ArticleNature methods2025
Fast-adaptive super-resolution lattice light-sheet microscopy for rapid, long-term, near-isotropic subcellular imaging.
Article in Nature methods, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
6 citing papers in PubMed.
- Endothelial Continuum and Capillary Specialization in Pulmonary Vascular Development.Arteriosclerosis, thrombosis, and vascular biology · 2026Review
- Article
- Physics-informed multi-encoder adaptive optics enables rapid aberration correction for intravital microscopy of deep complex tissue.Nature communications · 2026Article
- Super-Resolution Structured Illumination Microscopy to Study Endocytosis and Cellular Nanoparticle Uptake.ACS nano · 2026Review
- When imaging technology meets single-cell omics: new paradigm in developmental biology.Cell regeneration (London, England) · 2025Review
- Physics-Inspired Single-Particle Tracking Accelerated with Parallelism.bioRxiv : the preprint server for biology · 2025Article
Corrections and comments
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Authors and funding
19 authors.
Funding
Abstract
Lattice light-sheet microscopy provides a crucial observation window into intra- and intercellular physiology of living specimens but at the diffraction-limited resolution or anisotropic super-resolution with structured illumination. Here we present meta-learning-empowered reflective lattice light-sheet virtual structured illumination microscopy (Meta-rLLS-VSIM), which upgrades lattice light-sheet microscopy to a near-isotropic super resolution of ~120 nm laterally and ~160 nm axially without modifications of the core optical system or loss of other live-cell imaging metrics. Moreover, we devised an adaptive online training approach by synergizing the front-end imaging system and back-end meta-learning framework, which alleviated the demand for training data by tenfold and reduced the total time for data acquisition and model training down to tens of seconds. We demonstrate the versatile functionalities of Meta-rLLS-VSIM by imaging a variety of bioprocesses with ultrahigh spatiotemporal resolution for hundreds of multicolor volumes, delineating the nanoscale distributions, dynamics and interaction patterns of multiple organelles in embryos and eukaryotic cells.
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Registered trials
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.