Network Analysis and Visualization of Mouse Retina Connectivity Data.
Ontology highlight
ABSTRACT:
SUBMITTER: Pailthorpe BA
PROVIDER: S-EPMC4944929 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Angiogenesis requires coordinated changes in cell shape of endothelial cells (ECs), orchestrated by the actin cytoskeleton. The mechanisms that regulate this rearrangement in vivo are poorly understood - largely because of the difficulty to visualize filamentous actin (F-actin) structures with sufficient resolution. Here, we use transgenic mice expressing Lifeact-EGFP to visualize F-actin in ECs. We show that in the retina, Lifeact-EGFP expression is largely restricted to ECs allowing detailed visualization of F-actin in ECs in situ. Lifeact-EGFP labels actin associated with cell-cell junctions, apical and basal membranes and highlights actin-based structures such as filopodia and stress fiber-like cytoplasmic bundles. We also show that in the skin and the skeletal muscle, Lifeact-EGFP is highly expressed in vascular mural cells (vMCs), enabling vMC imaging. In summary, our results indicate that the Lifeact-EGFP transgenic mouse in combination with the postnatal retinal angiogenic model constitutes an excellent system for vascular cell biology research. Our approach is ideally suited to address structural and mechanistic details of angiogenic processes, such as endothelial tip cell migration and fusion, EC polarization or lumen formation.
| S-EPMC3480364 | biostudies-literature
Project description:Protein networks have become a popular tool for analyzing and visualizing the often long lists of proteins or genes obtained from proteomics and other high-throughput technologies. One of the most popular sources of such networks is the STRING database, which provides protein networks for more than 2000 organisms, including both physical interactions from experimental data and functional associations from curated pathways, automatic text mining, and prediction methods. However, its web interface is mainly intended for inspection of small networks and their underlying evidence. The Cytoscape software, on the other hand, is much better suited for working with large networks and offers greater flexibility in terms of network analysis, import, and visualization of additional data. To include both resources in the same workflow, we created stringApp, a Cytoscape app that makes it easy to import STRING networks into Cytoscape, retains the appearance and many of the features of STRING, and integrates data from associated databases. Here, we introduce many of the stringApp features and show how they can be used to carry out complex network analysis and visualization tasks on a typical proteomics data set, all through the Cytoscape user interface. stringApp is freely available from the Cytoscape app store: http://apps.cytoscape.org/apps/stringapp .
| S-EPMC6800166 | biostudies-literature
Project description:We developed a multi-unit microscope for all-optical inter-layers circuits interrogation. The system performs two-photon (2P) functional imaging and 2P multiplexed holographic optogenetics at axially distinct planes. We demonstrated the capability of the system to map, in the mouse retina, the functional connectivity between rod bipolar cells (RBCs) and ganglion cells (GCs) by activating single or defined groups of RBCs while recording the evoked response in the GC layer with cell-type specificity and single-cell resolution. We then used a logistic model to probe the functional connectivity between cell types by deriving the "cellular receptive field" describing how RBCs impact each GC type. With the capability to simultaneously image and control neuronal activity at axially distinct planes, the system enables a precise interrogation of multi-layered circuits. Understanding this information transfer is a promising avenue to dissect complex neural circuits and understand the neural basis of computations.
| S-EPMC9421538 | biostudies-literature
Project description:Metabolomics-based approaches are becoming increasingly popular to interrogate the chemical basis for phenotypic differences in biological systems. Successful metabolomics studies employ multivariate data analysis to compare large and highly complex datasets. A primary tool for unsupervised statistical analyses, principal component analysis (PCA), relies on the selection of a subsection of a maximum of three components from a larger model to visually represent similarity. The use of only three principal components limits the comprehensiveness of the model and can mask discrimination between samples. We have developed a new statistical metric, the composite score (CS), as a univariate statistic that incorporates multiple principal components to calculate a correlation matrix that enables quantitative comparisons of sample similarity between samples within one dataset based upon measured metabolome profiles. Composite score values were tabulated using profiles of complex extracts of dietary supplements from the plant Hydrastis canadensis (goldenseal) as a case study. Several outliers were unambiguously identified, and a PCA composite score network was developed to provide a graphical representation of the composite score matrix. Comparison with visualization using PCA score plots or dendrograms from hierarchical clustering analysis (HCA) demonstrates the utility of the composite score to as a tool for metabolomics studies that seek to quantify similarity among samples. An R-script for the calculation of composite score has been made available.
| S-EPMC6948848 | biostudies-literature
Project description:SummaryOne approach to infer functions of new proteins from their homologs utilizes visualization of an all-against-all pairwise similarity network (A2ApsN) that exploits the speed of BLAST and avoids the complexity of multiple sequence alignment. However, identifying functions of the protein clusters in A2ApsN is never trivial, due to a lack of linking characterized proteins to their relevant information in current software packages. Given the database errors introduced by automatic annotation transfer, functional deduction should be made from proteins with experimental studies, i.e. 'reference proteins'. Here, we present a web server, termed Pclust, which provides a user-friendly interface to visualize the A2ApsN, placing emphasis on such 'reference proteins' and providing access to their full information in source databases, e.g. articles in PubMed. The identification of 'reference proteins' and the ease of cross-database linkage will facilitate understanding the functions of protein clusters in the network, thus promoting interpretation of proteins of interest.AvailabilityThe Pclust server is freely available at http://prodata.swmed.edu/pclust
| S-EPMC3789550 | biostudies-literature
Project description:PurposeSimultaneous dual labeling to visualize specific RNA and protein content within the same formalin-fixed paraffin embedded (FFPE) section can be technically challenging and usually impossible, because of variables such as tissue fixation time and pretreatment methods to access the target RNA or protein. Within a specific experiment, ocular tissue sections can be a precious commodity. Thus, the ability to easily and consistently detect and localize cell-specific expression of RNA and protein within a single slide would be advantageous. In this study, we describe a simplified and reliable method for combined in situ hybridization (ISH) and immunohistochemistry (IHC) for detection of mRNA and protein, respectively, within the same FFPE ocular tissue.MethodsWhole mouse eyes were prepared for 5 micron FFPE sections after fixation for 3, 24, 48 or 72 h. Customized probes from Advanced Cell Diagnostics to detect mRNA for vascular endothelial growth factor (VEGF), hypoxia-inducible factor 1-alpha (HIF-1α), and hypoxia-inducible factor 2-alpha (HIF-2α) were used for ISH. Various parameters were tested using the novel RNAscope method for ISH and optimized for compatibility with subsequent IHC for glial fibrillary acidic protein (GFAP) or GS-lectin within the same tissue section. Dual fluorescent visualization of Fast Red ISH and Alexa Fluor 488 IHC signal was observed with confocal microscopy.ResultsA fixation time of 72 h was found to be optimal for ISH and subsequent IHC. The RNAscope probes for VEGF, HIF-1α, and HIF-2α mRNA all gave a strong Fast Red signal with both 48 h and 72 h fixed tissue, but the optimal IHC signal for either GFAP or GS-lectin within a retinal tissue section after ISH processing was observed with 72 h fixation. A pretreatment boiling time of 15 min and a dilution factor of 1:15 for the pretreatment protease solution were found to be optimal and necessary for successful ISH visualization with 72 h FFPE ocular tissue.ConclusionsThe protocol presented here provides a simple and reliable method to simultaneously detect mRNA and protein within the same paraffin-embedded ocular tissue section. The procedure, after preparation of FFPE sections, can be performed over a 2-day or 4-day period. We provide an optimization strategy that may be adapted for any RNAscope probe set and antibody for determining retinal or ocular cell-specific patterns of expression.
| S-EPMC4169891 | biostudies-literature
Project description:Data visualization is an essential element of biological research, required for obtaining insights and formulating new hypotheses on mechanisms of health and disease. NaviCell Web Service is a tool for network-based visualization of 'omics' data which implements several data visual representation methods and utilities for combining them together. NaviCell Web Service uses Google Maps and semantic zooming to browse large biological network maps, represented in various formats, together with different types of the molecular data mapped on top of them. For achieving this, the tool provides standard heatmaps, barplots and glyphs as well as the novel map staining technique for grasping large-scale trends in numerical values (such as whole transcriptome) projected onto a pathway map. The web service provides a server mode, which allows automating visualization tasks and retrieving data from maps via RESTful (standard HTTP) calls. Bindings to different programming languages are provided (Python and R). We illustrate the purpose of the tool with several case studies using pathway maps created by different research groups, in which data visualization provides new insights into molecular mechanisms involved in systemic diseases such as cancer and neurodegenerative diseases.
| S-EPMC4489283 | biostudies-literature
Project description:PurposeEndoplasmic reticulum (ER) stress activates inositol requiring enzyme 1 (IRE1), a key regulator of the unfolded protein response. The ER stress activated indicator (ERAI) transgenic mouse expresses a yellow fluorescent GFP variant (Venus) when IRE1 is activated by ER stress. We tested whether ERAI mice would allow for real-time longitudinal studies of ER stress in living mouse eyes.MethodsWe chemically and genetically induced ER stress, and qualitatively and quantitatively studied the Venus signal by fluorescence ophthalmoscopy. We determined retinal cell types that contribute to the signal by immunohistology, and we performed molecular and biochemical assays using whole retinal lysates to assess activity of the IRE1 pathway.ResultsWe found qualitative increase in vivo in fluorescence signal at sites of intravitreal tunicamycin injection in ERAI eyes, and quantitative increase in ERAI mice mated to RhoP23H mice expressing ER stress-inducing misfolded rhodopsin protein. As expected, we found that increased Venus signal arose primarily from photoreceptors in RhoP23H/+;ERAI mice. We found increased Xbp1S and XBP1s transcriptional target mRNA levels in RhoP23H/+;ERAI retinas compared to Rho+/+;ERAI retinas, and that Venus signal increased in ERAI retinas as a function of age.ConclusionsFluorescence ophthalmoscopy of ERAI mice enables in vivo visualization of retinas undergoing ER stress. ER stress activated indicator mice enable identification of individual retinal cells undergoing ER stress by immunohistochemistry. ER stress activated indicator mice show higher Venus signal at older ages, likely arising from amplification of basal retinal ER stress levels by GFP's inherent stability.
| S-EPMC4627472 | biostudies-literature
Project description:Schizophrenia is a severe global health problem, with over half of such patients experiencing auditory verbal hallucinations (AVHs). A better understanding of the neural correlates differentiating patients experiencing AVHs from patients not experiencing AVHs and healthy controls may identify targets that lead to better treatment strategies for AVHs. Employing 2 data-driven, voxel-based measure of functional connectivity, we studied 46 patients with schizophrenia or schizoaffective disorder (28 experiencing AVHs and 18 not experiencing AVHs). Twenty healthy controls matched for age, gender, ethnicity, education level, handedness, and estimated verbal intelligence were included for comparison. The intrinsic connectivity distribution (ICD) was used to model each voxel's connectivity to the rest of the brain using a Weibull distribution. To investigate lateralization of connectivity, we used cross-hemisphere ICD, a method that separates the contribution of each hemisphere to interrogate connectivity laterality. Patients with AVHs compared with patients without AVHs exhibited significantly decreased whole-brain connectivity in the medial prefrontal cortex and posterior cingulate cortex, less lateralized connectivity in left putamen, and more lateralized connectivity in left interior frontal gyrus. Correlations with Auditory Hallucination Rating Scale (AHRS) and post hoc seed connectivity analyses revealed significantly altered network connectivity. Using the results from all analyses comparing the patient groups and correlations with AHRS, we identified a potential AVH network, consisting of 25 nodes, showing substantial overlap with the default mode network and language processing networks. This network as a whole, instead of individual nodes, may represent actionable targets for interventions.
| S-EPMC6403094 | biostudies-literature
Project description:SummaryNAViGaTOR is a powerful graphing application for the 2D and 3D visualization of biological networks. NAViGaTOR includes a rich suite of visual mark-up tools for manual and automated annotation, fast and scalable layout algorithms and OpenGL hardware acceleration to facilitate the visualization of large graphs. Publication-quality images can be rendered through SVG graphics export. NAViGaTOR supports community-developed data formats (PSI-XML, BioPax and GML), is platform-independent and is extensible through a plug-in architecture.AvailabilityNAViGaTOR is freely available to the research community from http://ophid.utoronto.ca/navigator/. Installers and documentation are provided for 32- and 64-bit Windows, Mac, Linux and Unix.Contactjuris@ai.utoronto.caSupplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC2788933 | biostudies-literature