MetaboLomics is often confused with metaboNomics, probably because they mean exactly the same thing. They were both terms coined around a similar time, so pick your favourite and run with it. Metabolomics is probably more common and is usually used in conjunction with liquid chromatography mass spectrometry (LCMS) experiments, whereas metabonomics is usually used in NMR based studies. The technology has existed for some time but has gained significant momentum in recent years, coupled with higher resolution mass spectrometers. If you believe everything Wikipedia tells you (who doesn’t?) metabolomics is the study of all metabolites in a biological cell, tissue, organ or organism, which are the end products of cellular processes. So essentially it is the study of small molecules, typically less than 1500Da, which are produced by cells.
There are broadly two ways to obtain metabolomics data; by NMR or mass spectrometry (MS). NMR has been used more historically, but MS based approaches are increasingly common and will be the focus here. Bias is introduced with any single technique so it is advisable to use both techniques where possible, but of course this is not always practical and typically not a requirement for publication. The potential applications for metabolomics are expansive. It has been employed in single/co-culture, plant and crop, bio fluids, and organ based experiments. Not at the same time, of course. Like the boom in gut microbiome studies in all aspects of disease, I expect metabolomics to follow suit. In my primary research I have employed LCMS based metabolomics to explore the metabolites in stool, in the hope of increasing the understanding of cellular processes in the gut and developing potential biomarkers to aid in disease diagnosis. This type of work is not to be undertaken lightly and requires expansive optimisation. The analysis of the resulting data is also hugely complex and requires many user and computational hours to be invested. Following initial analysis it is also important to return to the samples and carry out MSn based targeted analysis on the compounds of interest. For absolute quantification and identification a standard of the compound should be run at various concentrations. As this technology grows, so too will methods with groups dedicating years to developing techniques, which everyone can benefit from. An example of this is passing a sample through a C18 column and HILIC column before injection into the MS separating both the hydrophobic and hydrophilic compounds, respectively.
Proteomics, the study of all proteins in any given sample, can be seen as the link between genomics and metabolomics. It is assumed that an increase/decrease in protein abundance reflects potentially important up-/down- regulation in response to a variable, for example disease or nutrient availability. There is an overwhelming amount of methods and techniques for proteomics, the choice of which should be dictated by the primary research question. Unlike genomics, gel based methods still provide good resolution and feature commonly in publications over the last decade. Two-dimensional (2D) gels separate proteins based on charge and size, where the proteins are visualised as spots. This is semi-quantitative as, all things even, the intensity of a spot reflects the relative abundance of the protein. SDS-PAGE gels are one-dimensional where proteins are separated electrophoretically according to size. Identification of spots or bands is usually necessary and involves excision from the gel and digestion, giving smaller peptide fragments. For multiple reasons, which won’t be discussed here, gel-based methods can be circumvented with samples digested in-solution into peptides and processed directly by liquid chromatography mass spectrometry (LCMS). Following acquisition of MS/MS data, peptides can identified using online databases, such as MASCOT, which maps the MS/MS fingerprint of peptides against genome sequence data to infer protein identity and cellular function. This overview is just the tip of the proteomics iceberg and I encourage anyone thinking of embarking in proteomics to understand the relative merits of all available methods.
As systems biology is increasingly applied to complex research questions, the field of omics will continue to expand and evolve. While these technologies become more accessible, with capacity to generate huge amounts of data, it is important researchers understand the technologies and implement them accordingly. Undertaking any omics experiment is NOT a quick way of amassing data, not when done properly anyway. To quote a leading metabolomics expert from the recent proteomics methods forum – “If you are thinking about doing metabolomics, don’t”. I wouldn’t go this far, my advice would be – “If you are thinking about doing any omics, do so with a hypothesis in mind, appreciate the need to develop and optimise methods, and understand analysis of data is hugely complex and time consuming”.
Let me know in the comments section how you perceive ‘omics’, how you are getting on in your research, and if you have any questions I might be able to address.