Abstract
Mouse retinal vasculature is a well-recognized and commonly used animal model for angiogenesis and microvascular remodeling. Morphological features of retinal vasculature reflect the vessel’s biological functions, and are critical in understanding the physiological and pathological process of vascular development and disease. Here we developed a comprehensive software, Vessel Tech, using retinal vasculature images of postnatal mice. This pipeline can automatically process retinal vascular images, reconstruct vessel network with high accuracy and assess global and local vascular characteristics based on the recent machine-learning techniques. The development of Vessel Tech provides a powerful tool for vascular biologists.
References
Krishnan L, Chang CC, Nunes SS, Williams SK, Weiss JA, Hoying JB (2013) Manipulating the microvasculature and its microenvironment. Crit Rev Biomed Eng 41(2):91–123. https://doi.org/10.1615/critrevbiomedeng.2013008077
Corliss BA, Mathews C, Doty R, Rohde G, Peirce SM (2019) Methods to label, image, and analyze the complex structural architectures of microvascular networks. Microcirculation 26(5):e12520. https://doi.org/10.1111/micc.12520
Fruttiger M (2002) Development of the mouse retinal vasculature: angiogenesis versus vasculogenesis. Invest Ophthalmol Vis Sci 43(2):522–527
Franco CA, Jones ML, Bernabeu MO, Geudens I, Mathivet T, Rosa A, Lopes FM, Lima AP, Ragab A, Collins RT, Phng LK, Coveney PV, Gerhardt H (2015) Dynamic endothelial cell rearrangements drive developmental vessel regression. PLoS Biol 13(4):e1002125. https://doi.org/10.1371/journal.pbio.1002125
Fruttiger M (2007) Development of the retinal vasculature. Angiogenesis 10(2):77–88. https://doi.org/10.1007/s10456-007-9065-1
Korn C, Scholz B, Hu J, Srivastava K, Wojtarowicz J, Arnsperger T, Adams RH, Boutros M, Augustin HG, Augustin I (2014) Endothelial cell-derived non-canonical Wnt ligands control vascular pruning in angiogenesis. Development 141(8):1757–1766. https://doi.org/10.1242/dev.104422
Pashaei M, Kamangir H, Starek MJ, Tissot P (2020) Review and evaluation of deep learning architectures for efficient land cover mapping with UAS hyper-spatial imagery: a case study over a Wetland. Remote Sens 12(6):959
Niemisto A, Dunmire V, Yli-Harja O, Zhang W, Shmulevich I (2005) Robust quantification of in vitro angiogenesis through image analysis. IEEE Trans Med Imag 24(4):549–553. https://doi.org/10.1109/tmi.2004.837339
Zudaire E, Gambardella L, Kurcz C, Vermeren S (2011) A computational tool for quantitative analysis of vascular networks. PLoS One 6(11):e27385. https://doi.org/10.1371/journal.pone.0027385
Seaman ME, Peirce SM, Kelly K (2011) Rapid analysis of vessel elements (RAVE): a tool for studying physiologic, pathologic and tumor angiogenesis. PLoS One 6(6):e20807. https://doi.org/10.1371/journal.pone.0020807
Montoya-Zegarra JA, Russo E, Runge P, Jadhav M, Willrodt AH, Stoma S, Norrelykke SF, Detmar M, Halin C (2019) AutoTube: a novel software for the automated morphometric analysis of vascular networks in tissues. Angiogenesis 22(2):223–236. https://doi.org/10.1007/s10456-018-9652-3
Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Transact Syst Man Cybernet 9(1):62–66
Chazal F, Michel B (2017) An introduction to topological data analysis: fundamental and practical aspects for data scientists. https://arxiv.org/abs/1710.04019. Accessed June 26 2020
Kolaczyk ED (2009) Statistical Analysis of Network Data. Springer, New York
Pitulescu ME, Schmidt I, Benedito R, Adams RH (2010) Inducible gene targeting in the neonatal vasculature and analysis of retinal angiogenesis in mice. Nat Protoc 5(9):1518–1534. https://doi.org/10.1038/nprot.2010.113
Majumder S, Zhu G, Xu X, Senchanthisai S, Jiang D, Liu H, Xue C, Wang X, Coia H, Cui Z, Smolock EM, Libby RT, Berk BC, Pang J (2016) G-protein-coupled receptor-2-interacting protein-1 controls stalk cell fate by inhibiting delta-like 4-notch1 signaling. Cell Rep 17(10):2532–2541. https://doi.org/10.1016/j.celrep.2016.11.017
Zhu G, Lin Y, Liu H, Jiang D, Singh S, Li X, Yu Z, Fan L, Wang S, Rhen J, Li W, Xu Y, Ge J, Pang J (2018) Dll4-Notch1 signaling but not VEGF-A is essential for hyperoxia induced vessel regression in retina. Biochem Biophys Res Commun 507(1–4):400–406. https://doi.org/10.1016/j.bbrc.2018.11.051
Acknowledgements
We acknowledge Jay Hwang for editing the manuscript.
Funding
This work was supported by Jinjiang Pang’s Grants from the National Institutes of Health (R01 HL122777-05, R01 HL122777-06A1) and American Heart Association Innovative Project Award (19IPLOI34760446).
Author information
Authors and Affiliations
Corresponding authors
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Wang, X., Zhu, G., Wang, S. et al. Vessel tech: a high-accuracy pipeline for comprehensive mouse retinal vasculature characterization. Angiogenesis 24, 7–11 (2021). https://doi.org/10.1007/s10456-020-09752-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10456-020-09752-8