ICPLQuant – A software for non‐isobaric isotopic labeling proteomics

The main goal of many proteomics experiments is an accurate and rapid quantification and identification of regulated proteins in complex biological samples. The bottleneck in quantitative proteomics remains the availability of efficient software to evaluate and quantify the tremendous amount of mass spectral data acquired during a proteomics project. A new software suite, ICPLQuant, has been developed to accurately quantify isotope‐coded protein label (ICPL)‐labeled peptides on the MS level during LC‐MALDI and peptide mass fingerprint experiments. The tool is able to generate a list of differentially regulated peptide precursors for subsequent MS/MS experiments, minimizing time‐consuming acquisition and interpretation of MS/MS data. ICPLQuant is based on two independent units. Unit 1 performs ICPL multiplex detection and quantification and proposes peptides to be identified by MS/MS. Unit 2 combines MASCOT MS/MS protein identification with the quantitative data and produces a protein/peptide list with all the relevant information accessible for further data mining. The accuracy of quantification, selection of peptides for MS/MS‐identification and the automated output of a protein list of regulated proteins are demonstrated by the comparative analysis of four different mixtures of three proteins (Ovalbumin, Horseradish Peroxidase and Rabbit Albumin) spiked into the complex protein background of the DGPF Proteome Marker.     

Experimental evaluation of protein identification by an LC/MALDI/on-target digestion approach

Tryptic digestion of proteins continues to be a workhorse of proteomics. Traditional tryptic digestion requires several hours to generate an adequate protein digest. A number of enhanced accelerated digestion protocols have been developed in recent years. Nonetheless, a need still exists for new digestion strategies that meet the demands of proteomics for high-throughput and rapid detection and identification of proteins. We performed an evaluation of direct tryptic digestion of proteins on a MALDI target plate and the potential for integrating RP HPLC separation of protein with on-target tryptic digestion in order to achieve a rapid and effective identification of proteins in complex biological samples. To this end, we used a Tempo HPLC/MALDI target plate deposition hybrid instrument (ABI). The technique was evaluated using a number of soluble and membrane proteins and an MRC5 cell lysate. We demonstrated that direct deposition of proteins on a MALDI target plate after reverse-phase HPLC separation and subsequent tryptic digestion of the proteins on the target followed by MALDI TOF/TOF analysis provided substantial data (intact protein mass, peptide mass and peptide fragment mass) that allowed a rapid and unambiguous identification of proteins. The rapid protein separation and direct deposition of fractions on a MALDI target plate provided by the RP HPLC combined with off-line interfacing with the MALDI MS is a unique platform for rapid protein identification with improved sequence coverage. This simple and robust approach significantly reduces the sample handling and potential loss in large-scale proteomics experiments. This approach allows combination of peptide mass fingerprinting (PMF), MS/MS peptide fragment fingerprinting (PPF) and whole protein MS for both protein identification and structural analysis of proteins.     

Clinical-scale high-throughput human plasma proteome analysis: lung adenocarcinoma

Clinical proteomics requires the stable and reproducible analysis of a large number of human samples. We report a high-throughput comprehensive protein profiling system comprising a fully automated, on-line, two-dimensional microflow liquid chromatography/tandem mass spectrometry (2-D microLC-MS/MS) system for use in clinical proteomics. A linear ion-trap mass spectrometer (ITMS) also known as a 2-D ITMS instrument, which is characterized by high scan speed, was incorporated into the microLC-MS/MS system in order to obtain highly improved sensitivity and resolution in MS/MS acquisition. This system was used to evaluate bovine serum albumin and human 26S proteasome. Application of these high-throughput microLC conditions and the 2-D ITMS resulted in a 10-fold increase in sensitivity in protein identification. Additionally, peptide fragments from the 26S proteasome were identified three-fold more efficiently than by the conventional 3-D ITMS instrument. In this study, the 2-D microLC-MS/MS system that uses linear 2-D ITMS has been applied for the plasma proteome analysis of a few samples from healthy individuals and lung adenocarcinoma patients. Using the 2-D and 1-D microLC-MS/MS analyses, approximately 250 and 100 different proteins were detected, respectively, in each HSA- and IgG-depleted sample, which corresponds to only 0.4 microL of blood plasma. Automatic operation enabled the completion of a single run of the entire 1-D and 2-D microLC-MS/MS analyses within 11 h. Investigation of the data extracted from the protein identification datasets of both healthy and adenocarcinoma groups revealed that several of the group-specific proteins could be candidate protein disease markers expressed in the human blood plasma. Consequently, it was demonstrated that this high-throughput microLC-MS/MS protein profiling system would be practically applicable to the discovery of protein disease markers, which is the primary objective in clinical plasma proteome projects.     

Characterization of a high field Orbitrap mass spectrometer for proteome analysis

The field of proteomics continues to be driven by improvements in analytical technology, notably in peptide separation, quantitative MS, and informatics. In this study, we have characterized a hybrid linear ion trap high field Orbitrap mass spectrometer (Orbitrap Elite) for proteomic applications. The very high resolution available on this instrument allows 95% of all peptide masses to be measured with sub‐ppm accuracy that in turn improves protein identification by database searching. We further confirm again that mass accuracy in tandem mass spectra is a valuable parameter for improving the success of protein identification. The new CID rapid scan type of the Orbitrap Elite achieves similar performance as higher energy collision induced dissociation fragmentation and both allow the identification of hundreds of proteins from as little as 0.1 ng of protein digest on column. The new instrument outperforms its predecessor the Orbitrap Velos by a considerable margin on each metric assessed that makes it a valuable and versatile tool for MS‐based proteomics.     

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 proteomic study of the HUPO Plasma Proteome Project's pilot samples using an accurate mass and time tag strategy

Characterization of the human blood plasma proteome is critical to the discovery of routinely useful clinical biomarkers. We used an accurate mass and time (AMT) tag strategy with high-resolution mass accuracy cLC-FT-ICR MS to perform a global proteomic analysis of pilot study samples as part of the HUPO Plasma Proteome Project. HUPO reference serum and citrated plasma samples from African Americans, Asian Americans, and Caucasian Americans were analyzed, in addition to a Pacific Northwest National Laboratory reference serum and plasma. The AMT tag strategy allowed us to leverage two previously published "shotgun" proteomics experiments to perform global analyses on these samples in triplicate in less than 4 days total analysis time. A total of 722 (22% with multiple peptide identifications) International Protein Index redundant proteins, or 377 protein families by ProteinProphet, were identified over the six individual HUPO serum and plasma samples. The samples yielded a similar number of identified redundant proteins in the plasma samples (average 446 +/- 23) as found in the serum samples (average 440 +/- 20). These proteins were identified by an average of 956 +/- 35 unique peptides in plasma and 930 +/- 11 unique peptides in serum. In addition to this high-throughput analysis, the AMT tag approach was used with a Z-score normalization to compare relative protein abundances. This analysis highlighted both known differences in serum and citrated plasma such as fibrinogens, and reproducible differences in peptide abundances from proteins such as soluble activin receptor-like kinase 7b and glycoprotein m6b. The AMT tag strategy not only improved our sample throughput but also provided a basis for estimated quantitation.     

Multidimensional protein profiling technology and its application to human plasma proteome

In clinical and diagnostic proteomics, it is essential to develop a comprehensive and robust system for proteome analysis. Although multidimensional liquid chromatography/tandem mass spectrometry (LC/MS/MS) systems have been recently developed as powerful tools especially for identification of protein complexes, these systems still some drawbacks in their application to clinical research that requires an analysis of a large number of human samples. Therefore, in this study, we have constructed a technically simple and high throughput protein profiling system comprising a two-dimensional (2D)-LC/MS/MS system which integrates both a strong cation exchange (SCX) chromatography and a microLC/MS/MS system with micro-flowing reversed-phase chromatography. Using the microLC/MS/MS system as the second dimensional chromatography, SCX separation has been optimized as an off-line first dimensional peptide fractionation. To evaluate the performance of the constructed 2D-LC/MS/MS system, the results of detection and identification of proteins were compared using digests mixtures of 6 authentic proteins with those obtained using one-dimensional microLC/MS/MS system. The number of peptide fragments detected and the coverage of protein sequence were found to be more than double through the use of our newly built 2D-LC/MS/MS system. Furthermore, this multidimensional protein profiling system has been applied to plasma proteome in order to examine its feasibility for clinical proteomics. The experimental results revealed the identification of 174 proteins from one serum sample depleted HSA and IgG which corresponds to only 1 microL of plasma, and the total analysis run time was less than half a day, indicating a fairly high possibility of practicing clinical proteomics in a high throughput manner.     

Analysis of the Arabidopsis cytosolic ribosome proteome provides detailed insights into its components and their post-translational modification

Finding gene-specific peptides by mass spectrometry analysis to pinpoint gene loci responsible for particular protein products is a major challenge in proteomics especially in highly conserved gene families in higher eukaryotes. We used a combination of in silico approaches coupled to mass spectrometry analysis to advance the proteomics insight into Arabidopsis cytosolic ribosomal composition and its post-translational modifications. In silico digestion of all 409 ribosomal protein sequences in Arabidopsis defined the proportion of theoretical gene-specific peptides for each gene family and highlighted the need for low m/z cutoffs of MS ion selection for MS/MS to characterize low molecular weight, highly basic ribosomal proteins. We undertook an extensive MS/MS survey of the cytosolic ribosome using trypsin and, when required, chymotrypsin and pepsin. We then used custom software to extract and filter peptide match information from Mascot result files and implement high confidence criteria for calling gene-specific identifications based on the highest quality unambiguous spectra matching exclusively to certain in silico predicted gene- or gene family-specific peptides. This provided an in-depth analysis of the protein composition based on 1446 high quality MS/MS spectra matching to 795 peptide sequences from ribosomal proteins. These identified peptides from five gene families of ribosomal proteins not identified previously, providing experimental data on 79 of the 80 different types of ribosomal subunits. We provide strong evidence for gene-specific identification of 87 different ribosomal proteins from these 79 families. We also provide new information on 30 specific sites of co- and post-translational modification of ribosomal proteins in Arabidopsis by initiator methionine removal, N-terminal acetylation, N-terminal methylation, lysine N-methylation, and phosphorylation. These site-specific modification data provide a wealth of resources for further assessment of the role of ribosome modification in influencing translation in Arabidopsis.     

Increased proteome coverage by combining PAGE and peptide isoelectric focusing: Comparative study of gel‐based separation approaches

The in‐depth analysis of complex proteome samples requires fractionation of the sample into subsamples prior to LC‐MS/MS in shotgun proteomics experiments. We have established a 3D workflow for shotgun proteomics that relies on protein separation by 1D PAGE, gel fractionation, trypsin digestion, and peptide separation by in‐gel IEF, prior to RP‐HPLC‐MS/MS. Our results show that applying peptide IEF can significantly increase the number of proteins identified from PAGE subfractionation. This method delivers deeper proteome coverage and provides a large degree of flexibility in experimentally approaching highly complex mixtures by still relying on protein separation according to molecular weight in the first dimension.     

Applications of affinity chromatography in proteomics

Affinity chromatography is a powerful protein separation method that is based on the specific interaction between immobilized ligands and target proteins. Peptides can also be separated effectively by affinity chromatography through the use of peptide-specific ligands. Both two-dimensional electrophoresis (2-DE)- and non-2-DE-based proteomic approaches benefit from the application of affinity chromatography. Before protein separation by 2-DE, affinity separation is used primarily for preconcentration and pretreatment of samples. Those applications entail the removal of one protein or a class of proteins that might interfere with 2-DE resolution, the concentration of low-abundance proteins to enable them to be visualized in the gel, and the classification of total protein into two or more groups for further separation by gel electrophoresis. Non-2-DE-based approaches have extensively employed affinity chromatography to reduce the complexity of protein and peptide mixtures. Prior to mass spectrometry (MS), preconcentration and capture of specific proteins or peptides to enhance sensitivity can be accomplished by using affinity adsorption. Affinity purification of protein complexes followed by identification of proteins by MS serves as a powerful tool for generating a map of protein-protein interactions and cellular locations of complexes. Affinity chromatography of peptide mixtures, coupled with mass spectrometry, provides a tool for the study of protein posttranslational modification (PTM) sites and quantitative proteomics. Quantitation of proteomes is possible via the use of isotope-coded affinity tags and isolation of proteolytic peptides by affinity chromatography. An emerging area of proteomics technology development is miniaturization. Affinity chromatography is becoming more widely used for exploring PTM and protein-protein interactions, especially with a view toward developing new general tag systems and strategies of chemical derivatization on peptides for affinity selection. More applications of affinity-based purification can be expected, including increasing the resolution in 2-DE, improving the sensitivity of MS quantification, and incorporating purification as part of multidimensional liquid chromatography experiments.     

Analysis of human serum proteins by liquid phase isoelectric focusing and matrix-assisted laser desorption/ionization-mass spectrometry

Direct matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS) analysis of human serum yielded ion signals from only a fraction of the total number of peptides and proteins expected to be in the sample. We increased the number of peptide and protein ion signals observed in the MALDI-TOF mass spectra analysis of human serum by using a prefractionation protocol based on liquid phase isoelectric focusing electrophoresis. This pre-fractionation technique facilitated the MALDI-TOF MS detection of as many as 262 different peptide and protein ion signals from human serum. The results obtained from three replicate fractionation experiments on the same serum sample indicated that 148 different peptide and protein ion signals were reproducibly detected using our isoelectric focusing and MALDI-TOF MS protocol.     

Mascot file parsing and quantification (MFPaQ), a new software to parse, validate, and quantify proteomics data generated by ICAT and SILAC mass spectrometric analyses: application to the proteomics study of membrane proteins from primary human endothelial cells

Proteomics strategies based on nanoflow (nano-) LC-MS/MS allow the identification of hundreds to thousands of proteins in complex mixtures. When combined with protein isotopic labeling, quantitative comparison of the proteome from different samples can be achieved using these approaches. However, bioinformatics analysis of the data remains a bottleneck in large scale quantitative proteomics studies. Here we present a new software named Mascot File Parsing and Quantification (MFPaQ) that easily processes the results of the Mascot search engine and performs protein quantification in the case of isotopic labeling experiments using either the ICAT or SILAC (stable isotope labeling with amino acids in cell culture) method. This new tool provides a convenient interface to retrieve Mascot protein lists; sort them according to Mascot scoring or to user-defined criteria based on the number, the score, and the rank of identified peptides; and to validate the results. Moreover the software extracts quantitative data from raw files obtained by nano-LC-MS/MS, calculates peptide ratios, and generates a non-redundant list of proteins identified in a multisearch experiment with their calculated averaged and normalized ratio. Here we apply this software to the proteomics analysis of membrane proteins from primary human endothelial cells (ECs), a cell type involved in many physiological and pathological processes including chronic inflammatory diseases such as rheumatoid arthritis. We analyzed the EC membrane proteome and set up methods for quantitative analysis of this proteome by ICAT labeling. EC microsomal proteins were fractionated and analyzed by nano-LC-MS/MS, and database searches were performed with Mascot. Data validation and clustering of proteins were performed with MFPaQ, which allowed identification of more than 600 unique proteins. The software was also successfully used in a quantitative differential proteomics analysis of the EC membrane proteome after stimulation with a combination of proinflammatory mediators (tumor necrosis factor-alpha, interferon-gamma, and lymphotoxin alpha/beta) that resulted in the identification of a full spectrum of EC membrane proteins regulated by inflammation.     

Comparative evaluation of label-free SINQ normalized spectral index quantitation in the central proteomics facilities pipeline

Normalized spectral index quantification was recently presented as an accurate method of label‐free quantitation, which improved spectral counting by incorporating the intensities of peptide MS/MS fragment ions into the calculation of protein abundance. We present SINQ, a tool implementing this method within the framework of existing analysis software, our freely available central proteomics facilities pipeline (CPFP). We demonstrate, using data sets of protein standards acquired on a variety of mass spectrometers, that SINQ can rapidly provide useful estimates of the absolute quantity of proteins present in a medium‐complexity sample. In addition, relative quantitation of standard proteins spiked into a complex lysate background and run without pre‐fractionation produces accurate results at amounts above 1 fmol on column. We compare quantitation performance to various precursor intensity‐ and identification‐based methods, including the normalized spectral abundance factor (NSAF), exponentially modified protein abundance index (emPAI), MaxQuant, and Progenesis LC‐MS. We anticipate that the SINQ tool will be a useful asset for core facilities and individual laboratories that wish to produce quantitative MS data, but lack the necessary manpower to routinely support more complicated software workflows. SINQ is freely available to obtain and use as part of the central proteomics facilities pipeline, which is released under an open‐source license.     

The EIPeptiDi tool: enhancing peptide discovery in ICAT-based LC MS/MS experiments

Background  

Isotope-coded affinity tags (ICAT) is a method for quantitative proteomics based on differential isotopic labeling, sample digestion and mass spectrometry (MS). The method allows the identification and relative quantification of proteins present in two samples and consists of the following phases. First, cysteine residues are either labeled using the ICAT Light or ICAT Heavy reagent (having identical chemical properties but different masses). Then, after whole sample digestion, the labeled peptides are captured selectively using the biotin tag contained in both ICAT reagents. Finally, the simplified peptide mixture is analyzed by nanoscale liquid chromatography-tandem mass spectrometry (LC-MS/MS). Nevertheless, the ICAT LC-MS/MS method still suffers from insufficient sample-to-sample reproducibility on peptide identification. In particular, the number and the type of peptides identified in different experiments can vary considerably and, thus, the statistical (comparative) analysis of sample sets is very challenging. Low information overlap at the peptide and, consequently, at the protein level, is very detrimental in situations where the number of samples to be analyzed is high.     

Proteome analysis of Escherichia coli using high-performance liquid chromatography and Fourier transform ion cyclotron resonance mass spectrometry

The basic problem of complexity poses a significant challenge for proteomic studies. To date two-dimensional gel electrophoresis (2-DE) followed by enzymatic in-gel digestion of the peptides, and subsequent identification by mass spectrometry (MS) is the most commonly used method to analyze complex protein mixtures. However, 2-DE is a slow and labor-intensive technique, which is not able to resolve all proteins of a proteome. To overcome these limitations gel-free approaches are developed based on high performance liquid chromatography (HPLC) and Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS). The high resolution and excellent mass accuracy of FT-ICR MS provides a basis for simultaneous analysis of numerous compounds. In the present study, a small protein subfraction of an Escherichia coli cell lysate was prepared by size-exclusion chromatography and proteins were analyzed using C4 reversed phase (RP)-HPLC for pre-separation followed by C18 RP nanoHPLC/nanoESI FT-ICR MS for analysis of the peptide mixtures after tryptic digestion of the protein fractions. We identified 231 proteins and thus demonstrated that a combination of two RP separation steps - one on the protein and one on the peptide level - in combination with high-resolution FT-ICR MS has the potential to become a powerful method for global proteomics studies.     

Combining ultracentrifugation and peptide termini group-specific immunoprecipitation for multiplex plasma protein analysis

Blood plasma is a valuable source of potential biomarkers. However, its complexity and the huge dynamic concentration range of its constituents complicate its analysis. To tackle this problem, an immunoprecipitation strategy was employed using antibodies directed against short terminal epitope tags (triple X proteomics antibodies), which allow the enrichment of groups of signature peptides derived from trypsin-digested plasma. Isolated signature peptides are subsequently detected using MALDI-TOF/TOF mass spectrometry. Sensitivity of the immunoaffinity approach was, however, compromised by the presence of contaminant peaks derived from the peptides of nontargeted high abundant proteins. A closer analysis of the enrichment strategy revealed nonspecific peptide binding to the solid phase affinity matrix as the major source of the contaminating peptides. We therefore implemented a sucrose density gradient ultracentrifugation separation step into the procedure. This yielded a 99% depletion of contaminating peptides from a sucrose fraction containing 70% of the peptide-antibody complexes and enabled the detection of the previously undetected low abundance protein filamin-A. Assessment of this novel approach using 15 different triple X proteomics antibodies demonstrated a more consistent detection of a greater number of targeted peptides and a significant reduction in the intensity of nonspecific peptides. Ultracentrifugation coupled with immunoaffinity MS approaches presents a powerful tool for multiplexed plasma protein analysis without the requirement for demanding liquid chromatography separation techniques.     

Improving large-scale proteomics by clustering of mass spectrometry data

Tandem mass spectrometry (MS/MS), coupled with liquid chromatography (LC), is a powerful tool for the analysis and comparison of complex protein and peptide mixtures. However, the extremely large amounts of data that result from the process are very complex and difficult to analyze. We show how the clustering of similar spectra from multiple LC-MS/MS runs can help in data management and improve the analysis of complex peptide mixtures. The major effect of spectrum clustering is the reduction of the huge amounts of data to a manageable size. As a result, analysis time is shorter and more data can be stored for further analysis. Furthermore, spectrum quality improvement allows the identification of more peptides with greater confidence, the comparison of complex peptide mixtures is facilitated, and the entire proteomics project is presented in concise form. Pep-Miner is an advanced software tool that implements these clustering-based applications. It proved useful in several comparative proteomics projects involving lung cancer cells and various other cell types. In one of these projects, Pep-Miner reduced 517 000 spectra to 20 900 clusters and identified 2518 peptides derived from 830 proteins. Clustering and identification lasted less than two hours on an IBM Thinkpad T23 computer (laptop). Pep-Miner's unique properties make it a very useful tool for large-scale shotgun proteomics projects.     

The Plasma Proteome: High Abundance versus Low Abundance

The field of proteomics is rapidly turning towards targeted mass spectrometry (MS) methods to quantify putative markers or known proteins of biological interest. Historically, the enzyme-linked immunosorbent assay (ELISA) has been used for targeted protein analysis, but, unfortunately, it is limited by the excessive time required for antibody preparation, as well as concerns over selectivity. Despite the ability of proteomics to deliver increasingly quantitative measurements, owing to limited sensitivity, the leads generated are in the microgram per milliliter range. This stands in stark contrast to ELISA, which is capable of quantifying proteins at low picogram per milliliter levels. To bridge this gap, targeted liquid chromatography (LC) tandem MS (MS/MS) analysis of tryptic peptide surrogates using selected reaction monitoring detection has emerged as a viable option for rapid quantification of target proteins. The precision of this approach has been enhanced by the use of stable isotope-labeled peptide internal standards to compensate for variation in recovery and the influence of differential matrix effects. Unfortunately, the complexity of proteinaceous matrices, such as plasma, limits the usefulness of this approach to quantification in the mid-nanogram per milliliter range (medium-abundance proteins). This article reviews the current status of LC/MS/MS using selected reaction monitoring for protein quantification, and specifically considers the use of a single antibody to achieve superior enrichment of either the protein target or the released tryptic peptide. Examples of immunoaffinity-assisted LC/MS/MS are reviewed that demonstrate quantitative analysis of low-abundance proteins (subnanogram per milliliter range). A strategy based on this technology is proposed for the expedited evaluation of novel protein biomarkers, which relies on the synergy created from the complementary nature of MS and ELISA.     

Coupling immunoaffinity techniques with MS for quantitative analysis of low-abundance protein biomarkers

The field of proteomics is rapidly turning towards targeted mass spectrometry (MS) methods to quantify putative markers or known proteins of biological interest. Historically, the enzyme-linked immunosorbent assay (ELISA) has been used for targeted protein analysis, but, unfortunately, it is limited by the excessive time required for antibody preparation, as well as concerns over selectivity. Despite the ability of proteomics to deliver increasingly quantitative measurements, owing to limited sensitivity, the leads generated are in the microgram per milliliter range. This stands in stark contrast to ELISA, which is capable of quantifying proteins at low picogram per milliliter levels. To bridge this gap, targeted liquid chromatography (LC) tandem MS (MS/MS) analysis of tryptic peptide surrogates using selected reaction monitoring detection has emerged as a viable option for rapid quantification of target proteins. The precision of this approach has been enhanced by the use of stable isotope-labeled peptide internal standards to compensate for variation in recovery and the influence of differential matrix effects. Unfortunately, the complexity of proteinaceous matrices, such as plasma, limits the usefulness of this approach to quantification in the mid-nanogram per milliliter range (medium-abundance proteins). This article reviews the current status of LC/MS/MS using selected reaction monitoring for protein quantification, and specifically considers the use of a single antibody to achieve superior enrichment of either the protein target or the released tryptic peptide. Examples of immunoaffinity-assisted LC/MS/MS are reviewed that demonstrate quantitative analysis of low-abundance proteins (subnanogram per milliliter range). A strategy based on this technology is proposed for the expedited evaluation of novel protein biomarkers, which relies on the synergy created from the complementary nature of MS and ELISA.