Dynamic lateral organization of opioid receptors (kappa, muwt and muN40D ) in the plasma membrane at the nanoscale level.

Opioid receptors are important pharmacological targets for the management of numerous medical conditions (eg, severe pain), but they are also the gateway to the development of deleterious side effects (eg, opiate addiction). Opioid receptor signaling cascades are well characterized. However, quantitative information regarding their lateral dynamics and nanoscale organization in the plasma membrane remains limited. Since these dynamic properties are important determinants of receptor function, it is crucial to define them. Herein, the nanoscale lateral dynamics and spatial organization of kappa opioid receptor (KOP), wild type mu opioid receptor (MOPwt ), and its naturally occurring isoform (MOPN40D ) were quantitatively characterized using fluorescence correlation spectroscopy and photoactivated localization microscopy. Obtained results, supported by ensemble-averaged Monte Carlo simulations, indicate that these opioid receptors dynamically partition into different domains. In particular, significant exclusion from GM1 ganglioside-enriched domains and partial association with cholesterol-enriched domains was observed. Nanodomain size, receptor population density and the fraction of receptors residing outside of nanodomains were receptor-specific. KOP-containing domains were the largest and most densely populated, with the smallest fraction of molecules residing outside of nanodomains. The opposite was true for MOPN40D . Moreover, cholesterol depletion dynamically regulated the partitioning of KOP and MOPwt , whereas this effect was not observed for MOPN40D .

Molecular signatures of mu opioid receptor and somatostatin receptor 2 in pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC), a particularly aggressive malignancy, has been linked to atypical levels, certain mutations, and aberrant signaling of G-protein-coupled receptors (GPCRs). GPCRs have been challenging to target in cancer because they organize into complex networks in tumor cells. To dissect such networks with nanometer-scale precision, here we combine traditional biochemical approaches with superresolution microscopy methods. A novel interaction specific to PDAC is identified between mu opioid receptor (MOR) and somatostatin receptor 2 (SSTR2). Although MOR and SSTR2 did not colocalize in healthy pancreatic cells or matching healthy patient tissues, the pair did significantly colocalize in pancreatic cancer cells, multicellular tumor spheroids, and cancerous patient tissues. Moreover, this association in pancreatic cancer cells correlated with functional cross-talk and increased metastatic potential of cells. Coactivation of MOR and SSTR2 in PDAC cells led to increased expression of mesenchymal markers and decreased expression of an epithelial marker. Together these results suggest that the MOR-SSTR2 heteromer may constitute a novel therapeutic target for PDAC.

Superresolution localization methods.

Superresolution localization microscopy methods produce nanoscale images via a combination of intermittently active fluorescent probes and algorithms that can precisely determine the positions of these probes from single-molecule or few-molecule images. These algorithms vary widely in their underlying principles, complexity, and accuracy. In this review, we begin by surveying the principles of localization microscopy and describing the fundamental limits to localization precision. We then examine several different families of fluorophore localization algorithms, comparing their complexity, performance, and range of applicability (e.g., whether they require particular types of experimental information, are optimized for specific situations, or are more general). Whereas our focus is on the localization of single isotropic emitters in two dimensions, we also consider oriented dipoles, three-dimensional localization, and algorithms that can handle overlapping images of several nearby fluorophores. Throughout the review, we try to highlight practical advice for users of fluorophore localization algorithms, as well as open questions.

Theoretical limits on errors and acquisition rates in localizing switchable fluorophores.

A variety of recent imaging techniques are able to beat the diffraction limit in fluorescence microcopy by activating and localizing subsets of the fluorescent molecules in the specimen, and repeating this process until all of the molecules have been imaged. In these techniques there is a tradeoff between speed (activating more molecules per imaging cycle) and error rates (activating more molecules risks producing overlapping images that hide information on molecular positions), and so intelligent image processing approaches are needed to identify and reject overlapping images. We introduce here a formalism for defining error rates, derive a general relationship between error rates, image acquisition rates, and the performance characteristics of the image processing algorithms, and show that there is a minimum acquisition time irrespective of algorithm performance. We also consider algorithms that can infer molecular positions from images of overlapping blurs, and derive the dependence of the minimum acquisition time on algorithm performance.

Spatial distribution of VEGF isoforms and chemotactic signals in the vicinity of a tumor.

We propose a mathematical model that describes the formation of gradients of different isoforms of vascular endothelial growth factor (VEGF). VEGF is crucial in the process of tumor-induced angiogenesis, and recent experiments strongly suggest that the molecule is most potent when bound to the extracellular matrix (ECM). Using a system of reaction-diffusion equations, we study diffusion of VEGF, binding of VEGF to the ECM, and cleavage of VEGF from the ECM by matrix metalloproteases (MMPs). We find that spontaneous gradients of matrix-bound VEGF are possible for an isoform that binds weakly to the ECM (i.e. VEGF(165)), but cleavage by MMPs is required to form long-range gradients of isoforms that bind rapidly to the ECM (i.e. VEGF(189)). We also find that gradient strengths and ranges are regulated by MMPs. Finally, we find that VEGF molecules cleaved from the ECM may be distributed in patterns that are not conducive to chemotactic migration toward a tumor, depending on the spatial distribution of MMP molecules. Our model elegantly explains a number of in vivo observations concerning the significance of different VEGF isoforms, points to VEGF(165) as an especially significant therapeutic target and indicator of a tumor's angiogenic potential, and enables predictions that are subject to testing with in vitro experiments.