Evidence map›Paper›PMID 38374266›Full record

ArticleNature methods2024

VIBRANT: spectral profiling for single-cell drug responses.

Xinwen Liu, Lixue Shi, Zhilun Zhao, Jian Shu, Wei Min

Open access · greenAbstract read
In one paragraph

Article in Nature methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
15.2field-weighted citation impact, top 1% 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

17 citing papers in PubMed, 24 citations in OpenAlex.

  1. PLANCK: super-multiplex optical imaging without labeling.bioRxiv : the preprint server for biology · 2026
    Article
  2. Review
  3. Article
  4. Article
  5. Article
  6. Review
  7. Bond-Selective Imaging at the Frontier of Biomedicine.Chemical & biomedical imaging · 2025
    Article
  8. Vibrational Probes in Bioimaging and Chemical Biology.Chemical & biomedical imaging · 2025
    Article
  9. Article
  10. Article
  11. Review
  12. Review
  13. Review
  14. Article
  15. Article
  16. Detection of radiosensitive subpopulationsFrontiers in oncology · 2025
    Article
  17. 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

5 authors at 3 institutions in 2 countries.

Xinwen LiuDepartment of Chemistry, Columbia University, New York, NY, USA.ORCID http://orcid.org/0000-0002-9812-7813
Lixue ShiDepartment of Chemistry, Columbia University, New York, NY, USA.ORCID http://orcid.org/0000-0001-9712-5800
Zhilun ZhaoDepartment of Chemistry, Columbia University, New York, NY, USA.
Jian ShuCutaneous Biology Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.ORCID http://orcid.org/0000-0001-9390-9333
Wei MinDepartment of Chemistry, Columbia University, New York, NY, USA. wm2256@columbia.edu.ORCID http://orcid.org/0000-0003-2570-3557
Columbia University · USBroad Institute · USShanghai Medical College of Fudan University · CN

Funding

High-resolution volumetric imaging of metabolic activity in tissues and its application to tumor metabolismR01EB029523 · NIBIB · COLUMBIA UNIV NEW YORK MORNINGSIDE · PI MIN, WEI · 2020 to 2023
$1.6M
NIBIB NIH HHS R01 EB029523
6 · The paper itself

Abstract

High-content cell profiling has proven invaluable for single-cell phenotyping in response to chemical perturbations. However, methods with improved throughput, information content and affordability are still needed. We present a new high-content spectral profiling method named vibrational painting (VIBRANT), integrating mid-infrared vibrational imaging, multiplexed vibrational probes and an optimized data analysis pipeline for measuring single-cell drug responses. Three infrared-active vibrational probes were designed to measure distinct essential metabolic activities in human cancer cells. More than 20,000 single-cell drug responses were collected, corresponding to 23 drug treatments. The resulting spectral profile is highly sensitive to phenotypic changes under drug perturbation. Using this property, we built a machine learning classifier to accurately predict drug mechanism of action at single-cell level with minimal batch effects. We further designed an algorithm to discover drug candidates with new mechanisms of action and evaluate drug combinations. Overall, VIBRANT has demonstrated great potential across multiple areas of phenotypic screening.

Indexed as

NeoplasmsAlgorithmsHumansMachine Learning

Identifiers

PMID38374266
PMCPMC11214684
OpenAlexW4391931019

What OpenQuestion holds

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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.