Peptide fractionation in proteomics approaches

Peptide fractionation is extremely important in proteomics approaches. Full proteome characterization is desired from complex organisms, and with growing interest in post-translational modifications an extended protein sequence coverage is required. Peptide fractionation techniques have the great challenge of feeding current mass spectrometers in a way in which these issues are met. Peptide fractionation can be divided into three simple components: the column characteristics; the mobile phase; and peptide properties (charge, polarity, hydrophobicity and size). The current challenges are in the combination of these three components to allow comprehensive proteomics studies to be improved.     

Bioinformatics approaches in clinical proteomics

Protein expression profiling is increasingly being used to discover, validate and characterize biomarkers that can potentially be used for diagnostic purposes and to aid in pharmaceutical development. Correct analysis of data obtained from these experiments requires an understanding of the underlying analytic procedures used to obtain the data, statistical principles underlying high-dimensional data and clinical statistical tools used to determine the utility of the interpreted data. This review summarizes each of these steps, with the goal of providing the nonstatistician proteomics researcher with a working understanding of the various approaches that may be used by statisticians. Emphasis is placed on the process of mining high-dimensional data to identify a specific set of biomarkers that may be used in a diagnostic or other assay setting.     

Identification of the hydrophobic glycoproteins of Caenorhabditis elegans

Hydrophobic proteins such as integral membrane proteins are difficult to separate, and therefore to study, at a proteomics level. However, the Asn-linked (N-linked) carbohydrates (N-glycans) contained in membrane glycoproteins are important in differentiation, embryogenesis, inflammation, cancer and metastasis, and other vital cellular processes. Thus, the identification of these proteins and their sites of glycosylation in a well-characterized model organism is the first step toward understanding the mechanisms by which N-glycans and their associated proteins function in vivo. In this report, a proteomics method recently developed by our group was applied to identify 117 hydrophobic N-glycosylated proteins of Caenorhabditis elegans extracts by analysis of 195 glycopeptides containing 199 Asn-linked oligosaccharides. Most of the proteins identified are involved in cell adhesion, metabolism, or the transport of small molecules. In addition, there are 18 proteins for which no function is known or predictable by sequence homologies and two proteins which were previously predicted to exist only on the basis of genomic sequences in the C. elegans database. Because N-glycosylation is initiated in the lumen of the endoplasmic reticulum (ER), our data can be used to reassess the previously predicted subcellular localizations of these proteins. As well, the identification of N-glycosylation sites helps establish the membrane topology of the associated glycoproteins. Caenorhabditis elegans strains are presently available with mutations in 17 of the genes we have identified. The powerful genetic tools available for C. elegans can be used to make other strains with mutations in genes encoding N-glycosylated proteins and thereby determine N-glycan function.     

High-throughput proteomics using matrix-assisted laser desorption/ ionization mass spectrometry

It has become evident that the mystery of life will not be deciphered just by decoding its blueprint, the genetic code. In the life and biomedical sciences, research efforts are now shifting from pure gene analysis to the analysis of all biomolecules involved in the machinery of life. One area of these postgenomic research fields is proteomics. Although proteomics, which basically encompasses the analysis of proteins, is not a new concept, it is far from being a research field that can rely on routine and large-scale analyses. At the time the term proteomics was coined, a gold-rush mentality was created, promising vast and quick riches (i.e., solutions to the immensely complex questions of life and disease). Predictably, the reality has been quite different. The complexity of proteomes and the wide variations in the abundances and chemical properties of their constituents has rendered the use of systematic analytical approaches only partially successful, and biologically meaningful results have been slow to arrive. However, to learn more about how cells and, hence, life works, it is essential to understand the proteins and their complex interactions in their native environment. This is why proteomics will be an important part of the biomedical sciences for the foreseeable future. Therefore, any advances in providing the tools that make protein analysis a more routine and large-scale business, ideally using automated and rapid analytical procedures, are highly sought after. This review will provide some basics, thoughts and ideas on the exploitation of matrix-assisted laser desorption/ ionization in biological mass spectrometry – one of the most commonly used analytical tools in proteomics – for high-throughput analyses.     

HPLC techniques for proteomics analysis--a short overview of latest developments.

Due to the complex nature of the proteome, instrumentation and methods development for sample cleanup, fractionation, preconcentration, chromatographic separation and detection becomes urgent for the identification of peptides and proteins. Newly developed techniques and equipment for separation and detection, such as nano-HPLC and multidimensional HPLC for protein and peptide separation, enabled proteomics to experience dynamic growth during the past few years. In any proteomic analysis the most important and sometimes most difficult task is the separation of the complex mixture of proteins or peptides. This review describes some aspects and limitations of HPLC, both multidimensional and one-dimensional, in proteomics research without attempting to discuss all available HPLC methods, which would need far more space than available here.     

Bioinformatics in mass spectrometry data analysis for proteomics studies

Mass spectrometry is a technique widely employed for the identification and characterization of proteins. The role of bioinformatics is fundamental for the elaboration of mass spectrometry data due to the amount of data that this technique can produce. To process data efficiently, new software packages and algorithms are continuously being developed to improve protein identification and characterization in terms of high-throughput and statistical accuracy. However, many limitations exist concerning bioinformatics spectral data elaboration. This review aims to critically cover the recent and future developments of new bioinformatics approaches in mass spectrometry data analysis for proteomics studies.     

Tissue proteomics using capillary isoelectric focusing-based multidimensional separations

The capabilities of capillary isoelectric focusing-based multidimensional separations for performing proteome analysis from minute samples create new opportunities in the pursuit of biomarker discovery using enriched and selected cell populations procured from tissue specimens. In this article, recent advances in online integration of capillary isoelectric focusing with nano-reversed phase liquid chromatography for achieving high-resolution peptide and protein separations prior to mass spectrometry analysis are reviewed, along with its potential application to tissue proteomics. These proteome technological advances combined with recently developed tissue microdissection techniques, provide powerful tools for those seeking to gain a greater understanding at the global level of the cellular machinery associated with human diseases such as cancer.     

Absolute quantification strategies in proteomics based on mass spectrometry

The strong need for quantitative information in proteomics has fueled the development of mass spectrometry-based analytical methods that are able to determine protein abundances. This article reviews mass spectrometry experiments aimed at providing an absolute quantification of proteins. The experiments make use of the isotope-dilution concept by spiking a known amount of synthetic, isotope-labeled reference peptide into the analyte sample. Quantification is achieved by comparing the mass spectrometry signal intensities of the reference with an endogenous peptide that is generated upon proteolytic cleavage of the target protein. In an analogous manner, the level of post-translational modification at a distinct residue within a target protein can be determined. Among the strengths of absolute quantification are low detection limits reaching subfemtomole levels, a high dynamic range spanning approximately five orders of magnitude, low requirements for sample clean-up, and a fast and straightforward method development. Recent studies have demonstrated the compatibility of absolute quantification with various mass spectrometry readout techniques and sample purification steps such as 1D gel electrophoresis, size-exclusion chromatography, isoelectric peptide focusing, strong cation exchange and reversed phase or affinity chromatography. Under ideal conditions, quantification errors and coefficients of variation below 5% have been reported. However, the fact that at the start of the experiment the analyte is a protein and the internal standard is a peptide, severe quantification errors may result due to the selection of unsuitable reference peptides and/or imperfect protein proteolysis. Within the ensemble of mass spectrometry-based quantification methods, absolute quantification is the method of choice in cases where absolute numbers, many repetitive experiments or precise levels of post-translational modifications are required for a few, preselected species of interest. Consequently, prominent application areas include biomarker quantification, the study of post-translational modifications such as phosphorylation or ubiquitination and the comparison of concentrations of interacting proteins.     

Genome annotating proteomics pipelines: available tools

Proteomics based on tandem mass spectrometry is a powerful tool for identifying novel biomarkers and drug targets. Previously, a major bottleneck in high-throughput proteomics has been that the computational techniques needed to reliably identify proteins from proteomic data lagged behind the ability to collect the immense quantity of data generated. This is no longer the case, as fully automated pipelines for peptide and protein identification exist, and these are publicly and privately accessible. Such pipelines can automatically and rapidly generate high-confidence protein identifications from large datasets in a searchable format covering multiple experimental runs. However, the main challenge for the community now is to use these resources as they are, by taking full advantage of the pooling of information, so that the next barrier in our understanding of biology may be broken. There are currently two pipelines in the public domain that provide such potential: PeptideAtlas and the Genome Annotating Proteomic Pipeline. This review will introduce their features in the context of high-throughput proteomics, and provide indicative results as to their usefulness and usability through a side-by-side comparison of results obtained when processing a set of human plasma samples.     

Current trends in computational inference from mass spectrometry-based proteomics

Mass spectrometry offers a high-throughput approach to quantifying the proteome associated with a biological sample and hence has become the primary approach of proteomic analyses. Computation is tightly coupled to this advanced technological platform as a required component of not only peptide and protein identification, but quantification and functional inference, such as protein modifications and interactions. Proteomics faces several key computational challenges such as identification of proteins and peptides from tandem mass spectra as well as their quantitation. In addition, the application of proteomics to systems biology requires understanding the functional proteome, including how the dynamics of the cell change in response to protein modifications and complex interactions between biomolecules. This review presents an overview of recently developed methods and their impact on these core computational challenges currently facing proteomics.     

The human urinary proteome: combinational approaches to comprehensive mapping

The development methodologies for the assessment of the protein content of biological samples have been in the ‘eye of the storm’ in proteomics for almost two decades. The work of Zerefos et al. is a continuation of this trend, focusing on analysis of urinary proteins using a combination of separation methodologies. In this work, the authors employ a previously analyzed control urine sample. Three different methodologies are presented, involving the combination of classical separation approaches, such as SDS-PAGE, preparative electrophoresis and liquid chromatography and standard mass spectrometer instrumentation. Several hundred proteins were reported following the application of a typical proteomics workflow and the use of a data meta-analysis platform to enhance the credibility of the final output. This is also established through cross-method (within this study) as well as cross-study (comparison of this with other main studies in the field) data comparisons. Emphasis is placed on the presentation of experimental identifiers as well as information provided at the peptide level.     

Optimization of Data-Dependent Acquisition Parameters for Coupling High-Speed Separations with LC-MS/MS for Protein Identifications

Recent developments in chromatography, such as ultra-HPLC and superficially porous particles, offer significantly improved peptide separation. The narrow peak widths, often only several seconds, can permit a 15-min liquid chromatography run to have a similar peak capacity as a 60-min run using traditional HPLC approaches. In theory, these larger peak capacities should provide higher protein coverage and/or more protein identifications when incorporated into a proteomic workflow. We initially observed a decrease in protein coverage when implementing these faster chromatographic approaches, due to data-dependent acquisition (DDA) settings that were not properly set to match the narrow peak widths resulting from newly implemented, fast separation techniques. Oversampling of high-intensity peptides lead to low protein-sequence coverage, and tandem mass spectra (MS/MS) from lower-intensity peptides were of poor quality, as automated MS/MS events were occurring late on chromatographic peaks. These observations led us to optimize DDA settings to use these fast separations. Optimized DDA settings were applied to the analysis of Trypanosome brucei peptides, yielding peptide identifications at a rate almost five times faster than previously used methodologies. The described approach significantly improves protein identification workflows that use typical available instrumentation.     

The Use of Ammonium Formate as a Mobile-Phase Modifier for LC-MS/MS Analysis of Tryptic Digests

A major challenge facing current mass spectrometry (MS)-based proteomics research is the large concentration range displayed in biological systems, which far exceeds the dynamic range of commonly available mass spectrometers. One approach to overcome this limitation is to improve online reversed-phase liquid chromatography (RP-LC) separation methodologies. LC mobile-phase modifiers are used to improve peak shape and increase sample load tolerance. Trifluoroacetic acid (TFA) is a commonly used mobile-phase modifier, as it produces peptide separations that are far superior to other additives. However, TFA leads to signal suppression when incorporated with electrospray ionization (ESI), and thus, other modifiers, such as formic acid (FA), are used for LC-MS applications. FA exhibits significantly less signal suppression, but is not as effective of a modifier as TFA. An alternative mobile-phase modifier is the combination of FA and ammonium formate (AF), which has been shown to improve peptide separations. The ESI-MS compatibility of this modifier has not been investigated, particularly for proteomic applications. This work compares the separation metrics of mobile phases modified with FA and FA/AF and explores the use of FA/AF for the LC-MS analysis of tryptic digests. Standard tryptic-digest peptides were used for comparative analysis of peak capacity and sample load tolerance. The compatibility of FA/AF in proteomic applications was examined with the analysis of soluble proteins from canine prostate carcinoma tissue. Overall, the use of FA/AF improved online RP-LC separations and led to significant increases in peptide identifications with improved protein sequence coverage.     

Characterization and quantitation of membrane proteomes using multidimensional MS-based proteomic technologies

A major goal of proteomics is to develop methods that enable the systematic characterization of every protein within the cell or particular subcellular proteome using a single analytical platform. Although the equivalent has already been achieved in genomics, reaching this goal in proteomics represents a much greater challenge due to the wide dynamic range of protein expression, numerous post-translational modifications and remarkable physicochemical heterogeneity of proteins. A major analytical challenge has involved developing more effective means for proteome-scale investigations of membrane proteins, whose solubility differs drastically from that of cytoplasmic proteins. Fortunately, rapid progress has increased the ability to characterize this critically important class of proteins on a scale analogous to that of aqueous soluble proteins.     

Multidimensional protein identification technology: current status and future prospects

Protein profiling using high-throughput tandem mass spectrometry has become a powerful method for analyzing changes in global protein expression patterns in cells and tissues as a function of developmental, physiologic and disease processes. This review summarizes the utility and practical application of multidimensional protein identification technology as a platform for comprehensive proteomic profiling of complex biologic samples. The strengths and potential problems and limitations associated with this powerful technology are discussed, with an emphasis placed on one of the biggest challenges currently facing large-scale expression profiling projects – namely, data analysis. Complementary bioinformatic computational data mining strategies, such as clustering, functional annotation and statistical inference, are also discussed as these are increasingly necessary for interpreting the results of global proteomic profiling studies.     

High-throughput proteomics using Fourier transform ion cyclotron resonance mass spectrometry

The advent of high-throughput proteomic technologies for global detection and quantitation of proteins creates new opportunities and challenges for those seeking to gain greater understanding of the cellular machinery. Here, recent advances in high-resolution capillary liquid chromatography coupled to Fourier transform ion cyclotron resonance mass spectrometry are reviewed along with its potential application to high-throughput proteomics. These technological advances combined with quantitative stable isotope labeling methodologies provide powerful tools for expanding our understanding of biology at the system level.     

生物质谱与蛋白质组学

蛋白质组学是后基因组学时代最受关注的研究领域之一,其核心的鉴定技术——生物质谱近年来在仪器设计以及鉴定通量、分辨率和灵敏度等各方面均有质的飞跃,促进了蛋白质表达谱作图、定量蛋白质组分析、亚细胞器蛋白质组作图、蛋白质翻译后修饰以及蛋白质相互作用等蛋白质组研究各个领域的飞速发展。本综述了生物质谱技术的最新进展,及其在蛋白质组学研究中的应用。     

Cancer proteomics: many technologies,one goal

A major goal of the National Cancer Institute is to alleviate patient pain, suffering and death associated with cancer by the year 2015. This goal does not insinuate a cure for cancer, but rather the development of diagnostics and therapeutics that will eventually decrease cancer morbidity and mortality. A part of meeting this goal is to leverage the enormous data-gathering capabilities of proteomic technologies to discover disease-specific biomarkers in serum, plasma, urine, tissues and other biologic samples. The rapid advance in available technologies that have been spurred by the -omics era, has enabled biologic samples to be surveyed for biomarkers in ways never before possible. However, it is not yet clear which specific technologies will be the most successful. Therefore, proteomic laboratories within the National Cancer Institute are taking a multipronged approach to identify disease-specific biomarkers. This review discusses some of these approaches in their context of meeting the National Cancer Institute’s 2015 goal.     

Mass spectrometric approaches for characterizing bacterial proteomes

The emergence of advanced liquid chromatography mass spectrometry technologies for characterizing very complex mixtures of proteins has greatly propelled the field of proteomics, the goal of which is the simultaneous examination of all the proteins expressed by an organism. This research area represents a paradigm shift in molecular biology by attempting to provide a top-down qualitative and quantitative view of all the proteins (including their modifications and interactions) that are essential for an organism’s life cycle, rather than targeting a particular protein family. This level of global protein information about an organism such as a bacterium can be combined with genomic and metabolomic data to enable a systems biology approach for understanding how these organisms live and function.