! Automated protein digestion workflows for MS-based proteomics applications 1, 1 2 3 Gunnar Dittmar Oliver Popp , Guenter Boehm , Andreas Bruchmann 1Max Delbrück Center for Molecular Medicine, MDC, Berlin, Germany; 2CTC Analytics, Zwingen, Switzerland; 3Axel Semrau GmbH, Sprockhövel, Germany In-solution digest Data analysis ! 0.20 6000 0.15 0.10 2000 0.00 Peptides are generated in a multi-step digestion procedure. The first steps occur at highly denaturing conditions (8 M urea). Proteins are reduced (TCEP) and blocked by alkylation (chloroacetamide). These conditions ensure complete unfolding of the proteins so all parts of polypeptide are accessible for the protease. Since the protease has only limited activity in 8 M urea, the solution is diluted and a second protease, trypsin, is added. An additional incubation at 37°C finalises the digest and allows reproducible production of small peptides.! Realisation on the PAL! Gel pieces are placed in wells of a 96well plate equipped with a PTFE membrane that keeps the gel in the well but allows draining liquid by vacuum.! ! The robot arm transfers - step by step the washing solutions and reagents to the reagent wells with the gel. After each washing step, vacuum is applied to soak out the washing solvent. ot l 0 ob K R 0.05 R K ua K R 4000 an R 0.25 8000 M K K Means + SD ot K Trypsin Means + SD ob K K K Reduction Alkylation Digest l K Main digest Cut gel band Means of missed cleavage sites Peptide sequences identified Yeast lysate was digested both manually and using the PAL in five replicates and measured on an orbitrap mass spectrometer.! Peptide sequences identified after digestion with the PAL are slightly decreased. H o w e v e r, t h e o v e r a l l reproducibility, as reflected by the standard deviations (SD), is increased. Missed cleavages are slightly elevated in the automatic procedure. This can be explained by a better mixing of the samples in the manual procedure compared to the automated procedure where reactions take place in a 96-well plate. Cross-contaminations By using SILAC labelling, cross-contamination was evaluated. A yeast lysate from „light“-only cells was digested in wells next to a 1:1 mixture of „heavy“ and „light“ labelled lysate. As a control, a 1:1 mixture was placed next to another 1:1 mixture. In case of a contamination, an overall shift to „light“ is expected. Density plot Density More time for important ! steps in the analysis K LysC S- •In-solution digest! •In-gel digest ! gel membrane Vacuum Transfer of liquids is achieved by a syringe tool. Syringes of different volumes can be automatically exchanged during the process and are washed in a washing station with organic solvent and water. Multi-dispense can be realised by the Chronos software and allows speeding-up of the overall process while avoiding cross-contaminations. In-gel digest manual 10 ! The density plot shows the distribution of the H/L ratios of peptides for the contamination test and the control experiment. The curves almost overlap completely and no shift of the contaminant test to the left is visible, indicating that cross-contaminations are avoided by efficient washing of the syringe between each transfer step. robot 9 A representation of an RTC-PAL setup. Each module is variable and thus the robotic setup can be extended by introducing new tools.! Log 2 ( normalized Ratio H / L ) 8 gel membrane The setup allows extensive washing of the gel bands by transfer of organic and inorganic solvent in alternating steps for up to 20 times, each followed by 10 min of incubation. A self-constructed tray holder for a vacuum chamber that is connected to a vacuum pump. The vacuum is regulated by an electric valve controlled by the PAL software. A mixture of proteins was separated by SDS-PAGE and stained with Coomassie. Bands were cut out and digested by hand and by robot, respectively.! ! 7 The normal benchtop methods for the IGD and the ISD were adapted to make automation in a robotic setup possible. In addition, we modified a standard CTC-PAL robot setup with a vacuum chamber that can be controlled by the robot's software and facilitates the removal of large volumes of washing solvents required by IGD leading to enhanced sensitivity. solvent Vacuum 6 ! The PAL robot performs all liquid solvent transfer steps on a 96-well plate based setup. 5 Mass spectrometry (MS) based bottom-up proteomics is built upon large scale identification of peptides, and depends on proteins being efficiently converted to peptides by a protease of known specificity. The most common preparation methods are digestion in solution (ISD) or digestion of proteins separated on an SDS-PAGE gel, in-gel digestion (IGD). Both methods consist of a lengthy sequence of washing and chemical modification steps. To increase throughput and reproducibility, automation of these processes is highly desired. Contrary to other „omic“-applications, proteomics analysis by LC-MS/MS remains time-intensive, making the measurement the ratedetermining step in the pipeline. Thus the preparation of samples does not require a high- but rather a medium-throughput setup.! Summary SH Repetitive rigorous washing log10 Intensity MS measurement Pre-digest S- WORKFLOW Sample preparation SH S Reduce hands-on time on repetitive work! Increase consistency! Increase reproducibility Reduce time investment S ua Urea In-solution digest Iodoacetamide Chloroacetamide an Three reasons for automating workflows in a proteomic laboratory: Alkylation Reduction DTT / TCEP M Denaturation Quality control In-gel digest R Overview 200 300 400 500 Peptide ID The PAL system provides an affordable and reliable platform optimised for medium-throughput peptide preparation for shotgun-proteomics based mass spectrometry As a result, we gain more peptide identifications (412 vs. 270) in the automated procedure, most likely due to increased washing efficiency.
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