No abstract

Epidermal growth factor (EGF) family peptides are ligands for the EGF receptor (EGFR). Here, we elucidate functional differences among EGFR ligands and mechanisms underlying these distinctions. In 32D/EGFR myeloid and MCF10A breast cells, soluble amphiregulin (AR), transforming growth factor alpha (TGFα), neuregulin 2 beta, and epigen stimulate greater EGFR coupling to cell proliferation and DNA synthesis than do EGF, betacellulin, heparin-binding EGF-like growth factor, and epiregulin. EGF competitively antagonizes AR, indicating that its functional differences reflect dissimilar intrinsic activity at EGFR. EGF stimulates much greater phosphorylation of EGFR Tyr1045 than does AR. Moreover, the EGFR Y1045F mutation and z-cbl dominant-negative mutant of the c-cbl ubiquitin ligase potentiate the effect of EGF but not of AR. Both EGF and AR stimulate phosphorylation of EGFR Tyr992. However, the EGFR Y992F mutation and phospholipase C gamma inhibitor U73122 reduce the effect of AR much more than that of EGF. Expression of TGFα in 32D/EGFR cells causes greater EGFR coupling to cell proliferation than does expression of EGF. Moreover, expression of EGF in 32D/EGFR cells causes these cells to be largely refractory to stimulation with soluble EGF. Thus, EGFR ligands are functionally distinct in models of paracrine and autocrine signaling and EGFR coupling to biological responses may be specified by competition among functionally distinct EGFR ligands.

Experimental in vitro models that capture pathophysiological characteristics of human tumours are essential for basic and translational cancer biology. Here, we describe a fully synthetic hydrogel extracellular matrix designed to elicit key phenotypic traits of the pancreatic environment in culture. To enable the growth of normal and cancerous pancreatic organoids from genetically engineered murine models and human patients, essential adhesive cues were empirically defined and replicated in the hydrogel scaffold, revealing a functional role of laminin – integrin α3/α6 signalling in establishment and survival of pancreatic organoids. Altered tissue stiffness — a hallmark of pancreatic cancer — was recapitulated in culture by adjusting the hydrogel properties to engage mechano-sensing pathways and alter organoid growth. Pancreatic stromal cells were readily incorporated into the hydrogels and replicated phenotypic traits characteristic of the tumour environment in vivo . This model therefore recapitulates a pathologically remodelled tumour microenvironment for studies of normal and pancreatic cancer cells in vitro .

Many human gut bacteria of clinical relevance are extremely oxygen sensitive, hampering the investigation of crosstalk with host cells. Zhang et al. developed a gut-microbe physiomimetic platform for long-term continuous co-culture of super oxygen-sensitive bacterial species with primary human colon epithelium in the context of inflammation.

Organoid cultures are proving to be powerful in vitro models that closely mimic the cellular constituents of their native tissue. Organoids are typically expanded and cultured in a 3D environment using either naturally derived or synthetic extracellular matrices. Assessing the morphology and growth characteristics of these cultures has been difficult due to the many imaging artifacts that accompany the corresponding images. Unlike single cell cultures, there are no reliable automated segmentation techniques that allow for the localization and quantification of organoids in their 3D culture environment. Here we describe OrgaQuant, a deep convolutional neural network implementation that can locate and quantify the size distribution of human intestinal organoids in brightfield images. OrgaQuant is an end-to-end trained neural network that requires no parameter tweaking; thus, it can be fully automated to analyze thousands of images with no user intervention. To develop OrgaQuant, we created a unique dataset of manually annotated human intestinal organoid images with bounding boxes and trained an object detection pipeline using TensorFlow. We have made the dataset, trained model and inference scripts publicly available along with detailed usage instructions.