Exploring matrix effects and quantification performance in metabolomics experiments using artificial biological gradients.

Metabolomics has become an integral part of many life-science applications but is technically still very challenging. Numerous analytical approaches are needed as metabolites have very broad concentration ranges and extremely diverse chemical properties. Configuring a metabolomics pipeline and exploring its merits is a complex task that depends on effective and transparent evaluation procedures. Unfortunately, there are no widely applicable methods to evaluate how well acquired data can approximate actual concentration differences. Here, we introduce a powerful approach that provides semiquantitative calibration curves over a biologically defined concentration range for all detected compounds. By performing metabolomics on a stepwise gradient between two biological specimens, we obtain a data set where each peak would ideally show a linear dependency on the mixture ratio. An example gradient between extracts of tomato leaf and fruit demonstrates good calibration statistics for a large proportion of the peaks but also highlights cases with strong background-dependent signal interference. Analysis of artificial biological gradients is a general and inexpensive tool for calibration that greatly facilitates data interpretation, quality control and method comparisons.