Our scientists analyse all known and unknown metabolites in a sample using state-of-the-art technologies, namely a high- throughput GC-MS system, an accurate mass QTOF coupled to a UPLC and an high- sensitivity Triple-Quadrupole MS with the UF-Technology from Shimadzu for ultra-fast multi – component analysis of targeted metabolites. We offer customized solutions and adjust our methods depending on the sample and the compound class of interest. Due to the large variation in physical properties of metabolites we employ different extraction and separation protocols.
Metabolomic analysis is a strictly controlled process in four steps:
Samples can be body tissue or fluids, fruit, vegetables, microorganisms, or any other biological material. We have extraction protocols in place to assure high coverage and yield of metabolites.
II) Separation & Detection
The compounds will be separated due to their charge and size employing gas and liquid chromatography in parallel. After separation the metabolites are injected into the mass spectrometer for detection. The compound will be detected as a whole molecule or as a fragment. The metabolite can be identified with the detected accurate mass. The fragmentation pattern is very specific for each compound and thus can be matched with a database of known compounds. Unknown metabolites present in samples can be identified using high-resolution mass spectrometry.
The complexity and wealth of information retrieved in a single experiment renders the necessary solid bioinformatic support. With the help of proprietary software, specific algorithms and custom-build databases we evaluate the obtained raw data and generate meaningful datasets.
The most important and challenging part in metabolite profiling is probably the dataset analysis, the evaluation of the dataset within the biological context and the visualization of the generated results. Based on additional information it will be determined whether the dataset can be categorized in different groups of biological samples . Metabolites that differ significantly between the sampled groups mark specific phenotypes or traits and can potentially be applied as biomarkers.