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

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.