High-Throughput Compound Quality Assessment with High-Mass-Resolution Acoustic Ejection Mass Spectrometry: An Automatic Data Processing Toolkit.
Alandra QuinnGordana IvosevJefferson ChinRobert MongilloCristiano VeigaThomas R CoveyBrendon KapinosBhagyashree KhunteHui ZhangMatthew D TroutmanChang LiuPublished in: Analytical chemistry (2024)
Pharmacological screening heavily relies on the reliability of compound libraries. To ensure the accuracy of screening results, fast and reliable quality control (QC) of these libraries is essential. While liquid chromatography (LC) with ultraviolet (UV) or mass spectrometry (MS) detection has been employed for molecule QC on small sample sets, the analytical throughput becomes a bottleneck when dealing with large libraries. Acoustic ejection mass spectrometry (AEMS) is a high-throughput analytical platform that covers a broad range of chemical structural space. In this study, we present the utilization of an AEMS system equipped with a high-resolution MS analyzer for high-throughput compound QC. To facilitate efficient data processing, which is a key challenge for such a high-throughput application, we introduce an automatic data processing toolkit that allows for the high-throughput assessment of the sample standards' quantitative and qualitative characteristics, including purity calculation with the background processing option. Moreover, the toolkit includes a module for quantitatively comparing spectral similarity with the reference library. Integrating the described high-resolution AEMS system with the data processing toolkit effectively eliminates the analytical bottleneck, enabling a rapid and reliable compound quality assessment of large-scale compound libraries.
Keyphrases
- high throughput
- mass spectrometry
- liquid chromatography
- high resolution
- high resolution mass spectrometry
- tandem mass spectrometry
- gas chromatography
- electronic health record
- high performance liquid chromatography
- single cell
- simultaneous determination
- capillary electrophoresis
- big data
- quality control
- solid phase extraction
- deep learning
- machine learning
- multiple sclerosis
- systematic review
- ms ms
- magnetic resonance
- loop mediated isothermal amplification