Shotgun Proteomics Experiments Research Articles

Mass Spectrometric Detection of Formaldehyde-Crosslinked PBMC Proteins in Cell-Free DNA Blood Collection Tubes

Streck tubes are commonly used to collect blood samples to preserve cell-free circulating DNA. They contain imidazolidinyl urea as a formaldehyde-releasing agent to stabilize cells. We investigated whether the released formaldehyde leads to crosslinking of intracellular proteins. Therefore, we employed a shotgun proteomics experiment on human peripheral blood mononuclear cells (PBMCs) that were isolated from blood collected in Streck tubes, EDTA tubes, EDTA tubes containing formaldehyde, or EDTA tubes containing allantoin. The identified crosslinks were validated in parallel reaction monitoring LC/MS experiments. In total, we identified and validated 45 formaldehyde crosslinks in PBMCs from Streck tubes, which were also found in PBMCs from formaldehyde-treated blood, but not in EDTA- or allantoin-treated samples. Most were derived from cytoskeletal proteins and histones, indicating the ability of Streck tubes to fix cells. In addition, we confirm a previous observation that formaldehyde crosslinking of proteins induces a +24 Da mass shift more frequently than a +12 Da shift. The crosslinking capacity of Streck tubes needs to be considered when selecting blood-collection tubes for mass-spectrometry-based proteomics or metabolomic experiments.

HyperSpec: Ultrafast Mass Spectra Clustering in Hyperdimensional Space.

As current shotgun proteomics experiments can produce gigabytes of mass spectrometry data per hour, processing these massive data volumes has become progressively more challenging. Spectral clustering is an effective approach to speed up downstream data processing by merging highly similar spectra to minimize data redundancy. However, because state-of-the-art spectral clustering tools fail to achieve optimal runtimes, this simply moves the processing bottleneck. In this work, we present a fast spectral clustering tool, HyperSpec, based on hyperdimensional computing (HDC). HDC shows promising clustering capability while only requiring lightweight binary operations with high parallelism that can be optimized using low-level hardware architectures, making it possible to run HyperSpec on graphics processing units to achieve extremely efficient spectral clustering performance. Additionally, HyperSpec includes optimized data preprocessing modules to reduce the spectrum preprocessing time, which is a critical bottleneck during spectral clustering. Based on experiments using various mass spectrometry data sets, HyperSpec produces results with comparable clustering quality as state-of-the-art spectral clustering tools while achieving speedups by orders of magnitude, shortening the clustering runtime of over 21 million spectra from 4 h to only 24 min.

Triqler for Protein Summarization of Data from Data-Independent Acquisition Mass Spectrometry.

A frequent goal, or subgoal, when processing data from a quantitative shotgun proteomics experiment is a list of proteins that are differentially abundant under the examined experimental conditions. Unfortunately, obtaining such a list is a challenging process, as the mass spectrometer analyzes the proteolytic peptides of a protein rather than the proteins themselves. We have previously designed a Bayesian hierarchical probabilistic model, Triqler, for combining peptide identification and quantification errors into probabilities of proteins being differentially abundant. However, the model was developed for data from data-dependent acquisition. Here, we show that Triqler is also compatible with data-independent acquisition data after applying minor alterations for the missing value distribution. Furthermore, we find that it has better performance than a set of compared state-of-the-art protein summarization tools when evaluated on data-independent acquisition data.

Global detection of human variants and isoforms by deep proteome sequencing

An average shotgun proteomics experiment detects approximately 10,000 human proteins from a single sample. However, individual proteins are typically identified by peptide sequences representing a small fraction of their total amino acids. Hence, an average shotgun experiment fails to distinguish different protein variants and isoforms. Deeper proteome sequencing is therefore required for the global discovery of protein isoforms. Using six different human cell lines, six proteases, deep fractionation and three tandem mass spectrometry fragmentation methods, we identify a million unique peptides from 17,717 protein groups, with a median sequence coverage of approximately 80%. Direct comparison with RNA expression data provides evidence for the translation of most nonsynonymous variants. We have also hypothesized that undetected variants likely arise from mutation-induced protein instability. We further observe comparable detection rates for exon–exon junction peptides representing constitutive and alternative splicing events. Our dataset represents a resource for proteoform discovery and provides direct evidence that most frame-preserving alternatively spliced isoforms are translated.

Multiaspect Examinations of Possible Alternative Mappings of Identified Variant Peptides: A Case Study on the HEK293 Cell Line.

Adopting proteogenomics approach to validate single nucleotide variation events by identifying corresponding single amino acid variant peptides from mass spectrometry (MS)-based proteomics data facilitates translational and clinical research. Although variant peptides are usually identified from MS data with a stringent false discovery rate (FDR), FDR control could fail to eliminate dubious results caused by several issues; thus, postexamination to eliminate dubious results is required. However, comprehensive postexaminations of identification results are still lacking. Therefore, we propose a framework of three bottom-up levels, peptide–spectrum match, peptide, and variant event levels, that consists of rigorous 11-aspect examinations from the MS perspective to further confirm the reliability of variant events. As a proof of concept and showing feasibility, we demonstrate 11 examinations on the identified variant peptides from an HEK293 cell line data set, where various database search strategies were applied to maximize the number of identified variant PSMs with an FDR <1% for postexaminations. The results showed that only FDR criterion is insufficient to validate identified variant peptides and the 11 postexaminations can reveal low-confidence variant events detected by shotgun proteomics experiments. Therefore, we suggest that postexaminations of identified variant events based on the proposed framework are necessary for proteogenomics studies.

Proteomic cellular signatures of kinase inhibitor-induced cardiotoxicity

Drug Toxicity Signature Generation Center (DToxS) at the Icahn School of Medicine at Mount Sinai is one of the centers for the NIH Library of Integrated Network-Based Cellular Signatures (LINCS) program. Its key aim is to generate proteomic and transcriptomic signatures that can predict cardiotoxic adverse effects of kinase inhibitors approved by the Food and Drug Administration. Towards this goal, high throughput shotgun proteomics experiments (308 cell line/drug combinations +64 control lysates) have been conducted. Using computational network analyses, these proteomic data can be integrated with transcriptomic signatures, generated in tandem, to identify cellular signatures of cardiotoxicity that may predict kinase inhibitor-induced toxicity and enable possible mitigation. Both raw and processed proteomics data have passed several quality control steps and been made publicly available on the PRIDE database. This broad protein kinase inhibitor-stimulated human cardiomyocyte proteomic data and signature set is valuable for prediction of drug toxicities.

ClusterSheep: A Graphics Processing Unit-Accelerated Software Tool for Large-Scale Clustering of Tandem Mass Spectra from Shotgun Proteomics.

Modern shotgun proteomics experiments generate gigabytes of spectra every hour, only a fraction of which were utilized to form biological conclusions. Instead of being stored as flat files in public data repositories, this large amount of data can be better organized to facilitate data reuse. Clustering these spectra by similarity can be helpful in building high-quality spectral libraries, correcting identification errors, and highlighting frequently observed but unidentified spectra. However, large-scale clustering is time-consuming. Here, we present ClusterSheep, a method utilizing Graphics Processing Units (GPUs) to accelerate the process. Unlike previously proposed algorithms for this purpose, our method performs true pairwise comparison of all spectra within a precursor mass-to-charge ratio tolerance, thereby preserving the full cluster structures. ClusterSheep was benchmarked against previously reported clustering tools, MS-Cluster, MaRaCluster, and msCRUSH. The software tool also functions as an interactive visualization tool with a persistent state, enabling the user to explore the resulting clusters visually and retrieve the clustering results as desired.

Shotgun Proteomics Sample Processing Automated by an Open-Source Lab Robot.

Mass spectrometry-based shotgun proteomics experiments require multiple sample preparation steps, including enzymatic protein digestion and clean-up, which can take up significant person-hours of bench labor and present a source of batch-to-batch variability. Lab automation with pipetting robots can reduce manual work, maximize throughput, and increase research reproducibility. Still, the steep starting prices of standard automation stations make them unaffordable for many academic laboratories. This article describes a proteomics sample preparation workflow using an affordable, open-source automation system (The Opentrons OT-2), including instructions for setting up semi-automated protein reduction, alkylation, digestion, and clean-up steps; as well as accompanying open-source Python scripts to program the OT-2 system through its application programming interface.

PepFormer: End-to-End Transformer-Based Siamese Network to Predict and Enhance Peptide Detectability Based on Sequence Only

The detectability of peptides is fundamentally important in shotgun proteomics experiments. At present, there are many computational methods to predict the detectability of peptides based on sequential composition or physicochemical properties, but they all have various shortcomings. Here, we present PepFormer, a novel end-to-end Siamese network coupled with a hybrid architecture of a Transformer and gated recurrent units that is able to predict the peptide detectability based on peptide sequences only. Specially, we, for the first time, use contrastive learning and construct a new loss function for model training, greatly improving the generalization ability of our predictive model. Comparative results demonstrate that our model performs significantly better than state-of-the-art methods on benchmark data sets in two species (Homo sapiens and Mus musculus). To make the model more interpretable, we further investigate the embedded representations of peptide sequences automatically learnt from our model, and the visualization results indicate that our model can efficiently capture high-latent discriminative information, improving the predictive performance. In addition, our model shows a strong ability of cross-species transfer learning and adaptability, demonstrating that it has great potential in robust prediction of peptides detectability on different species. The source code of our proposed method can be found via https://github.com/WLYLab/PepFormer.

PTM-Shepherd: Analysis and Summarization of Post-Translational and Chemical Modifications From Open Search Results

AbstractOpen searching has proven to be an effective strategy for identifying both known and unknown modifications in shotgun proteomics experiments. Rather than being limited to a small set of user-specified modifications, open searches identify peptides with any mass shift that may correspond to a single modification or a combination of several modifications. Here we present PTM-Shepherd, a bioinformatics tool that automates characterization of post-translational modification profiles detected in open searches based on attributes, such as amino acid localization, fragmentation spectra similarity, retention time shifts, and relative modification rates. PTM-Shepherd can also perform multiexperiment comparisons for studying changes in modification profiles, e.g., in data generated in different laboratories or under different conditions. We demonstrate how PTM-Shepherd improves the analysis of data from formalin-fixed and paraffin-embedded samples, detects extreme underalkylation of cysteine in some data sets, discovers an artifactual modification introduced during peptide synthesis, and uncovers site-specific biases in sample preparation artifacts in a multicenter proteomics profiling study.

Missing Value Monitoring to Address Missing Values in Quantitative Proteomics.

Many classes of key functional proteins such as transcription factors or cell cycle proteins are present in the proteome at a very low concentration. These low-abundance proteins are almost entirely invisible to systematic quantitative analysis by classical data dependent proteomics methods (DDA). Moreover, DDA runs in shotgun proteomics experiments are plenty of missing values among the replicates due to the stochastic nature of the acquisition method, thus hampering the robustness of the quantitative analysis. Here, we have overcome these obstacles designing a robust workflow named missing value monitoring (MvM) in order to follow low abundance proteins dynamics.

A Primer and Guidelines for Shotgun Proteomic Analysis in Non-model Organisms.

During the last decade, we have witnessed outstanding advances in proteomics led mostly by great technological improvements in mass spectrometry field allowing high-throughput production of high-quality data used for massive protein identification and quantification. From a practical viewpoint, these advances have been mainly exploited in research projects involving model organisms with abundant genomic and proteomic information available in public databases. However, there is a growing number of organisms of high interest in different disciplines, such as ecological, biotechnological, and evolutionary research, yet poorly represented in these databases. Important advances in massive parallel sequencing technology and easy accessibility ofthis technology to many research laboratories have madenowadayspossibleto produce customized genomic and proteomic databases ofany organism. Along this line, the use of proteogenomic approaches by combining in the same analysis the data obtained from different omic levels has emerged as a very useful and powerful strategy to run shotgun proteomic experiments specially focused on non-model organisms. In this chapter, we provide detailed procedures to undertake shotgun quantitative proteomic experiments following either a label-free or an isobaric labeling approach in non-model organisms, emphasizing also a few key aspects related to experimental design and data analysis.

CoolTip: Low-Temperature Solid-Phase Extraction Microcolumn for Capturing Hydrophilic Peptides and Phosphopeptides

Reversed-phase solid-phase extraction (SPE) techniques are commonly used for desalting samples before LC/MS/MS in shotgun proteomics. However, hydrophilic peptides are often lost during the desalting step under the standard SPE conditions. Here, we describe a simple protocol in which a stop-and-go extraction tip packed with a poly(styrene-divinylbenzene) copolymer disc is used at 4 °C during sample loading without any organic solvent. Using this method, which we designate as the CoolTip protocol, we identified 2.9-fold more tryptic peptides and 6.1-fold more tryptic phosphopeptides from HeLa lysates than the standard SPE protocol for hydrophilic peptides, with a mobile phase of less than 8% acetonitrile in LC/MS/MS. There was no decrease in the recovery of hydrophobic peptides. CoolTip also provided better quantitative reproducibility in LC/MS/MS analysis. We anticipate that this protocol will provide improved performance in many kinds of shotgun proteomics experiments.

Discovery of biomarker candidates associated with the risk of short-term and mid/long-term relapse after infliximab withdrawal in Crohn’s patients: a proteomics-based study

ObjectiveA subset of Crohn’s disease (CD) patients experiences mid/long-term remission after infliximab withdrawal. Biomarkers are needed to identify those patients.DesignNew biomarkers of relapse were searched in the baseline serum of...

ProtyQuant: Comparing label-free shotgun proteomics datasets using accumulated peptide probabilities

Comparing multiple label-free shotgun proteomics datasets requires various data processing and formatting steps, including peptide-spectrum matching, protein inference, and quantification. Finally, the compilation of results files into a format that allows for downstream analyses. ProtyQuant performs protein inference and quantification calculations, and combines the results of individual datasets into plain text tables. These are lightweight, human-readable, and easy to import into databases or statistical software. ProtyQuant reads validated pepXML from proteomic workflows such as the Trans-Proteomic Pipeline (TPP), which makes it compatible with many commercial and free search engines. For protein inference and quantification, a modified version of the PIPQ program (He et al. 2016) was integrated. In contrast to simple spectral-counting, PIPQ sums up peptide probabilities. For assigning peptides to proteins, three algorithms are available: Multiple Counting, Equal Division, and Linear Programming. The accumulated peptide probabilities (app) are used for both tasks, protein probability estimation, and quantification. ProtyQuant was tested using a reference dataset for label-free shotgun proteomics, obtained from different concentrations of 48 human UPS proteins spiked into yeast lysate. Compared to ProteinProphet, ProtyQuant detected up to 126 (15%) more proteins in the mixture, applying an equal false positive rate (FPR). Using the app values for label-free quantification showed suitable sensitivity and linearity. Strikingly, the app values represent a realistic measure of ‘Protein Presence,’ an integral concept of protein probability and quantity. ProtyQuant provides a graphical user interface (GUI) and scripts for console-based processing. It is available (GNU GLP v3) for Windows, Linux, and Docker from https://bitbucket.org/lababi/protyquant/. SignificanceIntegrating data from multiple shot-gun proteomics experiments overwhelms non-expert researchers. ProtyQuant complements well-established workflows by aiding the comparison of proteins across samples. Importantly, the probability and abundance of proteins are seen from a holistic point of view. The accumulated peptide probability (app) as an integral measure of ‘Protein Presence’ demonstrated reliable performance for both protein identification and quantification. Using the app as a single measure facilitates the compilation of reports in comparative proteomics.

The Power of Three in Cannabis Shotgun Proteomics: Proteases, Databases and Search Engines.

Cannabis research has taken off since the relaxation of legislation, yet proteomics is still lagging. In 2019, we published three proteomics methods aimed at optimizing protein extraction, protein digestion for bottom-up and middle-down proteomics, as well as the analysis of intact proteins for top-down proteomics. The database of Cannabis sativa proteins used in these studies was retrieved from UniProt, the reference repositories for proteins, which is incomplete and therefore underrepresents the genetic diversity of this non-model species. In this fourth study, we remedy this shortcoming by searching larger databases from various sources. We also compare two search engines, the oldest, SEQUEST, and the most popular, Mascot. This shotgun proteomics experiment also utilizes the power of parallel digestions with orthogonal proteases of increasing selectivity, namely chymotrypsin, trypsin/Lys-C and Asp-N. Our results show that the larger the database the greater the list of accessions identified but the longer the duration of the search. Using orthogonal proteases and different search algorithms increases the total number of proteins identified, most of them common despite differing proteases and algorithms, but many of them unique as well.

Shot-gun proteomics: why thousands of unidentified signals matter.

Mass spectrometry-based proteomics has become a constitutional part of the multi-omics toolbox in yeast research, advancing fundamental knowledge of molecular processes and guiding decisions in strain and product developmental pipelines. Nevertheless, post-translational protein modifications (PTMs) continue to challenge the field of proteomics. PTMs are not directly encoded in the genome; therefore, they require a sensitive analysis of the proteome itself. In yeast, the relevance of post-translational regulators has already been established, such as for phosphorylation, which can directly affect the reaction rates of metabolic enzymes. Whereas, the selective analysis of single modifications has become a broadly employed technique, the sensitive analysis of a comprehensive set of modifications still remains a challenge. At the same time, a large number of fragmentation spectra in a typical shot-gun proteomics experiment remain unidentified. It has been estimated that a good proportion of those unidentified spectra originates from unexpected modifications or natural peptide variants. In this review, recent advancements in microbial proteomics for unrestricted protein modification discovery are reviewed, and recent research integrating this additional layer of information to elucidate protein interaction and regulation in yeast is briefly discussed.

Efficient Tandem LysC/Trypsin Digestion in Detergent Conditions.

All shotgun proteomics experiments rely on efficient proteolysis steps for sensitive peptide/protein identification and quantification. Previous reports suggest that the sequential tandem LysC/trypsin digest yields higher recovery of fully tryptic peptides than single-tryptic proteolysis. Based on the previous studies, it is assumed that the advantageous effect of tandem proteolysis requires a high sample denaturation state for the initial LysC digest. Therefore, to date, all systematic assessments of LysC/trypsin proteolysis are done in chaotropic environments such as urea. Here, sole trypsin is compared with LysC/trypsin and it is shown that tandem digestion can be carried with high efficiency in Mass Spectrometry-compatible detergents, thereby resulting in higher quantitative yields of fully cleaved peptides. It is further demonstrated that higher cleavage efficiency of tandem digests has a positive impact on absolute protein quantification using intensity-based absolute quantification (iBAQ) values. The results of the examination of divergent urea tandem conditions imply that beneficial effects of the initial LysC digest do not depend on the sample denaturation state, but, are mainly caused by different target specificities of LysC and trypsin. The observed detergent compatibility enables tandem digestion schemes to be implemented in efficient cellular solubilization proteomics procedures without the need for buffer exchange to chaotropic environments.

Data Imputation in Merged Isobaric Labeling-Based Relative Quantification Datasets.

The data-dependent acquisition in mass spectrometry-based proteomics combined with quantitative analysis using isobaric labeling (iTRAQ and TMT) inevitably introduces missing values in proteomic experiments where a number of LC-runs are combined, especially in the growing field of shotgun clinical proteomics, where the protein profiles from the proteomics analysis of several hundred patient samples are compared and correlated to clinical traits such as a specific disease or disease treatment in order to link specific outcomes to one or more proteins. In the context of clinical research it is evident that missing values in such datasets reduce the power of the downstream statistical analysis therefore may hampers the linking of the expression of disease traits to the expression of specific proteins that may be useful for prognostic, diagnostic, or predictive purposes. In our study, we tested three data imputation approaches initially developed for microarray data for the imputation of missing values in datasets that are generated by several runs of shotgun proteomic experiments and where the data were relative protein abundances based on isobaric tags (iTRAQ and TMT). Our conclusion is that imputation methods based on k Nearest Neighbors successfully impute missing values in datasets with up to 50% missing values.

Maximizing Tandem Mass Spectrometry Acquisition Rates for Shotgun Proteomics.

Advances in tandem mass spectrometry (MS/MS) acquisition rate have steadily led to increased performance in shotgun proteomics experiments. To that end, contemporary mass spectrometers are outfitted with multiple analyzers allowing for the simultaneous collection of survey (MS1) and MS/MS spectra. In the latest generation Orbitrap hybrid, MS/MS scans can be acquired at a high rate using the dual cell linear ion trap analyzer, all while the next precursor is being dissociated in a collision cell and a MS1 scan is occurring in the Orbitrap. Often overlooked in these experiments is that the ion trap scan duration is highly variable and dependent upon precursor mass. Here, we examine the use of various static mass-to-charge ratio scan ranges for ion trap MS/MS acquisition and determine performance relative to conventional dynamic mass-to-charge ratio range scanning. We demonstrate that a fixed mass-to-charge ratio scan range can generate 12% more MS/MS scans and more unique peptide identifications as compared to the standard dynamic approach, respectively.