This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: Whether in drug discovery, environmental analysis or metabolomics: anyone analyzing complex biological samples often needs to identify the small molecules they contain. Researchers at Friedrich Schiller University Jena, in collaboration with partners from the Helmholtz Zentrum München and the Technical University of Munich, have developed a method that addresses a problem in analytical chemistry that has persisted for decades.
The team, led by bioinformatician Prof. Sebastian Böcker, presents the new tool in Nature Methods. Biological samples such as blood, cell material or bacterial cultures can contain a vast number of different molecules.
While DNA and proteins can now be analyzed relatively well using established methods, the diversity of small molecules—known as metabolites—is particularly high. These include metabolic products, natural compounds, toxins, degradation products and many pharmaceutical substances. Liquid chromatography is frequently used to separate such molecules from one another.
In this process, a mixture of substances passes through a separation column; individual molecules are retained to varying degrees and therefore elute from the column at different times. This so-called "retention time" provides an important indication of which substance is present in a sample. But when exactly does a particular molecule elute from the column?
It is precisely this question that has occupied analytical chemistry for decades. "The difficulty lies in the fact that retention times depend heavily on the experimental conditions: on the column used, the solvent, the gradient, the pH, the temperature and even on seemingly minor technical changes," says Böcker. "If, for example, a tube in the apparatus is replaced, or if a new tube is slightly longer than the old one, the measured times can shift significantly." Previous models therefore often had to be trained or fine-tuned using data from the very same measurement system on which they were later to make predictions.
In practice, this means that researchers would first have to measure numerous standard substances before they could use the model effectively. This is time-consuming, expensive and, for many applications, simply not feasible. The new approach from Böcker's team is not limited to a single system but can make predictions even for new systems and unknown molecules.
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