Metabolomics: What is it all about?

What is the definition of metabolomics?

Metabolomics is a fast growing area that combines sophisticated analytical techniques for identifying and quantifying cellular metabolites with statistical and multivariant methods for information extraction and data interpretation. In the recent two decades, tremendous progress has been achieved in the sequencing of a variety of organisms. Simultaneously, significant resources were invested in developing analytical methods for analysing various cell products, such as gene expression (transcripts), proteins, and metabolites.

All of these so-called 'omics methods, such as genomics, transcriptomics, proteomics, and metabolomics, are thought to be crucial tools for understanding an organism's biology and response to environmental stimuli or genetic disruption.Because metabolites "operate as a spoken language, broadcasting messages from the genetic architecture and the environment" , metabolomics is thought to provide a direct "functional readout of an organism's physiological condition" . Analytical technologies have been used to examine metabolites in many species, tissues, and fluids.

Mass spectrometry along with various chromatographic separation techniques, such as liquid or gas chromatography or NMR, are the main tools for simultaneously analysing a large number of metabolites. Despite the sophistication and sensitivity of the technology, there are still a few bottlenecks in metabolomics. There is no one method available to study the full metabolome due to the great diversity of chemical structures and considerable variances in abundance. As a result, a number of complementary techniques for extracting, detecting, quantifying, and identifying as many metabolites as feasible must be developed.

Another issue in metabolomics is extracting information from large amounts of data collected by high-throughput analyzers and interpreting it in a biological context. The use of sophisticated statistical and multi-variant data analysis tools, such as cluster analysis, pathway mapping, comparative overlays, and heatmaps, has not only been an exciting and steep learning process for biochemists, but it has also demonstrated that current thinking about large data sets and distinguishing between noise and real sample-related information needs to change.