Progress on Single-Molecule Localization Algorithms for Super-Resolution Imaging
Lin Wanni, Jin Luhong, Xu Yingke*
(Department of Biomedical Engineering, Key Laboratory for Biomedical Engineering of Ministry of Education,Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal,Zhejiang University, Hangzhou 310027, China)
Abstract:The development of super-resolution microscopy has provided an unprecedented opportunity for biomedical research. The single-molecule localization based super-resolution imaging has been widely used in biomedical field due to its relatively simple hardware structure. Single-molecule localization microscopy utilizes the characteristics of fluorescence molecules to randomly activate and image a small number of discrete distributed fluorophores, and then achieves high-precision spatial localization of single molecules by fitting analysis. In this process, the algorithms for single-molecule localization and the speed of image processing are particularly critical. This review briefly elaborated the single-molecule localization algorithms according to their basic principles, including model selection, estimator selection and reconstruction result evaluation methods. Based on that, we classified and compared the capabilities of more than ten developed algorithms, and also selected a few representative algorithms to discuss and introduce their functions. We hope this review would provide reference for future related research.
林婉妮, 金璐红, 许迎科. 超分辨显微成像中荧光单分子定位算法的研究进展[J]. 中国生物医学工程学报, 2020, 39(2): 229-237.
Lin Wanni, Jin Luhong, Xu Yingke. Progress on Single-Molecule Localization Algorithms for Super-Resolution Imaging. Chinese Journal of Biomedical Engineering, 2020, 39(2): 229-237.
[1] Abbe E. Beitrge zur theorie des mikroskops undder mikroskopischen wahrnehmung [J]. Archiv für Mikroskopische Anatomie, 1873, 9(1): 413-468.
[2] Hell SW, Wichmann J. Breaking the diffraction resolution limit by stimulated-emission: stimulated emission-depletion fluorescence microscopy [J]. Optics Letters, 1994, 19(11): 780-782.
[3] Hell SW, Kroug M. Ground-state depletion fluorescence microscopy, a concept for breaking the diffraction resolution limit [J]. Applied Physics B: Lasers and Optics, 1995, 60(5): 495-497.
[4] Gustafsson MGL. Nonlinear structured-illumination microscopy: Wide-field fluorescence imaging with theoretically unlimited resolution [J]. Proceedings of the National Academy of Sciences of the United States of America, 2005, 102(37): 13081-13086.
[5] Betzig E, Patterson GH, Sougrat R, et al. Imaging intracellular fluorescent proteins at nanometer resolution [J]. Science, 2006, 313(5793): 1642-1645.
[6] Rust MJ, Bates M, Zhuang Xiaowei. Sub-diffraction-limit imaging by stochastic optical reconstruction microscopy (STORM) [J]. Nature Methods, 2006, 3(10):793-795.
[7] Nathan CS, Paul AS, Roger YT. A guide to choosing fluorescent proteins [J]. Nature Methods, 2005, 2(12): 905-909.
[8] Graham TD, Joshua CV. Evaluation of fluorophores for optimal performance in Localization-based super-resolution imaging [J]. Nature Methods, 2012, 8(12): 1027-1036.
[9] Colin EA, Marshall RA, Joseph DP. An oxygen scavenging system for improvement of dye stability in single-molecule fluorescence experiments [J]. Biophysical Journal, 2008, 94(5): 1826-1835.
[10] Richards B, Wolf E. Electromagnetic diffraction in optical systems II. Structure of the image field in an aplanatic system [J]. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 1959, 253(1274): 358-379.
[11] Frisken GS, Lanni F. Experimental test of an analytical model of aberration in an oil-immersion objective lens used in three-dimensional light microscopy [J]. Journal of the Optical Society of America, 1992, 9(1): 154-166.
[12] Abraham AV, Ram S, Chao J, et al. Quantitative study of single molecule location estimation techniques [J]. Optics Express, 2009, 17(26): 23352-23373.
[13] Kirshner H, Vonesch C, Unser M. Can localization microscopy benefit from approximation theory [C] //2013 IEEE 10th International Symposium on Biomedical Imaging. San Francisco: IEEE, 2013: 588-591.
[14] Stetson PB. DAOPHOT: A computer program for crowded-field stellar photometry [J]. Publications of the Astronomical Society of the Pacific, 1987, 99(613): 191-222.
[15] Smith CS, Joseph N, Rieger B, et al. Fast, single-molecule localization that achieves theoretically minimum uncertainty [J]. Nature Methods, 2010, 7(5): 373-375.
[16] Deschout H, Zanacchi FC, Mlodzianoski M, et al. Precisely and accurately localizing single emitters in fluorescence microscopy [J]. Nature Methods, 2014, 11(3): 253-266.
[17] Gordon M, Ha T, Selvin P. Single-molecule high-resolution imaging with photobleaching [J]. Proceedings of the National Academy of Sciences of the United States of America, 2004, 101(17): 6462-6465.
[18] Niu Lili, Yu Ji. Investigating intracellular dynamics of FtsZ cytoskeleton with photoactivation single-molecule tracking [J]. Biophysical Journal, 2008, 95(4): 2009-2016.
[19] Andersson SB. Localization of a fluorescent source without numerical fitting [J]. Optics Express, 2008, 16(23): 18714-18724.
[20] Shtengel G, Galbraith JA, Galbraith CG, et al. Interferometric fluorescent super-resolution microscopy resolves 3D cellular ultrastructure [J]. Proceedings of the National Academy of Sciences of the United States of America, 2009, 106(9): 3125-3130.
[21] Quan Tingwei, Li Pengcheng, Long Fan, et al. Ultra-fast, high-precision image analysis for localization-based super resolution microscopy [J]. Optics Express, 2010, 18(11): 11867-11876.
[22] Holden SJ, Uphoff S, Kapanidis AN. DAOSTORM: An algorithm for high-density super-resolution microscopy [J]. Nature Methods, 2011, 8(4): 279-280.
[23] Brede N, Lakadamyali M. GraspJ: an open source, real-time analysis package for super-resolution imaging [J]. Optical Nanoscopy, 2012, 102(3): 724-731.
[24] Li Yiming, Ishitsuka Y, Hedde PN, et al. Fast and efficient molecule detection in localization-based super-resolution microscopy by parallel adaptive histogram equalization [J].ACS Nano, 2013, 7(6): 5207-5214.
[25] Ovesn M, Krizek P, Borkovec J, et al. ThunderSTORM: A comprehensive ImageJ plugin for PALM and STORM data analysis and super-resolution imaging [J]. Bioinformatics, 2014, 30(16): 2389-2390.
[26] Starr R, Stahlheber S, Small A. Fast maximum likelihood algorithm for localization of fluorescent molecules [J]. Optics Letters, 2012, 37(3): 413-415.
[27] Babcock H, Sigal Y, Zhuang Xiaowei. A high-density 3D localization algorithm for stochastic optical reconstruction microscopy [J]. Optical Nanoscopy, 2012, 1(6): 1186-1196.
[28] Anthony SM, Granick S. Image analysis with rapid and accurate two-dimensional Gaussian fitting [J]. Langmuir, 2009, 25(14): 8152-8160.
[29] Zhu Lei, Zhang Wei, Elnatan D, et al. Faster STORM using compressed sensing [J]. Nature Methods, 2012, 9(7): 721-723.
[30] Smith CS, Joseph N, Rieger B, et al. Fast, single-molecule localization that achieves theoretically minimum uncertainty [J]. Nature Methods, 2010, 7(5): 373-375.
[31] Babcock HP, Moffitt JR, Cao Yunlong, et al. Fast compressed sensing analysis for super-resolution imaging using L1-homotopy [J]. Optics Express, 2013, 21(23): 28583-28596.
[32] Henriques R, Lelek M, Fornasiero EF, et al. QuickPALM: 3D real-time photoactivation nanoscopy image processing in ImageJ [J]. Nature Methods, 2010, 7(5): 339-340.
[33] Kechkar A, Nair D, Heilemann M, et al. Real-time analysis and visualization for single-molecule based super-resolution microscopy [J]. PLoS ONE, 2013, 8(4): e62918-e62918.
[34] Kthe U, Herrmannsdoerfer F, Kats I, et al. SimpleSTORM: a fast, self-calibrating reconstruction algorithm for localization microscopy [J]. Histochemistry and Cell Biology, 2014, 141(6): 613-627.
[35] Wolter S, Endesfelder U, van de Linde S, et al. Measuring localization performance of super-resolution algorithms on very active samples [J]. Optics Express, 2011, 19(8): 7020-7033.
[36] Min J, Vonesch C, Kirshner H, et al. FALCON: fast and unbiased reconstruction of high-density super-resolution microscopy data [J]. Scientific Reports, 2014, 4(1): 4577-4577.
[37] Bon P, Linares-Loyez J, Feyeux M, et al. Self-interference 3D super-resolution microscopy for deep tissue investigations [J]. Nature Methods, 2018, 15(6): 449-454.
[38] Li Yiming, Markus M, Hoess P, et al. Real-time 3d single-molecule localization using experimental point spread functions [J]. Nature Methods, 2018, 15(5): 367-369.
[39] Andrey A, Benoit L, Elena, R, et al. ZOLA-3D allows flexible 3D localization microscopy over an adjustable axial range [J]. Nature Communications, 2018, 9(1): 2409-2416.
[40] Soubies E, Blanc-Féraud L, Schaub S, et al. A 3D model with shape prior information for biological structures reconstruction using multiple-angle total internal reflection fluorescence microscopy [C]// 2014 IEEE 11th International Symposium on Biomedical Imaging. Beijing: IEEE, 2014: 608-611.
[41] Gustafsson N, Culley S, Ashdown G, et al. Fast live-cell conventional fluorophore nanoscopy with ImageJ through super-resolution radial fluctuations [J]. Nature Communications, 2016, 7(1): 12471-12471.
[42] Li Honglin, Joshua CV. Switchable fluorophores for single-molecule localization microscopy [J]. Chemical Review, 2018, 118(18): 9412-9454.
[43] Francesca P, Serebrovskaya EO, Faro AR, et al. Fast reversibly photoswitching red fluorescent proteins for live-cell RESOLFT nanoscopy [J]. Nature Methods, 2018, 15(8): 601-604.
[44] Pan Deng, Hu Zhe, Qiu Fengwu, et al. A general strategy for developing cell-permeable photo-modulatable organic fluorescent probes for live-cell super-resolution imaging [J]. Nature Communications, 2014, 5(1): 5573-5580.
[45] Rivenson Y, Grcs Z, Günaydin H, et al. Deep learning microscopy [J]. Optica, 2017, 4(11): 1437-1443.
[46] Elias N, Lucien E, Weiss TM, et al. Deep-STORM: Super-resolution single-molecule microscopy by deep learning [J]. Optica, 2018, 5(4): 458-464.
[47] Martin W, Uwe S, Tobias B, et al. Content-aware image restoration: pushing the limits of fluorescence microscopy [J]. Nature Methods, 2018, 15(12): 1090-1097.
[48] Jin Luhong, Xiu Peng, Zhou Xiaoxu, et al. 3D reconstruction of cortical microtubules using multi-angle total internal reflection fluorescence microscopy [C] // Proceedings of International Conference on Innovative Optical Health Science. Shanghai: SPIE, 2017: 1-9.
[49] Jin Luhong, Wu Jian, Xiu Peng, et al. High-resolution 3D reconstruction of microtubule structures by quantitative multi-angle total internal reflection fluorescence microscopy [J]. Optics Communications, 2016, 395: 16-23.