DeMix workflow for efficient identification of cofragmented peptides in high resolution data-dependent tandem mass spectrometry

PepPre: Promote Peptide Identification Using Accurate and Comprehensive Precursors

Accurate and comprehensive peptide precursor ions are crucial to tandem mass-spectrometry-based peptide identification. An identification engine can derive great advantages from the search space reduction enabled by credible and detailed precursors. Furthermore, by considering multiple precursors per spectrum, both the number of identifications and the spectrum explainability can be substantially improved. Here, we introduce PepPre, which detects precursors by decomposing peaks into multiple isotope clusters using linear programming methods. The detected precursors are scored and ranked, and the high-scoring ones are used for subsequent peptide identification. PepPre is evaluated both on regular and cross-linked peptide data sets and compared with 11 methods. The experimental results show that PepPre achieves a remarkable increase of 203% in PSM and 68% in peptide identifications compared to instrument software for regular peptides and 99% in PSM and 27% in peptide pair identifications for cross-linked peptides, surpassing the performance of all other evaluated methods. In addition to the increased identification numbers, further credibility evaluations evidence the reliability of the identified results. Moreover, by widening the isolation window of data acquisition from 2 to 8 Th, with PepPre, an engine is able to identify at least 64% more PSMs, thereby demonstrating the potential advantages of wide-window data acquisition. PepPre is open-source and available at http://peppre.ctarn.io.

[Antibody proteomics]

Antibodies represent an essential component of humoral immunity; therefore their study is important for molecular biology and medicine. The unique property of antibodies to specifically recognize and bind a certain molecular target (an antigen) determines their widespread application in treatment and diagnostics of diseases, as well as in laboratory and biotechnological practices. High specificity and affinity of antibodies is determined by the presence of primary structure variable regions, which are not encoded in the human genome and are unique for each antibody-producing B cell clone. Hence, there is little or no information about amino acid sequences of the variable regions in the databases. This differs identification of antibody primary structure from most of the proteomic studies because it requires either B cell genome sequencing or de novo amino acid sequencing of the antibody. The present review demonstrates some examples of proteomic and proteogenomic approaches and the methodological arsenal that proteomics can offer for studying antibodies, in particular, for identification of primary structure, evaluation of posttranslational modifications and application of bioinformatics tools for their decoding.

Reversed-Phase Liquid Chromatography of Peptides for Bottom-Up Proteomics: A Tutorial

The performance of the current bottom-up liquid chromatography hyphenated with mass spectrometry (LC-MS) analyses has undoubtedly been fueled by spectacular progress in mass spectrometry. It is thus not surprising that the MS instrument attracts the most attention during LC-MS method development, whereas optimizing conditions for peptide separation using reversed-phase liquid chromatography (RPLC) remains somewhat in its shadow. Consequently, the wisdom of the fundaments of chromatography is slowly vanishing from some laboratories. However, the full potential of advanced MS instruments cannot be achieved without highly efficient RPLC. This is impossible to attain without understanding fundamental processes in the chromatographic system and the properties of peptides important for their chromatographic behavior. We wrote this tutorial intending to give practitioners an overview of critical aspects of peptide separation using RPLC to facilitate setting the LC parameters so that they can leverage the full capabilities of their MS instruments. After briefly introducing the gradient separation of peptides, we discuss their properties that affect the quality of LC-MS chromatograms the most. Next, we address the in-column and extra-column broadening. The last section is devoted to key parameters of LC-MS methods. We also extracted trends in practice from recent bottom-up proteomics studies and correlated them with the current knowledge on peptide RPLC separation.

Trapped Ion Mobility Spectrometry Reduces Spectral Complexity in Mass Spectrometry-Based Proteomics

In bottom-up mass spectrometry-based proteomics, deep proteome coverage is limited by high cofragmentation rates. Cofragmentation occurs when more than one analyte is isolated by the quadrupole and the subsequent fragmentation event produces fragment ions of heterogeneous origin. One strategy to reduce cofragmentation rates is through effective peptide separation techniques such as chromatographic separation and, the more recently popularized, ion mobility (IM) spectrometry, which separates peptides by their collisional cross section. Here, we use a computational model to investigate the capability of the trapped IM spectrometry (TIMS) device at effectively separating peptide ions and quantify the separation power of the TIMS device in the context of a parallel accumulation-serial fragmentation (PASEF) workflow. We found that TIMS separation increases the number of interference-free MS1 peptide features 9.2-fold, while decreasing the average peptide density in precursor spectra 6.5-fold. In a data-dependent acquisition PASEF workflow, IM separation increases the number of spectra without cofragmentation by a factor of 4.1 and the number of high-quality spectra 17-fold. Using a categorical model, we estimate that this observed decrease in spectral complexity results in an increased likelihood for peptide spectral matches, which may improve peptide identification rates. In the context of a data-independent acquisition workflow, the reduction in spectral complexity resulting from IM separation is estimated to be equivalent to a 4-fold decrease in the isolation window width (from 25 to 6.5 Da). Our study demonstrates that TIMS separation decreases spectral complexity by reducing cofragmentation rates, suggesting that TIMS separation may contribute toward the high identification rates observed in PASEF workflows.

ChimST: An Efficient Spectral Library Search Tool for Peptide Identification from Chimeric Spectra in Data-Dependent Acquisition

Accurate and sensitive identification of peptides from MS/MS spectra is a very challenging problem in computational shotgun proteomics. To tackle this problem, spectral library search has been one of the competitive solutions. However, most existing library search tools were developed on the basis of one peptide per spectrum, which prevents them from working properly on chimeric spectra where two or more peptides are co-fragmented. In this work, we present a new library search tool called ChimST, which is particularly capable of reliably identifying multiple peptides from a chimeric spectrum. It starts with associating each query MS/MS spectrum with MS precursor features. For each precursor feature, there is a list of peptide candidates extracted from an input spectral library. Then, it takes one peptide candidate from each associated feature and scores how well they could collectively interpret the query spectrum. The highest-scoring set of peptide candidates are finally reported as the identification of the query spectrum. Our experimental tests show that ChimST could significantly outperform the three state-of-the-art library search tools, SpectraST, reSpect, and MSPLIT, in terms of the numbers of both peptide-spectrum matches and unique peptides, especially when the acquisition isolation window is broad.

DecoID improves identification rates in metabolomics through database-assisted MS/MS deconvolution

Chimeric MS/MS spectra contain fragments from multiple precursor ions and therefore hinder compound identification in metabolomics. Historically, deconvolution of these chimeric spectra has been challenging and relied on specific experimental methods that introduce variation in the ratios of precursor ions between multiple tandem mass spectrometry (MS/MS) scans. DecoID provides a complementary, method-independent approach where database spectra are computationally mixed to match an experimentally acquired spectrum by using LASSO regression. We validated that DecoID increases the number of identified metabolites in MS/MS datasets from both data-independent and data-dependent acquisition without increasing the false discovery rate. We applied DecoID to publicly available data from the MetaboLights repository and to data from human plasma, where DecoID increased the number of identified metabolites from data-dependent acquisition data by over 30% compared to direct spectral matching. DecoID is compatible with any user-defined MS/MS database and provides automated searching for some of the largest MS/MS databases currently available.

An integrated transcriptomic- and proteomic-based approach to evaluate the human skin sensitization potential of glyphosate and its commercial agrochemical formulations

We investigated the skin sensitization hazard of glyphosate, the surfactant polyethylated tallow amine (POEA) and two commercial glyphosate-containing formulations using different omics-technologies based on a human dendritic cell (DC)-like cell line. First, the GARD™skin assay, investigating changes in the expression of 200 transcripts upon cell exposure to xenobiotics, was used for skin sensitization prediction. POEA and the formulations were classified as skin sensitizers while glyphosate alone was classified as a non-sensitizer. Interestingly, the mixture of POEA together with glyphosate displayed a similar sensitizing prediction as POEA alone, indicating that glyphosate likely does not increase the sensitizing capacity when associated with POEA. Moreover, mass spectrometry analysis identified differentially regulated protein groups and predicted molecular pathways based on a proteomic approach in response to cell exposures with glyphosate, POEA and the glyphosate-containing formulations. Based on the protein expression data, predicted pathways were linked to immunologically relevant events and regulated proteins further to cholesterol biosynthesis and homeostasis as well as to autophagy, identifying novel aspects of DC responses after exposure to xenobiotics. In summary, we here present an integrative analysis involving advanced technologies to elucidate the molecular mechanisms behind DC activation in the skin sensitization process triggered by the investigated agrochemical materials. SIGNIFICANCE: The use of glyphosate has increased worldwide, and much effort has been made to improve risk assessments and to further elucidate the mechanisms behind any potential human health hazard of this chemical and its agrochemical formulations. In this context, omics-based techniques can provide a multiparametric approach, including several biomarkers, to expand the mechanistic knowledge of xenobiotics-induced toxicity. Based on this, we performed the integration of GARD™skin and proteomic data to elucidate the skin sensitization hazard of POEA, glyphosate and its two commercial mixtures, and to investigate cellular responses more in detail on protein level. The proteomic data indicate the regulation of immune response-related pathways and proteins associated with cholesterol biosynthesis and homeostasis as well as to autophagy, identifying novel aspects of DC responses after exposure to xenobiotics. Therefore, our data show the applicability of a multiparametric integrated approach for the mechanism-based hazard evaluation of xenobiotics, eventually complementing decision making in the holistic risk assessment of chemicals regarding their allergenic potential in humans.

[Modification of cysteine residues for mass spectrometry-based proteomic analysis: facts and artifacts]

Mass spectrometric proteomic analysis at the sample preparation stage involves the artificial reduction of disulfide bonds in proteins formed between cysteine residues. Such bonds, when preserved in their native state, complicate subsequent enzymatic hydrolysis and interpretation of the research results. To prevent the re-formation of the disulfide bonds, cysteine residues are protected by special groups, most often by alkylation. In this review, we consider the methods used to modify cysteine residues during sample preparation, as well as possible artifacts of this stage. Particularly, adverse reactions of the alkylating agents with other amino acid residues are described. The most common alkylating compound used to protect cysteine residues in mass spectrometric proteomic analysis is iodoacetamide. However, an analysis of the literature in this area indicates that this reagent causes more adverse reactions than other agents used, such as chloroacetamide and acrylamide. The latter can be recommended for wider use. In the review we also discuss the features of the cysteine residue modifications and their influence on the efficiency of the search for post-translational modifications and protein products of single nucleotide substitutions.

Prediction and Consequences of Cofragmentation in Metaproteomics

Metaproteomics can provide critical information about biological systems, but peptides are found within a complex background of other peptides. This complex background can change across samples, in some cases drastically. Cofragmentation, the coelution of peptides with similar mass to charge ratios, is one factor that influences which peptides are identified in an LC-MS/MS experiment: it is dependent on the nature and complexity of this dynamic background. Metaproteomics applications are particularly susceptible to cofragmentation-induced bias; they have vast protein sequence diversity and the abundance of those proteins can span many orders of magnitude. We have developed a mechanistic model that determines the number of potentially cofragmenting peptides in a given sample (called cobia, https://github.com/bertrand-lab/cobia ). We then used previously published data sets to validate our model, showing that the resulting peptide-specific score reflects the cofragmentation "risk" of peptides. Using an Antarctic sea ice edge metatranscriptome case study, we found that more rare taxonomic and functional groups are associated with higher cofragmentation bias. We also demonstrate how cofragmentation scores can be used to guide the selection of protein- or peptide-based biomarkers. We illustrate potential consequences of cofragmentation for multiple metaproteomic approaches, and suggest practical paths forward to cope with cofragmentation-induced bias.

pClean: An Algorithm To Preprocess High-Resolution Tandem Mass Spectra for Database Searching

Database searches of MS/MS spectra are the main approach to peptide/protein identification in proteomics. Since most database search engines only utilize a small portion of the original MS/MS signals for peptide detection, how to improve the quality of MS/MS signals is a primary concern for enhancement of the peptide/protein identification rate. A fundamental issue is that some noise MS signals, informative or uninformative, have to be filtered out prior to database searching. Herein, an integrative preprocessing algorithm was designed, termed pClean, which incorporates three modules to preprocess MS/MS spectra, such as the removal of isobaric-labeling related ions, the reduction in isotopic peaks, the deconvolution of ions with higher charges, and the clearance of uninformative MS/MS signals. In contrast to the currently available approaches to MS/MS data preprocessing, pClean enables treatment of MS/MS spectra with high mass accuracy and favors filtering for the labeling or nonlabeling of peptides. Data sets at various scales gained from mass spectrometers with high resolution were used to assess the quality of peptides identified after pClean treatment and to compare the pClean improvement with those of other software programs. On the basis of the analysis of peptides identified and the Mascot ion score, pClean was proven to be effective in the removal of mass spectral noise and the reduction of random matching. Compared with other software programs, pClean appeared to be beneficial in terms of preprocessing performances for the enhancement of confidence scores and the increase in peptides identified. pClean is available at https://github.com/AimeeD90/pClean_release .

Assessing the Relationship Between Mass Window Width and Retention Time Scheduling on Protein Coverage for Data-Independent Acquisition

Due to the technical advances of mass spectrometers, particularly increased scanning speed and higher MS/MS resolution, the use of data-independent acquisition mass spectrometry (DIA-MS) became more popular, which enables high reproducibility in both proteomic identification and quantification. The current DIA-MS methods normally cover a wide mass range, with the aim to target and identify as many peptides and proteins as possible and therefore frequently generate MS/MS spectra of high complexity. In this report, we assessed the performance and benefits of using small windows with, e.g., 5-m/z width across the peptide elution time. We further devised a new DIA method named RTwinDIA that schedules the small isolation windows in different retention time blocks, taking advantage of the fact that larger peptides are normally eluting later in reversed phase chromatography. We assessed the direct proteomic identification by using shotgun database searching tools such as MaxQuant and pFind, and also Spectronaut with an external comprehensive spectral library of human proteins. We conclude that algorithms like pFind have potential in directly analyzing DIA data acquired with small windows, and that the instrumental time and DIA cycle time, if prioritized to be spent on small windows rather than on covering a broad mass range by large windows, will improve the direct proteome coverage for new biological samples and increase the quantitative precision. These results further provide perspectives for the future convergence between DDA and DIA on faster MS analyzers.

SpotLight Proteomics-A IgG-Enrichment Phenotype Profiling Approach with Clinical Implications

Sarcoidosis is a systemic interstitial lung disease of unknown aetiology. Less invasive diagnostics are needed to decipher disease pathology and to distinguish sub-phenotypes. Here we test if SpotLight proteomics, which combines de novo MS/MS sequencing of enriched IgG and co-extracted proteins with subsequent label-free quantification of new and known peptides, can differentiate controls and sarcoidosis phenotypes (Löfgrens and non-Löfgrens syndrome, LS and nonLS). Intra-individually matched IgG enriched from serum and bronchial lavage fluid (BALF) from controls (n = 12), LS (n = 11) and nonLS (n = 12) were investigated. High-resolution mass-spectrometry SpotLight proteomics and uni- and multivariate-statistical analyses were used for data processing. Major differences were particularly observed in control-BALF versus sarcoidosis-BALF. However, interestingly, information obtained from BALF profiles was still present (but less prominent) in matched serum profiles. By using information from orthogonal partial least squares discriminant analysis (OPLS-DA) differentiating 1) sarcoidosis-BALF and control-BALF and 2) LS-BALF vs. nonLS-BALF, control-serum and sarcoidosis-serum (p = 0.0007) as well as LS-serum and nonLS-serum (p = 0.006) could be distinguished. Noteworthy, many factors prominent in identifying controls and patients were those associated with Fc-regulation, but also features from the IgG-Fab region and novel peptide variants. Differences between phenotypes were mostly IgG-specificity related. The results support the analytical utility of SpotLight proteomics which prospectively have potential to differentiate closely related phenotypes from a simple blood test.

Pyteomics 4.0: Five Years of Development of a Python Proteomics Framework

Many of the novel ideas that drive today's proteomic technologies are focused essentially on experimental or data-processing workflows. The latter are implemented and published in a number of ways, from custom scripts and programs, to projects built using general-purpose or specialized workflow engines; a large part of routine data processing is performed manually or with custom scripts that remain unpublished. Facilitating the development of reproducible data-processing workflows becomes essential for increasing the efficiency of proteomic research. To assist in overcoming the bioinformatics challenges in the daily practice of proteomic laboratories, 5 years ago we developed and announced Pyteomics, a freely available open-source library providing Python interfaces to proteomic data. We summarize the new functionality of Pyteomics developed during the time since its introduction.

Proteomic Analysis of Mouse Brain Subjected to Spaceflight

There is evidence that spaceflight poses acute and late risks to the central nervous system. To explore possible mechanisms, the proteomic changes following spaceflight in mouse brain were characterized. Space Shuttle Atlantis (STS-135) was launched from the Kennedy Space Center (KSC) on a 13-day mission. Within 3–5 h after landing, brain tissue was collected to evaluate protein expression profiles using quantitative proteomic analysis. Our results showed that there were 26 proteins that were significantly altered after spaceflight in the gray and/or white matter. While there was no overlap between the white and gray matter in terms of individual proteins, there was overlap in terms of function, synaptic plasticity, vesical activity, protein/organelle transport, and metabolism. Our data demonstrate that exposure to the spaceflight environment induces significant changes in protein expression related to neuronal structure and metabolic function. This might lead to a significant impact on brain structural and functional integrity that could affect the outcome of space missions.

Evaluation of Advanced Precursor Determination for Tandem Mass Tag (TMT)-Based Quantitative Proteomics across Instrument Platforms

Tandem mass tag (TMT)-based quantitation is a strong modality for quantitative proteomics, as samples can be multiplexed, creating large-scale data sets with high precision and minimal missing values. However, coisolation/cofragmentation of near isobaric, coeluting precursor peptide analytes has been well-documented to show ratio compression, compromising the accuracy of peptide/protein quantitation. Advanced peak determination (APD) is a new peak-picking algorithm that shows improved identification of peak detection in survey scans (MS1) to increase the number of precursors selected for unimolecular dissociation (MS2). To increase the number of these "features" selected for MS2 APD purposefully selects multiple peptide precursors of very similar m/ z that often derive from different proteins-a major source of ratio compression in TMT quantification. Here, we evaluate the effects of various data acquisition parameters combined with APD on ratio compression. We find that data acquisition with APD enabled results in more coisolated precursors, more mixed spectra, and in turn, fewer peptide spectral matches, especially at standard on-column loads. We conclude that APD should not be utilized for isobaric tagging, MS2-based experiments.

CharmeRT: Boosting Peptide Identifications by Chimeric Spectra Identification and Retention Time Prediction

Coeluting peptides are still a major challenge for the identification and validation of MS/MS spectra, but carry great potential. To tackle these problems, we have developed the here presented CharmeRT workflow, combining a chimeric spectra identification strategy implemented as part of the MS Amanda algorithm with the validation system Elutator, which incorporates a highly accurate retention time prediction algorithm. For high-resolution data sets this workflow identifies 38-64% chimeric spectra, which results in up to 63% more unique peptides compared to a conventional single search strategy.

Enhanced Global Post-translational Modification Discovery with MetaMorpheus

Correct identification of protein post-translational modifications (PTMs) is crucial to understanding many aspects of protein function in biological processes. G-PTM-D is a recently developed technique for global identification and localization of PTMs. Spectral file calibration prior to applying G-PTM-D, and algorithmic enhancements in the peptide database search significantly increase the accuracy, speed, and scope of PTM identification. We enhance G-PTM-D by using multinotch searches and demonstrate its effectiveness in identification of numerous types of PTMs including high-mass modifications such as glycosylations. The changes described in this work lead to a 20% increase in the number of identified modifications and an order of magnitude decrease in search time. The complete workflow is implemented in MetaMorpheus, a software tool that integrates the database search procedure, identification of coisolated peptides, spectral calibration, and the enhanced G-PTM-D workflow. Multinotch searches are also shown to be useful in contexts other than G-PTM-D by producing superior results when used instead of standard narrow-window and open database searches.

Targeted Feature Detection for Data-Dependent Shotgun Proteomics

Label-free quantification of shotgun LC–MS/MS data is the prevailing approach in quantitative proteomics but remains computationally nontrivial. The central data analysis step is the detection of peptide-specific signal patterns, called features. Peptide quantification is facilitated by associating signal intensities in features with peptide sequences derived from MS2 spectra; however, missing values due to imperfect feature detection are a common problem. A feature detection approach that directly targets identified peptides (minimizing missing values) but also offers robustness against false-positive features (by assigning meaningful confidence scores) would thus be highly desirable. We developed a new feature detection algorithm within the OpenMS software framework, leveraging ideas and algorithms from the OpenSWATH toolset for DIA/SRM data analysis. Our software, FeatureFinderIdentification (“FFId”), implements a targeted approach to feature detection based on information from identified peptides. This information is encoded in an MS1 assay library, based on which ion chromatogram extraction and detection of feature candidates are carried out. Significantly, when analyzing data from experiments comprising multiple samples, our approach distinguishes between “internal” and “external” (inferred) peptide identifications (IDs) for each sample. On the basis of internal IDs, two sets of positive (true) and negative (decoy) feature candidates are defined. A support vector machine (SVM) classifier is then trained to discriminate between the sets and is subsequently applied to the “uncertain” feature candidates from external IDs, facilitating selection and confidence scoring of the best feature candidate for each peptide. This approach also enables our algorithm to estimate the false discovery rate (FDR) of the feature selection step. We validated FFId based on a public benchmark data set, comprising a yeast cell lysate spiked with protein standards that provide a known ground-truth. The algorithm reached almost complete (>99%) quantification coverage for the full set of peptides identified at 1% FDR (PSM level). Compared with other software solutions for label-free quantification, this is an outstanding result, which was achieved at competitive quantification accuracy and reproducibility across replicates. The FDR for the feature selection was estimated at a low 1.5% on average per sample (3% for features inferred from external peptide IDs). The FFId software is open-source and freely available as part of OpenMS (www.openms.org).

Improvements in Mass Spectrometry Assay Library Generation for Targeted Proteomics

In data-independent acquisition mass spectrometry (DIA-MS), targeted extraction of peptide signals in silico using mass spectrometry assay libraries is a successful method for the identification and quantification of proteins. However, it remains unclear if high quality assay libraries with more accurate peptide ion coordinates can improve peptide target identification rates in DIA analysis. In this study, we systematically improved and evaluated the common algorithmic steps for assay library generation and demonstrate that increased assay quality results in substantially higher identification rates of peptide targets from mouse organ protein lysates measured by DIA-MS. The introduced changes are (1) a new spectrum interpretation algorithm, (2) reapplication of segmented retention time normalization, (3) a ppm fragment mass error matching threshold, (4) usage of internal peptide fragments, and (5) a multilevel false discovery rate calculation. Taken together, these changes yielded 14-36% more identified peptide targets at 1% assay false discovery rate and are implemented in three new open source tools, Fraggle, Tramler, and Franklin, available at https://github.com/fickludd/eviltools . The improved algorithms provide ways to better utilize discovery MS data, translating to substantially increased DIA performance and ultimately better foundations for drawing biological conclusions in DIA-based experiments.

MSFragger: ultrafast and comprehensive peptide identification in mass spectrometry-based proteomics

There is a need to better understand and handle the 'dark matter' of proteomics-the vast diversity of post-translational and chemical modifications that are unaccounted in a typical mass spectrometry-based analysis and thus remain unidentified. We present a fragment-ion indexing method, and its implementation in peptide identification tool MSFragger, that enables a more than 100-fold improvement in speed over most existing proteome database search tools. Using several large proteomic data sets, we demonstrate how MSFragger empowers the open database search concept for comprehensive identification of peptides and all their modified forms, uncovering dramatic differences in modification rates across experimental samples and conditions. We further illustrate its utility using protein-RNA cross-linked peptide data and using affinity purification experiments where we observe, on average, a 300% increase in the number of identified spectra for enriched proteins. We also discuss the benefits of open searching for improved false discovery rate estimation in proteomics.

Covariation of Peptide Abundances Accurately Reflects Protein Concentration Differences

Most implementations of mass spectrometry-based proteomics involve enzymatic digestion of proteins, expanding the analysis to multiple proteolytic peptides for each protein. Currently, there is no consensus of how to summarize peptides' abundances to protein concentrations, and such efforts are complicated by the fact that error control normally is applied to the identification process, and do not directly control errors linking peptide abundance measures to protein concentration. Peptides resulting from suboptimal digestion or being partially modified are not representative of the protein concentration. Without a mechanism to remove such unrepresentative peptides, their abundance adversely impacts the estimation of their protein's concentration. Here, we present a relative quantification approach, Diffacto, that applies factor analysis to extract the covariation of peptides' abundances. The method enables a weighted geometrical average summarization and automatic elimination of incoherent peptides. We demonstrate, based on a set of controlled label-free experiments using standard mixtures of proteins, that the covariation structure extracted by the factor analysis accurately reflects protein concentrations. In the 1% peptide-spectrum match-level FDR data set, as many as 11% of the peptides have abundance differences incoherent with the other peptides attributed to the same protein. If not controlled, such contradicting peptide abundance have a severe impact on protein quantifications. When adding the quantities of each protein's three most abundant peptides, we note as many as 14% of the proteins being estimated as having a negative correlation with their actual concentration differences between samples. Diffacto reduced the amount of such obviously incorrectly quantified proteins to 1.6%. Furthermore, by analyzing clinical data sets from two breast cancer studies, our method revealed the persistent proteomic signatures linked to three subtypes of breast cancer. We conclude that Diffacto can facilitate the interpretation and enhance the utility of most types of proteomics data.

SpotLight Proteomics: uncovering the hidden blood proteome improves diagnostic power of proteomics

The human blood proteome is frequently assessed by protein abundance profiling using a combination of liquid chromatography and tandem mass spectrometry (LC-MS/MS). In traditional sequence database search, many good-quality MS/MS data remain unassigned. Here we uncover the hidden part of the blood proteome via novel SpotLight approach. This method combines de novo MS/MS sequencing of enriched antibodies and co-extracted proteins with subsequent label-free quantification of new and known peptides in both enriched and unfractionated samples. In a pilot study on differentiating early stages of Alzheimer’s disease (AD) from Dementia with Lewy Bodies (DLB), on peptide level the hidden proteome contributed almost as much information to patient stratification as the apparent proteome. Intriguingly, many of the new peptide sequences are attributable to antibody variable regions, and are potentially indicative of disease etiology. When the hidden and apparent proteomes are combined, the accuracy of differentiating AD (n = 97) and DLB (n = 47) increased from ≈85% to ≈95%. The low added burden of SpotLight proteome analysis makes it attractive for use in clinical settings.

Multiplexed Post-Experimental Monoisotopic Mass Refinement (mPE-MMR) to Increase Sensitivity and Accuracy in Peptide Identifications from Tandem Mass Spectra of Cofragmentation

Mass spectrometry (MS)-based proteomics, which uses high-resolution hybrid mass spectrometers such as the quadrupole-orbitrap mass spectrometer, can yield tens of thousands of tandem mass (MS/MS) spectra of high resolution during a routine bottom-up experiment. Despite being a fundamental and key step in MS-based proteomics, the accurate determination and assignment of precursor monoisotopic masses to the MS/MS spectra remains difficult. The difficulties stem from imperfect isotopic envelopes of precursor ions, inaccurate charge states for precursor ions, and cofragmentation. We describe a composite method of utilizing MS data to assign accurate monoisotopic masses to MS/MS spectra, including those subject to cofragmentation. The method, "multiplexed post-experiment monoisotopic mass refinement" (mPE-MMR), consists of the following: multiplexing of precursor masses to assign multiple monoisotopic masses of cofragmented peptides to the corresponding multiplexed MS/MS spectra, multiplexing of charge states to assign correct charges to the precursor ions of MS/MS spectra with no charge information, and mass correction for inaccurate monoisotopic peak picking. When combined with MS-GF+, a database search algorithm based on fragment mass difference, mPE-MMR effectively increases both sensitivity and accuracy in peptide identification from complex high-throughput proteomics data compared to conventional methods.

Human IgM Antibodies to Malondialdehyde Conjugated With Albumin Are Negatively Associated With Cardiovascular Disease Among 60-Year-Olds

Background

Malondialdehyde (MDA) is generated during lipid peroxidation as in oxidized low‐density lipoprotein, but antibodies against oxidized low‐density lipoprotein show variable results in clinical studies. We therefore studied the risk of cardiovascular disease (CVD) associated with IgM antibodies against MDA conjugated with human albumin (anti‐MDA).

Methods and Results

In a 5‐ to 7‐year follow‐up of 60‐year‐old men and women from Stockholm County previously screened for cardiovascular risk factors (2039 men, 2193 women), 209 incident CVD cases (defined as new events of coronary heart disease, fatal and nonfatal myocardial infarction, ischemic stroke, and hospitalization for angina pectoris) and 620 age‐ and sex‐matched controls were tested for IgM anti‐MDA by ELISA. Antibody peptide/protein characterization was done using a proteomics de novo sequencing approach. After adjustment for smoking, body‐mass index, type 2 diabetes mellitus, hyperlipidemia, and hypertension, an increased CVD risk was observed in the low IgM anti‐MDA percentiles (below 10th and 25th) (odds ratio and 95% CI: 2.0; 1.19–3.36 and 1.67; 1.16–2.41, respectively). Anti‐MDA above the 66th percentile was associated with a decreased CVD risk (odds ratio 0.68; CI: 0.48–0.98). After stratification by sex, associations were only present among men. IgM anti‐MDA levels were lower among cases (median [interquartile range]: 141.0 [112.7–164.3] versus 147.4 [123.5–169.6]; P=0.0177), even more so among men (130.6 [107.7–155.3] versus 143.0 [120.1–165.2]; P=0.001). The IgM anti‐MDA variable region profiles are distinctly different and also more homologous in their content (correlates strongly with fewer peptides) than control antibodies (not binding MDA).

Conclusions

IgM anti‐MDA is a protection marker for CVD. This finding could have diagnostic and therapeutic implications.

Flexible Data Analysis Pipeline for High-Confidence Proteogenomics

Proteogenomics leverages information derived from proteomic data to improve genome annotations. Of particular interest are “novel” peptides that provide direct evidence of protein expression for genomic regions not previously annotated as protein-coding. We present a modular, automated data analysis pipeline aimed at detecting such “novel” peptides in proteomic data sets. This pipeline implements criteria developed by proteomics and genome annotation experts for high-stringency peptide identification and filtering. Our pipeline is based on the OpenMS computational framework; it incorporates multiple database search engines for peptide identification and applies a machine-learning approach (Percolator) to post-process search results. We describe several new and improved software tools that we developed to facilitate proteogenomic analyses that enhance the wealth of tools provided by OpenMS. We demonstrate the application of our pipeline to a human testis tissue data set previously acquired for the Chromosome-Centric Human Proteome Project, which led to the addition of five new gene annotations on the human reference genome.

Immunogenic HLA-DR-Presented Self-Peptides Identified Directly from Clinical Samples of Synovial Tissue, Synovial Fluid, or Peripheral Blood in Patients with Rheumatoid Arthritis or Lyme Arthritis

Human leukocyte antigen-antigen D related (HLA-DR) molecules are highly expressed in synovial tissue (ST), the target of the immune response in chronic inflammatory forms of arthritis. Here, we used LC-MS/MS to identify HLA-DR-presented self-peptides in cells taken directly from clinical samples: ST, synovial fluid mononuclear cells (SFMC), or peripheral blood mononuclear cells (PBMC) from five patients with rheumatoid arthritis (RA) and eight with Lyme arthritis (LA). We identified 1593 non-redundant HLA-DR-presented peptides, derived from 870 source proteins. A total of 67% of the peptides identified in SFMC and 55% of those found in PBMC were found in ST, but analysis of SFMC/PBMC also revealed new antigen-presented peptides. Peptides were synthesized and examined for reactivity with the patients' PBMC. To date, three autoantigens in RA and four novel autoantigens in LA, presented in ST and/or PBMC, were shown to be targets of T- and B-cell responses in these diseases; ongoing analyses may add to this list. Thus, immunoprecipitation and LC-MS/MS can now identify hundreds of HLA-DR-presented self-peptides from individual patients' tissues or fluids with mixed cell populations. Importantly, identification of HLA-DR-presented peptides from SFMC or PBMC allows testing of more patients, including those early in the disease. Direct analysis of clinical samples facilitates identification of novel immunogenic T-cell epitopes.

Yield of 6,000 proteins by 1D nLC-MS/MS without pre-fractionation

Mass spectrometry (MS) has dominated over other protein analysis methods that aspire to deliver rapid and sensitive protein annotation, due to its ability to acquire high-content biological information from samples of great complexity. Routinely, in-depth analysis of complex biological samples, such as total cell lysates, relies on the high separation power of two-dimensional liquid chromatography-tandem MS (2D LC-MS/MS), often combined with protein pre-fractionation. However, on the basis of recent advances in chromatographic and MS instrumentation, one-dimensional (1D) LC-MS/MS approaches have become the method-of-choice for high-volume/high-throughput protein experiments. Thousands of proteins can be identified in single-run LC-MS/MS experiments. In the present study a 1D LC-MS/MS approach was applied on whole-cell lysates of WM-266-4 human cells leading to identification of more than 5,300 protein groups, 6,000 proteins and 22,00 peptides, in a single run. Using no pre-fractionation steps, method optimization was achieved through experimentation on lysis and protein extraction solutions, as well as nLC gradient parameters.

Peptide de novo sequencing of mixture tandem mass spectra

The impact of mixture spectra deconvolution on the performance of four popular de novo sequencing programs was tested using artificially constructed mixture spectra as well as experimental proteomics data. Mixture fragmentation spectra are recognized as a limitation in proteomics because they decrease the identification performance using database search engines. De novo sequencing approaches are expected to be even more sensitive to the reduction in mass spectrum quality resulting from peptide precursor co‐isolation and thus prone to false identifications. The deconvolution approach matched complementary b‐, y‐ions to each precursor peptide mass, which allowed the creation of virtual spectra containing sequence specific fragment ions of each co‐isolated peptide. Deconvolution processing resulted in equally efficient identification rates but increased the absolute number of correctly sequenced peptides. The improvement was in the range of 20–35% additional peptide identifications for a HeLa lysate sample. Some correct sequences were identified only using unprocessed spectra; however, the number of these was lower than those where improvement was obtained by mass spectral deconvolution. Tight candidate peptide score distribution and high sensitivity to small changes in the mass spectrum introduced by the employed deconvolution method could explain some of the missing peptide identifications.

Dinosaur: A Refined Open-Source Peptide MS Feature Detector

In bottom-up mass spectrometry (MS)-based proteomics, peptide isotopic and chromatographic traces (features) are frequently used for label-free quantification in data-dependent acquisition MS but can also be used for the improved identification of chimeric spectra or sample complexity characterization. Feature detection is difficult because of the high complexity of MS proteomics data from biological samples, which frequently causes features to intermingle. In addition, existing feature detection algorithms commonly suffer from compatibility issues, long computation times, or poor performance on high-resolution data. Because of these limitations, we developed a new tool, Dinosaur, with increased speed and versatility. Dinosaur has the functionality to sample algorithm computations through quality-control plots, which we call a plot trail. From the evaluation of this plot trail, we introduce several algorithmic improvements to further improve the robustness and performance of Dinosaur, with the detection of features for 98% of MS/MS identifications in a benchmark data set, and no other algorithm tested in this study passed 96% feature detection. We finally used Dinosaur to reimplement a published workflow for peptide identification in chimeric spectra, increasing chimeric identification from 26% to 32% over the standard workflow. Dinosaur is operating-system-independent and is freely available as open source on https://github.com/fickludd/dinosaur.

DeMix-Q: Quantification-Centered Data Processing Workflow

For historical reasons, most proteomics workflows focus on MS/MS identification but consider quantification as the end point of a comparative study. The stochastic data-dependent MS/MS acquisition (DDA) gives low reproducibility of peptide identifications from one run to another, which inevitably results in problems with missing values when quantifying the same peptide across a series of label-free experiments. However, the signal from the molecular ion is almost always present among the MS(1)spectra. Contrary to what is frequently claimed, missing values do not have to be an intrinsic problem of DDA approaches that perform quantification at the MS(1)level. The challenge is to perform sound peptide identity propagation across multiple high-resolution LC-MS/MS experiments, from runs with MS/MS-based identifications to runs where such information is absent. Here, we present a new analytical workflow DeMix-Q (https://github.com/userbz/DeMix-Q), which performs such propagation that recovers missing values reliably by using a novel scoring scheme for quality control. Compared with traditional workflows for DDA as well as previous DIA studies, DeMix-Q achieves deeper proteome coverage, fewer missing values, and lower quantification variance on a benchmark dataset. This quantification-centered workflow also enables flexible and robust proteome characterization based on covariation of peptide abundances.

Proteomics Is Analytical Chemistry: Fitness-for-Purpose in the Application of Top-Down and Bottom-Up Analyses

Molecular mechanisms underlying health and disease function at least in part based on the flexibility and fine-tuning afforded by protein isoforms and post-translational modifications. The ability to effectively and consistently resolve these protein species or proteoforms, as well as assess quantitative changes is therefore central to proteomic analyses. Here we discuss the pros and cons of currently available and developing analytical techniques from the perspective of the full spectrum of available tools and their current applications, emphasizing the concept of fitness-for-purpose in experimental design based on consideration of sample size and complexity; this necessarily also addresses analytical reproducibility and its variance. Data quality is considered the primary criterion, and we thus emphasize that the standards of Analytical Chemistry must apply throughout any proteomic analysis.

Extracting Accurate Precursor Information for Tandem Mass Spectra by RawConverter

Extraction of data from the proprietary RAW files generated by Thermo Fisher mass spectrometers is the primary step for subsequent data analysis. High resolution and high mass accuracy data obtained by state-of-the-art mass spectrometers (e.g., Orbitraps) can significantly improve both peptide/protein identification and quantification. We developed RawConverter, a stand-alone software tool, to improve data extraction on RAW files from high-resolution Thermo Fisher mass spectrometers. RawConverter extracts full scan and MS(n) data from RAW files like its predecessor RawXtract; most importantly, it associates the accurate precursor mass-to-charge (m/z) value with the tandem mass spectrum. RawConverter accepts RAW data generated by either data-dependent acquisition (DDA) or data-independent acquisition (DIA). It generates output into MS1/MS2/MS3, MGF, or mzXML file formats, which fulfills the format requirements for most data identification and quantification tools. Using the tandem mass spectra extracted by RawConverter with corrected m/z values, 32.8%, 27.1%, and 84.1%, peptide spectra matches (PSMs) produce 17.4% (13.0%), 14.4% (11.5%), and 45.7% (36.2%) more peptide (protein) identifications than ProteoWizard, pXtract, and RawXtract, respectively. RawConverter is implemented in C# and is freely accessible at http://fields.scripps.edu/rawconv.

Enhancement of mass spectrometry performance for proteomic analyses using high-field asymmetric waveform ion mobility spectrometry (FAIMS)

Remarkable advances in mass spectrometry sensitivity and resolution have been accomplished over the past two decades to enhance the depth and coverage of proteome analyses. As these technological developments expanded the detection capability of mass spectrometers, they also revealed an increasing complexity of low abundance peptides, solvent clusters and sample contaminants that can confound protein identification. Separation techniques that are complementary and can be used in combination with liquid chromatography are often sought to improve mass spectrometry sensitivity for proteomics applications. In this context, high-field asymmetric waveform ion mobility spectrometry (FAIMS), a form of ion mobility that exploits ion separation at low and high electric fields, has shown significant advantages by focusing and separating multiply charged peptide ions from singly charged interferences. This paper examines the analytical benefits of FAIMS in proteomics to separate co-eluting peptide isomers and to enhance peptide detection and quantitative measurements of protein digests via native peptides (label-free) or isotopically labeled peptides from metabolic labeling or chemical tagging experiments.

reSpect: software for identification of high and low abundance ion species in chimeric tandem mass spectra

Most shotgun proteomics data analysis workflows are based on the assumption that each fragment ion spectrum is explained by a single species of peptide ion isolated by the mass spectrometer; however, in reality mass spectrometers often isolate more than one peptide ion within the window of isolation that contribute to additional peptide fragment peaks in many spectra. We present a new tool called reSpect, implemented in the Trans-Proteomic Pipeline (TPP), which enables an iterative workflow whereby fragment ion peaks explained by a peptide ion identified in one round of sequence searching or spectral library search are attenuated based on the confidence of the identification, and then the altered spectrum is subjected to further rounds of searching. The reSpect tool is not implemented as a search engine, but rather as a post-search engine processing step where only fragment ion intensities are altered. This enables the application of any search engine combination in the iterations that follow. Thus, reSpect is compatible with all other protein sequence database search engines as well as peptide spectral library search engines that are supported by the TPP. We show that while some datasets are highly amenable to chimeric spectrum identification and lead to additional peptide identification boosts of over 30% with as many as four different peptide ions identified per spectrum, datasets with narrow precursor ion selection only benefit from such processing at the level of a few percent. We demonstrate a technique that facilitates the determination of the degree to which a dataset would benefit from chimeric spectrum analysis. The reSpect tool is free and open source, provided within the TPP and available at the TPP website. Graphical Abstract ᅟ.

Discovery of protein acetylation patterns by deconvolution of peptide isomer mass spectra

Protein post-translational modifications (PTMs) play important roles in the control of various biological processes including protein–protein interactions, epigenetics and cell cycle regulation. Mass spectrometry-based proteomics approaches enable comprehensive identification and quantitation of numerous types of PTMs. However, the analysis of PTMs is complicated by the presence of indistinguishable co-eluting isomeric peptides that result in composite spectra with overlapping features that prevent the identification of individual components. In this study, we present Iso-PeptidAce, a novel software tool that enables deconvolution of composite MS/MS spectra of isomeric peptides based on features associated with their characteristic fragment ion patterns. We benchmark Iso-PeptidAce using dilution series prepared from mixtures of known amounts of synthetic acetylated isomers. We also demonstrate its applicability to different biological problems such as the identification of site-specific acetylation patterns in histones bound to chromatin assembly factor-1 and profiling of histone acetylation in cells treated with different classes of HDAC inhibitors.

Deciphering patterns of histone modifications that modulate chromatin structure and function is important, but remains challenging. Here the authors describe a method to uncover patterns of site-specific histone acetylation by deconvolution of overlapping peptide isomer mass spectra.

Peptide-Centric Proteome Analysis: An Alternative Strategy for the Analysis of Tandem Mass Spectrometry Data

In mass spectrometry-based bottom-up proteomics, data-independent acquisition is an emerging technique because of its comprehensive and unbiased sampling of precursor ions. However, current data-independent acquisition methods use wide precursor isolation windows, resulting in cofragmentation and complex mixture spectra. Thus, conventional database searching tools that identify peptides by interpreting individual tandem MS spectra are inherently limited in analyzing data-independent acquisition data. Here we discuss an alternative approach, peptide-centric analysis, which tests directly for the presence and absence of query peptides. We discuss how peptide-centric analysis resolves some limitations of traditional spectrum-centric analysis, and we outline the unique characteristics of peptide-centric analysis in general.

SuperQuant: A Data Processing Approach to Increase Quantitative Proteome Coverage

SuperQuant is a quantitative proteomics data processing approach that uses complementary fragment ions to identify multiple coisolated peptides in tandem mass spectra allowing for their quantification. This approach can be applied to any shotgun proteomics data set acquired with high mass accuracy for quantification at the MS(1) level. The SuperQuant approach was developed and implemented as a processing node within the Thermo Proteome Discoverer 2.x. The performance of the developed approach was tested using dimethyl-labeled HeLa lysate samples having a ratio between channels of 10(heavy):4(medium):1(light). Peptides were fragmented with collision-induced dissociation using isolation windows of 1, 2, and 4 Th while recording data both with high-resolution and low-resolution. The results obtained using SuperQuant were compared to those using the conventional ion trap-based approach (low mass accuracy MS(2) spectra), which is known to achieve high identification performance. Compared to the common high-resolution approach, the SuperQuant approach identifies up to 70% more peptide-spectrum matches (PSMs), 40% more peptides, and 20% more proteins at the 0.01 FDR level. It identifies more PSMs and peptides than the ion trap-based approach. Improvements in identifications resulted in up to 10% more PSMs, 15% more peptides, and 10% more proteins quantified on the same raw data. The developed approach does not affect the accuracy of the quantification and observed coefficients of variation between replicates of the same proteins were close to the values typical for other precursor ion-based quantification methods. The raw data is deposited to ProteomeXchange (PXD001907). The developed node is available for testing at https://github.com/caetera/SuperQuantNode.

Systematic optimization of long gradient chromatography mass spectrometry for deep analysis of brain proteome

The development of high-resolution liquid chromatography (LC) is essential for improving the sensitivity and throughput of mass spectrometry (MS)-based proteomics. Here we present systematic optimization of a long gradient LC-MS/MS platform to enhance protein identification from a complex mixture. The platform employed an in-house fabricated, reverse-phase long column (100 μm × 150 cm, 5 μm C18 beads) coupled to Q Exactive MS. The column was capable of achieving a peak capacity of ∼700 in a 720 min gradient of 10-45% acetonitrile. The optimal loading level was ∼6 μg of peptides, although the column allowed loading as many as 20 μg. Gas-phase fractionation of peptide ions further increased the number of peptide identification by ∼10%. Moreover, the combination of basic pH LC prefractionation with the long gradient LC-MS/MS platform enabled the identification of 96,127 peptides and 10,544 proteins at 1% protein false discovery rate in a post-mortem brain sample of Alzheimer's disease. Because deep RNA sequencing of the same specimen suggested that ∼16,000 genes were expressed, the current analysis covered more than 60% of the expressed proteome. Further improvement strategies of the LC/LC-MS/MS platform were also discussed.