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Deep3DSIM: super-resolution imaging of thick tissue using 3D structured illumination with adaptive optics

Abstract:
Three-dimensional structured illumination microscopy (3D-SIM) doubles the resolution of fluorescence imaging in all directions and enables optical sectioning with increased image contrast. However, 3D-SIM has not been widely applied to imaging deep in thick tissues due to its sensitivity to sample-induced aberrations, making the method difficult to apply beyond 10 µm in depth. Furthermore, 3D-SIM has not been available in an upright configuration, limiting its use for live imaging while manipulating the specimen, for example, with electrophysiology. Here, we have overcome these barriers by developing a novel upright 3D-SIM system (termed Deep3DSIM) that incorporates adaptive optics for aberration correction and remote focusing, reducing artefacts, improving contrast, restoring resolution, and eliminating the need to move the specimen or the objective lens in volume imaging. These advantages are equally applicable to inverted 3D-SIM systems. We demonstrate high-quality 3D-SIM imaging in various samples, including imaging more than 130 µm into the Drosophila brain.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.7554/eLife.102144

Authors

More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
ORCID:
0000-0002-2856-7602
More by this author
Institution:
University of Oxford
Division:
MSD
Department:
Biochemistry
Role:
Author
ORCID:
0000-0001-5539-2206
More by this author
Institution:
University of Oxford
Division:
MSD
Department:
Biochemistry
Role:
Author
ORCID:
0000-0003-3578-7301
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
ORCID:
0000-0003-2710-0186
More by this author
Institution:
University of Oxford
Division:
MSD
Department:
Biochemistry
Role:
Author


More from this funder
Funder identifier:
https://ror.org/00cwqg982
Grant:
[BB/M011224/1]


Publisher:
eLife Sciences Publications
Journal:
eLife More from this journal
Volume:
14
Article number:
e102144
Place of publication:
England
Publication date:
2025-10-28
Acceptance date:
2025-09-04
DOI:
EISSN:
2050-084X


Language:
English
Pubs id:
2307598
UUID:
uuid_ad49a8d0-9f4a-4e7d-ac36-6c7812ca8873
Local pid:
pubs:2307598
Deposit date:
2025-11-06
ARK identifier:

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