A deep learning method that identifies cellular heterogeneity using nanoscale nuclear features.

Cellular phenotypic heterogeneity is an important hallmark of many biological processes and understanding its origins remains a substantial challenge. This heterogeneity often reflects variations in the chromatin structure, influenced by factors such as viral infections and cancer, which dramatically reshape the cellular landscape. To address the challenge of identifying distinct cell states, we developed artificial intelligence of the nucleus (AINU), a deep learning method that can identify specific nuclear signatures at the nanoscale resolution. AINU can distinguish different cell states based on the spatial arrangement of core histone H3, RNA polymerase II or DNA from super-resolution microscopy images. With only a small number of images as the training data, AINU correctly identifies human somatic cells, human-induced pluripotent stem cells, very early stage infected cells transduced with DNA herpes simplex virus type 1 and even cancer cells after appropriate retraining. Finally, using AI interpretability methods, we find that the RNA polymerase II localizations in the nucleoli aid in distinguishing human-induced pluripotent stem cells from their somatic cells. Overall, AINU coupled with super-resolution microscopy of nuclear structures provides a robust tool for the precise detection of cellular heterogeneity, with considerable potential for advancing diagnostics and therapies in regenerative medicine, virology and cancer biology.

Implementation of esophageal endoscopic submucosal dissection in Spain: Results from the nationwide registry. / Implementación de la disección endoscópica submucosa esofágica en España: resultados del registro nacional.

INTRODUCTION AND AIMS: The outcomes of endoscopic submucosal dissection (ESD) in the esophagus have not been assessed in our country. Our primary aim was to analyze the effectiveness and safety of the technique. MATERIAL AND METHODS: Analysis of the prospectively maintained national registry of ESD. We included all superficial esophageal lesions removed by ESD in 17 hospitals (20 endoscopists) between January 2016 and December 2021. Subepithelial lesions were excluded. The primary outcome was curative resection. We conducted a survival analysis and used logistic regression analysis to assess predictors of non-curative resection. RESULTS: A total of 102 ESD were performed on 96 patients. The technical success rate was 100% and the percentage of en-bloc resection was 98%. The percentage of R0 and curative resection was 77.5% (n=79; 95%CI: 68%-84%) and 63.7% (n=65; 95%CI: 54%-72%), respectively. The most frequent histology was Barrett-related neoplasia (n=55 [53.9%]). The main reason for non-curative resection was deep submucosal invasion (n=25). The centers with a lower volume of ESD obtained worse results in terms of curative resection. The rate of perforation, delayed bleeding and post-procedural stenosis were 5%, 5% and 15.7%, respectively. No patient died or required surgery due to an adverse effect. After a median follow-up of 14months, 20patients (20.8%) underwent surgery and/or chemoradiotherapy, and 9 patients died (mortality 9.4%). CONCLUSIONS: In Spain, esophageal ESD is curative in approximately two out of three patients, with an acceptable risk of adverse events.

CartoCell, a high-content pipeline for 3D image analysis, unveils cell morphology patterns in epithelia.

Decades of research have not yet fully explained the mechanisms of epithelial self-organization and 3D packing. Single-cell analysis of large 3D epithelial libraries is crucial for understanding the assembly and function of whole tissues. Combining 3D epithelial imaging with advanced deep-learning segmentation methods is essential for enabling this high-content analysis. We introduce CartoCell, a deep-learning-based pipeline that uses small datasets to generate accurate labels for hundreds of whole 3D epithelial cysts. Our method detects the realistic morphology of epithelial cells and their contacts in the 3D structure of the tissue. CartoCell enables the quantification of geometric and packing features at the cellular level. Our single-cell cartography approach then maps the distribution of these features on 2D plots and 3D surface maps, revealing cell morphology patterns in epithelial cysts. Additionally, we show that CartoCell can be adapted to other types of epithelial tissues.

A quantitative biophysical principle to explain the 3D cellular connectivity in curved epithelia.

Epithelial cell organization and the mechanical stability of tissues are closely related. In this context, it has been recently shown that packing optimization in bended or folded epithelia is achieved by an energy minimization mechanism that leads to a complex cellular shape: the "scutoid". Here, we focus on the relationship between this shape and the connectivity between cells. We use a combination of computational, experimental, and biophysical approaches to examine how energy drivers affect the three-dimensional (3D) packing of tubular epithelia. We propose an energy-based stochastic model that explains the 3D cellular connectivity. Then, we challenge it by experimentally reducing the cell adhesion. As a result, we observed an increment in the appearance of scutoids that correlated with a decrease in the energy barrier necessary to connect with new cells. We conclude that tubular epithelia satisfy a quantitative biophysical principle that links tissue geometry and energetics with the average cellular connectivity.