Challenges:
- Metabolite samples require much care and degrade rapidly. They also degrade at different rates depending on the type of metabolite: for example, fatty acids will degrade more rapidly than amino acids or other misc compounds.
- Identification of metabolites, as the vast majority (90+%) that can be detected (in blood) remain unknown
- Correct separation of individual metabolites from spectrometer output (sometimes what looks like 2 metabolites on the output may actually be one)
- Mapping of metabolites back to their genetic / DNA origins
- Mapping of metabolites to pathways
- Analysis methods are not perfected. There is no standard way of normalising data and it suffers from large technical and sampling variability. For example: if you take a sample in the morning and then one in the evening, they may have entirely different profiles due to diet and just general human metabolic activity. Thus, experiments require much better design than, say, GWAS studies, in order to reduce confounding factors. The data also suffers from high missingness, which typically has to be imputed or excluded.
Network analysis
As to which I alluded in the challenges, we simply don't know much about the majority of metabolites, nor do we know to which pathways they belong, etc (even for the known ones). In a typical experiment, one would identify dozens of, for example, fatty acids with different length carbon chains. These may be part of separate biochemical processes or part of the same biochemical cascade... impossible to know. One can easily build a correlation network of the data but it may be entirely meaningless. My feeling is that, in the future, we will have to build metabolite networks using known biochemical pathways as a 'scaffold'. For example, we know (very well) pathways such as citric acid synthesis, vitamin D metabolism, etc. One could take that information and use it as a scaffold for the purposes of building metabolite networks. Without these types of guides, I fear that network analysis in metabolomics will be just meaningless.
