# Defining Blood Plasma and Serum Metabolome by GC-MS
## 6 Current Challenges and Prospects in Measuring Metabolites  
Metabolomics stays at the nexus of chemistry, biology, data science, chemometrics, and bioinformatics. The challenges of metabolomics cannot be solved without crossing scientific boundaries. Therefore, a detailed study of the metabolome requires coordinated teamwork at all stages: from collecting samples to interpreting the obtained data. Progress in metabolomics (and GC-MS-based metabolomics, in particular) is \"fueled\" by optimized strategies of sample preparations, numerous technological advances, initiatives on standardization, and collective efforts to generate and curate databases [204; 205]. Thus, the Human Metabolome Project [206], which was launched in 2005, has united and streamlined the efforts of the world metabolic community to collect information about the \"detectable\" human metabolome, mediated by all the other \"omics\" processes in various states of the organism [207]. Today we are witnessing tremendous growth of HMDB 5.0, released in 2021, contains information on more than 220 thousand metabolites, almost twice as much as in 2018 [47]. We believe that the reason for this is the popularization of large-scale omics research \"beyond genomics\" [208]. The trend of shifting from genome-wide association studies (GWAS) to metabolome-wide association studies (MWAS) has been gaining momentum since 2008. The results of consolidation of several omics layers appear more and more often [209; 210; 211; 212; 213]. Metabolomics quickly responds to public challenges: over 200 publications on COVID-19 metabolomics appeared in less than two years [214; 215; 216]. The spectrum of application of metabolic knowledge solo or as a part of multi-omics is broad: from microbial stains engineering [217] to precise and personalized health monitoring [218; 219; 220; 221]. Unfortunately, clinical metabolic tests are not registered yet [222]. Nevertheless, there are clear signs of the potential of integration of metabolomics into the clinical space [223], which is hampered by insufficiently effective design of data acquisition and (re-)processing [187; 224], imperfect standard operating procedures [225], lack of adequate quality controls [69] and unrepresentative samples collections [226]. From a technical point of view, we believe that for comprehensive and rapid metabolomics, the improvements in the system of separation of complex biological mixtures are the most anticipated and promising since the progress of mass spectrometry alone is unlikely to change our understanding of the metabolome qualitatively. As in proteomics, to provide comprehensive coverage of the metabolome requires a shift to multidimensional chromatography techniques, providing state-of-the-art separation [227]. Beyond instrumental challenges, a series of improvements are required to translate metabolomics in routine medical practice, which is not conceivable without extensive population-wide studies explaining how metabolome interacts with phenotype and health status [31]. If these obstacles are overcome, metabolomics tools have tremendous potential to provide solutions for precision medicine and life sciences research. Already today, there are entire platforms of the complete cycle (for example, Metabolon [228]), within the framework of which the design of the experiment, its implementation, and processing of the obtained data are carried out. Due to their circulating nature, liquid blood components -plasma and serum- are excellent matrices for metabolomic studies [33]. However, the diverse chemistry and wide dynamic range of blood metabolites require digging deeper and developing tailored analytical techniques to provide proper metabolome coverage.