thomas H., Gudbergsson, Johann M., Fliedner, Frederikke P., Obara, Elisabeth A. A., Hamerlik, Petra, Hansen, Anders E., Kjaer, Andreas, Hempel, Casper, Andresen, Thomas L.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 01-07-2021; Nature Publishing Group; Nature Portfolio
• Subjects: 13/51; 14/19; 14/28; 59; 631/1647/328; 631/61/350; 631/67/1922; 631/67/2321; 64/60; 692/308/2778; Animals; Biology; Biomedical and Life Sciences; Brain architecture; Brain cancer; Brain Neoplasms - blood supply; Brain tumors; Cell Line, Tumor; Drug development; Extravasation; Extravasation of Diagnostic and Therapeutic Materials - diagnostic imaging; Female; Glioblastoma - blood supply; Humans; Image processing; Learning algorithms; Life Sciences; Machine Learning; Mice; Microenvironments; Neuroimaging; Optical Imaging; Perfusion; Tumors
• ISSN: 2399-3642; 2399-3642
• DOI: 10.1038/s42003-021-02275-y

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.