del Pozo, M. A. | Search bioRxiv

bioRxiv2018

ECM deposition is driven by caveolin1-dependent regulation of exosomal biogenesis and cargo sorting

The composition and physical properties of the extracellular matrix (ECM) critically influence tumour cell behaviour, and ECM deposition and remodelling by stromal fibroblast populations is therefore pivotal for tumour progression. The molecular mechanisms by which stromal and tumour cell populations regulate ECM layering are poorly understood. Tumour-stroma interaction is critically dependent on cell-cell communication mediated by exosomes, small vesicles secreted by most cell types and generated within multivesicular bodies (MVBs). Here, we show that caveolin-1 (Cav1), an essential regulator of stromal remodelling and tumour cell fate, plays a central role in modulating both exosome biogenesis and exosomal protein cargo sorting through cholesterol-dependent mechanisms. Quantitative proteomics profiling revealed that a major share of Cav1-dependent exosomal cargoes are compsed of ECM proteins, one of the most important components being tenascin-C (TnC). Comparative functional assays demonstrated that Cav1 is required for fibroblast-derived exosomes to depose ECM and promote tumour cell invasiveness. Exosomes purified from Cav1WT cells, but not those from Cav1-null cells, were able to nucleate distant stromal niches in different organs in vivo. These findings suggest a key role for Cav1 as a cholesterol rheostat in MVBs, and seems to determine ECM deposition by eliciting ECM component sorting into specific exosome pools. These results, together with previous work, support a model in which Cav1 is a central regulatory hub for tumour-stroma interactions through a novel exosome-dependent ECM deposition mechanism.

cell biology↗

bioRxiv2018

Tox_(R)CNN: Deep Learning-Based Nuclei Profiling tool For Drug Toxicity Screening

Toxicity is an important factor in failed drug development, and its efficient identification and prediction is a major challenge in drug discovery. We have explored the potential of microscopy images of fluorescently labeled nuclei for the prediction of toxicity based on nucleus pattern recognition. Deep learning algorithms obtain abstract representations of images through an automated process, allowing them to efficiently classify complex patterns, and have become the state-of-the art in machine learning for computer vision. Here, deep convolutional neural networks (CNN) were trained to predict toxicity from images of DAPI-stained cells pre-treated with a set of drugs with differing toxicity mechanisms. Different cropping strategies were used for training CNN models, the nuclei-cropping-based Tox-CNN model outperformed other models classifying cells according to health status. Tox-CNN allowed automated extraction of feature maps that clustered compounds according to mechanism of action. Moreover, fully automated region-based CNNs (RCNN) were implemented to detect and classify nuclei, providing per-cell toxicity prediction from raw screening images. We validated both Tox-(R)CNN models for detection of pre-lethal toxicity from nuclei images, which proved to be more sensitive and have broader specificity than established toxicity readouts. These models predicted toxicity of drugs with mechanisms of action other than those they had been trained for and were successfully transferred to other cell assays. The Tox-(R)CNN models thus provide robust, sensitive, and cost-effective tools for in vitro screening of drug-induced toxicity. These models can be adopted for compound prioritization in drug screening campaigns, and could thereby increase the efficiency of drug discovery.\n\nAuthor summaryVisualization of nuclei using different microscopic approaches has for decades allowed the identification of cells undergoing cell death, based on changes in morphology, nuclear density, etc. However, this human-based visual analysis has not been traslated into quantitative tools able to objectively measure cytotoxicity in drug-exposed cells. We asked ourselves if it would be possible to train machines to detect cytotoxicity from microscopy images of fluorescently stained nuclei, without using specific toxicity labeling. Deep learning is the most powerful supervised machine learning methodology available, with exceptional abilities to solve computer vision tasks, and was thus selected for the development of a toxicity quantification tool. Two convolutional neural networks (CNN) were developed to classify cells based on health status: Tox-CNN, relying on prior cell segmentation and cropping of nuclei images, and Tox-RCNN which carries out fully-automated cell detection and classification. Both Tox-(R)CNN classification outputs provided sensitive screening readouts that detected pre-lethal toxicity and were validated for a broad array of toxicity pathways and cell assays. Tox-(R)CNN approaches excel in affordability and applicability to other in vitro toxicity readouts and constitute a robust screening tool for drug discovery.

pharmacology and toxicology↗

bioRxiv2017

Mechanochemical feedback and control of endocytosis and membrane tension

Plasma membrane tension is an important factor that regulates many key cellular processes. Membrane trafficking is tightly coupled to membrane tension and can modulate the latter by addition or removal of the membrane. However, the cellular pathway(s) involved in these processes are poorly understood. Here we find that, among a number of endocytic processes operating simultaneously at the cell surface, a dynamin and clathrin-independent pathway, the CLIC/GEEC (CG) pathway, is rapidly and specifically upregulated upon reduction of tension. On the other hand, inhibition of the CG pathway results in lower membrane tension, while up regulation significantly enhances membrane tension. We find that vinculin, a well-studied mechanotransducer, mediates the tension-dependent regulation of the CG pathway. Vinculin negatively regulates a key CG pathway regulator, GBF1, at the plasma membrane in a tension dependent manner. Thus, the CG pathway operates in a negative feedback loop with membrane tension which leads to a homeostatic regulation of membrane tension.

cell biology↗