1932

Abstract

Live-cell single-molecule experiments are now widely used to study complex biological processes such as signal transduction, self-assembly, active trafficking, and gene regulation. These experiments’ increased popularity results in part from rapid methodological developments that have significantly lowered the technical barriers to performing them. Another important advance is the development of novel statistical algorithms, which, by modeling the stochastic behaviors of single molecules, can be used to extract systemic parameters describing the in vivo biochemistry or super-resolution localization of biological molecules within their physiological environment. This review discusses recent advances in experimental and computational strategies for live-cell single-molecule studies, as well as a selected subset of biological studies that have utilized these new technologies.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-physchem-040215-112451
2016-05-27
2024-06-12
Loading full text...

Full text loading...

/deliver/fulltext/physchem/67/1/annurev-physchem-040215-112451.html?itemId=/content/journals/10.1146/annurev-physchem-040215-112451&mimeType=html&fmt=ahah

Literature Cited

  1. Ghosh RN, Webb WW. 1.  1994. Automated detection and tracking of individual and clustered cell surface low density lipoprotein receptor molecules. Biophys. J. 66:51301–18 [Google Scholar]
  2. Kusumi A, Sako Y, Yamamoto M. 2.  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. 65:52021–40 [Google Scholar]
  3. Dahan M, Lévi S, Luccardini C, Rostaing P, Riveau B, Triller A. 3.  2003. Diffusion dynamics of glycine receptors revealed by single-quantum dot tracking. Science 302:5644442–45 [Google Scholar]
  4. Liu Z, Lavis LD, Betzig E. 4.  2015. Imaging live-cell dynamics and structure at the single-molecule level. Mol. Cell 58:4644–59 [Google Scholar]
  5. Shcherbakova DM, Sengupta P, Lippincott-Schwartz J, Verkhusha VV. 5.  2014. Photocontrollable fluorescent proteins for superresolution imaging. Annu. Rev. Biophys. 43:303–29 [Google Scholar]
  6. van de Linde S, Heilemann M, Sauer M. 6.  2012. Live-cell super-resolution imaging with synthetic fluorophores. Annu. Rev. Phys. Chem. 63:1519–40 [Google Scholar]
  7. Patterson GH, Lippincott-Schwartz J. 7.  2002. A photoactivatable GFP for selective photolabeling of proteins and cells. Science 297:55881873–77 [Google Scholar]
  8. Ando R, Hama H, Yamamoto-Hino M, Mizuno H, Miyawaki A. 8.  2002. An optical marker based on the UV-induced green-to-red photoconversion of a fluorescent protein. PNAS 99:2012651–56 [Google Scholar]
  9. Betzig E, Patterson GH, Sougrat R, Lindwasser OW, Olenych S. 9.  et al. 2006. Imaging intracellular fluorescent proteins at nanometer resolution. Science 313:1642–45 [Google Scholar]
  10. Hess ST, Girirajan TPK, Mason MD. 10.  2006. Ultra-high resolution imaging by fluorescence photoactivation localization microscopy. Biophys. J. 91:114258–72 [Google Scholar]
  11. Hess ST, Gould TJ, Gudheti MV, Maas SA, Mills KD, Zimmerberg J. 11.  2007. Dynamic clustered distribution of hemagglutinin resolved at 40 nm in living cell membranes discriminates between raft theories. PNAS 104:4417370–75 [Google Scholar]
  12. Manley S, Gillette JM, Patterson GH, Shroff H, Hess HF. 12.  et al. 2008. High-density mapping of single-molecule trajectories with photoactivated localization microscopy. Nat. Methods 5:2155–57 [Google Scholar]
  13. Niu L, Yu J. 13.  2008. Investigating intracellular dynamics of FtsZ cytoskeleton with photoactivation single-molecule tracking. Biophys. J. 95:42009–16 [Google Scholar]
  14. Los GV, Encell LP, McDougall MG, Hartzell DD, Karassina N. 14.  et al. 2008. Halotag: a novel protein labeling technology for cell imaging and protein analysis. ACS Chem. Biol. 3:6373–82 [Google Scholar]
  15. Rust MJ, Bates M, Zhuang X. 15.  2006. Sub-diffraction-limit imaging by stochastic optical reconstruction microscopy (STORM). Nat. Methods 3:10793–96 [Google Scholar]
  16. Heilemann M, van de Linde S, Schüttpelz M, Kasper R, Seefeldt B. 16.  et al. 2008. Subdiffraction-resolution fluorescence imaging with conventional fluorescent probes. Angew. Chem. Int. Ed. 47:336172–76 [Google Scholar]
  17. Gautier A, Juillerat A, Heinis C, Corrêa IR, Kindermann M. 17.  et al. 2008. An engineered protein tag for multiprotein labeling in living cells. Chem. Biol. 15:2128–36 [Google Scholar]
  18. Chen Z, Jing C, Gallagher SS, Sheetz MP, Cornish VW. 18.  2012. Second-generation covalent TMP-tag for live cell imaging. J. Am. Chem. Soc. 134:3313692–99 [Google Scholar]
  19. Subach OM, Patterson GH, Ting L-M, Wang Y, Condeelis JS, Verkhusha VV. 19.  2011. A photoswitchable orange-to-far-red fluorescent protein, PSmOrange. Nat. Methods 8:9771–77 [Google Scholar]
  20. Landgraf D, Okumus B, Chien P, Baker TA, Paulsson J. 20.  2012. Segregation of molecules at cell division reveals native protein localization. Nat. Methods 9:5480–82 [Google Scholar]
  21. Sander JD, Joung JK. 21.  2014. CRISPR-Cas systems for editing, regulating and targeting genomes. Nat. Biotechnol. 32:4347–55 [Google Scholar]
  22. Lubeck E, Cai L. 22.  2012. Single-cell systems biology by super-resolution imaging and combinatorial labeling. Nat. Methods 9:7743–48 [Google Scholar]
  23. Shim S-H, Xia C, Zhong G, Babcock HP, Vaughan JC. 23.  et al. 2012. Super-resolution fluorescence imaging of organelles in live cells with photoswitchable membrane probes. PNAS 109:3513978–83 [Google Scholar]
  24. Axelrod D, Burghardt TP, Thompson NL. 24.  1984. Total internal reflection fluorescence. Annu. Rev. Biophys. Bioeng. 13:247–68 [Google Scholar]
  25. Ritter JG, Veith R, Veenendaal A, Siebrasse JP, Kubitscheck U. 25.  2010. Light sheet microscopy for single molecule tracking in living tissue. PLOS ONE 5:7e11639 [Google Scholar]
  26. Hu YS, Zimmerley M, Li Y, Watters R, Cang H. 26.  2014. Single-molecule super-resolution light-sheet microscopy. ChemPhysChem 15:4577–86 [Google Scholar]
  27. Voie AH, Burns DH, Spelman FA. 27.  1993. Orthogonal-plane fluorescence optical sectioning: three-dimensional imaging of macroscopic biological specimens. J. Microsc. 170:3229–36 [Google Scholar]
  28. Wu Y, Ghitani A, Christensen R, Santella A, Du Z. 28.  et al. 2011. Inverted selective plane illumination microscopy (iSPIM) enables coupled cell identity lineaging and neurodevelopmental imaging in Caenorhabditis elegans. PNAS 108:4317708–13 [Google Scholar]
  29. Zhao ZW, Roy R, Gebhardt JCM, Suter DM, Chapman AR, Xie XS. 29.  2014. Spatial organization of RNA polymerase II inside a mammalian cell nucleus revealed by reflected light-sheet superresolution microscopy. PNAS 111:2681–86 [Google Scholar]
  30. Planchon TA, Gao L, Milkie DE, Davidson MW, Galbraith JA. 30.  et al. 2011. Rapid three-dimensional isotropic imaging of living cells using Bessel beam plane illumination. Nat. Methods 8:5417–23 [Google Scholar]
  31. Chen B-C, Legant WR, Wang K, Shao L, Milkie DE. 31.  et al. 2014. Lattice light-sheet microscopy: imaging molecules to embryos at high spatiotemporal resolution. Science 346:62081257998 [Google Scholar]
  32. Gao L, Shao L, Higgins CD, Poulton JS, Peifer M. 32.  et al. 2012. Noninvasive imaging beyond the diffraction limit of 3D dynamics in thickly fluorescent specimens. Cell 151:61370–85 [Google Scholar]
  33. Huisken J, Swoger J, Bene FD, Wittbrodt J, Stelzer EHK. 33.  2004. Optical sectioning deep inside live embryos by selective plane illumination microscopy. Science 305:56861007–9 [Google Scholar]
  34. Shao L, Kner P, Rego EH, Gustafsson MGL. 34.  2011. Super-resolution 3D microscopy of live whole cells using structured illumination. Nat. Methods 8:121044–46 [Google Scholar]
  35. Uyemura T, Takagi H, Yanagida T, Sako Y. 35.  2005. Single-molecule analysis of epidermal growth factor signaling that leads to ultrasensitive calcium response. Biophys. J. 88:53720–30 [Google Scholar]
  36. Tokunaga M, Imamoto N. 36.  2002. Single molecule imaging of nucleocytoplasmic transport in cells and quantitative analysis of interaction with nuclear pores. Biophys. J. 82:44a [Google Scholar]
  37. Tokunaga M, Imamoto N, Sakata-Sogawa K. 37.  2008. Highly inclined thin illumination enables clear single-molecule imaging in cells. Nat. Methods 5:2159–61 [Google Scholar]
  38. Konopka CA, Bednarek SY. 38.  2008. Variable-angle epifluorescence microscopy: a new way to look at protein dynamics in the plant cell cortex. Plant J. 53:1186–96 [Google Scholar]
  39. Saxton MJ, Jacobson K. 39.  1997. Single-particle tracking: applications to membrane dynamics. Annu. Rev. Biophys. Biomol. Struct. 26:1373–99 [Google Scholar]
  40. Saxton MJ. 40.  1997. Single-particle tracking: the distribution of diffusion coefficients. Biophys. J. 72:41744–53 [Google Scholar]
  41. Qian H, Sheetz MP, Elson EL. 41.  1991. Single particle tracking. Analysis of diffusion and flow in two-dimensional systems. Biophys. J. 60:4910–21 [Google Scholar]
  42. Grebenkov DS. 42.  2011. Probability distribution of the time-averaged mean-square displacement of a Gaussian process. Phys. Rev. E 84:3031124 [Google Scholar]
  43. Berglund AJ. 43.  2010. Statistics of camera-based single-particle tracking. Phys. Rev. E 82:1011917 [Google Scholar]
  44. Thompson RE, Larson DR, Webb WW. 44.  2002. Precise nanometer localization analysis for individual fluorescent probes. Biophys. J. 82:52775–83 [Google Scholar]
  45. Ober RJ, Ram S, Ward ES. 45.  2004. Localization accuracy in single-molecule microscopy. Biophys. J. 86:21185–200 [Google Scholar]
  46. Martin DS, Forstner MB, Käs JA. 46.  2002. Apparent subdiffusion inherent to single particle tracking. Biophys. J. 83:42109–17 [Google Scholar]
  47. Savin T, Doyle PS. 47.  2005. Static and dynamic errors in particle tracking microrheology. Biophys. J. 88:1623–38 [Google Scholar]
  48. Deverall MA, Gindl E, Sinner E-K, Besir H, Ruehe J. 48.  et al. 2005. Membrane lateral mobility obstructed by polymer-tethered lipids studied at the single molecule level. Biophys. J. 88:31875–86 [Google Scholar]
  49. Saxton MJ. 49.  1993. Lateral diffusion in an archipelago. Single-particle diffusion. Biophys. J. 64:61766–80 [Google Scholar]
  50. Masson J-B, Dionne P, Salvatico C, Renner M, Specht CG. 50.  et al. 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. 106:174–83 [Google Scholar]
  51. Koo P, Weitzmann M, Sabanaygam C, van Golen K, Mochrie S. 51.  2015. Extracting diffusive states of Rho GTPase in live cells: towards in vivo biochemistry. PLOS Comput. Biol. 11:e1004297 [Google Scholar]
  52. Dempster AP, Laird NM, Rubin DB. 52.  1977. Maximum likelihood from incomplete data via the EM algorithm. J. R. Stat. Soc. Ser. B Methodol. 39:11–38 [Google Scholar]
  53. Boyer D, Dean DS, Mejía-Monasterio C, Oshanin G. 53.  2012. Optimal estimates of the diffusion coefficient of a single Brownian trajectory. Phys. Rev. E 85:3031136 [Google Scholar]
  54. Rabiner L. 54.  1989. A tutorial on hidden Markov models and selected applications in speech recognition. Proc. IEEE 77:2257–86 [Google Scholar]
  55. Qin F, Auerbach A, Sachs F. 55.  2000. Hidden Markov modeling for single channel kinetics with filtering and correlated noise. Biophys. J. 79:41928–44 [Google Scholar]
  56. Venkataramanan L, Sigworth FJ. 56.  2002. Applying hidden Markov models to the analysis of single ion channel activity. Biophys. J. 82:41930–42 [Google Scholar]
  57. McKinney SA, Joo C, Ha T. 57.  2006. Analysis of single-molecule fret trajectories using hidden Markov modeling. Biophys. J. 91:51941–51 [Google Scholar]
  58. Bronson JE, Fei J, Hofman JM, Gonzalez RL, Wiggins CH. 58.  2009. Learning rates and states from biophysical time series: a Bayesian approach to model selection and single-molecule FRET data. Biophys. J. 97:123196–205 [Google Scholar]
  59. Chung I, Akita R, Vandlen R, Toomre D, Schlessinger J, Mellman I. 59.  2010. Spatial control of EGF receptor activation by reversible dimerization on living cells. Nature 464:7289783–87 [Google Scholar]
  60. Persson F, Lindén M, Unoson C, Elf J. 60.  2013. Extracting intracellular diffusive states and transition rates from single-molecule tracking data. Nat. Methods 10:3265–69 [Google Scholar]
  61. MacKay DJC. 61.  2003. Information Theory, Inference and Learning Algorithms Cambridge, UK: Cambridge Univ. Press, 1st ed.. [Google Scholar]
  62. Monnier N, Barry Z, Park HY, Su K-C, Katz Z. 62.  et al. 2015. Inferring transient particle transport dynamics in live cells. Nat. Methods 12:838–40 [Google Scholar]
  63. Steele RJ, Raftery A. 63.  2010. Performance of Bayesian model selection criteria for Gaussian mixture models. Frontiers of Statistical Decision Making and Bayesian Analysis M-H Chen, DK Dey, P Müller, D Sun, K Ye 113–130 New York: Springer [Google Scholar]
  64. Schütz GJ, Schindler H, Schmidt T. 64.  1997. Single-molecule microscopy on model membranes reveals anomalous diffusion. Biophys. J. 73:21073–80 [Google Scholar]
  65. Metzler R, Klafter J. 65.  2000. The random walk's guide to anomalous diffusion: a fractional dynamics approach. Phys. Rep. 339:11–77 [Google Scholar]
  66. Bel G, Barkai E. 66.  2005. Weak ergodicity breaking in the continuous-time random walk. Phys. Rev. Lett. 94:24240602 [Google Scholar]
  67. Weigel AV, Simon B, Tamkun MM, Krapf D. 67.  2011. Ergodic and nonergodic processes coexist in the plasma membrane as observed by single-molecule tracking. PNAS 108:166438–43 [Google Scholar]
  68. Wang B, Anthony SM, Bae SC, Granick S. 68.  2009. Anomalous yet Brownian. PNAS 106:3615160–64 [Google Scholar]
  69. Wang B, Kuo J, Bae SC, Granick S. 69.  2012. When Brownian diffusion is not Gaussian. Nat. Mater. 11:6481–85 [Google Scholar]
  70. Jacobson K, Sheets ED, Simson R. 70.  1995. Revisiting the fluid mosaic model of membranes. Science 268:52161441–42 [Google Scholar]
  71. Feder TJ, Brust-Mascher I, Slattery JP, Baird B, Webb WW. 71.  1996. Constrained diffusion or immobile fraction on cell surfaces: a new interpretation. Biophys. J. 70:62767–73 [Google Scholar]
  72. Shroff H, Galbraith CG, Galbraith JA, Betzig E. 72.  2008. Live-cell photoactivated localization microscopy of nanoscale adhesion dynamics. Nat. Methods 5:417–23 [Google Scholar]
  73. Huang F, Hartwich TMP, Rivera-Molina FE, Lin Y, Duim WC. 73.  et al. 2013. Video-rate nanoscopy using sCMOS camera-specific single-molecule localization algorithms. Nat. Methods 10:7653–58 [Google Scholar]
  74. Small AR, Parthasarathy R. 74.  2014. Superresolution localization methods. Annu. Rev. Phys. Chem. 65:1107–25 [Google Scholar]
  75. Lee S-H, Shin JY, Lee A, Bustamante C. 75.  2012. Counting single photoactivatable fluorescent molecules by photoactivated localization microscopy (PALM). PNAS 109:4317436–41 [Google Scholar]
  76. Puchner EM, Walter JM, Kasper R, Huang B, Lim WA. 76.  2013. Counting molecules in single organelles with superresolution microscopy allows tracking of the endosome maturation trajectory. PNAS 110:4016015–20 [Google Scholar]
  77. Annibale P, Vanni S, Scarselli M, Rothlisberger U, Radenovic A. 77.  2011. Quantitative photo activated localization microscopy: unraveling the effects of photoblinking. PLOS ONE 6:7e22678 [Google Scholar]
  78. Rollins GC, Shin JY, Bustamante C, Pressé S. 78.  2015. Stochastic approach to the molecular counting problem in superresolution microscopy. PNAS 112:2E110–18 [Google Scholar]
  79. Cox S, Rosten E, Monypenny J, Jovanovic-Talisman T, Burnette DT. 79.  et al. 2012. Bayesian localization microscopy reveals nanoscale podosome dynamics. Nat. Methods 9:2195–200 [Google Scholar]
  80. Hu YS, Nan X, Sengupta P, Lippincott-Schwartz J, Cang H. 80.  2013. Accelerating 3B single-molecule super-resolution microscopy with cloud computing. Nat. Methods 10:296–97 [Google Scholar]
  81. Babcock HP, Moffitt JR, Cao Y, Zhuang X. 81.  2013. Fast compressed sensing analysis for super-resolution imaging using L1-homotopy. Opt. Express 21:2328583 [Google Scholar]
  82. Zhu L, Zhang W, Elnatan D, Huang B. 82.  2012. Faster storm using compressed sensing. Nat. Methods 9:7721–23 [Google Scholar]
  83. Mukamel EA, Babcock H, Zhuang X. 83.  2012. Statistical deconvolution for superresolution fluorescence microscopy. Biophys. J. 102:102391–400 [Google Scholar]
  84. Dertinger T, Colyer R, Iyer G, Weiss S, Enderlein J. 84.  2009. Fast, background-free, 3D super-resolution optical fluctuation imaging (SOFI). PNAS 106:5222287–92 [Google Scholar]
  85. Deng Y, Sun M, Lin P-H, Ma J, Shaevitz JW. 85.  2014. Spatial covariance reconstructive (SCORE) super-resolution fluorescence microscopy. PLOS ONE 9:4e94807 [Google Scholar]
  86. Dedecker P, Mo GCH, Dertinger T, Zhang J. 86.  2012. Widely accessible method for superresolution fluorescence imaging of living systems. PNAS 109:2710909–14 [Google Scholar]
  87. Cho S, Jang J, Song C, Lee H, Ganesan P. 87.  et al. 2013. Simple super-resolution live-cell imaging based on diffusion-assisted Förster resonance energy transfer. Sci. Rep. 3:1208 [Google Scholar]
  88. Wu H. 88.  2013. Higher-order assemblies in a new paradigm of signal transduction. Cell 153:2287–92 [Google Scholar]
  89. Manz BN, Groves JT. 89.  2010. Spatial organization and signal transduction at intercellular junctions. Nat. Rev. Mol. Cell Biol. 11:5342–52 [Google Scholar]
  90. Dustin ML, Groves JT. 90.  2012. Receptor signaling clusters in the immune synapse. Annu. Rev. Biophys. 41:543–56 [Google Scholar]
  91. Falkenberg CV, Blinov ML, Loew LM. 91.  2013. Pleomorphic ensembles: formation of large clusters composed of weakly interacting multivalent molecules. Biophys. J. 105:112451–60 [Google Scholar]
  92. Irvine DJ, Purbhoo MA, Krogsgaard M, Davis MM. 92.  2002. Direct observation of ligand recognition by T cells. Nature 419:845–49 [Google Scholar]
  93. Douglass AD, Vale RD. 93.  2005. Single-molecule microscopy reveals plasma membrane microdomains created by protein–protein networks that exclude or trap signaling molecules in T cells. Cell 121:937–50 [Google Scholar]
  94. Huppa JB, Axmann M, Mörtelmaier MA, Lillemeier BF, Newell EW. 94.  et al. 2010. TCR–peptide–MHC interactions in situ show accelerated kinetics and increased affinity. Nature 463:963–67 [Google Scholar]
  95. Huang J, Zarnitsyna VI, Liu B, Edwards LJ, Jiang N. 95.  et al. 2010. The kinetics of two-dimensional TCR and pMHC interactions determine T-cell responsiveness. Nature 464:932–36 [Google Scholar]
  96. O’Donoghue GP, Pielak RM, Smoligovets AA, Lin JJ, Groves JT. 96.  2013. Direct single molecule measurement of TCR triggering by agonist pMHC in living primary T cells. eLife 2:e00778 [Google Scholar]
  97. Lemmon MA, Schlessinger J. 97.  2010. Cell signaling by receptor tyrosine kinases. Cell 141:71117–34 [Google Scholar]
  98. Low-Nam ST, Lidke KA, Cutler PJ, Roovers RC, van Bergen en Henegouwen PMP. 98.  et al. 2011. ErbB1 dimerization is promoted by domain co-confinement and stabilized by ligand binding. Nat. Struct. Mol. Biol. 18:111244–49 [Google Scholar]
  99. Casaletto JB, McClatchey AI. 99.  2012. Spatial regulation of receptor tyrosine kinases in development and cancer. Nat. Rev. Cancer 12:6387–400 [Google Scholar]
  100. Kasai RS, Suzuki KGN, Prossnitz ER, Koyama-Honda I, Nakada C. 100.  et al. 2011. Full characterization of GPCR monomer-dimer dynamic equilibrium by single molecule imaging. J. Cell Biol. 192:3463–80 [Google Scholar]
  101. Bouzigues C, Dahan M. 101.  2007. Transient directed motions of GABA(A) receptors in growth cones detected by a speed correlation index. Biophys. J. 92:2654–60 [Google Scholar]
  102. Hern JA, Baig AH, Mashanov GI, Birdsall B, Corrie JET. 102.  et al. 2010. Formation and dissociation of M1 muscarinic receptor dimers seen by total internal reflection fluorescence imaging of single molecules. PNAS 107:62693–98 [Google Scholar]
  103. Calebiro D, Rieken F, Wagner J, Sungkaworn T, Zabel U. 103.  et al. 2013. Single-molecule analysis of fluorescently labeled G-protein–coupled receptors reveals complexes with distinct dynamics and organization. PNAS 110:2743–48 [Google Scholar]
  104. Kasai RS, Kusumi A. 104.  2014. Single-molecule imaging revealed dynamic GPCR dimerization. Curr. Opin. Cell Biol. 27:78–86 [Google Scholar]
  105. Jaqaman K, Kuwata H, Touret N, Collins R, Trimble WS. 105.  et al. 2011. Cytoskeletal control of CD36 diffusion promotes its receptor and signaling function. Cell 146:4593–606 [Google Scholar]
  106. Rice SA. 106.  1985. Diffusion-Limited Reactions Amsterdam: Elsevier [Google Scholar]
  107. Oh D, Ogiue-Ikeda M, Jadwin JA, Machida K, Mayer BJ, Yu J. 107.  2012. Fast rebinding increases dwell time of Src homology 2 (SH2)-containing proteins near the plasma membrane. PNAS 109:3514024–29 [Google Scholar]
  108. Pawson T. 108.  2004. Specificity in signal transduction: from phosphotyrosine-SH2 domain interactions to complex cellular systems. Cell 116:2191–203 [Google Scholar]
  109. McKeithan TW. 109.  1995. Kinetic proofreading in T-cell receptor signal transduction. PNAS 92:115042–46 [Google Scholar]
  110. Li G, Ruan X, Auerbach RK, Sandhu KS, Zheng M. 110.  et al. 2012. Extensive promoter-centered chromatin interactions provide a topological basis for transcription regulation. Cell 148:1–284–98 [Google Scholar]
  111. Cisse II, Izeddin I, Causse SZ, Boudarene L, Senecal A. 111.  et al. 2013. Real-time dynamics of RNA polymerase II clustering in live human cells. Science 341:6146664–67 [Google Scholar]
  112. Izeddin I, Récamier V, Bosanac L, Cissé II, Boudarene L. 112.  et al. 2014. Single-molecule tracking in live cells reveals distinct target-search strategies of transcription factors in the nucleus. eLife 3:e02230 [Google Scholar]
  113. Liu Z, Legant WR, Chen B-C, Li L, Grimm JB. 113.  et al. 2014. 3D imaging of Sox2 enhancer clusters in embryonic stem cells. eLife 3:e04236 [Google Scholar]
  114. Berg OG, Winter RB, Von Hippel PH. 114.  1981. Diffusion-driven mechanisms of protein translocation on nucleic acids. 1. Models and theory. Biochemistry 20:246929–48 [Google Scholar]
  115. Hammar P, Leroy P, Mahmutovic A, Marklund EG, Berg OG, Elf J. 115.  2012. The lac repressor displays facilitated diffusion in living cells. Science 336:60881595–98 [Google Scholar]
  116. Chen J, Zhang Z, Li L, Chen B-C, Revyakin A. 116.  et al. 2014. Single-molecule dynamics of enhanceosome assembly in embryonic stem cells. Cell 156:61274–85 [Google Scholar]
  117. Hering H, Sheng M. 117.  2001. Dentritic spines: structure, dynamics and regulation. Nat. Rev. Neurosci. 2:12880–88 [Google Scholar]
  118. Frost NA, Shroff H, Kong H, Betzig E, Blanpied TA. 118.  2010. Single-molecule discrimination of discrete perisynaptic and distributed sites of actin filament assembly within dendritic spines. Neuron 67:186–99 [Google Scholar]
  119. Tatavarty V, Kim E-J, Rodionov V, Yu J. 119.  2009. Investigating sub-spine actin dynamics in rat hippocampal neurons with super-resolution optical imaging. PLOS ONE 4:11e7724 [Google Scholar]
  120. Chazeau A, Mehidi A, Nair D, Gautier JJ, Leduc C. 120.  et al. 2014. Nanoscale segregation of actin nucleation and elongation factors determines dendritic spine protrusion. EMBO J. 33:2745–64 [Google Scholar]
  121. Tatavarty V, Das S, Yu J. 121.  2012. Polarization of actin cytoskeleton is reduced in dendritic protrusions during early spine development in hippocampal neuron. Mol. Biol. Cell 23:163167–77 [Google Scholar]
  122. Hoze N, Nair D, Hosy E, Sieben C, Manley S. 122.  et al. 2012. Heterogeneity of AMPA receptor trafficking and molecular interactions revealed by superresolution analysis of live cell imaging. PNAS 109:4217052–57 [Google Scholar]
  123. Nägerl UV, Triller A. 123.  2014. Investigating AMPA Receptor Diffusion and Nanoscale Organization at Synapses with High-Density Single-Molecule Tracking Methods New York: Springer [Google Scholar]
  124. Nair D, Hosy E, Petersen JD, Constals A, Giannone G. 124.  et al. 2013. Super-resolution imaging reveals that AMPA receptors inside synapses are dynamically organized in nanodomains regulated by PSD95. J. Neurosci. 33:3213204–24 [Google Scholar]
  125. Das S, Yin T, Yang Q, Zhang J, Wu YI, Yu J. 125.  2015. Single-molecule tracking of small GTPase Rac1 uncovers spatial regulation of membrane translocation and mechanism for polarized signaling. PNAS 112:3E267–76 [Google Scholar]
/content/journals/10.1146/annurev-physchem-040215-112451
Loading
/content/journals/10.1146/annurev-physchem-040215-112451
Loading

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error