Histochem Cell Biol DOI 10.1007/s00418-014-1214-1

Review

High‑density single‑particle tracking: quantifying molecule organization and dynamics at the nanoscale Jean‑Baptiste Sibarita 

Accepted: 13 March 2014 © Springer-Verlag Berlin Heidelberg 2014

Abstract  The organization and dynamics of proteins are fundamental parameters for cellular function. Their study, at the single-molecule level, provides precise information on molecular interactions. Over the last 30 years, the singleparticle tracking imaging technique has proven its capability to efficiently quantify such parameters in many biological systems, with nanometric accuracy and millisecond temporal resolutions. Nevertheless, the low concentration of labeling required for single-molecule imaging usually prevents the extraction of large statistics. The advent of high-density single-molecule-based super-resolution techniques has revolutionized the field, allowing monitoring of thousands of biomolecules in the minute timescale and providing unprecedented insight into the molecular organization and dynamics of cellular compounds. In this issue, I will review the main principles of single-particle tracking, a highly interdisciplinary technique at the interface between microscopy, image analysis and labeling strategies. I will point out the advantages brought by high-density single-particle tracking which will be illustrated with a few recent biological results. Keywords  Single molecule · Localization · Tracking · Videomicroscopy · Photoswitching · Diffusion · Mean square displacement

Introduction Protein organization and mobility are fundamental to cellular communication and function. Their study provides J.-B. Sibarita (*)  Interdisciplinary Institute for Neuroscience, CNRS UMR 5297, University of Bordeaux, 33000 Bordeaux, France e-mail: jean‑baptiste.sibarita@u‑bordeaux2.fr

insights into their local environment and their interactions with macromolecular complexes, which can strongly differ between normal and pathological conditions. There are multiple techniques that allow quantifying protein mobility. The most popular are fluorescence recovery after photobleaching (FRAP), correlation spectroscopy-based techniques (FCS) and single-particle tracking (SPT). FRAP (Axelrod et al. 1976) and FCS (Madge et al. 1972) are bulk methods developed in the 1970s, which provide information on the average dynamics of molecules. They are unfortunately limited by the diffraction of light, preventing the resolution of molecular organization below 250 nm at the best. In addition, they are single-point measurements, making it difficult to describe cellular heterogeneity. In contrast, single-particle tracking techniques allow the quantification of protein dynamics with close to molecular resolution. While FRAP and FCS average the mobility of a very large number of molecules, SPT can reveal individual dynamics. Nevertheless, the low labeling density required to reach the single-molecule conditions of traditional SPT techniques strongly limits the statistics that can be obtained, while the precise description of many biological processes often requires a very large number of measurements. In addition, rare events are not discernible with an averaging method and require a large number of single-molecule trajectories, which is almost impossible to obtain using standard SPT methods. This has recently changed thanks to the advent of photoactivable fluorescent proteins (PA-FP) (Ando et al. 2002; Patterson and Lippincott-Schwartz 2002) and their use in single-molecule-based super-resolution microscopy (Betzig et al. 2006; Hess et al. 2006). The combination of photoactivation localization microscopy (PALM) and SPT, called sptPALM (Manley et al. 2008), allows monitoring of genetically encoded fluorescent proteins with high density. Similarly, it is possible to perform high-density

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single-molecule tracking of endogenous membrane proteins using photocaged organic fluorophores (Adams and Tsien 1993) or by stochastic labeling (Giannone et al. 2010). These complementary techniques enable the localization and tracking of a dense population of biomolecules, overcoming some limits of traditional SPT techniques.

Principle SPT is as single-molecule technique, developed for the first time in the early 1980s to quantify the diffusion coefficients of single receptors using fluorescent probes (Barak and Webb 1982), and reported later with the capability to localize and track individual membrane molecules at the nanometer scale (Gelles et al. 1988). Sporadically used at the beginning, its adoption has been significantly boosted with the progress of labeling strategies and imaging techniques. Today, it is possible to study the complex biological processes with nanometric accuracy and millisecond temporal resolution. Like any single-molecule technique, SPT requires that each particle to track is separated by a minimum distance of d  = 0.61λ/NA, corresponding the resolution limit of light microscopy (Abbe 1873); λ denotes the wavelength of the light and NA the numerical aperture of the objective. When the distance between molecules is greater than d, it becomes possible to localize individual particles, by Gaussian fitting or alternative methods, with a precision below the diffraction limit. The accuracy of localization is mainly dependent on the number of collected photons per molecule, per image (Thompson et al. 2002; Kubitscheck et al. 2000). Using in vitro experiments and organic fluorophores in deoxygenating solution, it was possible to track the movements of motor proteins with 1.5 nm resolution (Yildiz et al. 2004). In living cells, under optimal TIRF illumination conditions, expected resolutions are typically in the range of 30 nm for the brightest fluorophores. However, the main limitation of traditional SPT techniques resides in the poor statistics that can be obtained. Indeed, the required minimum distance between fluorescent molecules together with the stochastic labeling imposes a maximum labeling density of 0.5 molecule per μm2. This is about three orders of magnitude less than in dense macromolecular complexes, making generalization difficult and rare events almost impossible to find. The advent of super-resolution light microscopy, and more particularly the localization-based techniques, has completely revolutionized our understanding of biological mechanisms at the single-cell level. Single-molecule-based techniques rely on the cumulative spatial localization of fluorescently tagged markers, requiring both dedicated acquisition equipment and sophisticated detection algorithms.

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They offer the capabilities to count, locate and track the movement of a huge number of biomolecules in their cellular environment as well as in vitro. The initial localization-based super-resolution schema, based on genetically encoded PA-FP (Betzig et al. 2006; Hess et al. 2006) or organic photoswitchable fluorophores (Rust et al. 2006; Heilemann et al. 2008; Folling et al. 2008), was to maximize the number of photons collected per frame in order to achieve the best possible localization accuracy. By lowering the excitation power of the illumination system, and adjusting the photoconversion illumination power accordingly, it is possible to keep molecules fluorescent over a sufficient number of frames in order to capture their motion.

Fluorescent probes There are mainly two complementary labeling strategies to perform high-density single-molecule tracking; either by expressing the protein of interest with a genetically encoded photoactivable fluorescent protein or by targeting it with an antibody coupled with an organic fluorophore. Genetically encoded fluorescent proteins (Tsien 1998) have the advantage of being highly specific and non-invasive and remain ideal to monitor proteins in living cells. Nevertheless, their photophysics in terms of photon yield are still not ideal for tracking applications, leading to short trajectories and reduced localization accuracy. In addition, it is generally required to overexpress the protein of interest, leading to non-physiological conditions which can possibly perturb the dynamics and the biological system in general. Since the pioneer work on PA-GFP (Patterson and Lippincott-Schwartz 2002), many photoactivable proteins have been developed. The most popular for sptPALM, i.e., the ones providing the best photophysics for localization and tracking purpose, is PA-mCherry (Subach et al. 2009), Eos-FP (Wiedenmann et al. 2004) and Dendra2 (Gurskaya et al. 2006). Genetically encoded photoconvertible fluorescent proteins offer the great capability to localize and track membrane and intracellular targets with high specificity, in various biological organisms like bacteria (Persson et al. 2013) or mammalian cells (Gebhardt et al. 2013; Cisse et al. 2013). To improve localization accuracy, it is possible to use nanobodies labeled with organic fluorophores and targeted against fluorescent fusion proteins (Ries et al. 2012). Organic fluorophores are much brighter, photostable and enable labeling of endogeneous proteins. Indeed, while single fluorescent proteins typically emit about 105 photons (Kubitscheck et al. 2000), single organic fluorophores can be 2–3 orders of magnitude brighter (Dempsey et al. 2011; Yildiz et al. 2003). Their small molecular sizes are expected to cause less perturbation then FP on protein

Histochem Cell Biol

function. Nevertheless, they have to be targeted using antibodies, which have limited affinities and less specificity. In addition, they are usually limited to surface labeling, cell permeabilization required to target intracellular structures being non-physiological. But the recent advent of chemical tag-based (SNAP-, CLIP- and HALO-tag) protein labeling (Mazza et al. 2012; Chen et al. 2013), click-chemistry reaction (Zessin et al. 2012; Lukinavicius et al. 2013) allows overcoming this limit, opening intracellular single-protein imaging with a rich choice of fluorophores. High-density SPT with organic fluorophores can be performed using photocaged fluorophores (Adams and Tsien 1993; Mitchison 1989), which can become fluorescent upon absorption of violet light or stochastic labeling (Giannone et al. 2010, 2013). Stochastic labeling, called uPAINT, is strictly restricted to membrane labeling, but it allows monitoring the labeling in real time, enabling for example single-molecule FRET applications (Winckler et al. 2013).

Data acquisition

observation of samples in close proximity to the coverslip interface. Wide-field illumination or spinning-disk confocal microscopes (Graf et al. 2005; Tanaami et al. 2002) are also suitable for single-molecule imaging, but the limited contrast due to out of focus signal and the bleaching of single molecule located away from the imaging plane strongly reduce SPT performance. So far, SPT has mainly been used to monitor protein movements in 2D. Nevertheless, like any localization technique, it is compatible with the analysis of tridimensional movement using 3D single-molecule localization methods, like astigmatism (Holtzer et al. 2007; Huang et al. 2008), biplane (Juette et al. 2008) or double helix (Thompson et al. 2010).

Data analysis In addition to provide nanometer scale resolution, SPT has the ability to discriminate between the various modes of motions of tracked molecules. In most cases, tracking implies a two-step process: first, objects are independently segmented in each frame, and secondly, the detected objects are linked frame after frame to define object tracks (Fig. 2).

High-density SPT does not require a complex experimental setup. Basically, any single-molecule-based super-resolution microscope with a temperature regulation system for live cell recording offers single-molecule tracking capabilities. Nevertheless, for collecting the large amount of data required for high-density SPT, full-field techniques are preferred over scanning ones. In addition, acquisition speed is essential since it will limit the capability to monitor fast dynamic processes. The most popular experimental setups are the ones providing the highest signal to noise ratio at the single-molecule level. TIRF microscopy (Axelrod 1989; Wazawa and Ueda 2005) and oblique illumination (Tokunaga et al. 2008) are certainly the most popular techniques since they provide high contrast, low background and consequently the highest spatial and temporal resolutions (Fig. 1). They are unfortunately restricted to the

Single-molecule detection is usually achieved using Gaussian fitting due to its good localization performance (Cheezum et al. 2001; Abraham et al. 2009). Nevertheless, the large number of molecules to identify in localization-based super-resolution microscopy makes this computation very time consuming in the absence of hardware acceleration. To overcome this issue, alternative approaches (Hedde et al. 2009; Henriques et al. 2010; Wolter et al. 2010; Izeddin et al. 2012; Parthasarathy 2012) and massively parallel implementations of

Fig. 1  Main illumination systems used for high-density SPT. a To monitor biomolecules labeled using genetically encoded photoactivable fluorescent proteins or photocaged organic fluorophores, only a sparse subset of the fluorophores are turned fluorescent for a few frames before being turned non-fluorescent and replaced by another subset of fluorescent molecules. In TIRF mode, only the molecules in close proximity to the coverslip interface are imaged. It is possi-

ble to use any other illumination system, like oblique illumination of wide field, at the expense of contrast and localization accuracy. b In uPAINT, antibodies recognizing the surface protein of interest labeled with organic fluorophores are released in the solution. Under oblique illumination, only the fluorophores in the light sheet are imaged. The stochastic labeling is monitored in real time and the targeted protein motion is recorded before the bleaching of the fluorophore

Single‑molecule localization

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Fig. 2  Framework for trajectory computation from singlemolecule data. First, acquired images are analyzed to extract the coordinates of all the molecules in all frames. Then, all the localizations are pooled to create a density map, or superresolution intensity image. Finally, localized molecules are connected through time to compute molecule trajectories. Trajectories are further analyzed to extract their diffusion properties and classified

Gaussian fitting (Smith et al. 2010; Kechkar et al. 2013) have been recently proposed. Localization accuracy depends on the fluorescent probes and the acquisition parameters, but mostly relies on the signal to noise ratio per localized molecule. In the case of Gaussian fitting, it is a function of the number of collected photons, the image sampling and the background noise (Thompson et al. 2002; Ober et al. 2004). The localization accuracy will define the threshold below which we will be able to resolve confined nanoscale motion from immobilization. Single‑molecule tracking Once localized in all the images, identified molecules need to be connected through time to extract their trajectories and dynamics. Multi-target tracking is a difficult task in computer vision. A large number of methods have been developed over the last 30 years (Kalaidzidis 2009) and many software packages are available (Chenouard et al. 2014). For multi-target tracking, a prior motion model must be specified to impose some constraints and to find the “best” solution in some sense among the huge amount of possible solutions for matching. In the statistical framework, the two best known approaches are the joint probabilistic data-association filter (Fortmann et al. 1983) and the multiple hypothesis tracker (Reid 1979). Such frameworks have been successfully proposed in biology (Genovesio and Olivo-Marin 2004; Tvarusko et al. 1999; Serge et al. 2008), but their major drawback is that they are very time consuming and require assumptions about probability distributions which does not necessarily hold (Ingemar 1993). Global methods have been proposed to overcome such limits (Racine et al. 2006; Jaqaman et al. 2008), providing the most probable global solution. As a general rule, to reduce the computation

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time due to the numerous molecules of high-density SPT and avoid improbable connections, it is convenient to restrict the area of possible reconnection by the maximum diffusion coefficient or the maximum velocity of molecules. Trajectory analysis Trajectory analysis is an important step for the understanding of the dynamic behavior of the biological object of interest. It allows not only to better understand how proteins are transported and what mechanism is related, but also how they interact with other proteins. One common analysis is based on the mean square displacement (MSD) analysis (Fig. 3). In two dimensions, it measures the area r2 explored by a molecule over time. The MSD is widely used to extract diffusion characteristics of molecules from their trajectories. Common models to describe MSD functions are Brownian, directional and confined motions. These models can be fitted to data in order to quantify and classify the different kind of motions and their proportions (Kusumi et al. 1993; Saxton and Jacobson 1997). Brownian motion is characterized by the diffusion coefficient D, and its model is expressed as r2(t) = 4Dt. Analytical formulations of other types of motions, like confined (or immobilized), anomalous diffusion or directed movements, are detailed in Fig. 3. For each MSD curve, the diffusion coefficient D can be computed by fitting of the first four points of the MSD using r2 = 4Dt linear equation. This provides a unique diffusion coefficient per trajectory, which may not be appropriate when a molecule changes its motion (e.g., gets trapped during the trajectory observation). Therefore, it may be more appropriate to compute the instantaneous diffusion coefficient, which is calculated for each time point of the

Histochem Cell Biol Fig. 3  Left mean square displacement (MSD) for the different types of motions. Right mathematical models used to describe the different types of motions from the MSD curves

trajectory by linear fitting of the 4 following points of the MSD. MSD curves provide information on protein motion, which is a direct indicator of their interaction with other proteins. A straightforward method to perform this analysis is to fit the MSD curves with the general model r2 = tα + k and to sort protein motion type according to α (Saxton 1993, 1995). Trajectories with α  1.1 directed motion. It becomes possible to classify the different types of motion and their relative proportion. For example, the ratio between the

number of freely moving molecules and the number of confined ones provides information on the fraction of interacting versus non-interacting molecules. Their mapping on the intensity image allows their spatial characterization and their possible correlation with molecular organization at the nanoscale. It is important to notice that the localization accuracy defines the minimum resolvable radius of confinement of the system. Below this limit, it would not be possible to distinguish between immobilization and confinement. Figures 4 and 5 illustrate two biological applications taking advantage of high-density SPT imaging techniques,

Fig. 4  Nanoscale organization of integrins inside focal adhesion sites revealed by sptPALM. a β3-integrins are immobilized more frequently inside versus outside FAs: sptPALM of integrin-β3-WTmEOS2 in mouse embryonic fibroblasts. Trajectories overlaid on FAs (gray) are color coded to show their diffusion modes: diffusive (cyan), confined (green) and immobile (red). b Integrin immobilizations correlate with integrin activation: sptPALM of integrinβ3-N305T, a point mutation stabilizing the active conformation

of β3-integrin. c Variation of the MSD over time for trajectories of integrin-β3-WT-mEOS2 inside (plain) and outside FAs (dashed) with the same color code as in a. d Same as c for CAAX, a control protein anchored to the inner leaflet of the plasma membrane. e Distribution of LOG(D) for integrin-β3-WT (black) and integrin-β3-N305T (blue) (mean for cells). Gray areas including D values inferior to 0.008 μm2  s−1 represent immobile trajectories. Adapted from (Rossier et al. 2012)

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Fig. 5  Nanoscale organization of AMPA receptors in functional nanodomains revealed by sptPALM and uPAINT. a Comparison between epifluorescence, super-resolution intensity image, trajectories and diffusion map of a neuron expressing Eos::GluA1. The super-resolution intensity image is obtained by sptPALM from a sequence of 20,000 images of sparse single photoconverted molecules. The images of trajectories represent the individual trajectories longer than eight frames (20 ms per frame) of activated single Eos molecules. The diffusion map corresponds to the average instantaneous diffusion coefficients computed from the mean square displacement (MSD) of each trajectory. Scale bar in LOG(D(μm2 s−1)). b The left panel shows the diffraction limited fluorescent image of a spine expressing EOS::GluA1.

Middle panel shows the sptPALM intensity image. Nanodomains are identified by dashed circles. On the right panel, a subset of singlemolecule trajectories, where strongly confined trajectories are indicated in red and diffusive and weakly confined trajectories are indicated in cyan and green, respectively. AMPA receptors are strongly immobilized inside nanodomains and move freely outside. c Average mean square diffusion plots for single-molecule trajectories observed exclusively inside nanodomains (In), exclusively outside nanodomains (Out), or exchanging between inside and outside nanodomains (In/Out). This confirms the dynamics behavior of AMPA receptors in and out nanodomains. Adapted from (Nair et al. 2013)

revealing the nanoscale organization and the function of integrin proteins (Rossier et al. 2012) (Fig. 4) and post-synaptic receptors (Nair et al. 2013) (Fig. 5). High-density SPT techniques allow retrieving molecule’s trajectories with a density higher than two orders of magnitude compared to standard SPT. Histograms of the molecule’s diffusion constants measured on a large number of trajectories can thus be obtained to characterize the global mobility of the protein of interest (Fig. 4e) (Rossier et al. 2012; Nair et al. 2013; Heidbreder et al. 2012). It is also possible to map molecule motilities in a color-coded image to reveal cellular heterogeneity and reveal areas of interactions (Fig. 5a) (Manley et al. 2008). Nevertheless, these analysis methods provide statistics on interactions and where these interactions take place, but they are still limited to the analysis of individual trajectories. They cannot for example provide quantifications on the strength of molecular interactions. Recent advanced based on inference analysis (Hoze et al. 2012; Masson et al. 2014) or hidden Markov models (Persson et al. 2013) methods take advantage of the

high-density of trajectories, including the short ones, to extract and identify biophysical properties of molecules, like potential wells or protein interaction maps.

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Conclusion and perspectives Over the last 30 years, single-particle tracking has been demonstrated to be a powerful imaging technique to monitor protein dynamics in living cells. Its combination with photoconvertible fluorophores used for super-resolution microscopy has dramatically expanded its capability in terms of statistical analysis, which has remained until very recently its major limitation compared to bulk imaging. We are today capable of monitoring in routine thousands of biomolecules in a single-cell experiment and extracting their diffusion characteristics and interaction maps. Within the same experiment, it is possible to precisely correlate these interaction maps with the spatial organization of the same protein with nanometric resolution. Nevertheless, such experiments are still restricted to

Histochem Cell Biol

the study of one protein at the time. Simultaneous monitoring of multiple proteins would allow for more detailed study of protein–protein organization and interactions, and monitoring of real-time co-protein movements at the single-molecule level. Even if these events are statistically very rare, they could be compensated for by the very large statistics offered by high-density SPT. Such observation of multiple tagged proteins is already technically possible thanks to the multitude of available spectrally separated fluorophores and the possible combination between genetically encoded fluorescent proteins and organic fluorophores. Finally, single-molecule experiments have been so far mostly restricted to studies in cell cultures, where single molecule has optimal performances. There is a real challenge to push such investigations into more physiological and intact samples, like tissue or brain slices (Biermann et al. 2014), combining for example emerging in-depth single-molecule imaging (Cella Zanacchi et al. 2011; York et al. 2011; Gebhardt et al. 2013; Abrahamsson et al. 2013) and single-particle tracking techniques. This would help in gaining more information about the dynamic molecular organization in complex tissues and verifying or comparing findings obtained from cell culture. Acknowledgments  I acknowledge financial support from the CNRS, the French Agence Nationale pour la Recherche (Synapse2DT), the Regional Council of Aquitaine, and the Fondation pour la Recherche Médicale. I also would like to thank Daniel Choquet, Eric Hosy, Deepak Nair, Adel Kechkar, Corey Butler, Laurent Cognet, Olivier Rossier, Gregory Giannone and Matthieu Sainlos for their collaboration and feedback.

References Abbe E (1873) E. Beiträge zur Theorie des Mikroskops und der mikroskopischen Wahrnehmung. Arch Mikr Anat 9:413–468 Abraham AV, Ram S, Chao J, Ward ES, Ober RJ (2009) Quantitative study of single molecule location estimation techniques. Opt Express 17(26):23352–23373. doi:10.1364/OE.17.023352 Abrahamsson S, Chen J, Hajj B, Stallinga S, Katsov AY, Wisniewski J, Mizuguchi G, Soule P, Mueller F, Dugast Darzacq C, Darzacq X, Wu C, Bargmann CI, Agard DA, Dahan M, Gustafsson MG (2013) Fast multicolor 3D imaging using aberration-corrected multifocus microscopy. Nat Methods 10(1):60–63. doi:10.1038/nmeth.2277 Adams SR, Tsien RY (1993) Controlling cell chemistry with caged compounds. Annu Rev Physiol 55:755–784. doi:10.1146/annurev. ph.55.030193.003543 Ando R, Hama H, Yamamoto-Hino M, Mizuno H, Miyawaki A (2002) An optical marker based on the UV-induced green-tored photoconversion of a fluorescent protein. Proc Natl Acad Sci USA 99(20):12651–12656. doi:10.1073/pnas.202320599 Axelrod D (1989) Total internal reflection fluorescence microscopy. Methods Cell Biol 30:245–270 Axelrod D, Koppel DE, Schlessinger J, Elson E, Webb WW (1976) Mobility measurement by analysis of fluorescence photobleaching recovery kinetics. Biophys J 16(9):1055–1069. doi:10.1016/ S0006-3495(76)85755-4

Barak LS, Webb WW (1982) Diffusion of low density lipoprotein-receptor complex on human fibroblasts. J Cell Biol 95(3):846–852 Betzig E, Patterson GH, Sougrat R, Lindwasser OW, Olenych S, Bonifacino JS, Davidson MW, Lippincott-Schwartz J, Hess HF (2006) Imaging intracellular fluorescent proteins at nanometer resolution. Science 313(5793):1642–1645. doi:10.1126/science.1127344 Biermann B, Sokoll S, Klueva J, Missler M, Wiegert JS, Sibarita JB, Heine M (2014) Imaging of molecular surface dynamics in brain slices using single-particle tracking. Nat Commun 5:3024. doi:10 .1038/ncomms4024 Cella Zanacchi F, Lavagnino Z, Perrone Donnorso M, Del Bue A, Furia L, Faretta M, Diaspro A (2011) Live-cell 3D superresolution imaging in thick biological samples. Nat Methods 8(12):1047–1049. doi:10.1038/nmeth.1744 Cheezum MK, Walker WF, Guilford WH (2001) Quantitative comparison of algorithms for tracking single fluorescent particles. Biophys J 81(4):2378–2388. doi:10.1016/S0006-3495(01)75884-5 Chen Z, Cornish VW, Min W (2013) Chemical tags: inspiration for advanced imaging techniques. Curr Opin Chem Biol 17(4):637– 643. doi:10.1016/j.cbpa.2013.05.018 Chenouard N, Smal I, de Chaumont F, Maska M, Sbalzarini IF, Gong Y, Cardinale J, Carthel C, Coraluppi S, Winter M, Cohen AR, Godinez WJ, Rohr K, Kalaidzidis Y, Liang L, Duncan J, Shen H, Xu Y, Magnusson KE, Jalden J, Blau HM, Paul-Gilloteaux P, Roudot P, Kervrann C, Waharte F, Tinevez JY, Shorte SL, Willemse J, Celler K, van Wezel GP, Dan HW, Tsai YS, de Solorzano CO, Olivo-Marin JC, Meijering E (2014) Objective comparison of particle tracking methods. Nat Methods. doi:10.1038/nm eth.2808 Cisse II, Izeddin I, Causse SZ, Boudarene L, Senecal A, Muresan L, Dugast-Darzacq C, Hajj B, Dahan M, Darzacq X (2013) Realtime dynamics of RNA polymerase II clustering in live human cells. Science 341(6146):664–667. doi:10.1126/science.1239053 Dempsey GT, Vaughan JC, Chen KH, Bates M, Zhuang X (2011) Evaluation of fluorophores for optimal performance in localization-based super-resolution imaging. Nat Methods 8(12):1027– 1036. doi:10.1038/nmeth.1768 Folling J, Bossi M, Bock H, Medda R, Wurm CA, Hein B, Jakobs S, Eggeling C, Hell SW (2008) Fluorescence nanoscopy by ground-state depletion and single-molecule return. Nat Methods 5(11):943–945. doi:10.1038/nmeth.1257 Fortmann TE, Bar-Shalom Y, Sheffe M (1983) Sonar tracking of multiple targets using joint probabilistic data association. IEEE J Oceanic Eng 8(3):173–184 Gebhardt JC, Suter DM, Roy R, Zhao ZW, Chapman AR, Basu S, Maniatis T, Xie XS (2013) Single-molecule imaging of transcription factor binding to DNA in live mammalian cells. Nat Methods 10(5):421–426. doi:10.1038/nmeth.2411 Gelles J, Schnapp BJ, Sheetz MP (1988) Tracking kinesindriven movements with nanometre-scale precision. Nature 331(6155):450–453. doi:10.1038/331450a0 Genovesio A, Olivo-Marin JC (2004) Split and merge data association filter for dense multitarget tracking. ICPR 4:677–680 Giannone G, Hosy E, Levet F, Constals A, Schulze K, Sobolevsky AI, Rosconi MP, Gouaux E, Tampe R, Choquet D, Cognet L (2010) Dynamic super resolution imaging of endogenous proteins on living cells at ultra-high density. Biophys J 99(4):1303–1310. doi:10.1016/j.bpj.2010.06.005 Giannone G, Hosy E, Sibarita JB, Choquet D, Cognet L (2013) Highcontent super-resolution imaging of live cell by uPAINT. Methods Mol Biol 950:95–110. doi:10.1007/978-1-62703-137-0_7 Graf R, Rietdorf J, Zimmermann T (2005) Live cell spinning disk microscopy. Adv Biochem Eng Biotechnol 95:57–75 Gurskaya NG, Verkhusha VV, Shcheglov AS, Staroverov DB, Chepurnykh TV, Fradkov AF, Lukyanov S, Lukyanov KA (2006)

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Engineering of a monomeric green-to-red photoactivatable fluorescent protein induced by blue light. Nat Biotechnol 24(4):461– 465. doi:10.1038/nbt1191 Hedde PN, Fuchs J, Oswald F, Wiedenmann J, Nienhaus GU (2009) Online image analysis software for photoactivation localization microscopy. Nat Methods 6(10):689–690. doi:10.1038/nm eth1009-689 Heidbreder M, Zander C, Malkusch S, Widera D, Kaltschmidt B, Kaltschmidt C, Nair D, Choquet D, Sibarita JB, Heilemann M (2012) TNF-alpha influences the lateral dynamics of TNF receptor I in living cells. Biochim Biophys Acta 1823(10):1984–1989. doi:10.1016/j.bbamcr.2012.06.026 Heilemann M, van de Linde S, Schuttpelz M, Kasper R, Seefeldt B, Mukherjee A, Tinnefeld P, Sauer M (2008) Subdiffraction-resolution fluorescence imaging with conventional fluorescent probes. Angew Chem Int Ed Engl 47(33):6172–6176. doi:10.1002/a nie.200802376 Henriques R, Lelek M, Fornasiero EF, Valtorta F, Zimmer C, Mhlanga MM (2010) QuickPALM: 3D real-time photoactivation nanoscopy image processing in ImageJ. Nat Methods 7(5):339–340. doi:10.1038/nmeth0510-339 Hess ST, Girirajan TP, Mason MD (2006) Ultra-high resolution imaging by fluorescence photoactivation localization microscopy. Biophys J 91(11):4258–4272. doi:10.1529/biophysj.106.091116 Holtzer L, Meckel T, Schmidt T (2007) Nanometric three-dimensional tracking of individual quantum dots in cells. Appl Phys Lett 90:053902 Hoze N, Nair D, Hosy E, Sieben C, Manley S, Herrmann A, Sibarita JB, Choquet D, Holcman D (2012) Heterogeneity of AMPA receptor trafficking and molecular interactions revealed by superresolution analysis of live cell imaging. Proc Natl Acad Sci USA 109(42):17052–17057. doi:10.1073/pnas.1204589109 Huang B, Wang W, Bates M, Zhuang X (2008) Three-dimensional super-resolution imaging by stochastic optical reconstruction microscopy. Science 319(5864):810–813. doi:10.1126/ science.1153529 Ingemar JC (1993) A review of statistical data association techniques for motion correspondence. J Comp Vis 10(1):53–66 Izeddin I, Boulanger J, Racine V, Specht CG, Kechkar A, Nair D, Triller A, Choquet D, Dahan M, Sibarita JB (2012) Wavelet analysis for single molecule localization microscopy. Opt Express 20(3):2081–2095. doi:10.1364/OE.20.002081 Jaqaman K, Loerke D, Mettlen M, Kuwata H, Grinstein S, Schmid SL, Danuser G (2008) Robust single-particle tracking in live-cell time-lapse sequences. Nat Methods 5(8):695–702. doi:10.1038/n meth.1237 Juette MF, Gould TJ, Lessard MD, Mlodzianoski MJ, Nagpure BS, Bennett BT, Hess ST, Bewersdorf J (2008) Three-dimensional sub-100 nm resolution fluorescence microscopy of thick samples. Nat Methods 5(6):527–529. doi:10.1038/nmeth.1211 Kalaidzidis Y (2009) Multiple objects tracking in fluorescence microscopy. J Math Biol 58(1–2):57–80. doi:10.1007/ s00285-008-0180-4 Kechkar A, Nair D, Heilemann M, Choquet D, Sibarita JB (2013) Real-time analysis and visualization for single-molecule based super-resolution microscopy. PLoS ONE 8(4):e62918. doi:10.1371/journal.pone.0062918 Kubitscheck U, Kuckmann O, Kues T, Peters R (2000) Imaging and tracking of single GFP molecules in solution. Biophys J 78(4):2170–2179. doi:10.1016/S0006-3495(00)76764-6 Kusumi A, Sako Y, Yamamoto M (1993) 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(5):2021–2040. doi:10.1016/S0006-3495(93)81253-0

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Histochem Cell Biol Lukinavicius G, Umezawa K, Olivier N, Honigmann A, Yang G, Plass T, Mueller V, Reymond L, Correa IR Jr, Luo ZG, Schultz C, Lemke EA, Heppenstall P, Eggeling C, Manley S, Johnsson K (2013) A near-infrared fluorophore for live-cell super-resolution microscopy of cellular proteins. Nat Chem 5(2):132–139. doi:10. 1038/nchem.1546 Madge DE, Elson EL, Webb WW (1972) Thermodynamics fluctuations in a reacting system: measurement by fluorescence correlation spectroscopy. Phys Rev Lett 29:705–708 Manley S, Gillette JM, Patterson GH, Shroff H, Hess HF, Betzig E, Lippincott-Schwartz J (2008) High-density mapping of singlemolecule trajectories with photoactivated localization microscopy. Nat Methods 5(2):155–157. doi:10.1038/nmeth.1176 Masson JB, Dionne P, Salvatico C, Renner M, Specht CG, Triller A, Dahan M (2014) Mapping the energy and diffusion landscapes of membrane proteins at the cell surface using high-density single-molecule imaging and Bayesian Inference: application to the multiscale dynamics of glycine receptors in the neuronal membrane. Biophys J 106(1):74–83. doi:10.1016/j.bpj.2013.10.027 Mazza D, Abernathy A, Golob N, Morisaki T, McNally JG (2012) A benchmark for chromatin binding measurements in live cells. Nucleic Acids Res 40(15):e119. doi:10.1093/nar/gks701 Mitchison TJ (1989) Polewards microtubule flux in the mitotic spindle: evidence from photoactivation of fluorescence. J Cell Biol 109(2):637–652 Nair D, Hosy E, Petersen JD, Constals A, Giannone G, Choquet D, Sibarita JB (2013) Super-resolution imaging reveals that AMPA receptors inside synapses are dynamically organized in nanodomains regulated by PSD95. J Neurosci 33(32):13204–13224. doi: 10.1523/JNEUROSCI.2381-12.2013 Ober RJ, Ram S, Ward ES (2004) Localization accuracy in singlemolecule microscopy. Biophys J 86(2):1185–1200. doi:10.1016/ S0006-3495(04)74193-4 Parthasarathy R (2012) Rapid, accurate particle tracking by calculation of radial symmetry centers. Nat Methods 9(7):724–726. doi: 10.1038/nmeth.2071 Patterson GH, Lippincott-Schwartz J (2002) A photoactivatable GFP for selective photolabeling of proteins and cells. Science 297(5588):1873–1877. doi:10.1126/science.1074952 Persson F, Linden M, Unoson C, Elf J (2013) Extracting intracellular diffusive states and transition rates from single-molecule tracking data. Nat Methods 10(3):265–269. doi:10.1038/nmeth.2367 Racine V, Hertzog A, Jouanneau J, Salamero J, Kervrann C, Sibarita JB (2006) Multiple-target tracking of 3D fluorescent objects based on simulated annealing. IEEE Int Symp Biomed Imaging:1020–1023 Reid DB (1979) An algorithm for tracking multiple targets. IEEE Trans Autom Control 24(6):846–854 Ries J, Kaplan C, Platonova E, Eghlidi H, Ewers H (2012) A simple, versatile method for GFP-based super-resolution microscopy via nanobodies. Nat Methods 9(6):582–584. doi:10.1038/nmeth.1991 Rossier O, Octeau V, Sibarita JB, Leduc C, Tessier B, Nair D, Gatterdam V, Destaing O, Albiges-Rizo C, Tampe R, Cognet L, Choquet D, Lounis B, Giannone G (2012) Integrins beta(1) and beta(3) exhibit distinct dynamic nanoscale organizations inside focal adhesions. Nat Cell Biol 14(10):1057–1067. doi:10.1038/ncb2588 Rust MJ, Bates M, Zhuang X (2006) Sub-diffraction-limit imaging by stochastic optical reconstruction microscopy (STORM). Nat Methods 3(10):793–795. doi:10.1038/nmeth929 Saxton MJ (1993) Lateral diffusion in an archipelago. Singleparticle diffusion. Biophys J 64(6):1766–1780. doi:10.1016/ S0006-3495(93)81548-0 Saxton MJ (1995) Single-particle tracking: effects of corrals. Biophys J 69(2):389–398. doi:10.1016/S0006-3495(95)79911-8

Histochem Cell Biol Saxton MJ, Jacobson K (1997) Single-particle tracking: applications to membrane dynamics. Ann Rev Biophys Biomol Struct 26:373– 399. doi:10.1146/annurev.biophys.26.1.373 Serge A, Bertaux N, Rigneault H, Marguet D (2008) Dynamic multiple-target tracing to probe spatiotemporal cartography of cell membranes. Nat Methods 5(8):687–694. doi:10.1038/nmeth.1233 Smith CS, Joseph N, Rieger B, Lidke KA (2010) Fast, single-molecule localization that achieves theoretically minimum uncertainty. Nat Methods 7(5):373–375. doi:10.1038/nmeth.1449 Subach FV, Patterson GH, Manley S, Gillette JM, LippincottSchwartz J, Verkhusha VV (2009) Photoactivatable mCherry for high-resolution two-color fluorescence microscopy. Nat Methods 6(2):153–159. doi:10.1038/nmeth.1298 Tanaami T, Otsuki S, Tomosada N, Kosugi Y, Shimizu M, Ishida H (2002) High-speed 1-frame/ms scanning confocal microscope with a microlens and Nipkow disks. Appl Opt 41(22):4704–4708 Thompson RE, Larson DR, Webb WW (2002) Precise nanometer localization analysis for individual fluorescent probes. Biophys J 82(5):2775–2783. doi:10.1016/S0006-3495(02)75618-X Thompson MA, Lew MD, Badieirostami M, Moerner WE (2010) Localizing and tracking single nanoscale emitters in three dimensions with high spatiotemporal resolution using a double-helix point spread function. Nano Lett 10(1):211–218. doi:10.1021/nl903295p Tokunaga M, Imamoto N, Sakata-Sogawa K (2008) Highly inclined thin illumination enables clear single-molecule imaging in cells. Nat Methods 5(2):159–161. doi:10.1038/nmeth1171 Tsien RY (1998) The green fluorescent protein. Ann Rev Biochem 67:509–544. doi:10.1146/annurev.biochem.67.1.509 Tvarusko W, Bentele M, Misteli T, Rudolf R, Kaether C, Spector DL, Gerdes HH, Eils R (1999) Time-resolved analysis and

visualization of dynamic processes in living cells. Proc Natl Acad Sci USA 96(14):7950–7955 Wazawa T, Ueda M (2005) Total internal reflection fluorescence microscopy in single molecule nanobioscience. Adv Biochem Eng Biotechnol 95:77–106 Wiedenmann J, Ivanchenko S, Oswald F, Schmitt F, Rocker C, Salih A, Spindler KD, Nienhaus GU (2004) EosFP, a fluorescent marker protein with UV-inducible green-to-red fluorescence conversion. Proc Natl Acad Sci U S A 101(45):15905–15910. doi:10. 1073/pnas.0403668101 Winckler P, Lartigue L, Giannone G, De Giorgi F, Ichas F, Sibarita JB, Lounis B, Cognet L (2013) Identification and super-resolution imaging of ligand-activated receptor dimers in live cells. Sci Rep 3:2387. doi:10.1038/srep02387 Wolter S, Schuttpelz M, Tscherepanow M, Van de Linde S, Heilemann M, Sauer M (2010) Real-time computation of subdiffraction-resolution fluorescence images. J Microsc 237(1):12–22. doi:10.1111/j.1365-2818.2009.03287.x Yildiz A, Forkey JN, McKinney SA, Ha T, Goldman YE, Selvin PR (2003) Myosin V walks hand-over-hand: single fluorophore imaging with 1.5-nm localization. Science 300(5628):2061– 2065. doi:10.1126/science.1084398 Yildiz A, Tomishige M, Vale RD, Selvin PR (2004) Kinesin walks hand-over-hand. Science 303(5658):676–678. doi:10.1126/ science.1093753 York AG, Ghitani A, Vaziri A, Davidson MW, Shroff H (2011) Confined activation and subdiffractive localization enables whole-cell PALM with genetically expressed probes. Nat Methods 8(4):327– 333. doi:10.1038/nmeth.1571 Zessin PJM, Finan K, Heilemann M (2012) Super-resolution fluorescence imaging of chromosomal DNA. J Struct Biol 177:344–348

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