Evidence map›Paper›PMID 38535320›Full record

ArticleMetabolites2024

Evaluation of Oil-Absorbing Film for Imprint Desorption Electrospray Ionization Mass Spectrometry Imaging (IDESI-MSI) on Biological Samples.

Jiedong Li, Ruolun Wei, Yifan Meng, Richard N Zare

Abstract read
In one paragraph

Article in Metabolites, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

4 authors.

Jiedong LiDepartment of Chemistry and Biochemistry, University of California, Santa Barbara, Santa Barbara, CA 93106, USA.
Ruolun WeiDepartment of Neurosurgery, Stanford University School of Medicine, Stanford, CA 94305, USA.
Yifan MengDepartment of Chemistry, Stanford University, Stanford, CA 94305, USA.ORCID 0000-0001-6897-3595
Richard N ZareDepartment of Chemistry, Stanford University, Stanford, CA 94305, USA.

Funding

United States Air Force Office of Scientific Research AFOSR FA9550- 21- 1- 0170
6 · The paper itself

Abstract

Imprint Desorption Electrospray Ionization Mass Spectrometry Imaging (IDESI-MSI) has proven to be a robust and reliable tool for chemically imaging biological samples such as fungi, animal tissues, and plants, but the choice of the imprint substrate is crucial. It must effectively transfer maximum amounts of species from the sample while preserving the original spatial distribution of detected molecules. In this study, we explored the potential of utilizing an oil-absorbing film, known for its soft nature and excellent lipophilicity, as an imprint substrate for IDESI-MSI on biological samples. To assess the transfer efficiency of the amounts of molecules and molecular patterns, we conducted experiments using mouse brain tissue. The result shows that more than 90% of the analytes can be transferred to the oil-absorbing film from the original tissue. A comparison of IDESI-MSI results between the oil-absorbing film and the original tissue demonstrates the material's capability to transfer most molecules from the original tissue and retain images of different analytes with high spatial fidelity. We extended our investigation to plant imaging, where we applied IDESI-MSI to a cross-section of okra. The oil-absorbing film exhibited promise in this context as well. These findings suggest that IDESI-MSI utilizing the oil-absorbing film holds potential across various research fields, including biological metabolism, chemistry, and clinical research, making this technique widely applicable.

Indexed as

imprint DESI-MSImolecular imagingoil-absorbing film

Identifiers

PMID38535320
PMCPMC10972117

What OpenQuestion holds

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Registered trials

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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.