DOCSLIB.ORG

Bioimage Informatics for Experimental Biology*

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text

Load more

ANRV376-BB38-16 ARI 27 March 2009 11:9 Bioimage Informatics for Experimental Biology∗ Jason R. Swedlow,1 Ilya G. Goldberg,2 Kevin W. Eliceiri,3 and the OME Consortium4 1Wellcome Trust Centre for Gene Regulation and Expression, College of Life Sciences, University of Dundee, Dundee DD1 5EH, Scotland, United Kingdom; email: [email protected] 2Image Informatics and Computational Biology Unit, Laboratory of Genetics, National Institute on Aging, IRP, NIH Biomedical Research Center, Baltimore MD 21224; email: [email protected] 3Laboratory for Optical and Computational Instrumentation, University of Wisconsin at Madison, Madison, Wisconsin 53706; email: [email protected] 4http://openmicroscopy.org/site/about/development-teams Annu. Rev. Biophys. 2009. 38:327–46 Key Words The Annual Review of Biophysics is online at microscopy, file formats, image management, image analysis, image biophys.annualreviews.org processing This article’s doi: 10.1146/annurev.biophys.050708.133641 Abstract Copyright c 2009 by Annual Reviews. Over the past twenty years there have been great advances in light mi- Annu. Rev. Biophys. 2009.38:327-346. Downloaded from arjournals.annualreviews.org All rights reserved croscopy with the result that multidimensional imaging has driven a rev- by NORTHWESTERN UNIVERSITY - Evanston Campus on 05/17/10. For personal use only. 1936-122X/09/0609-0327$20.00 olution in modern biology. The development of new approaches of data ∗The U.S. Government has the right to retain a acquisition is reported frequently, and yet the significant data manage- nonexclusive, royalty-free license in and to any ment and analysis challenges presented by these new complex datasets copyright covering this paper. remain largely unsolved. As in the well-developed field of genome bioin- formatics, central repositories are and will be key resources, but there is a critical need for informatics tools in individual laboratories to help man- age, share, visualize, and analyze image data. In this article we present the recent efforts by the bioimage informatics community to tackle these challenges, and discuss our own vision for future development of bioimage informatics solutions. 327 ANRV376-BB38-16 ARI 27 March 2009 11:9 Contents INTRODUCTION .................. 328 SUPPORT FOR DATA FLEXIBLE INFORMATICS TRANSLATION—BIO- FOR EXPERIMENTAL FORMATS ........................ 334 BIOLOGY ........................ 329 Utility ............................. 334 Proprietary File Formats ........... 329 Modularity ........................ 335 Experimental Protocols ............ 330 Flexibility ......................... 335 Image Result Management ......... 330 Extensibility ....................... 335 Remote Image Access .............. 330 DATA MANAGEMENT Image Processing and Analysis ...... 330 APPLICATIONS: OME Distributed Processing ............. 330 AND OMERO .................... 336 Image Data and Interoperability .... 330 OMERO ENHANCEMENTS: TOWARDS BIOIMAGE BETA3ANDBETA4.............. 338 INFORMATICS .................. 331 OMERO.blitz ..................... 338 BUILDING BY AND FOR Structured Annotations............. 339 THE COMMUNITY ............. 331 OMERO.search ................... 339 DELIVERING ON THE PROMISE: OMERO.java ...................... 339 STANDARDIZED FILE OMERO.editor.................... 339 FORMATS VERSUS “JUST PUT OMERO.web...................... 339 IT IN A DATABASE” ............. 332 OMERO.scripts ................... 340 OME: A COMMUNITY-BASED OMERO.fs ........................ 340 EFFORT TO DEVELOP IMAGE WORKFLOW-BASED DATA INFORMATICS TOOLS ......... 333 ANALYSIS: WND-CHARM ...... 340 THE FOUNDATION: THE OME USABILITY ......................... 342 DATA MODEL ................... 334 SUMMARY AND FUTURE OME FILE FORMATS............... 334 IMPACT.......................... 342 INTRODUCTION a five-dimensional structure—space, time, and channel. High content screening (HCS) and Modern imaging systems have enabled a new fluorescence lifetime, polarization, and corre- kind of discovery in cellular and developmen- lation are all examples of new modalities that tal biology. With spatial resolutions running further increase the complexity of the mod- from millimeters to nanometers, analysis of cell Annu. Rev. Biophys. 2009.38:327-346. Downloaded from arjournals.annualreviews.org ern microscopy dataset. However, multidimen- and molecular structure and dynamics is now by NORTHWESTERN UNIVERSITY - Evanston Campus on 05/17/10. For personal use only. sional data acquisition generates a significant routinely possible across a range of biological data problem: A typical four-year project gen- systems. The development of fluorescent re- erates hundreds of gigabytes of images, per- porters, most notably in the form of geneti- haps on many different proprietary data ac- cally encoded fluorescent proteins (FPs), com- quisition systems, making hypothesis-driven bined with increasingly sophisticated imaging research dependent on data management, systems has enabled direct study of molecular visualization, and analysis. structure and dynamics (6, 52). Cell and tissue Bioinformatics is a mature science that imaging assays have scaled to include all three forms the cornerstone of much of modern spatial dimensions, a temporal component, and HCS: high content biology. Modern biologists routinely use ge- the use of spectral separation to measure mul- screening nomic databases to inform their experiments. tiple molecules such that a single image is now 328 Swedlow et al. ANRV376-BB38-16 ARI 27 March 2009 11:9 In fact these databases are well-crafted multi- to manage a single laboratory’s data that are layered applications that include defined data comparable to that used to deliver genomic se- structures, application programming interfaces quence applications and databases to the whole Application (APIs), and use standardized user interfaces to community? Why can’t the tools used in ge- programming enable querying, browsing, and visualization of nomics be immediately adapted to imaging? interface (API): an the underlying genome sequences. These fa- Are image informatics tools from other fields interface providing cilities serve as a great model of the sophis- appropriate for biological microscopy? In this one software program tication necessary to deliver complex, hetero- article, we address these questions, discuss the or library easy access to its functionality geneous datasets to bench biologists. However, requirements for successful image informatics with full knowledge of most genomic resources work on the basis of de- solutions for biological microscopy, and con- the underlying code or fined data structures with defined formats and sider the future directions that these applica- data structures known identifiers that all applications can ac- tions must take to deliver effective solutions for cess (they also employ expert staff to monitor biological microscopy. systems and databases, a resource that is rarely available in individual laboratories). There is no single agreed data format, but a defined num- FLEXIBLE INFORMATICS FOR ber are used in various applications, depend- EXPERIMENTAL BIOLOGY ing on the exact application (e.g., FASTA and Experimental imaging data are by their very EMBL files). These files are accessed through a nature heterogeneous and dynamic. The chal- number of defined software libraries that trans- lenge is to capture the evolving nature of an late data into defined data structures that can be experiment in data structures that by their very used for further analysis and visualization. Be- nature are specifically typed and static, for later cause a relatively small number of sequence data recall, analysis, and comparison. Achieving this generation and collation centers exist, standards goal in imaging applications means solving a have been relatively easy to declare and support. number of problems. Nonetheless, a key to the successful use of these data was the development of software applica- tions, designed for use by bench biologists as Proprietary File Formats well as specialist bioinformaticists, that enabled There are over 50 different proprietary file querying and discovery based on genomic data formats (PFFs) used in commercial and aca- held by and served from central data resources. demic image acquisition software packages for Given this paradigm, the same facility light microscopy (34). This number signifi- should in principle be available for all biological cantly increases if electron microscopy, new imaging data (as well as proteomics and, soon, HCS systems, tissue imaging systems, and deep sequencing). In contrast to centralized other new modes of imaging modalities are genomics resources, in most cases, these meth- included. Regardless of the specific applica- Annu. Rev. Biophys. 2009.38:327-346. Downloaded from arjournals.annualreviews.org ods are used for defined experiments in indi- tion, almost all store data in their own PFFs. by NORTHWESTERN UNIVERSITY - Evanston Campus on 05/17/10. For personal use only. vidual laboratories or facilities, and the number Each of these formats includes the binary data of image datasets recorded by a single postdoc- (i.e., the values in the pixels) and the meta- toral fellow (hundreds to thousands) can easily data (i.e., the data that describes the binary rival the number of genomes that have been data). Metadata include physical pixel sizes, sequenced to date. For the continued develop- time stamps, spectral ranges, and any other ment and application of experimental biology measurements or values required
Recommended publications
  • Bioimage Analysis Tools

    Bioimage Analysis Tools

    Bioimage Analysis Tools Kota Miura, Sébastien Tosi, Christoph Möhl, Chong Zhang, Perrine Paul-Gilloteaux, Ulrike Schulze, Simon Norrelykke, Christian Tischer, Thomas Pengo To cite this version: Kota Miura, Sébastien Tosi, Christoph Möhl, Chong Zhang, Perrine Paul-Gilloteaux, et al.. Bioimage Analysis Tools. Kota Miura. Bioimage Data Analysis, Wiley-VCH, 2016, 978-3-527-80092-6. hal- 02910986 HAL Id: hal-02910986 https://hal.archives-ouvertes.fr/hal-02910986 Submitted on 3 Aug 2020 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. 2 Bioimage Analysis Tools 1 2 3 4 5 6 Kota Miura, Sébastien Tosi, Christoph Möhl, Chong Zhang, Perrine Pau/-Gilloteaux, - Ulrike Schulze,7 Simon F. Nerrelykke,8 Christian Tischer,9 and Thomas Penqo'" 1 European Molecular Biology Laboratory, Meyerhofstraße 1, 69117 Heidelberg, Germany National Institute of Basic Biology, Okazaki, 444-8585, Japan 2/nstitute for Research in Biomedicine ORB Barcelona), Advanced Digital Microscopy, Parc Científic de Barcelona, dBaldiri Reixac 1 O, 08028 Barcelona, Spain 3German Center of Neurodegenerative
  • Other Departments and Institutes Courses 1

    Other Departments and Institutes Courses 1

    Other Departments and Institutes Courses 1 Other Departments and Institutes Courses About Course Numbers: 02-250 Introduction to Computational Biology Each Carnegie Mellon course number begins with a two-digit prefix that Spring: 12 units designates the department offering the course (i.e., 76-xxx courses are This class provides a general introduction to computational tools for biology. offered by the Department of English). Although each department maintains The course is divided into two halves. The first half covers computational its own course numbering practices, typically, the first digit after the prefix molecular biology and genomics. It examines important sources of biological indicates the class level: xx-1xx courses are freshmen-level, xx-2xx courses data, how they are archived and made available to researchers, and what are sophomore level, etc. Depending on the department, xx-6xx courses computational tools are available to use them effectively in research. may be either undergraduate senior-level or graduate-level, and xx-7xx In the process, it covers basic concepts in statistics, mathematics, and courses and higher are graduate-level. Consult the Schedule of Classes computer science needed to effectively use these resources and understand (https://enr-apps.as.cmu.edu/open/SOC/SOCServlet/) each semester for their results. Specific topics covered include sequence data, searching course offerings and for any necessary pre-requisites or co-requisites. and alignment, structural data, genome sequencing, genome analysis, genetic variation, gene and protein expression, and biological networks and pathways. The second half covers computational cell biology, including biological modeling and image analysis. It includes homework requiring Computational Biology Courses modification of scripts to perform computational analyses.
  • Bioimage Informatics

    Bioimage Informatics

    Bioimage Informatics Lecture 22, Spring 2012 Empirical Performance Evaluation of Bioimage Informatics Algorithms Image Database; Content Based Image Retrieval Emerging Applications: Molecular Imaging Lecture 22 April 23, 2012 1 Outline • Empirical performance evaluation of bioimage Informatics algorithm • Image database; content-based image retrieval • Introduction to molecular imaging 2 • Empirical performance evaluation of bioimage Informatics algorithm • Image database; content-based image retrieval • Introduction to molecular imaging 3 Overview of Algorithm Evaluation (I) • Two sets of data are required - Input data - Corresponding correct output (ground truth) • Sources of input data - Actual/experimental data, often from experiments - Synthetic data, often from computer simulation • Actual/experimental data - Essential for performance evaluation - May be costly to acquire - May not be representative - Ground truth often is unknown Overview of Algorithm Evaluation (II) • What exactly is ground truth? Ground truth is a term used in cartography, meteorology, analysis of aerial photographs, satellite imagery and a range of other remote sensing techniques in which data are gathered at a distance. Ground truth refers to information that is collected "on location." - http://en.wikipedia.org/wiki/Ground_truth • Synthetic (simulated) data Advantages - Ground truth is known - Usually low-cost Disadvantages - Difficult to fully represent the original data 5 Overview of Algorithm Evaluation (III) • Simulated data Advantages - Ground truth is known - Usually low-cost Disadvantages - Difficult to fully represent the original data • Realistic synthetic data - To use as much information from real experimental data as possible • Quality control of manual analysis Test Protocol Development (I) • Implementation - Source codes are not always available and often are on different platforms. • Parameter setting - This is one of the most challenging issues.
  • Bioimage Informatics 2019

    Bioimage Informatics 2019

    BioImage Informatics 2019 Co-hosted by: October 2-4, 2019 Allen Institute 615 Westlake Avenue N Seattle, WA 98109 alleninstitute.org/bioimage-informatics twitter #bioimage19 THANKS TO OUR SPONSORS BioImage Informatics 2019 Wednesday, October 2 8:00-9:00am Registration and continental breakfast 9:00-9:15am Welcome and introductory remarks, BioImage Informatics Local Planning Committee • Michael Hawrylycz, Ph.D., Allen Institute for Brain Science (co-chair) • Winfried Wiegraebe, Ph.D., Allen Institute for Cell Science (co-chair) • Forrest Collman, Ph.D., Allen Institute for Brain Science • Greg Johnson, Ph.D., Allen Institute for Cell Science • Lydia Ng, Ph.D., Allen Institute for Brain Science • Kaitlyn Casimo, Ph.D., Allen Institute 9:15-10:15am Opening keynote Gaudenz Danuser, UT Southwestern Medical Center “Inference of causality in molecular pathways from live cell images” Defining cause-effect relations among molecular components is the holy grail of every cell biological study. It turns out that for many of the molecular pathways this task is far from trivial. Many of the pathways are characterized by functional overlap and nonlinear interactions between components. In this configuration, perturbation of one component may result in a measureable shift of the pathway output – commonly referred to as a phenotype – but it is strictly impossible to interpret the phenotype in terms of the roles the targeted component plays in the unperturbed system. This caveat of perturbation approaches applies equally to genetic perturbations, which lead to long-term adaptation, and more acute pharmacological and optogenetic approaches, which often induce short-term adaptation. For nearly two decades my lab has been devoted to circumventing this problem by exploiting basal fluctuations of unperturbed systems to establish cause-and-effect cascades between pathway components.
  • A Bioimage Informatics Platform for High-Throughput Embryo Phenotyping James M

    A Bioimage Informatics Platform for High-Throughput Embryo Phenotyping James M

    Briefings in Bioinformatics, 19(1), 2018, 41–51 doi: 10.1093/bib/bbw101 Advance Access Publication Date: 14 October 2016 Paper A bioimage informatics platform for high-throughput embryo phenotyping James M. Brown, Neil R. Horner, Thomas N. Lawson, Tanja Fiegel, Simon Greenaway, Hugh Morgan, Natalie Ring, Luis Santos, Duncan Sneddon, Lydia Teboul, Jennifer Vibert, Gagarine Yaikhom, Henrik Westerberg and Ann-Marie Mallon Corresponding author: James Brown, MRC Harwell Institute, Harwell Campus, Oxfordshire, OX11 0RD. Tel. þ44-0-1235-841237; Fax: +44-0-1235-841172; E-mail: [email protected] Abstract High-throughput phenotyping is a cornerstone of numerous functional genomics projects. In recent years, imaging screens have become increasingly important in understanding gene–phenotype relationships in studies of cells, tissues and whole organisms. Three-dimensional (3D) imaging has risen to prominence in the field of developmental biology for its ability to capture whole embryo morphology and gene expression, as exemplified by the International Mouse Phenotyping Consortium (IMPC). Large volumes of image data are being acquired by multiple institutions around the world that encom- pass a range of modalities, proprietary software and metadata. To facilitate robust downstream analysis, images and metadata must be standardized to account for these differences. As an open scientific enterprise, making the data readily accessible is essential so that members of biomedical and clinical research communities can study the images for them- selves without the need for highly specialized software or technical expertise. In this article, we present a platform of soft- ware tools that facilitate the upload, analysis and dissemination of 3D images for the IMPC.
  • View of Above Observations and Arguments, the Fol- Scopy Environment) [9]

    View of Above Observations and Arguments, the Fol- Scopy Environment) [9]

    Loyek et al. BMC Bioinformatics 2011, 12:297 http://www.biomedcentral.com/1471-2105/12/297 SOFTWARE Open Access BioIMAX: A Web 2.0 approach for easy exploratory and collaborative access to multivariate bioimage data Christian Loyek1*, Nasir M Rajpoot2, Michael Khan3 and Tim W Nattkemper1 Abstract Background: Innovations in biological and biomedical imaging produce complex high-content and multivariate image data. For decision-making and generation of hypotheses, scientists need novel information technology tools that enable them to visually explore and analyze the data and to discuss and communicate results or findings with collaborating experts from various places. Results: In this paper, we present a novel Web2.0 approach, BioIMAX, for the collaborative exploration and analysis of multivariate image data by combining the webs collaboration and distribution architecture with the interface interactivity and computation power of desktop applications, recently called rich internet application. Conclusions: BioIMAX allows scientists to discuss and share data or results with collaborating experts and to visualize, annotate, and explore multivariate image data within one web-based platform from any location via a standard web browser requiring only a username and a password. BioIMAX can be accessed at http://ani.cebitec. uni-bielefeld.de/BioIMAX with the username “test” and the password “test1” for testing purposes. Background key challenge of bioimage informatics is to present the In the last two decades, innovative biological and biome- large amount of image data in a way that enables them dical imaging technologies greatly improved our under- to be browsed, analyzed, queried or compared with standing of structures of molecules, cells and tissue and other resources [2].
  • Principles of Bioimage Informatics: Focus on Machine Learning of Cell Patterns

    Principles of Bioimage Informatics: Focus on Machine Learning of Cell Patterns

    Principles of Bioimage Informatics: Focus on Machine Learning of Cell Patterns Luis Pedro Coelho1,2,3, Estelle Glory-Afshar3,6, Joshua Kangas1,2,3, Shannon Quinn2,3,4, Aabid Shariff1,2,3, and Robert F. Murphy1,2,3,4,5,6 1 Joint Carnegie Mellon University–University of Pittsburgh Ph.D. Program in Computational Biology 2 Lane Center for Computational Biology, Carnegie Mellon University 3 Center for Bioimage Informatics, Carnegie Mellon University 4 Department of Biological Sciences, Carnegie Mellon University 5 Machine Learning Department, Carnegie Mellon University 6 Department of Biomedical Engineering, Carnegie Mellon University Abstract. The field of bioimage informatics concerns the development and use of methods for computational analysis of biological images. Tra- ditionally, analysis of such images has been done manually. Manual an- notation is, however, slow, expensive, and often highly variable from one expert to another. Furthermore, with modern automated microscopes, hundreds to thousands of images can be collected per hour, making man- ual analysis infeasible. This field borrows from the pattern recognition and computer vi- sion literature (which contain many techniques for image processing and recognition), but has its own unique challenges and tradeoffs. Fluorescence microscopy images represent perhaps the largest class of biological images for which automation is needed. For this modality, typical problems include cell segmentation, classification of phenotypical response, or decisions regarding differentiated responses (treatment vs. control setting). This overview focuses on the problem of subcellular lo- cation determination as a running example, but the techniques discussed are often applicable to other problems. 1 Introduction Bioimage informatics employs computational and statistical techniques to ana- lyze images and related metadata.
  • Introduction to Bio(Medical)- Informatics

    Introduction to Bio(Medical)- Informatics

    Introduction to Bio(medical)- Informatics Kun Huang, PhD Raghu Machiraju, PhD Department of Biomedical Informatics Department of Computer Science and Engineering The Ohio State University Outline • Overview of bioinformatics • Other people’s (public) data • Basic biology review • Basic bioinformatics techniques • Logistics 2 Technical areas of bioinformatics IMMPORT https://immport.niaid.nih.gov/immportWeb/home/home.do?loginType=full# Databases and Query Databases and Query 547 5 Visualization 6 Visualization http://supramap.osu.edu 7 Network 8 Visualization and Visual Analytics § Visualizing genome data annotation 9 Network Mining Hopkins 2007, Nature Biotechnology, Network pharmacology 10 Role of Bioinformatics – Hypothesis Generation Hypothesis generation Bioinformatics Hypothesis testing 11 Training for Bioinformatics § Biology / medicine § Computing – programming, algorithm, pattern recognition, machine learning, data mining, network analysis, visualization, software engineering, database, etc § Statistics – hypothesis testing, permutation method, multivariate analysis, Bayesian method, etc Beyond Bioinformatics • Translational Bioinformatics • Biomedical Informatics • Medical Informatics • Nursing Informatics • Imaging Informatics • Bioimage Informatics • Clinical Research Informatics • Pathology Informatics • … • Health Analytics • … • Legal Informatics • Business Informatics Source: Department of Biomedical Informatics 13 Beyond Bioinformatics Basic Science Clinical Research Informatics Biomedical Informatics Theories & Methods
  • Bioimagexd: an Open, General-Purpose and High-Throughput Image-Processing Platform

    Bioimagexd: an Open, General-Purpose and High-Throughput Image-Processing Platform

    FOCUS ON BIOIMAGE INFORMATICS PERSPECTIVE BioImageXD: an open, general-purpose and high-throughput image-processing platform Pasi Kankaanpää1, Lassi Paavolainen2, Silja Tiitta1, Mikko Karjalainen2, Joacim Päivärinne1, Jonna Nieminen1, Varpu Marjomäki2, Jyrki Heino1 & Daniel J White2,3 BioImageXD puts open-source computer science BioImageXD in practice with user-friendly, auto- tools for three-dimensional visualization and mated, multiparametric image analyses of the move- analysis into the hands of all researchers, through a ment, clustering and internalization of integrins. user-friendly graphical interface tuned to the needs of biologists. BioImageXD has no restrictive licenses Criteria in BioImageXD development or undisclosed algorithms and enables publication Open. Our goal was to develop software that is trans- of precise, reproducible and modifiable workflows. parent, with open and documented source code and It allows simple construction of processing pipelines no undisclosed algorithms or hidden processing the and should enable biologists to perform challenging user is unaware of or cannot control. It is our view that analyses of complex processes. We demonstrate its software for scientific data analysis, in contrast to engi- performance in a study of integrin clustering in neering, is best when it is not a ‘black box’. Different response to selected inhibitors. implementations of the same method can often give dif- Bioimaging has emerged as one of the key tools in bio- ferent results, the reasons for which only become clear medical research. New imaging devices are appearing by comparing the source codes. Increasingly (and cor- rapidly, producing large multidimensional images with rectly in our view), disclosure of source code for image- several data channels in medium-to-high throughput.
  • Bioimage Informatics for Plant Sciences Blobs and Curves: Object-Based Colocalisation for Plant Cells

    Bioimage Informatics for Plant Sciences Blobs and Curves: Object-Based Colocalisation for Plant Cells

    ORE Open Research Exeter TITLE Blobs and curves: object-based colocalisation for plant cells AUTHORS Nelson, CJ; Duckney, P; Hawkins, TJ; et al. JOURNAL Functional Plant Biology DEPOSITED IN ORE 10 March 2017 This version available at http://hdl.handle.net/10871/26430 COPYRIGHT AND REUSE Open Research Exeter makes this work available in accordance with publisher policies. A NOTE ON VERSIONS The version presented here may differ from the published version. If citing, you are advised to consult the published version for pagination, volume/issue and date of publication Bioimage Informatics For Plant Sciences Blobs and Curves: Object-Based Colocalisation for Plant Cells Carl J. Nelson (A), Patrick Duckney, (B), Timothy J. Hawkins (B), Michael J. Deeks (C), Philippe Laissue (D), Patrick J. Hussey (B) and Boguslaw Obara (A)(*) (A) School of Engineering and Computing Sciences, Durham University, Durham, UK (B) School of Biological and Biomedical Sciences, Durham University, Durham, UK (C) College of Life and Environmental Sciences, University of Exeter, Exeter, UK (D) School of Biological Sciences, University of Essex, Colchester, UK (*) Corresponding author (email: [email protected]) Abstract: Quantifying the colocalisation of labels is a major application of fluorescent microscopy in plant biology. Pixel-based, i.e. Pearson's coefficient, quantification of colocalisation is a limited tool that gives limited information for further analysis. We show how applying bioimage informatics tools to a commonplace experiment allows further quantifiable results to be extracted. We use our object-based colocalisation technique to extract distance information, show temporal changes and demonstrate the advantages, and pitfalls, of using bioimage informatics for plant science.
  • A Short Course on Bioimage Informatics Lecture 1: Basic Concepts and Techniques

    A Short Course on Bioimage Informatics Lecture 1: Basic Concepts and Techniques

    A Short Course on Bioimage Informatics Lecture 1: Basic Concepts and Techniques Ge Yang (杨戈) Department of Biomedical Engineering & Department of Computational Biology Carnegie Mellon University August 10, 2015 1 Acknowledgements • Dr. Chao Tang • Dr. Luhua Lai • Students, faculty, and staff of the Center for Quantitative Biology, Peking University • Dr. Jing Yuan (袁静) • Youjun Xu (徐优俊) • 111 Visiting Scholar Program, Ministry of Education • College of Chemistry and Molecular Engineering, Peking University • School of Engineering & School of Computer Science, Carnegie Mellon University 2 Outline • Background • What is bioimage informatics? • Where does it come from? • Where is it going? • Basic concepts of image formation and collection • Overview of bioimage informatics techniques • Summary 3 • Background • What is bioimage informatics? • Where does it come from? • Where is it going? • Basic concepts of image formation and collection • Overview of biological image analysis techniques • Summary 4 What information can we extract from images? 刘奇 5 More Examples of Biological Images Typical Readouts from Images • From static images, e.g. immunofluorescence images - Location - Shape/morphology - Spatial distribution: e.g. spatial patterns - Spatial relation: e.g. protein colocalization • From dynamic images, e.g. live cell time-lapse images - All the readouts above, which now may be time-dependent - In other words, spatiotemporal dynamics/processes • Computational analysis techniques are required to extract these quantitative readouts from images. 7 Relations of Imaging and Image Analysis with Other Biological Research Tools • None of these readouts can be directly provided by e.g. genetics or biochemistry. • However, understanding these readouts often requires close integration of computational image analysis with experimental analysis using genetics and/or biochemistry techniques.
  • Phenotypic Image Analysis Software Tools for Exploring and Understanding Big Image Data from Cell-Based Assays

    Phenotypic Image Analysis Software Tools for Exploring and Understanding Big Image Data from Cell-Based Assays

    Cell Systems Review Phenotypic Image Analysis Software Tools for Exploring and Understanding Big Image Data from Cell-Based Assays Kevin Smith,1,2 Filippo Piccinini,3 Tamas Balassa,4 Krisztian Koos,4 Tivadar Danka,4 Hossein Azizpour,1,2 and Peter Horvath4,5,* 1KTH Royal Institute of Technology, School of Electrical Engineering and Computer Science, Lindstedtsvagen€ 3, 10044 Stockholm, Sweden 2Science for Life Laboratory, Tomtebodavagen€ 23A, 17165 Solna, Sweden 3Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, Via P. Maroncelli 40, Meldola, FC 47014, Italy 4Synthetic and Systems Biology Unit, Hungarian Academy of Sciences, Biological Research Center (BRC), Temesva´ ri krt. 62, 6726 Szeged, Hungary 5Institute for Molecular Medicine Finland, University of Helsinki, Tukholmankatu 8, 00014 Helsinki, Finland *Correspondence: [email protected] https://doi.org/10.1016/j.cels.2018.06.001 Phenotypic image analysis is the task of recognizing variations in cell properties using microscopic image data. These variations, produced through a complex web of interactions between genes and the environ- ment, may hold the key to uncover important biological phenomena or to understand the response to a drug candidate. Today, phenotypic analysis is rarely performed completely by hand. The abundance of high-dimensional image data produced by modern high-throughput microscopes necessitates computa- tional solutions. Over the past decade, a number of software tools have been developed to address this need. They use statistical learning methods to infer relationships between a cell’s phenotype and data from the image. In this review, we examine the strengths and weaknesses of non-commercial phenotypic image analysis software, cover recent developments in the field, identify challenges, and give a perspective on future possibilities.