CagA-ASPP2 complex mediates loss of cell polarity and favors H. pylori colonization of human gastric organoids.

The main risk factor for stomach cancer, the third most common cause of cancer death worldwide, is infection with Helicobacter pylori bacterial strains that inject cytotoxin-associated gene A (CagA). As the first described bacterial oncoprotein, CagA causes gastric epithelial cell transformation by promoting an epithelial-to-mesenchymal transition (EMT)-like phenotype that disrupts junctions and enhances motility and invasiveness of the infected cells. However, the mechanism by which CagA disrupts gastric epithelial cell polarity to achieve its oncogenicity is not fully understood. Here we found that the apoptosis-stimulating protein of p53 2 (ASPP2), a host tumor suppressor and an important CagA target, contributes to the survival of cagA-positive H. pylori in the lumen of infected gastric organoids. Mechanistically, the CagA-ASPP2 interaction is a key event that promotes remodeling of the partitioning-defective (PAR) polarity complex and leads to loss of cell polarity of infected cells. Blockade of cagA-positive H. pylori ASPP2 signaling by inhibitors of the EGFR (epidermal growth factor receptor) signaling pathway-identified by a high-content imaging screen-or by a CagA-binding ASPP2 peptide, prevents the loss of cell polarity and decreases the survival of H. pylori in infected organoids. These findings suggest that maintaining the host cell-polarity barrier would reduce the detrimental consequences of infection by pathogenic bacteria, such as H. pylori, that exploit the epithelial mucosal surface to colonize the host environment.

Motion sensing superpixels (MOSES) is a systematic computational framework to quantify and discover cellular motion phenotypes.

Correct cell/cell interactions and motion dynamics are fundamental in tissue homeostasis, and defects in these cellular processes cause diseases. Therefore, there is strong interest in identifying factors, including drug candidates that affect cell/cell interactions and motion dynamics. However, existing quantitative tools for systematically interrogating complex motion phenotypes in timelapse datasets are limited. We present Motion Sensing Superpixels (MOSES), a computational framework that measures and characterises biological motion with a unique superpixel 'mesh' formulation. Using published datasets, MOSES demonstrates single-cell tracking capability and more advanced population quantification than Particle Image Velocimetry approaches. From > 190 co-culture videos, MOSES motion-mapped the interactions between human esophageal squamous epithelial and columnar cells mimicking the esophageal squamous-columnar junction, a site where Barrett's esophagus and esophageal adenocarcinoma often arise clinically. MOSES is a powerful tool that will facilitate unbiased, systematic analysis of cellular dynamics from high-content time-lapse imaging screens with little prior knowledge and few assumptions.

Characterization of Biological Motion Using Motion Sensing Superpixels.

Precise spatiotemporal regulation is the foundation for the healthy development and maintenance of living organisms. All cells must correctly execute their function in the right place at the right time. Cellular motion is thus an important dynamic readout of signaling in key disease-relevant molecular pathways. However despite the rapid advancement of imaging technology, a comprehensive quantitative description of motion imaged under different imaging modalities at all spatiotemporal scales; molecular, cellular and tissue-level is still lacking. Generally, cells move either 'individually' or 'collectively' as a group with nearby cells. Current computational tools specifically focus on one or the other regime, limiting their general applicability. To address this, we recently developed and reported a new computational framework, Motion Sensing Superpixels (MOSES). Incorporating the individual advantages of single cell trackers for individual cell and particle image velocimetry (PIV) for collective cell motion analyses, MOSES enables 'mesoscale' analysis of both single-cell and collective motion over arbitrarily long times. At the same time, MOSES readily complements existing single-cell tracking workflows with additional characterization of global motion patterns and interaction analysis between cells and also operates directly on PIV extracted motion fields to yield rich motion trajectories analogous for single-cell tracks suitable for high-throughput motion phenotyping. This protocol provides a step-by-step practical guide for those interested in applying MOSES to their own datasets. The protocol highlights the salient features of a MOSES analysis and demonstrates the ease-of-use and wide applicability of MOSES to biological imaging through demo experimental analyses with ready-to-use code snippets of four datasets from different microscope modalities; phase-contrast, fluorescent, lightsheet and intra-vital microscopy. In addition we discuss critical points of consideration in the analysis.

Single cell RNA-seq reveals profound transcriptional similarity between Barrett's oesophagus and oesophageal submucosal glands.

Barrett's oesophagus is a precursor of oesophageal adenocarcinoma. In this common condition, squamous epithelium in the oesophagus is replaced by columnar epithelium in response to acid reflux. Barrett's oesophagus is highly heterogeneous and its relationships to normal tissues are unclear. Here we investigate the cellular complexity of Barrett's oesophagus and the upper gastrointestinal tract using RNA-sequencing of single cells from multiple biopsies from six patients with Barrett's oesophagus and two patients without oesophageal pathology. We find that cell populations in Barrett's oesophagus, marked by LEFTY1 and OLFM4, exhibit a profound transcriptional overlap with oesophageal submucosal gland cells, but not with gastric or duodenal cells. Additionally, SPINK4 and ITLN1 mark cells that precede morphologically identifiable goblet cells in colon and Barrett's oesophagus, potentially aiding the identification of metaplasia. Our findings reveal striking transcriptional relationships between normal tissue populations and cells in a premalignant condition, with implications for clinical practice.