PN-ImTLSM facilitates high-throughput low background single-molecule localization microscopy deep in the cell

Boxin Xue; Caiwei Zhou; Yizhi Qin; Yongzheng Li; Yuao Sun; Lei Chang; Shipeng Shao; Yongliang Li; Mengling Zhang; Chaoying Sun; Renxi He; Qian Peter Su; Yujie Sun

doi:10.52601/bpr.2021.210014

Volume 7 Issue 4

Sep. 2021

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Article Navigation > Biophysics Reports > 2021 > 7(4): 313-325

Boxin Xue, Caiwei Zhou, Yizhi Qin, Yongzheng Li, Yuao Sun, Lei Chang, Shipeng Shao, Yongliang Li, Mengling Zhang, Chaoying Sun, Renxi He, Qian Peter Su, Yujie Sun. PN-ImTLSM facilitates high-throughput low background single-molecule localization microscopy deep in the cell[J]. Biophysics Reports, 2021, 7(4): 313-325. doi: 10.52601/bpr.2021.210014

Citation:

Boxin Xue, Caiwei Zhou, Yizhi Qin, Yongzheng Li, Yuao Sun, Lei Chang, Shipeng Shao, Yongliang Li, Mengling Zhang, Chaoying Sun, Renxi He, Qian Peter Su, Yujie Sun. PN-ImTLSM facilitates high-throughput low background single-molecule localization microscopy deep in the cell[J]. Biophysics Reports, 2021, 7(4): 313-325. doi: 10.52601/bpr.2021.210014

Citation:

Boxin Xue, Caiwei Zhou, Yizhi Qin, Yongzheng Li, Yuao Sun, Lei Chang, Shipeng Shao, Yongliang Li, Mengling Zhang, Chaoying Sun, Renxi He, Qian Peter Su, Yujie Sun. PN-ImTLSM facilitates high-throughput low background single-molecule localization microscopy deep in the cell[J]. Biophysics Reports, 2021, 7(4): 313-325. doi: 10.52601/bpr.2021.210014

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PN-ImTLSM facilitates high-throughput low background single-molecule localization microscopy deep in the cell

doi: 10.52601/bpr.2021.210014

Boxin Xue^{1, 4, #},
Caiwei Zhou^{2, #},
Yizhi Qin^{1, #},
Yongzheng Li^{1, 2},
Yuao Sun¹,
Lei Chang^{1, 5},
Shipeng Shao^{1, 6},
Yongliang Li⁷,
Mengling Zhang¹,
Chaoying Sun¹,
Renxi He¹,
Qian Peter Su^{1, 8},
Yujie Sun^{1, 3
,
,}

1.
State Key Laboratory of Membrane Biology, Biomedical Pioneer Innovation Center (BIOPIC), School of Life Sciences, Peking University, Beijing 100871, China
2.
2
2.
Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
3.
School of Future Technology, Peking University, Beijing 100871, China
4.
College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
5.
Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou 510005, China
6.
Beijing Institute of Heart Lung and Blood Vessel Disease, Beijing Anzhen Hospital, Capital Medical University, Beijing 100029, China
7.
Department of Geriatric Dentistry, Peking University School and Hospital of Stomatology, Beijing 100081, China
8.
School of Biomedical Engineering, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia

Funds: We thank Prof. Bin Dong and Dr. Haimiao Zhang from School of Mathematical Sciences, Peking University for discussion on deep learning. We are grateful to School of Mathematical Sciences, Peking University and High-performance Computing Platform of Peking University for providing computing resources and platforms for scientific computing. We thank Prof. Wei Guo of the University of Pennsylvania for providing the Hela-S3 cell line (PubMed ID: 5733811). We thank Prof. Congying Wu of Peking University Institute of Systems Biomedicine (PKUISB) for providing the YAP-HaloTag plasmid. We thank Standard Imaging Co., Ltd. for their assistance in sample preparation. We thank Dr. Xuanze Chen, Coollight Co., Ltd. and Jingdao Technology Co., Ltd. for their help on the laser combination system. We thank Dr. Fengrong Chen for help on machining. We thank Prof. Bo Huang of the University of California, San Francisco for providing the Insight3 software. This work is supported by grants from the National Key R&D Program of China for Y. Sun (2017YFA0505300) and the National Science Foundation of China for Y. Sun (21825401). B. Xue and Y. Sun conceived the project and designed the experiments. Q.-P. Su provided useful advice for designing the project. B. Xue and Y. Qin constructed the ImTLSM system. Y. Qin constructed the large-field homogeneous illumination optical path. B. Xue and Y. Qin performed the data acquisition. C. Zhou and B. Xue performed the coding work and data analysis. Y. Li, Y. Sun, L. Chang, S. Shao, M. Zhang, C. Sun and R. He performed sample preparation. B. Xue, C. Zhou, Y. Qin, Q.-P. Su, Y. Li and Y. Sun wrote the manuscript with input from all authors.

More Information

Corresponding author: sun_yujie@pku.edu.cn (Y. Sun)
Received Date: 11 May 2021
Accepted Date: 15 June 2021
Publish Date: 17 September 2021

Abstract

Abstract

When imaging the nucleus structure of a cell, the out-of-focus fluorescence acts as background and hinders the detection of weak signals. Light-sheet fluorescence microscopy (LSFM) is a wide-field imaging approach which has the best of both background removal and imaging speed. However, the commonly adopted orthogonal excitation/detection scheme is hard to be applied to single-cell imaging due to steric hindrance. For LSFMs capable of high spatiotemporal single-cell imaging, the complex instrument design and operation largely limit their throughput of data collection. Here, we propose an approach for high-throughput background-free fluorescence imaging of single cells facilitated by the Immersion Tilted Light Sheet Microscopy (ImTLSM). ImTLSM is based on a light-sheet projected off the optical axis of a water immersion objective. With the illumination objective and the detection objective placed opposingly, ImTLSM can rapidly patrol and optically section multiple individual cells while maintaining single-molecule detection sensitivity and resolution. Further, the simplicity and robustness of ImTLSM in operation and maintenance enables high-throughput image collection to establish background removal datasets for deep learning. Using a deep learning model to train the mapping from epi-illumination images to ImTLSM illumination images, namely PN-ImTLSM, we demonstrated cross-modality fluorescence imaging, transforming the epi-illumination image to approach the background removal performance obtained with ImTLSM. We demonstrated that PN-ImTLSM can be generalized to large-field homogeneous illumination imaging, thereby further improving the imaging throughput. In addition, compared to commonly used background removal methods, PN-ImTLSM showed much better performance for areas where the background intensity changes sharply in space, facilitating high-density single-molecule localization microscopy. In summary, PN-ImTLSM paves the way for background-free fluorescence imaging on ordinary inverted microscopes.

# Boxin Xue, Caiwei Zhou and Yizhi Qin contributed equally to the work.

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References(40)

References

[1]	Axelrod D (1981) Cell-substrate contacts illuminated by total internal reflection fluorescence. J Cell Biol 89: 141−145 doi: 10.1083/jcb.89.1.141
[2]	Bai C, Liu C, Yu X, Peng T, Min J, Yan S, Dan D, Yao B (2019) Imaging enhancement of light-sheet fluorescence microscopy via deep learning. IEEE Photon Technol Lett 31(22): 1803−1806 doi: 10.1109/LPT.2019.2948030
[3]	Barbastathis G, Ozcan A, Situ G (2019) On the use of deep learning for computational imaging. Optica 6(8): 921−943 doi: 10.1364/OPTICA.6.000921
[4]	Belthangady C, Royer LA (2019) Applications, promises, and pitfalls of deep learning for fluorescence image reconstruction. Nat Methods 16(12): 1215−1225 doi: 10.1038/s41592-019-0458-z
[5]	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. Science 313(5793): 1642−1645 doi: 10.1126/science.1127344
[6]	Bouchard MB, Voleti V, Mendes CS, Lacefield C, Grueber WB, Mann RS, Bruno RM, Hillman EMC (2015) Swept confocally-aligned planar excitation (SCAPE) microscopy for high-speed volumetric imaging of behaving organisms. Nat Photon 9(2): 113−119 doi: 10.1038/nphoton.2014.323
[7]	Chen B-C, Legant WR, Wang K, Shao L, Milkie DE, Davidson MW, Janetopoulos C, Wu XS, Hammer JA, Liu Z, English BP, Mimori-Kiyosue Y, Romero DP, Ritter AT, Lippincott-Schwartz J, Fritz-Laylin L, Mullins RD, Mitchell DM, Bembenek JN, Reymann A-C, Böhme R, Grill SW, Wang JT, Seydoux G, Tulu US, Kiehart DP, Betzig E (2014) Lattice light-sheet microscopy: imaging molecules to embryos at high spatiotemporal resolution. Science 346(6208): 1257998 doi: 10.1126/science.1257998
[8]	Corsetti S, Wijesinghe P, Poulton PB, Sakata S, Vyas K, Simon Herrington C, Nylk J, Gasparoli F, Dholakia K (2020) Widefield light sheet microscopy using an Airy beam combined with deep-learning super-resolution. OSA Continuum 3(4): 1068−1083 doi: 10.1364/OSAC.391644
[9]	Deschamps J, Rowald A, Ries J (2016) Efficient homogeneous illumination and optical sectioning for quantitative single-molecule localization microscopy. Opt Express 24(24): 28080−28090 doi: 10.1364/OE.24.028080
[10]	Douglass KM, Sieben C, Archetti A, Lambert A, Manley S (2016) Super-resolution imaging of multiple cells by optimized flat-field epi-illumination. Nat Photon 10: 705−708 doi: 10.1038/nphoton.2016.200
[11]	Fang C, Yu T, Chu T, Feng W, Zhao F, Wang X, Huang Y, Li Y, Wan P, Mei W, Zhu D, Fei P (2021) Minutes-timescale 3D isotropic imaging of entire organs at subcellular resolution by content-aware compressed-sensing light-sheet microscopy. Nat Commun 12(1): 107 doi: 10.1038/s41467-020-20329-3
[12]	Galland R, Grenci G, Aravind A, Viasnoff V, Studer V, Sibarita J-B (2015) 3D high- and super-resolution imaging using single-objective SPIM. Nat Methods 12(7): 641−644 doi: 10.1038/nmeth.3402
[13]	Gebhardt JCM, Suter DM, Roy R, Zhao ZW, Chapman AR, Basu S, Maniatis T, Xie XS (2013) Single-molecule imaging of transcription factor binding to DNA in live mammalian cells. Nat Methods 10(5): 421−426 doi: 10.1038/nmeth.2411
[14]	Geng J, Zhang X, Prabhu S, Shahoei SH, Nelson ER, Swanson KS, Anastasio MA, Smith AM (2021) 3D microscopy and deep learning reveal the heterogeneity of crown-like structure microenvironments in intact adipose tissue. Sci Adv 7(8): eabe2480 doi: 10.1126/sciadv.abe2480
[15]	Gustavsson A-K, Petrov PN, Lee MY, Shechtman Y, Moerner WE (2018) 3D single-molecule super-resolution microscopy with a tilted light sheet. Nat Commun 9(1): 123 doi: 10.1038/s41467-017-02563-4
[16]	He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. Paper presented at: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770-778. doi: https://doi.org/10.1109/CVPR.2016.90
[17]	Hess ST, Girirajan TPK, Mason MD (2006) Ultra-high resolution imaging by fluorescence photoactivation localization microscopy. Biophys J 91(11): 4258−4272 doi: 10.1529/biophysj.106.091116
[18]	Huisken J, Swoger J, Del Bene F, Wittbrodt J, Stelzer EHK (2004) Optical sectioning deep inside live embryos by selective plane illumination microscopy. Science 305(5686): 1007−1009 doi: 10.1126/science.1100035
[19]	Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. Proceedings of the ICML 37: 448−456
[20]	Keller PJ, Schmidt AD, Wittbrodt J, Stelzer EHK (2008) Reconstruction of zebrafish early embryonic development by scanned light sheet microscopy. Science 322(5904): 1065−1069
[21]	Kim J, Wojcik M, Wang Y, Moon S, Zin EA, Marnani N, Newman ZL, Flannery JG, Xu K, Zhang X (2019) Oblique-plane single-molecule localization microscopy for tissues and small intact animals. Nat Methods 16(9): 853−857 doi: 10.1038/s41592-019-0510-z
[22]	Li J, Luisier F, Blu T (2018) PURE-LET image deconvolution. IEEE Trans Image Proc 27(1): 92−105 doi: 10.1109/TIP.2017.2753404
[23]	Manton JD, Rees EJ (2016) triSPIM: light sheet microscopy with isotropic super-resolution. Opt Lett 41(18): 4170−4173 doi: 10.1364/OL.41.004170
[24]	Möckl L, Roy AR, Petrov PN, Moerner WE (2020) Accurate and rapid background estimation in single-molecule localization microscopy using the deep neural network BGnet. Proc Natl Acad Sci USA 117(1): 60−67 doi: 10.1073/pnas.1916219117
[25]	Nehme E, Freedman D, Gordon R, Ferdman B, Weiss LE, Alalouf O, Naor T, Orange R, Michaeli T, Shechtman Y (2020) DeepSTORM3D: dense 3D localization microscopy and PSF design by deep learning. Nat Methods 17: 734−740 doi: 10.1038/s41592-020-0853-5
[26]	Ovesný M, Křížek P, Borkovec J, Švindrych Z, Hagen GM (2014) ThunderSTORM: a comprehensive ImageJ plug-in for PALM and STORM data analysis and super-resolution imaging. Bioinformatics 30(16): 2389−2390 doi: 10.1093/bioinformatics/btu202
[27]	Ren D, Zuo W, Hu Q, Zhu P, Meng D (2019) Progressive image deraining networks: a better and simpler baseline. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3932-3941. doi: https://doi.org/10.1109/CVPR.2019.00406
[28]	Rust MJ, Bates M, Zhuang X (2006) Sub-diffraction-limit imaging by stochastic optical reconstruction microscopy (STORM). Nat Methods 3(10): 793−796 doi: 10.1038/nmeth929
[29]	Shao S, Xue B, Sun Y (2018) Intranucleus single-molecule imaging in living cells. Biophys J 115(2): 181−189 doi: 10.1016/j.bpj.2018.05.017
[30]	Shi X, Chen Z, Wang H, Yeung D-Y, Wong W-K, Woo W-C (2015) Convolutional LSTM network: a machine learning approach for precipitation nowcasting. arXiv e-prints, arXiv:1506.04214. NIPS'15: Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 1, pp. 802–810
[31]	Siedentopf H, Zsigmondy R (1902) Uber sichtbarmachung und größenbestimmung ultramikoskopischer teilchen, mit besonderer anwendung auf goldrubingläser. Annalen der Physik 315(1): 1−39 doi: 10.1002/andp.19023150102
[32]	Stehr F, Stein J, Schueder F, Schwille P, Jungmann R (2019) Flat-top TIRF illumination boosts DNA-PAINT imaging and quantification. Nat Commun 10(1): 1268 doi: 10.1038/s41467-019-09064-6
[33]	Strnad P, Gunther S, Reichmann J, Krzic U, Balazs B, de Medeiros G, Norlin N, Hiiragi T, Hufnagel L, Ellenberg J (2016) Inverted light-sheet microscope for imaging mouse pre-implantation development. Nat Methods 13(2): 139−142 doi: 10.1038/nmeth.3690
[34]	Theer P, Dragneva D, Knop M (2016) πSPIM: high NA high resolution isotropic light-sheet imaging in cell culture dishes. Sci Rep 6(1): 32880 doi: 10.1038/srep32880
[35]	Tokunaga M, Imamoto N, Sakata-Sogawa K (2008) Highly inclined thin illumination enables clear single-molecule imaging in cells. Nat Methods 5(2): 159−161 doi: 10.1038/nmeth1171
[36]	Voelkel R, Weible KJ (2008) Laser beam homogenizing: limitations and constraints. Proceedings of the SPIE 7102: 71020J doi: 10.1117/12.799400
[37]	Wang H, Rivenson Y, Jin Y, Wei Z, Gao R, Günaydın H, Bentolila LA, Kural C, Ozcan A (2019) Deep learning enables cross-modality super-resolution in fluorescence microscopy. Nat Methods 16(1): 103−110 doi: 10.1038/s41592-018-0239-0
[38]	Yang B, Chen X, Wang Y, Feng S, Pessino V, Stuurman N, Cho NH, Cheng KW, Lord SJ, Xu L, Xie D, Mullins RD, Leonetti MD, Huang B (2019) Epi-illumination SPIM for volumetric imaging with high spatial-temporal resolution. Nat Methods 16(6): 501−504 doi: 10.1038/s41592-019-0401-3
[39]	Zhao F, Zhu L, Fang C, Yu T, Zhu D, Fei P (2020) Deep-learning super-resolution light-sheet add-on microscopy (Deep-SLAM) for easy isotropic volumetric imaging of large biological specimens. Biomed Opt Express 11, 7273-7285
[40]	Zhao Z, Xin B, Li L, Huang Z-L (2017) High-power homogeneous illumination for super-resolution localization microscopy with large field-of-view. Opt Exp 25(12): 13382−13395 doi: 10.1364/OE.25.013382