Compressed Sensing STORM Super-Resolution Image Reconstruction Based on Noise Correction-Principal Component Analysis Preprocessing Algorithm

PAN Wenhui1 CHEN Bingling1 ZHANG Jianguo1 GU Zhenyu1 XIONG Jia1 ZHANG Dan1 YANG Zhigang1 QU Junle1

(1.Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, Center for Biomedical Photonics, College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen, Guangdong Province, China 518060)

【Abstract】The low temporal resolution of stochastic optical reconstruction microscopy (STORM) limits its ability to observe the dynamic events in live cells. Further, the post-processing analysis and reconstruction algorithms have an important effect on super-resolution images. In this study, we report a new noise-correction principal component analysis method for single-molecule localization microscopy against fluorescent spot overlapping and excessive background noise in a single frame of images owing to high-density labeling and high camera-sampling frequency. The proposed method can improve the positioning accuracy of existing localization methods by preprocessing the raw images acquired by the single molecule localization microscopy before reconstruction. In addition, this method can accurately distinguish the overlapping molecules. Therefore, it is suitable for samples exhibiting a high fluorophore density. Thus, the proposed method improves the temporal resolution of super-resolution imaging, providing a powerful technical support for the STORM imaging of live cells.

【Keywords】 biotechnology; stochastic optical reconstruction microscopy; principal component analysis; denoising algorithm; super-resolution optical imaging;

【DOI】

【Funds】 National Natural Science Foundation of China (61875131, 61525503) Key projects of Guangdong Provincial Department of Education (2015KGJHZ002, 2016KCXTD007) Innovation Team Project of Guangdong Natural Science Foundation (2014A030312008) Shenzhen Basic Research Project (JCYJ20170818100931714, JCYJ20180305125549234, JCYJ20170412105003520)

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    References

    [1] Yu J. Single-molecule studies in live cells [J]. Annual Review of Physical Chemistry, 2016, 67 (1): 565–585.

    [2] Xia T, Li N, Fang X H. Single-molecule fluorescence imaging in living cells [J]. Annual Review of Physical Chemistry, 2013, 64 (1): 459–480.

    [3] Thompson M A, Lew M D, Moerner W E. Extending microscopic resolution with single-molecule imaging and active control [J]. Annual Review of Biophysics, 2012, 41 (1): 321–342.

    [4] Joo C, Balci H, Ishitsuka Y, et al. Advances in single-molecule fluorescence methods for molecular biology [J]. Annual Review of Biochemistry, 2008, 77 (1): 51–76.

    [5] Zhou X, Dan D, Qian J, et al. Super-resolution reconstruction theory in structured illumination microscopy [J]. Acta Optica Sinica, 2017, 37 (3): 0318001. (in Chinese)

    [6] Rust M J, Bates M, Zhuang X W. Sub-diffraction-limit imaging by stochastic optical reconstruction microscopy (STORM) [J]. Nature Methods, 2006, 3 (10): 793–796.

    [7] Huang B, Wang W, Bates M, et al. Three-dimensional super-resolution imaging by stochastic optical reconstruction microscopy [J]. Science, 2008, 319 (5864): 810–813.

    [8] Pan W H, Li W, Qu J H, et al. Research progress on organic fluorescent probes for single molecule localization microscopy [J]. Chinese Journal of Applied Chemistry, 2019, 36 (3): 269–281 (in Chinese).

    [9] Gordon M P, Ha T, Selvin P R. 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.

    [10] Jones S A, Shim S H, He J, et al. Fast, three-dimensional super-resolution imaging of live cells [J]. Nature Methods, 2011, 8 (6): 499–505.

    [11] Lee A, Tsekouras K, Calderon C, et al. Unraveling the thousand word picture: an introduction to super-resolution data analysis [J]. Chemical Reviews, 2017, 117 (11): 7276–7330.

    [12] Sage D, Kirshner H, Pengo T, et al. Quantitative evaluation of software packages for single-molecule localization microscopy [J]. Nature Methods, 2015, 12 (8): 717–724.

    [13] Small A, Stahlheber S. Fluorophore localization algorithms for super-resolution microscopy [J]. Nature Methods, 2014, 11 (3): 267–279.

    [14] Robbins M S, Hadwen B J. The noise performance of electron multiplying charge-coupled devices [J]. IEEE Transactions on Electron Devices, 2003, 50 (5): 1227–1232.

    [15] Zhu L, Zhang W, Elnatan D, et al. Faster STORM using compressed sensing [J]. Nature Methods, 2012, 9 (7): 721–723.

    [16] Ilovitsh T, Meiri A, Ebeling C G, et al. Improved localization accuracy in stochastic super-resolution fluorescence microscopy by K-factor image deshadowing [J]. Biomedical Optics Express, 2014, 5 (1): 244–258.

    [17] Jolliffe I T. Principal component analysis [M]. 2nd ed. New York: Springer-Verlag, 2002.

    [18] Le Marois A, Labouesse S, Suhling K, et al. Noise-Corrected Principal Component Analysis of fluorescence lifetime imaging data [J]. Journal of Biophotonics, 2017, 10 (9): 1124–1133.

    [19] Liu X, Zhang B, Luo J W, et al. 4-D reconstruction for dynamic fluorescence diffuse optical tomography [J]. IEEE Transactions on Medical Imaging, 2012, 31 (11): 2120–2132.

    [20] Prats-Montalbán J M, de Juan A, Ferrer A. Multivariate image analysis: a review with applications [J]. Chemometrics and Intelligent Laboratory Systems, 2011, 107 (1): 1–23.

    [21] Pedersen F, Bergströme M, Bengtsson E, et al. Principal component analysis of dynamic positron emission tomography images [J]. European Journal of Nuclear Medicine, 1994, 21 (12): 1285–1292.

    [22] Liu J X, Du B, Deng Y Q, et al. Terahertz-spectral identification of organic compounds based on differential PCA-SVM method [J]. Chinese Journal of Lasers, 2019, 46 (6): 0614039 (in Chinese).

    [23] Quan T W, Zeng S Q, Huang Z L. Localization capability and limitation of electron-multiplying charge-coupled, scientific complementary metal-oxide semiconductor, and charge-coupled devices for superresolution imaging [J]. Journal of Biomedical Optics, 2010, 15 (6): 066005.

    [24] Thompson R E, Larson D R, Webb W W. Precise nanometer localization analysis for individual fluorescent probes [J]. Biophysical Journal, 2002, 82 (5): 2775–2783.

    [25] O′Connor D V, Phillips D. Time-correlated single photon counting [M]. USA: Academic Press, 1984: 1–35.

    [26] Wang Z, Bovik A C, Sheikh H R, et al. Image quality assessment: from error visibility to structural similarity [J]. IEEE Transactions on Image Processing, 2004, 13 (4): 600–612.

    [27] Collection of reference datasets [DB/OL]. (2018-11-30)[2019-10-17]. http://bigwww.epfl.ch/smlm/datasets/.

    [28] Nieuwenhuizen R P J, Lidke K A, Bates M, et al. Measuring image resolution in optical nanoscopy [J]. Nature Methods, 2013, 10 (6): 557–562.

This Article

ISSN:0258-7025

CN: 31-1339/TN

Vol 47, No. 02, Pages 302-310

February 2020

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Abstract

  • 1 Introduction
  • 2 NC-PCA algorithm
  • 3 Noise reduction effect of NC-PCA algorithm
  • 4 Conclusion
  • References