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Zhang H, Kang DH, Piantino M, Tominaga D, Fujimura T, Nakatani N, Taylor JN, Furihata T, Matsusaki M, Fujita S. Rapid Quantification of Microvessels of Three-Dimensional Blood-Brain Barrier Model Using Optical Coherence Tomography and Deep Learning Algorithm. Biosensors (Basel) 2023; 13:818. [PMID: 37622905 PMCID: PMC10452445 DOI: 10.3390/bios13080818] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 08/02/2023] [Accepted: 08/09/2023] [Indexed: 08/26/2023]
Abstract
The blood-brain barrier (BBB) is a selective barrier that controls the transport between the blood and neural tissue features and maintains brain homeostasis to protect the central nervous system (CNS). In vitro models can be useful to understand the role of the BBB in disease and assess the effects of drug delivery. Recently, we reported a 3D BBB model with perfusable microvasculature in a Transwell insert. It replicates several key features of the native BBB, as it showed size-selective permeability of different molecular weights of dextran, activity of the P-glycoprotein efflux pump, and functionality of receptor-mediated transcytosis (RMT), which is the most investigated pathway for the transportation of macromolecules through endothelial cells of the BBB. For quality control and permeability evaluation in commercial use, visualization and quantification of the 3D vascular lumen structures is absolutely crucial. Here, for the first time, we report a rapid, non-invasive optical coherence tomography (OCT)-based approach to quantify the microvessel network in the 3D in vitro BBB model. Briefly, we successfully obtained the 3D OCT images of the BBB model and further processed the images using three strategies: morphological imaging processing (MIP), random forest machine learning using the Trainable Weka Segmentation plugin (RF-TWS), and deep learning using pix2pix cGAN. The performance of these methods was evaluated by comparing their output images with manually selected ground truth images. It suggested that deep learning performed well on object identification of OCT images and its computation results of vessel counts and surface areas were close to the ground truth results. This study not only facilitates the permeability evaluation of the BBB model but also offers a rapid, non-invasive observational and quantitative approach for the increasing number of other 3D in vitro models.
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Affiliation(s)
- Huiting Zhang
- AIST-Osaka University Advanced Photonics and Biosensing Open Innovation Laboratory, National Institute of Advanced Industrial Science and Technology (AIST), 2-1 Yamadaoka, Suita 565-0871, Osaka, Japan; (H.Z.); (J.N.T.); (M.M.)
- Graduate School of Engineering, Osaka University, 2-1 Yamadaoka, Suita 565-0871, Osaka, Japan; (D.-H.K.); (M.P.)
| | - Dong-Hee Kang
- Graduate School of Engineering, Osaka University, 2-1 Yamadaoka, Suita 565-0871, Osaka, Japan; (D.-H.K.); (M.P.)
| | - Marie Piantino
- Graduate School of Engineering, Osaka University, 2-1 Yamadaoka, Suita 565-0871, Osaka, Japan; (D.-H.K.); (M.P.)
| | - Daisuke Tominaga
- Cellular and Molecular Biotechnology Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), 1-1-1 Higashi, Tsukuba 305-8565, Ibaraki, Japan;
| | - Takashi Fujimura
- SCREEN Holdings Co., Ltd., 322 Furukawa-cho, Hazukashi, Fushimi-ku, Kyoto 612-8486, Kyoto, Japan; (T.F.); (N.N.)
| | - Noriyuki Nakatani
- SCREEN Holdings Co., Ltd., 322 Furukawa-cho, Hazukashi, Fushimi-ku, Kyoto 612-8486, Kyoto, Japan; (T.F.); (N.N.)
| | - J. Nicholas Taylor
- AIST-Osaka University Advanced Photonics and Biosensing Open Innovation Laboratory, National Institute of Advanced Industrial Science and Technology (AIST), 2-1 Yamadaoka, Suita 565-0871, Osaka, Japan; (H.Z.); (J.N.T.); (M.M.)
| | - Tomomi Furihata
- School of Pharmacy, Tokyo University of Pharmacy and Life Sciences, 1432-1 Horinouchi, Hachioji 192-0392, Tokyo, Japan;
| | - Michiya Matsusaki
- AIST-Osaka University Advanced Photonics and Biosensing Open Innovation Laboratory, National Institute of Advanced Industrial Science and Technology (AIST), 2-1 Yamadaoka, Suita 565-0871, Osaka, Japan; (H.Z.); (J.N.T.); (M.M.)
- Graduate School of Engineering, Osaka University, 2-1 Yamadaoka, Suita 565-0871, Osaka, Japan; (D.-H.K.); (M.P.)
| | - Satoshi Fujita
- AIST-Osaka University Advanced Photonics and Biosensing Open Innovation Laboratory, National Institute of Advanced Industrial Science and Technology (AIST), 2-1 Yamadaoka, Suita 565-0871, Osaka, Japan; (H.Z.); (J.N.T.); (M.M.)
- Graduate School of Engineering, Osaka University, 2-1 Yamadaoka, Suita 565-0871, Osaka, Japan; (D.-H.K.); (M.P.)
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Strobel HA, Schultz A, Moss SM, Eli R, Hoying JB. Quantifying Vascular Density in Tissue Engineered Constructs Using Machine Learning. Front Physiol 2021; 12:650714. [PMID: 33986691 PMCID: PMC8110917 DOI: 10.3389/fphys.2021.650714] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Accepted: 04/06/2021] [Indexed: 12/29/2022] Open
Abstract
Given the considerable research efforts in understanding and manipulating the vasculature in tissue health and function, making effective measurements of vascular density is critical for a variety of biomedical applications. However, because the vasculature is a heterogeneous collection of vessel segments, arranged in a complex three-dimensional architecture, which is dynamic in form and function, it is difficult to effectively measure. Here, we developed a semi-automated method that leverages machine learning to identify and quantify vascular metrics in an angiogenesis model imaged with different modalities. This software, BioSegment, is designed to make high throughput vascular density measurements of fluorescent or phase contrast images. Furthermore, the rapidity of assessments makes it an ideal tool for incorporation in tissue manufacturing workflows, where engineered tissue constructs may require frequent monitoring, to ensure that vascular growth benchmarks are met.
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Affiliation(s)
- Hannah A Strobel
- Tissue Modeling, Advanced Solutions Life Sciences, Manchester, NH, United States
| | - Alex Schultz
- Innovations Laboratory, Advanced Solutions Life Sciences, Louisville, KY, United States
| | - Sarah M Moss
- Tissue Modeling, Advanced Solutions Life Sciences, Manchester, NH, United States
| | - Rob Eli
- Innovations Laboratory, Advanced Solutions Life Sciences, Louisville, KY, United States
| | - James B Hoying
- Tissue Modeling, Advanced Solutions Life Sciences, Manchester, NH, United States
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Ghavami S, Bayat M, Fatemi M, Alizad A. Quantification of Morphological Features in Non-Contrast-Enhanced Ultrasound Microvasculature Imaging. IEEE Access 2020; 8:18925-18937. [PMID: 32328394 PMCID: PMC7179329 DOI: 10.1109/access.2020.2968292] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
There are significant differences in microvascular morphological features in diseased tissues, such as cancerous lesions, compared to noncancerous tissue. Quantification of microvessel morphological features could play an important role in disease diagnosis and tumor classification. However, analyzing microvessel morphology in ultrasound Doppler is a challenging task due to limitations associated with this technique. Our main objective is to provide methods for quantifying morphological features of microvasculature obtained by ultrasound Doppler imaging. To achieve this goal, we propose multiple image enhancement techniques and appropriate morphological feature extraction methods that enable quantitative analysis of microvasculature structures. Vessel segments obtained by the skeletonization of the regularized microvasculature images are further analyzed to satisfy other constraints, such as vessel segment diameter and length. Measurements of some morphological metrics, such as tortuosity, depend on preserving large vessel trunks. To address this issue, additional filtering methods are proposed. These methods are tested on in vivo images of breast lesion and thyroid nodule microvasculature, and the outcomes are discussed. Initial results show that using vessel morphological features allows for differentiation between malignant and benign breast lesions (p-value < 0.005) and thyroid nodules (p-value < 0.01). This paper provides a tool for the quantification of microvasculature images obtained by non-contrast ultrasound imaging, which may serve as potential biomarkers for the diagnosis of some diseases.
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Affiliation(s)
- Siavash Ghavami
- Department of Radiology, Mayo Clinic College of Medicine and Science, Rochester, MN, 55905, USA
| | - Mahdi Bayat
- Department of Physiology and Biomedical Engineering, Mayo Clinic College of Medicine and Science, Rochester, MN, 55905, USA
| | - Mostafa Fatemi
- Department of Physiology and Biomedical Engineering, Mayo Clinic College of Medicine and Science, Rochester, MN, 55905, USA
| | - Azra Alizad
- Department of Radiology, Mayo Clinic College of Medicine and Science, Rochester, MN, 55905, USA
- Department of Physiology and Biomedical Engineering, Mayo Clinic College of Medicine and Science, Rochester, MN, 55905, USA
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Righi M, Belleri M, Presta M, Giacomini A. Quantification of 3D Brain Microangioarchitectures in an Animal Model of Krabbe Disease. Int J Mol Sci 2019; 20:E2384. [PMID: 31091708 PMCID: PMC6567268 DOI: 10.3390/ijms20102384] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2019] [Revised: 05/08/2019] [Accepted: 05/11/2019] [Indexed: 12/16/2022] Open
Abstract
We performed a three-dimensional (3D) analysis of the microvascular network of the cerebral cortex of twitcher mice (an authentic model of Krabbe disease) using a restricted set of indexes that are able to describe the arrangement of the microvascular tree in CD31-stained sections. We obtained a near-linear graphical "fingerprint" of the microangioarchitecture of wild-type and twitcher animals that describes the amounts, spatial dispersion, and spatial relationships of adjacent classes of caliber-filtered microvessels. We observed significant alterations of the microangioarchitecture of the cerebral cortex of twitcher mice, whereas no alterations occur in renal microvessels, which is keeping with the observation that kidney is an organ that is not affected by the disease. This approach may represent an important starting point for the study of the microvascular changes that occur in the central nervous system (CNS) under different physiopathological conditions.
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Affiliation(s)
- Marco Righi
- Consiglio Nazionale delle Ricerche, Institute of Neuroscience, Via Vanvitelli 32, 20129 Milano, Italy.
| | - Mirella Belleri
- Unit of Experimental Oncology and Immunology, Department of Molecular and Translational Medicine, University of Brescia, 25123 Brescia, Italy.
| | - Marco Presta
- Unit of Experimental Oncology and Immunology, Department of Molecular and Translational Medicine, University of Brescia, 25123 Brescia, Italy.
| | - Arianna Giacomini
- Unit of Experimental Oncology and Immunology, Department of Molecular and Translational Medicine, University of Brescia, 25123 Brescia, Italy.
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