Technical Platforms

We use a wide range of analytical and computational tools in our research. In addition to commercial technologies such as RNA Seq, we use many technical platforms pioneered in our group or elsewhere at the MPIMP. These include quantitative methods that allow us to measure absolute transcript abundance, sensitive high-throughput activity assays for enzymes, high-resolution methods to analyse a wide range of metabolites, measurement of growth with high temporal and spatial resolution, and user-friendly data visualization tools. We use a suite of growth conditions to reveal the impact of the genotype and of changes in carbon and nutrient status on metabolism, growth and development. We place an emphasis on obtaining quantitative data that allows modelling and provides insights that cannot be obtained from less precise information.

Visualisation and data analysis tools

We have developed the MapMan gene ontology for rapid automated annotation of genomes, and for analysis and visualisation of large transcriptomic data sets (Thimm et al. 2004; Lohse et al. 2012; Schwache et al. 2019; cooperation with Björn Usadel TU Aachen)

Robotized quantitative RT-PCR

We complement RNASeq with large-scale use of quantitative reverse transcription-PCR (qRT-PCR) to analyze transcripts (see also Infrastructure Group Dirk Hincha). qRT-PCR is extremely sensitive and can be readily adapted to measure new sets of genes-of-interest in large numbers of samples (Czechowski et al., 2004; 2005). We spike known amounts of artificial RNA species into plant samples before RNA extraction and cDNA synthesis, allowing us to measure absolute amounts of transcripts and ribosomal RNA species (Piques et al., 2009; Flis et al. 2015).

Robotised platform for the measurement of enzyme activities

Changes of gene expression are typically measured by analysing transcript levels. However, these do not necessarily lead to changes in the levels of the encoded proteins (Stitt and Gibon 2014). We have established a robotised platform to measure the activities of over 40 enzymes in central carbon and nitrogen metabolism in optimised conditions (Gibon et al., 2004). The high throughput and sensitivity of this unique platform gives us a much more comprehensive overview of enzyme activities than is possible by traditional methods. Enzymes operate within complex metabolic networks, and measurement of multiple enzyme activities on this scale provides important insights into the structure and regulation of metabolic networks. We used this platform to identify cases where enzyme activities can be predicted from the changes of transcript levels and cases where protein turnover plays a dominant role (Piques et al., 2009). We also use it to analyse Arabidopsis ecotypes for natural variation in the activities of large numbers of enzymes, to identify physiological traits that correlate with different enzyme activities (e.g. Sulpice et al., 2010), and to identify genetic loci that regulate enzyme activities in Arabidopsis (Kuerentjes et al., 2008; Fusari et al., 2017)) and crop plants (e.g. Zhang et al., 2010; 2015; Wallace et al., 2014).

Measurement of metabolites and fluxes using liquid chromatography – tandem mass spectrometry

We use liquid chromatography linked to tandem mass spectrometry to quantify metabolites in central metabolism. This powerful method separates metabolites based on their physicochemical properties, and then by mass, followed by fragmentation of the molecule and detection of the fragments that provide a characteristic ‘fingerprint’ for a given metabolite (Lunn et al., 2006; Arrivault et al., 2009; 2015). This technique is combined with 13CO2 labelling to measure fluxes in metabolism. The principle is that replacement of a 12C atom (the predominant form of carbon) by a 13C atom leads to a mass shift of +1 in the molecule, which can be detected in the mass spectrometer. When samples are harvested at different times after supplying 13CO2 and analysed, it is possible to see how quickly the 13C is spreading through the different metabolites and pathways (Szecowka et al., 2013; Arrivault et al., 2017).

Measurement of growth

Growth is a complex process that involves cell division, the synthesis of cellular components like protein and the cell wall, and the uptake of ions and water to drive cell expansion. As mature plant cells contain a large central vacuole much of the physical increase in size is due to cell expansion. We have developed methods to measure the synthesis of protein and cell wall, in which we supply 13CO2 and analyse the rate of 13C incorporation into protein and into cell wall polysaccharides using mass spectrometry (Ishihara et al., 2015; 2017). We have also developed methods that use special infra-red cameras that are able to provide a 3-dimensional image of a plant to measure growth and deconvolute it from rhythmic leaf movement (also called hyponasty) (Apelt et al., 2015; 2017). By combining these techniques, we learn when metabolism is needed to supply energy and precursors for protein and cell wall synthesis, and when it is required to supply energy to pump ions and other small molecules to drive cell expansion.

References: Apelt et al. (2015) Plant J 82, 693–706; Apelt et al. (2017) Plant Physiol. 174, 1949-68; Arrivault et al. (2009) Plant J. 59, 824-839; Arrivault et al. (2015) Anal. Chem. 87, 6896-6904; Arrivault et al. (2017) J Exp. Bot.68, 283-298; Czechowski et al. (2004) Plant J. 38, 366-379; Czechowski et al. (2005) Plant Physiol. 139, 5-17; Flis et al. (2015) Open Biology 5, 150042, Fusari et al. (2017) Plant Cell 29, 2349-2373; Gibon et al. (2014) Plant Cell: 16, 3304-3325; Ishihara et al. (2015) Plant Physiol. 168, 74-93; Ishihara et al. (2017) Plant J. 91, 416-429; Keurentjes et al. 82008) Genome Biol. 9:R129; Lohse et al. (2012) Nucleic Acids Research 40, W622-W627; Lunn et al. (2006) Biochem. J. 397, 139-148; Piques et al. (2009) Mol. Syst. Biol. 5, 314; Schwache et al. (2019) Mol. Plant 12, 879-892; Stitt and Gibon (2014) Trends Plant Sci.19, 256- 265; Sulpice et al. (2010) Plant Cell 22: 2872–2893; Szecowka et al. (2013) Plant Cell. 25, 694-714; Thimm et al. (2004) Plant J. 37, 914-939; Wallace et al. (2014) PLoS Genet 10(12): e1004845; Zhang et al. (2010) Plant Physiol. 154, 1753–1765; Zhang et al. (2015) Plant Physiol. 168, 575-83.


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