High-Resolution Episcopic Microscopy (HREM) in Multimodal Imaging Approaches

Effects of Tulp4 deficiency on murine embryonic development and adult phenotype

Genetically engineered mouse models have the potential to unravel fundamental biological processes and provide mechanistic insights into the pathogenesis of human diseases. We have previously observed that germline genetic variation at the TULP4 locus influences clinical characteristics in patients with myeloproliferative neoplasms. To elucidate the role of TULP4 in pathological and physiological processes in vivo, we generated a Tulp4 knockout mouse model. Systemic Tulp4 deficiency exerted a strong impact on embryonic development in both Tulp4 homozygous null (Tulp4-/-) and heterozygous (Tulp4+/-) knockout mice, the former exhibiting perinatal lethality. High-resolution episcopic microscopy (HREM) of day 14.5 embryos allowed for the identification of multiple developmental defects in Tulp4-/- mice, including severe heart defects. Moreover, in Tulp4+/- embryos HREM revealed abnormalities of several organ systems, which per se do not affect prenatal or postnatal survival. In adult Tulp4+/- mice, extensive examinations of hematopoietic and cardiovascular features, involving histopathological surveys of multiple tissues as well as blood counts and immunophenotyping, did not provide evidence for anomalies as observed in corresponding embryos. Finally, evaluating a potential obesity-related phenotype as reported for other TULP family members revealed a trend for increased body weight of Tulp4+/- mice. RESEARCH HIGHLIGHTS: To study the role of the TULP4 gene in vivo, we generated a Tulp4 knockout mouse model. Correlative analyses involving HREM revealed a strong impact of Tulp4 deficiency on murine embryonic development.

A protocol for high-resolution episcopic microscopy and 3D volumetric analyses of the adult mouse brain

The rapid evolution of different imaging modalities in the last two decades has enabled the investigation of the role of different genes in development and disease to be studied in a range of model organisms. However, selection of the appropriate imaging technique depends on a number of constraints, including cost, time, image resolution, size of the sample, computational complexity and processing power. Here, we use the adult mouse central nervous system to investigate whether High-Resolution Episcopic Microscopy (HREM) can provide an effective means to study the volume of individual subregions within the brain. We find that HREM can provide precise volume quantification of different structures within the mouse brain, albeit with limitations regarding the time involved for analysis and the necessity of some estimations.

U-Net based vessel segmentation for murine brains with small micro-magnetic resonance imaging reference datasets

Identification and quantitative segmentation of individual blood vessels in mice visualized with preclinical imaging techniques is a tedious, manual or semiautomated task that can require weeks of reviewing hundreds of levels of individual data sets. Preclinical imaging, such as micro-magnetic resonance imaging (μMRI) can produce tomographic datasets of murine vasculature across length scales and organs, which is of outmost importance to study tumor progression, angiogenesis, or vascular risk factors for diseases such as Alzheimer's. Training a neural network capable of accurate segmentation results requires a sufficiently large amount of labelled data, which takes a long time to compile. Recently, several reasonably automated approaches have emerged in the preclinical context but still require significant manual input and are less accurate than the deep learning approach presented in this paper-quantified by the Dice score. In this work, the implementation of a shallow, three-dimensional U-Net architecture for the segmentation of vessels in murine brains is presented, which is (1) open-source, (2) can be achieved with a small dataset (in this work only 8 μMRI imaging stacks of mouse brains were available), and (3) requires only a small subset of labelled training data. The presented model is evaluated together with two post-processing methodologies using a cross-validation, which results in an average Dice score of 61.34% in its best setup. The results show, that the methodology is able to detect blood vessels faster and more reliably compared to state-of-the-art vesselness filters with an average Dice score of 43.88% for the used dataset.

Lossy Image Compression in a Preclinical Multimodal Imaging Study

The growing use of multimodal high-resolution volumetric data in pre-clinical studies leads to challenges related to the management and handling of the large amount of these datasets. Contrarily to the clinical context, currently there are no standard guidelines to regulate the use of image compression in pre-clinical contexts as a potential alleviation of this problem. In this work, the authors study the application of lossy image coding to compress high-resolution volumetric biomedical data. The impact of compression on the metrics and interpretation of volumetric data was quantified for a correlated multimodal imaging study to characterize murine tumor vasculature, using volumetric high-resolution episcopic microscopy (HREM), micro-computed tomography (µCT), and micro-magnetic resonance imaging (µMRI). The effects of compression were assessed by measuring task-specific performances of several biomedical experts who interpreted and labeled multiple data volumes compressed at different degrees. We defined trade-offs between data volume reduction and preservation of visual information, which ensured the preservation of relevant vasculature morphology at maximum compression efficiency across scales. Using the Jaccard Index (JI) and the average Hausdorff Distance (HD) after vasculature segmentation, we could demonstrate that, in this study, compression that yields to a 256-fold reduction of the data size allowed to keep the error induced by compression below the inter-observer variability, with minimal impact on the assessment of the tumor vasculature across scales.

Mapping the functional anatomy and topography of the cardiac autonomic innervation for selective cardiac neuromodulation using MicroCT

Background: Vagus nerve stimulation (VNS) has gained great importance as a promising therapy for a myriad of diseases. Of particular interest is the therapy of cardiovascular diseases, such as heart failure or atrial fibrillation using selective cardiac VNS. However, there is still a lack of organ-specific anatomical knowledge about the fascicular anatomy and topography of the cardiac branch (CB), which diminishes the therapeutic possibilities for selective cardiac neuromodulation. Here, we established a topographical and anatomical map of the superior cardiac VN in two animal species to dissect cervical and cardiac VN morphology.

Methods: Autonomic nerves including superior CBs were harvested from domestic pigs and New Zeeland rabbits followed by imaging with microcomputed tomography (µCT) and 3D rendering. The data were analyzed in terms of relevant topographical and anatomical parameters.

Results: Our data showed that cardiac vagal fascicles remained separated from other VN fascicles up to 22.19 mm (IQR 14.02–41.30 mm) in pigs and 7.68 mm (IQR 4.06–12.77 mm) in rabbits from the CB point and then started merging with other fascicles. Exchanges of nerve fascicles between sympathetic trunk (ST) and VN were observed in 3 out of 11 nerves, which might cause additional unwanted effects in unselective VNS. Our 3D rendered digital model of the cardiac fascicles was generated showing that CB first remained on the medial side where it branched off the VN, as also shown in the µCT data of 11 pig nerves, and then migrated towards the ventromedial site the further it was traced cranially.

Conclusion: Our data provided an anatomical map of the cardiac vagal branches including cervical VN and ST for future approaches of selective cardiac neurostimulation, indicating the best position of selective cardiac VNS just above the CB point.

Functional and multiscale 3D structural investigation of brain tissue through correlative in vivo physiology, synchrotron microtomography and volume electron microscopy

Understanding the function of biological tissues requires a coordinated study of physiology and structure, exploring volumes that contain complete functional units at a detail that resolves the relevant features. Here, we introduce an approach to address this challenge: Mouse brain tissue sections containing a region where function was recorded using in vivo 2-photon calcium imaging were stained, dehydrated, resin-embedded and imaged with synchrotron X-ray computed tomography with propagation-based phase contrast (SXRT). SXRT provided context at subcellular detail, and could be followed by targeted acquisition of multiple volumes using serial block-face electron microscopy (SBEM). In the olfactory bulb, combining SXRT and SBEM enabled disambiguation of in vivo-assigned regions of interest. In the hippocampus, we found that superficial pyramidal neurons in CA1a displayed a larger density of spine apparati than deeper ones. Altogether, this approach can enable a functional and structural investigation of subcellular features in the context of cells and tissues.