An overview of the proteomics, glycomics and metabolomics expertise and capabilities within the Translational Metabolic Laboratory of the Radboudumc. We're interested in collaboration with academic and industrial partners, either bilateral or as part of multi-partner consortia.
2013-09-03 Radboudumc NCMLS Technical ForumAlain van Gool
The document provides information about the Radboud Proteomics Center. It summarizes that the center was established in 2003 to initiate, coordinate and facilitate proteomics research activities. It offers technological tools and knowledge transfer for proteomics research. The center has played a crucial role in numerous research projects using various proteomics approaches like bottom-up proteomics, targeted proteomics, and top-down proteomics. It provides expertise, resources, and services to both internal and external academic and industrial researchers.
Proteomics and its applications
Proteomics involves the analysis of the entire complement of proteins in a cell, tissue or organism. It assesses protein activities, modifications, localization and interactions. Proteomics uses techniques like gel electrophoresis, mass spectrometry and liquid chromatography to separate and identify proteins. These techniques can be applied to discover disease biomarkers, develop diagnostic tools, and gain insights into disease pathogenesis and treatment. Proteomics has applications in studying various diseases including cancer, diabetes and infections. It provides insights into cellular processes and systems biology.
Proteomics is the study of the complete set of proteins expressed in an organism under particular conditions. It aims to understand protein expression in response to changing conditions like disease. Tools in proteomics include cell lysis, fractionation, protein concentration and quantification, digestion, and peptide cleanup prior to mass spectrometry analysis. Key techniques discussed are molecular techniques like SAGE, separation techniques like gel electrophoresis and chromatography, and protein identification techniques like mass spectrometry.
1. Proteomics is the study of proteomes, which are the entire set of proteins expressed by a genome.
2. Mass spectrometry combined with separation techniques like liquid chromatography are the main tools used in proteomics to identify and characterize proteins.
3. Modern proteomics utilizes multidimensional separation methods like multiple liquid chromatography columns or liquid chromatography coupled with capillary electrophoresis prior to mass spectrometry to better resolve complex protein mixtures.
Techniques used for separation in proteomicsNilesh Chandra
Proteomics aims to characterize the complete set of proteins in a biological system. It faces challenges due to sample complexity and wide protein concentration ranges. Common separation techniques include 2D electrophoresis, 2D-DIGE, ICAT, SILAC, iTRAQ, MudPIT, and protein microarrays. Mass spectrometry is central to protein identification. Data analysis is challenging due to the large datasets and lack of standardization. Effective proteomics requires optimized multi-step workflows combining separation, labeling, mass spectrometry, and bioinformatics.
Proteomics is the study of the proteome, which is the entire set of proteins expressed by a genome or cell. It involves the large-scale study of proteins, including their structures and functions. The document discusses key aspects of proteomics, including what constitutes a proteome, why studying the proteome is important, and how proteomics compares and contrasts with genomics. It also describes various techniques used in proteomics, such as sample preparation, two-dimensional gel electrophoresis, detection technologies like mass spectrometry, and bioinformatics tools for protein identification and analysis of expression profiles.
This document provides an overview of proteomics. It discusses the goals of proteomics including global protein analysis, expression, function, and biomarker discovery. It covers different types of proteomics like expression, structural, and functional proteomics. Methods for protein measurement like mass spectrometry and 2D gel electrophoresis are described. The document discusses clinical applications of proteomics in areas like cancer, infectious diseases, CNS disorders and cardiovascular disease. It also touches on challenges like target discovery and costs, as well as future perspectives and conclusions on the potential of proteomics.
2013-09-03 Radboudumc NCMLS Technical ForumAlain van Gool
The document provides information about the Radboud Proteomics Center. It summarizes that the center was established in 2003 to initiate, coordinate and facilitate proteomics research activities. It offers technological tools and knowledge transfer for proteomics research. The center has played a crucial role in numerous research projects using various proteomics approaches like bottom-up proteomics, targeted proteomics, and top-down proteomics. It provides expertise, resources, and services to both internal and external academic and industrial researchers.
Proteomics and its applications
Proteomics involves the analysis of the entire complement of proteins in a cell, tissue or organism. It assesses protein activities, modifications, localization and interactions. Proteomics uses techniques like gel electrophoresis, mass spectrometry and liquid chromatography to separate and identify proteins. These techniques can be applied to discover disease biomarkers, develop diagnostic tools, and gain insights into disease pathogenesis and treatment. Proteomics has applications in studying various diseases including cancer, diabetes and infections. It provides insights into cellular processes and systems biology.
Proteomics is the study of the complete set of proteins expressed in an organism under particular conditions. It aims to understand protein expression in response to changing conditions like disease. Tools in proteomics include cell lysis, fractionation, protein concentration and quantification, digestion, and peptide cleanup prior to mass spectrometry analysis. Key techniques discussed are molecular techniques like SAGE, separation techniques like gel electrophoresis and chromatography, and protein identification techniques like mass spectrometry.
1. Proteomics is the study of proteomes, which are the entire set of proteins expressed by a genome.
2. Mass spectrometry combined with separation techniques like liquid chromatography are the main tools used in proteomics to identify and characterize proteins.
3. Modern proteomics utilizes multidimensional separation methods like multiple liquid chromatography columns or liquid chromatography coupled with capillary electrophoresis prior to mass spectrometry to better resolve complex protein mixtures.
Techniques used for separation in proteomicsNilesh Chandra
Proteomics aims to characterize the complete set of proteins in a biological system. It faces challenges due to sample complexity and wide protein concentration ranges. Common separation techniques include 2D electrophoresis, 2D-DIGE, ICAT, SILAC, iTRAQ, MudPIT, and protein microarrays. Mass spectrometry is central to protein identification. Data analysis is challenging due to the large datasets and lack of standardization. Effective proteomics requires optimized multi-step workflows combining separation, labeling, mass spectrometry, and bioinformatics.
Proteomics is the study of the proteome, which is the entire set of proteins expressed by a genome or cell. It involves the large-scale study of proteins, including their structures and functions. The document discusses key aspects of proteomics, including what constitutes a proteome, why studying the proteome is important, and how proteomics compares and contrasts with genomics. It also describes various techniques used in proteomics, such as sample preparation, two-dimensional gel electrophoresis, detection technologies like mass spectrometry, and bioinformatics tools for protein identification and analysis of expression profiles.
This document provides an overview of proteomics. It discusses the goals of proteomics including global protein analysis, expression, function, and biomarker discovery. It covers different types of proteomics like expression, structural, and functional proteomics. Methods for protein measurement like mass spectrometry and 2D gel electrophoresis are described. The document discusses clinical applications of proteomics in areas like cancer, infectious diseases, CNS disorders and cardiovascular disease. It also touches on challenges like target discovery and costs, as well as future perspectives and conclusions on the potential of proteomics.
Functional proteomics, methods and toolsKAUSHAL SAHU
INTRODUCTION
HISTORY
DEFINITION
PROTEOMICS
FUNCTIONAL PROTEOMICS
PROTEOMICS SOFTWARE
PROTEOMICS ANALYSIS
TOOLS FOR PROTEOM ANALYSIS
DIFFERENTS METHODS FOR STUDY OF FUNCTIONAL PROTEOMICS
APLLICATIONS
LIMITATIONS
CONCLUSION
Proteomics studies play an increasing role in the field of biology. The use of mass spectrometry (MS) in combination with a range of separation methods is the main principal methodology for proteomics. The two principal approaches to identifying and characterizing proteins using MS are the “bottom-up”, which analyze peptides by proteolytic digestion, and “top-down”, which analyze intact proteins.
This document provides an overview of common proteomics techniques. It describes proteomics as the study of proteins including their roles, structures, localization, interactions and other factors. The key techniques discussed include molecular techniques like DNA microarrays and yeast two-hybrid analysis, separation techniques like gel electrophoresis and chromatography, protein identification methods like mass spectroscopy and Edman sequencing, and protein structure determination methods like NMR, X-ray crystallography and computational prediction. The document provides examples and details of several of these techniques.
Proteome analysis studies the array of proteins expressed in a biological system under particular conditions. It can help understand cellular pathways and biological processes by characterizing protein complexes. Proteome analysis is also used to discover disease biomarkers for diagnostics and drug development by identifying protein expression changes. Key developments driving the field include improvements to 2D electrophoresis for separating proteins, mass spectrometry for analyzing separated proteins, and bioinformatics tools for searching protein databases. Common steps in proteome analysis involve separating proteins, digesting them into peptides, and identifying peptides and proteins using mass spectrometry techniques like MALDI-TOF-MS and ESI-Q-IT-MS.
Proteomics is the study of the proteome, which is the full set of proteins present in an organism. It allows the study of post-translational modifications and protein interactions. Proteomics can be used to identify disease biomarkers, which are biological indicators used for diagnosis and prognosis. Mass spectrometry plays a key role in proteomics by allowing the identification of molecules like proteins and peptides. Developments like electrospray ionization and matrix-assisted laser desorption/ionization allow the ionization and vaporization of large biomolecules for mass spectrometry analysis.
Proteomics uses techniques from molecular biology, biochemistry, and genetics to analyze proteins produced by genes. Mass spectrometry is commonly used in proteomics to identify proteins. Techniques like isotope-coded affinity tags (ICAT) allow comparative analysis of protein expression between samples by labeling proteins with stable isotopes before mass spectrometry analysis. ICAT involves labeling cysteine-containing peptides from two samples with either light or heavy isotopic reagents, mixing the samples, then using mass spectrometry to quantify differences in protein expression between the original samples based on mass shifts between labeled peptides.
Proteomics and its applications in phytopathologyAbhijeet Kashyap
Dear friends, I Abhijeet kashyap presenting the basics of proteomics to you all . Proteomics is the large-scale study of proteins, particularly their structures and functions.Proteomics helps in understanding the structure and function of different proteins as well as protein-protein interactions of an organism.
Genomics and Proteomics provides an overview of genomics and proteomics methods and their applications in medicine. It discusses how the fields of genomics and molecular biology emerged through key advances in DNA structure, recombinant DNA technology, PCR, and automated DNA sequencing. The document also reviews epigenetics, genomic and proteomic techniques including blotting, PCR, microarrays, and mass spectrometry. Applications of these methods in clinical settings are described such as genomic tests for disease diagnosis, prognosis, and personalized medicine. Proteomics uses mass spectrometry to discover biomarkers for diseases like cancer.
The document discusses proteomics, which is the study of the proteome or total protein complement of a biological system. Proteomics aims to understand protein expression, functions, interactions, and modifications through various analytical techniques and faces many challenges due to the complexity of proteins. Key approaches in proteomics include expression profiling to compare protein levels between healthy and disease states, structural analysis to determine protein structures, and network mapping to study protein interactions. Mass spectrometry and bioinformatics tools play important roles in proteomic studies, which have applications in characterizing protein complexes and identifying disease biomarkers.
This document discusses microbial proteomics and various proteomics techniques. It describes structural proteomics which analyzes protein structures to identify gene functions and interaction sites. Interaction proteomics analyzes protein-protein interactions to determine functions. Expression proteomics identifies proteins differentially expressed between related samples like healthy and diseased tissue. Microbial proteomics studies microorganism proteins using techniques like mass spectrometry, electrospray ionization, and matrix-assisted laser desorption-ionization. Gel-based separation techniques including 1D, 2D, and 3D gel electrophoresis are also discussed.
A brief introfuction of label-free protein quantification methodsCreative Proteomics
If you want to know more about our services, please visit https://www.creative-proteomics.com/services/label-free-quantification.htm.
Label-free protein quantification is a mass spectrometry-based method for identifying and quantifying relative changes in two or more biological samples instead of using a stable isotope-containing compound to label proteins.
This document discusses protein extraction and fractionation techniques used in food proteomics. It begins by outlining various methods for disrupting plant cell walls including mechanical, ultrasonic, pressure and temperature-based techniques. It then describes approaches for solubilizing and precipitating proteins from foods using organic solvents and aqueous solutions. Key steps in a typical proteomics workflow are outlined including protein extraction, separation, identification and data analysis. The challenges of analyzing complex food proteomes due to heterogeneity and abundance differences are also noted. Finally, an integrated view of various extraction and fractionation methods employed in food proteomics is presented.
This document summarizes proteomics and metabolomics techniques for mapping biochemical regulations. It discusses how proteomics uses techniques like gel electrophoresis, liquid chromatography, and mass spectrometry to separate and identify proteins on a large scale. Metabolomics similarly aims to analyze all metabolites in a biological system using techniques like fingerprinting, profiling, and integrating with other omics data. Together, proteomics and metabolomics provide multiple levels of insight into cellular processes by examining changes in gene expression, protein abundance, and metabolic activity.
The document discusses the field of proteomics, which involves the systematic analysis of proteins in cells under various conditions. It defines key terms like proteome and describes technologies used in proteomics like mass spectrometry, protein separation techniques, and protein analysis methods. The document also outlines several applications of proteomics in medicine, such as in diagnosing and studying diseases like diabetes and rheumatoid arthritis, as well as its use in analyzing changes that occur during the aging process.
The document discusses proteomics, which is the study of the entire complement of proteins in a cell or organism. It defines key proteomics terms like proteome and describes techniques used in proteomics like protein separation, 2D gel electrophoresis, mass spectrometry, and protein digestion. The goals of proteomics include detecting and comparing protein expression profiles to understand biological processes and discover drug targets. Proteomics provides important insights not available through genomics alone.
This document discusses the application of clinical proteomics in disease diagnosis and biomarker discovery. It provides an overview of how proteomics methodologies like mass spectrometry and protein microarrays can be used to identify protein biomarkers for various diseases from body fluids. Specific examples are given of proteomics studies that have discovered protein biomarker patterns or specific proteins that can improve diagnosis of cancers like colorectal cancer and breast cancer compared to single biomarkers. Biomarkers identified for other diseases like Alzheimer's disease and diabetic nephropathy through proteomics are also summarized.
Gel Based Proteomics and Protein Sequences AnalysisGelica F
Two-dimensional gel electrophoresis (2DE) is the standard method for quantitative proteome analysis. It combines protein separation based on isoelectric focusing and molecular weight. In the first dimension, proteins are separated based on their isoelectric point using immobilized pH gradients. In the second dimension, proteins are separated by molecular weight using SDS-PAGE. The separated protein spots are then analyzed using mass spectrometry to identify individual proteins. 2DE provides high resolution and the ability to analyze thousands of proteins simultaneously, but it also has limitations including irreproducibility and inability to resolve all proteins.
The document discusses protein-protein interactions (PPIs), including an introduction to PPIs, the types of interactions, techniques used to study them like X-ray crystallography, NMR spectroscopy and cryo-electron microscopy, and factors that affect PPIs. It also covers methods to investigate PPIs such as affinity purification coupled with mass spectrometry and yeast two-hybrid screening. Applications of understanding PPIs include developing therapeutic drugs and identifying functions of unknown proteins.
Peptide Mass Fingerprinting (PMF) and Isotope Coded Affinity Tags (ICAT)Suresh Antre
Analytical technique for identifying unknown protein. The peptide mass are compared to database containing the theoretical peptide masses of all known protein sequences.
Proteins – Basics you need to know for ProteomicsLionel Wolberger
The document provides an overview of key concepts in proteomics, including:
1) It discusses protein structure and function, the 20 common amino acids, and post-translational modifications that proteins undergo.
2) It introduces common techniques used in proteomics like chromatography, electrophoresis, mass spectrometry, and bioinformatics.
3) It summarizes protein analysis methods like gel electrophoresis, isoelectric focusing, and immunological assays used to detect and purify proteins of interest.
This document provides an overview of using XCMS, an open-source software package for metabolomics data preprocessing and analysis. It discusses:
1) Installing and loading the required XCMS packages in R.
2) Preparing raw metabolomics data, which can be in various open formats like netCDF.
3) Using XCMS functions to identify peaks, match peaks across samples, correct retention time drift, and fill in any missing peaks.
4) Generating reports to analyze and visualize results, identifying statistically significant differences in metabolite intensities between samples.
Functional proteomics, methods and toolsKAUSHAL SAHU
INTRODUCTION
HISTORY
DEFINITION
PROTEOMICS
FUNCTIONAL PROTEOMICS
PROTEOMICS SOFTWARE
PROTEOMICS ANALYSIS
TOOLS FOR PROTEOM ANALYSIS
DIFFERENTS METHODS FOR STUDY OF FUNCTIONAL PROTEOMICS
APLLICATIONS
LIMITATIONS
CONCLUSION
Proteomics studies play an increasing role in the field of biology. The use of mass spectrometry (MS) in combination with a range of separation methods is the main principal methodology for proteomics. The two principal approaches to identifying and characterizing proteins using MS are the “bottom-up”, which analyze peptides by proteolytic digestion, and “top-down”, which analyze intact proteins.
This document provides an overview of common proteomics techniques. It describes proteomics as the study of proteins including their roles, structures, localization, interactions and other factors. The key techniques discussed include molecular techniques like DNA microarrays and yeast two-hybrid analysis, separation techniques like gel electrophoresis and chromatography, protein identification methods like mass spectroscopy and Edman sequencing, and protein structure determination methods like NMR, X-ray crystallography and computational prediction. The document provides examples and details of several of these techniques.
Proteome analysis studies the array of proteins expressed in a biological system under particular conditions. It can help understand cellular pathways and biological processes by characterizing protein complexes. Proteome analysis is also used to discover disease biomarkers for diagnostics and drug development by identifying protein expression changes. Key developments driving the field include improvements to 2D electrophoresis for separating proteins, mass spectrometry for analyzing separated proteins, and bioinformatics tools for searching protein databases. Common steps in proteome analysis involve separating proteins, digesting them into peptides, and identifying peptides and proteins using mass spectrometry techniques like MALDI-TOF-MS and ESI-Q-IT-MS.
Proteomics is the study of the proteome, which is the full set of proteins present in an organism. It allows the study of post-translational modifications and protein interactions. Proteomics can be used to identify disease biomarkers, which are biological indicators used for diagnosis and prognosis. Mass spectrometry plays a key role in proteomics by allowing the identification of molecules like proteins and peptides. Developments like electrospray ionization and matrix-assisted laser desorption/ionization allow the ionization and vaporization of large biomolecules for mass spectrometry analysis.
Proteomics uses techniques from molecular biology, biochemistry, and genetics to analyze proteins produced by genes. Mass spectrometry is commonly used in proteomics to identify proteins. Techniques like isotope-coded affinity tags (ICAT) allow comparative analysis of protein expression between samples by labeling proteins with stable isotopes before mass spectrometry analysis. ICAT involves labeling cysteine-containing peptides from two samples with either light or heavy isotopic reagents, mixing the samples, then using mass spectrometry to quantify differences in protein expression between the original samples based on mass shifts between labeled peptides.
Proteomics and its applications in phytopathologyAbhijeet Kashyap
Dear friends, I Abhijeet kashyap presenting the basics of proteomics to you all . Proteomics is the large-scale study of proteins, particularly their structures and functions.Proteomics helps in understanding the structure and function of different proteins as well as protein-protein interactions of an organism.
Genomics and Proteomics provides an overview of genomics and proteomics methods and their applications in medicine. It discusses how the fields of genomics and molecular biology emerged through key advances in DNA structure, recombinant DNA technology, PCR, and automated DNA sequencing. The document also reviews epigenetics, genomic and proteomic techniques including blotting, PCR, microarrays, and mass spectrometry. Applications of these methods in clinical settings are described such as genomic tests for disease diagnosis, prognosis, and personalized medicine. Proteomics uses mass spectrometry to discover biomarkers for diseases like cancer.
The document discusses proteomics, which is the study of the proteome or total protein complement of a biological system. Proteomics aims to understand protein expression, functions, interactions, and modifications through various analytical techniques and faces many challenges due to the complexity of proteins. Key approaches in proteomics include expression profiling to compare protein levels between healthy and disease states, structural analysis to determine protein structures, and network mapping to study protein interactions. Mass spectrometry and bioinformatics tools play important roles in proteomic studies, which have applications in characterizing protein complexes and identifying disease biomarkers.
This document discusses microbial proteomics and various proteomics techniques. It describes structural proteomics which analyzes protein structures to identify gene functions and interaction sites. Interaction proteomics analyzes protein-protein interactions to determine functions. Expression proteomics identifies proteins differentially expressed between related samples like healthy and diseased tissue. Microbial proteomics studies microorganism proteins using techniques like mass spectrometry, electrospray ionization, and matrix-assisted laser desorption-ionization. Gel-based separation techniques including 1D, 2D, and 3D gel electrophoresis are also discussed.
A brief introfuction of label-free protein quantification methodsCreative Proteomics
If you want to know more about our services, please visit https://www.creative-proteomics.com/services/label-free-quantification.htm.
Label-free protein quantification is a mass spectrometry-based method for identifying and quantifying relative changes in two or more biological samples instead of using a stable isotope-containing compound to label proteins.
This document discusses protein extraction and fractionation techniques used in food proteomics. It begins by outlining various methods for disrupting plant cell walls including mechanical, ultrasonic, pressure and temperature-based techniques. It then describes approaches for solubilizing and precipitating proteins from foods using organic solvents and aqueous solutions. Key steps in a typical proteomics workflow are outlined including protein extraction, separation, identification and data analysis. The challenges of analyzing complex food proteomes due to heterogeneity and abundance differences are also noted. Finally, an integrated view of various extraction and fractionation methods employed in food proteomics is presented.
This document summarizes proteomics and metabolomics techniques for mapping biochemical regulations. It discusses how proteomics uses techniques like gel electrophoresis, liquid chromatography, and mass spectrometry to separate and identify proteins on a large scale. Metabolomics similarly aims to analyze all metabolites in a biological system using techniques like fingerprinting, profiling, and integrating with other omics data. Together, proteomics and metabolomics provide multiple levels of insight into cellular processes by examining changes in gene expression, protein abundance, and metabolic activity.
The document discusses the field of proteomics, which involves the systematic analysis of proteins in cells under various conditions. It defines key terms like proteome and describes technologies used in proteomics like mass spectrometry, protein separation techniques, and protein analysis methods. The document also outlines several applications of proteomics in medicine, such as in diagnosing and studying diseases like diabetes and rheumatoid arthritis, as well as its use in analyzing changes that occur during the aging process.
The document discusses proteomics, which is the study of the entire complement of proteins in a cell or organism. It defines key proteomics terms like proteome and describes techniques used in proteomics like protein separation, 2D gel electrophoresis, mass spectrometry, and protein digestion. The goals of proteomics include detecting and comparing protein expression profiles to understand biological processes and discover drug targets. Proteomics provides important insights not available through genomics alone.
This document discusses the application of clinical proteomics in disease diagnosis and biomarker discovery. It provides an overview of how proteomics methodologies like mass spectrometry and protein microarrays can be used to identify protein biomarkers for various diseases from body fluids. Specific examples are given of proteomics studies that have discovered protein biomarker patterns or specific proteins that can improve diagnosis of cancers like colorectal cancer and breast cancer compared to single biomarkers. Biomarkers identified for other diseases like Alzheimer's disease and diabetic nephropathy through proteomics are also summarized.
Gel Based Proteomics and Protein Sequences AnalysisGelica F
Two-dimensional gel electrophoresis (2DE) is the standard method for quantitative proteome analysis. It combines protein separation based on isoelectric focusing and molecular weight. In the first dimension, proteins are separated based on their isoelectric point using immobilized pH gradients. In the second dimension, proteins are separated by molecular weight using SDS-PAGE. The separated protein spots are then analyzed using mass spectrometry to identify individual proteins. 2DE provides high resolution and the ability to analyze thousands of proteins simultaneously, but it also has limitations including irreproducibility and inability to resolve all proteins.
The document discusses protein-protein interactions (PPIs), including an introduction to PPIs, the types of interactions, techniques used to study them like X-ray crystallography, NMR spectroscopy and cryo-electron microscopy, and factors that affect PPIs. It also covers methods to investigate PPIs such as affinity purification coupled with mass spectrometry and yeast two-hybrid screening. Applications of understanding PPIs include developing therapeutic drugs and identifying functions of unknown proteins.
Peptide Mass Fingerprinting (PMF) and Isotope Coded Affinity Tags (ICAT)Suresh Antre
Analytical technique for identifying unknown protein. The peptide mass are compared to database containing the theoretical peptide masses of all known protein sequences.
Proteins – Basics you need to know for ProteomicsLionel Wolberger
The document provides an overview of key concepts in proteomics, including:
1) It discusses protein structure and function, the 20 common amino acids, and post-translational modifications that proteins undergo.
2) It introduces common techniques used in proteomics like chromatography, electrophoresis, mass spectrometry, and bioinformatics.
3) It summarizes protein analysis methods like gel electrophoresis, isoelectric focusing, and immunological assays used to detect and purify proteins of interest.
This document provides an overview of using XCMS, an open-source software package for metabolomics data preprocessing and analysis. It discusses:
1) Installing and loading the required XCMS packages in R.
2) Preparing raw metabolomics data, which can be in various open formats like netCDF.
3) Using XCMS functions to identify peaks, match peaks across samples, correct retention time drift, and fill in any missing peaks.
4) Generating reports to analyze and visualize results, identifying statistically significant differences in metabolite intensities between samples.
This document provides an overview of plant proteomics techniques, including 2D gel electrophoresis, mass spectrometry, and software analysis. 2D gel electrophoresis separates proteins by isoelectric focusing based on pH in the first dimension, followed by SDS-PAGE based on size in the second dimension. Spots are visualized, excised, digested, and identified using mass spectrometry. Software performs matching, detection, quantitation, and annotation of protein spots across gels to identify differentially expressed proteins.
Proteomics is the large-scale study of proteins and how they function. [1] It uses techniques like mass spectrometry and protein chips to study post-translational modifications and interactions that cannot be predicted from genomic data alone. [2] Proteomics provides insights into biological processes by identifying proteins, analyzing modifications, detecting interactions, and comparing expression levels between cell states. [3] Studying proteomics is necessary to understand how genes are functionally expressed at the protein level.
The document discusses the field of proteomics, which is the large-scale study of proteins, including their functions and structures. It defines proteomics and describes several areas within it, such as functional proteomics, expressional proteomics, and structural proteomics. It outlines typical proteomics experiments and some key methods used, including two-dimensional electrophoresis, mass spectrometry, and protein-protein interaction prediction methods like phylogenetic profiling.
Proteomics is the study of the structure and function of proteins. It involves identifying and quantifying the proteins expressed by a genome or cell type. Key aspects of proteomics include protein separation techniques like gel electrophoresis, mass spectrometry to identify proteins, and analyzing protein interactions and post-translational modifications. While genomes provide the blueprint, proteomics helps understand the diversity of proteins expressed and how they function together to direct cellular activities. It is a promising tool for disease diagnosis by identifying protein biomarkers.
Proteomics is the large-scale study of proteins, including their structures, functions, and interactions. It has become an important technology for understanding biological systems on a global scale. Mass spectrometry plays a key role in proteomic analysis by allowing researchers to identify and characterize proteins and their post-translational modifications like phosphorylation. There are challenges in analyzing post-translational modifications since proteins exist in multiple modified forms, but methods like affinity enrichment and tandem mass spectrometry are used to map modifications and locate them on protein sequences.
Proteomic analysis involves fractionating and enriching cells or tissue to isolate proteins, then further breaking the proteins into peptides. The peptides are separated using chromatography and introduced into a mass spectrometer to determine their mass-to-charge ratios. Data-dependent acquisition is used to automatically select peptides for fragmentation and sequencing to identify the proteins present. Proteomics provides information about protein expression levels, post-translational modifications, interactions, and dynamics that complement genomics and transcriptomics data.
The document discusses representing imaging mass spectrometry (MS) data. It describes imzML, a common data standard for MS imaging data. It also outlines how MS imaging data can be submitted to the ProteomeXchange repository via the PRIDE database. MS imaging generates data from tissue sections, and imzML encodes both the raw data files and metadata about the images. Submitting to ProteomeXchange involves uploading raw data files, result files, and metadata descriptions to allow sharing and reuse of MS imaging experiments.
Ezose Sciences Inc. is a relatively new biotech company located in New Jersey that focuses on glycomics and biomarker discovery. It has completed multiple collaborations and utilizes its GlycanMap technology. The pharmaceutical industry is facing pressures to control expenses as major drugs lose patent protection, while biomarkers, personalized medicine, and outsourced research and development represent growth opportunities. Glycomics is an underdeveloped field that could provide insights into disease, drug response and cellular processes.
This document summarizes the use of desorption electrospray ionization mass spectrometry (DESI-MS) for various applications including tissue imaging, analysis of drugs and metabolites, food analysis, and quantitation of pharmaceutical molecules. DESI-MS allows direct analysis of samples without sample preparation or matrix application. It provides high sensitivity, molecular weight range from 50-5000 Da, and spatial resolution of 50um-2mm. DESI-MS has been used for applications such as imaging of lipids in rat brain tissue, screening of synthetic cannabinoids, quantitation of drugs in plasma, and analysis of food products like corn chips.
This document discusses the principles and workflow of pre-processing untargeted metabolomics data from LC-MS experiments. It describes the key steps as: feature detection to identify signals in the raw data, feature grouping and filtering, alignment of samples to correct for retention time shifts, and annotation of features with putative metabolite identities and quantitative information. The resulting table contains information on possible metabolites detected across multiple samples for subsequent statistical analysis.
MS (and NMR) data standards in Metabolomics why, how and some caveatsSteffen Neumann
The document discusses data standards for MS (mass spectrometry) and NMR (nuclear magnetic resonance) in metabolomics. It begins with an overview of the metabolomics workflow and data processing pipelines. It then discusses several existing data standards including netCDF and mzML, providing examples of how they encode metadata and spectral data. The document notes tools for working with these formats like OpenMS and ProteoWizard, and libraries for parsing the formats in different programming languages. Finally, it discusses challenges in data preprocessing and peak picking for different MS instruments and formats.
DEVNET-1148 Leveraging Cisco OpenStack Private Cloud for DevelopersCisco DevNet
The document discusses developing applications on Cisco OpenStack Private Cloud. It describes setting up a development environment using CoreOS, Docker, Gitlab, Jenkins, Slack, and Ansible. The environment automates the deployment of load balancers, web servers, and other application components from source code commits using continuous integration and continuous delivery practices. Jenkins is configured to build Docker containers from code commits and deploy them to instances in the private cloud.
The document provides guidance on creating challenges to solve problems through open innovation. It discusses why challenges are useful, what challenge design entails, and the basics of setting up a challenge, including: researching the problem space, providing background information, establishing judging criteria, securing funding, and running the challenge. The overall process involves diversifying input, letting others help find solutions, and amplifying creativity through open participation in challenges.
IoS-XR SW: partnering with Elastic: an overviewCisco DevNet
A session in the DevNet Zone at Cisco Live, Berlin. Monitoring network equipment is hard, too many protocols each with their own tools for monitoring, MIBs always delivered as an afterthought, SNMP falls over at scale. We’ll introduce IOS-XR Telemetry, a push based fire hose of data, and our integration with ELK, a stack of open source tools for collecting all that data, indexing it, and turning it into graphs that are actionable.
This document discusses emerging digital marketing trends for 2016. It finds that the top trend is the rise of video across digital platforms, as consumers are increasingly consuming video content on websites, social media, and in online ads. It also discusses other trends like influencer marketing, increased "buy now" functionality on social media, using social listening to inform brand decisions, and mapping cross-device consumer journeys. The overarching theme is that all of these trends are focused on putting consumers at the center of marketing strategies. Brands will need to respond by soliciting consumer input and feedback to better understand and meet consumer needs.
This document outlines Randy T. Nobleza's curriculum for 2014-2015 and 2015-2016 focusing on info shops as archival/curatorial projects and their sustainability. It describes various events, projects, and activities involving info shops, autonomous spaces, conferences and reading groups in the Philippines and Asia exploring political economy, knowledge practices, and transitions in the region.
1) The document discusses the use of protein and metabolite biomarkers in personalized healthcare, noting that over 100 biomarkers are now included in drug labels and 16 companion diagnostics are needed.
2) It describes how companion diagnostics can help determine a drug's metabolism, efficacy, or safety for a patient. Systems biology approaches that integrate multi-omic data are important for developing personalized treatment approaches.
3) The Radboud Center for Proteomics, Glycomics and Metabolomics performs various 'omics analyses including proteomics, glycoproteomics, metabolomics, and top-down proteomics to discover and validate biomarkers for personalized healthcare applications like diagnosing rare diseases, detecting inborn errors of metabolism, and characterizing
SMB 28112013 Alain van Gool - Technologiecentra RadboudumcSMBBV
The document describes the Radboud Centre for Proteomics, Glycomics & Metabolomics at the Radboud University Medical Center. The center aims to translate research into biomarkers and diagnostics using proteomics, glycomics, and metabolomics expertise. It has key experts in these areas and provides services to both internal and external partners for research projects, biomarkers identification, and diagnostic test development. An example is described where glycoprofiling was used to diagnose a rare genetic disease and identify a dietary intervention as a successful personalized therapy.
2015 01-06 Oudejaarssymposium Personalized Healthcare, GroningenAlain van Gool
Personalized healthcare is moving beyond just targeted medicine to become more patient-centered. Biomarkers are playing an evolving role, from diagnosis to translational medicine to personalized healthcare. Radboud University Medical Center aims to have a significant impact on healthcare through their focus on personalized healthcare and including the patient as a partner. Their integrated translational research and diagnostic laboratory develops biomarkers through various omics technologies for personalized diagnosis and treatment.
Outlining the proces and lessons learned in organising the technological infrastructure at the Radboud university medical center, to shape the Radboudumc Technology Centers, supporting our mission in enabling personalized healthcare.
Proteomics Modules designed to bring clinically relevant data, at any point, into the Drug Discovery Process. 1000s of proteins are plated from primary cells and are used to trap autoantibodies from diseased patients' blood sera. Results put a spotlight on highest probability targets.
2015 09-14 Precision Medicine 2015, London, Alain van GoolAlain van Gool
Outline of my view hoe personalized health(care) is more than just targeted medicines, also including personal motivation and actions towards disease prevention. It also outlines 4 key factors that should be in order for optimal personalized health(care): 1. start with patients first, 2. Accelerate translation research to application, 3. Copy best practice, 4. Spread the word.
2021 03-25 11th World Clinical Biomarkers & Companion Diagnostics, Alain van ...Alain van Gool
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Overview Radboudumc Center for Proteomics, Glycomics and Metabolomics april 2015
1. Translational Metabolic Laboratory
Using Proteomics, Glycomics and Metabolomics to
translate Research to Biomarkers to Diagnostics
April 2015
Translational Metabolic Laboratory, Department of Laboratory Medicine
https://www.radboudumc.nl/Research/ProteomicsMetabolomicsGlycomics/
2. Radboudumc
• Mission: “To have a significant impact on healthcare”
• Strategic focus on Personalized Healthcare through “the
patient as partner”
• Core activities:
• Patient care
• Research
• Education
• 11.000 colleagues
• 50 departments
• 3.000 students
• 1.000 beds
• First academic centre outside US to fully implement EPIC
4. Personalized Healthcare @ Radboudumc
People are different Stratification by multilevel diagnosis
+Patient’s preference of treatment
Exchange experiences in
care communities Select personalized therapy
Population
Patient
Molecule
4
6. Opening Radboud Research Facilities, 2nd Oct 2014
Point of contact: Alain van Gool
About 250 dedicated people working in 18 Technology Centers, ~1600 users (internal, external), ~140 consortia
www.radboudumc.nl/research/technologycenters/
6
Genomics
Bioinformatics
Animal
studies
Stem
cells
Translational
neuroscience
Image-guided
treatment
Imaging
Microscopy
Biobank
Health
economics
Mass
Spectrometry
Radboudumc
Technology
Centers
Investigational
products
Clinical
trials
EHR-based
research
Statistics
Human
physiology
Data
stewardship
Molecule
Flow
cytometry
7. 7
About 250 dedicated people working in 18 Technology Centers, ~1600 users (internal, external), ~140 consortia
www.radboudumc.nl/research/technologycenters/
• Proteins
• Metabolites
• Drugs
• PK-PD
• Preclinical
• Clinical
• Behavioural
• Preclinical
• Animal facility
• Systematic review
• Cell analysis
• Sorting
• Pediatric
• Adult
• Phase 1, 2, 3, 4
• Vaccines
• Pharmaceutics
• Radio-isotopes
• Malaria parasites
• Management
• Analysis
• Sharing
• Cloud computing
• DNA
• RNA
• Internal
• External
• Early HTA
• Evidence-based
surgery
• Field lab
• Statistics
• Biological
• Structural
• Preclinical
• Clinical• Economic
viability
• Decision
analysis
• Experimental design
• Biostatistical advice
• Electronic Health Records
• Big Data
• Best practice
• In vivo
• Functional
diagnostics
• iPSC
• Organoids
9. Research Biomarkers Diagnostics
Department of Laboratory Medicine, Radboudumc
Integrated Translational Research and Diagnostic Laboratory, 220 fte, yearly budget ~ 28M euro.
Close interaction with Dept of Genetics, Pathology and Medical Microbiology
Specialities:
• Proteomics, glycomics, metabolomics
• Enzymatic assays
• Neurochemistry
• Cellulair immunotherapy
• Immunomonitoring
Areas of disease:
• Metabolic diseases
• Mitochondrial diseases
• Lysosomal /glycosylation disorders
• Neuroscience
• Nefrology
• Iron metabolism
• Autoimmunity
• Immunodeficiency
• Transplantation
In development:
• ~500 Biomarkers
• Early and late stage
• Analytical development
• Clinical validation
Assay formats:
• Immunoassay
• Turbidicity assays
• Flow cytometry
• DNA sequencing
• Mass spectrometry
• Experimental human (-ized)
invitro and invivo models for
inflammation and
immunosuppression
Validated assays*:
• ~ 1000 assays
• 3.000.000 tests/year
Areas of application:
• Personalized healthcare
• Diagnosis
• Prognosis
• Mechanism of disease
• Mechanism of drug action
Department of Laboratory Medicine
*CCKL accreditation/RvA/EFI
www.laboratorymedicine.nl
9
10. One genome → multiple proteomes/metabolomes
• The proteomes and metabolomes are the functional output of
the genome
• 21.000 genes → approximately 500.000 possible proteins and
isoforms and biochemical metabolites
• Proteomes define and reflect the functional state of a cell or
organism at a certain time under certain conditions
• Proteomes and metabolome change depending on stimuli and
challenges; most cell/tissue signalling occurs through rapid
protein changes
• Proteomics and metabolomics are strong approaches to
identify and analyse metabolic changes of cell/tissue/organism
• Unique added value of proteomics:
• Protein expression
• Post-translational modifications
• Protein complex formation + function
12. Proteomics MetabolomicsGlycomics
Mass spectrometry – NMR based, 20 dedicated fte, part of diagnostic laboratory (Department of
Laboratory Medicine), close interaction with Radboudumc scientists and external partners
Translational Metabolic Laboratory – Laboratory Medicine
Ron Wevers, Jolein Gloerich, Alain van Gool, Leo Kluijtmans, Dirk Lefeber, Hans Wessels, et al
Research Biomarkers Diagnostics
13. Mass spectrometry – NMR based, 20 dedicated fte, part of diagnostic laboratory (Department
Laboratory Medicine), close interaction with Radboudumc scientists and external partners
Key experts:
Proteomics
Jolein Gloerich
Hans Wessels
Alain van Gool
Glycomics
Monique Scherpenzeel
Dirk Lefeber
Metabolomics
Leo Kluijtmans
Ron Wevers
Translational Metabolic Laboratory – Laboratory Medicine
14. Research
• Projects
• Service
External
• Projects
• Service
Patient care
• Health care focus
• Biomarkers, diagnostics
• Consortia (NL, EU)
Key features:
• Expertise centre rather than service facility
• Focus to translate Research to Biomarkers to Diagnostics
• Application of many years Omics expertise to customer’s specific needs
• Ambition to grow with long-term strategic projects, collaborations, staff and impact
Translational Metabolic Laboratory – Laboratory Medicine
15. Radboud Proteomics Center
Bottom up
proteomics
Top down
proteomics
Targeted
proteomics
Peptide-based
Differential Protein Profiling
Relative Quantitation
Intact protein-based
Post Translational Modifications
Research Biomarkers Diagnostics
Peptide-based
Selected biomarkers
Quantitative analysis
16. Proteomics techniques
• Peptide-based identification of proteins
• Differential protein expression profiling
(labelfree/labeled)
• Suitable for very complex samples
(in combination with fractionation)
• Focus on research
Whole proteome analysis
Protein complex isolation and characterization
Bottom up
Proteomics
17. Applications • Differential protein expression in:
• Health/disease
• Time
• Before/after treatment
• Protein-protein interactions:
• Protein complexes
• Protein correlation profiling
Up regulatedDown regulated
Instruments:
Bottom up
proteomics
18. Proteins Peptides Data Analysis
Phase1
RP pH2.7
LC-MS/MS
Trypsin
1D LC MS/MS workflow
CONTROLS
CONDITION 1
CONDITION 2
• Body fluids
• Circulating vesicles
• Tissues
• Cells
• Organelles
• Membranes
• Protein complexes
• Single proteins
Samples:
Bottom up
proteomics
19. Example cellular proteome profiling
Sample: HEK293 whole cell proteome (1 µg tryptic digest of urea extract)
1D LC-M/MS proteomics analysis
Retention time
m/z
400
600
800
1000
1200
1400
m/z
10 20 30 40 50 60 Time [min]
Blue: signal intensity in MS
Pink dots: precursors selected for MS/MS
Detected peaks in MS spectra 1.584.599
Detected isotope patterns in MS spectra 130.172
Total number of MS/MS spectra 22.743
Av. Absolute Mass Deviation [ppm] 2,8972
Matched MS/MS spectra 5.603
Identified NR peptides 4.537
Identified proteins 1.321
False Discovery Rate 0,98%
Bottom up
proteomics
In 1 scan:
20. Proteins Peptides RP pH10 UPLC 20 fractions
Phase1Phase2
20 fractions RP pH2.7
LC-MS/MS
Data processing
Statistical
analysis
400
600
800
1000
1200
m/z
20 30 40 50 Time [min]
Trypsin
CONTROLS
CONDITION 1
CONDITION 2
2D LC (RP x RP) MS/MS workflow
Bottom up
proteomics
23. Example tissue profiling project
Protein expression
(positive controls)
GO Protein distributions
Cellular compartments
LFQ scatter plot
Biological replicates
y= 0.9834x + 130390
R2=0.9842
Q: downstream effects of transgene?
Hippocampus tissue of Transgenic mice
4 Conditions: WT, TG, WT treated, TG treated with drug
5 Biological replicates; 2D LC-MS/MS analysis (20 fractions, 1 hour gradient)
Label-Free Quantitation (LFQ – MaxQuant)
• LC-MS/MS analyses: 400
• MS spectra: 1.937.394
• MS/MS spectra: 2.323.458
• Detected isotope patterns: 66.602.271
• Isotope patterns sequenced: 1.295.489
• Average absolute mass deviation: 1,38 ppm
• 1,3 Terrabyte data
PCA analysis – loading plot
Bottom up
proteomics
• Matched MS/MS spectra to peptides: 500.317
• Identified proteins: 3.187
• Quantified proteins: 2.365 (≥2 peptides/protein)
• Differential proteins: 276 (p<0.05)
• Average CV < 21%*
* Combining biological and technical reproducability
Transgene
Downstream
24. Proteins SDS-PAGE 9 Gel slices 9 in-gel digests
Phase1Phase2
9 Samples RP pH2.7
LC-MS/MS
Data processing
Statistical
analysis
400
600
800
1000
1200
m/z
20 30 40 50 Time [min]
Gel enhanced LC-MS/MS workflow
Trypsin
Bottom up
proteomics
25. Example of cellular proteome profiling project
Q: downstream miRNA effects on proteome?
A375 melanoma cell line
miRNA treated versus control
3 Biological replicates
GeLC-MS/MS analysis (5 slices, 1 hour LC gradients)
Label-Free Quantitation (LFQ – MaxQuant)
• Identified proteins: 1.932
• Quantified proteins: 1.379 (≥2 peptides/protein)
• Differential proteins: 337 (p<0.05) / 151 (p<0.01)
• Good reproducibility (average CV < 20%)*
• Data analysis: 70% overlap LC-MS/MS and RNA-Seq data
* Combination biological and technical reproducability
PCA loading plot
Chromatogram and ion map of a gel fraction
Collaboration with Radboudumc, InteRNA, TNO (DTL hotel project)
Already
suspected outlier
26. Conclusions
Example of cellular proteome profiling project
Results
Samples
Up
regulated
Down
regulated
Differential analysis
-10
-5
0
5
10 ∞
∞
178 Differentially
expressed proteins
Results
Gene ontology: cellular localization
• 3,824 identified proteins (98.7% cell specific)
• 2,550 quantified proteins (≥ 2 peptides/protein)
• 178 differential proteins due to treatment:
• 138 proteins upregulated
• 40 proteins downregulated
• Good basis for follow-up pharmaco-proteomics
Q: how does proteome cell
line x look like?
Q: First look at effect
treatment on proteome
(feasibility)
→ GeLC-MS/MS approach
Bottom up
proteomics
27. Cluster: 28S mt-Ribosome
Cluster: 39S mt-Ribosome
Cluster: F1F0 ATP synthase
Cluster: cytochrome b-c1 complex
Cluster: NADH dehydrogenase & TCP1
Cluster: trifunctional enzyme & isocitrate dehydrogenase
Cluster: cytochrome C oxidase & mt-Ribosomal subcomplex
Example of complexome analysis project
Bottom up
proteomics
Collaboration with NCMD, Bob Lightowlers
Q: What subcomplexes in mitochondrial proteome?
HEK293 Mitochondrial fraction
2 BN gel lanes (4-12% AA & 5-15% AA)
24 gel slices per gel lane
• Migration profiles for 953 proteins
• Unambiguous ID of 24 known complexes
• Validation of 8 implied interactors of the mt-Ribosome
• 11 novel putative interactors of the mt-Ribosome
Hierarchical clustering
29. Q: Changes in exosome proteome related to clinical phenotype?
Samples: - urine exosomes from patients with rejection after renal transplantation
- 4 subject groups (CTRL, REJ, CMV, BK)
Approach: - Gel enhanced 1D LC-MS/MS analysis (9 fractions)
- Per subject group: 2 different pools of multiple patients
- 2 separate experiments (LTQ FT Ultra & MaXis 4G)
Results: - Robust sample preparation is crucial
- In total 521 proteins identified
- Exosome enrichment confirmed by gene ontology classification (Cellular Components)
Collaboration with Department of Urology
Example of urine exosome analysis project
Bottom up
proteomics
31. Q: Effect of two bacterial growth conditions?
Desulfobacillus bacterium
2 Different growth conditions; 2 Biological replicates
GeLC-MS/MS analysis (9 slices, 1 hour gradient)
Label-Free Quantitation (LFQ – MaxQuant)
• Identified proteins: 1.228
• Quantified proteins: 950
• Differential proteins: 245 (p<0.05) / 109 (p<0.01)
• Excellent reproducibility (average CV < 10%)*
* Biological replicates: technical reproducability likely better
Protein expression example
Example of biotechnology project
LFQ scatter plot
Biological replicates
y= 1.0167x -
49244
R2=0.998
PCA loading plot
PC1 (72.9%)
PC2(14.7%)
Collaboration with external client
Bottom up
proteomics
32. Radboud Proteomics Center
Bottom up
proteomics
Top down
proteomics
Targeted
proteomics
Peptide-based
Differential Protein Profiling
Relative Quantitation
Intact protein-based
Post Translational Modifications
Research Biomarkers Diagnostics
Peptide-based
Selected biomarkers
Quantitative analysis
33. Proteomics techniques
• Intact protein analysis
• Post-translational modification
• Analysis of low to medium
complexity samples
Top down
proteomics
LC-MS Ion map of protein complex with MS spectrum of one subunit
Deconvoluted protein spectrum
Instruments:
34. Applications
• Characterization of intact
proteins:
• Post-translational
modifications
• Protein processing
• Splice variants
• Protein complex analysis
• Composition
• Complex-specific subunit
variants
• Quality control of biotech
products
Top down
proteomics
Quantitative analysis of intact protein isoforms
Collaboration with Floris van Delft (Synnafix)
35. Complexes Native
Electrophoresis
60 Gel slices 60 in-gel digests
Phase1Phase2
60 Samples RP pH2.7
LC-MS/MS
Data processing
Complexome
Profile
400
600
800
1000
1200
m/z
20 30 40 50 Time [min]
Bottom-up Complexome Profiling workflow
Trypsin
36. Complexes Native
Electrophoresis
Gel slice of
interest
Protein extraction,
reduction and SPE
Phase1Phase2
Protein
sample
LC-MS/MS with
fraction collection
Data processing
Top-Down
profiling
Top-Down Complexome Profiling workflow
Survey View
500
1000
1500
2000
2500
m/z
10 20 30 40 50 60 70 Time [min]
15
24
23
11 128 10 16 17 26
18
209
19
222114
13
12 13 14 15 16 17 18 19 20 Time[min]
0.0
0.5
1.0
1.5
2.0
2.5
7x10
Intens.
37. Phase3 Integrated Complexome Profiling workflow
Protein fractions
of interest
Peptides RP pH2.7
LC-MS/MS
Peptide MS2
level Data
nESI-MS/MS
Protein MS2
level data
Characterized
proteoform
Trypsin
38. Example of complexome analysis
Survey View
500
1000
1500
2000
2500
m/z
10 20 30 40 50 60 70 Time [min]
'1009.7168
10+
'1121.7954
9+
'1261.8938
8+
'1442.0208
7+ '1682.1905
6+
'2018.4295
5+
+MS, 56.8-58.7min #3408-3522
0
1
2
3
4
5
4x10
Intens.
1000 1200 1400 1600 1800 2000 2200 m/z
5+
6+
7+
8+
9+
10+
5+
6+7+
8+
9+
10+
1.682 m/z Da
Q: Composition of mitochondrial
complex 1?
• Y. lipolytica complex 1 as a model
for human
• 42 established subunits (7 mtDNA,
35 nDNA)
• Unknown mature subunit forms
• Unknown and dynamic post-
translational modifications
• Study: Combine Top-Down and
Bottom-Up characterization of all
subunits
Collaboration with Ulrich Brandt
Top down
proteomics
42. Top down / bottom up analysis of subunit protein (13,2 kDa)
Top-Down LC-MS/MS (ETD)
Top-Down NSI-MS/MS (ETD)
Bottom-Up LC-MS/MS (CID & ETD)
Matched peptide sequences in red, amino acids matched as ETD fragment ions are marked yellow (only for Top-Down data)
Hypothesized protein form
• N-terminus processing: Targeting sequence cleavage at S18
• C-terminus processing: None
• Additional PTMs: None
Top down
proteomics
44. Characterization of complex subunits
Q: Composition of mitochondrial complex 1?
•Predicted: 42 subunits (7 mtDNA, 35 nDNA)
•Detected: 240 protein subunit isoforms
(truncations, PTMs)
•Straight but time-consuming path to subunit
characterization
Top down
proteomics
45. Intact complexome analysis from tissue biopsies
Pilot study:
• Native tissue biopsies
• Isolate membrane complexes
• Separate and isolate complexes using Blue Native gels
• LC-MS/MS analysis
• Data analysis
Tissue 1
(n=3)
Tissue 2
(n=3)
Subunit
Subunit – tissue 1
Subunit – tissue 2
• Identified protein sequence of subunit
• Deduce simulated sequences from database
• Determine fit with experimental data
Top down
proteomics
46. Example of diagnostic top-down proteomics
• 12 families with liver disease and dilated cardiomyopathy (5-20 years)
• Initial clinical assessment didn’t yield clear cause of symptoms
• Specific sugar loss of serum transferrin identified via glycoproteomics
ChipCube-LC- Q-tof MS
• Outcome 1: Explanation of disease
• Outcome 2: Dietary intervention as succesful personalized therapy
• Outcome 3: Glycoprofile transferrin developed and applied as diagnostic test
• Genetic defect in glycosylation enzyme (PGM1) identified via exome sequencing
{Tegtmeyer et al, NEJM 370;6: 533 (2014)}
Genomics Glycomics Metabolomics
Top down
proteomics
By Monique van Scherpenzeel, Dirk Lefeber
47. Radboud Proteomics Center
Bottom up
proteomics
Top down
proteomics
Targeted
proteomics
Peptide-based
Differential Protein Profiling
Relative Quantitation
Intact protein-based
Post Translational Modifications
Peptide-based
Selected biomarkers
Quantitative analysis
Research Biomarkers Diagnostics
48. Proteomics techniques
• Peptide-based
• Sensitive quantitative analysis
• Suitable for very complex
samples
Targeted
proteomics
Nature Methods:
Method of the year 2012
protein expression data
Data Analysis
Protein A isoform 1
Protein A isoform 2
Protein B
49. Applications
(Absolute) quantitation of protein biomarkers:
• Biomarker research: Quantitative analysis of specific set of proteins
• Biomarker validation: Validation and prioritization of selected biomarkers
• Diagnostics: Analysis of qualified biomarkers
Targeted
proteomics
Research Diagnostics
Instruments:
50. Biomarker innovation gap
• Imbalance between biomarker discovery, validation and application
• Many more biomarkers discovered than available as diagnostic test
50
51. Selection of
biomarkers
Single Reaction Monitoring workflow
Phase1
Selection of
optimal
peptides
• Unique
• Best detectable
in LC-MS
Optimize detection by
selecting optimal transitions
Phase2
Proteins Peptides Data AnalysisRP pH2.7
LC-MS/MS
Trypsin
Isotope
labeled
standards
Isotope
labeled
standards
Targeted
proteomics
55. Glycosylation markers in human medicin
• Biomarker for disease and therapy monitoring: rheumatoid arthritis,
oncology, hepatitis
• MUC2 glycosylation in colon carinoma
• Human blood groups (A, B, O, AB)
• CDTect (Carbohydrate-Deficient transferrin)
• Infectious diseases
• IgA nephropathy
1% of genes directly involved in glycosylation
About 50% of proteins is glycosylated
IgA
60. Example: Intact glycoprotein biomarker
• 12 families with liver disease and dilated cardiomyopathy (5-20 years)
• Initial clinical assessment didn’t yield clear cause of symptoms
• Specific sugar loss of serum transferrin identified via glycoproteomics
ChipCube-LC- Q-tof MS
• Outcome 1: Explanation of disease
• Outcome 2: Dietary intervention as succesful personalized therapy
• Outcome 3: Glycoprofile transferrin developed and applied as diagnostic test
• Genetic defect in glycosylation enzyme (PGM1) identified via exome sequencing
{Tegtmeyer et al, NEJM 370;6: 533 (2014)}
Genomics Glycomics Metabolomics
60
61. Example: Glycopeptide profiling
• Optimized procedure using simple sample prep of plasma
• Detection of ~12.000 unique deconvoluted monoisotopic masses per
single analysis (> 50% are glycopeptides)
500
1000
1500
2000
m/z
5 10 15 20 25 30 35 40 Time [min]
Proof of principle study:
Monique van Scherpenzeel, Dirk Lefeber, Hans Wessels, Alain van Gool
Translational Metabolic Laboratory, Radboudumc, unpublished data
62. Example: Glycan analysis by nanoChip-QTOF MS
• High-resolution glycoprofiling
• Microfluidic chip system results in simplified operating conditions, increased
reproducibility and robustness
• CHIP formats: C18, Carbograph, C8, HILIC, phosphopeptides, PNGaseF
63. Bio-informatics :
• Coupling with public glyco-databases
• Annotation of glycan linkages
Glycan profiling in serum
B4GalT1
64. • Proteomics
• Bottom-up (shot-gun) proteomics
• Targeted proteomics
• Top-down proteomics
• Glycomics
• Glycan profiling
• (Targeted) Glycoproteomics
• Metabolomics
• Untargeted metabolomics
• Targeted metabolite profiling
Translational Metabolic Laboratory – Laboratory Medicine
Research Biomarkers Diagnostics
Key experts:
Jolein Gloerich
Hans Wessels
Alain van Gool
Monique Scherpenzeel
Dirk Lefeber
Leo Kluijtmans
Ron Wevers
69. A blind study
Plasma sample choice : Dr. C.D.G Huigen
Analytical chemistry : E. van der Heeft
Chemometrics : Dr. U.F.H. Engelke
Diagnosis : Prof. dr. R.A. Wevers;
Dr. L.A.J. Kluijtmans
Test 10 samples from 10 patients with 5 different
Inborn Error of Metabolism’s
21 controls
70. The blind study
MSUD (2) → leucine, isoleucine, valine, 3-methyl-2-oxovaleric acid
Aminoacylase I deficiency (2) → N-acetylglutamine, N-acetylglutamic acid,
N-acetylalanine, N-acetylserine, N-acetylasparagine, N-acetylglycine
Prolinemia type II (2) → proline, 1-pyrroline-5-carboxylic acid
Hyperlysinemia (2) → pipecolic acid, lysine, homoarginine, homocitrulline
3-Hydroxy-3-methylglutaryl-CoA lyase deficiency (2) → 3-methylglutaryl-carnitine, 3-
methylglutaconic acid, 3-hydroxy-2-methylbutanoic acid, 3-hydroxy-3-methylglutaric acid
Diagnostic metabolites found in blood plasma
• Correct diagnosis in all 10 patients
• Five different IEM’s identified by
differential metabolites
• The approach works!!!
• Validated method diagnostic SOP
• Planned for execution in line with genetics
72. Human
samples
Plasma, CSF (urine)
Controls vs. patient
QTOF Mass Spectrometry
- Reverse phase liquid chromatography
- Positive and negative mode
- Features
XCMS
Alignment
Peak comparison
> 10,000 Features
Personalized metabolic diagnostics
Xanthine Uric acid
72
Full metabolite profile:
Highly suspected of
xanthinuria
73. • Proteomics
• Bottom-up (shot-gun) proteomics
• Targeted proteomics
• Top-down proteomics
• Glycomics
• Glycan profiling
• (Targeted) Glycoproteomics
• Metabolomics
• Untargeted metabolomics
• Targeted metabolite profiling
Translational Metabolic Laboratory – Laboratory Medicine
Research Biomarkers Diagnostics
Key experts:
Jolein Gloerich
Hans Wessels
Alain van Gool
Monique Scherpenzeel
Dirk Lefeber
Leo Kluijtmans
Ron Wevers
74. A problem in biomarker land
Imbalance between biomarker discovery and application.
• Gap 1: Strong focus on discovery of new biomarkers, few biomarkers progress
beyond initial publication to multi-center clinical validation.
• Gap 2: Insufficient demonstrated added value of new clinical biomarker and
limited development of a commercially viable diagnostic biomarker test.
Discovery Clinical
validation/confirmation
Diagnostic
test
Number of
biomarkers
Gap 1
Gap 2
74
The innovation gap in biomarker
research & development
75. Some numbers
Data obtained from Thomson Reuters Integrity Biomarker Module
Eg Biomarkers in time: Prostate cancer
May 2011: 2,231 biomarkers
Nov 2012: 6,562 biomarkers
Oct 2013: 8,358 biomarkers
25 Sep 2014: 9,975 biomarkers with
31,403 biomarker uses
EU: CE marking
USA: LDT, 510(k), PMA
76. Shared biomarker research through open innovation
We need to set up a open innovation network to share biomarker knowledge and
jointly develop and validate biomarkers (at level of NL and EU):
1. Assay development of (diagnostic) biomarkers
2. Clinical biomarker quantification/validation/confirmation
Shared knowledge,
technologies and objectives
Funding: NL – STW; EU - Horizon2020, IMI; Fast track pharma funds
78. Biomarker Development Center (Netherlands)
STW perspectief grant
Biomarker Development Center
Public-private partnership 4 years
Project grant 4.3M Eur of which 2.2M government,
and 2.1M industry (0.9M cash/1.2M kind)
Close interactions with:
- Clinicians (biomarker application)
- Industry
- Patient stakeholder associations
Open
Innovation
Network !
80. healthy disease disease +
treatment
Challenge: how to identify subpopulations in
Personalized Healthcare?
healthy disease disease +
treatment
• Biomarkers in populations often have a wide range
• Within this range, subpopulations can behave quite differently
• Chemometric methods dealing with multiple biomarker data points are needed
to reveal such individual differences and enable personalized medicine
(Source: Jasper Engel, Lionel Blanchet, Udo Engelke, Ron Wevers and Lutgarde Buydens)
80