3D quantification of zebrafish cerebrovascular architecture by automated image analysis of light sheet fluorescence microscopy datasets
Posted on: 3 September 2020
Preprint posted on 10 August 2020
Article now published in Development at http://dx.doi.org/10.1242/dev.199720
Categories: cell biology, developmental biology
Background
Zebrafish transgenic lines are considered an unrivalled model for dynamic three dimensional (3D) in vivo vascular imaging. The lack of robust automated approaches to quantify vascular anatomy in 3D is a significant limitation in the field, which prevents high throughput analysis, and sometimes may prevent detection of subtle phenotypes. The development of an automated 3D vascular quantification pipeline requires many obstacles to be overcome. LSFM datasets are often terabytes in size, rendering data handling, processing, and analysis computationally demanding. Due to its complexity, very few previous studies have attempted to quantitatively characterise the zebrafish cranial vasculature. In their work, Kugler et al present the first easily applicable 3D image analysis pipeline for zebrafish vasculature, and apply it to various different datasets.
Moreover, the authors implemented this as a workflow in the open-source image analysis software Fiji, and make available detailed documentation for its use.
Key findings and developments
The authors applied their pipeline to cerebral vasculature, and performed registration allowing examination of vascular patterning similarity and variability between individuals or groups. Manual analysis and visual inspection allowed identification of eleven anatomical landmarks for registration:
1-2) The left and right prosencephalic arteries.
3-4) The junction points of the prosencephalic arteries with the anterior cerebral vein.
5-6) The junction of the left and right posterior communicating segment to the metencephalic artery.
7-8) The highest curvature point of the anterior cerebral vein.
9-10) The left posterior junction point of posterior cerebral vein and primordial hindbrain channel
11) Junction point of middle cerebral vein and dorsal longitudinal vein.
Automatic and manual registration approaches significantly increased similarity, bringing the vasculature into one spatial coordinate system. Upon applying both methods to different embryos, the authors found that in the main cerebral vessels there is a high degree of anatomical similarity between fish. Altogether, a common coordinate system allows for consistent automated placement of regions of interest, which improves speed and reproducibility of quantification.
The workflow for 3D vascular parameter quantification allows quantification of vascular similarity, vascular volume, surface area, density, network length, branching points, average radius and complexity.
The authors applied their pipeline to embryos with or without blood flow to study the parameters previously defined, and determined that the only parameter that remained relatively unchanged was vascular density, suggesting that the pipeline allows identification of significant differences between fish groups with different conditions.
The 3D quantification was applied to multiple fish lines with different genetic or pharmacologically induced alterations. This included:
- Loss of jagged 1a which leads to decreased network length and branching points
- Loss of jagged 1b which results in reduced vascular volume, surface area and network length.
- Loss of dll4 which results in a decrease in all vascular parameters in the brain vasculature except radius and complexity.
- Loss of ccbe1 which leads to reduced volume, surface area, branching points and network length.
- Chemical inhibition of VEGF signaling, which reduced most of the measured vascular parameters.
- Notch signaling inhibition results in hyper-vascularization, and an increase in all measured vascular parameters, but only significantly so in vascular volume and surface area.
- NOS inhibition shows no change in any measured parameter.
- Wnt inhibition and activation does not lead to a statistically significant change in any of the quantified vascular parameters.
- F-actin polymerization inhibition and Myosin II inhibition resulted in a reduction in almost all vascular parameters.
- 24h exposure to glucose- which is thought to result in cellular swelling and impact angiogenesis- resulted in no significant impact on cerebrovascular topology.
- 24h exposure to DMSO, to decrease membrane rigidity, does not lead to significant changes to vascular topology.
The data shows that the 3D analysis pipeline hereby presented has a great potential to gain novel insights into the role of proteins, signaling pathways, and chemical modulators.
Finally, the authors compared regional similarities across fish, and left-right symmetry. For left-right symmetry analysis, the authors developed an image analysis workflow to assess topological similarities between left and right vasculature automatically. A high level of symmetry was found across various days throughout embryo development.
Altogether, the authors provide a comprehensive 3D quantification approach for zebrafish cerebrovascular characterisation with significant potential for increasing our understanding of the vasculature.
What I like about this preprint
I think the pipeline developed here has great potential and applicability for multiple research questions in the area of vascular biology, as shown also by the proof of concept applications hereby explored. I like open science, and the pipeline generated by the authors is consistent with this philosophy.
References
1. Kugler EC, et al 3D quantification of zebrafish cerebrovascular architecture by image analysis of light sheet fluorescence microscopy datasets, bioRxiv, 2020.
2. Kugler EC, et al Enhancement and Segmentation Workflow for the Developing Zebrafish Vasculature, J Imaging 5 (1), 2019.
3. Kugler EC, et al, Segmentation of the Zebrafish Brain Vasculature from Light Sheet Fluorescence Microscopy Datasets, bioRxiv, 2020.
doi: https://doi.org/10.1242/prelights.24427
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