Biomarker discovery studies

For most biomarkers discovery studies, sample size was not large enough,... 

It is therefore not surprising that the interest in PyMS as a typing tool diminished at the turn of the twenty-first century and hence why taxonomists have turned to MS-based methods that use soft ionization methods such as electrospray ionization (ESI-MS) and matrix-assisted laser desorption ionization (MALDI MS). These methods generate information-rich spectra of metabolites and proteins, and because the molecular ion is seen, the potential for biomarker discovery is being realized. The analyses of ESI-MS and MALDI-MS data will still need chemometric methods, and it is hoped that researchers in these areas can look back and learn from the many PyMS studies where machine learning was absolutely necessary to turn the complex pyrolysis MS data into knowledge of bacterial identities. 

Biomarker Discovery in Renal Cell Carcinoma Applying Proteome-Based Studies in Combination with Serology... 

Although the coining of the term metabolomics has been relatively new, an explosion of publications has occurred in this field plus a realization that many researchers already were doing similar studies, albeit without an -omic tag before the word. It has not been possible to review all the applications of metabolomics fully, and the applications will increase. In addition to the development of new applications, the development of the analytical approaches will also take center stage as researchers push back the limits of detection of NMR spectroscopy, mass spectrometry, and other analytical approaches. In addition to these wet lab developments, both the pattern recognition approaches used to process metabolomics and the metabolomic databases used to identify metabolites need to be developed or expanded. In this respect, an excellent place to start on the arduous journey to biomarker discovery through metabolomics is the current metabolomic databases found on the web that make standard spectra freely available (68-70). 

In the pharmaceutical industry, the techniques are being used to examine off-target effects particularly for the early identification of toxicity. MOA can be studied through metabolomics and can also be used as a quality control tool for complex mixtures such as foods or herbal medicines. Similarly, the tools and expertise of natural products chemists are essential in metabolomics, particularly in biomarker discovery (see also Volume 9). Biomarker discovery via untargeted metabolomics can lead to metabolite signatures (nuclear magnetic resonance (NMR) spectroscopy, mass spectrometry (MS), etc.) that are not present in current metabolomics databases. This is particularly true for plant secondary metabolism studies and nonmammalian metabolites. Structure elucidation then becomes critical to understanding the metabolomics results and for biomarker development. 

In 2009, Ladd et al. studied diagnostic BMs in cancer by nanoproteomics approaches based on a combination of antibody microarrays and a SPRi detection system. This work also showed the potential of SPRi as a suitable methodology for studying serum profiles in biomarker discovery projects (32). 

New biomarker discovery from such studies can lead to early detection of deleterious effects. In a more recent study, researchers validated toxicological protein markers from an in vivo system (rat liver) as well as from an in vitro system (human HepG2 cell line) [86]. They reported a total of 11 protein markers with reactivity toward multiple toxic compounds and no reactivity toward nontoxic compounds. An important conclusion from this work is that cells in culture can be used as an in vitro toxicity testing system to assess hepatotoxicity. However, in the future, a much more extensive study may be required to identify a larger group of toxicology markers to detect more diverse types of toxic reactions. 

While the above study utilized urine, probably no biofluid has attracted the attention of the proteomic biomarker discovery field as serum and plasma. Although the analysis of serum by RPLC-MS has almost become routine, such is not the case for CE-MS. In a proof of principle study to show the efficacy of using CE-MS to find differentially abundant proteins in serum, the group of Sassi et al. analyzed groups of sera that were spiked with different concentrations of known standard peptides. The groups of sera were analyzed by CE-MS and the standard peptides were successfully identified as being differentially abundant with a success rate of 95%. 

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