An analysis modality for vascular structures combining tissue-clearing technology and topological data analysis

• Published in: Nature communications Vol. 13; no. 1; pp. 5239 - 17
• Main Authors: Takahashi, Kei, Abe, Ko, Kubota, Shimpei I., Fukatsu, Noriaki, Morishita, Yasuyuki, Yoshimatsu, Yasuhiro, Hirakawa, Satoshi, Kubota, Yoshiaki, Watabe, Tetsuro, Ehata, Shogo, Ueda, Hiroki R., Shimamura, Teppei, Miyazono, Kohei
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 12.09.2022; Nature Publishing Group; Nature Portfolio
• Subjects: 14; 14/1; 14/5; 631/114/1564; 631/1647/245/2221; 64; 64/60; Animal models; Animals; Blood; Blood vessels; Brain - diagnostic imaging; Clearing; Computational neuroscience; Data Analysis; Evaluation; Feature extraction; Humanities and Social Sciences; Image resolution; Imaging, Three-Dimensional - methods; Learning algorithms; Lymphatic system; Lymphatic Vessels - diagnostic imaging; Machine learning; Mathematical models; Medical imaging; Mice; multidisciplinary; Neuroimaging; Science; Science (multidisciplinary); Signal classification; Signal processing; Technology; Three dimensional imaging; Topology
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-022-32848-2

The blood and lymphatic vasculature networks are not yet fully understood even in mouse because of the inherent limitations of imaging systems and quantification methods. This study aims to evaluate the usefulness of the tissue-clearing technology for visualizing blood and lymphatic vessels in adult mouse. Clear, unobstructed brain/body imaging cocktails and computational analysis (CUBIC) enables us to capture the high-resolution 3D images of organ- or area-specific vascular structures. To evaluate these 3D structural images, signals are first classified from the original captured images by machine learning at pixel base. Then, these classified target signals are subjected to topological data analysis and non-homogeneous Poisson process model to extract geometric features. Consequently, the structural difference of vasculatures is successfully evaluated in mouse disease models. In conclusion, this study demonstrates the utility of CUBIC for analysis of vascular structures and presents its feasibility as an analysis modality in combination with 3D images and mathematical frameworks. Understanding blood and lymphatic vasculature networks is currently limited by existing imaging systems and quantification methods. Here the authors use the tissue clearing method CUBIC to generate 3D images, machine learning to capture the signals, and extract geometric features by topological data analysis.