ArticleNature communications2021
Robust adaptive optics for localization microscopy deep in complex tissue.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed, 50 citations in OpenAlex.
- Graph-based modeling of optical system enables adaptive optics on dynamic samples with self-calibration.iScience · 2026Article
- Physics-informed multi-encoder adaptive optics enables rapid aberration correction for intravital microscopy of deep complex tissue.Nature communications · 2026Article
- Aberration-aware 3D localization microscopy via self-supervised neural-physics learning.Nature communications · 2026Article
- Adaptive optical correction for in vivo two-photon fluorescence microscopy with neural fields.Nature methods · 2026Article
- Interferometric ultra-high resolution 3D imaging through brain sections.Nature communications · 2026Article
- Dual deconvolution in multiphoton structured illumination microscopy for deep-tissue super-resolution imaging.Nature communications · 2026Article
- Review
- In-depth single molecule localization microscopy using adaptive optics and single objective light-sheet microscopy.Nature communications · 2025Article
- Model-based aberration corrected microscopy inside a glass tube.Journal of microscopy · 2025Article
- Super-resolution imaging of the neuronal cytoskeleton.Npj imaging · 2024Review
- Measurement precision bounds on aberrated single-molecule emission patterns.Optics express · 2024Article
- Adaptive optics in an oblique plane microscope.Biomedical optics express · 2024Article
- Adaptive Optics in an Oblique Plane Microscope.bioRxiv : the preprint server for biology · 2024Article
- Deep learning-driven adaptive optics for single-molecule localization microscopy.Nature methods · 2023Article
- Label-free adaptive optics single-molecule localization microscopy for whole zebrafish.Nature communications · 2023Article
- NeuWS: Neural wavefront shaping for guidestar-free imaging through static and dynamic scattering media.Science advances · 2023Article
- Adaptive optics for optical microscopy [Invited].Biomedical optics express · 2023Review
- Active image optimization for lattice light sheet microscopy in thick samples.Biomedical optics express · 2022Article
- SOLEIL: single-objective lens inclined light sheet localization microscopy.Biomedical optics express · 2022Article
- Adaptive optics in single objective inclined light sheet microscopy enables three-dimensional localization microscopy in adultFrontiers in neuroscience · 2022Article
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Single-Molecule Localization Microscopy (SMLM) provides the ability to determine molecular organizations in cells at nanoscale resolution, but in complex biological tissues, where sample-induced aberrations hamper detection and localization, its application remains a challenge. Various adaptive optics approaches have been proposed to overcome these issues, but the exact performance of these methods has not been consistently established. Here we systematically compare the performance of existing methods using both simulations and experiments with standardized samples and find that they often provide limited correction or even introduce additional errors. Careful analysis of the reasons that underlie this limited success enabled us to develop an improved method, termed REALM (Robust and Effective Adaptive Optics in Localization Microscopy), which corrects aberrations of up to 1 rad RMS using 297 frames of blinking molecules to improve single-molecule localization. After its quantitative validation, we demonstrate that REALM enables to resolve the periodic organization of cytoskeletal spectrin of the axon initial segment even at 50 μm depth in brain tissue.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.