No abstract

Congenital heart disease (CHD) affects up to 1 % of live births1. Although a genetic etiology is indicated by an increased recurrence risk2,3, sporadic occurrence suggests that CHD genetics is complex4. Here, we show that hypoplastic left heart syndrome (HLHS), a severe CHD, is multigenic and genetically heterogeneous. Using mouse forward genetics, we report what is, to our knowledge, the first isolation of HLHS mutant mice and identification of genes causing HLHS. Mutations from seven HLHS mouse lines showed multigenic enrichment in ten human chromosome regions linked to HLHS5–7. Mutations in Sap130 and Pcdha9, genes not previously associated with CHD, were validated by CRISPR–Cas9 genome editing in mice as being digenic causes of HLHS. We also identified one subject with HLHS with SAP130 and PCDHA13 mutations. Mouse and zebrafish modeling showed that Sap130 mediates left ventricular hypoplasia, whereas Pcdha9 increases penetrance of aortic valve abnormalities, both signature HLHS defects. These findings show that HLHS can arise genetically in a combinatorial fashion, thus providing a new paradigm for the complex genetics of CHD.

In vivo cell tracking by MRI can provide means to observe biological processes and monitor cell therapy directly. Immune cells, e.g., macrophages, play crucial roles in many pathophysiological processes, including organ rejection, inflammation, autoimmune diseases, cancer, atherosclerotic plaque formation, numerous neurological disorders, etc. The current gold standard for diagnosing and staging rejection after organ transplantation is biopsy, which is not only invasive, but also prone to sampling errors. Here, we report a noninvasive approach using MRI to detect graft rejection after solid organ transplantation. In addition, we present the feasibility of imaging individual macrophages in vivo by MRI in a rodent heterotopic working-heart transplantation model using a more sensitive contrast agent, the micrometer-sized paramagnetic iron oxide particle, as a methodology to detect acute cardiac rejection.cardiac rejection ͉ detection of single macrophage ͉ micrometer-sized iron oxide particle ͉ nanometer-sized iron oxide particle

Abstract-The paper presents a novel stochastic active contour scheme (STACS) for automatic image segmentation designed to overcome some of the unique challenges in cardiac MR images such as problems with low contrast, papillary muscles, and turbulent blood flow. STACS minimizes an energy functional that combines stochastic region-based and edge-based information with shape priors of the heart and local properties of the contour. The minimization algorithm solves, by the level set method, the Euler-Lagrange equation that describes the contour evolution. STACS includes an annealing schedule that balances dynamically the weight of the different terms in the energy functional. Three particularly attractive features of STACS are: 1) ability to segment images with low texture contrast by modeling stochastically the image textures; 2) robustness to initial contour and noise because of the utilization of both edge and region-based information; 3) ability to segment the heart from the chest wall and the undesired papillary muscles due to inclusion of heart shape priors. Application of STACS to a set of 48 real cardiac MR images shows that it can successfully segment the heart from its surroundings such as the chest wall and the heart structures (the left and right ventricles and the epicardium.) We compare STACS' automatically generated contours with manually-traced contours, or the "gold standard," using both area and edge similarity measures. This assessment demonstrates very good and consistent segmentation performance of STACS.Index Terms-Active contour, cardiac magnetic resonance imaging (cardiac MRI), chamfer method, energy minimization, image segmentation, level set, shape and area similarities, stochastic model, stochastic relaxation.