Phenotyping the central nervous system of the embryonic mouse by magnetic resonance microscopy

4-Hydroxybenzoic acid rescues multisystemic disease and perinatal lethality in a mouse model of mitochondrial disease

Coenzyme Q (CoQ) deficiency syndrome is conventionally treated with limited efficacy using exogenous CoQ10. Poor outcomes result from low absorption and bioavailability of CoQ10 and the clinical heterogenicity of the disease. Here, we demonstrate that supplementation with 4-hydroxybenzoic acid (4HB), the precursor of the benzoquinone ring in the CoQ biosynthetic pathway, completely rescues multisystemic disease and perinatal lethality in a mouse model of CoQ deficiency. 4HB stimulates endogenous CoQ biosynthesis in tissues of Coq2 mutant mice, normalizing mitochondrial function and rescuing cardiac insufficiency, edema, and neurodevelopmental delay. In contrast, exogenous CoQ10 supplementation falls short in fully restoring the phenotype. The treatment is translatable to human use, as proven by in vitro studies in skin fibroblasts from patients with pathogenic variants in COQ2. The therapeutic approach extends to other disorders characterized by deficiencies in the production of 4HB and early steps of CoQ biosynthesis and instances of secondary CoQ deficiency.

A Deep Learning Approach for Segmentation, Classification, and Visualization of 3-D High-Frequency Ultrasound Images of Mouse Embryos

Segmentation and mutant classification of high-frequency ultrasound (HFU) mouse embryo brain ventricle (BV) and body images can provide valuable information for developmental biologists. However, manual segmentation and identification of BV and body requires substantial time and expertise. This article proposes an accurate, efficient and explainable deep learning pipeline for automatic segmentation and classification of the BV and body. For segmentation, a two-stage framework is implemented. The first stage produces a low-resolution segmentation map, which is then used to crop a region of interest (ROI) around the target object and serve as the probability map of the autocontext input for the second-stage fine-resolution refinement network. The segmentation then becomes tractable on high-resolution 3-D images without time-consuming sliding windows. The proposed segmentation method significantly reduces inference time (102.36-0.09 s/volume ≈ 1000× faster) while maintaining high accuracy comparable to previous sliding-window approaches. Based on the BV and body segmentation map, a volumetric convolutional neural network (CNN) is trained to perform a mutant classification task. Through backpropagating the gradients of the predictions to the input BV and body segmentation map, the trained classifier is found to largely focus on the region where the Engrailed-1 (En1) mutation phenotype is known to manifest itself. This suggests that gradient backpropagation of deep learning classifiers may provide a powerful tool for automatically detecting unknown phenotypes associated with a known genetic mutation.

Diverse application of MRI for mouse phenotyping

Small animal models, particularly mouse models, of human diseases are becoming an indispensable tool for biomedical research. Studies in animal models have provided important insights into the etiology of diseases and accelerated the development of therapeutic strategies. Detailed phenotypic characterization is essential, both for the development of such animal models and mechanistic studies into disease pathogenesis and testing the efficacy of experimental therapeutics. MRI is a versatile and noninvasive imaging modality with excellent penetration depth, tissue coverage, and soft tissue contrast. MRI, being a multi-modal imaging modality, together with proven imaging protocols and availability of good contrast agents, is ideally suited for phenotyping mutant mouse models. Here we describe the applications of MRI for phenotyping structural birth defects involving the brain, heart, and kidney in mice. The versatility of MRI and its ease of use are well suited to meet the rapidly increasing demands for mouse phenotyping in the coming age of functional genomics. Birth Defects Research 109:758-770, 2017. © 2017 Wiley Periodicals, Inc.

Brain size regulations by cbp haploinsufficiency evaluated by in-vivo MRI based volumetry

The Rubinstein-Taybi Syndrome (RSTS) is a congenital disease that affects brain development causing severe cognitive deficits. In most cases the disease is associated with dominant mutations in the gene encoding the CREB binding protein (CBP). In this work, we present the first quantitative analysis of brain abnormalities in a mouse model of RSTS using magnetic resonance imaging (MRI) and two novel self-developed automated algorithms for image volumetric analysis. Our results quantitatively confirm key syndromic features observed in RSTS patients, such as reductions in brain size (−16.31%, p < 0.05), white matter volume (−16.00%, p < 0.05), and corpus callosum (−12.40%, p < 0.05). Furthermore, they provide new insight into the developmental origin of the disease. By comparing brain tissues in a region by region basis between cbp^+/− and cbp^+/+ littermates, we found that cbp haploinsufficiency is specifically associated with significant reductions in prosencephalic tissue, such us in the olfactory bulb and neocortex, whereas regions evolved from the embryonic rhombencephalon were spared. Despite the large volume reductions, the proportion between gray-, white-matter and cerebrospinal fluid were conserved, suggesting a role of CBP in brain size regulation. The commonalities with holoprosencephaly and arhinencephaly conditions suggest the inclusion of RSTS in the family of neuronal migration disorders.