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Quantitative and Comparative Analysis of Global Patterns of (Microtubule) Cytoskeleton Organization with CytoskeletonAnalyzer2D

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Plant Cell Morphogenesis

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1992))

Abstract

The microtubule cytoskeleton plays important roles in cell morphogenesis. To investigate the mechanisms of cytoskeletal organization, for example, during growth or development, in genetic studies, or in response to environmental stimuli, image analysis tools for quantitative assessment are needed. Here, we present a method for texture measure-based quantification and comparative analysis of global microtubule cytoskeleton patterns and subsequent visualization of output data. In contrast to other approaches that focus on the extraction of individual cytoskeletal fibers and analysis of their orientation relative to the growth axis, CytoskeletonAnalyzer2D quantifies cytoskeletal organization based on the analysis of local binary patterns. CytoskeletonAnalyzer2D thus is particularly well suited to study cytoskeletal organization in cells where individual fibers are difficult to extract or which lack a clearly defined growth axis, such as leaf epidermal pavement cells. The tool is available as ImageJ plugin and can be combined with publicly available software and tools, such as R and Cytoscape, to visualize similarity networks of cytoskeletal patterns.

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References

  1. Staiger CJ, Lloyd CW (1991) The plant cytoskeleton. Curr Opin Cell Biol 3:33–42

    Article  CAS  PubMed  Google Scholar 

  2. Wasteneys GO, Yang Z (2004) New views on the plant cytoskeleton. Plant Physiol 136:3884–3891

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Wasteneys GO, Yang Z (2004) The cytoskeleton becomes multidisciplinary. Plant Physiol 136:3853–3854

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Lloyd C, Hussey P (2001) Microtubule-associated proteins in plants–why we need a map. Nat Rev Mol Cell Biol 2:40–47

    Article  CAS  PubMed  Google Scholar 

  5. Chakrabortty B, Blilou I, Scheres B, Mulder BM (2018) A computational framework for cortical microtubule dynamics in realistically shaped plant cells. PLoS Comp Biol 14:e1005959

    Article  Google Scholar 

  6. Wasteneys GO (2002) Microtubule organization in the green kingdom: chaos or self-order? J Cell Sci 115:1345–1354

    CAS  PubMed  Google Scholar 

  7. Chen X, Wu S, Liu Z, Friml J (2016) Environmental and endogenous control of cortical microtubule orientation. Trends Cell Biol 26:409–419

    Article  CAS  PubMed  Google Scholar 

  8. Matschegewski C, Staehlke S, Birkholz H, Lange R, Beck U et al (2012) Automatic actin filament quantification of osteoblasts and their morphometric analysis on microtextured silicon-titanium arrays. Materials (Basel) 5:1176–1195

    Article  CAS  Google Scholar 

  9. Hamada T, Nagasaki-Takeuchi N, Kato T, Fujiwara M, Sonobe S et al (2013) Purification and characterization of novel microtubule-associated proteins from Arabidopsis cell suspension cultures. Plant Physiol 163:1804–1816

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Akita K, Higaki T, Kutsuna N, Hasezawa S (2015) Quantitative analysis of microtubule orientation in interdigitated leaf pavement cells. Plant Signal Behav 10:e1024396

    Article  PubMed  PubMed Central  Google Scholar 

  11. Sugiyama Y, Wakazaki M, Toyooka K, Fukuda H, Oda Y (2017) A novel plasma membrane-anchored protein regulates xylem cell-wall deposition through microtubule-dependent lateral inhibition of Rho GTPase domains. Curr Biol 27:2522–2528

    Article  CAS  PubMed  Google Scholar 

  12. Belteton SA, Sawchuk MG, Donohoe BS, Scarpella E, Szymanski DB (2018) Reassessing the roles of PIN proteins and anticlinal microtubules during pavement cell morphogenesis. Plant Physiol 176:432–449

    Article  CAS  PubMed  Google Scholar 

  13. Armour WJ, Barton DA, Law AM, Overall RL (2015) Differential growth in periclinal and anticlinal walls during lobe formation in Arabidopsis cotyledon pavement cells. Plant Cell 27:2484–2500

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Boudaoud A, Burian A, Borowska-Wykret D, Uyttewaal M, Wrzalik R et al (2014) FibrilTool, an ImageJ plug-in to quantify fibrillar structures in raw microscopy images. Nat Protoc 9:457–463

    Article  CAS  PubMed  Google Scholar 

  15. Wang XL, Zhang J, Yuan M, Ehrhardt DW, Wang ZY et al (2012) Arabidopsis microtubule destabilizing protein40 is involved in brassinosteroid regulation of hypocotyl elongation. Plant Cell 24:4012–4025

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Liu X, Yang Q, Wang Y, Wang L, Fu Y et al (2018) Brassinosteroids regulate pavement cell growth by mediating BIN2-induced microtubule stabilization. J Exp Bot 69:1037–1049

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Higaki T, Kutsuna N, Sano T, Kondo N, Hasezawa S (2010) Quantification and cluster analysis of actin cytoskeletal structures in plant cells: role of actin bundling in stomatal movement during diurnal cycles in Arabidopsis guard cells. Plant J 61:156–165

    Article  CAS  PubMed  Google Scholar 

  18. Faulkner C, Zhou J, Evrard A, Bourdais G, MacLean D et al (2017) An automated quantitative image analysis tool for the identification of microtubule patterns in plants. Traffic 18:683–693

    Article  CAS  PubMed  Google Scholar 

  19. Jacques E, Buytaert J, Wells DM, Lewandowski M, Bennett MJ et al (2013) MicroFilament Analyzer, an image analysis tool for quantifying fibrillar orientation, reveals changes in microtubule organization during gravitropism. Plant J 74:1045–1058

    Article  CAS  PubMed  Google Scholar 

  20. Püspöki Z, Storath M, Sage D, Unser M (2016) Transforms and operators for directional bioimage analysis: A survey. Adv Anat Embryol Cell Biol 219:69–93

    Article  PubMed  Google Scholar 

  21. Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Learn 24:971–987

    Article  Google Scholar 

  22. Bürstenbinder K, Möller B, Plötner R, Stamm G, Hause G et al (2017) The IQD family of calmodulin-binding proteins links calcium signaling to microtubules, membrane subdomains, and the nucleus. Plant Physiol 173:1692–1708

    Article  PubMed  PubMed Central  Google Scholar 

  23. Möller B, Glaß M, Misiak D, Posch S (2016) MiToBo–A toolbox for image processing and analysis. J Open Res Software 4:e17

    Article  Google Scholar 

  24. Möller B, Poeschl Y, Plötner R, Bürstenbinder K (2017) PaCeQuant: A tool for high-throughput quantification of pavement cell shape characteristics. Plant Physiol 175:998–1017

    Article  PubMed  PubMed Central  Google Scholar 

  25. R Development Core Team (2008) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria

    Google Scholar 

  26. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT et al (2003) Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res 13:2498–2504

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Möller B, Piltz E, Bley N (2014) Quantification of actin structures using unsupervised pattern analysis techniques. In: 22nd International conference on pattern recognition, IEEE, Stockholm, pp 3251–3256. https://doi.org/10.1109/ICPR.2014.560, Electronic ISBN: 978-1-4799-5209-0, Print ISSN: 1051-4651. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6977272&isnumber=6976709

  28. Zirkel A, Lederer M, Stohr N, Pazaitis N, Hüttelmaier S (2013) IGF2BP1 promotes mesenchymal cell properties and migration of tumor-derived cells by enhancing the expression of LEF1 and SNAI2 (SLUG). Nucleic Acids Res 41:6618–6636

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Cutler SR, Ehrhardt DW, Griffitts JS, Somerville CR (2000) Random GFP :: cDNA fusions enable visualization of subcellular structures in cells of Arabidopsis at a high frequency. Proc Natl Acad Sci USA 97:3718–3723

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Nelson BK, Cai X, Nebenführ A (2007) A multicolored set of in vivo organelle markers for co-localization studies in Arabidopsis and other plants. Plant J 51:1126–1136

    Article  CAS  PubMed  Google Scholar 

  31. Gutierrez R, Lindeboom JJ, Paredez AR, Emons AM, Ehrhardt DW (2009) Arabidopsis cortical microtubules position cellulose synthase delivery to the plasma membrane and interact with cellulose synthase trafficking compartments. Nat Cell Biol 11:797–806

    Article  CAS  PubMed  Google Scholar 

  32. Abe T, Hashimoto T (2005) Altered microtubule dynamics by expression of modified alpha-tubulin protein causes right-handed helical growth in transgenic Arabidopsis plants. Plant J 43:191–204

    Article  CAS  PubMed  Google Scholar 

  33. Marc J, Granger CL, Brincat J, Fisher DD, Kao T et al (1998) A GFP-MAP4 reporter gene for visualizing cortical microtubule rearrangements in living epidermal cells. Plant Cell 10:1927–1940

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Schindelin J, Arganda-Carreras I, Frise E, Kaynig V, Longair M et al (2012) Fiji: an open-source platform for biological-image analysis. Nat Methods 9:676–682

    Article  CAS  PubMed  Google Scholar 

  35. RstudioTeam (2015) RStudio: integrated development for R. RstudioTeam, Boston, MA. http://www.rstudio.com/

    Google Scholar 

  36. Shamloul M, Trusa J, Mett V, Yusibov V (2014) Optimization and utilization of Agrobacterium-mediated transient protein production in Nicotiana. J Vis Exp 86:51204

    Google Scholar 

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Acknowledgments

This work was supported by IPB core funding (Leibniz Association) from the Federal Republic of Germany and the state of Saxony-Anhalt.

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Correspondence to Katharina Bürstenbinder .

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Möller, B., Zergiebel, L., Bürstenbinder, K. (2019). Quantitative and Comparative Analysis of Global Patterns of (Microtubule) Cytoskeleton Organization with CytoskeletonAnalyzer2D. In: Cvrčková, F., Žárský, V. (eds) Plant Cell Morphogenesis. Methods in Molecular Biology, vol 1992. Humana, New York, NY. https://doi.org/10.1007/978-1-4939-9469-4_10

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  • DOI: https://doi.org/10.1007/978-1-4939-9469-4_10

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-4939-9468-7

  • Online ISBN: 978-1-4939-9469-4

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