Biology subjects

Ushiku, T.

Publications and source records attributed to Ushiku, T..

5 recordsLinked to original sources

bioRxiv2025

Muc6-expressing gastric isthmus progenitors contribute to regeneration and metaplasia supported by myeloid-mesenchymal interactions

Gastric mucosal homeostasis is maintained by tissue-resident stem and progenitor cells residing in the isthmus region. Following mucosal injury, surviving cells contribute to regeneration, coinciding with characteristic pathological changes such as atrophic gastritis and metaplasia. To comprehensively understand the cellular dynamics involved in this process, we performed single-cell and spatial transcriptomics using newly generated transgenic mice. In human samples and mouse models, loss of gastric chief cells precedes, and even induces, loss of parietal cells during the progression of atrophy and metaplasia, validating the causal relationship underlying the decrease of these two lineages. Single-cell analysis confirmed robust stemness and metaplastic changes in the Muc6-expressing neck lineage following either chief or parietal cell ablation, and lineage-tracing experiments revealed that Muc6-expressing isthmus progenitors serve as a source of metaplasia and regeneration. Mechanistically, mucosal injury recruits IL-1-expressing myeloid cells, which stimulates NRG1 production in stromal fibroblasts, leading to mucosal proliferation and regeneration mediated by Myc activation in isthmus progenitors. These findings highlight the injury-responsible stem cell-like function of Muc6-expressing isthmal progenitors, which play a critical role in mucosal homeostasis and disease progression. Visual abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=109 SRC="FIGDIR/small/648856v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@10fc2eaorg.highwire.dtl.DTLVardef@1c5b82corg.highwire.dtl.DTLVardef@1be747dorg.highwire.dtl.DTLVardef@d1d722_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗

bioRxiv2022

Can SARS-CoV-2 transmit from a dead body?

Although it has been 2.5 years since the COVID-19 pandemic began, the transmissibility of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) from a dead infected body remains unclear, and often, in Japan bereaved family members are not allowed to view in-person a loved one who has died from COVID-19. In this study, we analyzed the possibility of SARS-CoV-2 transmission from a dead body by using the hamster model. We also analyzed the effect of Angel-care--in which the pharynx, nostril, and rectum are plugged--and embalming on reducing transmissibility from dead bodies. We found that SARS-CoV-2 could be transmitted from the body of animals that died within a few days of infection; however, Angel-care and embalming were effective in preventing transmission from the dead body. These results suggest that protection from infection is essential when in contact with a SARS-CoV-2-infected dead body, and that sealing the cavities of a dead body is an important infection control step if embalming is not done. ImportanceWe found that SARS-CoV-2 could be transmitted from a dead body presumably via postmortem gases. However, we also found that postmortem care, such as plugging the pharynx, nostrils, and rectum, or embalming could prevent transmission from the dead body. These results indicate that protection from infection is essential when handling infected corpses, and that appropriate care of SARS-CoV-2-infected corpses is important.

microbiology↗

bioRxiv2022

Single-Cell Transcriptome Analyses Reveal the Cell Diversity and Developmental Features of Human Gastric and Metaplastic Mucosa

The stomach is an important digestive organ with a variety of biological functions. However, due to the complexity of its cellular and glandular composition, the precise cellular biology has yet to be elucidated. In this study, we conducted single-cell RNA sequence analysis of the human stomach and constructed a 137,610-cell dataset, the largest cell atlas reported to date. By integrating this single-cell analysis with spatial cellular distribution analysis, we were able to clarify novel aspects of the developmental and tissue homeostatic ecosystems in the human stomach. We identified LEFTY1+ as a potential stem cell marker in both gastric and intestinal metaplastic glands. We also revealed skewed distribution patterns for PDGFRA+BMP4+WNT5A+ fibroblasts that play pivotal roles in, or even precede, the phenotypic changes from gastric to metaplastic mucosa. Our extensive dataset will function as a fundamental resource in investigations of the stomach, including studies on development, aging, and carcinogenesis.

genomics↗

bioRxiv2022

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↗

bioRxiv2020

Deep Texture Representations as a Universal Encoder for Pan-cancer Histology

Cancer histological images contain rich biological and clinical information, but quantitative representation can be problematic and has prevented direct comparison and accumulation of large-scale datasets. Here we show that deep texture representations (DTRs) produced by a bilinear Convolutional Neural Network, express cancer morphology well in an unsupervised manner, and work as a universal encoder for cancer histology. DTRs are useful for content-based image retrieval, enabling quick retrieval of histologically similar images from optimally area selected datasets of 7,175 cases from The Cancer Genome Atlas. Via comprehensive comparison with driver and clinically actionable gene mutations, we have successfully predicted 309 combinations of genomic features and cancer types from hematoxylin and eosin-stained images at high accuracy (AUC > 0.70 and q < 0.02). With its mounting capabilities on accessible devices such as smartphones, DTR-based encoding for cancer histology has a potentially strong impact on global equalization for cancer diagnosis and targeted therapies.

cancer biology↗