Evidence map›Paper›PMID 36812197›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2023

Video-rate Raman-based metabolic imaging by Airy light-sheet illumination and photon-sparse detection.

Lochlann Dunn, Haokun Luo, Nava R Subedi, Ramachandran Kasu, Armando G McDonald, Demetrios N Christodoulides, Andreas E Vasdekis

Abstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
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

4 citing papers in PubMed.

  1. Space-time light-sheet microscopy.Nature communications · 2026
    Article
  2. Near-zero photon bioimaging by fusing deep learning and ultralow-light microscopy.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  3. Review
  4. Video-rate Raman-based metabolic imaging by Airy light-sheet illumination and photon-sparse detection.Proceedings of the National Academy of Sciences of the United States of America · 2023
    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

7 authors.

Lochlann DunnDepartment of Physics, University of Idaho, Moscow, ID 83844-0903.
Haokun LuoThe College of Optics and Photonics, University of Central Florida, Orlando, FL 32816-2700.
Nava R SubediDepartment of Physics, University of Idaho, Moscow, ID 83844-0903.
Ramachandran KasuDepartment of Physics, University of Idaho, Moscow, ID 83844-0903.ORCID 0000-0002-6774-5184
Armando G McDonaldDepartment of Forest, Rangeland, and Fire Sciences, University of Idaho, Moscow, ID 83844-1132.ORCID 0000-0001-5877-4082
Demetrios N ChristodoulidesThe College of Optics and Photonics, University of Central Florida, Orlando, FL 32816-2700.
Andreas E VasdekisDepartment of Physics, University of Idaho, Moscow, ID 83844-0903.

Funding

U.S. Department of Energy (DOE) DE-SC0019249U.S. Department of Energy (DOE) DE-SC0022282
6 · The paper itself

Abstract

Despite its massive potential, Raman imaging represents just a modest fraction of all research and clinical microscopy to date. This is due to the ultralow Raman scattering cross-sections of most biomolecules that impose low-light or photon-sparse conditions. Bioimaging under such conditions is suboptimal, as it either results in ultralow frame rates or requires increased levels of irradiance. Here, we overcome this tradeoff by introducing Raman imaging that operates at both video rates and 1,000-fold lower irradiance than state-of-the-art methods. To accomplish this, we deployed a judicially designed Airy light-sheet microscope to efficiently image large specimen regions. Further, we implemented subphoton per pixel image acquisition and reconstruction to confront issues arising from photon sparsity at just millisecond integrations. We demonstrate the versatility of our approach by imaging a variety of samples, including the three-dimensional (3D) metabolic activity of single microbial cells and the underlying cell-to-cell variability. To image such small-scale targets, we again harnessed photon sparsity to increase magnification without a field-of-view penalty, thus, overcoming another key limitation in modern light-sheet microscopy.

Indexed as

LightingMicroscopyImaging, Three-DimensionalPhotonslight-sheetmetabolismmicroscopysingle cell

Identifiers

PMID36812197
PMCPMC9992822

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.