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High-Throughput Multiplexed Quantitative Proteomics for Personalized Medicine Applications (Due to an overwhelming response, registration for the above session has been closed.)
Clinical-Scale Discovery Proteomics in Human Body Fluids Loic Dayon, Ph.D. Scientist, Nestle Institute of Health Sciences, Research, Switzerland
Profiling of proteomes from blood, plasma, and other human body fluids via high-throughput quantitative pipelines is key for clinical research and biomarker discovery. Holistic proteome maps allow assessing concomitantly multi-parameters of the health state. Over many years, multiple mass spectrometry research groups have pursued the relevant goal of measuring “complete proteomes” (the “protein number race”). Much less attention has been given to the “sample number race” that can ultimately lead to sufficient statistical power and deliver robust and reproducible biological findings, by taking into account diversity and variability of human populations. An end-to-end integrated solution that encompasses automated sample preparation (Dayon et al., 2014; Núñez Galindo et al., 2015), robust mass spectrometric analysis, and high-content straightforward processing of data (Corthésy et al., in preparation) has been developed, characterized, validated and applied. Hundreds to thousands of human clinical samples have been profiled in a few weeks. Analytical and - most importantly - biological readouts were shown to be robust, accurate and highly informative (Cominetti et al., 2016). This human body fluid profiling approach was applied to metabolic and brain health research projects. Potential biomarkers were identified in those studies for clinical purposes, but also more general proteome observations could be made and replicated at the population level. For research use only. Not for use in diagnostic procedures.
Zurich-Cancer-Maps: Turning Clinical Biopsies into Searchable Digital Biobanks Bernd Wollscheid, Ph.D. Head of Proteomics, Inst. Molecular Systems Biology, ETH Zurich, Switzerland
Clinical specimens are unique, finite and cannot be reproduced. They are also a critical foundation for most Personalized Medicine (PM) projects. At present, clinical specimens such as blood, tissue or cell samples are annotated and stored in biospecimen banks (Biobanks) that are typically associated with and maintained by a specific clinical center. In the Zurich-Cancer-Maps project, we aim to generate a “Digital Biobank” from clinical specimens where the genomic and expressed, transcriptomic, proteomic and metabolomic information is recorded and presented in searchable, digital files that are stored, along with the clinical metadata in a clinical information management system. Recent advances in quantitative proteomics have made it now possible to create accurate, detailed digital protein profiles of clinical samples in an efficient manner. Access to such a “Digital Biobank” offers potential benefits in terms of tumor typing and personalized medicine approaches to treatment. This seminar will share information about the Zurich-Cancer-Maps project, details regarding conversion of biopsy samples into digital protein profiles using data independent analysis (DIA) on Orbitrap mass spectrometers, and potential use of these profiles for cancer research.
For research use only. Not for use in diagnostic procedures.
Simple Cell Glycoproteomics: Discovery and Applications Sergey Vakhrushev, Ph.D. Associate Professor, Dept. Cellular Molecular Medicine, University of Copenhagen, Denmark
Site-specific O-glycosylation is emerging as an important concept for regulating protein processing and functions. However, full understanding of the nature and functions of this abundant type of protein glycosylation is severely hampered by lack of tools for proteome-wide characterization of O-glycan structures at specific sites in O-glycoproteins. The challenge of high throughput O-glycoproteomics has been partly met by recent advances in tagging strategies, genetic engineering of cells, and ETD based mass spectrometry. Advances in analysis of O-glycoproteins have been made and proteome-wide analysis of O-glycosylation sites is becoming available. The O-GalNAc glycoproteome is orchestrated by a large polypeptide GalNAc-T iso-enzyme family, and the roles of these each isoforms are poorly understood. Most of our current knowledge of these isoenzymes is based on in vitro enzyme assays with short peptide substrates. We have now expanded on this strategy and applied a quantitative approach to show non-redundant O-glycosylation performed by a single polypeptide Gal-NAc-T using differential analysis of O-glycoproteomes produced in an isogenic cell model with and without knock-out or knock-in of GALNTs. Here we discuss different aspects of quantitative O-glycoproteomics applied to the analysis of human cell lines, body fluids and tissues.
Unlocking the Low Mass Range with Trap-HCD for Glycan Analysis Christopher Ashwood Ph.D. Candidate, Macquarie University, Sydney , Australia
Typical ion trap analysis of glycans involves LC-ESI-MS/MS with collision-induced dissociation (CID) the fragmentation method of choice. One limitation of this approach is the inability to detect fragments with m/z values 1/3rd of the precursor ion, leaving the lower mass range of glycan fragments undetected and unable to be used for structural annotation. This presentation will describe the use of Pulsed Q Collision Induced Dissociation (PQD) and Ion-Trap Higher-Energy Collision Dissociation (tHCD) methods on the Thermo Scientific™ LTQ Velos™ Pro to allow detection of these lower mass range glycan fragments. The additional value these fragments give to standard and unusual glycan characterization will also be demonstrated.
Maximizing S- Nitrosylation Detection: Quantifying Pathological Diversity Jenny van Eyk, Ph.D. Director, Cedars-Sinai Medical Center, USA
Redox-switches involve the post-translational modification (oxPTM) of cysteine residues as a consequence of cell’s changing redox-environment. However, all cysteine is not equally reactive and a subset of these amino acid residues can oscillate from the reduced to oxidized state through a continuum of PTMs depending on the magnitude of oxidative stimuli; each of which with the potential to confer a different functional effects. Of the oxPTMs, S-nitrosation (SNO) is considered the most labile and readily reversible of the oxidative modifications making it a strong candidate for redox-signaling. Classic biotin-switch assay and its derivatives are the most common methods used for detecting SNO. In this approach, the labile SNO group is selectively replaced with a single stable tag. To date, a variety of thiol-reactive tags have been introduced. However, these methods have not produced a consistent data set, which suggests an incomplete capture and or detection. Using 2 mass spectrometry-based tags we developed a dual-labeling approach using Cys-TMT and Iodo-TMT to maximize the overall detection of SNO. We uncovered a previously unrecognized labeling bias derived when only one label is used that is the result of label-specific reactivity to two different cysteine subpopulations. Using the dual labeling approach we were able to identify the maximal SNO-modified cysteine”ome” for wild-type and S-nitrosoglutathione reductase knockout mouse hearts (S-nitrosoglutathione reductase is a negative regulator of S-nitrosoglutathione production) and nitric oxide-induced in various cell lines. We went on to determine the in vivo SNO cysteine”ome” in the heart, a very redox sensitive system, from muscular dystrophy animals with two different perturbation - exercise and TRPC6 channel manipulation. It is clear that in vivo there is a class of hyper-reactive cysteine residues already modified under physiological conditions but there is an important subset that can be regulated. Of these, specific kinase pathways were indicated. Zooming in on the role of SNO on these selective kinases we have begun to determine the relationship between redox-flux, signaling and oxidative stress.
Personalized ‘omics Profiling' of S. cerevisiae Strains Isolated from Differing Environments Daniel Lopez-Ferrer, Ph.D. Senior Marketing Specialist, Thermo Fisher Scientific, USA
Systems biology represents a shift in the way biology has been studied in the last century. The ability to identify and quantitate biomolecular components to a high degree of detail on a genomic scale allows us to understand and elucidate global regulatory networks in biological systems. In this work, we first put Orbitrap™ DDA, Orbitrap DIA, and QTOF DIA technologies head-to-head to evaluate the sensitivity and number of peptides identified and quantified and demonstrate that Orbitrap DDA technology outperforms DIA analyses significantly. DDA acquisition used in conjunction with the label-free quantitation node in Thermo Scientific™ Proteome Discoverer™ 2.2 software allows for the quantification of 95% of the identified peptides in the study using the MS1 scan to integrate the peptide signals. In addition, over 90% of the quantified peptides have CVs below 15%. Later on, we used label free proteomics in addition to whole genome DNA sequence, whole transcriptome (RNA-seq), the genomic binding locations of 50 transcription factors and 4 histone modification types (ChIP-seq) to define the individual biological components for 5 well-characterized haploid Saccharomyces cerevisiae yeast strains originally isolated from different environments: 2 wine strains, 2 laboratory strains and 1 clinical isolate. Integration of these datasets represents a start to the eventual goal of understanding how an entire biological system operates, and how such systems vary between genetically differing individuals within a species.
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