Metabolite Fingerprinting by Infrared Matrix-Assisted Laser Desorption Electrospray Ionization Mass Spectrometry.

IF 2.5 4区 医学 Q3 BIOCHEMICAL RESEARCH METHODS SLAS Technology Pub Date : 2025-03-13 DOI:10.1016/j.slast.2025.100272
Alena N Joignant, Fan Pu, Shaun M McLoughlin, James W Sawicki, Andrew J Radosevich, Renze Ma, Jon D Williams, Sujatha M Gopalakrishnan, Nathaniel L Elsen
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引用次数: 0

Abstract

The adoption of mass spectrometry for high-throughput screening in drug discovery has become increasingly prevalent and has enabled label-free screening against diverse targets. Cellular assays for phenotypic screening, however, are primarily conducted by microscopy as there remain many challenges associated with conducting phenotypic screens via ultra-high throughput mass spectrometry. Following a simple on-plate extraction, infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI) was employed to directly sample the cell lysate at a speed of one sample per second with high mass resolution. A549 cells were treated with compounds identified as hits in literature, including a recently reported glutaminase cellular screen. Among the test compounds were confirmed glutaminase inhibitors, proposed nuisance compounds, and cell-active but enzyme-inactive compounds. Filtered data were further processed in R for dimensionality reduction and unsupervised clustering. The general nature of dimensionality reduction enables the immediate use of this method in applications other than glutaminase inhibition. Though we observed that all compounds affected the intracellular conversion of glutamine to glutamate, there were clear metabolic differences between the biochemically active compounds and the off-target false hits. Moreover, two nuisance compounds were observed to cluster separately from the confirmed glutaminase inhibitors in the observed metabolite fingerprints. This proof-of-concept work establishes a workflow that enables high-throughput mass spectrometry-based phenotypic screening. The methods proposed herein, at the throughput enabled by IR-MALDESI, could offer a new avenue for the discovery of novel drugs.

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来源期刊
SLAS Technology
SLAS Technology Computer Science-Computer Science Applications
CiteScore
6.30
自引率
7.40%
发文量
47
审稿时长
106 days
期刊介绍: SLAS Technology emphasizes scientific and technical advances that enable and improve life sciences research and development; drug-delivery; diagnostics; biomedical and molecular imaging; and personalized and precision medicine. This includes high-throughput and other laboratory automation technologies; micro/nanotechnologies; analytical, separation and quantitative techniques; synthetic chemistry and biology; informatics (data analysis, statistics, bio, genomic and chemoinformatics); and more.
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