DIANA--algorithmic improvements for analysis of data-independent acquisition MS data

Proteome-wide copy-number estimation from transcriptomics

Protein copy numbers constrain systems-level properties of regulatory networks, but proportional proteomic data remain scarce compared to RNA-seq. We related mRNA to protein statistically using best-available data from quantitative proteomics and transcriptomics for 4366 genes in 369 cell lines. The approach starts with a protein's median copy number and hierarchically appends mRNA-protein and mRNA-mRNA dependencies to define an optimal gene-specific model linking mRNAs to protein. For dozens of cell lines and primary samples, these protein inferences from mRNA outmatch stringent null models, a count-based protein-abundance repository, empirical mRNA-to-protein ratios, and a proteogenomic DREAM challenge winner. The optimal mRNA-to-protein relationships capture biological processes along with hundreds of known protein-protein complexes, suggesting mechanistic relationships. We use the method to identify a viral-receptor abundance threshold for coxsackievirus B3 susceptibility from 1489 systems-biology infection models parameterized by protein inference. When applied to 796 RNA-seq profiles of breast cancer, inferred copy-number estimates collectively re-classify 26-29% of luminal tumors. By adopting a gene-centered perspective of mRNA-protein covariation across different biological contexts, we achieve accuracies comparable to the technical reproducibility of contemporary proteomics.

Waves of regulated protein expression and phosphorylation rewire the proteome to drive gametogenesis in budding yeast

Sexually reproducing eukaryotes employ a developmentally regulated cell division program-meiosis-to generate haploid gametes from diploid germ cells. To understand how gametes arise, we generated a proteomic census encompassing the entire meiotic program of budding yeast. We found that concerted waves of protein expression and phosphorylation modify nearly all cellular pathways to support meiotic entry, meiotic progression, and gamete morphogenesis. Leveraging this comprehensive resource, we pinpointed dynamic changes in mitochondrial components and showed that phosphorylation of the FoF1-ATP synthase complex is required for efficient gametogenesis. Furthermore, using cryoET as an orthogonal approach to visualize mitochondria, we uncovered highly ordered filament arrays of Ald4ALDH2, a conserved aldehyde dehydrogenase that is highly expressed and phosphorylated during meiosis. Notably, phosphorylation-resistant mutants failed to accumulate filaments, suggesting that phosphorylation regulates context-specific Ald4ALDH2 polymerization. Overall, this proteomic census constitutes a broad resource to guide the exploration of the unique sequence of events underpinning gametogenesis.

Network-based elucidation of colon cancer drug resistance mechanisms by phosphoproteomic time-series analysis

Aberrant signaling pathway activity is a hallmark of tumorigenesis and progression, which has guided targeted inhibitor design for over 30 years. Yet, adaptive resistance mechanisms, induced by rapid, context-specific signaling network rewiring, continue to challenge therapeutic efficacy. Leveraging progress in proteomic technologies and network-based methodologies, we introduce Virtual Enrichment-based Signaling Protein-activity Analysis (VESPA)-an algorithm designed to elucidate mechanisms of cell response and adaptation to drug perturbations-and use it to analyze 7-point phosphoproteomic time series from colorectal cancer cells treated with clinically-relevant inhibitors and control media. Interrogating tumor-specific enzyme/substrate interactions accurately infers kinase and phosphatase activity, based on their substrate phosphorylation state, effectively accounting for signal crosstalk and sparse phosphoproteome coverage. The analysis elucidates time-dependent signaling pathway response to each drug perturbation and, more importantly, cell adaptive response and rewiring, experimentally confirmed by CRISPR knock-out assays, suggesting broad applicability to cancer and other diseases.

MassDash: A Web-Based Dashboard for Data-Independent Acquisition Mass Spectrometry Visualization

With the increased usage and diversity of methods and instruments being applied to analyze Data-Independent Acquisition (DIA) data, visualization is becoming increasingly important to validate automated software results. Here we present MassDash, a cross-platform DIA mass spectrometry visualization and validation software for comparing features and results across popular tools. MassDash provides a web-based interface and Python package for interactive feature visualizations and summary report plots across multiple automated DIA feature detection tools, including OpenSwath, DIA-NN, and dreamDIA. Furthermore, MassDash processes peptides on the fly, enabling interactive visualization of peptides across dozens of runs simultaneously on a personal computer. MassDash supports various multidimensional visualizations across retention time, ion mobility, m/z, and intensity, providing additional insights into the data. The modular framework is easily extendable, enabling rapid algorithm development of novel peak-picker techniques, such as deep-learning-based approaches and refinement of existing tools. MassDash is open-source under a BSD 3-Clause license and freely available at https://github.com/Roestlab/massdash, and a demo version can be accessed at https://massdash.streamlit.app.

AlphaPept: a modern and open framework for MS-based proteomics

In common with other omics technologies, mass spectrometry (MS)-based proteomics produces ever-increasing amounts of raw data, making efficient analysis a principal challenge. A plethora of different computational tools can process the MS data to derive peptide and protein identification and quantification. However, during the last years there has been dramatic progress in computer science, including collaboration tools that have transformed research and industry. To leverage these advances, we develop AlphaPept, a Python-based open-source framework for efficient processing of large high-resolution MS data sets. Numba for just-in-time compilation on CPU and GPU achieves hundred-fold speed improvements. AlphaPept uses the Python scientific stack of highly optimized packages, reducing the code base to domain-specific tasks while accessing the latest advances. We provide an easy on-ramp for community contributions through the concept of literate programming, implemented in Jupyter Notebooks. Large datasets can rapidly be processed as shown by the analysis of hundreds of proteomes in minutes per file, many-fold faster than acquisition. AlphaPept can be used to build automated processing pipelines with web-serving functionality and compatibility with downstream analysis tools. It provides easy access via one-click installation, a modular Python library for advanced users, and via an open GitHub repository for developers.

Acquisition and Analysis of DIA-Based Proteomic Data: A Comprehensive Survey in 2023

Data-independent acquisition (DIA) mass spectrometry (MS) has emerged as a powerful technology for high-throughput, accurate, and reproducible quantitative proteomics. This review provides a comprehensive overview of recent advances in both the experimental and computational methods for DIA proteomics, from data acquisition schemes to analysis strategies and software tools. DIA acquisition schemes are categorized based on the design of precursor isolation windows, highlighting wide-window, overlapping-window, narrow-window, scanning quadrupole-based, and parallel accumulation-serial fragmentation-enhanced DIA methods. For DIA data analysis, major strategies are classified into spectrum reconstruction, sequence-based search, library-based search, de novo sequencing, and sequencing-independent approaches. A wide array of software tools implementing these strategies are reviewed, with details on their overall workflows and scoring approaches at different steps. The generation and optimization of spectral libraries, which are critical resources for DIA analysis, are also discussed. Publicly available benchmark datasets covering global proteomics and phosphoproteomics are summarized to facilitate performance evaluation of various software tools and analysis workflows. Continued advances and synergistic developments of versatile components in DIA workflows are expected to further enhance the power of DIA-based proteomics.

Data-Driven Tool for Cross-Run Ion Selection and Peak-Picking in Quantitative Proteomics with Data-Independent Acquisition LC-MS/MS

Proteomics provides molecular bases of biology and disease, and liquid chromatography-tandem mass spectrometry (LC-MS/MS) is a platform widely used for bottom-up proteomics. Data-independent acquisition (DIA) improves the run-to-run reproducibility of LC-MS/MS in proteomics research. However, the existing DIA data processing tools sometimes produce large deviations from true values for the peptides and proteins in quantification. Peak-picking error and incorrect ion selection are the two main causes of the deviations. We present a cross-run ion selection and peak-picking (CRISP) tool that utilizes the important advantage of run-to-run consistency of DIA and simultaneously examines the DIA data from the whole set of runs to filter out the interfering signals, instead of only looking at a single run at a time. Eight datasets acquired by mass spectrometers from different vendors with different types of mass analyzers were used to benchmark our CRISP-DIA against other currently available DIA tools. In the benchmark datasets, for analytes with large content variation among samples, CRISP-DIA generally resulted in 20 to 50% relative decrease in error rates compared to other DIA tools, at both the peptide precursor level and the protein level. CRISP-DIA detected differentially expressed proteins more efficiently, with 3.3 to 90.3% increases in the numbers of true positives and 12.3 to 35.3% decreases in the false positive rates, in some cases. In the real biological datasets, CRISP-DIA showed better consistencies of the quantification results. The advantages of assimilating DIA data in multiple runs for quantitative proteomics were demonstrated, which can significantly improve the quantification accuracy.

State-of-the-art computational methods to predict protein-protein interactions with high accuracy and coverage

Prediction of protein-protein interactions (PPIs) commonly involves a significant computational component. Rapid recent advances in the power of computational methods for protein interaction prediction motivate a review of the state-of-the-art. We review the major approaches, organized according to the primary source of data utilized: protein sequence, protein structure, and protein co-abundance. The advent of deep learning (DL) has brought with it significant advances in interaction prediction, and we show how DL is used for each source data type. We review the literature taxonomically, present example case studies in each category, and conclude with observations about the strengths and weaknesses of machine learning methods in the context of the principal sources of data for protein interaction prediction.

A Comparative Analysis of Data Analysis Tools for Data-Independent Acquisition Mass Spectrometry

Data-independent acquisition (DIA) mass spectrometry-based proteomics generates reproducible proteome data. The complex processing of the DIA data has led to the development of multiple data analysis tools. In this study, we assessed the performance of five tools (OpenSWATH, EncyclopeDIA, Skyline, DIA-NN, and Spectronaut) using six DIA datasets obtained from TripleTOF, Orbitrap, and TimsTOF Pro instruments. By comparing identification and quantification metrics and examining shared and unique cross-tool identifications, we evaluated both library-based and library-free approaches. Our findings indicate that library-free approaches outperformed library-based methods when the spectral library had limited comprehensiveness. However, our results also suggest that constructing a comprehensive library still offers benefits for most DIA analyses. This study provides comprehensive guidance for DIA data analysis tools, benefiting both experienced and novice users of DIA-mass spectrometry technology.

Proteome-wide copy-number estimation from transcriptomics

Protein copy numbers constrain systems-level properties of regulatory networks, but absolute proteomic data remain scarce compared to transcriptomics obtained by RNA sequencing. We addressed this persistent gap by relating mRNA to protein statistically using best-available data from quantitative proteomics-transcriptomics for 4366 genes in 369 cell lines. The approach starts with a central estimate of protein copy number and hierarchically appends mRNA-protein and mRNA-mRNA dependencies to define an optimal gene-specific model that links mRNAs to protein. For dozens of independent cell lines and primary prostate samples, these protein inferences from mRNA outmatch stringent null models, a count-based protein-abundance repository, and empirical protein-to-mRNA ratios. The optimal mRNA-to-protein relationships capture biological processes along with hundreds of known protein-protein interaction complexes, suggesting mechanistic relationships are embedded. We use the method to estimate viral-receptor abundances of CD55-CXADR from human heart transcriptomes and build 1489 systems-biology models of coxsackievirus B3 infection susceptibility. When applied to 796 RNA sequencing profiles of breast cancer from The Cancer Genome Atlas, inferred copy-number estimates collectively reclassify 26% of Luminal A and 29% of Luminal B tumors. Protein-based reassignments strongly involve a pharmacologic target for luminal breast cancer (CDK4) and an α-catenin that is often undetectable at the mRNA level (CTTNA2). Thus, by adopting a gene-centered perspective of mRNA-protein covariation across different biological contexts, we achieve accuracies comparable to the technical reproducibility limits of contemporary proteomics. The collection of gene-specific models is assembled as a web tool for users seeking mRNA-guided predictions of absolute protein abundance (http://janeslab.shinyapps.io/Pinferna).

A Galaxy of informatics resources for MS-based proteomics

INTRODUCTION

Continuous advances in mass spectrometry (MS) technologies have enabled deeper and more reproducible proteome characterization and a better understanding of biological systems when integrated with other 'omics data. Bioinformatic resources meeting the analysis requirements of increasingly complex MS-based proteomic data and associated multi-omic data are critically needed. These requirements included availability of software that would span diverse types of analyses, scalability for large-scale, compute-intensive applications, and mechanisms to ease adoption of the software.

AREAS COVERED

The Galaxy ecosystem meets these requirements by offering a multitude of open-source tools for MS-based proteomics analyses and applications, all in an adaptable, scalable, and accessible computing environment. A thriving global community maintains these software and associated training resources to empower researcher-driven analyses.

EXPERT OPINION

The community-supported Galaxy ecosystem remains a crucial contributor to basic biological and clinical studies using MS-based proteomics. In addition to the current status of Galaxy-based resources, we describe ongoing developments for meeting emerging challenges in MS-based proteomic informatics. We hope this review will catalyze increased use of Galaxy by researchers employing MS-based proteomics and inspire software developers to join the community and implement new tools, workflows, and associated training content that will add further value to this already rich ecosystem.

Biomarker Reproducibility Challenge: A Review of Non-Nucleotide Biomarker Discovery Protocols from Body Fluids in Breast Cancer Diagnosis

Breast cancer has now become the most commonly diagnosed cancer, accounting for one in eight cancer diagnoses worldwide. Non-invasive diagnostic biomarkers and associated tests are superlative candidates to complement or improve current approaches for screening, early diagnosis, or prognosis of breast cancer. Biomarkers detected from body fluids such as blood (serum/plasma), urine, saliva, nipple aspiration fluid, and tears can detect breast cancer at its early stages in a minimally invasive way. The advancements in high-throughput molecular profiling (omics) technologies have opened an unprecedented opportunity for unbiased biomarker detection. However, the irreproducibility of biomarkers and discrepancies of reported markers have remained a major roadblock to clinical implementation, demanding the investigation of contributing factors and the development of standardised biomarker discovery pipelines. A typical biomarker discovery workflow includes pre-analytical, analytical, and post-analytical phases, from sample collection to model development. Variations introduced during these steps impact the data quality and the reproducibility of the findings. Here, we present a comprehensive review of methodological variations in biomarker discovery studies in breast cancer, with a focus on non-nucleotide biomarkers (i.e., proteins, lipids, and metabolites), highlighting the pre-analytical to post-analytical variables, which may affect the accurate identification of biomarkers from body fluids.

Network-centric analysis of co-fractionated protein complex profiles using SECAT

The Size-Exclusion Chromatography Analysis Toolkit (SECAT) elucidates protein complex dynamics using co-fractionated bottom-up mass spectrometry (CF-MS) data. Here, we present a protocol for the network-centric analysis and interpretation of CF-MS profiles using SECAT. We describe the technical steps for preprocessing, scoring, semi-supervised machine learning, and quantification, including common pitfalls and their solutions. We further provide guidance for data export, visualization, and the interpretation of SECAT results to discover dysregulated proteins and interactions, supporting new hypotheses and biological insights. For complete details on the use and execution of this protocol, please refer to Rosenberger et al. (2020).1.

Network-based elucidation of colon cancer drug resistance by phosphoproteomic time-series analysis

Aberrant signaling pathway activity is a hallmark of tumorigenesis and progression, which has guided targeted inhibitor design for over 30 years. Yet, adaptive resistance mechanisms, induced by rapid, context-specific signaling network rewiring, continue to challenge therapeutic efficacy. By leveraging progress in proteomic technologies and network-based methodologies, over the past decade, we developed VESPA-an algorithm designed to elucidate mechanisms of cell response and adaptation to drug perturbations-and used it to analyze 7-point phosphoproteomic time series from colorectal cancer cells treated with clinically-relevant inhibitors and control media. Interrogation of tumor-specific enzyme/substrate interactions accurately inferred kinase and phosphatase activity, based on their inferred substrate phosphorylation state, effectively accounting for signal cross-talk and sparse phosphoproteome coverage. The analysis elucidated time-dependent signaling pathway response to each drug perturbation and, more importantly, cell adaptive response and rewiring that was experimentally confirmed by CRISPRko assays, suggesting broad applicability to cancer and other diseases.

A Novel Blood Proteomic Signature for Prostate Cancer

Prostate cancer is the most common malignant tumour in men. Improved testing for diagnosis, risk prediction, and response to treatment would improve care. Here, we identified a proteomic signature of prostate cancer in peripheral blood using data-independent acquisition mass spectrometry combined with machine learning. A highly predictive signature was derived, which was associated with relevant pathways, including the coagulation, complement, and clotting cascades, as well as plasma lipoprotein particle remodeling. We further validated the identified biomarkers against a second cohort, identifying a panel of five key markers (GP5, SERPINA5, ECM1, IGHG1, and THBS1) which retained most of the diagnostic power of the overall dataset, achieving an AUC of 0.91. Taken together, this study provides a proteomic signature complementary to PSA for the diagnosis of patients with localised prostate cancer, with the further potential for assessing risk of future development of prostate cancer. Data are available via ProteomeXchange with identifier PXD025484.

Meiotic nuclear pore complex remodeling provides key insights into nuclear basket organization

Nuclear pore complexes (NPCs) are large proteinaceous assemblies that mediate nuclear compartmentalization. NPCs undergo large-scale structural rearrangements during mitosis in metazoans and some fungi. However, our understanding of NPC remodeling beyond mitosis remains limited. Using time-lapse fluorescence microscopy, we discovered that NPCs undergo two mechanistically separable remodeling events during budding yeast meiosis in which parts or all of the nuclear basket transiently dissociate from the NPC core during meiosis I and II, respectively. Meiosis I detachment, observed for Nup60 and Nup2, is driven by Polo kinase-mediated phosphorylation of Nup60 at its interface with the Y-complex. Subsequent reattachment of Nup60-Nup2 to the NPC core is facilitated by a lipid-binding amphipathic helix in Nup60. Preventing Nup60-Nup2 reattachment causes misorganization of the entire nuclear basket in gametes. Strikingly, meiotic nuclear basket remodeling also occurs in the distantly related fission yeast, Schizosaccharomyces pombe. Our study reveals a conserved and developmentally programmed aspect of NPC plasticity, providing key mechanistic insights into the nuclear basket organization.

Benchmarking of analysis strategies for data-independent acquisition proteomics using a large-scale dataset comprising inter-patient heterogeneity

Numerous software tools exist for data-independent acquisition (DIA) analysis of clinical samples, necessitating their comprehensive benchmarking. We present a benchmark dataset comprising real-world inter-patient heterogeneity, which we use for in-depth benchmarking of DIA data analysis workflows for clinical settings. Combining spectral libraries, DIA software, sparsity reduction, normalization, and statistical tests results in 1428 distinct data analysis workflows, which we evaluate based on their ability to correctly identify differentially abundant proteins. From our dataset, we derive bootstrap datasets of varying sample sizes and use the whole range of bootstrap datasets to robustly evaluate each workflow. We find that all DIA software suites benefit from using a gas-phase fractionated spectral library, irrespective of the library refinement used. Gas-phase fractionation-based libraries perform best against two out of three reference protein lists. Among all investigated statistical tests non-parametric permutation-based statistical tests consistently perform best.

Data independent acquisition (DIA) has been gaining momentum in clinical proteomics. Here, the authors create a benchmark dataset comprising inter-patient heterogeneity to compare popular DIA data analysis workflows for identifying differentially abundant proteins.

The impact of genomic variation on protein phosphorylation states and regulatory networks

Genomic variation impacts on cellular networks by affecting the abundance (e.g., protein levels) and the functional states (e.g., protein phosphorylation) of their components. Previous work has focused on the former, while in this context, the functional states of proteins have largely remained neglected. Here, we generated high-quality transcriptome, proteome, and phosphoproteome data for a panel of 112 genomically well-defined yeast strains. Genetic effects on transcripts were generally transmitted to the protein layer, but specific gene groups, such as ribosomal proteins, showed diverging effects on protein levels compared with RNA levels. Phosphorylation states proved crucial to unravel genetic effects on signaling networks. Correspondingly, genetic variants that cause phosphorylation changes were mostly different from those causing abundance changes in the respective proteins. Underscoring their relevance for cell physiology, phosphorylation traits were more strongly correlated with cell physiological traits such as chemical compound resistance or cell morphology, compared with transcript or protein abundance. This study demonstrates how molecular networks mediate the effects of genomic variants to cellular traits and highlights the particular importance of protein phosphorylation.

A comprehensive LFQ benchmark dataset on modern day acquisition strategies in proteomics

In the last decade, a revolution in liquid chromatography-mass spectrometry (LC-MS) based proteomics was unfolded with the introduction of dozens of novel instruments that incorporate additional data dimensions through innovative acquisition methodologies, in turn inspiring specialized data analysis pipelines. Simultaneously, a growing number of proteomics datasets have been made publicly available through data repositories such as ProteomeXchange, Zenodo and Skyline Panorama. However, developing algorithms to mine this data and assessing the performance on different platforms is currently hampered by the lack of a single benchmark experimental design. Therefore, we acquired a hybrid proteome mixture on different instrument platforms and in all currently available families of data acquisition. Here, we present a comprehensive Data-Dependent and Data-Independent Acquisition (DDA/DIA) dataset acquired using several of the most commonly used current day instrumental platforms. The dataset consists of over 700 LC-MS runs, including adequate replicates allowing robust statistics and covering over nearly 10 different data formats, including scanning quadrupole and ion mobility enabled acquisitions. Datasets are available via ProteomeXchange (PXD028735).

DIAMetAlyzer allows automated false-discovery rate-controlled analysis for data-independent acquisition in metabolomics

The extraction of meaningful biological knowledge from high-throughput mass spectrometry data relies on limiting false discoveries to a manageable amount. For targeted approaches in metabolomics a main challenge is the detection of false positive metabolic features in the low signal-to-noise ranges of data-independent acquisition results and their filtering. Another factor is that the creation of assay libraries for data-independent acquisition analysis and the processing of extracted ion chromatograms have not been automated in metabolomics. Here we present a fully automated open-source workflow for high-throughput metabolomics that combines data-dependent and data-independent acquisition for library generation, analysis, and statistical validation, with rigorous control of the false-discovery rate while matching manual analysis regarding quantification accuracy. Using an experimentally specific data-dependent acquisition library based on reference substances allows for accurate identification of compounds and markers from data-independent acquisition data in low concentrations, facilitating biomarker quantification.

Democratizing data-independent acquisition proteomics analysis on public cloud infrastructures via the Galaxy framework

BACKGROUND

Data-independent acquisition (DIA) has become an important approach in global, mass spectrometric proteomic studies because it provides in-depth insights into the molecular variety of biological systems. However, DIA data analysis remains challenging owing to the high complexity and large data and sample size, which require specialized software and vast computing infrastructures. Most available open-source DIA software necessitates basic programming skills and covers only a fraction of a complete DIA data analysis. In consequence, DIA data analysis often requires usage of multiple software tools and compatibility thereof, severely limiting the usability and reproducibility.

FINDINGS

To overcome this hurdle, we have integrated a suite of open-source DIA tools in the Galaxy framework for reproducible and version-controlled data processing. The DIA suite includes OpenSwath, PyProphet, diapysef, and swath2stats. We have compiled functional Galaxy pipelines for DIA processing, which provide a web-based graphical user interface to these pre-installed and pre-configured tools for their use on freely accessible, powerful computational resources of the Galaxy framework. This approach also enables seamless sharing workflows with full configuration in addition to sharing raw data and results. We demonstrate the usability of an all-in-one DIA pipeline in Galaxy by the analysis of a spike-in case study dataset. Additionally, extensive training material is provided to further increase access for the proteomics community.

CONCLUSION

The integration of an open-source DIA analysis suite in the web-based and user-friendly Galaxy framework in combination with extensive training material empowers a broad community of researches to perform reproducible and transparent DIA data analysis.

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.

A Prostate Cancer Proteomics Database for SWATH-MS Based Protein Quantification

Simple Summary

Prostate cancer is the third most frequent cancer in men worldwide, with a notable increase in prevalence over the past two decades. The PSA is the only well-established protein biomarker for prostate cancer diagnosis, staging, and surveillance. It frequently leads to inaccurate diagnosis and overtreatment since it is an organ-specific biomarker rather than a tumour-specific biomarker. As a result, one of the primary goals of prostate cancer proteome research is to identify novel biomarkers that can be used with or instead of PSA, particularly in non-invasive blood samples. Thousands of peptides or assays were detected in blood samples from patients with low- to high-grade prostate cancer and healthy individuals, allowing data processing of sequential window acquisition of all theoretical mass spectra (SWATH-MS). By assisting in the detection of prostate cancer biomarkers in blood samples, this useful resource will improve our understanding of the role of proteomics in prostate cancer diagnosis and risk assessment.

Abstract

Prostate cancer is the most frequent form of cancer in men, accounting for more than one-third of all cases. Current screening techniques, such as PSA testing used in conjunction with routine procedures, lead to unnecessary biopsies and the discovery of low-risk tumours, resulting in overdiagnosis. SWATH-MS is a well-established data-independent (DI) method requiring prior knowledge of targeted peptides to obtain valuable information from SWATH maps. In response to the growing need to identify and characterise protein biomarkers for prostate cancer, this study explored a spectrum source for targeted proteome analysis of blood samples. We created a comprehensive prostate cancer serum spectral library by combining data-dependent acquisition (DDA) MS raw files from 504 patients with low, intermediate, or high-grade prostate cancer and healthy controls, as well as 304 prostate cancer-related protein in silico assays. The spectral library contains 114,684 transitions, which equates to 18,479 peptides translated into 1227 proteins. The robustness and accuracy of the spectral library were assessed to boost confidence in the identification and quantification of prostate cancer-related proteins across an independent cohort, resulting in the identification of 404 proteins. This unique database can facilitate researchers to investigate prostate cancer protein biomarkers in blood samples. In the real-world use of the spectrum library for biomarker detection, using a signature of 17 proteins, a clear distinction between the validation cohort’s pre- and post-treatment groups was observed. Data are available via ProteomeXchange with identifier PXD028651.

Extending the Proteomic Characterization of Candida albicans Exposed to Stress and Apoptotic Inducers through Data-Independent Acquisition Mass Spectrometry

Candida albicans is a commensal fungus that causes systemic infections in immunosuppressed patients. In order to deal with the changing environment during commensalism or infection, C. albicans must reprogram its proteome. Characterizing the stress-induced changes in the proteome that C. albicans uses to survive should be very useful in the development of new antifungal drugs. We studied the C. albicans global proteome after exposure to hydrogen peroxide (H₂O₂) and acetic acid (AA), using a data-independent acquisition mass spectrometry (DIA-MS) strategy. More than 2,000 C. albicans proteins were quantified using an ion library previously constructed using data-dependent acquisition mass spectrometry (DDA-MS). C. albicans responded to treatment with H₂O₂ with an increase in the abundance of many proteins involved in the oxidative stress response, protein folding, and proteasome-dependent catabolism, which led to increased proteasome activity. The data revealed a previously unknown key role for Prn1, a protein similar to pirins, in the oxidative stress response. Treatment with AA resulted in a general decrease in the abundance of proteins involved in amino acid biosynthesis, protein folding, and rRNA processing. Almost all proteasome proteins declined, as did proteasome activity. Apoptosis was observed after treatment with H₂O₂ but not AA. A targeted proteomic study of 32 proteins related to apoptosis in yeast supported the results obtained by DIA-MS and allowed the creation of an efficient method to quantify relevant proteins after treatment with stressors (H₂O₂, AA, and amphotericin B). This approach also uncovered a main role for Oye32, an oxidoreductase, suggesting this protein as a possible apoptotic marker common to many stressors.

IMPORTANCE Fungal infections are a worldwide health problem, especially in immunocompromised patients and patients with chronic disorders. Invasive candidiasis, caused mainly by C. albicans, is among the most common fungal diseases. Despite the existence of treatments to combat candidiasis, the spectrum of drugs available is limited. For the discovery of new drug targets, it is essential to know the pathogen response to different stress conditions. Our study provides a global vision of proteomic remodeling in C. albicans after exposure to different agents, such as hydrogen peroxide, acetic acid, and amphotericin B, that can cause apoptotic cell death. These results revealed the significance of many proteins related to oxidative stress response and proteasome activity, among others. Of note, the discovery of Prn1 as a key protein in the defense against oxidative stress as well the increase in the abundance of Oye32 protein when apoptotic process occurred point them out as possible drug targets.

Robust optimization of SWATH-MS workflow for human blood serum proteome analysis using a quality by design approach

Background

It is not enough to optimize proteomics assays. It is critical those assays are robust to operating conditions. Without robust assays, proteomic biomarkers are unlikely to translate readily into the clinic. This study outlines a structured approach to the identification of a robust operating window for proteomics assays and applies that method to Sequential Window Acquisition of all Theoretical Spectra Mass Spectroscopy (SWATH-MS).

Methods

We used a sequential quality by design approach exploiting a fractional screening design to first identify critical SWATH-MS parameters, then using response surface methods to identify a robust operating window with good reproducibility, before validating those settings in a separate validation study.

Results

The screening experiment identified two critical SWATH-MS parameters. We modelled the number of proteins and reproducibility as a function of those parameters identifying an operating window permitting robust maximization of the number of proteins quantified in human serum. In a separate validation study, these settings were shown to give good proteome-wide coverage and high quantification reproducibility.

Conclusions

Using design of experiments permits identification of a robust operating window for SWATH-MS. The method gives a good understanding of proteomics assays and greater data-driven confidence in SWATH-MS performance.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12014-021-09323-z.

Alendronate-induced Perturbation of the Bone Proteome and Microenvironmental Pathophysiology

Objectives: Bisphosphonates (BPs) are powerful inhibitors of osteoclastogenesis and are used to prevent osteoporotic bone loss and reduce the risk of osteoporotic fracture in patients suffering from postmenopausal osteoporosis. Patients with breast cancer or gynecological malignancies being treated with BPs or those receiving bone-targeted therapy for metastatic prostate cancer are at increased risk of bisphosphonate-related osteonecrosis of the jaw (BRONJ). Although BPs markedly ameliorate osteoporosis, their adverse effects largely limit the clinical application of these drugs. This study focused on providing a deeper understanding of one of the most popular BPs, the alendronate (ALN)-induced perturbation of the bone proteome and microenvironmental pathophysiology. Methods: To understand the molecular mechanisms underlying ALN-induced side-effects, an unbiased and global proteomics approach combined with big data bioinformatics was applied. This was followed by biochemical and functional analyses to determine the clinicopathological mechanisms affected by ALN. Results: The findings from this proteomics study suggest that the RIPK3/Wnt/GSK3/β-catenin signaling pathway is significantly perturbed upon ALN treatment, resulting in abnormal angiogenesis, inflammation, anabolism, remodeling, and mineralization in bone cells in an in vitro cell culture system. Conclusion: Our investigation into potential key signaling mechanisms in response to ALN provides a rational basis for suppressing BP-induced adverse effect and presents various therapeutic strategies.

A Simple Optimization Workflow to Enable Precise and Accurate Imputation of Missing Values in Proteomic Data Sets

Missing values in proteomic data sets have real consequences on downstream data analysis and reproducibility. Although several imputation methods exist to handle missing values, no single imputation method is best suited for a diverse range of data sets, and no clear strategy exists for evaluating imputation methods for clinical DIA-MS data sets, especially at different levels of protein quantification. To navigate through the different imputation strategies available in the literature, we have established a strategy to assess imputation methods on clinical label-free DIA-MS data sets. We used three DIA-MS data sets with real missing values to evaluate eight imputation methods with multiple parameters at different levels of protein quantification: a dilution series data set, a small pilot data set, and a clinical proteomic data set comparing paired tumor and stroma tissue. We found that imputation methods based on local structures within the data, like local least-squares (LLS) and random forest (RF), worked well in our dilution series data set, whereas imputation methods based on global structures within the data, like BPCA, performed well in the other two data sets. We also found that imputation at the most basic protein quantification level-fragment level-improved accuracy and the number of proteins quantified. With this analytical framework, we quickly and cost-effectively evaluated different imputation methods using two smaller complementary data sets to narrow down to the larger proteomic data set's most accurate methods. This acquisition strategy allowed us to provide reproducible evidence of the accuracy of the imputation method, even in the absence of a ground truth. Overall, this study indicates that the most suitable imputation method relies on the overall structure of the data set and provides an example of an analytic framework that may assist in identifying the most appropriate imputation strategies for the differential analysis of proteins.

Cerebrospinal fluid proteome maps detect pathogen-specific host response patterns in meningitis

Meningitis is a potentially life-threatening infection characterized by the inflammation of the leptomeningeal membranes. Many different viral and bacterial pathogens can cause meningitis, with differences in mortality rates, risk of developing neurological sequelae, and treatment options. Here, we constructed a compendium of digital cerebrospinal fluid (CSF) proteome maps to define pathogen-specific host response patterns in meningitis. The results revealed a drastic and pathogen-type specific influx of tissue-, cell-, and plasma proteins in the CSF, where, in particular, a large increase of neutrophil-derived proteins in the CSF correlated with acute bacterial meningitis. Additionally, both acute bacterial and viral meningitis result in marked reduction of brain-enriched proteins. Generation of a multiprotein LASSO regression model resulted in an 18-protein panel of cell- and tissue-associated proteins capable of classifying acute bacterial meningitis and viral meningitis. The same protein panel also enabled classification of tick-borne encephalitis, a subgroup of viral meningitis, with high sensitivity and specificity. The work provides insights into pathogen-specific host response patterns in CSF from different disease etiologies to support future classification of pathogen type based on host response patterns in meningitis.

The Role of Data-Independent Acquisition for Glycoproteomics

Data-independent acquisition (DIA) is now an emerging method in bottom–up proteomics and capable of achieving deep proteome coverage and accurate label-free quantification. However, for post-translational modifications, such as glycosylation, DIA methodology is still in the early stage of development. The full characterization of glycoproteins requires site-specific glycan identification as well as subsequent quantification of glycan structures at each site. The tremendous complexity of glycosylation represents a significant analytical challenge in glycoproteomics. This review focuses on the development and perspectives of DIA methodology for N- and O-linked glycoproteomics and posits that DIA-based glycoproteomics could be a method of choice to address some of the challenging aspects of glycoproteomics. First, the current challenges in glycoproteomics and the basic principles of DIA are briefly introduced. DIA-based glycoproteomics is then summarized and described into four aspects based on the actual samples. Finally, we discussed the important challenges and future perspectives in the field. We believe that DIA can significantly facilitate glycoproteomic studies and contribute to the development of future advanced tools and approaches in the field of glycoproteomics.

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Protein glycosylation and challenges in glycoproteomics.

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Data-independent acquisition for deglycosylated and intact N-linked glycopeptides.

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Unbiased screening of oxonium ions from all glycopeptide precursors.

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Glyco–data-independent acquisition on mucin-type O-glycopeptides.

SECAT: Quantifying Protein Complex Dynamics across Cell States by Network-Centric Analysis of SEC-SWATH-MS Profiles

Protein-protein interactions (PPIs) play critical functional and regulatory roles in cellular processes. They are essential for macromolecular complex formation, which in turn constitutes the basis for protein interaction networks that determine the functional state of a cell. We and others have previously shown that chromatographic fractionation of native protein complexes in combination with bottom-up mass spectrometric analysis of consecutive fractions supports the multiplexed characterization and detection of state-specific changes of protein complexes. In this study, we extend co-fractionation and mass spectrometric data analysis to perform quantitative, network-based studies of proteome organization, via the size-exclusion chromatography algorithmic toolkit (SECAT). This framework explicitly accounts for the dynamic nature and rewiring of protein complexes across multiple cell states and samples, thus, elucidating molecular mechanisms that are differentially implemented across different experimental settings. Systematic analysis of multiple datasets shows that SECAT represents a highly scalable and effective methodology to assess condition/state-specific protein-network state. A record of this paper's transparent peer review process is included in the Supplemental Information.

Application of SWATH mass spectrometry in the identification of circulating proteins does not predict future weight gain in early psychosis

Weight gain is a common consequence of treatment with antipsychotic drugs in early psychosis, leading to further morbidity and poor treatment adherence. Identifying tools that can predict weight change in early psychosis may contribute to better-individualised treatment and adherence. Recently we showed that proteomic profiling with sequential window acquisition of all theoretical fragment ion spectra (SWATH) mass spectrometry (MS) can identify individuals with pre-diabetes more likely to experience weight change in relation to lifestyle change. We investigated whether baseline proteomic profiles predicted weight change over time using data from the BeneMin clinical trial of the anti-inflammatory antibiotic, minocycline, versus placebo. Expression levels for 844 proteins were determined by SWATH proteomics in 83 people (60 men and 23 women). Hierarchical clustering analysis and principal component analysis of baseline proteomics data did not reveal distinct separation between the proteome profiles of participants in different weight change categories. However, individuals with the highest weight loss had higher Positive and Negative Syndrome Scale (PANSS) scores. Our findings imply that mode of treatment i.e. the pharmacological intervention for psychosis may be the determining factor in weight change after diagnosis, rather than predisposing proteomic dynamics.

Strategies to enable large-scale proteomics for reproducible research

Reproducible research is the bedrock of experimental science. To enable the deployment of large-scale proteomics, we assess the reproducibility of mass spectrometry (MS) over time and across instruments and develop computational methods for improving quantitative accuracy. We perform 1560 data independent acquisition (DIA)-MS runs of eight samples containing known proportions of ovarian and prostate cancer tissue and yeast, or control HEK293T cells. Replicates are run on six mass spectrometers operating continuously with varying maintenance schedules over four months, interspersed with ~5000 other runs. We utilise negative controls and replicates to remove unwanted variation and enhance biological signal, outperforming existing methods. We also design a method for reducing missing values. Integrating these computational modules into a pipeline (ProNorM), we mitigate variation among instruments over time and accurately predict tissue proportions. We demonstrate how to improve the quantitative analysis of large-scale DIA-MS data, providing a pathway toward clinical proteomics.

Clinical proteomics critically depends on the ability to acquire highly reproducible data over an extended period of time. Here, the authors assess reproducibility over four months across different mass spectrometers and develop a computational approach to mitigate variation among instruments over time.

Complex-centric proteome profiling by SEC-SWATH-MS for the parallel detection of hundreds of protein complexes

Most catalytic, structural and regulatory functions of the cell are carried out by functional modules, typically complexes containing or consisting of proteins. The composition and abundance of these complexes and the quantitative distribution of specific proteins across different modules are therefore of major significance in basic and translational biology. However, detection and quantification of protein complexes on a proteome-wide scale is technically challenging. We have recently extended the targeted proteomics rationale to the level of native protein complex analysis (complex-centric proteome profiling). The complex-centric workflow described herein consists of size exclusion chromatography (SEC) to fractionate native protein complexes, data-independent acquisition mass spectrometry to precisely quantify the proteins in each SEC fraction based on a set of proteotypic peptides and targeted, complex-centric analysis where prior information from generic protein interaction maps is used to detect and quantify protein complexes with high selectivity and statistical error control via the computational framework CCprofiler (https://github.com/CCprofiler/CCprofiler). Complex-centric proteome profiling captures most proteins in complex-assembled state and reveals their organization into hundreds of complexes and complex variants observable in a given cellular state. The protocol is applicable to cultured cells and can potentially also be adapted to primary tissue and does not require any genetic engineering of the respective sample sources. At present, it requires ~8 d of wet-laboratory work, 15 d of mass spectrometry measurement time and 7 d of computational analysis.

The use of missing values in proteomic data-independent acquisition mass spectrometry to enable disease activity discrimination

MOTIVATION

Data-independent acquisition mass spectrometry allows for comprehensive peptide detection and relative quantification than standard data-dependent approaches. While less prone to missing values, these still exist. Current approaches for handling the so-called missingness have challenges. We hypothesized that non-random missingness is a useful biological measure and demonstrate the importance of analysing missingness for proteomic discovery within a longitudinal study of disease activity.

RESULTS

The magnitude of missingness did not correlate with mean peptide concentration. The magnitude of missingness for each protein strongly correlated between collection time points (baseline, 3 months, 6 months; R = 0.95-0.97, confidence interval = 0.94-0.97) indicating little time-dependent effect. This allowed for the identification of proteins with outlier levels of missingness that differentiate between the patient groups characterized by different patterns of disease activity. The association of these proteins with disease activity was confirmed by machine learning techniques. Our novel approach complements analyses on complete observations and other missing value strategies in biomarker prediction of disease activity.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A Global Screen for Assembly State Changes of the Mitotic Proteome by SEC-SWATH-MS

Living systems integrate biochemical reactions that determine the functional state of each cell. Reactions are primarily mediated by proteins. In proteomic studies, these have been treated as independent entities, disregarding their higher-level organization into complexes that affects their activity and/or function and is thus of great interest for biological research. Here, we describe the implementation of an integrated technique to quantify cell-state-specific changes in the physical arrangement of protein complexes concurrently for thousands of proteins and hundreds of complexes. Applying this technique to a comparison of human cells in interphase and mitosis, we provide a systematic overview of mitotic proteome reorganization. The results recall key hallmarks of mitotic complex remodeling and suggest a model of nuclear pore complex disassembly, which we validate by orthogonal methods. To support the interpretation of quantitative SEC-SWATH-MS datasets, we extend the software CCprofiler and provide an interactive exploration tool, SECexplorer-cc.

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Global quantification of assembly state changes in the mitotic proteome

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Improved performance over thermostability measurement of proteome states

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Discovery of a mitotic disassembly intermediate of the nuclear pore complex

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Introduction of SECexplorer-cc, a publicly available online platform

Removing the Hidden Data Dependency of DIA with Predicted Spectral Libraries

Data-independent acquisition (DIA) generates comprehensive yet complex mass spectrometric data, which imposes the use of data-dependent acquisition (DDA) libraries for deep peptide-centric detection. Here, it is shown that DIA can be redeemed from this dependency by combining predicted fragment intensities and retention times with narrow window DIA. This eliminates variation in library building and omits stochastic sampling, finally making the DIA workflow fully deterministic. Especially for clinical proteomics, this has the potential to facilitate inter-laboratory comparison.

The GTPase Nog1 co-ordinates the assembly, maturation and quality control of distant ribosomal functional centers

Eukaryotic ribosome precursors acquire translation competence in the cytoplasm through stepwise release of bound assembly factors, and proofreading of their functional centers. In case of the pre-60S, these steps include removal of placeholders Rlp24, Arx1 and Mrt4 that prevent premature loading of the ribosomal protein eL24, the protein-folding machinery at the polypeptide exit tunnel (PET), and the ribosomal stalk, respectively. Here, we reveal that sequential ATPase and GTPase activities license release factors Rei1 and Yvh1 to trigger Arx1 and Mrt4 removal. Drg1-ATPase activity removes Rlp24 from the GTPase Nog1 on the pre-60S; consequently, the C-terminal tail of Nog1 is extracted from the PET. These events enable Rei1 to probe PET integrity and catalyze Arx1 release. Concomitantly, Nog1 eviction from the pre-60S permits peptidyl transferase center maturation, and allows Yvh1 to mediate Mrt4 release for stalk assembly. Thus, Nog1 co-ordinates the assembly, maturation and quality control of distant functional centers during ribosome formation.

DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput

We present an easy-to-use integrated software suite, DIA-NN, that exploits deep neural networks and new quantification and signal correction strategies for the processing of data-independent acquisition (DIA) proteomics experiments. DIA-NN improves the identification and quantification performance in conventional DIA proteomic applications, and is particularly beneficial for high-throughput applications, as it is fast and enables deep and confident proteome coverage when used in combination with fast chromatographic methods.

Reproducibility, Specificity and Accuracy of Relative Quantification Using Spectral Library-based Data-independent Acquisition

Currently data-dependent acquisition (DDA) is the method of choice for mass spectrometry-based proteomics discovery experiments, but data-independent acquisition (DIA) is steadily becoming more important. One of the most important requirements to perform a DIA analysis is the availability of suitable spectral libraries for peptide identification and quantification. Several studies were performed addressing the evaluation of spectral library performance for protein identification in DIA measurements. But so far only few experiments estimate the effect of these libraries on the quantitative level.In this work we created a gold standard spike-in sample set with known contents and ratios of proteins in a complex protein matrix that allowed a detailed comparison of DIA quantification data obtained with different spectral library approaches. We used in-house generated sample-specific spectral libraries created using varying sample preparation approaches and repeated DDA measurement. In addition, two different search engines were tested for protein identification from DDA data and subsequent library generation. In total, eight different spectral libraries were generated, and the quantification results compared with a library free method, as well as a default DDA analysis. Not only the number of identifications on peptide and protein level in the spectral libraries and the corresponding DIA analysis results was inspected, but also the number of expected and identified differentially abundant protein groups and their ratios.We found, that while libraries of prefractionated samples were generally larger, there was no significant increase in DIA identifications compared with repetitive non-fractionated measurements. Furthermore, we show that the accuracy of the quantification is strongly dependent on the applied spectral library and whether the quantification is based on peptide or protein level. Overall, the reproducibility and accuracy of DIA quantification is superior to DDA in all applied approaches.Data has been deposited to the ProteomeXchange repository with identifiers PXD012986, PXD012987, PXD012988 and PXD014956.

Bioinformatics approaches for improving seminal plasma proteome analysis

Reproduction efficiency of male animals is one of the key factors influencing the sustainability of livestock. Mass spectrometry (MS) based proteomics has become an important tool for studying seminal plasma proteomes. In this review, we summarize bioinformatics analysis strategies for current proteomics approaches, for identifying novel biomarkers of reproductive robustness.

Precision Profiling of the Cardiovascular Post-Translationally Modified Proteome: Where There Is a Will, There Is a Way

There is an exponential increase in biological complexity as initial gene transcripts are spliced, translated into amino acid sequence, and post-translationally modified. Each protein can exist as multiple chemical or sequence-specific proteoforms, and each has the potential to be a critical mediator of a physiological or pathophysiological signaling cascade. Here, we provide an overview of how different proteoforms come about in biological systems and how they are most commonly measured using mass spectrometry-based proteomics and bioinformatics. Our goal is to present this information at a level accessible to every scientist interested in mass spectrometry and its application to proteome profiling. We will specifically discuss recent data linking various protein post-translational modifications to cardiovascular disease and conclude with a discussion for enablement and democratization of proteomics across the cardiovascular and scientific community. The aim is to inform and inspire the readership to explore a larger breadth of proteoform, particularity post-translational modifications, related to their particular areas of expertise in cardiovascular physiology.

Identification of two abundant Aerococcus urinae cell wall-anchored proteins

Aerococcus urinae is an emerging pathogen that causes urinary tract infections, bacteremia and infective endocarditis. The mechanisms through which A. urinae cause infection are largely unknown. The aims of this study were to describe the surface proteome of A. urinae and to analyse A. urinae genomes in search for genes encoding surface proteins. Two proteins, denoted Aerococcal surface protein (Asp) 1 and 2, were through the use of mass spectrometry based proteomics found to quantitatively dominate the aerococcal surface. The presence of these proteins on the surface was also shown using ELISA with serum from rabbits immunized with the recombinant Asp. These proteins had a signal sequence in the amino-terminal end and a cell wall-sorting region in the carboxy-terminal end, which contained an LPATG-motif, a hydrophobic domain and a positively charged tail. Twenty-three additional A. urinae genomes were sequenced using Illumina HiSeq technology. Six different variants of asp genes were found (denoted asp1-6). All isolates had either one or two of these asp-genes located in a conserved locus, designated Locus encoding Aerococcal Surface Proteins (LASP). The 25 genomes had in median 13 genes encoding LPXTG-proteins (range 6-24). For other Gram-positive bacteria, cell wall-anchored surface proteins with an LPXTG-motif play a key role for virulence. Thus, it will be of great interest to explore the function of the Asp proteins of A. urinae to establish a better understanding of the molecular mechanisms by which A. urinae cause disease.

A quantitative Streptococcus pyogenes-human protein-protein interaction map reveals localization of opsonizing antibodies

A fundamental challenge in medical microbiology is to characterize the dynamic protein–protein interaction networks formed at the host–pathogen interface. Here, we generate a quantitative interaction map between the significant human pathogen, Streptococcus pyogenes, and proteins from human saliva and plasma obtained via complementary affinity-purification and bacterial-surface centered enrichment strategies and quantitative mass spectrometry. Perturbation of the network using immunoglobulin protease cleavage, mixtures of different concentrations of saliva and plasma, and different S. pyogenes serotypes and their isogenic mutants, reveals how changing microenvironments alter the interconnectivity of the interaction map. The importance of host immunoglobulins for the interaction with human complement proteins is demonstrated and potential protective epitopes of importance for phagocytosis of S. pyogenes cells are localized. The interaction map confirms several previously described protein–protein interactions; however, it also reveals a multitude of additional interactions, with possible implications for host–pathogen interactions involving other bacterial species.

Characterizing host-pathogen protein interactions can help elucidate the molecular basis of bacterial infections. Here, the authors use an integrative proteomics approach to generate a quantitative map of protein interactions between Streptococcus pyogenes and human saliva and plasma.

Comparative analysis of mRNA and protein degradation in prostate tissues indicates high stability of proteins

Deterioration of biomolecules in clinical tissues is an inevitable pre-analytical process, which affects molecular measurements and thus potentially confounds conclusions from cohort analyses. Here, we investigate the degradation of mRNA and protein in 68 pairs of adjacent prostate tissue samples using RNA-Seq and SWATH mass spectrometry, respectively. To objectively quantify the extent of protein degradation, we develop a numerical score, the Proteome Integrity Number (PIN), that faithfully measures the degree of protein degradation. Our results indicate that protein degradation only affects 5.9% of the samples tested and shows negligible correlation with mRNA degradation in the adjacent samples. These findings are confirmed by independent analyses on additional clinical sample cohorts and across different mass spectrometric methods. Overall, the data show that the majority of samples tested are not compromised by protein degradation, and establish the PIN score as a generic and accurate indicator of sample quality for proteomic analyses.

Protein degradation in clinical samples is largely unexplored. Here, the authors analyze the transcriptome and proteome of clinical tissue samples and develop an algorithm to assess protein degradation, showing that protein degradation is negligible in most tissue samples and does not correlate with transcript degradation.

[Preliminary Study on the Biological Markers for I-IIb Stage Non-small Cell Lung Cancer Based on a Serum-peptidomics]

背景与目的

非小细胞肺癌是发病率最高的一种肺恶性肿瘤，早发现、早诊断、早治疗是其治疗原则，因其发病隐匿，缺乏早期筛查的特异性标志物，多数患者就诊时已处于晚期阶段，导致其5年生存率较低，预后不理想。探索一种敏感的生物学标志物，是目前肺癌诊疗的重点。本文基于差异多肽组学技术，旨在筛选Ⅰ期-Ⅱb期非小细胞肺癌的血清生物标志物。

方法

利用纳升超高效液相色谱联合四级杆轨道阱质谱技术，比较和筛选正常健康人群、肺部良性病变患者、非小细胞肺癌患者血清中的差异多肽片段，通过对目标肽段的鉴别和定量分析，探寻非小细胞肺癌早期诊断的肿瘤生物标志物。

结果

通过与人类蛋白质组数据库进行比对，共鉴定到545个多肽，分别来自118个蛋白。将各组样本中肽段的谱图数进行比较，共筛选出201个差异多肽，经定量分析发现峰面积的变异系数（coefficient of variation, CV）≤30%的目标肽段共计7个，其中在各组间呈趋势变化的有2条肽段，分别是肺癌组中表达下调的肽段QGAKIPKPEASFSPR和肺癌组中表达上调的肽段CDDYRLC，它们分别来自于中间α球蛋白抑制因子H4蛋白（ITIH4）和基质γ-羧基谷氨酸蛋白（MGP）。

结论

将血清多肽组学用于筛选肿瘤标志物的研究，可为非小细胞肺癌的早期诊断提供新线索，其中ITIH4与MGP蛋白水解的特异性肽段是潜在早期非小细胞肺癌患者的血清标志物。

Complex-centric proteome profiling by SEC-SWATH-MS

Proteins are major effectors and regulators of biological processes that can elicit multiple functions depending on their interaction with other proteins. The organization of proteins into macromolecular complexes and their quantitative distribution across these complexes is, therefore, of great biological and clinical significance. In this paper, we describe an integrated experimental and computational technique to quantify hundreds of protein complexes in a single operation. The method consists of size exclusion chromatography (SEC) to fractionate native protein complexes, SWATH/DIA mass spectrometry to precisely quantify the proteins in each SEC fraction, and the computational framework CCprofiler to detect and quantify protein complexes by error‐controlled, complex‐centric analysis using prior information from generic protein interaction maps. Our analysis of the HEK293 cell line proteome delineates 462 complexes composed of 2,127 protein subunits. The technique identifies novel sub‐complexes and assembly intermediates of central regulatory complexes while assessing the quantitative subunit distribution across them. We make the toolset CCprofiler freely accessible and provide a web platform, SECexplorer, for custom exploration of the HEK293 proteome modularity.

Data-independent acquisition-based SWATH-MS for quantitative proteomics: a tutorial

Many research questions in fields such as personalized medicine, drug screens or systems biology depend on obtaining consistent and quantitatively accurate proteomics data from many samples. SWATH-MS is a specific variant of data-independent acquisition (DIA) methods and is emerging as a technology that combines deep proteome coverage capabilities with quantitative consistency and accuracy. In a SWATH-MS measurement, all ionized peptides of a given sample that fall within a specified mass range are fragmented in a systematic and unbiased fashion using rather large precursor isolation windows. To analyse SWATH-MS data, a strategy based on peptide-centric scoring has been established, which typically requires prior knowledge about the chromatographic and mass spectrometric behaviour of peptides of interest in the form of spectral libraries and peptide query parameters. This tutorial provides guidelines on how to set up and plan a SWATH-MS experiment, how to perform the mass spectrometric measurement and how to analyse SWATH-MS data using peptide-centric scoring. Furthermore, concepts on how to improve SWATH-MS data acquisition, potential trade-offs of parameter settings and alternative data analysis strategies are discussed.

Identification of small molecules using accurate mass MS/MS search

Tandem mass spectral library search (MS/MS) is the fastest way to correctly annotate MS/MS spectra from screening small molecules in fields such as environmental analysis, drug screening, lipid analysis, and metabolomics. The confidence in MS/MS-based annotation of chemical structures is impacted by instrumental settings and requirements, data acquisition modes including data-dependent and data-independent methods, library scoring algorithms, as well as post-curation steps. We critically discuss parameters that influence search results, such as mass accuracy, precursor ion isolation width, intensity thresholds, centroiding algorithms, and acquisition speed. A range of publicly and commercially available MS/MS databases such as NIST, MassBank, MoNA, LipidBlast, Wiley MSforID, and METLIN are surveyed. In addition, software tools including NIST MS Search, MS-DIAL, Mass Frontier, SmileMS, Mass++, and XCMS2 to perform fast MS/MS search are discussed. MS/MS scoring algorithms and challenges during compound annotation are reviewed. Advanced methods such as the in silico generation of tandem mass spectra using quantum chemistry and machine learning methods are covered. Community efforts for curation and sharing of tandem mass spectra that will allow for faster distribution of scientific discoveries are discussed.