Automated Sample Preparation Workflows for Quantitative Proteomics Applications
Poster Jul 31, 2015
Oliver Popp1, Lucas Luethy2, Tamara Kanashova1, HaAn Nguyen1, Julia Kikuchi1, Guenter Boehm2, Thomas Blenkers3, Andreas Bruchmann3, Gunnar Dittmar1
The post-lysis labelling using DML allows for the rapid analysis and quantification of all tissue samples, while not requiring the metabolic incorporation of an isotopic label. This is an advantage in comparison to the expensive and time consuming labelling with isotope labelled amino acids (SILAC), while allowing the same quantification steps using the MS1 signal in a shot-gun experiment. Proteins are digested to peptides using our automated ISD approach on a PAL RTC robotic system. Peptides are labelled in a 96-well format whereby the PAL RTC transfers the labelling reagents to the sample plate followed by incubation periods. Phosphopeptides (PP) are enriched by using 96-well plates equipped with filters that retain titanium oxide beads combined with a vacuum chamber.
Ship diesel exhaust particles are a growing concern for coastal regions. These particles can carry different chemical loads and are know to be engulfed into cells if they reach the alveolar parts of the lung. Here the carbon core and the chemical load can have severe effects on the health of the lung cell and tissue. Using cells incubated with aerosol particles collected on a ship diesel engine the biological response was characterized by metabolic SILAC or DML labelling. In order to minimize the experimental variations both sample sets (6 replicates each) were processed on the PAL RTC based automated setup. Our quantitative proteomic data reveals that both SILAC and DML lead to well quantifiable data. Due to the chemical modification of the peptides during the DML procedure the chromatographic separation as well as the ionization of the peptides changed. This lead two deep data sets. The bioinformatic analysis revealed that both techniques complement each other, since different peptides have been identified in both experiments.
Using Elemental Analysis For Discrimination Of Pinot Noir Wines From Six Different Districts In An AvaPoster
The determination of geographical origin of wine is gaining increased interest by researchers and federal agencies around the world, partially due to increased fraud with regards to place of origin labelling. For wine, multi-elemental profiling of macro, micro, and trace elements has been proposed for determination of authenticity. Commercial wines from different wineries in 5 different neighborhoods within one AVA show characteristic elemental fingerprints. Macro, micro and trace elements as well as elemental ratios contribute to the observed separation, indicating the involvement of multiple factors and underlying mechanisms, including location and soil composition, elemental uptake by vine and rootstock, viticulture and nutrient management, water sources, and small differences in the different wineries.READ MORE
Fast arsenic speciation analysis of wines and rice with LC-ICP-QQQPoster
This method was designed in response to recent and proposed food standards, both international and national, that limit inorganic arsenic rather than total, organic, or individual arsenic species such as arsenite (AsIII) and arsenate (AsV). Analysis time is 10x faster than the current FDA regulatory method, increasing sample throughput, avoided spectral interferences and dramatically increased sensitivity. Validation data from two laboratories demonstrate the method’s accuracy and reproducibility of both wine and rice matrices in a single analytical batch.READ MORE
Proteomics and Substrate Based MS Imaging of Xenobiotic Metabolising Enzymes in Ex Vivo Human Skin and a Human Living Skin Equivalent ModelPoster
Untargeted proteomics analysis showed that human skin and a commercially available living skin equivalent model exhibit a similar distribution of xenobiotic metabolising enzymes. A new technique, substrate based mass spectrometry imaging (SB-MSI) was developed during this study.READ MORE