Mass Accuracy Check Using Common Background Peaks for Improving Metabolome Data Quality in Chemical Isotope Labeling LC-MS.

Abstract

Chemical isotope labeling (CIL) LC-MS is a highly sensitive and quantitative method for metabolome analysis. Because of a large number of peaks detectable in a sample and the need of running many samples in a metabolomics project, any significant change in mass measurement accuracy during the whole period of running samples can adversely affect the downstream peak alignment and quantitative analysis. Herein, we report a rapid method to check the mass accuracy of individual spectra in each CIL LC-MS run in order to flag up any run containing spectra with accuracy drift that falls outside the expected error. The flagged run may be re-run or discarded before merging with other runs for peak alignment and analysis. This method is based on the observation that some background signals are commonly detected in almost all spectra collected in CIL LC-MS runs. A mass accuracy check (MAC) software program has been developed to first find the common background mass peaks and then use them as mass references to calculate any mass shifts over the course of multiple sample runs. Using a metabolome dataset of 324 human cerebrospinal fluid (CSF) samples and 35 quality control (QC) samples produced by CIL LC-MS, we show that this accuracy check method can streamline the initial raw data processing for downstream analysis in metabolomics.