A comparison of MS/MS‐based,stable‐isotope‐labeled,quantitation performance on ESI‐quadrupole TOF and MALDI‐TOF/TOF mass spectrometers

The peptide‐based quantitation accuracy and precision of LC‐ESI (QSTAR Elite) and LC‐MALDI (4800 MALDI TOF/TOF) were compared by analyzing identical Escherichia coli tryptic digests containing iTRAQ‐labeled peptides of defined abundances (1:1, 2.5:1, 5:1, and 10:1). Only 51.4% of QSTAR spectra were used for quantitation by ProteinPilot Software versus 66.7% of LC‐MALDI spectra. The average protein sequence coverages for LC‐ESI and LC‐MALDI were 24.0 and 18.2% (14.9 and 8.4 peptides per protein), respectively. The iTRAQ‐based expression ratios determined by ProteinPilot from the 57 467 ESI‐MS/MS and 26 085 MALDI‐MS/MS spectra were analyzed for measurement accuracy and reproducibility. When the relative abundances of peptides within a sample were increased from 1:1 to 10:1, the mean ratios calculated on both instruments differed by only 0.7–6.7% between platforms. In the 10:1 experiment, up to 64.7% of iTRAQ ratios from LC‐ESI MS/MS spectra failed S/N thresholds and were excluded from quantitation, while only 0.1% of the equivalent LC‐MALDI iTRAQ ratios were rejected. Re‐analysis of an archived LC‐MALDI sample set stored for 5 months generated 3715 MS/MS spectra for quantitation, compared with 3845 acquired originally, and the average ratios differed by only 3.1%. Overall, MS/MS‐based peptide quantitation performance of offline LC‐MALDI was comparable with on‐line LC‐ESI, which required threefold less time. However, offline LC‐MALDI allows the re‐analysis of archived HPLC‐separated samples.     

Systematic Evaluation of Immobilized Trypsin‐Based Fast Protein Digestion for Deep and High‐Throughput Bottom‐Up Proteomics

Immobilized trypsin (IM) has been recognized as an alternative to free trypsin (FT) for accelerating protein digestion 30 years ago. However, some questions of IM still need to be answered. How does the solid matrix of IM influence its preference for protein cleavage and how well can IM perform for deep bottom‐up proteomics compared to FT? By analyzing Escherichia coli proteome samples digested with amine or carboxyl functionalized magnetic bead–based IM (IM‐N or IM‐C) or FT, it is observed that IM‐N with the nearly neutral solid matrix, IM‐C with the negatively charged solid matrix, and FT have similar cleavage preference considering the microenvironment surrounding the cleavage sites. IM‐N (15 min) and FT (12 h) both approach 9000 protein identifications (IDs) from a mouse brain proteome. Compared to FT, IM‐N has no bias in the digestion of proteins that are involved in various biological processes, are located in different components of cells, have diverse functions, and are expressed in varying abundance. A high‐throughput bottom‐up proteomics workflow comprising IM‐N‐based rapid protein cleavage and fast CZE‐MS/MS enables the completion of protein sample preparation, CZE‐MS/MS analysis, and data analysis in only 3 h, resulting in 1000 protein IDs from the mouse brain proteome.     

QCMAP: An Interactive Web‐Tool for Performance Diagnosis and Prediction of LC‐MS Systems

The increasing role played by liquid chromatography‐mass spectrometry (LC‐MS)‐based proteomics in biological discovery has led to a growing need for quality control (QC) on the LC‐MS systems. While numerous quality control tools have been developed to track the performance of LC‐MS systems based on a pre‐defined set of performance factors (e.g., mass error, retention time), the precise influence and contribution of the performance factors and their generalization property to different biological samples are not as well characterized. Here, a web‐based application (QCMAP) is developed for interactive diagnosis and prediction of the performance of LC‐MS systems across different biological sample types. Leveraging on a standardized HeLa cell sample run as QC within a multi‐user facility, predictive models are trained on a panel of commonly used performance factors to pinpoint the precise conditions to a (un)satisfactory performance in three LC‐MS systems. It is demonstrated that the learned model can be applied to predict LC‐MS system performance for brain samples generated from an independent study. By compiling these predictive models into our web‐application, QCMAP allows users to benchmark the performance of their LC‐MS systems using their own samples and identify key factors for instrument optimization. QCMAP is freely available from: http://shiny.maths.usyd.edu.au/QCMAP/ .     

Twoplex 12/13C6 aniline stable isotope and linkage‐specific sialic acid labeling 2D‐LC‐MS workflow for quantitative N‐glycomics

Quantitative glycomics represents an actively expanding research field ranging from the discovery of disease‐associated glycan alterations to the quantitative characterization of N‐glycans on therapeutic proteins. Commonly used analytical platforms for comparative relative quantitation of complex glycan samples include MALDI‐TOF‐MS or chromatographic glycan profiling with subsequent data alignment and statistical evaluation. Limitations of such approaches include run‐to‐run technical variation and the potential introduction of subjectivity during data processing. Here, we introduce an offline 2D LC‐MS^E workflow for the fractionation and relative quantitation of twoplex isotopically labeled N‐linked oligosaccharides using neutral ¹²C₆ and ¹³C₆ aniline (Δmass = 6 Da). Additional linkage‐specific derivatization of sialic acids using 4‐(4,6‐dimethoxy‐1,3,5‐trizain‐2‐yl)‐4‐methylmorpholinium chloride offered simultaneous and advanced in‐depth structural characterization. The potential of the method was demonstrated for the differential analysis of structurally defined N‐glycans released from serum proteins of patients diagnosed with various stages of colorectal cancer. The described twoplex ¹²C₆/¹³C₆ aniline 2D LC‐MS platform is ideally suited for differential glycomic analysis of structurally complex N‐glycan pools due to combination and analysis of samples in a single LC‐MS injection and the associated minimization in technical variation.     

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.     

Label‐free profiling of skeletal muscle using high‐definition mass spectrometry

We report automated and time‐efficient (2 h per sample) profiling of muscle using ultra‐performance LC coupled directly with high‐definition MS (HDMS^E). Soluble proteins extracted from rat gastrocnemius (n = 10) were digested with trypsin and analyzed in duplicate using a 90 min RPLC gradient. Protein identification and label‐free quantitation were performed from HDMS^E spectra analyzed using Progenesis QI for Proteomics software. In total 1514 proteins were identified. Of these, 811 had at least three unique peptides and were subsequently used to assess the dynamic range and precision of LC‐HDMS^E label‐free profiling. Proteins analyzed by LC‐HDMS^E encompass the entire complement of glycolytic, β‐oxidation, and tricarboxylic acid enzymes. In addition, numerous components of the electron transport chain and protein kinases involved in skeletal muscle regulation were detected. The dynamic range of protein abundances spanned four orders of magnitude. The correlation between technical replicates of the ten biological samples was R² = 0.9961 ± 0.0036 (95% CI = 0.9940 – 0.9992) and the technical CV averaged 7.3 ± 6.7% (95% CI = 6.87 – 7.79%). This represents the most sophisticated label‐free profiling of skeletal muscle to date.     

Enhanced recovery of lyophilized peptides in shotgun proteomics by using an LC‐ESI‐MS compatible surfactant

LC‐ESI/MS/MS‐based shotgun proteomics is currently the most commonly used approach for the identification and quantification of proteins in large‐scale studies of biomarker discovery. In the past several years, the shotgun proteomics technologies have been refined toward further enhancement of proteome coverage. In the complex series of protocols involved in shotgun proteomics, however, loss of proteolytic peptides during the lyophilization step prior to the LC/MS/MS injection has been relatively neglected despite the fact that the dissolution of the hydrophobic peptides in lyophilized samples is difficult in 0.05–0.1% TFA or formic acid, causing substantial loss of precious peptide samples. In order to prevent the loss of peptide samples during this step, we devised a new protocol using Invitrosol (IVS), a commercially available surfactant compatible with ESI‐MS; by dissolving the lyophilized peptides in IVS, we show improved recovery of hydrophobic peptides, leading to enhanced coverage of proteome. Thus, the use of IVS in the recovery step of lyophilized peptides will help the shotgun proteomics analysis by expanding the proteome coverage, which would significantly promote the discovery and development of new diagnostic markers and therapeutic targets.     

Gel‐free mass spectrometry analysis of Drosophila melanogaster heads

Membrane proteins play key roles in several fundamental biological processes such as cell signalling, energy metabolism and transport. Despite the significance, these still remain an under‐represented group in proteomics datasets. Herein, a bottom‐up approach to analyse an enriched membrane fraction from Drosophila melanogaster heads using multidimensional liquid chromatography (LC) coupled with tandem‐mass spectrometry (MS/MS) that relies on complete solubilisation and digestion of proteins, is reported. An enriched membrane fraction was prepared using equilibrium density centrifugation on a discontinuous sucrose gradient, followed by solubilisation using the filter‐aided sample preparation (FASP), tryptic and sequential chymotryptic digestion of proteins. Peptides were separated by reversed‐phase (RP) LC at high pH in the first dimension and acidic RP‐LC in the second dimension coupled directly to an Orbitrap Velos Pro mass spectrometer. A total number of 4812 proteins from 114 865 redundant and 38 179 distinct peptides corresponding to 4559 genes were identified in the enriched membrane fraction from fly heads. These included brain receptors, transporters and channels that are most important elements as drug targets or are linked to disease. Data are available via ProteomeXchange with identifier PXD001712 ( http://proteomecentral.proteomexchange.org/dataset/PXD001712 ).     

Bottom‐up proteome analysis of E. coli using capillary zone electrophoresis‐tandem mass spectrometry with an electrokinetic sheath‐flow electrospray interface

The Escherichia coli proteome was digested with trypsin and fractionated using SPE on a C18 SPE column. Seven fractions were collected and analyzed by CZE‐ESI‐MS/MS. The separation was performed in a 60‐cm‐long linear polyacrylamide‐coated capillary with a 0.1% v/v formic acid separation buffer. An electrokinetic sheath‐flow electrospray interface was used to couple the separation capillary with an Orbitrap‐Velos operating in higher‐energy collisional dissociation mode. Each CZE‐ESI‐MS/MS run lasted 50 min and total MS time was 350 min. A total of 23 706 peptide spectra matches, 4902 peptide IDs, and 871 protein group IDs were generated using MASCOT with false discovery rate less than 1% on the peptide level. The total mass spectrometer analysis time was less than 6 h, the sample identification rate (145 proteins/h) was more than two times higher than previous studies of the E. coli proteome, and the amount of sample consumed (<1 μg) was roughly fourfold less than previous studies. These results demonstrate that CZE is a useful tool for the bottom‐up analysis of prokaryote proteomes.     

Proteomics of mouse liver microsomes: Performance of different protein separation workflows for LC‐MS/MS

The mouse liver microsome proteome was investigated using ion trap MS combined with three separation workflows including SDS‐PAGE followed by reverse‐phase LC of in‐gel protein digestions (519 proteins identified); 2‐D LC of protein digestion (1410 proteins); whole protein separation on mRP heat‐stable column followed by 2‐D LC of protein digestions from each fraction (3‐D LC; 3703 proteins). The higher number of proteins identified in the workflow corresponded to the lesser percentage of run‐to‐run reproducibility. Gel‐based method yielded a number of predicted membrane proteins similar to LC‐based workflows.     

Comparative analysis of soybean plasma membrane proteins under osmotic stress using gel‐based and LC MS/MS‐based proteomics approaches

To study the soybean plasma membrane proteome under osmotic stress, two methods were used: a gel‐based and a LC MS/MS‐based proteomics method. Two‐day‐old seedlings were subjected to 10% PEG for 2 days. Plasma membranes were purified from seedlings using a two‐phase partitioning method and their purity was verified by measuring ATPase activity. Using the gel‐based proteomics, four and eight protein spots were identified as up‐ and downregulated, respectively, whereas in the nanoLC MS/MS approach, 11 and 75 proteins were identified as up‐ and downregulated, respectively, under PEG treatment. Out of osmotic stress responsive proteins, most of the transporter proteins and all proteins with high number of transmembrane helices as well as low‐abundance proteins could be identified by the LC MS/MS‐based method. Three homologues of plasma membrane H⁺‐ATPase, which are transporter proteins involved in ion efflux, were upregulated under osmotic stress. Gene expression of this protein was increased after 12 h of stress exposure. Among the identified proteins, seven proteins were mutual in two proteomics techniques, in which calnexin was the highly upregulated protein. Accumulation of calnexin in plasma membrane was confirmed by immunoblot analysis. These results suggest that under hyperosmotic conditions, calnexin accumulates in the plasma membrane and ion efflux accelerates by upregulation of plasma membrane H⁺‐ATPase protein.     

Helicobacter pylori proteomics by 2‐DE/MS, 1‐DE‐LC/MS and functional data mining

With its predicted proteome of 1550 proteins (data set Etalon) Helicobacter pylori 26695 represents a perfect model system of medium complexity for investigating basic questions in proteomics. We analyzed urea‐solubilized proteins by 2‐DE/MS (data set 2‐DE) and by 1‐DE‐LC/MS (Supprot); proteins insoluble in 9 M urea but solubilized by SDS (Pellet); proteins precipitating in the Sephadex layer at the application side of IEF (Sephadex) by 1‐DE‐LC/MS; and proteins precipitating close to the application side within the IEF gel by LC/MS (Startline). The experimental proteomics data of H. pylori comprising 567 proteins (protein coverage: 36.6%) were stored in the Proteome Database System for Microbial Research ( http://www.mpiib‐berlin.mpg.de/2D‐PAGE/ ), which gives access to raw mass spectra (MALDI‐TOF/TOF) in T2D format, as well as to text files of peak lists. For data mining the protein mapping and comparison tool PROMPT ( http://webclu.bio.wzw.tum.de/prompt/ ) was used. The percentage of proteins with transmembrane regions, relative to all proteins detected, was 0, 0.2, 0, 0.5, 3.8 and 6.3% for 2‐DE, Supprot, Startline, Sephadex, Pellet, and Etalon, respectively. 2‐DE does not separate membrane proteins because they are insoluble in 9 M urea/70 mM DTT and 2% CHAPS. SDS solubilizes a considerable portion of the urea‐insoluble proteins and makes them accessible for separation by SDS‐PAGE and LC. The 2‐DE/MS analysis with urea‐solubilized proteins and the 1‐DE‐LC/MS analysis with the urea‐insoluble protein fraction (Pellet) are complementary procedures in the pursuit of a complete proteome analysis. Access to the PROMPT‐generated diagrams in the Proteome Database allows the mining of experimental data with respect to other functional aspects.     

A proteomics assay to detect eight CBRN‐relevant toxins in food

A proteomics assay was set up to analyze food substrates for eight toxins of the CBRN (chemical, biological, radiological and nuclear) threat, namely ricin, Clostridium perfringens epsilon toxin (ETX), Staphylococcus aureus enterotoxins (SEA, SEB and SED), shigatoxins from Shigella dysenteriae and entero‐hemorragic Escherichia coli strains (STX1 and STX2) and Campylobacter jejuni cytolethal distending toxin (CDT). The assay developed was based on an antibody‐free sample preparation followed by bottom‐up LC‐MS/MS analysis operated in targeted mode. Highly specific detection and absolute quantification were obtained using isotopically labeled proteins (PSAQ standards) spiked into the food matrix. The sensitivity of the assay for the eight toxins was lower than the oral LD50 which would likely be used in a criminal contamination of food supply. This assay should be useful in monitoring biological threats. In the public‐health domain, it opens the way for multiplex investigation of food‐borne toxins using targeted LC‐MS/MS.     

Life‐style changes of a halophilic archaeon analyzed by quantitative proteomics

Quantitative proteomics based on isotopic labeling has become the method of choice to accurately determine changes in protein abundance in highly complex mixtures. Isotope‐coded protein labeling (ICPL), which is based on the nicotinoylation of proteins at lysine residues and free N‐termini was used as a simple, reliable and fast method for the comparative analysis of three different cellular states of the halophilic archaeon Halobacterium salinarum through pairwise comparison. The labeled proteins were subjected to SDS‐PAGE, in‐gel digested and the proteolytic peptides were separated by LC and analyzed by MALDI‐TOF/TOF MS. Automated quantitation was performed by comparing the MS peptide signals of ¹²C and ¹³C nicotinoylated isotopic peptide pairs. The transitions between (i) aerobic growth in complex versus synthetic medium and (ii) aerobic versus anaerobic/phototrophic growth, both in complex medium, provide a wide span in nutrient and energy supply for the cell and thus allowed optimal studies of proteome changes. In these two studies, 559 and 643 proteins, respectively, could be quantified allowing a detailed analysis of the adaptation of H. salinarum to changes of its living conditions. The subtle cellular response to a wide variation of nutrient and energy supply demonstrates a fine tuning of the cellular protein inventory.     

Improving MHC‐I Ligand Identifications from LC‐MS/MS Data by Incorporating Allelic Peptide Motifs

MHC class I (MHC‐I)‐bound ligands play a pivotal role in CD8 T cell immunity and are hence of major interest in understanding and designing immunotherapies. One of the most commonly utilized approaches for detecting MHC ligands is LC‐MS/MS. Unfortunately, the effectiveness of current algorithms to identify MHC ligands from LC‐MS/MS data is limited because the search algorithms used were originally developed for proteomics approaches detecting tryptic peptides. Consequently, the analysis often results in inflated false discovery rate (FDR) statistics and an overall decrease in the number of peptides that pass FDR filters. Andreatta et al. describe a new scoring tool (MS‐rescue) for peptides from MHC‐I immunopeptidome datasets. MS‐rescue incorporates the existence of MHC‐I peptide motifs to rescore peptides from ligandome data. The tool is demonstrated here using peptides assigned from LC‐MS/MS data with PEAKs software but can be deployed on data from any search algorithm. This new approach increased the number of peptides identified by up to 20–30% and promises to aid the discovery of novel MHC‐I ligands with immunotherapeutic potential.     

Pathway Palette: A rich internet application for peptide‐, protein‐ and network‐oriented analysis of MS data

Recent improvements in proteomic technologies have collectively yielded data sets that far exceed the capabilities of typical low‐throughput interpretation strategies. Unfortunately, tools designed to leverage the “peptide‐centric” content of MS‐based proteomics lag the current rate of data production. Here, we describe Pathway Palette ( http://blaispathways.dfci.harvard.edu ), a freely accessible internet application that enables researchers to easily transition from peptides to biological pathways, while simultaneously retaining the qualitative and quantitative aspects of the underlying MS data.     

Definitive screening design enables optimization of LC–ESI–MS/MS parameters in proteomics

In proteomics, more than 100,000 peptides are generated from the digestion of human cell lysates. Proteome samples have a broad dynamic range in protein abundance; therefore, it is critical to optimize various parameters of LC–ESI–MS/MS to comprehensively identify these peptides. However, there are many parameters for LC–ESI–MS/MS analysis. In this study, we applied definitive screening design to simultaneously optimize 14 parameters in the operation of monolithic capillary LC–ESI–MS/MS to increase the number of identified proteins and/or the average peak area of MS1. The simultaneous optimization enabled the determination of two-factor interactions between LC and MS. Finally, we found two parameter sets of monolithic capillary LC–ESI–MS/MS that increased the number of identified proteins by 8.1% or the average peak area of MS1 by 67%. The definitive screening design would be highly useful for high-throughput analysis of the best parameter set in LC–ESI–MS/MS systems.