Automated data acquisition and analysis for high-resolution Fourier transform mass spectrometry of proteins and peptides

This thesis details the progress made in automating the data acquisition and data processing of MS and MS/MS data from Fourier transform ion cyclotron resonance mass spectrometers. These instruments have made great strides in the past few years, moving from labor-intensive, largely manual systems to the current flagship of the Kelleher group ??? a 12 tesla hybrid LTQ FT capable of analyzing hundreds of proteins per week with minimal user intervention. In early automation work centered around an 8.5 tesla hybrid quadrupole FT-ICR mass spectrometer, three strategies for automated operation of the instrument were developed. In one method, a charge state deconvolution algorithm was used to identify species of interest for isolation using a SWIFT waveform, which were then fragmented in the ICR cell using IRMPD. In another method, broad 20-60 m/z wide sections of the mass spectrum of a complex mixture were fragmented in parallel and this multiplexed fragment data was analyzed using an iterative algorithm. In the third method, samples were analyzed using a THRASH-based "quad march" method where data were acquired using consecutive, wide (20-60 m/z) isolation windows, analyzed using the THRASH algorithm, and then selected species were selected for MS/MS fragmentation. Results from the application of this platform to a survey of the Methanosarcina acetivorans proteome are presented. Later work focused on a commercial 12 tesla hybrid linear ion trap FT-ICR mass spectrometer. This automation scheme used hybrid online/offline data acquisition to take advantages of the features of both online LC-MS (efficient separation and rapid data collection) and offline direct infusion MS/MS (project-wide target selection, better MS/MS data). The workflow for the online portion of this scheme is a modified form of the THRASH-based "quad march" data acquisition scheme from the earlier 8.5 T automation work. The centerpiece of this - iii - platform is a database known as the Automation Warehouse, which acts as a repository for the intact mass data observed in a proteome project and stores the overall state of the project. Custom software binds together the raw data, the data stored in the warehouse and ProSight. Results from the application of this platform to a survey of proteins from HeLa cell nuclei are presented. In an attempt to begin applying the above platforms to membrane proteins, MS analysis of a putative cross-link in the active site of the C-type heme-copper oxygen reductase from Vibrio cholera was performed. Though the sequences in the region differed, other members of the HCO superfamily contained a similar cross-link (confirmed by mass spectrometry and crystal structures) that is important in the catalytic cycle of the enzyme. Computer modeling of the C-type oxidase suggested that the cross-link would be present, though the key amino acid would be located on a different helix. MS/MS analysis of a tryptic digest confirmed the presence of the cross-link and the evolutionary migration of the key amino acid

Power and limitations of electrophoretic separations in proteomics strategies

Proteomics can be defined as the large-scale analysis of proteins. Due to the complexity of biological systems, it is required to concatenate various separation techniques prior to mass spectrometry. These techniques, dealing with proteins or peptides, can rely on chromatography or electrophoresis. In this review, the electrophoretic techniques are under scrutiny. Their principles are recalled, and their applications for peptide and protein separations are presented and critically discussed. In addition, the features that are specific to gel electrophoresis and that interplay with mass spectrometry (i.e., protein detection after electrophoresis, and the process leading from a gel piece to a solution of peptides) are also discussed

Proteomic Approaches within the NCI Early Detection Research Network for the Discovery and Identification of Cancer Biomarkers

In the postgenome era, proteomics provides a powerful approach for the analysis of normal and transformed cell functions, for the identification of disease-specific targets, and for uncovering novel endpoints for the evaluation of chemoprevention agents and drug toxicity. Unfortunately, the genomic information that has greatly expounded the genetic basis of cancer does not allow an accurate prediction of what is actually occurring at the protein level within a given cell type at any given time. The gene expression program of a given cell is affected by numerous factors in the in vivo environment resulting from tissue complexity and organ system orchestration, with cells acting in concert with each other and responding to changes in their microenvironment. Repositories of genomic information can be considered master “inventory lists” of genes and their maps, which need to be supplemented with protein-derived information. The National Cancer Institute's Early Detection Research Network is employing proteomics, or “protein walking”, in the discovery and evaluation of biomarkers for cancer detection and for the identification of high-risk subjects. Armed with microdissection techniques, including the use of Laser Capture Microdissection (LCM) to procure pure populations of cells directly from human tissue, the Network is facilitating the development of technologies that can overcome the problem of tissue heterogeneity and address the need to identify markers in easily accessible biological fluids. Proteomic approaches complement plasma-based assays of circulating DNA for cancer detection and risk assessment. LCM, coupled with downstream proteomics applications, such as two-dimensional polyacrylamide gel electrophoresis and SELDI (surface enhanced laser desorption ionization) separation followed by mass spectrometry (MS) analysis, may greatly facilitate the characterization and identification of protein expression changes that track normal and disease phenotypes. We highlight recent work from Network investigators to demonstrate the potential of proteomics to identify proteins present in cancer tissues and body fluids that are relevant for cancer screening.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/73353/1/j.1749-6632.2001.tb03870.x.pd

Proteomic approaches in biological and medical sciences: principles and applications

After the first introduction of the concept of “proteome” more than 10 years ago, large-scale studies of protein expression, localization, activities and interactions have gained an exponential increase of interest, leading to extensive research and technology development. Proteomics is expansively applied in many areas, ranging from basic research, various disease and malignant tumors diagnostic and biomarker discovery to therapeutic applications. Several proteomics approaches have been developed for protein separation and identification, and for the characterization of protein function and structure. Two-dimensional gel electrophoresis, chromatography, capillary electrophoresis and mass spectrometry have become the most used proteomics methods. These techniques are also under constant development. This review provides an overview of the main techniques and their combinations, used in proteomics. The emphasis is made on description of advantages and disadvantages of each technique, to navigate in selection of the best application for solving a specific problem.Развитие протеомики за последние 10 лет стимулировало возрастающий интерес к ее применению для решения важных проблем современной биологии и медицины. Сегодня протеомика активно используется в фундаментальных исследованиях, поиске биомаркеров, диагностике опухолей. Широкомасштабное изучение экспрессии, локализации, активности и взаимодействий белков привело к специализации протеомных технологий и методов. Поэтому вопрос подбора наиболее подходящих методов для решения специфических проблем является особо актуальным. Двухмерный электрофорез, жидкостная хроматография, капиллярный электрофорез и масс-спектрометрия являются наиболее развитыми технологиями протеомики. В этом обзоре проанализированы сильные и слабые стороны этих технологий. Авторы надеются, что этот анализ поможет читателям выбрать наиболее эффективную методику для решения соответствующих научных задач

Exploring Information Technologies to Support Shotgun Proteomics

Shotgun proteomics refers to the direct analysis of complex protein mixtures to create a profile of the proteins present in the cell. These profiles can be used to study the underlying biological basis for cancer development. Closely studying the profiles as the cancer proliferates reveals the molecular interactions in the cell. They provide clues to researchers on potential drug targets to treat the disease. A little more than a decade old, shotgun proteomics is a relatively new form of discovery, one that is data intensive and requires complex data analysis. Early studies indicated a gap between the ability to analyze biological samples with a mass spectrometer and the information systems available to process and analyze this data. This thesis reflects on an automated proteomic information system at the University of Colorado Central Analytical Facility. Investigators there are using cutting edge proteomic techniques to analyze melanoma cell lines responsible for skin cancer in patients. The paper will provide insight on key design processes in the development of an Oracle relational database and automation system to support high-throughput shotgun proteomics in the facility. It will also discuss significant contributions, technologies, software, a data standard, and leaders in the field developing solutions and products in proteomics

Automated sample preparation for streamlined proteomic profiling of clinical specimens

The genetic information of all life is encoded within DNA molecules that are translated into functional entities, so-called proteins. They are responsible for operating and controlling a vast array of molecular mechanisms in any biological system and ubiquitous in (patho)physiology as a result. Besides, proteins are the primary target of drugs and can have a central role as biomarkers for diagnostic, prognostic, or predictive purposes. Here, many regulatory mechanisms and spatiotemporal influences prevent an accurate prediction of a proteins’ abundance and its associated functionality based on the genome information alone. Nowadays, it has become possible to measure and quantify thousands of proteins simultaneously, however, involving comprehensive sample preparation procedures. Currently, no universally standardized method enables a routine application of proteome profiling in a clinical environment. In this thesis, an automated workflow for the efficient processing of the most common and quantity-limited specimens is described. In order to demonstrate the usefulness of the end-to- end pipeline, which was termed autoSP3, it was applied to the proteome profiling of histologically defined and WHO recognized growth patterns of pulmonary adenocarcinoma (ADC) that currently have a limited clinical implication. Secondly, we investigated the proteome composition of a molecularly well-defined cohort of Ependymoma (EPN) pediatric brain tumors. Despite the availability of substantial NGS data and their ability to differentiate nine distinct subgroups, the majority of tumors remained without a functional insight. Here, the proteome profiling could provide a missing link and emphasize several subgroup-specific protein targets. In summary, this thesis describes the optimization of SP3 and its automation into a robust and cost-efficient pipeline for quantity-limited sample preparation and biological insight into the proteome composition of ADC growth patterns and EPN tumor subgroups

Review on Proteomics Technologies and Its Application for Crop Improvement

Proteomics is the study of proteins and their interactions in a cell. Within the wide field of functional OMICS, proteomics has become a useful tool and the emphasis is shifting from genomics to the protein compliment of the human organism. Because proteome reflects more accurately on the dynamic state of a cell, tissue, or organism, much is expected from proteomics to yield better disease markers for diagnosis and therapy monitoring. Hence the present review was to review proteomics technologies and their applications for crop improvement. The advent of proteomics technologies for global detection and quantitation of proteins creates new opportunities and challenges for those seeking to gain greater understanding of diseases. High-throughput proteomics technologies combining with advanced bioinformatics are extensively used to identify molecular signatures of diseases based on protein pathways and signalling cascades. Mass spectrometry plays a vital role in proteomics and has become an indispensable tool for molecular and cellular biology. However, future developments may enable faster and more sensitive proteomics studies and Proteomics alone cannot provide all the information required for understanding cellular processes. Therefore Complementary approaches in genomics, metabolomics and bioinformatics will have to be used together with proteomics to permits a more holistic view of biological systems and their alterations in disease, so that the maximum benefit can be realized. Keywords: Bioinformatics, mass spectrometry, proteomics, Two-dimensional electrophoresi

Metallomics in environmental and health related research: Current status and perspectives

Metals and metalloids play distinct roles in human health, either beneficial or toxic, depending on their concentrations and species. There is an increasing interest in metals uptake, trafficking, function, and exertion in microorganisms to maintain and advance human health. Metallomics, an emerging research area, focuses on elucidation of metals/metalloids location, distribution, speciation, and behavior in living organisms. This paper briefly summarized the recent progress on the methodology development of metallomics including various techniques, i. e. multiple dimensional liquid chromatography-inductively coupled plasma mass spectrometry (LC-ICPMS), gel electrophoresis-laser ablation-inductively coupled plasma mass spectrometry (GE-LA-ICPMS), synchrotron X-ray fluorescent spectroscopy (XFS), and the applications of metallomics in environmental and health care. © 2012 The Author(s).published_or_final_versio