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Nano LC Accuracy for Proteomics Applications

Presenters: Thermo Fisher Scientific and Eksigent

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Nano LC is becoming a standard technique in the chromatography world, driven by recent advancements in proteomics that would require decreasing inner diameter (ID) of a liquid chromatography (LC) column to allow for smaller samples and to increase sensitivity. Nano LC is becoming the preferred tool for identification of proteins. This technique provides a high degree of selectivity and reliably identifies multiple sample components in a complex mixture.

This webinar provides an opportunity for lab professionals to understand how advancements in Nano LC systems meet the needs of proteomics researchers and have a positive business impact in the lab.

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Comparative MS-based protein analysis for biotechnological applications

M. Akeroyd1, R.A.M. van der Hoeven1, P. Klaassen1, M.W.E.M. van Tilborg1, M.M.A. Olsthoorn1, Qing-Yuan Yin2 1 DSM Biotechnology Center, Delft, The Netherlands 2 DSM EBA White Biotech, China
Presented: Thursday, December 17, 2009, 3pm (CET), 9 am (EDT) Duration: 45 minutes

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We are developing a method for fast, comparative analysis of proteins in large numbers of microbial samples. The method includes a generic sample preparation, nanoLC-MS/MS followed by protein identification and label-free quantification. The label-free quantification is based on spectral counting and machine-learning with a program called APEX (Absolute Protein Expression, Lu et al. Nature Biotech 25, 1, 2007).

Total protein content or cell numbers are determined, followed by BSA spiking as an internal control and cell lysis in case of intracellular protein analysis. Next, proteins are isolated by precipitation and subsequently digested with Trypsin. The resulting peptides are analyzed with nanoLC-MS/MS using multiple injections per sample on an EASY-nLC-LTQ setup. Database searches are performed in dedicated databases using the Sorcerer 2 search engine. Search results are processed using the Trans-Proteome Pipeline (TPP) and APEX.

This workflow has been applied in several of DSM’s white-biotechnology projects. These projects are aiming for the development and optimisation of strains producing renewable alternatives to crude oil. The strength of the method becomes clear when rational genetic engineering is applied for strain improvement, since cloned genes and gene knock-outs can directly be confirmed at the protein level using our method. Monitoring of random mutagenesis strain improvement can also be performed using our method in order to identify potential knock-out or over-expression genes for further improvements. The reproducible nanoLC gradients run on the EASY-nLC are essential for these experiments.

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Biomarker Discovery using Proteomics

Thomas Kislinger, Ph.D., Division of Cancer Genomics and Proteomics, Ontario Cancer Institute (OCI), Toronto Medical Discovery Tower, Canada

Presented: March 12, 2008, Duration: 45 minutes

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Epithelial ovarian cancer is the most lethal gynecological malignancy, and disease-specific biomarkers are urgently needed to improve diagnosis, prognosis, and to predict and monitor treatment efficiency. We present an in-depth proteomic analysis of selected biochemical fractions of human ovarian cancer ascites, resulting in the stringent and confident identification of over 2500 proteins. Integrated computational analysis of the ascites proteome combined with several recently published proteomic data sets of human plasma, urine, 59 ovarian cancer related microarray data sets, and protein-protein interactions from the Interologous Interaction Database I2D resulted in a short-list of 80 putative biomarkers. The presented proteomics analysis provides a significant resource for ovarian cancer research, and a framework for biomarker discovery. Technical challenges of proteomics-based biomarker discovery projects and future work will also be discussed.

Proteomic profiling has emerged as a useful approach to identify tissue alterations in disease states, including vascular diseases. Intrauterine Growth Restriction (IUGR) and preeclampsia are placental vascular diseases that affect ~7% of pregnancies, yet surprising little is known about their genetic causes. The mouse is usually considered the ideal model organism for human disease yet no extensive comparisons have been made between the human and mouse placenta, and to other tissues. We have recently developed and applied organellar proteomics to reveal tissue and organelle selectivity in six mouse tissues (Kislinger et al. Cell 2006: 125; 173-186). We have applied Multidimensional Protein Identification Technology (MudPIT) to derive an extensive profile of the transcriptional and translational environment of the highly vascular exchange region of the mouse and human placenta. First, we generated extensive organelle (cytosol, mitochondria, membrane and nucleus) proteome profiles of healthy, microdissected mouse labyrinth (E 17.5) and human villous (term) tissues.  The same samples were also profiled by Affymetrix microarrays to further increase the biological information. We identified over 4,500 proteins with high confidence (FDR ~1%) and detected ~9,000 gene transcripts expressed by microarray in both tissues. Of these over 3,000 proteins and ~7,000 transcripts are co-expressed orthologs. Detailed bioinformatics analysis using available phenotype, Gene Ontology, protein-protein interaction and pathway information has generated a panel of ~130 proteins with orthologous expression that have known placental or vascular phenotypes in mouse and are likely to be involved in placental disease in humans; follow-up analyses are currently in progress. Our future goal is comparative proteomics profiling of established mouse vascular disease models, to obtain a better molecular understanding of the disease and to identify potential blood-based biomarkers.

 

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Characterization and Mapping of post-translational modifications in proteomics

Presenter: Professor Ole N. Jensen, Protein Research Group, Department of Biochemistry and Molecular Biology, University of Southern Denmark
Presented: February 4, 2008, Duration: 45 minutes

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Post-translational modifications generate tremendous diversity, complexity and heterogeneity of gene products, and their determination is one of the main challenges in proteomics research. Recent developments in mass spectrometry based approaches for systematic, qualitative and quantitative determination of modified proteins promise to bring new insights on the dynamics and spatio-temporal control of protein activities by post-translational modifications, and reveal their roles in biological processes and pathogenic conditions. Combinations of affinity-based enrichment and extraction methods, multidimensional separation technologies and mass spectrometry are particularly attractive for systematic investigation of post-translationally modified proteins in proteomics applications in cell signaling and epigenetics.

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In vivo proteomics in Drosophila melanogaster by tandem affinity purification of protein complexes and analysis by ProteinCenter software.

Presenter: Dr. J. S. Rees, Cambridge Centre for Proteomics

Presented: October 31, 2007, Duration: 45 minutes

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J. S. Rees1, S. S. Hester1, M. Bern3 , D. J. St. Johnston2 and K. S. Lilley1  

1 Cambridge Centre for Proteomics and 2 Gurdon Institute, University of Cambridge, Tennis Court Road, Cambridge, CB2 1QW, UK and 3 Proxeon A/S Staermosegaardsvej 6, DK-5230 Odense M, Denmark.

In order to characterize in vivo interactions in Drosophila melanogaster, and further our understanding of developmental processes, triple tagged YFP D. melanogaster traps with Flag and Strep affinity tags were generated to isolate multi-protein complexes in a high throughput fashion. The incorporated Flag and Strep affinity tags allow the target protein to be isolated from its native environment, by parallel or tandem affinity purification, along with any associating proteins. Each resulting pulldown eluate is analysed in a single run by LC-MS and the bait and associating proteins are then identified using the MASCOT search engine in conjunction with Flybase.
At the data analysis level, we have utilised Proxeon's ProteinCenter software that allows us to visually and statistically compare multiple parallel pull down data sets and therefore only identify true binding partners with high confidence. By analysing many lines together we can determine common contaminants immediately as well as pulldown specific contaminants. This data is used to compile a non exhaustive exclusion list used in the data dependant settings for the upstream MS/MS analysis and thus increase the performance of the LTQ-Orbitrap. The capability of the Venn diagrams function allows the comparison of hits identified in the different pulldown methods and the overlap observed. Also, the biological annotation feature is useful in confirming relevant in vivo interactions.
This data will be a valuable catalogue of the D. melanogaster proteome and will compliment the ‘in vitro' data generated from the yeast 2-hybrid interaction screens.

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