1932

Abstract

Fluorescent tools have revolutionized our ability to probe biological dynamics, particularly at the cellular level. Fluorescent sensors have been developed on several platforms, utilizing either small-molecule dyes or fluorescent proteins, to monitor proteins, RNA, DNA, small molecules, and even cellular properties, such as pH and membrane potential. We briefly summarize the impressive history of tool development for these various applications and then discuss the most recent noteworthy developments in more detail. Particular emphasis is placed on tools suitable for single-cell analysis and especially live-cell imaging applications. Finally, we discuss prominent areas of need in future fluorescent tool development—specifically, advancing our capability to analyze and integrate the plethora of high-content data generated by fluorescence imaging.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-physiol-022516-034055
2017-02-10
2024-10-06
Loading full text...

Full text loading...

/deliver/fulltext/physiol/79/1/annurev-physiol-022516-034055.html?itemId=/content/journals/10.1146/annurev-physiol-022516-034055&mimeType=html&fmt=ahah

Literature Cited

  1. Spence MTZ, Johnson ID. 1.  2010. Molecular Probes Handbook: A Guide to Fluorescent Probes and Labeling Technologies Carlsbad, CA: Life Tech. Corp, 11th ed.. [Google Scholar]
  2. Lavis LD, Raines RT. 2.  2008. Bright ideas for chemical biology. ACS Chem. Biol. 3:3142–55 [Google Scholar]
  3. Lavis LD, Raines RT. 3.  2014. Bright building blocks for chemical biology. ACS Chem. Biol. 9:4855–66 [Google Scholar]
  4. Wysocki LM, Lavis LD. 4.  2011. Advances in the chemistry of small molecule fluorescent probes. Curr. Opin. Chem. Biol. 15:6752–59 [Google Scholar]
  5. Grimm JB, English BP, Chen J, Slaughter JP, Zhang Z. 5.  et al. 2015. A general method to improve fluorophores for live-cell and single-molecule microscopy. Nat. Methods 12:3244–50 [Google Scholar]
  6. Lukinavičius G, Umezawa K, Olivier N, Honigmann A, Yang G. 6.  et al. 2013. A near-infrared fluorophore for live-cell super-resolution microscopy of cellular proteins. Nat. Chem. 5:2132–39 [Google Scholar]
  7. Lukinavičius G, Blaukopf C, Pershagen E, Schena A, Reymond L. 7.  et al. 2015. SiR-Hoechst is a far-red DNA stain for live-cell nanoscopy. Nat. Commun. 6:8497 [Google Scholar]
  8. Lukinavičius G, Reymond L, D'Este E, Masharina A, Göttfert F. 8.  et al. 2014. Fluorogenic probes for live-cell imaging of the cytoskeleton. Nat. Methods 11:7731–33 [Google Scholar]
  9. Grimm JB, Sung AJ, Legant WR, Hulamm P, Matlosz SM. 9.  et al. 2013. Carbofluoresceins and carborhodamines as scaffolds for high-contrast fluorogenic probes. ACS Chem. Biol. 8:61303–10 [Google Scholar]
  10. Giepmans BNG, Adams SR, Ellisman MH, Tsien RY. 10.  2006. The fluorescent toolbox for assessing protein location and function. Science 312:217–24 [Google Scholar]
  11. Snapp EL. 11.  2009. Fluorescent proteins: a cell biologist's user guide. Trends Cell Biol 19:11649–55 [Google Scholar]
  12. Chudakov DM, Matz MV, Lukyanov S, Lukyanov KA. 12.  2010. Fluorescent proteins and their applications in imaging living cells and tissues. Physiol. Rev. 90:31103–63 [Google Scholar]
  13. Shaner NC, Lambert GG, Chammas A, Ni Y, Cranfill PJ. 13.  et al. 2013. A bright monomeric green fluorescent protein derived from Branchiostoma lanceolatum. Nat. Methods 10:5407–9 [Google Scholar]
  14. Bajar BT, Wang ES, Lam AJ, Kim BB, Jacobs CL. 14.  et al. 2016. Improving brightness and photostability of green and red fluorescent proteins for live cell imaging and FRET reporting. Sci. Rep. 6:20889 [Google Scholar]
  15. Shemiakina II, Ermakova GV, Cranfill PJ, Baird MA, Evans RA. 15.  et al. 2012. A monomeric red fluorescent protein with low cytotoxicity. Nat. Commun. 3:1204 [Google Scholar]
  16. Hense A, Prunsche B, Gao P, Ishitsuka Y, Nienhaus K, Nienhaus GU. 16.  2015. Monomeric garnet, a far-red fluorescent protein for live-cell STED imaging. Sci. Rep. 5:18006 [Google Scholar]
  17. Costantini LM, Baloban M, Markwardt ML, Rizzo M, Guo F. 17.  et al. 2015. A palette of fluorescent proteins optimized for diverse cellular environments. Nat. Commun. 6:7670 [Google Scholar]
  18. Cranfill PJ, Sell BR, Baird MA, Allen JR, Lavagnino Z. 18.  et al. 2016. Quantitative assessment of fluorescent proteins. Nat. Methods 13:557–62 [Google Scholar]
  19. Stadler C, Rexhepaj E, Singan VR, Murphy RF, Pepperkok R. 19.  et al. 2013. Immunofluorescence and fluorescent-protein tagging show high correlation for protein localization in mammalian cells. Nat. Methods 10:4315–23 [Google Scholar]
  20. Giraldez T, Hughes TE, Sigworth FJ. 20.  2005. Generation of functional fluorescent BK channels by random insertion of GFP variants. J. Gen. Physiol. 126:5429–38 [Google Scholar]
  21. Chen X, Zaro JL, Shen W-C. 21.  2013. Fusion protein linkers: property, design and functionality. Adv. Drug Deliv. Rev. 65:101357–69 [Google Scholar]
  22. Costantini LM, Snapp EL. 22.  2013. Fluorescent proteins in cellular organelles: serious pitfalls and some solutions. DNA Cell Biol 32:11622–27 [Google Scholar]
  23. Ratz M, Testa I, Hell SW, Jakobs S. 23.  2015. CRISPR/Cas9-mediated endogenous protein tagging for RESOLFT super-resolution microscopy of living human cells. Sci. Rep. 5:9592 [Google Scholar]
  24. Stewart-Ornstein J, Lahav G. 24.  2016. Dynamics of CDKN1A in single cells defined by an endogenous fluorescent tagging toolkit. Cell Rep 14:71800–11 [Google Scholar]
  25. Cabantous S, Terwilliger TC, Waldo GS. 25.  2005. Protein tagging and detection with engineered self-assembling fragments of green fluorescent protein. Nat. Biotechnol. 23:1102–7 [Google Scholar]
  26. Kamiyama D, Sekine S, Barsi-Rhyne B, Hu J, Chen B. 26.  et al. 2016. Versatile protein tagging in cells with split fluorescent protein. Nat. Commun. 7:11046 [Google Scholar]
  27. Wang J, Xie J, Schultz PG. 27.  2006. A genetically encoded fluorescent amino acid. J. Am. Chem. Soc. 128:278738–39 [Google Scholar]
  28. Summerer D, Chen S, Wu N, Deiters A, Chin JW, Schultz PG. 28.  2006. A genetically encoded fluorescent amino acid. PNAS 103:269785–89 [Google Scholar]
  29. Lee HS, Guo J, Lemke EA, Dimla RD, Schultz PG. 29.  2009. Genetic incorporation of a small, environmentally sensitive, fluorescent probe into proteins in Saccharomyces cerevisiae. J. Am. Chem. Soc. 131:3612921–23 [Google Scholar]
  30. Kuhn SM, Rubini M, Müller MA, Skerra A. 30.  2011. Biosynthesis of a fluorescent protein with extreme pseudo-Stokes shift by introducing a genetically encoded non-natural amino acid outside the fluorophore. J. Am. Chem. Soc. 133:113708–11 [Google Scholar]
  31. Lang K, Chin JW. 31.  2014. Cellular incorporation of unnatural amino acids and bioorthogonal labeling of proteins. Chem. Rev. 114:94764–806 [Google Scholar]
  32. Charbon G, Brustad E, Scott KA, Wang J, Løbner-Olesen A. 32.  et al. 2011. Subcellular protein localization by using a genetically encoded fluorescent amino acid. ChemBioChem 12:121818–21 [Google Scholar]
  33. Abe R, Shiraga K, Ebisu S, Takagi H, Hohsaka T. 33.  2010. Incorporation of fluorescent non-natural amino acids into N-terminal tag of proteins in cell-free translation and its dependence on position and neighboring codons. J. Biosci. Bioeng. 110:132–38 [Google Scholar]
  34. Dean KM, Palmer AE. 34.  2014. Advances in fluorescence labeling strategies for dynamic cellular imaging. Nat. Chem. Biol. 10:7512–23 [Google Scholar]
  35. Cserép GB, Herner A, Kele P. 35.  2015. Bioorthogonal fluorescent labels: a review on combined forces. Methods Appl. Fluoresc. 3:4042001 [Google Scholar]
  36. Keppler A, Gendreizig S, Gronemeyer T, Pick H, Vogel H, Johnsson K. 36.  2003. A general method for the covalent labeling of fusion proteins with small molecules in vivo. Nat. Biotechnol. 21:186–89 [Google Scholar]
  37. Gautier A, Juillerat A, Heinis C, Corrêa IR, Kindermann M. 37.  et al. 2008. An engineered protein tag for multiprotein labeling in living cells. Chem. Biol. 15:2128–36 [Google Scholar]
  38. Los G V, Encell LP, McDougall MG, Hartzell DD, Karassina N. 38.  et al. 2008. Halotag: a novel protein labeling technology for cell imaging and protein analysis. ACS Chem. Biol. 3:6373–82 [Google Scholar]
  39. Miller LW, Cai Y, Sheetz MP, Cornish VW. 39.  2005. In vivo protein labeling with trimethoprim conjugates: a flexible chemical tag. Nat. Methods 2:4255–57 [Google Scholar]
  40. Sun X, Zhang A, Baker B, Sun L, Howard A. 40.  et al. 2011. Development of snap-tag fluorogenic probes for wash-free fluorescence imaging. ChemBioChem 12:142217–26 [Google Scholar]
  41. Adams SR, Tsien RY. 41.  2008. Preparation of the membrane-permeant biarsenicals FlAsH-EDT2 and ReAsH-EDT2 for fluorescent labeling of tetracysteine-tagged proteins. Nat. Protoc. 3:91527–34 [Google Scholar]
  42. Adams SR, Campbell RE, Gross LA, Martin BR, Walkup GK. 42.  et al. 2002. New biarsenical ligands and tetracysteine motifs for protein labeling in vitro and in vivo: synthesis and biological applications. J. Am. Chem. Soc. 124:216063–76 [Google Scholar]
  43. Halo TL, Appelbaum J, Hobert EM, Balkin DM, Schepartz A. 43.  2009. Selective recognition of protein tetraserine motifs with a cell-permeable, pro-fluorescent bis-boronic acid. J. Am. Chem. Soc. 131:2438–39 [Google Scholar]
  44. Howarth M, Takao K, Hayashi Y, Ting AY. 44.  2005. Targeting quantum dots to surface proteins in living cells with biotin ligase. PNAS 102:217583–88 [Google Scholar]
  45. Slavoff SA, Chen I, Choi Y-A, Ting AY. 45.  2008. Expanding the substrate tolerance of biotin ligase through exploration of enzymes from diverse species. J. Am. Chem. Soc. 130:41160–62 [Google Scholar]
  46. Baskin JM, Prescher JA, Laughlin ST, Agard NJ, Chang PV. 46.  et al. 2007. Copper-free click chemistry for dynamic in vivo imaging. PNAS 104:4316793–97 [Google Scholar]
  47. Uttamapinant C, White KA, Baruah H, Thompson S, Fernández-Suárez M. 47.  et al. 2010. A fluorophore ligase for site-specific protein labeling inside living cells. PNAS 107:2410914–19 [Google Scholar]
  48. Cohen JD, Thompson S, Ting AY. 48.  2011. Structure-guided engineering of a Pacific Blue fluorophore ligase for specific protein imaging in living cells. Biochemistry 50:388221–25 [Google Scholar]
  49. Meyer T, Muyldermans S, Depicker A. 49.  De 2014. Nanobody-based products as research and diagnostic tools. Trends Biotechnol 32:5263–70 [Google Scholar]
  50. Kaiser PD, Maier J, Traenkle B, Emele F, Rothbauer U. 50.  2014. Recent progress in generating intracellular functional antibody fragments to target and trace cellular components in living cells. Biochim. Biophys. Acta 1844:111933–42 [Google Scholar]
  51. Kimura H, Hayashi-Takanaka Y, Stasevich TJ, Sato Y. 51.  2015. Visualizing posttranslational and epigenetic modifications of endogenous proteins in vivo. Histochem. Cell Biol. 144:2101–9 [Google Scholar]
  52. Gueorguieva D, Li S, Walsh N, Mukerji A, Tanha J, Pandey S. 52.  2006. Identification of single-domain, Bax-specific intrabodies that confer resistance to mammalian cells against oxidative-stress-induced apoptosis. FASEB J 20:142636–38 [Google Scholar]
  53. McNeil PL, Warder E. 53.  1987. Glass beads load macromolecules into living cells. J. Cell Sci. 88:Pt. 5669–78 [Google Scholar]
  54. Hayashi-Takanaka Y, Yamagata K, Wakayama T, Stasevich TJ, Kainuma T. 54.  et al. 2011. Tracking epigenetic histone modifications in single cells using Fab-based live endogenous modification labeling. Nucleic Acids Res 39:156475–88 [Google Scholar]
  55. Sato Y, Mukai M, Ueda J, Muraki M, Stasevich TJ. 55.  et al. 2013. Genetically encoded system to track histone modification in vivo. Sci. Rep. 3:2436 [Google Scholar]
  56. Hayashi-Takanaka Y, Stasevich TJ, Kurumizaka H, Nozaki N, Kimura H. 56.  2014. Evaluation of chemical fluorescent dyes as a protein conjugation partner for live cell imaging. PLOS ONE 9:9e106271 [Google Scholar]
  57. Szent-Gyorgyi C, Schmidt BF, Schmidt BA, Creeger Y, Fisher GW. 57.  et al. 2008. Fluorogen-activating single-chain antibodies for imaging cell surface proteins. Nat. Biotechnol. 26:2235–40 [Google Scholar]
  58. Ozhalici-Unal H, Pow CL, Marks SA, Jesper LD, Silva GL. 58.  et al. 2008. A rainbow of fluoromodules: a promiscuous scFv protein binds to and activates a diverse set of fluorogenic cyanine dyes. J. Am. Chem. Soc. 130:3812620–21 [Google Scholar]
  59. Yates BP, Peck MA, Berget PB. 59.  2013. Directed evolution of a fluorogen-activating single chain antibody for function and enhanced brightness in the cytoplasm. Mol. Biotechnol. 54:3829–41 [Google Scholar]
  60. Szent-Gyorgyi C, Schmidt BF, Fitzpatrick JAJ, Bruchez MP. 60.  2010. Fluorogenic dendrons with multiple donor chromophores as bright genetically targeted and activated probes. J. Am. Chem. Soc. 132:3211103–9 [Google Scholar]
  61. Tanenbaum ME, Gilbert LA, Qi LS, Weissman JS, Vale RD. 61.  2014. A protein-tagging system for signal amplification in gene expression and fluorescence imaging. Cell 159:3635–46 [Google Scholar]
  62. Viswanathan S, Williams ME, Bloss EB, Stasevich TJ, Speer CM. 62.  et al. 2015. High-performance probes for light and electron microscopy. Nat. Methods 12:6568–76 [Google Scholar]
  63. Huh W-K, Falvo JV, Gerke LC, Carroll AS, Howson RW. 63.  et al. 2003. Global analysis of protein localization in budding yeast. Nature 425:686–91 [Google Scholar]
  64. Simpson JC, Wellenreuther R, Poustka A, Pepperkok R, Wiemann S. 64.  2000. Systematic subcellular localization of novel proteins identified by large-scale cDNA sequencing. EMBO Rep 1:3287–92 [Google Scholar]
  65. Shcherbakova DM, Sengupta P, Lippincott-Schwartz J, Verkhusha VV. 65.  2014. Photocontrollable fluorescent proteins for superresolution imaging. Annu. Rev. Biophys. 43:303–29 [Google Scholar]
  66. van de Linde S, Sauer M. 66.  2014. How to switch a fluorophore: from undesired blinking to controlled photoswitching. Chem. Soc. Rev. 43:41076–87 [Google Scholar]
  67. Knop M, Edgar BA. 67.  2014. Tracking protein turnover and degradation by microscopy: photo-switchable versus time-encoded fluorescent proteins. Open Biol 4:4140002 [Google Scholar]
  68. Pletnev S, Subach FV, Dauter Z, Wlodawer A, Verkhusha VV. 68.  2010. Understanding blue-to-red conversion in monomeric fluorescent timers and hydrolytic degradation of their chromophores. J. Am. Chem. Soc. 132:72243–53 [Google Scholar]
  69. Khmelinskii A, Keller PJ, Bartosik A, Meurer M, Barry JD. 69.  et al. 2012. Tandem fluorescent protein timers for in vivo analysis of protein dynamics. Nat. Biotechnol. 30:7708–14 [Google Scholar]
  70. Khmelinskii A, Meurer M, Ho C-T, Besenbeck B, Füller J. 70.  et al. 2016. Incomplete proteasomal degradation of green fluorescent proteins in the context of tandem fluorescent protein timers. Mol. Biol. Cell 27:2360–70 [Google Scholar]
  71. Mattiazzi Usaj M, Styles EB, Verster AJ, Friesen H, Boone C, Andrews BJ. 71.  2016. High-content screening for quantitative cell biology. Trends Cell Biol 26:8598–611 [Google Scholar]
  72. Newman JRS, Ghaemmaghami S, Ihmels J, Breslow DK, Noble M. 72.  et al. 2006. Single-cell proteomic analysis of S. cerevisiae reveals the architecture of biological noise. Nature 441:840–46 [Google Scholar]
  73. Chong YT, Koh JLY, Friesen H, Duffy K, Cox MJ. 73.  et al. 2015. Yeast proteome dynamics from single cell imaging and automated analysis. Cell 161:61413–24 [Google Scholar]
  74. Mazumder A, Pesudo LQ, McRee S, Bathe M, Samson LD. 74.  2013. Genome-wide single-cell-level screen for protein abundance and localization changes in response to DNA damage in S. cerevisiae. Nucleic Acids Res 41:209310–24 [Google Scholar]
  75. Barteneva NS, Fasler-Kan E, Vorobjev IA. 75.  2012. Imaging flow cytometry: coping with heterogeneity in biological systems. J. Histochem. Cytochem. 60:10723–33 [Google Scholar]
  76. Krutzik PO, Clutter MR, Trejo A, Nolan GP. 76.  2011. Fluorescent cell barcoding for multiplex flow cytometry. Curr. Protoc. Cytom. 6:Unit 6.31 [Google Scholar]
  77. Torres NP, Ho B, Brown GW. 77.  2016. High-throughput fluorescence microscopic analysis of protein abundance and localization in budding yeast. Crit. Rev. Biochem. Mol. Biol. 51:2110–19 [Google Scholar]
  78. Wachsmuth M, Conrad C, Bulkescher J, Koch B, Mahen R. 78.  et al. 2015. High-throughput fluorescence correlation spectroscopy enables analysis of proteome dynamics in living cells. Nat. Biotechnol. 33:4384–89 [Google Scholar]
  79. Snijder B, Sacher R, Rämö P, Damm E-M, Liberali P, Pelkmans L. 79.  2009. Population context determines cell-to-cell variability in endocytosis and virus infection. Nature 461:520–23 [Google Scholar]
  80. Stracy M, Uphoff S, Garza de Leon F, Kapanidis AN. 80.  2014. In vivo single-molecule imaging of bacterial DNA replication, transcription, and repair. FEBS Lett 588:193585–94 [Google Scholar]
  81. Chao JA, Yoon YJ, Singer RH. 81.  2012. Imaging translation in single cells using fluorescent microscopy. Cold Spring Harb. Perspect. Biol. 4:111–12 [Google Scholar]
  82. Uphoff S, Kapanidis AN. 82.  2014. Studying the organization of DNA repair by single-cell and single-molecule imaging. DNA Repair 20:32–40 [Google Scholar]
  83. Femino AM. 83.  1998. Visualization of single RNA transcripts in situ. Science 280:585–90 [Google Scholar]
  84. Raj A, van den Bogaard P, Rifkin SA, van Oudenaarden A, Tyagi S. 84.  2008. Imaging individual mRNA molecules using multiple singly labeled probes. Nat. Methods 5:10877–79 [Google Scholar]
  85. Xiao L, Guo J. 85.  2015. Multiplexed single-cell in situ RNA analysis by reiterative hybridization. Anal. Methods 7:177290–95 [Google Scholar]
  86. Chen KH, Boettiger AN, Moffitt JR, Wang S, Zhuang X. 86.  2015. Spatially resolved, highly multiplexed RNA profiling in single cells. Science 348:aaa6090 [Google Scholar]
  87. Nelles DA, Fang MY, O'Connell MR, Xu JL, Markmiller SJ. 87.  et al. 2016. Programmable RNA tracking in live cells with CRISPR/Cas9. Cell 165:2488–96 [Google Scholar]
  88. Tyagi S. 88.  2009. Imaging intracellular RNA distribution and dynamics in living cells. Nat. Methods 6:5331–38 [Google Scholar]
  89. Seferos DS, Giljohann DA, Hill HD, Prigodich AE, Mirkin CA. 89.  2007. Nano-flares: probes for transfection and mRNA detection in living cells. J. Am. Chem. Soc. 129:5015477–79 [Google Scholar]
  90. Babendure JR, Adams SR, Tsien RY. 90.  2003. Aptamers switch on fluorescence of triphenylmethane dyes. J. Am. Chem. Soc. 125:4814716–17 [Google Scholar]
  91. Constantin TP, Silva GL, Robertson KL, Hamilton TP, Fague K. 91.  et al. 2008. Synthesis of new fluorogenic cyanine dyes and incorporation into RNA fluoromodules. Org. Lett. 10:81561–64 [Google Scholar]
  92. Paige JS, Wu KY, Jaffrey SR. 92.  2011. RNA mimics of green fluorescent protein. Science 333:642–46 [Google Scholar]
  93. Filonov GS, Moon JD, Svensen N, Jaffrey SR. 93.  2014. Broccoli: rapid selection of an RNA mimic of green fluorescent protein by fluorescence-based selection and directed evolution. J. Am. Chem. Soc. 136:4616299–308 [Google Scholar]
  94. Autour A, Westhof E, Ryckelynck M. 94.  2016. ISpinach: a fluorogenic RNA aptamer optimized for in vitro applications. Nucleic Acids Res 44:6gkw083 [Google Scholar]
  95. You M, Jaffrey SR. 95.  2015. Structure and mechanism of RNA mimics of green fluorescent protein. Annu. Rev. Biophys. 44:187–206 [Google Scholar]
  96. Buxbaum AR, Haimovich G, Singer RH. 96.  2014. In the right place at the right time: visualizing and understanding mRNA localization. Nat. Rev. Mol. Cell Biol. 16:295–109 [Google Scholar]
  97. Garcia JF, Parker R. 97.  2015. MS2 coat proteins bound to yeast mRNAs block 5′ to 3′ degradation and trap mRNA decay products: implications for the localization of mRNAs by MS2-MCP system. RNA 21:81393–95 [Google Scholar]
  98. Wu B, Chen J, Singer RH. 98.  2014. Background free imaging of single mRNAs in live cells using split fluorescent proteins. Sci. Rep. 4:3615 [Google Scholar]
  99. Ozawa T, Natori Y, Sato M, Umezawa Y. 99.  2007. Imaging dynamics of endogenous mitochondrial RNA in single living cells. Nat. Methods 4:5413–19 [Google Scholar]
  100. O'Connell MR, Oakes BL, Sternberg SH, East-Seletsky A, Kaplan M, Doudna JA. 100.  2014. Programmable RNA recognition and cleavage by CRISPR/Cas9. Nature 516:7530263–66 [Google Scholar]
  101. Chen B, Gilbert LA, Cimini BA, Schnitzbauer J, Zhang W. 101.  et al. 2013. Dynamic imaging of genomic loci in living human cells by an optimized CRISPR/Cas system. Cell 155:71479–91 [Google Scholar]
  102. Collins AR. 102.  2004. The comet assay for DNA damage and repair: principles, applications, and limitations. Mol. Biotechnol. 26:3249–61 [Google Scholar]
  103. Glei M, Hovhannisyan G, Pool-Zobel BL. 103.  2009. Use of Comet-FISH in the study of DNA damage and repair: Review. Mutat. Res. 681:133–43 [Google Scholar]
  104. Condie AG, Yan Y, Gerson SL, Wang Y. 104.  2015. A fluorescent probe to measure DNA damage and repair. PLOS ONE 10:8e0131330 [Google Scholar]
  105. Toga T, Kuraoka I, Watanabe S, Nakano E, Takeuchi S. 105.  et al. 2014. Fluorescence detection of cellular nucleotide excision repair of damaged DNA. Sci. Rep. 4:5578 [Google Scholar]
  106. Tkach JM, Yimit A, Lee AY, Riffle M, Costanzo M. 106.  et al. 2012. Dissecting DNA damage response pathways by analysing protein localization and abundance changes during DNA replication stress. Nat. Cell Biol. 14:9966–76 [Google Scholar]
  107. Chan J, Dodani SC, Chang CJ. 107.  2012. Reaction-based small-molecule fluorescent probes for chemoselective bioimaging. Nat. Chem. 4:12973–84 [Google Scholar]
  108. Purvis JE, Lahav G. 108.  2013. Encoding and decoding cellular information through signaling dynamics. Cell 152:5945–56 [Google Scholar]
  109. Newman RH, Fosbrink MD, Zhang J. 109.  2011. Genetically encodable fluorescent biosensors for tracking signaling dynamics in living cells. Chem. Rev. 111:53614–66 [Google Scholar]
  110. Eggeling L, Bott M, Marienhagen J. 110.  2015. Novel screening methods–biosensors. Curr. Opin. Biotechnol. 35:30–36 [Google Scholar]
  111. Carter KP, Young AM, Palmer AE. 111.  2014. Fluorescent sensors for measuring metal ions in living systems. Chem. Rev. 114:84564–601 [Google Scholar]
  112. Strack RL, Jaffrey SR. 112.  2013. New approaches for sensing metabolites and proteins in live cells using RNA. Curr. Opin. Chem. Biol. 17:4651–55 [Google Scholar]
  113. Rose T, Goltstein PM, Portugues R, Griesbeck O. 113.  2014. Putting a finishing touch on GECIs. Front. Mol. Neurosci. 7:88 [Google Scholar]
  114. Lock JT, Parker I, Smith IF. 114.  2015. A comparison of fluorescent Ca2+ indicators for imaging local Ca2+ signals in cultured cells. Cell Calcium 58:6638–48 [Google Scholar]
  115. Broussard GJ, Liang R, Tian L. 115.  2014. Monitoring activity in neural circuits with genetically encoded indicators. Front. Mol. Neurosci. 7:97 [Google Scholar]
  116. Palmer AE, Qin Y, Park JG, McCombs JE. 116.  2011. Design and application of genetically encoded biosensors. Trends Biotechnol 29:3144–52 [Google Scholar]
  117. Akerboom J, Rivera JDV, Guilbe MMR, Malavé ECA, Hernandez HH. 117.  et al. 2009. Crystal structures of the GCaMP calcium sensor reveal the mechanism of fluorescence signal change and aid rational design. J. Biol. Chem. 284:106455–64 [Google Scholar]
  118. Wang Q, Shui B, Kotlikoff MI, Sondermann H. 118.  2008. Structural basis for calcium sensing by GCaMP2. Structure 16:121817–27 [Google Scholar]
  119. Wardill TJ, Chen T-W, Schreiter ER, Hasseman JP, Tsegaye G. 119.  et al. 2013. A neuron-based screening platform for optimizing genetically-encoded calcium indicators. PLOS ONE 8:10e77728 [Google Scholar]
  120. Chen T-W, Wardill TJ, Sun Y, Pulver SR, Renninger SL. 120.  et al. 2013. Ultrasensitive fluorescent proteins for imaging neuronal activity. Nature 499:295–300 [Google Scholar]
  121. Thestrup T, Litzlbauer J, Bartholomäus I, Mues M, Russo L. 121.  et al. 2014. Optimized ratiometric calcium sensors for functional in vivo imaging of neurons and T lymphocytes. Nat. Methods 11:2175–82 [Google Scholar]
  122. Dana H, Mohar B, Sun Y, Narayan S, Gordus A. 122.  et al. 2016. Sensitive red protein calcium indicators for imaging neural activity. eLife 5:e12727 [Google Scholar]
  123. Lohse MJ, Nuber S, Hoffmann C. 123.  2012. Fluorescence/bioluminescence resonance energy transfer techniques to study G-protein-coupled receptor activation and signaling. Pharmacol. Rev. 64:2299–336 [Google Scholar]
  124. Sridharan R, Zuber J, Connelly SM, Mathew E, Dumont ME. 124.  2014. Fluorescent approaches for understanding interactions of ligands with G protein coupled receptors. Biochim. Biophys. Acta. 1838:1 Pt. A15–33 [Google Scholar]
  125. Prével C, Pellerano M, Van TNN, Morris MC. 125.  2014. Fluorescent biosensors for high throughput screening of protein kinase inhibitors. Biotechnol. J. 9:2253–65 [Google Scholar]
  126. Regot S, Hughey JJ, Bajar BT, Carrasco S, Covert MW. 126.  2014. High-sensitivity measurements of multiple kinase activities in live single cells. Cell 157:71724–34 [Google Scholar]
  127. Sakabe M, Asanuma D, Kamiya M, Iwatate RJ, Hanaoka K. 127.  et al. 2013. Rational design of highly sensitive fluorescence probes for protease and glycosidase based on precisely controlled spirocyclization. J. Am. Chem. Soc. 135:1409–14 [Google Scholar]
  128. Sanman LE, Bogyo M. 128.  2014. Activity-based profiling of proteases. Annu. Rev. Biochem. 83:249–73 [Google Scholar]
  129. Hertel F, Zhang J. 129.  2014. Monitoring of post-translational modification dynamics with genetically encoded fluorescent reporters. Biopolymers 101:2180–87 [Google Scholar]
  130. Peterka DS, Takahashi H, Yuste R. 130.  2011. Imaging voltage in neurons. Neuron 69:19–21 [Google Scholar]
  131. Canepari M, Zecevic D, Bernus O. 131.  2015. Membrane Potential Imaging in the Nervous System and Heart New York: Springer [Google Scholar]
  132. Jercog P, Rogerson T, Schnitzer MJ. 132.  2016. Large-scale fluorescence calcium-imaging methods for studies of long-term memory in behaving mammals. Cold Spring Harb. Perspect. Biol. 8:a021824 [Google Scholar]
  133. St-Pierre F, Chavarha M, Lin MZ. 133.  2015. Designs and sensing mechanisms of genetically encoded fluorescent voltage indicators. Curr. Opin. Chem. Biol. 27:31–38 [Google Scholar]
  134. Gong Y. 134.  2015. The evolving capabilities of rhodopsin-based genetically encoded voltage indicators. Curr. Opin. Chem. Biol. 27:84–89 [Google Scholar]
  135. Jung A, Garcia JE, Kim E, Yoon B-J, Baker BJ. 135.  2015. Linker length and fusion site composition improve the optical signal of genetically encoded fluorescent voltage sensors. Neurophotonics 2:2021012 [Google Scholar]
  136. Shcherbakova DM, Shemetov AA, Kaberniuk AA, Verkhusha VV. 136.  2015. Natural photoreceptors as a source of fluorescent proteins, biosensors, and optogenetic tools. Annu. Rev. Biochem. 84:519–50 [Google Scholar]
  137. Sakaue-Sawano A, Kurokawa H, Morimura T, Hanyu A, Hama H. 137.  et al. 2008. Visualizing spatiotemporal dynamics of multicellular cell-cycle progression. Cell 132:3487–98 [Google Scholar]
  138. Spencer SL, Cappell SD, Tsai F-C, Overton KW, Wang CL, Meyer T. 138.  2013. The proliferation-quiescence decision is controlled by a bifurcation in CDK2 activity at mitotic exit. Cell 155:2369–83 [Google Scholar]
  139. Kaur A, Kolanowski JL, New EJ. 139.  2016. Reversible fluorescent probes for biological redox states. Angew. Chem. 55:51602–13 [Google Scholar]
  140. Winterbourn CC. 140.  2014. The challenges of using fluorescent probes to detect and quantify specific reactive oxygen species in living cells. Biochim. Biophys. Acta 1840:2730–38 [Google Scholar]
  141. Pouvreau S. 141.  2014. Genetically encoded reactive oxygen species (ROS) and redox indicators. Biotechnol. J. 9:2282–93 [Google Scholar]
  142. Lukyanov KA, Belousov V V. 142.  2014. Genetically encoded fluorescent redox sensors. Biochim. Biophys. Acta 1840:2745–56 [Google Scholar]
  143. Zhao Y, Yang Y. 143.  2015. Profiling metabolic states with genetically encoded fluorescent biosensors for NADH. Curr. Opin. Biotechnol. 31:86–92 [Google Scholar]
  144. Zhao Y, Hu Q, Cheng F, Su N, Wang A. 144.  et al. 2015. Sonar, a highly responsive NAD+/NADH sensor, allows high-throughput metabolic screening of anti-tumor agents. Cell Metab 21:5777–89 [Google Scholar]
  145. Morikawa TJ, Fujita H, Kitamura A, Horio T, Yamamoto J. 145.  et al. 2016. Dependence of fluorescent protein brightness on protein concentration in solution and enhancement of it. Sci. Rep. 6:22342 [Google Scholar]
  146. Altschuler SJ, Wu LF. 146.  2010. Cellular heterogeneity: do differences make a difference?. Cell 141:4559–63 [Google Scholar]
  147. Kang J, Hsu C-H, Wu Q, Liu S, Coster AD. 147.  et al. 2016. Improving drug discovery with high-content phenotypic screens by systematic selection of reporter cell lines. Nat. Biotechnol. 34:170–77 [Google Scholar]
  148. Davis DM, Purvis JE. 148.  2015. Computational analysis of signaling patterns in single cells. Semin. Cell Dev. Biol. 37:35–43 [Google Scholar]
  149. Okada H, Ohnuki S, Roncero C, Konopka JB, Ohya Y. 149.  2014. Distinct roles of cell wall biogenesis in yeast morphogenesis as revealed by multivariate analysis of high-dimensional morphometric data. Mol. Biol. Cell 25:2222–33 [Google Scholar]
  150. Snijder B, Sacher R, Rämö P, Liberali P, Mench K. 150.  et al. 2012. Single-cell analysis of population context advances RNAi screening at multiple levels. Mol. Syst. Biol. 8:579 [Google Scholar]
  151. Kimura A, Celani A, Nagao H, Stasevich T, Nakamura K. 151.  2015. Estimating cellular parameters through optimization procedures: elementary principles and applications. Front. Physiol. 6:60 [Google Scholar]
  152. Chenouard N, Smal I, de Chaumont F, Maška M, Sbalzarini IF. 152.  et al. 2014. Objective comparison of particle tracking methods. Nat. Methods 11:3281–89 [Google Scholar]
  153. Wiesmann V, Franz D, Held C, Münzenmayer C, Palmisano R, Wittenberg T. 153.  2015. Review of free software tools for image analysis of fluorescence cell micrographs. J. Microsc. 257:139–53 [Google Scholar]
  154. Cohen AR. 154.  2014. Extracting meaning from biological imaging data. Mol. Biol. Cell 25:223470–73 [Google Scholar]
  155. Winter MR, Liu M, Monteleone D, Melunis J, Hershberg U. 155.  et al. 2015. Computational image analysis reveals intrinsic multigenerational differences between anterior and posterior cerebral cortex neural progenitor cells. Stem Cell Rep 5:4609–20 [Google Scholar]
  156. Horvath P, Wild T, Kutay U, Csucs G. 156.  2011. Machine learning improves the precision and robustness of high-content screens: using nonlinear multiparametric methods to analyze screening results. J. Biomol. Screen. 16:91059–67 [Google Scholar]
  157. Sommer C, Gerlich DW. 157.  2013. Machine learning in cell biology-teaching computers to recognize phenotypes. J. Cell Sci. 126:245529–39 [Google Scholar]
  158. Smith K, Horvath P. 158.  2014. Active learning strategies for phenotypic profiling of high-content screens. J. Biomol. Screen. 19:5685–95 [Google Scholar]
  159. Abraham Y, Zhang X, Parker CN. 159.  2014. Multiparametric analysis of screening data: growing beyond the single dimension to infinity and beyond. J. Biomol. Screen. 19:5628–39 [Google Scholar]
  160. Wait E, Winter M, Bjornsson C, Kokovay E, Wang Y. 160.  et al. 2014. Visualization and correction of automated segmentation, tracking and lineaging from 5-D stem cell image sequences. BMC Bioinform. 15:1328 [Google Scholar]
/content/journals/10.1146/annurev-physiol-022516-034055
Loading
/content/journals/10.1146/annurev-physiol-022516-034055
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