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BatchDeconvolution: a Fiji plugin for increasing deconvolution workflow

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Deconvolution microscopy is a very useful, software-based technique allowing to deblur microscopy images and increase both lateral and axial resolutions. It can be used along with many of fluorescence microscopy imaging techniques. By increasing axial resolution, it also enables three-dimensional imaging using a basic wide-field fluorescence microscope. Unfortunately, commercially available deconvolution software is expensive, while freely available programs have limited capabilities of a batch file processing. In this work we presentBatchDeconvolution, a Fiji plugin that bridges two programs that we used subsequentlyin an image deconvolution pipeline: PSF Generator and DeconvolutionLab2, both from Biomedical Imaging Group, EPFL. Our software provides a simple way to perform a batch processing of multiple microscopy files with minimal working time required from the user.
Rocznik
Strony
1--5
Opis fizyczny
Bibliogr. 22 poz., rys., tab.
Twórcy
  • Department of Molecular and Interfacial Biophysics, Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, Krakow, Poland
  • Institute of Physics, Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, 11 Lojasiewicza Street, 30-348 Krakow, Poland. Phone: +48 12 664 48 19
autor
  • Department of Molecular and Interfacial Biophysics, Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, Krakow, Poland
Bibliografia
  • 1. Drummen G. Fluorescent probes and fluorescence (microscopy) techniques - illuminating biological and biomedical research. Molecules 2012;17:14067-90.
  • 2. Combs CA, Shroff H. Fluorescence microscopy: a concise guide to current imaging methods. Curr Protoc Neurosci 2017;79. https://doi.org/10.1002/cpns.29.
  • 3. Fischer RS, Wu Y, Kanchanawong P, Shroff H, Waterman CM. Microscopy in 3D: a biologist’s toolbox. Trends Cell Biol 2011;21: 682-91.
  • 4. Sibarita JB. Deconvolution microscopy. Adv Biochem Eng Biotechnol 2005;95:201-43.
  • 5. McNally JG, Karpova T, Cooper J, Conchello JA. Three-dimensional imaging by deconvolution microscopy. Methods 1999;19:373-85.
  • 6. Lee JS, Wee TLE, Brown CM. Calibration of wide-field deconvolution microscopy for quantitative fluorescence imaging. J Biomol Tech 2014;25:31-40.
  • 7. Cole RW, Jinadasa T, Brown CM. Measuring and interpreting point spread functions to determine confocal microscope resolution and ensure quality control. Nat Protoc 2011;6: 1929-41.
  • 8. Kirshner H, Aguet F, Sage D, Unser M. 3-D PSF fitting for fluorescence microscopy: implementation and localization application. J Microsc 2013;249:13-25.
  • 9. Sage D, Donati L, Soulez F, Fortun D, Schmit G, Seitz A, et al. DeconvolutionLab2: an open-source software for deconvolution microscopy. Methods 2017;115:28-41.
  • 10. Swedlow JR. Quantitative fluorescence microscopy and image deconvolution. In: Methods in cell biology. Cambridge, MA: Academic Press; 2013:407-26 pp.
  • 11. Boutet de Monvel J, Le Calvez S, Ulfendahl M. Image restoration for confocal microscopy: improving the limits of deconvolution, with application to the visualization of the mammalian hearing organ. Biophys J 2001;80:2455-70.
  • 12. Day KJ, La Riviere PJ, Chandler T, Bindokas VP, Ferrier NJ, Glick BS. ` Improved deconvolution of very weak confocal signals. F1000Research 2017;6:787.
  • 13. Dougherty R. Extensions of DAMAS and benefits and limitations of deconvolution in beamforming. In: 11th AIAA/CEAS aeroacoustics conference. Reston, Virigina: American Institute of Aeronautics and Astronautics; 2005.
  • 14. Wendykier P. High performance Java software for image processing. Atlanta: Emory University; 2009.
  • 15. Schindelin J, Arganda-Carreras I, Frise E, Kaynig V, Longair M, Pietzsch T, et al. Fiji: an open-source platform for biologicalimage analysis. Nat Methods 2012;9:676-82.
  • 16. Schneider CA, Rasband WS, Eliceiri KW. NIH Image to ImageJ: 25 years of image analysis. Nat Methods 2012;9:671-5.
  • 17. Tse JR, Engler AJ. Preparation of hydrogel substrates with tunable mechanical properties. Curr Protoc Cell Biol 2010;47.
  • 18. Linkert M, Rueden CT, Allan C, Burel J-M, Moore W, Patterson A, et al. Metadata matters: access to image data in the real world. J Cell Biol 2010;189:777-82.
  • 19. The open microscopy environment. Image repository. Available from: https://downloads.openmicroscopy.org/images/ [accessed 2020 Apr 3].
  • 20. Baster Z, Li L, Rajfur Z, Huang C. Talin2 mediates secretion and trafficking of matrix metallopeptidase 9 during invadopodium formation. Biochim Biophys Acta Mol Cell Res 2020;1867: 118693.
  • 21. Wendykier P, Nagy JG. Large-scale image deblurring in Java. In: Bubak M, van Albada GD, Dongarra J, Sloot PMA, editors Computational science - ICCS 2008, 8th international conference Kraków, Poland, June 23-25, 2008, Proceedings, Part I. Springer, Berlin, Heidelberg; 2008 721-30 pp.
  • 22. Gibson SF, Lanni F. Diffraction by a circular aperture as a model for three-dimensional optical microscopy. J Opt Soc Am A 1989;6: 1357. Available from: https://www.osapublishing.org/abstract.cfm?URI=josaa-6-9-1357 [accessed 2017 Jun 29].
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-54f1715b-e66d-4297-8cf8-c394967c6664
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