Concentration and Brightness Imaging for Fluorescent Molecules in Cells: Statistical Image Analysis by Empirical Bayes Method

Chapter 1

1. Youker, R.T., and H. Teng. 2014. Measuring protein dynamics in live cells: protocols and practical considerations for fluorescence fluctuation microscopy. J. Biomed. Opt. 19:090801.

2. Bag, N., and T. Wohland. 2014. Imaging Fluorescence Fluctuation Spectroscopy: New Tools for Quantitative Bioimaging. Annu. Rev. Phys. Chem. 65:225–248.

3. Migueles-Ramirez, R.A., A.G. Velasco-Feliz, R. Pinto-Camara, C.D. Wood, and A. Guerrero. 2017. Fluorescence fluctuation spectroscopy in living cells. Microsc. imaging Sci. Pract. approaches to Appl. Res. Educ. 138–151.

4. Elson, E.L., and D. Magde. 1974. Fluorescence correlation spectroscopy. I. Conceptual basis and theory. Biopolymers. 13:1–27.

5. Digman, M.A., R. Dalal, A.F. Horwitz, and E. Gratton. 2008. Mapping the Number of Molecules and Brightness in the Laser Scanning Microscope. Biophys. J. 94:2320–2332.

6. Oakley, R.H., and J.A. Cidlowski. 2013. The biology of the glucocorticoid receptor: New signaling mechanisms in health and disease. J. Allergy Clin. Immunol. 132:1033–1044.

7. Robertson, S., J.P. Hapgood, and A. Louw. 2013. Glucocorticoid receptor concentration and the ability to dimerize influence nuclear translocation and distribution. Steroids. 78:182–194.

8. Nicolaides, N.C., Z. Galata, T. Kino, G.P. Chrousos, and E. Charmandari. 2010. The human glucocorticoid receptor: molecular basis of biologic function. Steroids. 75:1–12.

Chapter 2

1. Qian, H., and E.L. Elson. 1990. On the analysis of high order moments of fluorescence fluctuations. Biophys. J. 57:375–380.

2. Digman, M.A., R. Dalal, A.F. Horwitz, and E. Gratton. 2008. Mapping the Number of Molecules and Brightness in the Laser Scanning Microscope. Biophys. J. 94:2320–2332.

3. Hendrix, J., W. Schrimpf, M. Höller, and D.C. Lamb. 2013. Pulsed Interleaved Excitation Fluctuation Imaging. Biophys. J. 105:848–861.

4. Hillesheim, L.N., and J.D. Müller. 2003. The Photon Counting Histogram in Fluorescence Fluctuation Spectroscopy with Non-Ideal Photodetectors. Biophys. J. 85:1948–1958.

Chapter 3

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Abdollah-Nia, F., Gelfand, M.P., Van Orden, A. (2017b) Artifact-Free and Detection-Profile-Independent Higher-Order Fluorescence Correlation Spectroscopy for Microsecond-Resolved Kinetics. 2.Mixtures and Reactions. J. Phys. Chem. B 121:2388–2399. https://doi.org/10.1021/acs.jpcb.7b00408

Bag, N., Wohland, T. (2014) Imaging Fluorescence Fluctuation Spectroscopy: New Tools for Quantitative Bioimaging. Annu. Rev. Phys. Chem. 65:225–248. https://doi.org/10.1146/annurev- physchem-040513-103641

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Elson, E.L., Magde, D. (1974) Fluorescence correlation spectroscopy. I. Conceptual basis and theory.Biopolymers 13:1–27. https://doi.org/10.1002/bip.1974.360130102

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Fukushima, R., Yamamoto, J., Ishikawa, H., Kinjo, M. (2018) Two-detector number and brightness analysis reveals spatio-temporal oligomerization of proteins in living cells. Methods 140–141:161–171. https://doi.org/10.1016/j.ymeth.2018.03.007

Gambin, Y., Polinkovsky, M., Francois, B., Giles, N., Bhumkar, A., Sierecki, E. (2016) Confocal Spectroscopy to Study Dimerization, Oligomerization and Aggregation of Proteins: A Practical Guide. Int. J. Mol. Sci. 17:655. https://doi.org/10.3390/ijms17050655

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Hennen, J., Hur, K.-H., Saunders, C.A., Luxton, G.W.G., Mueller, J.D. (2017) Quantitative Brightness Analysis of Protein Oligomerization in the Nuclear Envelope. Biophys. J. 113:138–147. https://doi.org/10.1016/j.bpj.2017.05.044

Hillesheim, L.N., Müller, J.D. (2003) The Photon Counting Histogram in Fluorescence Fluctuation Spectroscopy with Non-Ideal Photodetectors. Biophys. J. 85:1948–1958. https://doi.org/10.1016/S0006-3495(03)74622-0

Huet, S., Avilov, S. V., Ferbitz, L., Daigle, N., Cusack, S., Ellenberg, J. (2010) Nuclear Import and Assembly of Influenza A Virus RNA Polymerase Studied in Live Cells by Fluorescence Cross- Correlation Spectroscopy. J. Virol. 84:1254–1264. https://doi.org/10.1128/JVI.01533-09

Hur, K.-H., Macdonald, P.J., Berk, S., Angert, C.I., Chen, Y., Mueller, J.D. (2014) Quantitative Measurement of Brightness from Living Cells in the Presence of Photodepletion. PLoS One 9:e97440. https://doi.org/10.1371/journal.pone.0097440

Hur, K.-H., Mueller, J.D. (2015) Quantitative Brightness Analysis of Fluorescence Intensity Fluctuations in E. Coli. PLoS One 10:e0130063. https://doi.org/10.1371/journal.pone.0130063

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Chapter 4

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Chapter 5

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46. Culbertson, C.T., S.C. Jacobson, and J. Michael Ramsey. 2002. Diffusion coefficient measurements in microfluidic devices. Talanta. 56:365–73.

47. Amrhein, V., S. Greenland, and B. McShane. 2019. Scientists rise up against statistical significance. Nature. 567:305–307.

48. Johnson, D.H. 1999. The Insignificance of Statistical Significance Testing. J. Wildl. Manage.63:763.

49. Lemoine, N.P. 2019. Moving beyond noninformative priors: why and how to choose weakly informative priors in Bayesian analyses. Oikos. 128:912–928.

50. Gelman, A. 2006. Prior distributions for variance parameters in hierarchical models (comment on article by Browne and Draper). Bayesian Anal. 1:515–534.

51. Polson, N.G., and J.G. Scott. 2012. On the Half-Cauchy Prior for a Global Scale Parameter.Bayesian Anal. 7:887–902.

52. R Core Team. 2020. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.r-project.org/.

53. Stan Development Team. 2020. RStan: the R interface to Stan. http://mc-stan.org/.

SUPPORTING REFERENCES

1. Bédard, G. 1967. Dead-time corrections to the statistical distribution of photoelectrons. Proc. Phys. Soc. 90.

2. Ackermann, J., and H. Hogreve. 2010. Small dead-time expansion in counting distributions and moments. Nucl. Instruments Methods Phys. Res. Sect. A Accel. Spectrometers, Detect. Assoc. Equip. 614:297–302.

3. Hillesheim, L.N., and J.D. Müller. 2003. The Photon Counting Histogram in Fluorescence Fluctuation Spectroscopy with Non-Ideal Photodetectors. Biophys. J. 85:1948–1958.

4. Hillesheim, L.N., and J.D. Müller. 2005. The dual-color photon counting histogram with non- ideal photodetectors. Biophys. J. 89:3491–3507.

5. Bishop, C.M. 2006. Pattern Recognition and Machine Learning. Springer, Germany.

6. Fukushima, R., J. Yamamoto, H. Ishikawa, and M. Kinjo. 2018. Two-detector number and brightness analysis reveals spatio-temporal oligomerization of proteins in living cells. Methods. 140–141:161–171.

7. Oura, M., J. Yamamoto, H. Ishikawa, S. Mikuni, R. Fukushima, and M. Kinjo. 2016. Polarization-dependent fluorescence correlation spectroscopy for studying structural properties of proteins in living cell. Sci. Rep. 6:31091.

8. Pack, C., K. Saito, M. Tamura, and M. Kinjo. 2006. Microenvironment and Effect of Energy Depletion in the Nucleus Analyzed by Mobility of Multiple Oligomeric EGFPs. Biophys. J. 91:3921–3936.

9. Takahasi, H., and M. Mori. 1973. Double exponential formulas for numerical integration. Publ. Res. Inst. Math. Sci. 9:721–741.

10. Müller, C.B., A. Loman, V. Pacheco, F. Koberling, D. Willbold, W. Richtering, and J. Enderlein. 2008. Precise measurement of diffusion by multi-color dual-focus fluorescence correlation spectroscopy. Europhys. Lett. 83:46001.

11. Culbertson, C.T., S.C. Jacobson, and J. Michael Ramsey. 2002. Diffusion coefficient measurements in microfluidic devices. Talanta. 56:365–73.

12. Amrhein, V., S. Greenland, and B. McShane. 2019. Scientists rise up against statistical significance. Nature. 567:305–307.

13. Johnson, D.H. 1999. The Insignificance of Statistical Significance Testing. J. Wildl. Manage.63:763.

14. Lemoine, N.P. 2019. Moving beyond noninformative priors: why and how to choose weakly informative priors in Bayesian analyses. Oikos. 128:912–928.

15. Gelman, A. 2006. Prior distributions for variance parameters in hierarchical models (comment on article by Browne and Draper). Bayesian Anal. 1:515–534.

16. Polson, N.G., and J.G. Scott. 2012. On the Half-Cauchy Prior for a Global Scale Parameter.Bayesian Anal. 7:887–902.

17. Gelman, A., J. Carlin, H. Stern, and D. Rubin. 2004. Bayesian data analysis. 3rd ed. Chapman & Hall/CRC, UK.

18. R Core Team. 2020. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.r-project.org/.

19. Stan Development Team. 2020. RStan: the R interface to Stan. http://mc-stan.org/.