A methodology has been developed that improves the identification and description of biomedical markers

Metabolomics is a technology in the field of omic sciences that studies metabolites, small molecules present in the blood and tissues that reflect the activity and metabolic status of the body. Some can be decisive in indicating the risk, the onset or the course that a specific disease will have: these are known as markers. But finding a marker among thousands of metabolites is no easy task. Current analytical strategies based on mass spectrometry can detect hundreds and even thousands of metabolites, but identifying their chemical structure is a challenge that is still to be solved. Now a research team from the Universitat Rovira i Virgili, the Pere Virgili Health Research Institute (IISPV) and CIBERDEM has developed a methodology that significantly improves the ability to identify and describe these markers. The results of the research have been published in the scientific journal Nature Methods.

Mass spectrometry provides spectral data on the presence of metabolites in samples of biomedical interest. With this methodology, the research team has been able to use the chemical information found in human metabolome databases and project it en masse onto spectral data and signals. By so doing, they have been able to decipher which signals contain relevant biological information. They have shown that a large majority of the signals in the sample analysed are redundant or nonspecific and they have substantially improved the ability to identify and describe metabolites.

"If we imagine a place where there are hundreds of people talking, with current techniques we can see that many people are talking to each other, but we would have great difficulty in deciphering the content of a single conversation. The technique we have developed enables us to identify the conversations that interest us and the content much more quickly,” explains the researcher Òscar Yanes, from the Department of Electronic, Electrical and Automatic Engineering and the URV-Metabolic Platform IISPV-CIBERDEM.

More specifically, the research team has managed to filter up to 90% of all non-specific signals, and increase fourfold the ability to identify and describe markers and understand their chemical structure. This is an important step in deciphering the human metabolome, which is the set of metabolites involved in the metabolism of a cell, tissue or organ.

This advance is also an improvement for many technological applications, especially in the field of biomedical research, as it makes it possible to identify markers more easily. The technology developed by this research team, which is part of the Metabolomics Interdisciplinary Laboratory (MIL @ b) group —recognized by the Catalan government— has been patented and they are working to put it on the market.

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