19 research outputs found
Abstract B06: HTSvis: An user-friendly application for analysis of arrayed high-throughput experiments by interactive data representations
No full textAbstract
The presented application HTSvis provides an user-interface tool for interactive analysis of arrayed high-throughput screening experiments. Arrayed formats are broadly used for cell-based screens which help to reveal mechanisms of complex diseases such as cancer. Technological advances allow to screen tens of thousands experimental conditions in a single experiments resulting in large datasets with complicated annotation files. Software packages for standardized analysis with a minimized programming effort have been developed accordingly. Even though such pipelines provide comprehensive analysis options, interpretation of the results remains a challenge as the user has to manually compare files and possibly re-run the analysis to prosecute certain hypotheses. Here we present HTSvis, a web application that visualizes data from arrayed screens. The data can be read-in as result files of the established R package CellHTS2 or as any spreadsheet table. This enables the user to feed in data which has been statistically analyzed whilst preserving flexibility to analyze data independent of the CellHTS2 package. The application provides several informative data representations like heatmaps, scatter plots and tables. Within the application, the data can be browsed in an ad hoc manner via the implemented user interface. This allows the user to assess data quality, compare experiments, identify measurements of interest and to verify generated hypotheses in an exploratory manner. The direct allocation of data points to experimental conditions is preserved in all representations facilitating an intuitive interpretation. HTSvis is easy to install, data input is straight forward and no programming skills are required for the usage. The application is compatible with common web browsers and can be set up on local computers as well as on server clusters which allows parallel access for multiple users. Summarized, HTSvis provides a helpful tool for interactive data analysis and interpretation for both, users with and without programming skills. Thereby, the application also promotes the cooperation and exchange of ideas between bioinformaticians and experimental biologists or between collaborators.
Citation Format: Christian Scheeder, Florian Heigwer, Michael Boutros. HTSvis: An user-friendly application for analysis of arrayed high-throughput experiments by interactive data representations [abstract]. In: Proceedings of the AACR Precision Medicine Series: Opportunities and Challenges of Exploiting Synthetic Lethality in Cancer; Jan 4-7, 2017; San Diego, CA. Philadelphia (PA): AACR; Mol Cancer Ther 2017;16(10 Suppl):Abstract nr B06.</jats:p
Abstract A23: Multi-parametric genetic interactions map dynamic genetic network rewiring upon anti-proliferative treatment
No full textAbstract
Signaling pathways are often characterized as rather static networks whose outcome is the result of state changes of pathway components, e.g. by protein phosphorylation or other post-translational modifications. These state changes along signaling networks have been systematically studied using, for example proteomics methods. However it remained unknown how networks rewire under the influence of external stimuli, such as anti-cancer drug treatment. To analyze dynamic rewiring of a signaling network, we performed an arrayed high-throughput co-RNAi screen in a Drosophila melanogaster cell line (Dmel-2). Therin, we assessed statistic genetic interactions measured by 26 880 pairwise RNAi experiments under MEK1/2 inhibitor treatment over the time course of 96 hours. As phenotypic readout we conducted high-content imaging of DNA, cytoskeleton and microtubule markers and extracted &gt;150 cellular features from 4 423 680 images each representing a specific condition, and marker. Together these features precisely characterize ~100 000 alleviating and 160 000 aggravating synergistic effects resulting from combinatorial perturbations. While only 2 % of all interactions are explained by cell viability the vast majority of dynamic differential interactions is explained by other features. Correlation of genetic interaction profiles across those features allows us to precisely how genes change pathway affiliation under different conditions. Our results show that, among others, key signaling nodes e.g. ERK1/2 or the Mediator complex build different connections within genetic networks depending on the environmental conditions and reveal yet unknown synthetic interactions. Using the confidence we gain from time resolved measurements of different cellular features we could identify numerous interactions which could resolve mechanisms of resistance to MEK inhibitor driven treatments and reveal potential new therapeutic targets.
Citation Format: Florian Heigwer, Christian Scheeder, Thilo Miersch, Claudia Blass, Michael Boutros. Multi-parametric genetic interactions map dynamic genetic network rewiring upon anti-proliferative treatment [abstract]. In: Proceedings of the AACR Precision Medicine Series: Opportunities and Challenges of Exploiting Synthetic Lethality in Cancer; Jan 4-7, 2017; San Diego, CA. Philadelphia (PA): AACR; Mol Cancer Ther 2017;16(10 Suppl):Abstract nr A23.</jats:p
Superresolution microscopy localizes endogenous Dvl2 to Wnt signaling-responsive biomolecular condensates
No full textDuring organismal development, homeostasis, and disease, Dishevelled (Dvl) proteins act as key signaling factors in beta-catenin–dependent and beta-catenin–independent Wnt pathways. While their importance for signal transmission has been genetically demonstrated in many organisms, our mechanistic understanding is still limited. Previous studies using overexpressed proteins showed Dvl localization to large, punctate-like cytoplasmic structures that are dependent on its DIX domain. To study Dvl’s role in Wnt signaling, we genome engineered an endogenously expressed Dvl2 protein tagged with an mEos3.2 fluorescent protein for superresolution imaging. First, we demonstrate the functionality and specificity of the fusion protein in beta-catenin–dependent and beta-catenin–independent signaling using multiple independent assays. We performed live-cell imaging of Dvl2 to analyze the dynamic formation of the supramolecular cytoplasmic Dvl2_mEos3.2 condensates. While overexpression of Dvl2_mEos3.2 mimics the previously reported formation of abundant large “puncta,” supramolecular condensate formation at physiological protein levels is only observed in a subset of cells with approximately one per cell. We show that, in these condensates, Dvl2 colocalizes with Wnt pathway components at gamma-tubulin and CEP164-positive centrosomal structures and that the localization of Dvl2 to these condensates is Wnt dependent. Single-molecule localization microscopy using photoactivated localization microscopy (PALM) of mEos3.2 in combination with DNA-PAINT demonstrates the organization and repetitive patterns of these condensates in a cell cycle–dependent manner. Our results indicate that the localization of Dvl2 in supramolecular condensates is coordinated dynamically and dependent on cell state and Wnt signaling levels. Our study highlights the formation of endogenous and physiologically regulated biomolecular condensates in the Wnt pathways at single-molecule resolution
HTSvis: a web app for exploratory data analysis and visualization of arrayed high-throughput screens
No full textAbstract
Summary
Arrayed high-throughput screens (HTS) cover a broad range of applications using RNAi or small molecules as perturbations and specialized software packages for statistical analysis have become available. However, exploratory data analysis and integration of screening results has remained challenging due to the size of the data sets and the lack of user-friendly tools for interpretation and visualization of screening results. Here we present HTSvis, a web application to interactively visualize raw data, perform quality control and assess screening results from single to multi-channel measurements such as image-based screens. Per well aggregated raw and analyzed data of various assay types and scales can be loaded in a generic tabular format.
Availability and implementation
HTSvis is distributed as an open-source R package, downloadable from https://github.com/boutroslab/HTSvis and can also be accessed at http://htsvis.dkfz.de.
Supplementary information
Supplementary data are available at Bioinformatics online.
</jats:sec
CytoData-2019 original challenge raw data
No full textAll data was originally published in: Breinig et al. Molcular System Biology, 2015The data was re-analyzed from raw images and split into training and validation data.EBImage was used to segement cells and extract features.Segemented cells were cropped. Validation cells were anonymyzed.The task was to learn the annotated Mode of action (MOA) or target of the chemcial that each validation cells was treated with after learning from the training cells. MOA is coded by folder name in the image and the "target" column in the csv files.Cells in the same well were targeted by the same chemcial and thus share their MOA. This information can be used to solve the challenge, however the more interesting/challenging part is the classification of MOA from looking at a single cell only.Files and rows in feature space data are mapped 1:1 by their barcodes.</div
HTSvis: A web app for exploratory data analysis and visualization of arrayed high-throughput screens
No full textAbstractThe analysis and visualization of arrayed high-throughput screens (HTS), such as cell-based RNAi or small-molecule HTS experiments, requires specialized computational methods. Software packages such as the R/Bioconductor package cellHTS have been developed to support the analysis and are broadly used by the high-throughput screening community. However, exploratory data analysis and integration of screening results remains challenging due to the size of produced data tables in multi-channel experiments and the lack of user-friendly tools to integrate and visualize screening results. Here we present HTSvis, an R/Shiny open-source web application for interactive visualization and exploratory analysis of arrayed high-throughput data. Using a light-weight infrastructure suitable for desktop computers, HTSvis can be used to visualize raw data, perform quality control and interactively visualize screening results from single- to multi-channel measurements, such as image-based, screens. Input data can either be a result file obtained upon analysis with cellHTS or a generic table with raw or analyzed data from, e.g. a high-content microscopy screen. HTSvis can be downloaded from http://github.com/boutroslab/HTSvis.</jats:p
High-throughput cell quantification assays for use in cell purification development - enabling technologies for cell production
No full textCharacterization and spatial distribution of mesoplastics in an arable soil
No full textExtraction of plastic particles from soil is challenging and, thus, exceptionally little spatial information on plastic distribution at the field scale has been gathered. However, for environmental risk assessment, adequate sampling should complement coherent plastic profiling. In this study, we investigated the spatial distribution of mesoplastics (MePs; from >5 mm up to 130 mm) in arable soil (Haplic Cambisol) managed intensively by 12 years of compost application. Geo-referenced samples (n = 128) and five different sampling designs (n = 45) of variable sampling volume (from 2 to 300 L) were collected at a three hectare study site in Northern Germany (0–30 cm soil depth). Soil properties such as pH and soil organic carbon (SOC) were measured to evaluate dispersion measures of these data. In total, we found 259 MePs with a predominance of transparent packaging foils made of polyethylene and coloured fibres of polypropylene. Average particle metrics were a projection area of 47 (3–400) mm2, a Feret diameter of 18.5 (5.4–130) mm and a mass of 1.89 (0.11–221) mg. Caution is advised when measuring the particle mass due to still strongly adhering soil material, especially for fibre bundles with 0.544 mg soil mg−1 particle. We recommend using a 0.1 mol L−1 tetrasodium pyrophosphate solution to purify MePs by removing attached soil before weighing for further environmental risk assessment. The MePs count with a median value of 0.50 (0–3.2) particles kg−1 and median mass of 2.26 (0–221) mg kg−1 featured the highest coefficient of variation (CV) with 103% and 187%, respectively. This is 10–20 times larger in comparison to the CV of SOC (9.2%) and even 50–93 times larger than CV of soil pH (2.2%). This leads to the need of larger sample numbers to delineate plastic metrics in comparison with soil properties to identify a reliable mean value of the field within a predefined allowable error. Mesoplastics in the soil were characterized by a pure nugget effect variogram (no spatial correlation), revealed no intrafield variability and the sample volume yielded inconclusive results. Sampling for plastics in soil should either (i) drastically increase the sample number for a single field or (ii) communicate transparently that the allowable error is by far enhanced in comparison with classical soil properties like pH and SOC. More systematic studies featuring geo-spatial analysis of MePs and smaller-sized plastics in soils are required to propose adequate sampling designs across multiple land uses and plastics fingerprints. A larger database would, thereupon, pave the way for best-practice guides on how to treat ‘outliers’ and search for robust estimators for spatial mapping of plastics in soils
