Separating the wheat from the chaff: a prioritisation pipeline for the analysis of metabolomics datasets

Abstract

Liquid Chromatography Mass Spectrometry (LC-MS) is a powerful and widely applied method for the study of biological systems, biomarker discovery and pharmacological interventions. LC-MS measurements are, however, significantly complicated by several technical challenges, including: (1) ionisation suppression/enhancement, disturbing the correct quantification of analytes, and (2) the detection of large amounts of separate derivative ions, increasing the complexity of the spectra, but not their information content. Here we introduce an experimental and analytical strategy that leads to robust metabolome profiles in the face of these challenges. Our method is based on rigorous filtering of the measured signals based on a series of sample dilutions. Such data sets have the additional characteristic that they allow a more robust assessment of detection signal quality for each metabolite. Using our method, almost 80% of the recorded signals can be discarded as uninformative, while important information is retained. As a consequence, we obtain a broader understanding of the information content of our analyses and a better assessment of the metabolites detected in the analyzed data sets. We illustrate the applicability of this method using standard mixtures, as well as cell extracts from bacterial samples. It is evident that this method can be applied in many types of LC-MS analyses and more specifically in untargeted metabolomics.

Metabolic adaptations of Leishmania donovani in relation to differentiation, drug resistance, and drug pressure

“…A9906 – preparation of this mixture is described by Jankevics et al . () was run at the start, the middle and the end of every analysis batch to aid accurate metabolite identification (Vincent et al ., ). In addition, a serial dilution of a pooled sample of all extracts was included to filter out a substantial part of the spurious signals (Jankevics et al ., ).…”

Missing value imputation strategies for metabolomics data

“…In this method, missing values are found using a zero-fill program, then, returning to the raw LC-MS data, the peak pairs are found, the intensity ratio is calculated and then entered into the table [18]. In another published study, data reprocessing has been explored within the theme of limit of detection, whereby it was suggested that variables can be filtered on dilution factor [19]. Although this is not explicitly explored for the analysis of missing values, more useful signals are retained while artefacts are removed; removing features that often contain a larger number of missing values and provide confusion to the statistical outcome.…”

LC–MS based global metabolite profiling: the necessity of high data quality

“…Examples of these softwares include the R-based CAMERA addition to XCMS (Smith et al 2006;Kuhl et al 2012), PUTMEDID-LCMS , PeakML/mzMatch (Scheltema et al 2011) and IDEOM (Creek et al 2012). Once the chemical space has been reduced to a list of experimental masses coupled to retention times (features), databases and mass spectral libraries may be used to afford plausible metabolite identities based on matching experimental m/z values against theoretical values included in these databases.…”