Mass Spectrometry-based Untargeted Metabolomics

National Institute Of Environmental Health Sciences

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Abstract

The Metabolomics Core Facility (MCF) completed a variety of projects during this period in support of the objectives of the NIEHS and NIH. The MCF analyzed >2000 unique samples from NIEHS and 6 other ICs. Research was performed with 30 unique investigators and more than 35 different researchers. The primary effort this year was improving upon untargeted metabolomics methods developed in the prior year (including protocols for sample extraction, data acquisition, and data analysis) while expanding the capacity (number of samples) as well as adding additional capabilities (services). In support of the increasing capability an additional ultra-high performance liquid chromatography - high resolution mass spectrometry system was purchased with installation planned. Further, the robotic liquid handling system (Hamilton Starlet) in the Core was programmed to facilitate the sample processing and extraction of biofluid samples. An untargeted metabolomics method is implemented using a reverse-phase liquid chromatography - mass spectrometry approach. The data are collected using a data-dependent method (DDA) in which MS1 and MS/MS information (used in identification) are collected concurrently. Over 600 chemical standards were analyzed, interpreted, and compiled into an MS/MS spectral database for annotation. Using the developed untargeted metabolomics method, the Core has analyzed human plasma and urine, murine kidney and liver homogenates, Drosophila extracts, and others. A typical experiment results in the annotation (putative identification) of approximately 300-600 chemicals with the remaining MS features remaining knowns. Such results are on par with established metabolomics cores and academic laboratories performing LC-MS based metabolomics analyses. In addition to the acquisition method, the Core has developed a series of R scripts and Jupyter Notebooks to facilitate data analysis, quality assurance, and quality control procedures including pooled quality control measures, linear response evaluation, dispersion ratio, and others. The result is a robust, high-quality untargeted metabolomics data acquisition method and data processing pipeline to serve NIEHS and NIH investigators.