Abstract
Super-resolution microscopy techniques have pushed the limit of optical imaging to unprecedented spatial resolutions. However, one of the frontiers in nanoscopy is its application to intact living organisms. Here we describe the implementation and application of super-resolution single-particle tracking photoactivated localization microscopy (sptPALM) to probe single-molecule dynamics of membrane proteins in live roots of the model plant Arabidopsis thaliana. We first discuss the advantages and limitations of sptPALM for studying the diffusion properties of membrane proteins and compare this to fluorescence recovery after photobleaching (FRAP) and fluorescence correlation spectroscopy (FCS). We describe the technical details for handling and imaging the samples for sptPALM, with a particular emphasis on the specificity of imaging plant cells, such as their thick cell walls or high degree of autofluorescence. We then provide a practical guide from data collection to image analyses. In particular, we introduce our sptPALM_viewer software and describe how to install and use it for analyzing sptPALM experiments. Finally, we report an R statistical analysis pipeline to analyze and compare sptPALM experiments. Altogether, this protocol should enable plant researchers to perform sptPALM using a benchmarked reproducible protocol. Routinely, the procedure takes 3–4 h of imaging followed by 3–4 d of image processing and data analysis.
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Data availability
All the raw data used to generate Figs. 4–8 and Extended Data Figs. 1–3 are available at http://bioserv.cbs.cnrs.fr/DOWNLOAD/sptPALM_data/.
Code availability
The code used for the analysis of TrackMate or MTT data have been uploaded to https://github.com/jbfiche/sptPALM/, and explanations on how to run an analysis can be found in the README.md file. A standalone application and a test set are available from http://bioserv.cbs.cnrs.fr/DOWNLOAD/sptPALM_data/. Additional codes (including FIJI plugins and R code for the statistics) as well as data used for the figures are accessible from http://bioserv.cbs.cnrs.fr/DOWNLOAD/sptPALM_data/. Additional advice on how to use them can be obtained from the authors upon reasonable request.
References
Jacobson, K., Liu, P. & Lagerholm, B. C. The lateral organization and mobility of plasma membrane components. Cell 177, 806–819 (2019).
Jaillais, Y. & Ott, T. The nanoscale organization of the plasma membrane and its importance in signaling: a proteolipid perspective. Plant Physiol. 182, 1682–1696 (2020).
Grossmann, G. et al. Green light for quantitative live-cell imaging in plants. J. Cell Sci. 131, jcs209270 (2018).
Donaldson, L. Autofluorescence in plants. Molecules 25, 2393 (2020).
Wang, L., Xue, Y., Xing, J., Song, K. & Lin, J. Exploring the spatiotemporal organization of membrane proteins in living plant cells. Ann. Rev. Plant Biol. 69, 525–551 (2018).
Cui, Y. et al. Single-particle tracking for the quantification of membrane protein dynamics in living plant cells. Mol. Plant 11, 1315–1327 (2018).
Wang, L. et al. Spatiotemporal dynamics of the BRI1 receptor and its regulation by membrane microdomains in living Arabidopsis cells. Mol. Plant 8, 1334–1349 (2015).
Wang, X. et al. Single-molecule fluorescence imaging to quantify membrane protein dynamics and oligomerization in living plant cells. Nat. Protoc. 10, 2054–2063 (2015).
Hao, H. et al. Clathrin and membrane microdomains cooperatively regulate RbohD dynamics and activity in Arabidopsis. Plant Cell 26, 1729–1745 (2014).
Li, X. et al. Single-molecule analysis of PIP2;1 dynamics and partitioning reveals multiple modes of Arabidopsis plasma membrane aquaporin regulation. Plant Cell 23, 3780–3797 (2011).
Wang, Q. et al. Single-particle analysis reveals shutoff control of the Arabidopsis ammonium transporter AMT1;3 by clustering and internalization. Proc. Natl Acad. Sci. USA 110, 13204–13209 (2013).
McKenna, J. F. et al. The cell wall regulates dynamics and size of plasma-membrane nanodomains in Arabidopsis. Proc. Natl Acad. Sci. USA 116, 12857–12862 (2019).
Martiniere, A. et al. Cell wall constrains lateral diffusion of plant plasma-membrane proteins. Proc. Natl Acad. Sci. USA 109, 12805–12810 (2012).
Platre, M. P. et al. Developmental control of plant Rho GTPase nano-organization by the lipid phosphatidylserine. Science 364, 57–62 (2019).
Martiniere, A. et al. Osmotic stress activates two reactive oxygen species pathways with distinct effects on protein nanodomains and diffusion. Plant Physiol. 179, 1581–1593 (2019).
Hosy, E., Martiniere, A., Choquet, D., Maurel, C. & Luu, D. T. Super-resolved and dynamic imaging of membrane proteins in plant cells reveal contrasting kinetic profiles and multiple confinement mechanisms. Mol. Plant 8, 339–342 (2015).
Simon, M. L. et al. A PtdIns(4)P-driven electrostatic field controls cell membrane identity and signalling in plants. Nat. Plants 2, 16089 (2016).
Li, X., Xing, J., Qiu, Z., He, Q. & Lin, J. Quantification of membrane protein dynamics and interactions in plant cells by fluorescence correlation spectroscopy. Mol. Plant 9, 1229–1239 (2016).
Gronnier, J. et al. Structural basis for plant plasma membrane protein dynamics and organization into functional nanodomains. eLife 6, e26404 (2017).
Perraki, A. et al. REM1.3’s phospho-status defines its plasma membrane nanodomain organization and activity in restricting PVX cell-to-cell movement. PLoS Pathog. 14, e1007378 (2018).
Smokvarska, M. et al. A plasma membrane nanodomain ensures signal specificity during osmotic signaling in plants. Curr. Biol. 30, 4654–4664 (2020).
Manley, S. et al. High-density mapping of single-molecule trajectories with photoactivated localization microscopy. Nat. Methods 5, 155–157 (2008).
Baddeley, D. & Bewersdorf, J. Biological insight from super-resolution microscopy: what we can learn from localization-based images. Annu. Rev. Biochem. 87, 965–989 (2018).
Betzig, E. et al. Imaging intracellular fluorescent proteins at nanometer resolution. Science 313, 1642–1645 (2006).
Thompson, R. E., Larson, D. R. & Webb, W. W. Precise nanometer localization analysis for individual fluorescent probes. Biophys. J. 82, 2775–2783 (2002).
Schermelleh, L. et al. Super-resolution microscopy demystified. Nat. Cell Biol. 21, 72–84 (2019).
Li, Y. et al. Real-time 3D single-molecule localization using experimental point spread functions. Nat. Methods 15, 367–369 (2018).
Huang, B., Wang, W., Bates, M. & Zhuang, X. Three-dimensional super-resolution imaging by stochastic optical reconstruction microscopy. Science 319, 810–813 (2008).
Siemons, M. et al. Comparing strategies for deep astigmatism-based single-molecule localization microscopy. Biomed. Opt. Express 11, 735–751 (2020).
Izeddin, I. et al. PSF shaping using adaptive optics for three-dimensional single-molecule super-resolution imaging and tracking. Opt. Express 20, 4957–4967 (2012).
Shechtman, Y., Sahl, S. J., Backer, A. S. & Moerner, W. E. Optimal point spread function design for 3D imaging. Phys. Rev. Lett. 113, 133902 (2014).
Zhang, X. et al. Phosphorylation-mediated dynamics of nitrate transceptor NRT1.1 regulate auxin flux and nitrate signaling in lateral root growth. Plant Physiol. 181, 480–498 (2019).
Xing, J. et al. Secretion of phospholipase Dδ functions as a regulatory mechanism in plant innate immunity. Plant Cell 31, 3015–3032 (2019).
Zhao, Y., Man, Y., Wen, J., Guo, Y. & Lin, J. Advances in imaging plant cell walls. Trends Plant Sci. 24, 867–878 (2019).
Zhang, X., Cui, Y., Yu, M. & Lin, J. Single-molecule techniques for imaging exo-endocytosis coupling in cells. Trends Plant Sci. 24, 879–880 (2019).
Yu, M. et al. The dynamics and endocytosis of Flot1 protein in response to flg22 in Arabidopsis. J. Plant Physiol. 215, 73–84 (2017).
Wudick, M. M. et al. Subcellular redistribution of root aquaporins induced by hydrogen peroxide. Mol. Plant 8, 1103–1114 (2015).
Cui, Y. et al. Sterols regulate endocytic pathways during flg22-induced defense responses in Arabidopsis. Development 145, dev165688 (2018).
Gronnier, J. et al. FERONIA regulates FLS2 plasma membrane nanoscale dynamics to modulate plant immune signaling. Preprint at https://www.biorxiv.org/content/10.1101/2020.07.20.212233v2 (2020).
Johnson, A. & Vert, G. Single event resolution of plant plasma membrane protein endocytosis by TIRF microscopy. Front. Plant Sci. 8, 612 (2017).
Johnson, A. et al. Experimental toolbox for quantitative evaluation of clathrin-mediated endocytosis in the plant model Arabidopsis. J. Cell Sci. 133, jcs248062 (2020).
Konopka, C. A., Backues, S. K. & Bednarek, S. Y. Dynamics of Arabidopsis dynamin-related protein 1C and a clathrin light chain at the plasma membrane. Plant Cell 20, 1363–1380 (2008).
Konopka, C. A. & Bednarek, S. Y. Variable-angle epifluorescence microscopy: a new way to look at protein dynamics in the plant cell cortex. Plant J. 53, 186–196 (2008).
Wan, Y., Xue, Y., Li, R. & Lin, J. Application of variable angle total internal reflection fluorescence microscopy to investigate protein dynamics in intact plant cells. Methods Mol. Biol. 1363, 123–132 (2016).
Serge, A., Bertaux, N., Rigneault, H. & Marguet, D. Dynamic multiple-target tracing to probe spatiotemporal cartography of cell membranes. Nat. Methods 5, 687–694 (2008).
Rouger, V. et al. Mapping molecular diffusion in the plasma membrane by multiple-target tracing (MTT). J. Vis. Exp. e3599 (2012).
Durand-Smet, P., Spelman, T. A., Meyerowitz, E. M. & Jönsson, H. Cytoskeletal organization in isolated plant cells under geometry control. Proc. Natl Acad. Sci. USA 117, 17399–17408 (2020).
Los, G. V. et al. HaloTag: a novel protein labeling technology for cell imaging and protein analysis. ACS Chem. Biol. 3, 373–382 (2008).
Chen, J. et al. Single-molecule dynamics of enhanceosome assembly in embryonic stem cells. Cell 156, 1274–1285 (2014).
Holcman, D. et al. Single particle trajectories reveal active endoplasmic reticulum luminal flow. Nat. Cell Biol. 20, 1118–1125 (2018).
Jin, D. et al. Nanoparticles for super-resolution microscopy and single-molecule tracking. Nat. Methods 15, 415–423 (2018).
Banaz, N., Mäkelä, J. & Uphoff, S. Choosing the right label for single-molecule tracking in live bacteria: side-by-side comparison of photoactivatable fluorescent protein and Halo tag dyes. J. Phys. D Appl. Phys. 52, 064002 (2019).
Varela, J. A. et al. Single nanoparticle tracking of N-methyl-d-aspartate receptors in cultured and intact brain tissue. Neurophotonics 3, 041808 (2016).
Varela, J. A. et al. Targeting neurotransmitter receptors with nanoparticles in vivo allows single-molecule tracking in acute brain slices. Nat. Commun. 7, 10947 (2016).
Freeman, S. A. et al. Transmembrane pickets connect cyto- and pericellular skeletons forming barriers to receptor engagement. Cell 172, 305–317 (2018).
Groc, L. et al. Surface trafficking of neurotransmitter receptor: comparison between single-molecule/quantum dot strategies. J. Neurosci. 27, 12433–12437 (2007).
Liu, H. et al. Visualizing long-term single-molecule dynamics in vivo by stochastic protein labeling. Proc. Natl Acad. Sci. USA 115, 343–348 (2018).
Grimm, J. B. et al. Bright photoactivatable fluorophores for single-molecule imaging. Nat. Methods 13, 985–988 (2016).
Iwatate, R. J. et al. Covalent self-labeling of tagged proteins with chemical fluorescent dyes in BY-2 Cells and Arabidopsis seedlings. Plant Cell 32, 3081–3094 (2020).
Lippincott-Schwartz, J., Altan-Bonnet, N. & Patterson, G. H. Photobleaching and photoactivation: following protein dynamics in living cells. Nat. Cell Biol. 5, S7–S14 (2003).
Kang, M., Day, C. A., Kenworthy, A. K. & DiBenedetto, E. Simplified equation to extract diffusion coefficients from confocal FRAP data. Traffic 13, 1589–1600 (2012).
Wawrezinieck, L., Rigneault, H., Marguet, D. & Lenne, P. F. Fluorescence correlation spectroscopy diffusion laws to probe the submicron cell membrane organization. Biophys. J. 89, 4029–4042 (2005).
Xiang, L., Chen, K., Yan, R., Li, W. & Xu, K. Single-molecule displacement mapping unveils nanoscale heterogeneities in intracellular diffusivity. Nat. Methods 17, 524–530 (2020).
Lenne, P. F. et al. Dynamic molecular confinement in the plasma membrane by microdomains and the cytoskeleton meshwork. EMBO J. 25, 3245–3256 (2006).
Rose, M., Hirmiz, N., Moran-Mirabal, J. M. & Fradin, C. Lipid diffusion in supported lipid bilayers: a comparison between line-scanning fluorescence correlation spectroscopy and single-particle tracking. Membranes 5, 702–721 (2015).
Hosy, E., Martiniere, A., Choquet, D., Maurel, C. & Luu, D. T. Super-resolved and dynamic imaging of membrane proteins in plant cells reveal contrasting kinetic profiles and multiple confinement mechanisms. Mol. Plant 8, 339–342 (2014).
Sibarita, J. B. High-density single-particle tracking: quantifying molecule organization and dynamics at the nanoscale. Histochem. Cell Biol. 141, 587–595 (2014).
McKinney, S. A., Murphy, C. S., Hazelwood, K. L., Davidson, M. W. & Looger, L. L. A bright and photostable photoconvertible fluorescent protein. Nat. Methods 6, 131–133 (2009).
Zhang, M. et al. Rational design of true monomeric and bright photoactivatable fluorescent proteins. Nat. Methods 9, 727–729 (2012).
De Zitter, E. et al. Mechanistic investigation of mEos4b reveals a strategy to reduce track interruptions in sptPALM. Nat. Methods 16, 707–710 (2019).
Subach, F. V., Patterson, G. H., Renz, M., Lippincott-Schwartz, J. & Verkhusha, V. V. Bright monomeric photoactivatable red fluorescent protein for two-color super-resolution sptPALM of live cells. J. Am. Chem. Soc. 132, 6481–6491 (2010).
Durisic, N., Laparra-Cuervo, L., Sandoval-Alvarez, A., Borbely, J. S. & Lakadamyali, M. Single-molecule evaluation of fluorescent protein photoactivation efficiency using an in vivo nanotemplate. Nat. Methods 11, 156–162 (2014).
Dahlberg, P. D. et al. Identification of PAmKate as a red photoactivatable fluorescent protein for cryogenic super-resolution imaging. J. Am. Chem. Soc. 140, 12310–12313 (2018).
Hussein, W. & Berlin, S. Red photoactivatable genetic optical-indicators. Front. Cell. Neurosci. 14, 113 (2020).
van de Linde, S. Single-molecule localization microscopy analysis with ImageJ. J. Phys. D. Appl. Phys. 52, 203002 (2019).
Tinevez, J. Y. et al. TrackMate: an open and extensible platform for single-particle tracking. Methods 115, 80–90 (2017).
Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).
Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 9, 671–675 (2012).
Kao, H. P. & Verkman, A. S. Tracking of single fluorescent particles in three dimensions: use of cylindrical optics to encode particle position. Biophys. J. 67, 1291–1300 (1994).
Gebhardt, J. C. M. et al. Single-molecule imaging of transcription factor binding to DNA in live mammalian cells. Nat. Methods 10, 421–426 (2013).
Izeddin, I. et al. Single-molecule tracking in live cells reveals distinct target-search strategies of transcription factors in the nucleus. eLife 3, e02230 (2014).
Kusumi, A., Sako, Y. & Yamamoto, M. Confined lateral diffusion of membrane receptors as studied by single particle tracking (nanovid microscopy). Effects of calcium-induced differentiation in cultured epithelial cells. Biophys. J. 65, 2021–2040 (1993).
Wieser, S. & Schutz, G. J. Tracking single molecules in the live cell plasma membrane-do’s and don’t’s. Methods 46, 131–140 (2008).
Michalet, X. Mean square displacement analysis of single-particle trajectories with localization error: Brownian motion in an isotropic medium. Phys. Rev. E 82, 041914 (2010).
Deschout, H. et al. Precisely and accurately localizing single emitters in fluorescence microscopy. Nat. Methods 11, 253–266 (2014).
Reynolds, D. A. Gaussian mixture models. in Encyclopedia of Biometrics (eds Li, S.Z. & Jain, A. K.) (Springer, 2009).
Dempster, A. P., Laird, N. M. & Rubin, D. B. Maximum likelihood from incomplete data via the EM algorithm. J. R. Stat. Soc. Series B Stat. Methodol. 39, 1–22 (1977).
Azzalini, A. A class of distributions which includes the normal ones. Scand. J. Stat. 12, 171–178 (1985).
Prates, M. O., Lachos, V. H. & Barbosa Cabral, C. R. mixsmsn: fitting finite mixture of scale mixture of skew-normal distributions. J. Stat. Soft. https://doi.org/10.18637/jss.v054.i12 (2013).
Dowle, M. et al. Package ‘data. table’. Extension of ‘data. frame (https://cran.r-project.org/web/packages/data.table/index.html, 2019).
Scrucca, L., Fop, M., Murphy, T. B. & Raftery, A. E. mclust 5: clustering, classification and density estimation using Gaussian finite mixture models. R J. 8, 289–317 (2016).
Mattheyses, A. L., Shaw, K. & Axelrod, D. Effective elimination of laser interference fringing in fluorescence microscopy by spinning azimuthal incidence angle. Micros. Res. Tech. 69, 642–647 (2006).
Fiolka, R., Belyaev, Y., Ewers, H. & Stemmer, A. Even illumination in total internal reflection fluorescence microscopy using laser light. Micros. Res. Tech. 71, 45–50 (2008).
Levet, F. et al. SR-Tesseler: a method to segment and quantify localization-based super-resolution microscopy data. Nat. Methods 12, 1065–1071 (2015).
Milo, R. & Phillips, R. What are the time scales for diffusion in cells? in Cell Biology by the Numbers 256–260 (Garland Science, 2016).
Acknowledgements
We acknowledge the contribution of SFR Biosciences (UMS3444/CNRS, US8/Inserm, ENS de Lyon, UCBL) facilities: C. Lionnet, E. Chattre and J. Brocard. We thank A. Johnson and G. Vert for their initial input and advice in setting up TIRF microscopy. Y.J. is funded by ERC no. 3363360-APPL under FP/2007-2013 and ANR caLIPSO (ANR18-CE13-0025). Y.J. and A.M. are funded by the innovative project iRhobot from the department of Biologie et Amélioration des Plantes of INRAE. A.M. is funded by the French National Agency ANR CellOsmo (ANR-19-CE20-0008-01). C.B. is funded by the Austrian Science Fund (FWF W1225). We acknowledge support by France-BioImaging (ANR-10-INBS-04, ‘Investments for the future’).
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V.B., M.P.P. and A.M. set up the imaging conditions and performed imaging. J.B.F. established the sptPALM analysis pipeline and coded the sptPALM_viewer software. V.B., A.M. and J.B.F. performed image analyses. C.B. established the statistical analysis pipeline and performed statistics. V.B., J.B.F., C.B., M.P.P., M.N., A.M. and Y.J. wrote the manuscript.
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Related links
Key references using this protocol:
Platre, M. P. et al. Science 364, 57–62 (2019): https://doi.org/10.1126/science.aav9959
Martinière, A. et al. Plant Physiol. 179, 1581–1593 (2019): https://doi.org/10.1104/pp.18.01065
Smokvarska, M. et al. Curr. Biol. 30, 4654–4664 (2020): https://doi.org/10.1016/j.cub.2020.09.013
Extended data
Extended Data Fig. 1 Fluorescence intensity of a typical mEOS2 sub-diffractive spot along time.
a, Pictures showing a single molecule through time (20 ms between each picture) and (b) corresponding trace of fluorescent intensity. Note that the signal intensity observed is not continuous, and the OFF state varies in duration between seconds and milliseconds. This blinking behavior is typical of single-molecule observation. Scale bar, 1 µm.
Extended Data Fig. 2 Example of false tracks identification.
a, Example of mis-reconnected tracks (white arrow). b, Example of track with a very long duration (white arrowhead), indicating that it is background fluorescence rather than true signal. The color gradient from blue, green to red indicates the early versus late recorded positions. Scale bars, 1 µm.
Extended Data Fig. 3 Mixture modeling reveals two latent subpopulations of Lti6B-mEos2 and PIP2;1-mEos2 molecules.
GMM (a and c) and FMSMSN (b and d) fits. Either a GMM-2V (a) or a FMSMSN-2 (d) model is retained for Lti6b and PIP2;1, respectively, using the BIC criterion. See the legend in Fig. 8c for more details.
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Bayle, V., Fiche, JB., Burny, C. et al. Single-particle tracking photoactivated localization microscopy of membrane proteins in living plant tissues. Nat Protoc 16, 1600–1628 (2021). https://doi.org/10.1038/s41596-020-00471-4
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DOI: https://doi.org/10.1038/s41596-020-00471-4
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