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

Abstract

LC-MS based global metabolite profiling currently lacks detailed guidelines to demonstrate that the obtained data is of high enough analytical quality. Insufficient data quality may result in the failure to generate a hypothesis, or in the worst case, a false or skewed hypothesis. After assessing the literature, it is apparent that an analytically focused summary and critical discussion related to this subject would be beneficial for both beginners and experts engaged in this field. A particular focus will be placed on data quality, which we here define as the degree to which a set of parameters fulfills predetermined criteria, similar to the established guidelines for targeted analysis. However, several of these parameters are difficult to assess since holistic approaches measure thousands of metabolites in parallel and seldom include predefined knowledge of which metabolites will differ between sample groups. In this review, the following parameters will be discussed in detail: reproducibility, selectivity, certainty of metabolite identification and metabolite coverage. The review systematically describes the generic workflow for LC-MS based global metabolite profiling and highlights how each separate part may affect data quality. The last part of the review describes how data quality can be evaluated as well as identifies areas where additional improvement is needed. In this review, we provide our own analytical opinions in regards to evaluation and, to some extent, improvement of data quality.

Advanced LC–MS-based methods to study the co-occurrence and metabolization of multiple mycotoxins in cereals and cereal-based food

“…Both targeted and untargeted LC-HRMS-based metabolomic studies follow a general workflow consisting of several steps: (1) experimental design, (2) sample preparation, (3) chromatographic separation and MS detection, (4) data acquisition, (5) data processing, and (6) data analysis and interpretation. These workflows were recently summarized in several comprehensive reviews [86][87][88]. Several studies dealing with the biotransformation of mycotoxins in plants using a recently developed untargeted stable isotope labeling (SIL)-assisted approach using 13 C-labeled standards in combination with LC-HRMS have been published [89][90][91].…”

β-N-Methylamino-l-alanine (BMAA) perturbs alanine, aspartate and glutamate metabolism pathways in human neuroblastoma cells as determined by metabolic profiling

“…It is important to state that not all small molecules ionize with the same efficiency during the MS-analysis thus providing a plausible explanation to why serine was not detected either as [M+H], [M−H] or possible adducts (Engskog et al 2016). However, there was a perturbation of the glycine, serine and threonine metabolism pathways in the BMAA-treated cells.…”

“…Lock-mass correction was performed using a solution of 2 ng/μl leucine-enkephalin in acetonitrile:0.1% formic acid in water (50:50 v/v). Stable signal intensity, mass accuracy and retention time were monitored by repeated injections of the matrix (QC sample) to ensure a stabile system (Want et al 2010; Vorkas et al 2015; Engskog et al 2016). Moreover, the QC sample was injected in triplicates in regular intervals throughout the analytical run to assess repeatability and overall system performance across the analytical batch (Want et al 2010; Engskog et al 2016).…”

“…A criterion of CV <30% was used to afford analytically reliable features for multivariate and univariate analysis (Bijlsma et al 2006; Dunn et al 2012; Vorkas et al 2015; Engskog et al 2016). Data were Pareto scaled, as implemented in the SIMCA-P+ package, prior to further analysis.…”

“…This information can be used to build a hypothesis related to the response. It is important to note that the resulting hypothesis only becomes relevant if the generated data is analytically reliable, thus numerous principles for quality assurance should be employed (Engskog et al 2016; Vorkas et al 2015). The analytical techniques of choice for detection of small metabolites in complex biological matrices is either nuclear magnetic resonance (NMR) spectroscopy or high resolution mass spectrometry (HRMS) or a combination of both to ensure a wider metabolome coverage (Lindon and Nicholson 2008; Zhang et al 2010, 2013; Theodoridis et al 2012; Vuckovic 2012).…”