Proteomics and metabolomics analyses

In this chapter, we will snmmarize the current status of plant genome and transcriptome projects, describe strategies and examples that exploit the resulting information in the context of plant natural product research, and give an overview of how proteome and metabolome analyses are being used to promote the field. 

These new omics technologies will also enable us to efficiently identify novel compounds of value to both the Agrochemical and Pharmaceutical industries. It is of crucial importance that the data that results from high throughput analyses of the genome, transcriptome, proteome and metabolome of crop plants and foeir relatives is archived in an appropriately structured and indexed manner. This will enable it to be fully integrated and data-mined over an extensive period as our ability to formulate increasingly sophisticated queries on these data sets develops. 

Given the sensitive, label-free, and multiplexed analysis that MS can provide and the automated, low-volume fluidic operations enabled by microfluidics, the combination of microfluidics with ESI-MS is expected to see increased use for chemical and biochemical analyses in the years to come. In particular, the ability of microfluidics to process minute volumes and perform sample handling operations in a high-throughput, automated fashion can extend MS-based proteomics and metabolomics studies to samples that are too small for current processes, including in-depth chemical analysis of single cells. 

As the frequency of ion motion can be determined with high precision, the mass determination results with high accuracy below 1 ppm and very high mass resolution above 500000 (Figure 2.190). The mass precision and resolution depends on the measurement time of the trapped ions. Orbitrap MS systems are applied in LC-MS since many years for proteomics and metabolomics especially in life sciences, but also show its high capabilities in the small molecular domain for multi-residue trace analyses of, for example, pesticides or drugs, both in LC-MS (Fiirst and Bernsmann, 2010 Kaklamanos et al., 2013) and GC-MS applications (Peterson et al, 2009, 2010). 

With respect to technical differences, it should be emphasized that, for metabolomic analyses, there is no single analytical technique that allows determination of all low-molecular-weight compounds, unlike genomic and proteomic analyses. In metabolomics, various complementary analytical techniques are often used in combination with advanced bioinformatics tools for analysis of large data sets. 

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