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Shinbo, K.

Publications and source records attributed to Shinbo, K..

2 recordsLinked to original sources

A patient-derived LMX1B variant causes tissue-specific manifestations of nail-patella syndrome in mice

Nail-patella syndrome (NPS) is a multisystem disorder caused by pathogenic variants in LMX1B and is characterized by dysplasia of the nails and patellae as well as extraskeletal complications such as progressive nephropathy and glaucoma. We generated a CRISPR/Cas9 knock-in mouse carrying the R252Q substitution, corresponding to a human LMX1B variant associated with renal-predominant disease. Phenotypic analysis revealed that homozygous mice were viable, but they displayed marked growth retardation and severe bilateral ocular opacity. Interestingly, while this model exhibited clear skeletal and ocular defects, the renal phenotype was relatively mild, although increased urinary albumin excretion, focal glomerular basement membrane abnormalities, and subtle changes in renal gene expression were detected. Beyond the classical NPS hallmarks, mutant mice also displayed midbrain morphological abnormalities, suggesting broader developmental consequences of this LMX1B variant. This patient-derived variant model not only recapitulates the pleiotropic features of NPS but also demonstrates organ-specific susceptibility to the R252Q substitution, providing a foundation for elucidating the complex molecular mechanisms underlying multisystem disease.

genetics↗

Beyond pathologist-level annotation of large-scale cancer histology for semantic segmentation using immunofluorescence restaining

Numerous cancer histopathology specimens have been collected and digitised as whole slide images over the past few decades. A comprehensive evaluation of the distribution of various cells in a section of tumour tissue can provide valuable information for understanding cancer and making accurate cancer diagnoses. Deep learning is one of the most suitable techniques to achieve these goals; however, the collection of large, unbiased training data has been a barrier to producing accurate segmentation models. Here, we developed a pipeline to generate SegPath, the largest annotation dataset that is over one order of magnitude larger than publicly available annotations, for the segmentation of haematoxylin and eosin (H&E)-stained sections for eight major cell types. The pipeline used H&E-stained sections that were destained and subsequently immunofluorescence-stained with carefully selected antibodies. The results showed that SegPath is comparable to, or significantly outperforms, conventional pathologist annotations. Moreover, we revealed that annotations by pathologists are biased toward typical morphologies; however, the model trained on SegPath can overcome this limitation. Our results provide foundational datasets for the histopathology machine learning community.

pathology↗