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Abstract
The need for reproducible and comparable results is of increasing importance in non-targeted metabolomic studies, especially when differences between experimental groups are small. Liquid chromatography–mass spectrometry spectra are often acquired batch-wise so that necessary calibrations and cleaning of the instrument can take place. However this may introduce further sources of variation, such as differences in the conditions under which the acquisition of individual batches is performed. Quality control (QC) samples are frequently employed as a means of both judging and correcting this variation. Here we show that the use of QC samples can lead to problems. The non-linearity of the response can result in substantial differences between the recorded intensities of the QCs and experimental samples, making the required adjustment difficult to predict. Furthermore, changes in the response profile between one QC interspersion and the next cannot be accounted for and QC based correction can actually exacerbate the problems by introducing artificial differences. “Background correction” methods utilise all experimental samples to estimate the variation over time rather than relying on the QC samples alone. We compare non-QC correction methods with standard QC correction and demonstrate their success in reducing differences between replicate samples and their potential to highlight differences between experimental groups previously hidden by instrumental variation.
Highlights
Non-targeted metabolomic studies seek to analyse as wide a range of metabolites as possible
The use of liquid chromatography-mass spectrometry (LC–MS) for this purpose has found a wide range of applications, including drug discovery (Korfmacher 2005), disease biomarker discovery (Lu et al 2008), pesticide (Zhang et al 2011) and herbicide (Shalaby et al 1992) analysis in agriculture, wastewater analysis (Kostich et al 2014) and the discovery of novel metabolites (Nakabayashi and Saito 2013)
It is clear from the Principal Components Analysis (PCA) of the scaled data that the majority of the variance is due to batch differences rather than experimental groups
Summary
Non-targeted metabolomic studies seek to analyse as wide a range of metabolites as possible. Many non-targeted approaches focus on qualitative results, such as biomarker discovery, and the need for reproducible and comparable results is imperative, especially when differences between experimental groups are small. A number of factors can cause differences in LC–MS response profiles between acquisitions. Many of these relate to chromatographic aspects, such as retention time drift or changes in peak shape (Lai et al 2009), but changes in the response of the mass spectrometer can be seen (Ohlsson and Wallmark 1999).
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