8
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      BactMAP: An R package for integrating, analyzing and visualizing bacterial microscopy data

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          High‐throughput analyses of single‐cell microscopy data are a critical tool within the field of bacterial cell biology. Several programs have been developed to specifically segment bacterial cells from phase‐contrast images. Together with spot and object detection algorithms, these programs offer powerful approaches to quantify observations from microscopy data, ranging from cell‐to‐cell genealogy to localization and movement of proteins. Most segmentation programs contain specific post‐processing and plotting options, but these options vary between programs and possibilities to optimize or alter the outputs are often limited. Therefore, we developed BactMAP (Bacterial toolbox for Microscopy Analysis & Plotting), a command‐line based R package that allows researchers to transform cell segmentation and spot detection data generated by different programs into various plots. Furthermore, BactMAP makes it possible to perform custom analyses and change the layout of the output. Because BactMAP works independently of segmentation and detection programs, inputs from different sources can be compared within the same analysis pipeline. BactMAP complies with standard practice in R which enables the use of advanced statistical analysis tools, and its graphic output is compatible with ggplot2, enabling adjustable plot graphics in every operating system. User feedback will be used to create a fully automated Graphical User Interface version of BactMAP in the future. Using BactMAP, we visualize key cell cycle parameters in Bacillus subtilis and Staphylococcus aureus, and demonstrate that the DNA replication forks in Streptococcus pneumoniae dissociate and associate before splitting of the cell, after the Z‐ring is formed at the new quarter positions. BactMAP is available from https://veeninglab.com/bactmap.

          Abstract

          High‐throughput analysis of single‐cell microscopy is a critical tool within the field of biology. We developed BactMAP, an R package that allows researchers to transform and combine cell segmentation and spot detection data by different programs, visualize the data and perform custom analysis. Using BactMAP, we visualized key cell cycle parameters in three Gram‐positive bacteria and analyzed the dynamics of the replisome in the human pathogen Streptococcus pneumoniae.

          Related collections

          Most cited references25

          • Record: found
          • Abstract: found
          • Article: not found

          MicrobeJ, a tool for high throughput bacterial cell detection and quantitative analysis

          Single cell analysis of bacteria and subcellular protein localization dynamics has shown that bacteria have elaborate life cycles, cytoskeletal protein networks, and complex signal transduction pathways driven by localized proteins. The volume of multi-dimensional images generated in such experiments and the computation time required to detect, associate, and track cells and subcellular features pose considerable challenges, especially for high-throughput experiments. Therefore, there is a need for a versatile, computationally efficient image analysis tool capable of extracting the desired relationships from images in a meaningful and unbiased way. Here we present MicrobeJ, a plug-in for the open-source platform ImageJ. MicrobeJ provides a comprehensive framework to process images derived from a wide variety of microscopy experiments with special emphasis on large image sets. It performs the most common intensity and morphology measurements as well as customized detection of poles, septa, fluorescent foci, and organelles, determines their sub-cellular localization with sub-pixel resolution, and tracks them over time. Because a dynamic link is maintained between the images, measurements, and all data representations derived from them, the editor and suite of advanced data presentation tools facilitates the image analysis process and provides a robust way to verify the accuracy and veracity of the data.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            High-throughput, subpixel precision analysis of bacterial morphogenesis and intracellular spatio-temporal dynamics.

            Bacteria display various shapes and rely on complex spatial organization of their intracellular components for many cellular processes. This organization changes in response to internal and external cues. Quantitative, unbiased study of these spatio-temporal dynamics requires automated image analysis of large microscopy datasets. We have therefore developed MicrobeTracker, a versatile and high-throughput image analysis program that outlines and segments cells with subpixel precision, even in crowded images and mini-colonies, enabling cell lineage tracking. MicrobeTracker comes with an integrated accessory tool, SpotFinder, which precisely tracks foci of fluorescently labelled molecules inside cells. Using MicrobeTracker, we discover that the dynamics of the extensively studied Escherichia coli Min oscillator depends on Min protein concentration, unveiling critical limitations in robustness within the oscillator. We also find that the fraction of MinD proteins oscillating increases with cell length, indicating that the oscillator has evolved to be most effective when cells attain an appropriate length. MicrobeTracker was also used to uncover novel aspects of morphogenesis and cell cycle regulation in Caulobacter crescentus. By tracking filamentous cells, we show that the chromosomal origin at the old-pole is responsible for most replication/separation events while the others remain largely silent despite contiguous cytoplasm. This surprising position-dependent silencing is regulated by division. © 2011 Blackwell Publishing Ltd.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Oufti: an integrated software package for high-accuracy, high-throughput quantitative microscopy analysis.

              With the realization that bacteria display phenotypic variability among cells and exhibit complex subcellular organization critical for cellular function and behavior, microscopy has re-emerged as a primary tool in bacterial research during the last decade. However, the bottleneck in today's single-cell studies is quantitative image analysis of cells and fluorescent signals. Here, we address current limitations through the development of Oufti, a stand-alone, open-source software package for automated measurements of microbial cells and fluorescence signals from microscopy images. Oufti provides computational solutions for tracking touching cells in confluent samples, handles various cell morphologies, offers algorithms for quantitative analysis of both diffraction and non-diffraction-limited fluorescence signals and is scalable for high-throughput analysis of massive datasets, all with subpixel precision. All functionalities are integrated in a single package. The graphical user interface, which includes interactive modules for segmentation, image analysis and post-processing analysis, makes the software broadly accessible to users irrespective of their computational skills.
                Bookmark

                Author and article information

                Contributors
                https://twitter.com/vrrenske
                https://twitter.com/Morten3891
                jan-willem.veening@unil.ch , https://twitter.com/JWVeening
                Journal
                Mol Microbiol
                Mol. Microbiol
                10.1111/(ISSN)1365-2958
                MMI
                Molecular Microbiology
                John Wiley and Sons Inc. (Hoboken )
                0950-382X
                1365-2958
                24 November 2019
                January 2020
                : 113
                : 1 ( doiID: 10.1111/mmi.v113.1 )
                : 297-308
                Affiliations
                [ 1 ] Department of Fundamental Microbiology Faculty of Biology and Medicine University of Lausanne Lausanne Switzerland
                [ 2 ] Molecular Genetics Group Groningen Biomolecular Sciences and Biotechnology Institute Centre for Synthetic Biology University of Groningen Groningen The Netherlands
                [ 3 ] Faculty of Chemistry, Biotechnology and Food Science Norwegian University of Life Sciences Ås Norway
                Author notes
                [*] [* ] Correspondence

                Jan‐Willem Veening, Department of Fundamental Microbiology, Faculty of Biology and Medicine, University of Lausanne, CH‐1015 Lausanne, Switzerland.

                Email: jan-willem.veening@ 123456unil.ch

                Author information
                https://orcid.org/0000-0001-7778-5289
                https://orcid.org/0000-0003-4448-9082
                https://orcid.org/0000-0002-3162-6634
                Article
                MMI14417
                10.1111/mmi.14417
                7027861
                31693257
                09864815-5f95-48b5-9215-c8a36884de2b
                © 2019 The Authors. Molecular Microbiology published by John Wiley & Sons Ltd

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 02 November 2019
                Page count
                Figures: 5, Tables: 1, Pages: 11, Words: 15106
                Funding
                Funded by: H2020 European Research Council , open-funder-registry 10.13039/100010663;
                Award ID: ERC consolidator grant 771534‐PneumoCaTChER
                Funded by: Norges Forskningsråd , open-funder-registry 10.13039/501100005416;
                Award ID: 250976
                Award ID: 296906
                Funded by: Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung , open-funder-registry 10.13039/501100001711;
                Award ID: 31003A_172861
                Award ID: 40AR40_185533
                Categories
                Research Article
                Research Articles
                Custom metadata
                2.0
                January 2020
                Converter:WILEY_ML3GV2_TO_JATSPMC version:5.7.5 mode:remove_FC converted:18.02.2020

                Microbiology & Virology
                bacillus subtilis,bacterial cell biology,chromosome segregation,dna replication,image analysis,rtools,single cell analysis,staphylococcus aureus,streptococcus pneumoniae

                Comments

                Comment on this article