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Automated sample preparation workflows for quantitative proteomics applications
Automated sample preparation workflows for quantitative proteomics applications
Submitted on 28 Aug 2015

Oliver Popp1, Lucas Luethy2, Tamara Kanashova1, HaAn Nguyen1, Julia Kikuchi1, Guenter Boehm2, Thomas Blenkers3, Andreas Bruchmann3, Gunnar Dittmar1
1Max Delbrück Center for Molecular Medicine in the Helmholtz Association, MDC, Berlin, Germany; 2CTC Analytics, Zwingen, Switzerland; 3Axel Semrau GmbH, Sprockhövel, Germany
This poster was presented at ASMS 2015
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Poster Abstract

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.

Preliminary data
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.

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