Zebrafish vascular quantification: a tool for quantification of three-dimensional zebrafish cerebrovascular architecture by automated image analysis

Abstract

Zebrafish transgenic lines and light sheet fluorescence microscopy allow in-depth insights into three-dimensional vascular development in vivo. However, quantification of the zebrafish cerebral vasculature in 3D remains highly challenging. Here, we describe and test an image analysis workflow for 3D quantification of the total or regional zebrafish brain vasculature, called zebrafish vasculature quantification “ZVQ”. It provides the first landmark- or object-based vascular inter-sample registration of the zebrafish cerebral vasculature, producing Population Average Maps allowing rapid assessment of intra- and inter-group vascular anatomy. ZVQ also extracts a range of quantitative vascular parameters from a user-specified Region of Interest including volume, surface area, density, branching points, length, radius, and complexity. Application of ZVQ to thirteen experimental conditions, including embryonic development, pharmacological manipulations and morpholino induced gene knockdown, shows ZVQ is robust, allows extraction of biologically relevant information and quantification of vascular alteration, and can provide novel insights into vascular biology. To allow dissemination, the code for quantification, a graphical user interface, and workflow documentation are provided. Together, ZVQ provides the first open-source quantitative approach to assess the 3D cerebrovascular architecture in zebrafish.

Analytical Approaches for the Segmentation of the Zebrafish Brain Vasculature

“…Although this review has focused on segmentation of the zebrafish brain vasculature, many concepts and principles discussed here apply to other data. Such wider applications include the following: Using simulated tubes could be beneficial to other tube‐like structures such as lymphatic vessels or certain neuron data. Experimental and computational alteration of the CNR is widely applicable to any data acquired with optical microscopy. Data with known large biological effect sizes can be used as a first assessment of segmentation and quantification performance, as shown by exsanguination; this is also applicable to animals without blood flow [such as tnn2a morphants (Sehnert et al., 2002)] or any other severe phenotypes. Detection of smaller biological effect sizes can take advantage of the differences between developmental stages as shown here and used previously (Daetwyler et al., 2019), or between groups treated using drugs with known vascular effects [such as anti‐VEGF treatment to inhibit vascular growth (Kugler et al., 2022)]. Double‐transgenic or dual labeling can be used to assess segmentation and improve segmentation outcomes, as previously shown for the mouse brain vasculature (Todorov et al., 2020). …”

“…Data with known large biological effect sizes can be used as a first assessment of segmentation and quantification performance, as shown by exsanguination; this is also applicable to animals without blood flow [such as tnn2a morphants (Sehnert et al, 2002)] or any other severe phenotypes. Detection of smaller biological effect sizes can take advantage of the differences between developmental stages as shown here and used previously (Daetwyler et al, 2019), or between groups treated using drugs with known vascular effects [such as anti-VEGF treatment to inhibit vascular growth (Kugler et al, 2022)]. Double-transgenic or dual labeling can be used to assess segmentation and improve segmentation outcomes, as previously shown for the mouse brain vasculature (Todorov et al, 2020).…”

“…Although certain characteristics of the cerebrovascular architecture may be obvious on visual inspection (such as missing or highly abnormal vessels), others may be too subtle for visual detection (such as diameter changes). Generally, 3D computational quantification of the vascular architecture is not only less labor‐intensive but is also more comprehensive than manual assessments, providing measurements of volume, diameter, length, and branching (Kugler et al., 2022). Additionally, computational analysis is often more reproducible and sensitive than visual/manual assessment, requiring fewer animals.…”

New functional vessels form after spinal cord injury in zebrafish

“…Blood vessels rapidly re-vascularise the injured spinal cord To examine how blood vessels respond to a SCI, we performed a contusion injury model in adult zebrafish (Hui et al, 2010) and imaged the blood vessels in cleared wholemount samples (Tg(kdrl:ras-mCherry) s896 ) at different days post injury (dpi). To quantify vessel length and morphology we used a 3D image analysis workflow developed by Kugler et al (Kugler et al, 2022), which measures vessel branch length and position, thickness and tortuosity (Supp. Fig.…”

“…Kugler analysis of vessels in wholemount spinal cords Data analysis of light sheet images was performed in Fiji, using an adapted 3D image analysis workflow developed by Kugler et al, 2022. Quantification of vessel branch length and position, thickness and tortuosity was done as described by the authors, with the following changes to the macro: manual regions of interest (ROIs) with 500x500x[z size] µm were defined for each spinal cord image using polygon tool and the threshold range for image segmentation was tested and adapted to our data set.…”