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DNA-based point accumulation in nanoscale topography (DNA-PAINT) is a well-established technique for single-molecule localization microscopy (SMLM), enabling resolution of up to a few nanometers. Traditionally, DNA-PAINT involves the utilization of tens of thousands of single-molecule fluorescent images to generate a single super-resolution image. This process can be time-consuming, which makes it unfeasible for many researchers. Here, we propose a simplified DNA-PAINT labeling method and a deep learning-enabled fast DNA-PAINT imaging strategy for subcellular structures, such as microtubules. By employing our method, super-resolution reconstruction can be achieved with only one-tenth of the raw data previously needed, along with the option of acquiring the widefield image. As a result, DNA-PAINT imaging is significantly accelerated, making it more accessible to a wider range of biological researchers.
Alvelid J, Damenti M, Sgattoni C, Testa I (2022) Event-triggered STED imaging. Nat Methods 19(10): 1268−1275
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
Chen B, Chang BJ, Roudot P, Zhou F, Sapoznik E, Marlar-Pavey M, Hayes JB, Brown PT, Zeng CW, Lambert T, Friedman JR, Zhang CL, Burnette DT, Shepherd DP, Dean KM, Fiolka RP (2022) Resolution doubling in light-sheet microscopy via oblique plane structured illumination. Nat Methods 19(11): 1419−1426
Chung KKH, Zhang Z, Kidd P, Zhang YD, Williams ND, Rollins B, Yang Y, Lin CX, Baddeley D, Bewersdorf J (2022) Fluorogenic DNA-PAINT for faster, low-background super-resolution imaging. Nat Methods 19(5): 554−559
Dai M, Jungmann R, Yin P (2016) Optical imaging of individual biomolecules in densely packed clusters. Nat Nanotechnol 11(9): 798−807
Filius M, Cui TJ, Ananth AN, Docter MW, Hegge JW, van der Oost J, Joo C (2020) High-speed super-resolution imaging using protein-assisted DNA-PAINT. Nano Lett 20(4): 2264−2270
Giannone G, Hosy E, Levet F, Constals A, Schulze K, Sobolevsky AI, Rosconi MP, Gouaux E, Tampe R, Choquet D, Cognet L (2010) Dynamic superresolution imaging of endogenous proteins on living cells at ultra-high density. Biophys J 99(4): 1303−1310
Gustafsson MG (2005) Nonlinear structured-illumination microscopy: wide-field fluorescence imaging with theoretically unlimited resolution. Proc Natl Acad Sci USA 102(37): 13081−13086
Hell SW, & Wichmann J (1994) Breaking the diffraction resolution limit by stimulated emission: stimulated-emission-depletion fluorescence microscopy. Opt Lett 19(11): 780−792
Iinuma R, Ke Y, Jungmann R, Schlichthaerle T, Woehrstein JB, Yin P (2014) Polyhedra self-assembled from DNA tripods and characterized with 3D DNA-PAINT. Science 344(6179): 65−69
Jin LH, Liu B, Zhao FQ, Hahn S, Dong BW, Song RY, Elston TC, Xu YK, Hahn KM (2020) Deep learning enables structured illumination microscopy with low light levels and enhanced speed. Nat Commun 11(1): 1934. https://doi.org/10.1038/s41467-41020-15784-x
Jungmann R, Avendano MS, Dai M, Woehrstein JB, Agasti SS, Feiger Z, Rodal A, Yin P (2016) Quantitative super-resolution imaging with qPAINT. Nat Methods 13(5): 439−442
Jungmann R, Avendano MS, Woehrstein JB, Dai M, Shih WM, Yin P (2014) Multiplexed 3D cellular super-resolution imaging with DNA-PAINT and Exchange-PAINT. Nat Methods 11(3): 313−318
Jungmann R, Steinhauer C, Scheible M, Kuzyk A, Tinnefeld P, Simmel FC (2010) Single-molecule kinetics and super-resolution microscopy by fluorescence imaging of transient binding on DNA origami. Nano Lett 10(11): 4756−4761
Marx V (2019) Machine learning, practically speaking. Nat Methods 16(6): 463−467
Nehme E, Weiss LE, Michaeli T, Shechtman Y (2018) Deep-STORM: super-resolution single-molecule microscopy by deep learning. Optica 5(4): 458−464
Oleksiievets N, Sargsyan Y, Thiele JC, Mougios N, Sograte-Idrissi S, Nevskyi O, Gregor I, Opazo F, Thoms S, Enderlein J, Tsukanov R (2022) Fluorescence lifetime DNA-PAINT for multiplexed super-resolution imaging of cells. Commun Biol 5(1): 38. https://doi.org/10.1038/s42003-021-02976-4
Ouyang W, Aristov A, Lelek M, Hao X, Zimmer C (2018) Deep learning massively accelerates super-resolution localization microscopy. Nat Biotechnol 36(5): 460−468
Rust MJ, Bates M, Zhuang X (2006) Sub-diffraction-limit imaging by stochastic optical reconstruction microscopy (STORM). Nat Methods 3(10): 793−795
Schindelin J, Arganda-Carreras I, Frise E, Kaynig V, Longair M, Pietzsch T, Preibisch S, Rueden C, Saalfeld S, Schmid B, Tinevez JY, White DJ, Hartenstein V, Eliceiri K, Tomancak P, Cardona A (2012) Fiji: an open-source platform for biological-image analysis. Nat Methods 9(7): 676−682
Schnitzbauer J, Strauss MT, Schlichthaerle T, Schueder F, Jungmann R (2017) Super-resolution microscopy with DNA-PAINT. Nat Protoc 12(6): 1198−1228
Schueder F, Stein J, Stehr F, Auer A, Sperl B, Strauss MT, Schwille P, Jungmann R (2019) An order of magnitude faster DNA-PAINT imaging by optimized sequence design and buffer conditions. Nat Methods 16(11): 1101−1104
Sharonov A, Hochstrasser RM (2006) Wide-field subdiffraction imaging by accumulated binding of diffusing probes. Proc Natl Acad Sci USA 103(50): 18911−18916
Tholen MME, Tas RP, Wang Y, Albertazzi L (2023) Beyond DNA: new probes for PAINT super-resolution microscopy. Chem Commun (Camb) 59(54): 8332−8342
Xie L, Dong P, Chen X, Hsieh TS, Banala S, De Marzio M, English BP, Qi Y, Jung SK, Kieffer-Kwon KR, Legant WR, Hansen AS, Schulmann A, Casellas R, Zhang B, Betzig E, Lavis LD, Chang HY, Tjian R, Liu Z (2020) 3D ATAC-PALM: super-resolution imaging of the accessible genome. Nat Methods 17(4): 430−436
Xu J, Ma H, Ma H, Jiang W, Mela CA, Duan M, Zhao S, Gao C, Hahm ER, Lardo SM, Troy K, Sun M, Pai R, Stolz DB, Zhang L, Singh S, Brand RE, Hartman DJ, Hu J, Hainer SJ, Liu Y (2020) Super-resolution imaging reveals the evolution of higher-order chromatin folding in early carcinogenesis. Nat Commun 11(1): 1899. https://doi.org/10.1038/s41467-41020-15718-41467
Zhu M, Zhang LH, Jin LH, Chen JC, Zhang YD, Xu YK (2022) DNA-PAINT imaging accelerated by machine learning. Front Chem 10: 864701. https://doi.org/10.863389/fchem.862022.864701
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