Evidence map›Paper›PMID 34131146›Full record

ArticleNature communications2021

Super-resolution label-free volumetric vibrational imaging.

Chenxi Qian, Kun Miao, Li-En Lin, Xinhong Chen, Jiajun Du, Lu Wei

Open access · goldAbstract read
In one paragraph

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 31 papers.

0numbers the graph read from it
0cells of the map it votes in
31citing papers in PubMed
11.2field-weighted citation impact, top 2% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

31 citing papers in PubMed, 62 citations in OpenAlex.

  1. Review
  2. Review
  3. GFP-free live neuron quantitative imaging reveals compartmentalization and growth dynamics of polyQ aggregates.Proceedings of the National Academy of Sciences of the United States of America · 2026
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  11. Review
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  15. Roadmap on Label-Free Super-Resolution Imaging.Laser & photonics reviews · 2023
    Article
  16. Review
  17. Article
  18. Article
  19. Far-field super-resolution chemical microscopy.Light, science & applications · 2023
    Review
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 1 institution in 1 country.

Chenxi Qian *Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA, USA.ORCID 0000-0003-4815-5565
Kun Miao *Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA, USA.ORCID 0000-0001-6567-3650
Li-En LinDivision of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA, USA.
Xinhong ChenDivision of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA.ORCID 0000-0003-0408-0813
Jiajun DuDivision of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA, USA.
Lu WeiDivision of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA, USA. lwei@caltech.edu.ORCID 0000-0001-9170-2283
California Institute of Technology · US

Funding

Super-resolution Chemical Imaging for BiomedicineDP2GM140919 · NIGMS · CALIFORNIA INSTITUTE OF TECHNOLOGY · PI WEI, LU · 2020 to 2020
$2.3M
NIGMS NIH HHS DP2 GM140919
6 · The paper itself

Abstract

Innovations in high-resolution optical imaging have allowed visualization of nanoscale biological structures and connections. However, super-resolution fluorescence techniques, including both optics-oriented and sample-expansion based, are limited in quantification and throughput especially in tissues from photobleaching or quenching of the fluorophores, and low-efficiency or non-uniform delivery of the probes. Here, we report a general sample-expansion vibrational imaging strategy, termed VISTA, for scalable label-free high-resolution interrogations of protein-rich biological structures with resolution down to 78 nm. VISTA achieves decent three-dimensional image quality through optimal retention of endogenous proteins, isotropic sample expansion, and deprivation of scattering lipids. Free from probe-labeling associated issues, VISTA offers unbiased and high-throughput tissue investigations. With correlative VISTA and immunofluorescence, we further validated the imaging specificity of VISTA and trained an image-segmentation model for label-free multi-component and volumetric prediction of nucleus, blood vessels, neuronal cells and dendrites in complex mouse brain tissues. VISTA could hence open new avenues for versatile biomedical studies.

Indexed as

AnimalsBrainFemaleFluorescenceFluorescent Antibody TechniqueHeLa CellsHumansImaging, Three-DimensionalMachine LearningMaleMiceMice, Inbred C57BLModels, TheoreticalStaining and LabelingZebrafish

Identifiers

PMID34131146
PMCPMC8206358
OpenAlexW3172002016

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.