Therapeutic targeting of vascular malformation in a zebrafish model of hereditary haemorrhagic telangiectasia.

Hereditary haemorrhagic telangiectasia (HHT) causes arteriovenous malformations (AVMs) in multiple organs to cause bleeding, neurological and other complications. HHT is caused by mutations in the BMP co-receptor endoglin. We characterised a range of vascular phenotypes in embryonic and adult endoglin mutant zebrafish and the effect of inhibiting different pathways downstream of Vegf signalling. Adult endoglin mutant zebrafish developed skin AVMs, retinal vascular abnormalities and cardiac enlargement. Embryonic endoglin mutants developed an enlarged basilar artery (similar to the previously described enlarged aorta and cardinal vein) and larger numbers of endothelial membrane cysts (kugeln) on cerebral vessels. Vegf inhibition prevented these embryonic phenotypes, leading us to investigate specific Vegf signalling pathways. Inhibiting mTOR or MEK pathways prevented abnormal trunk and cerebral vasculature phenotypes, whereas inhibiting Nos or Mapk pathways had no effect. Combined subtherapeutic mTOR and MEK inhibition prevented vascular abnormalities, confirming synergy between these pathways in HHT. These results indicate that the HHT-like phenotype in zebrafish endoglin mutants can be mitigated through modulation of Vegf signalling. Combined low-dose MEK and mTOR pathway inhibition could represent a novel therapeutic strategy in HHT.

Meeting in the Middle: Towards Successful Multidisciplinary Bioimage Analysis Collaboration.

With an increase in subject knowledge expertise required to solve specific biological questions, experts from different fields need to collaborate to address increasingly complex issues. To successfully collaborate, everyone involved in the collaboration must take steps to "meet in the middle". We thus present a guide on truly cross-disciplinary work using bioimage analysis as a showcase, where it is required that the expertise of biologists, microscopists, data analysts, clinicians, engineers, and physicists meet. We discuss considerations and best practices from the perspective of both users and technology developers, while offering suggestions for working together productively and how this can be supported by institutes and funders. Although this guide uses bioimage analysis as an example, the guiding principles of these perspectives are widely applicable to other cross-disciplinary work.

Analytical Approaches for the Segmentation of the Zebrafish Brain Vasculature.

With advancements in imaging techniques, data visualization allows new insights into fundamental biological processes of development and disease. However, although biomedical science is heavily reliant on imaging data, interpretation of datasets is still often based on subjective visual assessment rather than rigorous quantitation. This overview presents steps to validate image processing and segmentation using the zebrafish brain vasculature data acquired with light sheet fluorescence microscopy as a use case. Blood vessels are of particular interest to both medical and biomedical science. Specific image enhancement filters have been developed that enhance blood vessels in imaging data prior to segmentation. Using the Sato enhancement filter as an example, we discuss how filter application can be evaluated and optimized. Approaches from the medical field such as simulated, experimental, and augmented datasets can be used to gain the most out of the data at hand. Using such datasets, we provide an overview of how biologists and data analysts can assess the accuracy, sensitivity, and robustness of their segmentation approaches that allow extraction of objects from images. Importantly, even after optimization and testing of a segmentation workflow (e.g., from a particular reporter line to another or between immunostaining processes), its generalizability is often limited, and this can be tested using double-transgenic reporter lines. Lastly, due to the increasing importance of deep learning networks, a comparative approach can be adopted to study their applicability to biological datasets. In summary, we present a broad methodological overview ranging from image enhancement to segmentation with a mixed approach of experimental, simulated, and augmented datasets to assess and validate vascular segmentation using the zebrafish brain vasculature as an example. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC. HIGHLIGHTS: Simulated, experimental, and augmented datasets provide an alternative to overcome the lack of segmentation gold standards and phantom models for zebrafish cerebrovascular segmentation. Direct generalization of a segmentation approach to the data for which it was not optimized (e.g., different transgenics or antibody stainings) should be treated with caution. Comparison of different deep learning segmentation methods can be used to assess their applicability to data. Here, we show that the zebrafish cerebral vasculature can be segmented with U-Net-based architectures, which outperform SegNet architectures.

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

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 13 experimental conditions, including embryonic development, pharmacological manipulations and morpholino-induced gene knockdown, shows that 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.

The "Neuro-Glial-Vascular" Unit: The Role of Glia in Neurovascular Unit Formation and Dysfunction.

The neurovascular unit (NVU) is a complex multi-cellular structure consisting of endothelial cells (ECs), neurons, glia, smooth muscle cells (SMCs), and pericytes. Each component is closely linked to each other, establishing a structural and functional unit, regulating central nervous system (CNS) blood flow and energy metabolism as well as forming the blood-brain barrier (BBB) and inner blood-retina barrier (BRB). As the name suggests, the "neuro" and "vascular" components of the NVU are well recognized and neurovascular coupling is the key function of the NVU. However, the NVU consists of multiple cell types and its functionality goes beyond the resulting neurovascular coupling, with cross-component links of signaling, metabolism, and homeostasis. Within the NVU, glia cells have gained increased attention and it is increasingly clear that they fulfill various multi-level functions in the NVU. Glial dysfunctions were shown to precede neuronal and vascular pathologies suggesting central roles for glia in NVU functionality and pathogenesis of disease. In this review, we take a "glio-centric" view on NVU development and function in the retina and brain, how these change in disease, and how advancing experimental techniques will help us address unanswered questions.

Cerebrovascular development: mechanisms and experimental approaches.

The cerebral vasculature plays a central role in human health and disease and possesses several unique anatomic, functional and molecular characteristics. Despite their importance, the mechanisms that determine cerebrovascular development are less well studied than other vascular territories. This is in part due to limitations of existing models and techniques for visualisation and manipulation of the cerebral vasculature. In this review we summarise the experimental approaches used to study the cerebral vessels and the mechanisms that contribute to their development.

Cerebrovascular endothelial cells form transient Notch-dependent cystic structures in zebrafish.

We identify a novel endothelial membrane behaviour in transgenic zebrafish. Cerebral blood vessels extrude large transient spherical structures that persist for an average of 23 min before regressing into the parent vessel. We term these structures "kugeln", after the German for sphere. Kugeln are only observed arising from the cerebral vessels and are present as late as 28 days post fertilization. Kugeln do not communicate with the vessel lumen and can form in the absence of blood flow. They contain little or no cytoplasm, but the majority are highly positive for nitric oxide reactivity. Kugeln do not interact with brain lymphatic endothelial cells (BLECs) and can form in their absence, nor do they perform a scavenging role or interact with macrophages. Inhibition of actin polymerization, Myosin II, or Notch signalling reduces kugel formation, while inhibition of VEGF or Wnt dysregulation (either inhibition or activation) increases kugel formation. Kugeln represent a novel Notch-dependent NO-containing endothelial organelle restricted to the cerebral vessels, of currently unknown function.