ArticleJournal of visualized experiments : JoVE2022
Label-free Super-resolution Imaging Enabled by Vibrational Imaging of Swelled Tissue and Analysis.
Article in Journal of visualized experiments : JoVE, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 8 citations in OpenAlex.
- AI-Assisted Coherent Raman Scattering Microscopy for Clinical Translation.Chemical & biomedical imaging · 2026Review
- Emerging Technologies for Exploring the Cellular Mechanisms in Vascular Diseases.International journal of molecular sciences · 2025Review
- Advances in Super-resolution Stimulated Raman Scattering Microscopy.Chemical & biomedical imaging · 2024Review
- Super-resolution vibrational imaging based on photoswitchable Raman probe.Science advances · 2023Article
- Coherent Raman Microscopy: from instrumentation to applications.Journal of visualized experiments : JoVE · 2023Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
The universal utilization of fluorescence microscopy, especially super-resolution microscopy, has greatly advanced knowledge about modern biology. Conversely, the requirement of fluorophore labeling in fluorescent techniques poses significant challenges, such as photobleaching and non-uniform labeling of fluorescent probes and prolonged sample processing. In this protocol, the detailed working procedures of vibrational imaging of swelled tissue and analysis (VISTA) are presented. VISTA circumvents obstacles associated with fluorophores and achieves label-free super-resolution volumetric imaging in biological samples with spatial resolution down to 78 nm. The procedure is established by embedding cells and tissues in hydrogel, isotropically expanding the hydrogel sample hybrid, and visualizing endogenous protein distributions by vibrational imaging with stimulated Raman scattering microscopy. The method is demonstrated on both cells and mouse brain tissues. Highly correlative VISTA and immunofluorescence images were observed, validating the protein origin of imaging specificities. Exploiting such correlation, a machine learning-based image-segmentation algorithm was trained to achieve multi-component prediction of nuclei, blood vessels, neuronal cells, and dendrites from label-free mouse brain images. The procedure was further adapted to investigate pathological poly-glutamine (polyQ) aggregates in cells and amyloid-beta (Aβ) plaques in brain tissues with high throughput, justifying its potential for large-scale clinical samples.
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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.