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      Publishing and sharing multi-dimensional image data with OMERO

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          Abstract

          Imaging data are used in the life and biomedical sciences to measure the molecular and structural composition and dynamics of cells, tissues, and organisms. Datasets range in size from megabytes to terabytes and usually contain a combination of binary pixel data and metadata that describe the acquisition process and any derived results. The OMERO image data management platform allows users to securely share image datasets according to specific permissions levels: data can be held privately, shared with a set of colleagues, or made available via a public URL. Users control access by assigning data to specific Groups with defined membership and access rights. OMERO’s Permission system supports simple data sharing in a lab, collaborative data analysis, and even teaching environments. OMERO software is open source and released by the OME Consortium at www.openmicroscopy.org.

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          Phenotypic profiling of the human genome by time-lapse microscopy reveals cell division genes.

          Christian Conrad, Catherine Chapuis, Rolf Kabbe … (2010)
          Despite our rapidly growing knowledge about the human genome, we do not know all of the genes required for some of the most basic functions of life. To start to fill this gap we developed a high-throughput phenotypic screening platform combining potent gene silencing by RNA interference, time-lapse microscopy and computational image processing. We carried out a genome-wide phenotypic profiling of each of the approximately 21,000 human protein-coding genes by two-day live imaging of fluorescently labelled chromosomes. Phenotypes were scored quantitatively by computational image processing, which allowed us to identify hundreds of human genes involved in diverse biological functions including cell division, migration and survival. As part of the Mitocheck consortium, this study provides an in-depth analysis of cell division phenotypes and makes the entire high-content data set available as a resource to the community.
          Article 0 comments Cited 328 times – based on 0 reviews     Review now
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            Rapid Global Fitting of Large Fluorescence Lifetime Imaging Microscopy Datasets

            Sean C. Warren, Anca Margineanu, Dominic Alibhai … (2013)
            Fluorescence lifetime imaging (FLIM) is widely applied to obtain quantitative information from fluorescence signals, particularly using Förster Resonant Energy Transfer (FRET) measurements to map, for example, protein-protein interactions. Extracting FRET efficiencies or population fractions typically entails fitting data to complex fluorescence decay models but such experiments are frequently photon constrained, particularly for live cell or in vivo imaging, and this leads to unacceptable errors when analysing data on a pixel-wise basis. Lifetimes and population fractions may, however, be more robustly extracted using global analysis to simultaneously fit the fluorescence decay data of all pixels in an image or dataset to a multi-exponential model under the assumption that the lifetime components are invariant across the image (dataset). This approach is often considered to be prohibitively slow and/or computationally expensive but we present here a computationally efficient global analysis algorithm for the analysis of time-correlated single photon counting (TCSPC) or time-gated FLIM data based on variable projection. It makes efficient use of both computer processor and memory resources, requiring less than a minute to analyse time series and multiwell plate datasets with hundreds of FLIM images on standard personal computers. This lifetime analysis takes account of repetitive excitation, including fluorescence photons excited by earlier pulses contributing to the fit, and is able to accommodate time-varying backgrounds and instrument response functions. We demonstrate that this global approach allows us to readily fit time-resolved fluorescence data to complex models including a four-exponential model of a FRET system, for which the FRET efficiencies of the two species of a bi-exponential donor are linked, and polarisation-resolved lifetime data, where a fluorescence intensity and bi-exponential anisotropy decay model is applied to the analysis of live cell homo-FRET data. A software package implementing this algorithm, FLIMfit, is available under an open source licence through the Open Microscopy Environment.
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              An evaluation of confocal versus conventional imaging of biological structures by fluorescence light microscopy

              M. Fordham, WB Amos, JG White (1987)
              Scanning confocal microscopes offer improved rejection of out-of-focus noise and greater resolution than conventional imaging. In such a microscope, the imaging and condenser lenses are identical and confocal. These two lenses are replaced by a single lens when epi- illumination is used, making confocal imaging particularly applicable to incident light microscopy. We describe the results we have obtained with a confocal system in which scanning is performed by moving the light beam, rather than the stage. This system is considerably faster than the scanned stage microscope and is easy to use. We have found that confocal imaging gives greatly enhanced images of biological structures viewed with epifluorescence. The improvements are such that it is possible to optically section thick specimens with little degradation in the image quality of interior sections.
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                Author and article information

                Contributors
                Jason R. Swedlow:
                ORCID: http://orcid.org/0000-0002-2198-1958
                +44 1382 385819 , jrswedlow@dundee.ac.uk , http://www.openmicroscopy.org
                Journal
                Journal ID (nlm-ta): Mamm Genome
                Journal ID (iso-abbrev): Mamm. Genome
                Title: Mammalian Genome
                Publisher: Springer US (New York )
                ISSN (Print): 0938-8990
                ISSN (Electronic): 1432-1777
                Publication date (Electronic): 30 July 2015
                Publication date PMC-release: 30 July 2015
                Publication date (Print): 2015
                Volume: 26
                Issue: 9-10
                Pages: 441-447
                Affiliations
                [ ]Centre for Gene Regulation & Expression, University of Dundee, Dundee, Scotland UK
                [ ]Glencoe Software, Inc., Seattle, WA USA
                Author information
                http://orcid.org/0000-0002-2198-1958
                Article
                Publisher ID: 9587
                DOI: 10.1007/s00335-015-9587-6
                PMC ID: 4602067
                PubMed ID: 26223880
                SO-VID: 49709462-af41-41ef-9aab-f7ce7baf1878
                Copyright © © The Author(s) 2015
                License:

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

                History
                Date received : 3 June 2015
                Date accepted : 13 July 2015
                Categories
                Subject: Article
                Custom metadata
                issue-copyright-statement © Springer Science+Business Media New York 2015

                ScienceOpen disciplines: Genetics
                Data availability:
                ScienceOpen disciplines: Genetics

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