The inner surfaces of the human heart are covered by a complex network of muscular strands that is thought to be a vestige of embryonic development. 1,2 The function of these trabeculae in adults and their genetic architecture are unknown. To investigate this we performed a genome-wide association study using fractal analysis of trabecular morphology as an image-derived phenotype in 18,096 UK Biobank participants. We identified 16 significant loci containing genes associated with haemodynamic phenotypes and regulation of cytoskeletal arborisation. 3,4 Using biomechanical simulations and human observational data, we demonstrate that trabecular morphology is an important determinant of cardiac performance. Through genetic association studies with cardiac disease phenotypes and Mendelian randomisation, we find a causal relationship between trabecular morphology and cardiovascular disease risk. These findings suggest an unexpected role for myocardial trabeculae in the function of the adult heart, identify conserved pathways that regulate structural complexity, and reveal their influence on susceptibility to disease. MainThe chambers of the mature human heart have a complex inner surface whose function is unknown. Unlike the smooth endothelium of the great vessels, the endocardial surfaces of both ventricles are lined by a fenestrated network of muscular trabeculae which extend into the cavity. Their embryological development is driven by highly-conserved signalling pathways involving the endocardium-myocardium and extra-cellular matrix that regulate myocardial proliferation during cardiac morphogenesis. 2,[5][6][7][8][9] 1 Cell lineage tracing suggests that trabeculae have a molecular and developmental identity which is distinct from the compact myocardium. 10 The high surface area of trabeculae enables nutrient and oxygen diffusion from blood pool to myocardium before the coronary circulation is established. 1 Trabeculae are also vital to formation of the conduction system. 11 Theoretical analyses have proposed that their complex structure may contribute to efficient intra-ventricular flow patterns. 12-14 While hypertrabeculation is observed as a feature of some genetically-characterised cardiomyopathies, 15 the physiological function of trabeculae in adult hearts, their genetic architecture, and potential role in common disease have not been determined.The distinguishing trait of trabeculae is their branching morphology and the degree of such biological complexity in the heart can be quantified by fractal dimension (FD) analysis of cardiac magnetic resonance (CMR) imaging. 8 In a replicated genomewide association study (GWAS), using FD as an image-derived phenotype, we identify loci linked with trabecular morphology. Knockout models of loci-associated genes showed a marked decrease in trabecular complexity. Using biomechanical modelling and human observational data, we find a causal relationship between myocardial trabeculation and ventricular performance, with Mendelian randomisation showing that reduced trabecula...
Capturing highly dynamic biological processes at sub-cellular resolution is a recurring challenge in biology. Here we show that combining selective volume illumination with simultaneous acquisition of orthogonal lightfields yields 3D images with high, isotropic spatial resolution and free of reconstruction artefacts, thereby overcoming current limitations of lightfield microscopy implementations. We demonstrate Medaka heart and blood flow imaging with single-cell resolution and free of motion artefacts at volume rates up to 200Hz.
Light-field microscopy (LFM) has emerged as a powerful tool for fast volumetric image acquisition in biology, but its effective throughput and widespread use has been hampered by a computationally demanding and artefact-prone image reconstruction process. Here, we present a novel framework consisting of a hybrid light-field light-sheet microscope and deep learning-based volume reconstruction, where single light-sheet acquisitions continuously serve as training data and validation for the convolutional neural network reconstructing the LFM volume. Our network delivers high-quality reconstructions at video-rate throughput and we demonstrate the capabilities .
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