Abstract

Imaging across length scales and in depth has been an important pursuit of widefield optical imaging. This promises to reveal fine cellular detail within a widefield snapshot of a tissue sample. Current advances often sacrifice resolution through selective sub-sampling to provide a wide field of view in a reasonable time scale. We demonstrate a new avenue for recovering high-resolution images from sub-sampled data in light sheet microscopy using deep-learning super-resolution. We combine this with the use of a widefield Airy beam to achieve high-resolution imaging over extended fields of view and depths. We characterise our method on fluorescent beads as test targets. We then demonstrate improvements in imaging amyloid plaques in a cleared brain from a mouse model of Alzheimer’s disease, and in excised healthy and cancerous colon and breast tissues. This development can be widely applied in all forms of light sheet microscopy to provide a two-fold increase in the dynamic range of the imaged length scale. It has the potential to provide further insight into neuroscience, developmental biology, and histopathology.

Published by The Optical Society under the terms of the Creative Commons Attribution 4.0 License. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

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2019 (7)

S. Corsetti, F. Gunn-Moore, and K. Dholakia, “Light sheet fluorescence microscopy for neuroscience,” J. Neurosci. Methods 319, 16–27 (2019).
[Crossref]

E. M. Hillman, V. Voleti, W. Li, and H. Yu, “Light-sheet microscopy in neuroscience,” Annu. Rev. Neurosci. 42(1), 295–313 (2019).
[Crossref]

Z. Yang, K. L. H. Cole, Y. Qiu, I. M. L. Somorjai, P. Wijesinghe, J. Nylk, S. Cochran, G. C. Spalding, D. A. Lyons, and K. Dholakia, “Light sheet microscopy with acoustic sample confinement,” Nat. Commun. 10(1), 669 (2019).
[Crossref]

H. Wang, Y. Rivenson, Y. Jin, Z. Wei, R. Gao, H. Günaydın, L. A. Bentolila, C. Kural, and A. Ozcan, “Deep learning enables cross-modality super-resolution in fluorescence microscopy,” Nat. Methods 16(1), 103–110 (2019).
[Crossref]

H. Zhang, C. Fang, X. Xie, Y. Yang, W. Mei, D. Jin, and P. Fei, “High-throughput, high-resolution deep learning microscopy based on registration-free generative adversarial network,” Biomed. Opt. Express 10(3), 1044–1063 (2019).
[Crossref]

C. Belthangady and L. A. Royer, “Applications, promises, and pitfalls of deep learning for fluorescence image reconstruction,” Nat. Methods 16(12), 1215–1225 (2019).
[Crossref]

W. Yang, X. Zhang, Y. Tian, W. Wang, J.-H. Xue, and Q. Liao, “Deep learning for single image super-resolution: A brief review,” IEEE Trans. Multimedia 21(12), 3106–3121 (2019).
[Crossref]

2018 (2)

J. Nylk, K. McCluskey, M. A. Preciado, M. Mazilu, Z. Yang, F. J. Gunn-Moore, S. Aggarwal, J. A. Tello, D. E. K. Ferrier, and K. Dholakia, “Light-sheet microscopy with attenuation-compensated propagation-invariant beams,” Sci. Adv. 4(4), eaar4817 (2018).
[Crossref]

A.-K. Gustavsson, P. N. Petrov, and W. E. Moerner, “Light sheet approaches for improved precision in 3D localization-based super-resolution imaging in mammalian cells,” Opt. Express 26(10), 13122–13147 (2018).
[Crossref]

2017 (3)

A. K. Glaser, N. P. Reder, Y. Chen, E. F. McCarty, C. Yin, L. Wei, Y. Wang, L. D. True, and J. T. Liu, “Light-sheet microscopy for slide-free non-destructive pathology of large clinical specimens,” Nat. Biomed. Eng. 1(7), 0084 (2017).
[Crossref]

R. M. Power and J. Huisken, “A guide to light-sheet fluorescence microscopy for multiscale imaging,” Nat. Methods 14(4), 360–373 (2017).
[Crossref]

F. Fereidouni, Z. T. Harmany, M. Tian, A. Todd, J. A. Kintner, J. D. McPherson, A. D. Borowsky, J. Bishop, M. Lechpammer, S. G. Demos, and R. Levenson, “Microscopy with ultraviolet surface excitation for rapid slide-free histology,” Nat. Biomed. Eng. 1(12), 957–966 (2017).
[Crossref]

2016 (3)

P. Hoyer, G. de Medeiros, B. Balázs, N. Norlin, C. Besir, J. Hanne, H.-G. Kräusslich, J. Engelhardt, S. J. Sahl, S. W. Hell, and L. Hufnagel, “Breaking the diffraction limit of light-sheet fluorescence microscopy by RESOLFT,” Proc. Natl. Acad. Sci. 113(13), 3442–3446 (2016).
[Crossref]

J. Nylk, K. McCluskey, S. Aggarwal, J. A. Tello, and K. Dholakia, “Enhancement of image quality and imaging depth with Airy light-sheet microscopy in cleared and non-cleared neural tissue,” Biomed. Opt. Express 7(10), 4021–4033 (2016).
[Crossref]

N. Renier, E. L. Adams, C. Kirst, Z. Wu, R. Azevedo, J. Kohl, A. E. Autry, L. Kadiri, K. Umadevi Venkataraju, Y. Zhou, V. X. Wang, C. Y. Tang, O. Olsen, C. Dulac, P. Osten, and M. Tessier-Lavigne, “Mapping of brain activity by automated volume analysis of immediate early genes,” Cell 165(7), 1789–1802 (2016).
[Crossref]

2015 (2)

G. de Medeiros, N. Norlin, S. Gunther, M. Albert, L. Panavaite, U.-M. Fiuza, F. Peri, T. Hiiragi, U. Krzic, and L. Hufnagel, “Confocal multiview light-sheet microscopy,” Nat. Commun. 6(1), 8881 (2015).
[Crossref]

D. Li, L. Shao, B.-C. Chen, X. Zhang, M. Zhang, B. Moses, D. E. Milkie, J. R. Beach, J. A. Hammer, M. Pasham, T. Kirchhausen, M. A. Baird, M. W. Davidson, P. Xu, and E. Betzig, “Extended-resolution structured illumination imaging of endocytic and cytoskeletal dynamics,” Science 349(6251), aab3500 (2015).
[Crossref]

2014 (4)

T. Vettenburg, H. I. Dalgarno, J. Nylk, C. Coll-Lladó, D. E. Ferrier, T. Čižmár, F. J. Gunn-Moore, and K. Dholakia, “Light-sheet microscopy using an Airy beam,” Nat. Methods 11(5), 541–544 (2014).
[Crossref]

M. Ragazzi, S. Piana, C. Longo, F. Castagnetti, M. Foroni, G. Ferrari, G. Gardini, and G. Pellacani, “Fluorescence confocal microscopy for pathologists,” Mod. Pathol. 27(3), 460–471 (2014).
[Crossref]

B.-C. Chen, W. R. Legant, K. Wang, L. Shao, D. E. Milkie, M. W. Davidson, C. Janetopoulos, X. S. Wu, J. A. Hammer, Z. Liu, B. P. English, Y. Mimori-Kiyosue, D. P. Romero, A. T. Ritter, J. Lippincott-Schwartz, L. Fritz-Laylin, R. D. Mullins, D. M. Mitchell, J. N. Bembenek, A.-C. Reymann, R. Böhme, S. W. Grill, J. T. Wang, G. Seydoux, U. S. Tulu, D. P. Kiehart, and E. Betzig, “Lattice light-sheet microscopy: Imaging molecules to embryos at high spatiotemporal resolution,” Science 346(6208), 1257998 (2014).
[Crossref]

N. Renier, Z. Wu, D. J. Simon, J. Yang, P. Ariel, and M. Tessier-Lavigne, “iDISCO: A simple, rapid method to immunolabel large tissue samples for volume imaging,” Cell 159(4), 896–910 (2014).
[Crossref]

2013 (1)

N. G. Horton, K. Wang, D. Kobat, C. G. Clark, F. W. Wise, C. B. Schaffer, and C. Xu, “In vivo three-photon microscopy of subcortical structures within an intact mouse brain,” Nat. Photonics 7(3), 205–209 (2013).
[Crossref]

2011 (1)

M. Weber and J. Huisken, “Light sheet microscopy for real-time developmental biology,” Curr. Opin. Genet. & Dev. 21(5), 566–572 (2011).
[Crossref]

2009 (1)

J. Huisken and D. Y. R. Stainier, “Selective plane illumination microscopy techniques in developmental biology,” Development 136(12), 1963–1975 (2009).
[Crossref]

2008 (1)

J. Baumgartl, M. Mazilu, and K. Dholakia, “Optically mediated particle clearing using airy wavepackets,” Nat. Photonics 2(11), 675–678 (2008).
[Crossref]

2006 (1)

H. Oakley, S. L. Cole, S. Logan, E. Maus, P. Shao, J. Craft, A. Guillozet-Bongaarts, M. Ohno, J. Disterhoft, L. Van Eldik, R. Berry, and R. Vassar, “Intraneuronal β-amyloid aggregates, neurodegeneration, and neuron loss in transgenic mice with five familial Alzheimer’s disease mutations: Potential factors in amyloid plaque formation,” J. Neurosci. 26(40), 10129–10140 (2006).
[Crossref]

2005 (1)

M. G. L. Gustafsson, “Nonlinear structured-illumination microscopy: Wide-field fluorescence imaging with theoretically unlimited resolution,” Proc. Natl. Acad. Sci. 102(37), 13081–13086 (2005).
[Crossref]

1995 (1)

Adams, E. L.

N. Renier, E. L. Adams, C. Kirst, Z. Wu, R. Azevedo, J. Kohl, A. E. Autry, L. Kadiri, K. Umadevi Venkataraju, Y. Zhou, V. X. Wang, C. Y. Tang, O. Olsen, C. Dulac, P. Osten, and M. Tessier-Lavigne, “Mapping of brain activity by automated volume analysis of immediate early genes,” Cell 165(7), 1789–1802 (2016).
[Crossref]

Aggarwal, S.

J. Nylk, K. McCluskey, M. A. Preciado, M. Mazilu, Z. Yang, F. J. Gunn-Moore, S. Aggarwal, J. A. Tello, D. E. K. Ferrier, and K. Dholakia, “Light-sheet microscopy with attenuation-compensated propagation-invariant beams,” Sci. Adv. 4(4), eaar4817 (2018).
[Crossref]

J. Nylk, K. McCluskey, S. Aggarwal, J. A. Tello, and K. Dholakia, “Enhancement of image quality and imaging depth with Airy light-sheet microscopy in cleared and non-cleared neural tissue,” Biomed. Opt. Express 7(10), 4021–4033 (2016).
[Crossref]

Albert, M.

G. de Medeiros, N. Norlin, S. Gunther, M. Albert, L. Panavaite, U.-M. Fiuza, F. Peri, T. Hiiragi, U. Krzic, and L. Hufnagel, “Confocal multiview light-sheet microscopy,” Nat. Commun. 6(1), 8881 (2015).
[Crossref]

Ariel, P.

N. Renier, Z. Wu, D. J. Simon, J. Yang, P. Ariel, and M. Tessier-Lavigne, “iDISCO: A simple, rapid method to immunolabel large tissue samples for volume imaging,” Cell 159(4), 896–910 (2014).
[Crossref]

Autry, A. E.

N. Renier, E. L. Adams, C. Kirst, Z. Wu, R. Azevedo, J. Kohl, A. E. Autry, L. Kadiri, K. Umadevi Venkataraju, Y. Zhou, V. X. Wang, C. Y. Tang, O. Olsen, C. Dulac, P. Osten, and M. Tessier-Lavigne, “Mapping of brain activity by automated volume analysis of immediate early genes,” Cell 165(7), 1789–1802 (2016).
[Crossref]

Azevedo, R.

N. Renier, E. L. Adams, C. Kirst, Z. Wu, R. Azevedo, J. Kohl, A. E. Autry, L. Kadiri, K. Umadevi Venkataraju, Y. Zhou, V. X. Wang, C. Y. Tang, O. Olsen, C. Dulac, P. Osten, and M. Tessier-Lavigne, “Mapping of brain activity by automated volume analysis of immediate early genes,” Cell 165(7), 1789–1802 (2016).
[Crossref]

Baird, M. A.

D. Li, L. Shao, B.-C. Chen, X. Zhang, M. Zhang, B. Moses, D. E. Milkie, J. R. Beach, J. A. Hammer, M. Pasham, T. Kirchhausen, M. A. Baird, M. W. Davidson, P. Xu, and E. Betzig, “Extended-resolution structured illumination imaging of endocytic and cytoskeletal dynamics,” Science 349(6251), aab3500 (2015).
[Crossref]

Balázs, B.

P. Hoyer, G. de Medeiros, B. Balázs, N. Norlin, C. Besir, J. Hanne, H.-G. Kräusslich, J. Engelhardt, S. J. Sahl, S. W. Hell, and L. Hufnagel, “Breaking the diffraction limit of light-sheet fluorescence microscopy by RESOLFT,” Proc. Natl. Acad. Sci. 113(13), 3442–3446 (2016).
[Crossref]

Baumgartl, J.

J. Baumgartl, M. Mazilu, and K. Dholakia, “Optically mediated particle clearing using airy wavepackets,” Nat. Photonics 2(11), 675–678 (2008).
[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, M. Pasham, T. Kirchhausen, M. A. Baird, M. W. Davidson, P. Xu, and E. Betzig, “Extended-resolution structured illumination imaging of endocytic and cytoskeletal dynamics,” Science 349(6251), aab3500 (2015).
[Crossref]

Belthangady, C.

C. Belthangady and L. A. Royer, “Applications, promises, and pitfalls of deep learning for fluorescence image reconstruction,” Nat. Methods 16(12), 1215–1225 (2019).
[Crossref]

Bembenek, J. N.

B.-C. Chen, W. R. Legant, K. Wang, L. Shao, D. E. Milkie, M. W. Davidson, C. Janetopoulos, X. S. Wu, J. A. Hammer, Z. Liu, B. P. English, Y. Mimori-Kiyosue, D. P. Romero, A. T. Ritter, J. Lippincott-Schwartz, L. Fritz-Laylin, R. D. Mullins, D. M. Mitchell, J. N. Bembenek, A.-C. Reymann, R. Böhme, S. W. Grill, J. T. Wang, G. Seydoux, U. S. Tulu, D. P. Kiehart, and E. Betzig, “Lattice light-sheet microscopy: Imaging molecules to embryos at high spatiotemporal resolution,” Science 346(6208), 1257998 (2014).
[Crossref]

Bengio, Y.

I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in Advances in neural information processing systems, (2014), pp. 2672–2680.

Bentolila, L. A.

H. Wang, Y. Rivenson, Y. Jin, Z. Wei, R. Gao, H. Günaydın, L. A. Bentolila, C. Kural, and A. Ozcan, “Deep learning enables cross-modality super-resolution in fluorescence microscopy,” Nat. Methods 16(1), 103–110 (2019).
[Crossref]

Berry, R.

H. Oakley, S. L. Cole, S. Logan, E. Maus, P. Shao, J. Craft, A. Guillozet-Bongaarts, M. Ohno, J. Disterhoft, L. Van Eldik, R. Berry, and R. Vassar, “Intraneuronal β-amyloid aggregates, neurodegeneration, and neuron loss in transgenic mice with five familial Alzheimer’s disease mutations: Potential factors in amyloid plaque formation,” J. Neurosci. 26(40), 10129–10140 (2006).
[Crossref]

Besir, C.

P. Hoyer, G. de Medeiros, B. Balázs, N. Norlin, C. Besir, J. Hanne, H.-G. Kräusslich, J. Engelhardt, S. J. Sahl, S. W. Hell, and L. Hufnagel, “Breaking the diffraction limit of light-sheet fluorescence microscopy by RESOLFT,” Proc. Natl. Acad. Sci. 113(13), 3442–3446 (2016).
[Crossref]

Betzig, E.

D. Li, L. Shao, B.-C. Chen, X. Zhang, M. Zhang, B. Moses, D. E. Milkie, J. R. Beach, J. A. Hammer, M. Pasham, T. Kirchhausen, M. A. Baird, M. W. Davidson, P. Xu, and E. Betzig, “Extended-resolution structured illumination imaging of endocytic and cytoskeletal dynamics,” Science 349(6251), aab3500 (2015).
[Crossref]

B.-C. Chen, W. R. Legant, K. Wang, L. Shao, D. E. Milkie, M. W. Davidson, C. Janetopoulos, X. S. Wu, J. A. Hammer, Z. Liu, B. P. English, Y. Mimori-Kiyosue, D. P. Romero, A. T. Ritter, J. Lippincott-Schwartz, L. Fritz-Laylin, R. D. Mullins, D. M. Mitchell, J. N. Bembenek, A.-C. Reymann, R. Böhme, S. W. Grill, J. T. Wang, G. Seydoux, U. S. Tulu, D. P. Kiehart, and E. Betzig, “Lattice light-sheet microscopy: Imaging molecules to embryos at high spatiotemporal resolution,” Science 346(6208), 1257998 (2014).
[Crossref]

Bishop, J.

F. Fereidouni, Z. T. Harmany, M. Tian, A. Todd, J. A. Kintner, J. D. McPherson, A. D. Borowsky, J. Bishop, M. Lechpammer, S. G. Demos, and R. Levenson, “Microscopy with ultraviolet surface excitation for rapid slide-free histology,” Nat. Biomed. Eng. 1(12), 957–966 (2017).
[Crossref]

Böhme, R.

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Xu, B.

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Xu, C.

N. G. Horton, K. Wang, D. Kobat, C. G. Clark, F. W. Wise, C. B. Schaffer, and C. Xu, “In vivo three-photon microscopy of subcortical structures within an intact mouse brain,” Nat. Photonics 7(3), 205–209 (2013).
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D. Li, L. Shao, B.-C. Chen, X. Zhang, M. Zhang, B. Moses, D. E. Milkie, J. R. Beach, J. A. Hammer, M. Pasham, T. Kirchhausen, M. A. Baird, M. W. Davidson, P. Xu, and E. Betzig, “Extended-resolution structured illumination imaging of endocytic and cytoskeletal dynamics,” Science 349(6251), aab3500 (2015).
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W. Yang, X. Zhang, Y. Tian, W. Wang, J.-H. Xue, and Q. Liao, “Deep learning for single image super-resolution: A brief review,” IEEE Trans. Multimedia 21(12), 3106–3121 (2019).
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N. Renier, Z. Wu, D. J. Simon, J. Yang, P. Ariel, and M. Tessier-Lavigne, “iDISCO: A simple, rapid method to immunolabel large tissue samples for volume imaging,” Cell 159(4), 896–910 (2014).
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W. Yang, X. Zhang, Y. Tian, W. Wang, J.-H. Xue, and Q. Liao, “Deep learning for single image super-resolution: A brief review,” IEEE Trans. Multimedia 21(12), 3106–3121 (2019).
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E. M. Hillman, V. Voleti, W. Li, and H. Yu, “Light-sheet microscopy in neuroscience,” Annu. Rev. Neurosci. 42(1), 295–313 (2019).
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Zhang, M.

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Zhang, X.

W. Yang, X. Zhang, Y. Tian, W. Wang, J.-H. Xue, and Q. Liao, “Deep learning for single image super-resolution: A brief review,” IEEE Trans. Multimedia 21(12), 3106–3121 (2019).
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D. Li, L. Shao, B.-C. Chen, X. Zhang, M. Zhang, B. Moses, D. E. Milkie, J. R. Beach, J. A. Hammer, M. Pasham, T. Kirchhausen, M. A. Baird, M. W. Davidson, P. Xu, and E. Betzig, “Extended-resolution structured illumination imaging of endocytic and cytoskeletal dynamics,” Science 349(6251), aab3500 (2015).
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Zhou, Y.

N. Renier, E. L. Adams, C. Kirst, Z. Wu, R. Azevedo, J. Kohl, A. E. Autry, L. Kadiri, K. Umadevi Venkataraju, Y. Zhou, V. X. Wang, C. Y. Tang, O. Olsen, C. Dulac, P. Osten, and M. Tessier-Lavigne, “Mapping of brain activity by automated volume analysis of immediate early genes,” Cell 165(7), 1789–1802 (2016).
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Annu. Rev. Neurosci. (1)

E. M. Hillman, V. Voleti, W. Li, and H. Yu, “Light-sheet microscopy in neuroscience,” Annu. Rev. Neurosci. 42(1), 295–313 (2019).
[Crossref]

Biomed. Opt. Express (2)

Cell (2)

N. Renier, E. L. Adams, C. Kirst, Z. Wu, R. Azevedo, J. Kohl, A. E. Autry, L. Kadiri, K. Umadevi Venkataraju, Y. Zhou, V. X. Wang, C. Y. Tang, O. Olsen, C. Dulac, P. Osten, and M. Tessier-Lavigne, “Mapping of brain activity by automated volume analysis of immediate early genes,” Cell 165(7), 1789–1802 (2016).
[Crossref]

N. Renier, Z. Wu, D. J. Simon, J. Yang, P. Ariel, and M. Tessier-Lavigne, “iDISCO: A simple, rapid method to immunolabel large tissue samples for volume imaging,” Cell 159(4), 896–910 (2014).
[Crossref]

Curr. Opin. Genet. & Dev. (1)

M. Weber and J. Huisken, “Light sheet microscopy for real-time developmental biology,” Curr. Opin. Genet. & Dev. 21(5), 566–572 (2011).
[Crossref]

Development (1)

J. Huisken and D. Y. R. Stainier, “Selective plane illumination microscopy techniques in developmental biology,” Development 136(12), 1963–1975 (2009).
[Crossref]

IEEE Trans. Multimedia (1)

W. Yang, X. Zhang, Y. Tian, W. Wang, J.-H. Xue, and Q. Liao, “Deep learning for single image super-resolution: A brief review,” IEEE Trans. Multimedia 21(12), 3106–3121 (2019).
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J. Neurosci. (1)

H. Oakley, S. L. Cole, S. Logan, E. Maus, P. Shao, J. Craft, A. Guillozet-Bongaarts, M. Ohno, J. Disterhoft, L. Van Eldik, R. Berry, and R. Vassar, “Intraneuronal β-amyloid aggregates, neurodegeneration, and neuron loss in transgenic mice with five familial Alzheimer’s disease mutations: Potential factors in amyloid plaque formation,” J. Neurosci. 26(40), 10129–10140 (2006).
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J. Neurosci. Methods (1)

S. Corsetti, F. Gunn-Moore, and K. Dholakia, “Light sheet fluorescence microscopy for neuroscience,” J. Neurosci. Methods 319, 16–27 (2019).
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J. Opt. Soc. Am. A (1)

Mod. Pathol. (1)

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Nat. Biomed. Eng. (2)

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Nat. Commun. (2)

Z. Yang, K. L. H. Cole, Y. Qiu, I. M. L. Somorjai, P. Wijesinghe, J. Nylk, S. Cochran, G. C. Spalding, D. A. Lyons, and K. Dholakia, “Light sheet microscopy with acoustic sample confinement,” Nat. Commun. 10(1), 669 (2019).
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Nat. Methods (4)

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Opt. Express (1)

Proc. Natl. Acad. Sci. (2)

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Sci. Adv. (1)

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Science (2)

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Figures (6)

Fig. 1.
Fig. 1. Setup and acquisition process. (a) Schematic of the setup. NF: notch filter; WP: half-wave waveplate; PBS: polarising beam splitter; BS1, BS2: 50:50 beam splitters; PM: Airy phase mask; M1–4: mirrors; L1–4: lenses; CL: cylindrical lens; OBJ: objective; BP: bandpass filter; TL: tube lens. In the inset: A solid immersion lens (SIL) and an index-matching oil layer are used for imaging a sample on top of a glass coverslip. The sample is translated with the coverslip and (b) a series of images are recorded. (c) The image sequence is interpolated into the physical coordinate space.
Fig. 2.
Fig. 2. Maximum intensity projections in the $yz$ plane of 200-nm green fluorescent microspheres embedded in 1.5 $\%$ agarose recorded with the (a) Gaussian light sheet and (b) Airy light sheet. (c) Deconvolution of the experimental data shown in (b). (d) Experimentally determined full-width at half-maximum (FWHM) as function of depth of the sample.
Fig. 3.
Fig. 3. Validation of deep-learning super-resolution on fluorescent beads in agarose. (a–c) 200-nm sub-resolution beads imaged with (a) low resolution (LR), and (b) high resolution (HR) with a use of a zoom lens. In (b), the grayed out area is the LR image, representing the missed field of view. (c) The output of the network applied to the LR image recovers a high resolution over the whole field of view. (g) and (h) show the vertical and horizontal line profiles across a bead marked by a target. (d–f) 4.8- $\mu$ m beads imaged with (d) low resolution, (e) high resolution, and (f) output of the network applied to (d). The insets in (d–f) show a magnified view of beads marked by the box. (i) Vertical and (j) horozontal line profiles across the bead in (d–f).
Fig. 4.
Fig. 4. Images of cleared mouse brain tissue stained with thioflavin S. (a–f) Images taken at a depth of 400 $\mu$ m: (a) Gaussian light sheet, (b) deconvolved Airy light sheet and (c) network outputs of the deconvolved Airy data. (d), (e), and (f) are magnified images of the amyloid plaques shown in (a), (b) and (c), respectively. In (d–f) the red dashed lines indicate the (g) line profile through two different plaques. (h–j) Images taken at a depth of 1300 $\mu$ m: (h) Gaussian light sheet, (i) deconvolved Airy light sheet and (j) network outputs of the deconvolved Airy data.
Fig. 5.
Fig. 5. Images of healthy and cancerous colon tissue stained with acridine orange. The images of normal colon were acquired at different depths into the sample with both the (a–d) Gaussian and (e-h) Airy beam. The images of cancerous colon were also acquired at different depths into the sample with both the (i–k) Gaussian and (l-n) Airy beam. The Airy images are deconvolved and the deep learning algorithm is applied to them.
Fig. 6.
Fig. 6. Images of benign breast tissue and invasive breast cancer tissue stained with acriflavine. The images were acquired at different depths into the sample with both the Gaussian and the Airy beam. The deep learning algorithm is applied to the Airy images after deconvolution. (a) and (c) show the surface of benign breast tissue acquired with the Gaussian and Airy beam, respectively. (b) and (d) show images of the benign tissue acquired at a depth of 86 $\mu$ m with the Gaussian and Airy beam, respectively. (e) and (g) show images of the surface of breast cancer tissue acquired with the Gaussian and Airy beam, respectively. Insets (1) and (2) show an agglomeration of nuclei from regions marked by a yellow rectangle in (e) and (g) respectively. (f) and (h) show images of the cancer tissue acquired at a depth of 86 $\mu$ m with the Gaussian and Airy beam, respectively.

Equations (2)

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P ( u ) = exp ( ı 2 π α u 3 ) ,
I ^ i + 1 = [ D I ^ i PSF PSF ] I ^ i ,

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