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Nicolas N, Dinet V, Roux E. 3D imaging and morphometric descriptors of vascular networks on optically cleared organs. iScience 2023; 26:108007. [PMID: 37810224 PMCID: PMC10551892 DOI: 10.1016/j.isci.2023.108007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/10/2023] Open
Abstract
The vascular system is a multi-scale network whose functionality depends on its structure, and for which structural alterations can be linked to pathological shifts. Though biologists use multiple 3D imaging techniques to visualize vascular networks, the 3D image processing methodologies remain sources of biases, and the extraction of quantitative morphometric descriptors remains flawed. The article, first, reviews the current 3D image processing methodologies, and morphometric descriptors of vascular network images mainly obtained by light-sheet microscopy on optically cleared organs, found in the literature. Second, it proposes operator-independent segmentation and skeletonization methodologies using the freeware ImageJ. Third, it gives more extractable network-level (density, connectivity, fractal dimension) and segment-level (length, diameter, tortuosity) 3D morphometric descriptors and how to statistically analyze them. Thus, it can serve as a guideline for biologists using 3D imaging techniques of vascular networks, allowing the production of more comparable studies in the future.
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Affiliation(s)
- Nabil Nicolas
- University Bordeaux, INSERM, Biologie des maladies cardiovasculaires, U1034, F-33600 Pessac, France
| | - Virginie Dinet
- University Bordeaux, INSERM, Biologie des maladies cardiovasculaires, U1034, F-33600 Pessac, France
| | - Etienne Roux
- University Bordeaux, INSERM, Biologie des maladies cardiovasculaires, U1034, F-33600 Pessac, France
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Adamo A, Bruno A, Menallo G, Francipane MG, Fazzari M, Pirrone R, Ardizzone E, Wagner WR, D'Amore A. Blood Vessel Detection Algorithm for Tissue Engineering and Quantitative Histology. Ann Biomed Eng 2022; 50:387-400. [PMID: 35171393 PMCID: PMC8917109 DOI: 10.1007/s10439-022-02923-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Accepted: 01/17/2022] [Indexed: 11/29/2022]
Abstract
Immunohistochemistry for vascular network analysis plays a fundamental role in basic science, translational research and clinical practice. However, identifying vascularization in histological tissue images is time consuming and markedly depends on the operator’s experience. In this study, we present “blood vessel detection—BVD”, an automatic algorithm for quantitative analysis of blood vessels in immunohistochemical images. BVD is based on extraction and analysis of low-level image features and spatial filtering techniques, which do not require a training phase. BVD algorithm performance was comparatively evaluated on histological sections from three different in vivo experiments. Collectively, 173 independent images were analyzed, and the algorithm's results were compared to those obtained by human operators. The developed BVD algorithm proved to be a robust and versatile tool, being able to quantify number, area, and spatial distribution of blood vessels within all three considered histologic datasets. BVD is provided as an open-source application working on different operating systems. BVD is supported by a user-friendly graphical interface designed to facilitate large-scale analysis.
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Affiliation(s)
- A Adamo
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties, University of Palermo, 90100, Palermo, Italy.,McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA, 15219, USA.,Fondazione Ri.MED, 90133, Palermo, Italy
| | - A Bruno
- Department of Computing and Informatics in the Faculty of Science and Technology, Bournemouth University, Poole, BH12 5BB, UK
| | - G Menallo
- McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA, 15219, USA.,Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, MA, 01605, USA
| | - M G Francipane
- McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA, 15219, USA.,Fondazione Ri.MED, 90133, Palermo, Italy.,Department of Pathology, University of Pittsburgh, Pittsburgh, PA, 15206, USA
| | - M Fazzari
- Department of Pharmacology & Chemical Biology, University of Pittsburgh, Pittsburgh, PA, 15261, USA
| | - R Pirrone
- Department of Industrial and Digital Innovation, University of Palermo, 90100, Palermo, Italy
| | - E Ardizzone
- Department of Industrial and Digital Innovation, University of Palermo, 90100, Palermo, Italy
| | - W R Wagner
- McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA, 15219, USA.,Department of Surgery, University of Pittsburgh, Pittsburgh, PA, 15219, USA
| | - A D'Amore
- McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA, 15219, USA. .,Fondazione Ri.MED, 90133, Palermo, Italy. .,Department of Surgery, University of Pittsburgh, Pittsburgh, PA, 15219, USA. .,Department of Surgery and Bioengineering, McGowan Institute for Regenerative Medicine, University of Pittsburgh, 450 Technology Drive, Pittsburgh, PA, 15219, USA.
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Reschke M, DiRito JR, Stern D, Day W, Plebanek N, Harris M, Hosgood SA, Nicholson ML, Haakinson DJ, Zhang X, Mehal WZ, Ouyang X, Pober JS, Saltzman WM, Tietjen GT. A digital pathology tool for quantification of color features in histologic specimens. Bioeng Transl Med 2022; 7:e10242. [PMID: 35111944 PMCID: PMC8780932 DOI: 10.1002/btm2.10242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Revised: 07/14/2021] [Accepted: 07/18/2021] [Indexed: 11/12/2022] Open
Abstract
In preclinical research, histological analysis of tissue samples is often limited to qualitative or semiquantitative scoring assessments. The reliability of this analysis can be impaired by the subjectivity of these approaches, even when read by experienced pathologists. Furthermore, the laborious nature of manual image assessments often leads to the analysis being restricted to a relatively small number of images that may not accurately represent the whole sample. Thus, there is a clear need for automated image analysis tools that can provide robust and rapid quantification of histologic samples from paraffin-embedded or cryopreserved tissues. To address this need, we have developed a color image analysis algorithm (DigiPath) to quantify distinct color features in histologic sections. We demonstrate the utility of this tool across multiple types of tissue samples and pathologic features, and compare results from our program to other quantitative approaches such as color thresholding and hand tracing. We believe this tool will enable more thorough and reliable characterization of histological samples to facilitate better rigor and reproducibility in tissue-based analyses.
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Affiliation(s)
- Melanie Reschke
- Department of Molecular Biophysics & BiochemistryYale UniversityNew HavenConnecticutUSA
| | - Jenna R. DiRito
- Department of SurgeryYale School of MedicineNew HavenConnecticutUSA
| | - David Stern
- Department of SurgeryYale School of MedicineNew HavenConnecticutUSA
| | - Wesley Day
- Department of Biomedical EngineeringYale UniversityNew HavenConnecticutUSA
| | - Natalie Plebanek
- Department of Biomedical EngineeringYale UniversityNew HavenConnecticutUSA
| | - Matthew Harris
- Department of SurgeryYale School of MedicineNew HavenConnecticutUSA
| | | | | | | | - Xuchen Zhang
- Department of PathologyYale School of MedicineNew HavenConnecticutUSA
| | - Wajahat Z. Mehal
- Section of Digestive Diseases, Department of Internal MedicineYale University School of MedicineNew HavenConnecticutUSA
| | - Xinshou Ouyang
- Section of Digestive Diseases, Department of Internal MedicineYale University School of MedicineNew HavenConnecticutUSA
| | - Jordan S. Pober
- Department of ImmunobiologyYale UniversityNew HavenConnecticutUSA
| | - W. Mark Saltzman
- Department of Biomedical EngineeringYale UniversityNew HavenConnecticutUSA
| | - Gregory T. Tietjen
- Department of SurgeryYale School of MedicineNew HavenConnecticutUSA
- Department of Biomedical EngineeringYale UniversityNew HavenConnecticutUSA
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Wälchli T, Bisschop J, Miettinen A, Ulmann-Schuler A, Hintermüller C, Meyer EP, Krucker T, Wälchli R, Monnier PP, Carmeliet P, Vogel J, Stampanoni M. Hierarchical imaging and computational analysis of three-dimensional vascular network architecture in the entire postnatal and adult mouse brain. Nat Protoc 2021; 16:4564-4610. [PMID: 34480130 DOI: 10.1038/s41596-021-00587-1] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2020] [Accepted: 06/08/2021] [Indexed: 02/08/2023]
Abstract
The formation of new blood vessels and the establishment of vascular networks are crucial during brain development, in the adult healthy brain, as well as in various diseases of the central nervous system. Here, we describe a step-by-step protocol for our recently developed method that enables hierarchical imaging and computational analysis of vascular networks in postnatal and adult mouse brains. The different stages of the procedure include resin-based vascular corrosion casting, scanning electron microscopy, synchrotron radiation and desktop microcomputed tomography imaging, and computational network analysis. Combining these methods enables detailed visualization and quantification of the 3D brain vasculature. Network features such as vascular volume fraction, branch point density, vessel diameter, length, tortuosity and directionality as well as extravascular distance can be obtained at any developmental stage from the early postnatal to the adult brain. This approach can be used to provide a detailed morphological atlas of the entire mouse brain vasculature at both the postnatal and the adult stage of development. Our protocol allows the characterization of brain vascular networks separately for capillaries and noncapillaries. The entire protocol, from mouse perfusion to vessel network analysis, takes ~10 d.
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Affiliation(s)
- Thomas Wälchli
- Group of CNS Angiogenesis and Neurovascular Link, Neuroscience Center Zurich, and Division of Neurosurgery, University and University Hospital Zurich, Zurich, Switzerland. .,Division of Neurosurgery, University Hospital Zurich, Zurich, Switzerland. .,Group Brain Vasculature and Perivascular Niche, Division of Experimental and Translational Neuroscience, Krembil Brain Institute, Krembil Research Institute, Toronto Western Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada. .,Division of Neurosurgery, Department of Surgery, Toronto Western Hospital, University of Toronto, Toronto, Ontario, Canada.
| | - Jeroen Bisschop
- Group of CNS Angiogenesis and Neurovascular Link, Neuroscience Center Zurich, and Division of Neurosurgery, University and University Hospital Zurich, Zurich, Switzerland.,Division of Neurosurgery, University Hospital Zurich, Zurich, Switzerland.,Group Brain Vasculature and Perivascular Niche, Division of Experimental and Translational Neuroscience, Krembil Brain Institute, Krembil Research Institute, Toronto Western Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada.,Division of Neurosurgery, Department of Surgery, Toronto Western Hospital, University of Toronto, Toronto, Ontario, Canada.,Department of Physiology, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Arttu Miettinen
- Swiss Light Source, Paul Scherrer Institute, Villigen, Switzerland.,Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland.,Department of Physics, University of Jyväskylä, Jyväskylä, Finland
| | | | | | - Eric P Meyer
- Institute of Molecular Life Sciences, University of Zurich, Zurich, Switzerland
| | - Thomas Krucker
- Novartis Institutes for BioMedical Research Inc, Emeryville, CA, USA
| | - Regula Wälchli
- Department of Dermatology, Pediatric Skin Center, University Children's Hospital Zurich, Zurich, Switzerland
| | - Philippe P Monnier
- Department of Physiology, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada.,Krembil Research Institute, Vision Division, Krembil Discovery Tower, Toronto, Ontario, Canada.,Department of Ophthalmology and Vision Sciences, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Peter Carmeliet
- Laboratory of Angiogenesis and Vascular Metabolism, Department of Oncology, KU Leuven, Leuven, Belgium.,Laboratory of Angiogenesis and Vascular Metabolism, VIB Center for Cancer Biology, VIB, Leuven, Belgium
| | - Johannes Vogel
- Institute of Veterinary Physiology, Vetsuisse Faculty, University of Zurich, Zurich, Switzerland
| | - Marco Stampanoni
- Swiss Light Source, Paul Scherrer Institute, Villigen, Switzerland.,Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland
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