Label-free measurement of histone lysine methyltransferases activity by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry

Histone lysine methyltransferases (HKMTs) are enzymes that play an essential role in epigenetic regulation. Thus, identification of inhibitors specifically targeting these enzymes represents a challenge for the development of new antitumor therapeutics. Several methods for measuring HKMT activity are already available. Most of them use indirect measurement of the enzymatic reaction through radioactive labeling or antibody-recognized products or coupled enzymatic assays. Mass spectrometry (MS) represents an interesting alternative approach because it allows direct detection and quantification of enzymatic reactions and can be used to determine kinetics and to screen small molecules as potential inhibitors. Application of mass spectrometry to the study of HKMTs has not been fully explored yet. We describe here the development of a simple reliable label-free MALDI-TOF MS-based assay for the detection and quantification of peptide methylation, using SET7/9 as a model enzyme. Importantly, the use of expensive internal standard often required in mass spectrometry quantitative analysis is not necessary in this assay. This MS assay allowed us to determine enzyme kinetic parameters as well as IC₅₀ for a known inhibitor of this enzyme. Furthermore, a comparative study with an antibody-based immunosorbent assay showed that the MS assay is more reliable and suitable for the screening of inhibitors.     

Computational methods for the comparative quantification of proteins in label-free LCn-MS experiments

Liquid chromatography (LC) coupled to electrospray mass spectrometry (MS) is well established in high-throughput proteomics. The technology enables rapid identification of large numbers of proteins in a relatively short time. Comparative quantification of identified proteins from different samples is often regarded as the next step in proteomics experiments enabling the comparison of protein expression in different proteomes. Differential labeling of samples using stable isotope incorporation or conjugation is commonly used to compare protein levels between samples but these procedures are difficult to carry out in the laboratory and for large numbers of samples. Recently, comparative quantification of label-free LC(n)-MS proteomics data has emerged as an alternative approach. In this review, we discuss different computational approaches for extracting comparative quantitative information from label-free LC(n)-MS proteomics data. The procedure for computationally recovering the quantitative information is described. Furthermore, statistical tests used to evaluate the relevance of results will also be discussed.     

Xlink-identifier: an automated data analysis platform for confident identifications of chemically cross-linked peptides using tandem mass spectrometry

Chemical cross-linking combined with mass spectrometry provides a powerful method for identifying protein-protein interactions and probing the structure of protein complexes. A number of strategies have been reported that take advantage of the high sensitivity and high resolution of modern mass spectrometers. Approaches typically include synthesis of novel cross-linking compounds, and/or isotopic labeling of the cross-linking reagent and/or protein, and label-free methods. We report Xlink-Identifier, a comprehensive data analysis platform that has been developed to support label-free analyses. It can identify interpeptide, intrapeptide, and deadend cross-links as well as underivatized peptides. The software streamlines data preprocessing, peptide scoring, and visualization and provides an overall data analysis strategy for studying protein-protein interactions and protein structure using mass spectrometry. The software has been evaluated using a custom synthesized cross-linking reagent that features an enrichment tag. Xlink-Identifier offers the potential to perform large-scale identifications of protein-protein interactions using tandem mass spectrometry.     

Platform-independent and label-free quantitation of proteomic data using MS1 extracted ion chromatograms in skyline: application to protein acetylation and phosphorylation

Despite advances in metabolic and postmetabolic labeling methods for quantitative proteomics, there remains a need for improved label-free approaches. This need is particularly pressing for workflows that incorporate affinity enrichment at the peptide level, where isobaric chemical labels such as isobaric tags for relative and absolute quantitation and tandem mass tags may prove problematic or where stable isotope labeling with amino acids in cell culture labeling cannot be readily applied. Skyline is a freely available, open source software tool for quantitative data processing and proteomic analysis. We expanded the capabilities of Skyline to process ion intensity chromatograms of peptide analytes from full scan mass spectral data (MS1) acquired during HPLC MS/MS proteomic experiments. Moreover, unlike existing programs, Skyline MS1 filtering can be used with mass spectrometers from four major vendors, which allows results to be compared directly across laboratories. The new quantitative and graphical tools now available in Skyline specifically support interrogation of multiple acquisitions for MS1 filtering, including visual inspection of peak picking and both automated and manual integration, key features often lacking in existing software. In addition, Skyline MS1 filtering displays retention time indicators from underlying MS/MS data contained within the spectral library to ensure proper peak selection. The modular structure of Skyline also provides well defined, customizable data reports and thus allows users to directly connect to existing statistical programs for post hoc data analysis. To demonstrate the utility of the MS1 filtering approach, we have carried out experiments on several MS platforms and have specifically examined the performance of this method to quantify two important post-translational modifications: acetylation and phosphorylation, in peptide-centric affinity workflows of increasing complexity using mouse and human models.     

RIBAR and xRIBAR: Methods for reproducible relative MS/MS-based label-free protein quantification

Mass spectrometry-driven proteomics is increasingly relying on quantitative analyses for biological discoveries. As a result, different methods and algorithms have been developed to perform relative or absolute quantification based on mass spectrometry data. One of the most popular quantification methods are the so-called label-free approaches, which require no special sample processing, and can even be applied retroactively to existing data sets. Of these label-free methods, the MS/MS-based approaches are most often applied, mainly because of their inherent simplicity as compared to MS-based methods. The main application of these approaches is the determination of relative protein amounts between different samples, expressed as protein ratios. However, as we demonstrate here, there are some issues with the reproducibility across replicates of these protein ratio sets obtained from the various MS/MS-based label-free methods, indicating that the existing methods are not optimally robust. We therefore present two new methods (called RIBAR and xRIBAR) that use the available MS/MS data more effectively, achieving increased robustness. Both the accuracy and the precision of our novel methods are analyzed and compared to the existing methods to illustrate the increased robustness of our new methods over existing ones.     

Characterization of a TiO₂ enrichment method for label-free quantitative phosphoproteomics

Phosphorylation is a protein post-translational modification with key roles in the regulation of cell biochemistry and signaling. In-depth analysis of phosphorylation using mass spectrometry is permitting the investigation of processes controlled by phosphorylation at the system level. A critical step of these phosphoproteomics methods involves the isolation of phosphorylated peptides from the more abundant unmodified peptides produced by the digestion of cell lysates. Although different techniques to enrich for phosphopeptides have been reported, there are limited data on their suitability for direct quantitative analysis by MS. Here we report a TiO₂ based enrichment method compatible with large-scale and label-free quantitative analysis by LC–MS/MS. Starting with just 500 μg of protein, the technique reproducibly isolated hundreds of peptides, >85% of which were phosphorylated. These results were obtained by using relatively short LC–MS/MS gradient runs (45 min) and without any previous separation step. In order to characterize the performance of the method for quantitative analyses, we employed label-free LC–MS/MS using extracted ion chromatograms as the quantitative readout. After normalization, phosphopeptides were quantified with good precision (coefficient of variation was 20% on average, n = 900 phosphopeptides), linearity (correlation coefficients >0.98) and accuracy (deviations <20%). Thus, phosphopeptide ion signals correlated with the concentration of the respective phosphopeptide in samples, making the approach suitable for in-depth relative quantification of phosphorylation by label-free LC–MS/MS.     

Tissue proteomics by one-dimensional gel electrophoresis combined with label-free protein quantification

Label-free methods streamline quantitative proteomics of tissues by alleviating the need for metabolic labeling of proteins with stable isotopes. Here we detail and implement solutions to common problems in label-free data processing geared toward tissue proteomics by one-dimensional gel electrophoresis followed by liquid chromatography tandem mass spectrometry (geLC MS/MS). Our quantification pipeline showed high levels of performance in terms of duplicate reproducibility, linear dynamic range, and number of proteins identified and quantified. When applied to the liver of an adenomatous polyposis coli (APC) knockout mouse, we demonstrated an 8-fold increase in the number of statistically significant changing proteins compared to alternative approaches, including many more previously unidentified hydrophobic proteins. Better proteome coverage and quantification accuracy revealed molecular details of the perturbed energy metabolism.     

Mass spectrometry imaging is moving toward drug protein co-localization

Mass spectrometry (MS)-based technology provides label-free localization of molecules in tissue samples. Drugs, proteins, lipids and metabolites can easily be monitored in their environment. Resolution can be achieved down to the cellular level (10-20μm) for conventional matrix-assisted laser desorption/ionization (MALDI) imaging, or even to the subcellular level for more complex technologies such as secondary ionization mass spectrometry (SIMS) imaging. One question remains: are we going to be able to investigate functional relationships between drugs and proteins and compare with localized phenomena? This review describes the various spatial levels of investigation offered by mass spectrometry imaging (MSI), and the advantages and disadvantages compared with other labeling technologies.     

An assessment of software solutions for the analysis of mass spectrometry based quantitative proteomics data

Over the past decade, a series of experimental strategies for mass spectrometry based quantitative proteomics and corresponding computational methodology for the processing of the resulting data have been generated. We provide here an overview of the main quantification principles and available software solutions for the analysis of data generated by liquid chromatography coupled to mass spectrometry (LC-MS). Three conceptually different methods to perform quantitative LC-MS experiments have been introduced. In the first, quantification is achieved by spectral counting, in the second via differential stable isotopic labeling, and in the third by using the ion current in label-free LC-MS measurements. We discuss here advantages and challenges of each quantification approach and assess available software solutions with respect to their instrument compatibility and processing functionality. This review therefore serves as a starting point for researchers to choose an appropriate software solution for quantitative proteomic experiments based on their experimental and analytical requirements.     

Recent progress in liquid chromatography-based separation and label-free quantitative plant proteomics

Recent innovations in liquid chromatography-mass spectrometry (LC-MS)-based methods have facilitated quantitative and functional proteomic analyses of large numbers of proteins derived from complex samples without any need for protein or peptide labelling. Regardless of its great potential, the application of these proteomics techniques to plant science started only recently. Here we present an overview of label-free quantitative proteomics features and their employment for analysing plants. Recent methods used for quantitative protein analyses by MS techniques are summarized and major challenges associated with label-free LC-MS-based approaches, including sample preparation, peptide separation, quantification and kinetic studies, are discussed. Database search algorithms and specific aspects regarding protein identification of non-sequenced organisms are also addressed. So far, label-free LC-MS in plant science has been used to establish cellular or subcellular proteome maps, characterize plant-pathogen interactions or stress defence reactions, and for profiling protein patterns during developmental processes. Improvements in both, analytical platforms (separation technology and bioinformatics/statistical analysis) and high throughput nucleotide sequencing technologies will enhance the power of this method.     

A label-free quantification method by MS/MS TIC compared to SILAC and spectral counting in a proteomics screen

In order to assess the biological function of proteins and their modifications for understanding signaling mechanisms within cells as well as specific biomarkers to disease, it is important that quantitative information be obtained under different experimental conditions. Stable isotope labeling is a powerful method for accurately determining changes in the levels of proteins and PTMs; however, isotope labeling experiments suffer from limited dynamic range resulting in signal change ratios of less than approximately 20:1 using most commercial mass spectrometers. Label-free approaches to relative quantification in proteomics such as spectral counting have gained popularity since no additional chemistries are needed. Here, we show a label-free method for relative quantification based on the TIC from peptide MS/MS spectra collected from data-dependent runs can be used effectively as a quantitative measure and expands the dynamic range over isotope labeling experiments allowing for abundance differences up to approximately 60:1 in a screen for proteins that bind to phosphotyrosine residues.     

YPED:An Integrated Bioinformatics Suite and Database for Mass Spectrometry-based Proteomics Research

We report a significantly-enhanced bioinformatics suite and database for proteomics research called Yale Protein Expression Database(YPED) that is used by investigators at more than 300 institutions worldwide. YPED meets the data management, archival, and analysis needs of a high-throughput mass spectrometry-based proteomics research ranging from a singlelaboratory, group of laboratories within and beyond an institution, to the entire proteomics community. The current version is a significant improvement over the first version in that it contains new modules for liquid chromatography–tandem mass spectrometry(LC–MS/MS) database search results, label and label-free quantitative proteomic analysis, and several scoring outputs for phosphopeptide site localization. In addition, we have added both peptide and protein comparative analysis tools to enable pairwise analysis of distinct peptides/proteins in each sample and of overlapping peptides/proteins between all samples in multiple datasets. We have also implemented a targeted proteomics module for automated multiple reaction monitoring(MRM)/selective reaction monitoring(SRM) assay development. We have linked YPED's database search results and both label-based and label-free fold-change analysis to the Skyline Panorama repository for online spectra visualization. In addition, we have built enhanced functionality to curate peptide identifications into an MS/MS peptide spectral library for all of our protein database search identification results.     

A label-free mass spectrometry method for the quantification of protein isotypes

Successful quantitative mass spectrometry (MS) requires strategies to link the mass spectrometer response to the analyte abundance, with the response being dependent on more factors than just analyte abundance. Label-dependent strategies rely on the incorporation of an isotopically labeled internal standard into the sample. Current label-free strategies (performed without internal standards) are useful for analyzing samples that are unsuitable for isotopic labeling but are less accurate. Here we describe a label-free technique applicable to analysis of products from related genes (isotypes). This approach enables the invariant tryptic peptide sequences within the family to serve as “built-in” internal standards and the isotype-specific peptide sequences to report the amount of the various isotypes. A process of elimination segregates reliably trypsin-released standard and reporter peptides from unreliably released peptides. The specific MS response factors for these reporter and standard peptides can be determined using synthetic peptides. Analysis of HeLa tubulin digests revealed peptides from βI-, βII-, βIII-, βIVb-, and βV-tubulin, eight of which were suitable; along with five standard peptides for quantification of the β-tubulin isotypes. To show the utility of this method, we determined that βI-tubulin represented 77% and βIII-tubulin represented 3.2% of the total HeLa β-tubulin.     

Use of DNA ladders for reproducible protein fractionation by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) for quantitative proteomics

In proteomics, one-dimensional (1D) sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) is widely used for protein fractionation prior to mass spectrometric analysis to enhance the dynamic range of analysis and to improve the identification of low-abundance proteins. Such protein prefractionation works well for quantitation strategies if the proteins are labeled prior to separation. However, because of the poor reproducibility of cutting gel slices, especially when small amounts of samples are analyzed, its application in label-free and peptide-labeling quantitative proteomics methods has been greatly limited. To overcome this limitation, we developed a new strategy in which a DNA ladder is mixed with the protein sample before PAGE separation. After PAGE separation, the DNA ladder is stained to allow for easy, precise, and reproducible gel cutting. To this end, a novel visible DNA-staining method was developed. This staining method is fast, sensitive, and compatible with mass spectrometry. To evaluate the reproducibility of DNA-ladder-assisted gel cutting for quantitative protein fractionation, we used stable isotope labeling with amino acids in cell culture (SILAC). Our results show that the quantitative error associated with fractionation can be minimized using the DNA-assisted fractionation and multiple replicates of gel cutting. In conclusion, 1D PAGE fractionation in combination with DNA ladders can be used for label-free comparative proteomics without compromising quantitation.     

A review on recent developments in mass spectrometry instrumentation and quantitative tools advancing bacterial proteomics

Proteomics has evolved substantially since its early days, some 20 years ago. In this mini-review, we aim to provide an overview of general methodologies and more recent developments in mass spectrometric approaches used for relative and absolute quantitation of proteins. Enhancement of sensitivity of the mass spectrometers as well as improved sample preparation and protein fractionation methods are resulting in a more comprehensive analysis of proteomes. We also document some upcoming trends for quantitative proteomics such as the use of label-free quantification methods. Hopefully, microbiologists will continue to explore proteomics as a tool in their research to understand the adaptation of microorganisms to their ever changing environment. We encourage them to incorporate some of the described new developments in mass spectrometry to facilitate their analyses and improve the general knowledge of the fascinating world of microorganisms.     

Quantitative proteome analysis using differential stable isotopic labeling and microbore LC-MALDI MS and MS/MS

We demonstrate an approach for global quantitative analysis of protein mixtures using differential stable isotopic labeling of the enzyme-digested peptides combined with microbore liquid chromatography (LC) matrix-assisted laser desorption ionization (MALDI) mass spectrometry (MS). Microbore LC provides higher sample loading, compared to capillary LC, which facilitates the quantification of low abundance proteins in protein mixtures. In this work, microbore LC is combined with MALDI MS via a heated droplet interface. The compatibilities of two global peptide labeling methods (i.e., esterification to carboxylic groups and dimethylation to amine groups of peptides) with this LC-MALDI technique are evaluated. Using a quadrupole-time-of-flight mass spectrometer, MALDI spectra of the peptides in individual sample spots are obtained to determine the abundance ratio among pairs of differential isotopically labeled peptides. MS/MS spectra are subsequently obtained from the peptide pairs showing significant abundance differences to determine the sequences of selected peptides for protein identification. The peptide sequences determined from MS/MS database search are confirmed by using the overlaid fragment ion spectra generated from a pair of differentially labeled peptides. The effectiveness of this microbore LC-MALDI approach is demonstrated in the quantification and identification of peptides from a mixture of standard proteins as well as E. coli whole cell extract of known relative concentrations. It is shown that this approach provides a facile and economical means of comparing relative protein abundances from two proteome samples.     

To label or not to label: applications of quantitative proteomics in neuroscience research

Proteomics has provided researchers with a sophisticated toolbox of labeling-based and label-free quantitative methods. These are now being applied in neuroscience research where they have already contributed to the elucidation of fundamental mechanisms and the discovery of candidate biomarkers. In this review, we evaluate and compare labeling-based and label-free quantitative proteomic techniques for applications in neuroscience research. We discuss the considerations required for the analysis of brain and central nervous system specimens, the experimental design of quantitative proteomic workflows as well as the feasibility, advantages, and disadvantages of the available techniques for neuroscience-oriented questions. Furthermore, we assess the use of labeled standards as internal controls for comparative studies in humans and review applications of labeling-based and label-free mass spectrometry approaches in relevant model organisms and human subjects. Providing a comprehensive guide of feasible and meaningful quantitative proteomic methodologies for neuroscience research is crucial not only for overcoming current limitations but also for gaining useful insights into brain function and translating proteomics from bench to bedside.     

Quantitative proteome analysis of human plasma following in vivo lipopolysaccharide administration using 16O/18O labeling and the accurate mass and time tag approach

Identification of novel diagnostic or therapeutic biomarkers from human blood plasma would benefit significantly from quantitative measurements of the proteome constituents over a range of physiological conditions. Herein we describe an initial demonstration of proteome-wide quantitative analysis of human plasma. The approach utilizes postdigestion trypsin-catalyzed 16O/18O peptide labeling, two-dimensional LC-FTICR mass spectrometry, and the accurate mass and time (AMT) tag strategy to identify and quantify peptides/proteins from complex samples. A peptide accurate mass and LC elution time AMT tag data base was initially generated using MS/MS following extensive multidimensional LC separations to provide the basis for subsequent peptide identifications. The AMT tag data base contains >8,000 putative identified peptides, providing 938 confident plasma protein identifications. The quantitative approach was applied without depletion of high abundance proteins for comparative analyses of plasma samples from an individual prior to and 9 h after lipopolysaccharide (LPS) administration. Accurate quantification of changes in protein abundance was demonstrated by both 1:1 labeling of control plasma and the comparison between the plasma samples following LPS administration. A total of 429 distinct plasma proteins were quantified from the comparative analyses, and the protein abundances for 25 proteins, including several known inflammatory response mediators, were observed to change significantly following LPS administration.     

Quantitative Proteomics Using Reductive Dimethylation for Stable Isotope Labeling

Stable isotope labeling of peptides by reductive dimethylation (ReDi labeling) is a method to accurately quantify protein expression differences between samples using mass spectrometry. ReDi labeling is performed using either regular (light) or deuterated (heavy) forms of formaldehyde and sodium cyanoborohydride to add two methyl groups to each free amine. Here we demonstrate a robust protocol for ReDi labeling and quantitative comparison of complex protein mixtures. Protein samples for comparison are digested into peptides, labeled to carry either light or heavy methyl tags, mixed, and co-analyzed by LC-MS/MS. Relative protein abundances are quantified by comparing the ion chromatogram peak areas of heavy and light labeled versions of the constituent peptide extracted from the full MS spectra. The method described here includes sample preparation by reversed-phase solid phase extraction, on-column ReDi labeling of peptides, peptide fractionation by basic pH reversed-phase (BPRP) chromatography, and StageTip peptide purification. We discuss advantages and limitations of ReDi labeling with respect to other methods for stable isotope incorporation. We highlight novel applications using ReDi labeling as a fast, inexpensive, and accurate method to compare protein abundances in nearly any type of sample.     

Optimization of the isotope-coded affinity tag-labeling procedure for quantitative proteome analysis

The combination of isotope coded affinity tag (ICAT) reagents and tandem mass spectrometry constitutes a new method for quantitative proteomics. It involves the site-specific, covalent labeling of proteins with isotopically normal or heavy ICAT reagents, proteolysis of the combined, labeled protein mixture, followed by the isolation and mass spectrometric analysis of the labeled peptides. The method critically depends on labeling protocols that are specific, quantitative, general, robust, and reproducible. Here we describe the systematic evaluation of important parameters of the labeling protocol and describe optimized labeling conditions. The tested factors include the ICAT reagent concentration, the influence of the protein, SDS, and urea concentrations on the labeling reaction, and the reaction time. We demonstrate that using the optimized conditions specific and quantitative labeling was achieved on standard proteins as well as in complex protein mixtures such as a yeast cell lysate.