Bayesian localization microscopy based on intensity distribution of fluorophores

Abstract

Super-resolution microscopy techniques have overcome the limit of optical diffraction. Recently, the Bayesian analysis of Bleaching and Blinking data (3B) method has emerged as an important tool to obtain super-resolution fluorescence images. 3B uses the change in information caused by adding or removing fluorophores in the cell to fit the data. When adding a new fluorophore, 3B selects a random initial position, optimizes this position and then determines its reliability. However, the fluorophores are not evenly distributed in the entire image region, and the fluorescence intensity at a given position positively correlates with the probability of observing a fluorophore at this position. In this paper, we present a Bayesian analysis of Bleaching and Blinking microscopy method based on fluorescence intensity distribution (FID3B). We utilize the intensity distribution to select more reliable positions as the initial positions of fluorophores. This approach can improve the reconstruction results and significantly reduce the computational time. We validate the performance of our method using both simulated data and experimental data from cellular structures. The results confirm the effectiveness of our method.

Keywords

super-resolution / fluorescence image / 3B / intensity distribution

References

[1] 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. Science313: 1642-1645

[2] Cox S, Rosten E, Monypenny J, Jovanovic-Talisman T, Burnette DT, Lippincott-Schwartz J, Jones GE, Heintzmann R (2012) Bayesian localization microscopy reveals nanoscale podosome dynamics. Nat Methods9: 195-200

[3] Daostorm SS (2011) DAOSTORM: an algorithm for high-density super-resolution microscopy. Nat methods8: 279

[4] Deschout H, Cella Zanacchi F, Mlodzianoski M, Diaspro A, Bewersdorf J, Hess ST, Braeckmans K (2014) Precisely and accurately localizing single emitters in fluorescence microscopy. Nat Methods11: 253-266

[5] Ghahramani Z, Jordan MI (1997) Factorial hidden Markov models. Mach Learn29: 245-273

[6] Hell SW (2007) Far-fleld optical nanoscopy. Science316: 1153-1158

[7] Hess ST, Girirajan TP, Mason MD (2006) Ultra-high resolution imaging by fluorescence photoactivation localization microscopy. Biophys J91: 4258-4272

[8] Huang F, Schwartz SL, Byars JM, Lidke KA (2011) Simultaneous multiple-emitter fltting for single molecule super-resolution imaging. Biomed Opt Express2: 1377-1393

[9] Lidke KA (2012) Super resolution for common probes and common microscopes. Nat Methods9(139): 141

[10] Lippincott-Schwartz J, Manley S (2008) Putting super-resolution fluorescence microscopy to work. Nat Methods6: 21-23

[11] MacKay DJ (2003) Information theory, inference, and learning algorithms, vol 7 (Citeseer). Cambridge university press, Cambridge

[12] Mukamel EA, Babcock H, Zhuang X (2012) Statistical deconvolution for superresolution fluorescence microscopy. Biophys J102: 2391-2400

[13] Quan T, Zhu H, Liu X, Liu Y, Ding J, Zeng S, Huang Z-L (2011) Highdensity localization of active molecules using structured sparse model and Bayesian information criterion. Opt Express19: 16963-16974

[14] Ram S, Ward ES, Ober RJ (2006) Beyond Rayleigh’s criterion: a resolution measure with application to single-molecule microscopy. Proc Natl Acad Sci USA103: 4457-4462

[15] Rust MJ, Bates M, Zhuang X (2006) Sub-diffraction-limit imaging by stochastic optical reconstruction microscopy (STORM). Nat Methods3: 793-795

[16] Small AR (2009) Theoretical limits on errors and acquisition rates in localizing switchable fluorophores. Biophys J96: L16-L18

[17] Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process13: 600-612

[18] Wolter S, Endesfelder U, van de Linde S, Heilemann M, Sauer M (2011) Measuring localization performance of superresolution algorithms on very active samples. Opt Express19: 7020-7033

RIGHTS & PERMISSIONS

2014 This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.