Enabling Efficient and Confident Annotation of LC-MS Metabolomics Data through MS1 Spectrum and Time Prediction

被引:70
作者
Broeckling, Corey D. [6 ]
Ganna, Andrea [1 ,2 ]
Layer, Mark [3 ,5 ]
Brown, Kevin [3 ]
Sutton, Ben [3 ]
Ingelsson, Erik [4 ]
Peers, Graham [5 ]
Prenni, Jessica E. [6 ]
机构
[1] Massachusetts Gen Hosp, Dept Med, Broad Inst MIT & Harvard & Analyt & Translat Gene, Program Med & Populat Genet, Boston, MA 02114 USA
[2] Harvard Med Sch, Boston, MA 02114 USA
[3] Colorado State Univ, Res Software Facil Soil & Crop Sci, Ft Collins, CO 80523 USA
[4] Stanford Univ, Sch Med, Dept Med, Div Cardiovasc Med, Stanford, CA 94305 USA
[5] Colorado State Univ, Dept Biol, Ft Collins, CO 80523 USA
[6] Colorado State Univ, Prote & Metabol Facil, C-121 Microbiol Bldg,2021 Campus Delivery, Ft Collins, CO 80523 USA
关键词
SODIUM ADDUCT FORMATION; TANDEM MASS-SPECTRA; SPECTROMETRY DATA; RETENTION TIME; METABOLITE IDENTIFICATION; ESI-MS;
D O I
10.1021/acs.analchem.6b02479
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
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.
引用
收藏
页码:9226 / 9234
页数:9
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