Imaging Transient Blood Vessel Fusion Events by Correlative Volume Electron Microscopy

Extended abstract of a paper presented at Microscopy and Microanalysis 2010 in Portland, Oregon, USA, August 1 - August 5, 201

WASp-dependent actin cytoskeleton stability at the dendritic cell immunological synapse is required for extensive, functional T cell contacts

The immunological synapse is a highly structured and molecularly dynamic interface between communicating immune cells. Although the immunological synapse promotes T cell activation by dendritic cells, the specific organization of the immunological synapse on the dendritic cell side in response to T cell engagement is largely unknown. In this study, confocal and electron microscopy techniques were used to investigate the role of dendritic cell actin regulation in immunological synapse formation, stabilization, and function. In the dendritic cell-restricted absence of the Wiskott-Aldrich syndrome protein, an important regulator of the actin cytoskeleton in hematopoietic cells, the immunological synapse contact with T cells occupied a significantly reduced surface area. At a molecular level, the actin network localized to the immunological synapse exhibited reduced stability, in particular, of the actin-related protein-2/3-dependent, short-filament network. This was associated with decreased polarization of dendritic cell-associated ICAM-1 and MHC class II, which was partially dependent on Wiskott-Aldrich syndrome protein phosphorylation. With the use of supported planar lipid bilayers incorporating anti-ICAM-1 and anti-MHC class II antibodies, the dendritic cell actin cytoskeleton organized into recognizable synaptic structures but interestingly, formed Wiskott-Aldrich syndrome protein-dependent podosomes within this area. These findings demonstrate that intrinsic dendritic cell cytoskeletal remodeling is a key regulatory component of normal immunological synapse formation, likely through consolidation of adhesive interaction and modulation of immunological synapse stability

Method: automatic segmentation of mitochondria utilizing patch classification, contour pair classification, and automatically seeded level sets

<p>Abstract</p> <p>Background</p> <p>While progress has been made to develop automatic segmentation techniques for mitochondria, there remains a need for more accurate and robust techniques to delineate mitochondria in serial blockface scanning electron microscopic data. Previously developed texture based methods are limited for solving this problem because texture alone is often not sufficient to identify mitochondria. This paper presents a new three-step method, the Cytoseg process, for automated segmentation of mitochondria contained in 3D electron microscopic volumes generated through serial block face scanning electron microscopic imaging. The method consists of three steps. The first is a random forest patch classification step operating directly on 2D image patches. The second step consists of contour-pair classification. At the final step, we introduce a method to automatically seed a level set operation with output from previous steps.</p> <p>Results</p> <p>We report accuracy of the Cytoseg process on three types of tissue and compare it to a previous method based on Radon-Like Features. At step 1, we show that the patch classifier identifies mitochondria texture but creates many false positive pixels. At step 2, our contour processing step produces contours and then filters them with a second classification step, helping to improve overall accuracy. We show that our final level set operation, which is automatically seeded with output from previous steps, helps to smooth the results. Overall, our results show that use of contour pair classification and level set operations improve segmentation accuracy beyond patch classification alone. We show that the Cytoseg process performs well compared to another modern technique based on Radon-Like Features.</p> <p>Conclusions</p> <p>We demonstrated that texture based methods for mitochondria segmentation can be enhanced with multiple steps that form an image processing pipeline. While we used a random-forest based patch classifier to recognize texture, it would be possible to replace this with other texture identifiers, and we plan to explore this in future work.</p

Lymphatic vessel density and function in experimental bladder cancer

<p>Abstract</p> <p>Background</p> <p>The lymphatics form a second circulatory system that drains the extracellular fluid and proteins from the tumor microenvironment, and provides an exclusive environment in which immune cells interact and respond to foreign antigen. Both cancer and inflammation are known to induce lymphangiogenesis. However, little is known about bladder lymphatic vessels and their involvement in cancer formation and progression.</p> <p>Methods</p> <p>A double transgenic mouse model was generated by crossing a bladder cancer-induced transgenic, in which SV40 large T antigen was under the control of uroplakin II promoter, with another transgenic mouse harboring a <it>lacZ </it>reporter gene under the control of an NF-κB-responsive promoter (κB-<it>lacZ</it>) exhibiting constitutive activity of β-galactosidase in lymphatic endothelial cells. In this new mouse model (SV40-<it>lacZ</it>), we examined the lymphatic vessel density (LVD) and function (LVF) during bladder cancer progression. LVD was performed in bladder whole mounts and cross-sections by fluorescent immunohistochemistry (IHC) using LYVE-1 antibody. LVF was assessed by real-time <it>in vivo </it>imaging techniques using a contrast agent (biotin-BSA-Gd-DTPA-Cy5.5; Gd-Cy5.5) suitable for both magnetic resonance imaging (MRI) and near infrared fluorescence (NIRF). In addition, IHC of Cy5.5 was used for time-course analysis of co-localization of Gd-Cy5.5 with LYVE-1-positive lymphatics and CD31-positive blood vessels.</p> <p>Results</p> <p>SV40-<it>lacZ </it>mice develop bladder cancer and permitted visualization of lymphatics. A significant increase in LVD was found concomitantly with bladder cancer progression. Double labeling of the bladder cross-sections with LYVE-1 and Ki-67 antibodies indicated cancer-induced lymphangiogenesis. MRI detected mouse bladder cancer, as early as 4 months, and permitted to follow tumor sizes during cancer progression. Using Gd-Cy5.5 as a contrast agent for MRI-guided lymphangiography, we determined a possible reduction of lymphatic flow within the tumoral area. In addition, NIRF studies of Gd-Cy5.5 confirmed its temporal distribution between CD31-positive blood vessels and LYVE-1 positive lymphatic vessels.</p> <p>Conclusion</p> <p>SV40-<it>lacZ </it>mice permit the visualization of lymphatics during bladder cancer progression. Gd-Cy5.5, as a double contrast agent for NIRF and MRI, permits to quantify delivery, transport rates, and volumes of macromolecular fluid flow through the interstitial-lymphatic continuum. Our results open the path for the study of lymphatic activity <it>in vivo </it>and in real time, and support the role of lymphangiogenesis during bladder cancer progression.</p

Imaging Transient Blood Vessel Fusion Events in Zebrafish by Correlative Volume Electron Microscopy

The study of biological processes has become increasingly reliant on obtaining high-resolution spatial and temporal data through imaging techniques. As researchers demand molecular resolution of cellular events in the context of whole organisms, correlation of non-invasive live-organism imaging with electron microscopy in complex three-dimensional samples becomes critical. The developing blood vessels of vertebrates form a highly complex network which cannot be imaged at high resolution using traditional methods. Here we show that the point of fusion between growing blood vessels of transgenic zebrafish, identified in live confocal microscopy, can subsequently be traced through the structure of the organism using Focused Ion Beam/Scanning Electron Microscopy (FIB/SEM) and Serial Block Face/Scanning Electron Microscopy (SBF/SEM). The resulting data give unprecedented microanatomical detail of the zebrafish and, for the first time, allow visualization of the ultrastructure of a time-limited biological event within the context of a whole organism

A mechanically active heterotypic E-cadherin/N-cadherin adhesion enables fibroblasts to drive cancer cell invasion

Cancer-associated fibroblasts (CAFs) promote tumour invasion and metastasis. We show that CAFs exert a physical force on cancer cells that enables their collective invasion. Force transmission is mediated by a heterophilic adhesion involving N-cadherin at the CAF membrane and E-cadherin at the cancer cell membrane. This adhesion is mechanically active; when subjected to force it triggers β-catenin recruitment and adhesion reinforcement dependent on α-catenin/vinculin interaction. Impairment of E-cadherin/N-cadherin adhesion abrogates the ability of CAFs to guide collective cell migration and blocks cancer cell invasion. N-cadherin also mediates repolarization of the CAFs away from the cancer cells. In parallel, nectins and afadin are recruited to the cancer cell/CAF interface and CAF repolarization is afadin dependent. Heterotypic junctions between CAFs and cancer cells are observed in patient-derived material. Together, our findings show that a mechanically active heterophilic adhesion between CAFs and cancer cells enables cooperative tumour invasion

A model species for agricultural pest genomics: the genome of the Colorado potato beetle, Leptinotarsa decemlineata (Coleoptera: Chrysomelidae)

The Colorado potato beetle is one of the most challenging agricultural pests to manage. It has shown a spectacular ability to adapt to a variety of solanaceaeous plants and variable climates during its global invasion, and, notably, to rapidly evolve insecticide resistance. To examine evidence of rapid evolutionary change, and to understand the genetic basis of herbivory and insecticide resistance, we tested for structural and functional genomic changes relative to other arthropod species using genome sequencing, transcriptomics, and community annotation. Two factors that might facilitate rapid evolutionary change include transposable elements, which comprise at least 17% of the genome and are rapidly evolving compared to other Coleoptera, and high levels of nucleotide diversity in rapidly growing pest populations. Adaptations to plant feeding are evident in gene expansions and differential expression of digestive enzymes in gut tissues, as well as expansions of gustatory receptors for bitter tasting. Surprisingly, the suite of genes involved in insecticide resistance is similar to other beetles. Finally, duplications in the RNAi pathway might explain why Leptinotarsa decemlineata has high sensitivity to dsRNA. The L. decemlineata genome provides opportunities to investigate a broad range of phenotypes and to develop sustainable methods to control this widely successful pest

Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data

Abstract: Crohn Disease (CD) is a complex genetic disorder for which more than 140 genes have been identified using genome wide association studies (GWAS). However, the genetic architecture of the trait remains largely unknown. The recent development of machine learning (ML) approaches incited us to apply them to classify healthy and diseased people according to their genomic information. The Immunochip dataset containing 18,227 CD patients and 34,050 healthy controls enrolled and genotyped by the international Inflammatory Bowel Disease genetic consortium (IIBDGC) has been re-analyzed using a set of ML methods: penalized logistic regression (LR), gradient boosted trees (GBT) and artificial neural networks (NN). The main score used to compare the methods was the Area Under the ROC Curve (AUC) statistics. The impact of quality control (QC), imputing and coding methods on LR results showed that QC methods and imputation of missing genotypes may artificially increase the scores. At the opposite, neither the patient/control ratio nor marker preselection or coding strategies significantly affected the results. LR methods, including Lasso, Ridge and ElasticNet provided similar results with a maximum AUC of 0.80. GBT methods like XGBoost, LightGBM and CatBoost, together with dense NN with one or more hidden layers, provided similar AUC values, suggesting limited epistatic effects in the genetic architecture of the trait. ML methods detected near all the genetic variants previously identified by GWAS among the best predictors plus additional predictors with lower effects. The robustness and complementarity of the different methods are also studied. Compared to LR, non-linear models such as GBT or NN may provide robust complementary approaches to identify and classify genetic markers