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

Serhii Kostrikov, Kasper B. Johnsen, Thomas H. Braunstein, Johann M. Gudbergsson, Frederikke P. Fliedner, Elisabeth A.A. Obara, Petra Hamerlik, Anders E. Hansen, Andreas Kjaer, Casper Hempel*, Thomas L. Andresen

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

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.

Original languageEnglish
Article number815
JournalCommunications Biology
Volume4
Issue number1
Number of pages16
ISSN2399-3642
DOIs
Publication statusPublished - 2021

Bibliographical note

Funding Information:
Microscopy imaging and image analysis described in the present paper was done using the instruments at the Core Facility for Integrated Microscopy, Department of Biomedical Sciences, University of Copenhagen. We are thankful to Esben Christensen and Trine Bjørnbo Engel, Department of Health Technology, Technical University of Denmark, for providing syngeneic colorectal cancer tumor models. We thank Clara Prats Gavalda and Pablo Varas, Core Facility for Integrated Microscopy, Department of Biomedical Sciences, University of Copenhagen and Sebastian Rhode from Zeiss Microscopy for sharing their expertise. We are thankful to Nanna Elmstedt Bild, Department of Health Technology, Technical University of Denmark for assisting with creation of schematic drawings for the present paper. This research was funded by generous grants from the Lundbeck Foundation Research Initiative on Brain Barriers and Drug Delivery (Grant no. R155-2013-14113).

Publisher Copyright:
© 2021, The Author(s).

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