title: Enabling Efficient and Confident Annotation of LC-MS Metabolomics Data through MS1 Spectrum and Time Prediction.
abstract: Liquid chromatography coupled to electrospray ionization-mass spectrometry (LC-ESI-MS) is a versatile and robust platform for metabolomic analysis. However, while ESI is a soft ionization technique, in-source phenomena including multimerization, nonproton cation adduction, and in-source fragmentation complicate interpretation of MS data. Here, we report chromatographic and mass spectrometric behavior of 904 authentic standards collected under conditions identical to a typical nontargeted profiling experiment. The data illustrate that the often high level of complexity in MS spectra is likely to result in misinterpretation during the annotation phase of the experiment and a large overestimation of the number of compounds detected. However, our analysis of this MS spectral library data indicates that in-source phenomena are not random but depend at least in part on chemical structure. These nonrandom patterns enabled predictions to be made as to which in-source signals are likely to be observed for a given compound. Using the authentic standard spectra as a training set, we modeled the in-source phenomena for all compounds in the Human Metabolome Database to generate a theoretical in-source spectrum and retention time library. A novel spectral similarity matching platform was developed to facilitate efficient spectral searching for nontargeted profiling applications. Taken together, this collection of experimental spectral data, predictive modeling, and informatic tools enables more efficient, reliable, and transparent metabolite annotation.
authors: Proteomics and Metabolomics Facility, Colorado State University , C-121 Microbiology Building, 2021 Campus Delivery, Fort Collins, Colorado 80523, United States.
authors: Corey D
authors: CD
authors: Broeckling
authors: Program in Medical and Population Genetics, Broad Institute of MIT and Harvard and Analytical and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School , Boston, Massachusetts 02114, United States.
authors: Andrea
authors: A
authors: Ganna
authors: Research Software Facility, Soil and Crop Sciences, Colorado State University , Fort Collins, Colorado 80523, United States.|Department of Biology, Colorado State University , Fort Collins, Colorado 80523, United States.
authors: Mark
authors: M
authors: Layer
authors: Research Software Facility, Soil and Crop Sciences, Colorado State University , Fort Collins, Colorado 80523, United States.
authors: Kevin
authors: K
authors: Brown
authors: Research Software Facility, Soil and Crop Sciences, Colorado State University , Fort Collins, Colorado 80523, United States.
authors: Ben
authors: B
authors: Sutton
authors: Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine , Stanford, California 94305, United States.
authors: Erik
authors: E
authors: Ingelsson
authors: Department of Biology, Colorado State University , Fort Collins, Colorado 80523, United States.
authors: Graham
authors: G
authors: Peers
authors: Proteomics and Metabolomics Facility, Colorado State University , C-121 Microbiology Building, 2021 Campus Delivery, Fort Collins, Colorado 80523, United States.
authors: Jessica E
authors: JE
authors: Prenni
mesh_terms: D002853:Chromatography, Liquid / Q000379:methods*; D016208:Databases, Factual; D006801:Humans; D055442:Metabolome; D055432:Metabolomics / Q000379:methods*; D012984:Software; D021241:Spectrometry, Mass, Electrospray Ionization / Q000379:methods*