Optical tissue clearing and machine learning can precisely characterize extravasation and blood vessel architecture in brain tumors
Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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Optical tissue clearing and machine learning can precisely characterize extravasation and blood vessel architecture in brain tumors. / 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.
I: Communications Biology , Bind 4, Nr. 1, 815, 07.2021.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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TY - JOUR
T1 - Optical tissue clearing and machine learning can precisely characterize extravasation and blood vessel architecture in brain tumors
AU - Kostrikov, Serhii
AU - Johnsen, Kasper B.
AU - Braunstein, Thomas H.
AU - Gudbergsson, Johann M.
AU - Fliedner, Frederikke P.
AU - Obara, Elisabeth A. A.
AU - Hamerlik, Petra
AU - Hansen, Anders E.
AU - Kjaer, Andreas
AU - Hempel, Casper
AU - Andresen, Thomas L.
PY - 2021/7
Y1 - 2021/7
N2 - 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.
AB - 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.
KW - TARGETED DRUG-DELIVERY
KW - CYCLING HYPOXIA
KW - GLIOBLASTOMA
KW - GLIOMA
KW - DISEASE
KW - QUANTIFICATION
KW - NANOPARTICLES
KW - VISUALIZATION
KW - BEVACIZUMAB
KW - RESECTION
U2 - 10.1038/s42003-021-02275-y
DO - 10.1038/s42003-021-02275-y
M3 - Journal article
C2 - 34211069
VL - 4
JO - Communications Biology
JF - Communications Biology
SN - 2399-3642
IS - 1
M1 - 815
ER -
ID: 274614056