Biology subjects

Kugler, E. C.

Publications and source records attributed to Kugler, E. C..

3 recordsLinked to original sources

bioRxiv2020

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

Zebrafish transgenic lines and light sheet fluorescence microscopy allow in-depth insights into vascular development in vivo and 3D. However, robust quantification of the zebrafish cerebral vasculature in 3D remains a challenge, and would be essential to describe the vascular architecture. Here, we report an image analysis pipeline that allows 3D quantification of the total or regional zebrafish brain vasculature. This is achieved by landmark- or object-based inter-sample registration and extraction of quantitative parameters including vascular volume, surface area, density, branching points, length, radius, and complexity. Application of our analysis pipeline to a range of sixteen genetic or pharmacological manipulations shows that our quantification approach is robust, allows extraction of biologically relevant information, and provides novel insights into vascular biology. To allow dissemination, the code for quantification, a graphical user interface, and workflow documentation are provided. Together, we present the first 3D quantification approach to assess the whole 3D cerebrovascular architecture in zebrafish.

bioinformatics↗

bioRxiv2020

The effect of absent blood flow on the zebrafish cerebral and trunk vasculature

The role of blood flow is complex and context-dependent. In this study, we quantify the effect of the lack of blood flow on vascular development and compare its impact in two vascular beds, namely the cerebral and trunk vasculature, using zebrafish as preclinical model. We performed this by analysing vascular topology, endothelial cell number, apoptosis, and inflammatory response in animals with normal blood flow or absent blood flow. We find that absent blood flow reduced vascular area and endothelial cell number significantly in both examined vascular beds, but the effect is more severe in the cerebral vasculature. Similarly, while stereotypic vascular patterning in the trunk is maintained, intra-cerebral vessels show altered patterning. Absent blood flow lead to an increase in non-EC-specific apoptosis without increasing tissue inflammation, as quantified by cerebral immune cell numbers and nitric oxide. In conclusion, blood flow is essential for cellular survival in both the trunk and cerebral vasculature, but particularly intra-cerebral vessels are affected by the lack of blood flow, suggesting that responses to blood flow differ between these two vascular beds. Key pointsO_LIWe here use zebrafish as a model to quantitatively assess the impact of the lack of blood flow in development and compare its impact in two vascular beds, namely the cerebral to trunk vasculature. C_LIO_LIIn both vascular beds, vascular growth and endothelial cell number are reduced by lack of blood flow, with increasing effect size from 2-5 days post fertilisation. C_LIO_LIExamination of vascular patterning shows that while stereotypic patterning in the trunk is preserved, the intra-cerebral vasculature patterning is altered. C_LIO_LIWe found non-EC-specific cell death to be increased in both vascular beds, with a larger effect size in the brain, but that this cell death occurs without triggering tissue inflammation. C_LI

developmental biology↗

bioRxiv2020

Segmentation of the Zebrafish Brain Vasculature from Light Sheet Fluorescence Microscopy Datasets

Light sheet fluorescent microscopy allows imaging of zebrafish vascular development in great detail. However, interpretation of data often relies on visual assessment and approaches to validate image analysis steps are broadly lacking. Here, we compare different enhancement and segmentation approaches to extract the zebrafish cerebral vasculature, provide comprehensive validation, study segmentation robustness, examine sensitivity, apply the validated method to quantify embryonic cerebrovascular volume, and examine applicability to different transgenic reporter lines. The best performing segmentation method was used to train different deep learning networks for segmentation. We found that U-Net based architectures outperform SegNet. While there was a slight overestimation of vascular volume using the U-Net methodologies, variances were low, suggesting that sensitivity to biological changes would still be obtained. HighlightsO_LIGeneral filtering is less applicable than Sato enhancement to enhance zebrafish cerebral vessels. C_LIO_LIBiological data sets help to overcome the lack of segmentation gold-standards and phantom models. C_LIO_LISato enhancement followed by Otsu thresholding is highly accurate, robust, and sensitive. C_LIO_LIDirect generalization of the segmentation approach to transgenics, other than the one optimized for, should be treated with caution. C_LIO_LIDeep learning based segmentation is applicable to the zebrafish cerebral vasculature, with U-Net based architectures outperforming SegNet architectures. C_LI Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=147 SRC="FIGDIR/small/213843v1_ufig1.gif" ALT="Figure 1"> View larger version (88K): org.highwire.dtl.DTLVardef@6b45a3org.highwire.dtl.DTLVardef@a90c0borg.highwire.dtl.DTLVardef@55be1borg.highwire.dtl.DTLVardef@1340df9_HPS_FORMAT_FIGEXP M_FIG C_FIG

developmental biology↗