title: Predicting retention time in hydrophilic interaction liquid chromatography mass spectrometry and its use for peak annotation in metabolomics.
abstract: Liquid chromatography coupled to mass spectrometry (LCMS) is widely used in metabolomics due to its sensitivity, reproducibility, speed and versatility. Metabolites are detected as peaks which are characterised by mass-over-charge ratio (m/z) and retention time (rt), and one of the most critical but also the most challenging tasks in metabolomics is to annotate the large number of peaks detected in biological samples. Accuratem/zmeasurements enable the prediction of molecular formulae which provide clues to the chemical identity of peaks, but often a number of metabolites have identical molecular formulae. Chromatographic behaviour, reflecting the physicochemical properties of metabolites, should also provide structural information. However, the variation in rt between analytical runs, and the complicating factors underlying the observed time shifts, make the use of such information for peak annotation a non-trivial task. To this end, we conducted Quantitative Structure-Retention Relationship (QSRR) modelling between the calculated molecular descriptors (MDs) and the experimental retention times (rts) of 93 authentic compounds analysed using hydrophilic interaction liquid chromatography (HILIC) coupled to high resolution MS. A predictive QSRR model based on Random Forests algorithm outperformed a Multiple Linear Regression based model, and achieved a high correlation between predicted rts and experimental rts (Pearson's correlation coefficient = 0.97), with mean and median absolute error of 0.52 min and 0.34 min (corresponding to 5.1 and 3.2 % error), respectively. We demonstrate that rt prediction with the precision achieved enables the systematic utilisation of rts for annotating unknown peaks detected in a metabolomics study. The application of the QSRR model with the strategy we outlined enhanced the peak annotation process by reducing the number of false positives resulting from database queries by matching accurate mass alone, and enriching the reference library. The predicted rts were validated using either authentic compounds or ion fragmentation patterns.
authors.affiliation: AgResearch Grasslands Research Centre, Palmerston North, 4442 New Zealand.
authors.forename: Mingshu
authors.initials: M
authors.lastname: Cao
authors.affiliation: AgResearch Grasslands Research Centre, Palmerston North, 4442 New Zealand.
authors.forename: Karl
authors.initials: K
authors.lastname: Fraser
authors.affiliation: AgResearch Grasslands Research Centre, Palmerston North, 4442 New Zealand.
authors.forename: Jan
authors.initials: J
authors.lastname: Huege
authors.affiliation: AgResearch Grasslands Research Centre, Palmerston North, 4442 New Zealand.
authors.forename: Tom
authors.initials: T
authors.lastname: Featonby
authors.affiliation: Massey University, Institute of Agriculture and Environment, Palmerston North, New Zealand.
authors.forename: Susanne
authors.initials: S
authors.lastname: Rasmussen
authors.affiliation: AgResearch Grasslands Research Centre, Palmerston North, 4442 New Zealand.
authors.forename: Chris
authors.initials: C
authors.lastname: Jones
keywords: LCMS; Lolium perenne; Metabolite identification; Metabolomics; Peak annotation; QSRR