Abstract

To date, imaging systems have generally been designed to provide an even spatiotemporal resolution across the field of view (FOV). However, this becomes a fundamental limitation when we aim to simultaneously observe varying dynamics at different parts of the FOV. In conventional imaging systems, to capture fast dynamics occurring at only a small portion of the FOV, the entire imaging system’s sampling rate must be increased. This is a major problem if different parts of the FOV must rather be imaged at high spatial resolutions beyond the diffraction limit and require a sacrifice in temporal resolution. To answer this unmet challenge, we propose tunable SIM, which enables adaptive modulation of spatiotemporally varying structured illumination across different parts of the FOV. Using tunable SIM, we exploit the varying and designable spatiotemporal resolution to demonstrate simultaneous measurements of subdiffraction-limited changes in the actin fine structure of U87MG-EGFP-CD9 cells and the fast viscous flow inducing these structural changes.

© 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

Full Article  |  PDF Article

Corrections

12 August 2020: Typographical corrections were made to paragraph 1 of pages 1 and 5.


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References

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2019 (5)

L. Schermelleh, A. Ferrand, T. Huser, C. Eggeling, M. Sauer, O. Biehlmaier, and G. P. C. Drummen, “Super-resolution microscopy demystified,” Nat. Cell Biol. 21, 72–84 (2019).
[Crossref]

J. Fan, X. Huang, L. Li, S. Tan, and L. Chen, “A protocol for structured illumination microscopy with minimal reconstruction artifacts,” Biophys. Rep. 5, 80–90 (2019).
[Crossref]

A. Markwirth, M. Lachetta, V. Monkemoller, R. Heintzmann, W. Hubner, T. Huser, and M. Muller, “Video-rate multi-color structured illumination microscopy with simultaneous real-time reconstruction,” Nat. Commun. 10, 4315 (2019).
[Crossref]

A. Glia, P. Sukumar, A. Brimmo, M. Deliorman, M. A. Qasaimeh, B. L. Gray, and H. Becker, “Immuno-capture of cells in open microfluidics: microfluidic probes integrated with herringbone micro-mixers,” Proc. SPIE 10875, 108751F (2019).
[Crossref]

M. P. Edgar, G. M. Gibson, and M. J. Padgett, “Principles and prospects for single-pixel imaging,” Nat. Photonics 13, 13–20 (2019).
[Crossref]

2018 (6)

Y. Ma, D. Li, Z. J. Smith, D. Li, and K. Chu, “Structured illumination microscopy with interleaved reconstruction (SIMILR),” J. Biophoton. 11, e201700090 (2018).
[Crossref]

R. Forster, W. Muller, R. Richter, and R. Heintzmann, “Automated distinction of shearing and distortion artefacts in structured illumination microscopy,” Opt. Express 26, 20680–20694 (2018).
[Crossref]

Y. Guo, D. Li, S. Zhang, Y. Yang, J.-J. Liu, X. Wang, C. Liu, D. E. Milkie, R. P. Moore, and U. S. Tulu, “Visualizing intracellular organelle and cytoskeletal interactions at nanoscale resolution on millisecond timescales,” Cell 175, 1430–1442 (2018).
[Crossref]

J. Schnitzbauer, Y. Wang, S. Zhao, M. Bakalar, T. Nuwal, B. Chen, and B. Huang, “Correlation analysis framework for localization-based superresolution microscopy,” Proc. Natl. Acad. Sci. USA 115, 3219–3224 (2018).
[Crossref]

G. Vicidomini, P. Bianchini, and A. Diaspro, “STED super-resolved microscopy,” Nat. Methods 15, 173–182 (2018).
[Crossref]

X. Huang, J. Fan, L. Li, H. Liu, R. Wu, Y. Wu, L. Wei, H. Mao, A. Lal, P. Xi, L. Tang, Y. Zhang, Y. Liu, S. Tan, and L. Chen, “Fast, long-term, super-resolution imaging with Hessian structured illumination microscopy,” Nat. Biotechnol. 36, 451–459 (2018).
[Crossref]

2017 (3)

R. Heintzmann and T. Huser, “Super-resolution structured illumination microscopy,” Chem. Rev. 117, 13890–13908 (2017).
[Crossref]

J. Demmerle, C. Innocent, A. J. North, G. Ball, M. Muller, E. Miron, A. Matsuda, I. M. Dobbie, Y. Markaki, and L. Schermelleh, “Strategic and practical guidelines for successful structured illumination microscopy,” Nat. Protoc. 12, 988–1010 (2017).
[Crossref]

F. Strohl and C. F. Kaminski, “Speed limits of structured illumination microscopy,” Opt. Lett. 42, 2511–2514 (2017).
[Crossref]

2016 (5)

N. Chakrova, A. S. Canton, C. Danelon, S. Stallinga, and B. Rieger, “Adaptive illumination reduces photobleaching in structured illumination microscopy,” Biomed. Opt. Express 7, 4263–4274 (2016).
[Crossref]

L. Song, H.-W. Lu-Walther, R. Förster, A. Jost, M. Kielhorn, J. Zhou, and R. Heintzmann, “Fast structured illumination microscopy using rolling shutter cameras,” Meas. Sci. Technol. 27, 055401 (2016).
[Crossref]

R. Forster, K. Wicker, W. Muller, A. Jost, and R. Heintzmann, “Motion artefact detection in structured illumination microscopy for live cell imaging,” Opt. Express 24, 22121–22134 (2016).
[Crossref]

G. Narita, Y. Watanabe, and M. Ishikawa, “Dynamic projection mapping onto deforming non-rigid surface using deformable dot cluster marker,” IEEE Trans. Vis. Comput. Graphics 23, 1235–1248 (2016).
[Crossref]

M. Muller, V. Monkemoller, S. Hennig, W. Hubner, and T. Huser, “Open-source image reconstruction of super-resolution structured illumination microscopy data in ImageJ,” Nat. Commun. 7, 10980 (2016).
[Crossref]

2015 (6)

D. Li, L. Shao, B.-C. Chen, X. Zhang, M. Zhang, B. Moses, D. E. Milkie, J. R. Beach, J. A. Hammer, and M. Pasham, “Extended-resolution structured illumination imaging of endocytic and cytoskeletal dynamics,” Science 349, aab3500 (2015).
[Crossref]

M. Saxena, G. Eluru, and S. S. Gorthi, “Structured illumination microscopy,” Adv. Opt. Photon. 7, 241–275 (2015).
[Crossref]

A. Curd, A. Cleasby, K. Makowska, A. York, H. Shroff, and M. Peckham, “Construction of an instant structured illumination microscope,” Methods 88, 37–47 (2015).
[Crossref]

A. M. Sydor, K. J. Czymmek, E. M. Puchner, and V. Mennella, “Super-resolution microscopy: from single molecules to supramolecular assemblies,” Trends Cell Biol. 25, 730–748 (2015).
[Crossref]

S. W. Hell, S. J. Sahl, M. Bates, X. Zhuang, R. Heintzmann, M. J. Booth, J. Bewersdorf, G. Shtengel, H. Hess, P. Tinnefeld, A. Honigmann, S. Jakobs, I. Testa, L. Cognet, B. Lounis, H. Ewers, S. J. Davis, C. Eggeling, D. Klenerman, K. I. Willig, G. Vicidomini, M. Castello, A. Diaspro, and T. Cordes, “The 2015 super-resolution microscopy roadmap,” J. Phys. D 48, 443001 (2015).
[Crossref]

C. Kuang, Y. Ma, R. Zhou, J. Lee, G. Barbastathis, R. R. Dasari, Z. Yaqoob, and P. T. So, “Digital micromirror device-based laser-illumination Fourier ptychographic microscopy,” Opt. Express 23, 26999–27010 (2015).
[Crossref]

2014 (4)

2013 (3)

A. Jost and R. Heintzmann, “Superresolution multidimensional imaging with structured illumination microscopy,” Annu. Rev. Mater. Res. 43, 261–282 (2013).
[Crossref]

D. Dan, M. Lei, B. Yao, W. Wang, M. Winterhalder, A. Zumbusch, Y. Qi, L. Xia, S. Yan, and Y. Yang, “DMD-based LED-illumination super-resolution and optical sectioning microscopy,” Sci. Rep. 3, 1116 (2013).
[Crossref]

A. G. York, P. Chandris, D. D. Nogare, J. Head, P. Wawrzusin, R. S. Fischer, A. Chitnis, and H. Shroff, “Instant super-resolution imaging in live cells and embryos via analog image processing,” Nat. Methods 10, 1122–1126 (2013).
[Crossref]

2011 (1)

L. Saias, J. Autebert, L. Malaquin, and J.-L. Viovy, “Design, modeling and characterization of microfluidic architectures for high flow rate, small footprint microfluidic systems,” Lab Chip 11, 822–832 (2011).
[Crossref]

2010 (1)

B. Huang, H. Babcock, and X. Zhuang, “Breaking the diffraction barrier: super-resolution imaging of cells,” Cell 143, 1047–1058 (2010).
[Crossref]

2009 (1)

B. Huang, M. Bates, and X. Zhuang, “Super-resolution fluorescence microscopy,” Annu. Rev. Biochem. 78, 993–1016 (2009).
[Crossref]

2007 (1)

W. Choi, C. Fang-Yen, K. Badizadegan, S. Oh, N. Lue, R. R. Dasari, and M. S. Feld, “Tomographic phase microscopy,” Nat. Methods 4, 717–719 (2007).
[Crossref]

2006 (1)

E. Betzig, G. H. Patterson, R. Sougrat, O. W. Lindwasser, S. Olenych, J. S. Bonifacino, M. W. Davidson, J. Lippincott-Schwartz, and H. F. Hess, “Imaging intracellular fluorescent proteins at nanometer resolution,” Science 313, 1642–1645 (2006).
[Crossref]

2002 (1)

J. Sharpe, U. Ahlgren, P. Perry, B. Hill, A. Ross, J. Hecksher-Sørensen, R. Baldock, and D. Davidson, “Optical projection tomography as a tool for 3D microscopy and gene expression studies,” Science 296, 541–545 (2002).
[Crossref]

2000 (1)

M. G. L. Gustafsson, “Surpassing the lateral resolution limit by a factor of two using structured illumination microscopy,” J. Microsc. 198, 82–87 (2000).
[Crossref]

1999 (1)

R. Heintzmann and C. G. Cremer, “Laterally modulated excitation microscopy: improvement of resolution by using a diffraction grating,” Proc. SPIE 3568, 185–196 (1999).
[Crossref]

1997 (1)

1994 (1)

Ahlgren, U.

J. Sharpe, U. Ahlgren, P. Perry, B. Hill, A. Ross, J. Hecksher-Sørensen, R. Baldock, and D. Davidson, “Optical projection tomography as a tool for 3D microscopy and gene expression studies,” Science 296, 541–545 (2002).
[Crossref]

Ahmadi, A.

M. H. Winer, A. Ahmadi, and K. C. Cheung, “Application of a three-dimensional (3D) particle tracking method to microfluidic particle focusing,” Lab Chip 14, 1443–1451 (2014).
[Crossref]

Autebert, J.

L. Saias, J. Autebert, L. Malaquin, and J.-L. Viovy, “Design, modeling and characterization of microfluidic architectures for high flow rate, small footprint microfluidic systems,” Lab Chip 11, 822–832 (2011).
[Crossref]

Babcock, H.

B. Huang, H. Babcock, and X. Zhuang, “Breaking the diffraction barrier: super-resolution imaging of cells,” Cell 143, 1047–1058 (2010).
[Crossref]

Badizadegan, K.

W. Choi, C. Fang-Yen, K. Badizadegan, S. Oh, N. Lue, R. R. Dasari, and M. S. Feld, “Tomographic phase microscopy,” Nat. Methods 4, 717–719 (2007).
[Crossref]

Bakalar, M.

J. Schnitzbauer, Y. Wang, S. Zhao, M. Bakalar, T. Nuwal, B. Chen, and B. Huang, “Correlation analysis framework for localization-based superresolution microscopy,” Proc. Natl. Acad. Sci. USA 115, 3219–3224 (2018).
[Crossref]

Baldock, R.

J. Sharpe, U. Ahlgren, P. Perry, B. Hill, A. Ross, J. Hecksher-Sørensen, R. Baldock, and D. Davidson, “Optical projection tomography as a tool for 3D microscopy and gene expression studies,” Science 296, 541–545 (2002).
[Crossref]

Ball, G.

J. Demmerle, C. Innocent, A. J. North, G. Ball, M. Muller, E. Miron, A. Matsuda, I. M. Dobbie, Y. Markaki, and L. Schermelleh, “Strategic and practical guidelines for successful structured illumination microscopy,” Nat. Protoc. 12, 988–1010 (2017).
[Crossref]

Barbastathis, G.

Bates, M.

S. W. Hell, S. J. Sahl, M. Bates, X. Zhuang, R. Heintzmann, M. J. Booth, J. Bewersdorf, G. Shtengel, H. Hess, P. Tinnefeld, A. Honigmann, S. Jakobs, I. Testa, L. Cognet, B. Lounis, H. Ewers, S. J. Davis, C. Eggeling, D. Klenerman, K. I. Willig, G. Vicidomini, M. Castello, A. Diaspro, and T. Cordes, “The 2015 super-resolution microscopy roadmap,” J. Phys. D 48, 443001 (2015).
[Crossref]

B. Huang, M. Bates, and X. Zhuang, “Super-resolution fluorescence microscopy,” Annu. Rev. Biochem. 78, 993–1016 (2009).
[Crossref]

Beach, J. R.

D. Li, L. Shao, B.-C. Chen, X. Zhang, M. Zhang, B. Moses, D. E. Milkie, J. R. Beach, J. A. Hammer, and M. Pasham, “Extended-resolution structured illumination imaging of endocytic and cytoskeletal dynamics,” Science 349, aab3500 (2015).
[Crossref]

Becker, H.

A. Glia, P. Sukumar, A. Brimmo, M. Deliorman, M. A. Qasaimeh, B. L. Gray, and H. Becker, “Immuno-capture of cells in open microfluidics: microfluidic probes integrated with herringbone micro-mixers,” Proc. SPIE 10875, 108751F (2019).
[Crossref]

Betzig, E.

E. Betzig, G. H. Patterson, R. Sougrat, O. W. Lindwasser, S. Olenych, J. S. Bonifacino, M. W. Davidson, J. Lippincott-Schwartz, and H. F. Hess, “Imaging intracellular fluorescent proteins at nanometer resolution,” Science 313, 1642–1645 (2006).
[Crossref]

D. P. Hoffman and E. Betzig, “Tiled reconstruction improves structured illumination microscopy,” bioRxiv: 2020.2001.2006.895318 (2020).

Bewersdorf, J.

S. W. Hell, S. J. Sahl, M. Bates, X. Zhuang, R. Heintzmann, M. J. Booth, J. Bewersdorf, G. Shtengel, H. Hess, P. Tinnefeld, A. Honigmann, S. Jakobs, I. Testa, L. Cognet, B. Lounis, H. Ewers, S. J. Davis, C. Eggeling, D. Klenerman, K. I. Willig, G. Vicidomini, M. Castello, A. Diaspro, and T. Cordes, “The 2015 super-resolution microscopy roadmap,” J. Phys. D 48, 443001 (2015).
[Crossref]

Bianchini, P.

G. Vicidomini, P. Bianchini, and A. Diaspro, “STED super-resolved microscopy,” Nat. Methods 15, 173–182 (2018).
[Crossref]

Biehlmaier, O.

L. Schermelleh, A. Ferrand, T. Huser, C. Eggeling, M. Sauer, O. Biehlmaier, and G. P. C. Drummen, “Super-resolution microscopy demystified,” Nat. Cell Biol. 21, 72–84 (2019).
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Supplementary Material (3)

NameDescription
» Supplement 1       Supplementary Material
» Visualization 1       Simultaneous measurements of 100 nm scale sub-diffraction-limited structural changes in U87 cells and surrounding shear flow flowing at ∼ 800 um/s realized by tunable SIM.
» Visualization 2       Effects of shear stress on actin structure.

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Figures (7)

Fig. 1.
Fig. 1. Schematic of tunable SIM. (a) Simulation model containing both subcellular fine structure and time-varying objects in a single time sequence (${t_1} - {t_{15}}$). Gridlines are guides to the eye to show the uneven flow of beads. Simulated data acquired under different illumination conditions; (b) widefield, (c) SIM, and (d) tunable SIM, respectively. Reconstructed (e) widefield, (f) SIM, and (g) tunable SIM images. Widefield imaging misses SR imaging of subcellular fine structure, while SIM fails to track dynamic objects. Tunable SIM can arbitrarily obtain full temporal resolution or subdiffraction-limited resolution at different areas of the FOV to successfully image the subcellular structures and fast surrounding flow simultaneously. The fluid flow velocity map in Fig. 1(g) was generated by combining 15 images obtained at different time points (${t_1} - {t_{15}}$).
Fig. 2.
Fig. 2. Experimental setup. Incident light is modulated by binary patterns on a DMD. By selecting the first and zeroth diffraction orders using a custom pinhole, tunable SIM patterns with simultaneous sinusoidal 3D-SIM and widefield illumination at arbitrary regions are obtained (see Fig. S2 of Supplement 1 for details of illumination pattern generation). Inset: Blue lines show the binary patterns displayed on the DMD, green lines show the resulting filtered 3D SIM patterns on the object. DMD, digital micromirror device; L1-L4, lenses (${f_1} = {400}$, ${f_2} = {750}$, ${f_3} = {300}$, ${f_4} = {180}\;{\rm mm}$); PH, pinhole; OBJ, objective lens; DM, dichroic mirror; EF, emission filter; M, mirror.
Fig. 3.
Fig. 3. Tunable SIM flowchart. Image acquisition was initiated by applying conventional SIM patterns to the entire FOV, the first two images were used to make a binary image pair for region identification. Dynamic object regions were identified by performing an AND operation on the image pair. Tunable SIM illumination patterns were generated using the classification of dynamic and stationary regions. The acquired tunable SIM images were used for sequent tunable SIM pattern updates.
Fig. 4.
Fig. 4. 100 nm diameter nanobeads imaged by (a) widefield, (b) Wiener deconvolution, and (c) SIM. (d) Tunable SIM images of nanobeads with SR applied only to the areas bounded by the letters ‘UNIST’, and (e) a magnified subarea outlined by a white solid square in (d). (f) Averaged intensity plot of 20 random beads. Scale bars; 5 µm. 0.5 µm in insets of (a–c).
Fig. 5.
Fig. 5. Sample preparation. Cuboid shaped microfluidic chip was fabricated with polydimethylsiloxane (PDMS). U87MG-EGFP-CD9 cells were cultured in the channel for 72 h. Fluorescent beads were introduced into the channel with a constant pressure (2 µl/min) using a syringe pump.
Fig. 6.
Fig. 6. Analysis of fluid stream obtained with tunable SIM. (a) An example of tunable SIM raw data including U87 cells surrounded by beads flowing in a microfluidic channel. Region is visualized with yellow boundary. Segmented ellipsoidal-shaped beads are depicted in red. (b) Beads flowing at different depths expressed with different colored arrows in reconstructed tunable SIM image. (c) 3D fluid stream lines measured with tunable SIM. The target fluorescent cell is depicted in red. The position of a nonfluorescent cell can be observed and is visualized with a green boundary. (d) Velocity distribution map of a plane 10 µm from the channel floor. (e) Velocity plot for ROI 1 and 2 at different distances from the channel floor. Theoretical maximum flow velocity shown in orange dashed line. Scalebars; 10 µm.
Fig. 7.
Fig. 7. Visualization of stress induced morphological changes. (a) Comparison of reconstructed tunable SIM and widefield images. The fluid flow is visualized with arrows, indicating the direction and velocity. (b,c) Expanded widefield (upper column) and SIM image (lower column) of the subarea depicted with yellow dashed square in (a). (d,e) Dynamics of actin cytoskeletal structure observed by tunable SIM in the regions marked by the cyan and green dashed squares in (a). Structure at initial time point ${t_0}$ is shown in red, while the structure at corresponding time points ${t_0} + \Delta t$ are shown in green. Scalebars; 5 µm in (a), 2 µm in (b,c), and 0.5 µm in (d,e).

Equations (4)

Equations on this page are rendered with MathJax. Learn more.

I ( r ) = { O ( r ) P ( r ) } P S F ,
O ( r , t ) = O ( r s ) + O ( r d , t ) ,
P ( r , t ) = P S I M ( r s , t ) + P W F ( r d ) ,
I ( r , t ) = [ { O ( r s ) + O ( r d , t ) } { P S I M ( r s , t ) + P W F ( r d ) } ] P S F = { O ( r s ) P S I M ( r s , t ) + O ( r d , t ) P W F ( r d ) } P S F .

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