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3D quantification of zebrafish cerebrovascular architecture by automated image analysis of light sheet fluorescence microscopy datasets

E. C. Kugler, J. Frost, V. Silva, K. Plant, K. Chhabria, T. J.A. Chico, P. A. Armitage

Preprint posted on August 10, 2020 https://www.biorxiv.org/content/10.1101/2020.08.06.239905v2

Microscopy and zebrafish: Understanding vasculature– part III.

Selected by Mariana De Niz

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.

Figure 1. 3D image analysis pipeline for zebrafish vasculature. (From Ref 1).

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.

 

Posted on: 3rd September 2020

doi: https://doi.org/10.1242/prelights.24427

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  • Author's response

    Elisabeth Kugler, Timothy Chico, Paul Armitage shared

    Open questions 

    1.Can the registration method you developed be applied to samples which display much more movement effects- for instance circulation, heartbeat, peristalsis, breathing? Coming from a mouse model point of view, these are usual complications for vasculature analysis.

    The main challenge for vascular registration is actually the data sparsity, rather than motion artefacts. We examined motion artefacts and motion correction for our data previously in Kugler et al. 2019 (https://www.mdpi.com/2313-433X/5/1/14) to address inter-plane motion artefacts. Recent advances in optical gating and image post-processing can also be used to address motion artefacts. However, we appreciate that these need to be optimized for the specific biological question and imaging setup.

    2.Do you envisage that the pipeline you generated here is also applicable to other anatomical locations in zebrafish embryos?

    The most crucial step for registration and quantification is a reliable and robust segmentation of the vasculature. As we are interested in brain vessels, we optimized our enhancement and segmentation for those. When wanting to apply our suggested analysis approach to other vascular beds, we anticipate that the enhancement and segmentation will need some optimization to account for differences in signal, vessel diameters, and overall vascular topology.

    3.Do you have any recommendations for users, regarding whether specific dyes or reporters are more suitable to facilitate vascular analysis?

    From our experience in vivo imaging with transgenics delivers better data quality (i.e. higher vascular signal and less background) than for example antibody staining. However, even if using reporter lines one should spend time on examining their individual specifics. We started examining this for four commonly used transgenics in another preprint (https://www.biorxiv.org/content/10.1101/2020.07.21.213843v1).

    Future work will focus on how we can remove non-vascular information (e.g. unspecific skin signal) from our data, either during pre- or post-processing, as well as examining how our approach performs on other data, such as dyes.

    4.For the experimental conditions you explored, there were some which did not match previously published literature. What are your hypotheses for such differences?

    Many studies focussed on examining the trunk vasculature, while we focus on the brain vasculature, and that it is known that many factors effect different vascular beds in different, even opposite, ways.

    Our quantification data are really a proof of principle that objective quantification is needed when examining the impact of genetic or chemical manipulations.

    We examined six morpholino datasets, quantifying the impact of tnnt2a loss, showing for the first time that loss of jagged-1a, jagged-1b, or ccbe1 leads to cerebrovascular changes, and finding that notch1b probably has a conserved role in the zebrafish head and trunk vasculature. Our finding that dll4 loss results in a reduction of the cerebral vasculature contrasts previous findings in the zebrafish trunk, which emphasizes that different vascular beds can be impacted and respond differently.

    When examining nine datasets with chemical treatments, we were able to provide the first quantitative insights into the impact of VEGF inhibition, Notch inhibition, F-actin polymerization inhibition, Myosin II inhibition, glucose treatment and DMSO-treatment on the cerebral vasculature, which are in line with previous findings and assumptions. We were surprised by the lack of changes observed upon NOS inhibition, Wnt inhibition, and Wnt activation. However, we anticipate that effects might be observed more locally, and that changes in drug treatment duration or concentration might have more pronounced effects.

    5.What do you think are remaining limitations of the pipeline you hereby present, and what should users be aware of optimizing for their own work? (as this is an advantage of open access, and of making available the pipeline as you have done).

    It would be fantastic to see the community using and modifying our approach for their data needs. Currently there is no 3D digital atlas of the zebrafish brain vasculature available, as is the case for mice, so this would be something incredibly useful for the community to be able to automatically annotate and extract individual vessels.

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