MRMAssayDB: an integrated resource for validated targeted proteomics assays

Proteomic Evolution from Acute to Post-COVID-19 Conditions

Many COVID-19 survivors have post-COVID-19 conditions, and females are at a higher risk. We sought to determine (1) how protein levels change from acute to post-COVID-19 conditions, (2) whether females have a plasma protein signature different from that of males, and (3) which biological pathways are associated with COVID-19 when compared to restrictive lung disease. We measured protein levels in 74 patients on the day of admission and at 3 and 6 months after diagnosis. We determined protein concentrations by multiple reaction monitoring (MRM) using a panel of 269 heavy-labeled peptides. The predicted forced vital capacity (FVC) and diffusing capacity of the lungs for carbon monoxide (DLCO) were measured by routine pulmonary function testing. Proteins associated with six key lipid-related pathways increased from admission to 3 and 6 months; conversely, proteins related to innate immune responses and vasoconstriction-related proteins decreased. Multiple biological functions were regulated differentially between females and males. Concentrations of eight proteins were associated with FVC, %, and they together had c-statistics of 0.751 (CI:0.732-0.779); similarly, concentrations of five proteins had c-statistics of 0.707 (CI:0.676-0.737) for DLCO, %. Lipid biology may drive evolution from acute to post-COVID-19 conditions, while activation of innate immunity and vascular regulation pathways decreased over that period. (ProteomeXchange identifiers: PXD041762, PXD029437).

Bioinformatics Tools and Knowledgebases to Assist Generating Targeted Assays for Plasma Proteomics

In targeted proteomics experiments, selecting the appropriate proteotypic peptides as surrogate for the target protein is a crucial pre-acquisition step. This step is largely a bioinformatics exercise that involves integrating information on the peptides and proteins and using various software tools and knowledgebases. We present here a few resources that automate and simplify the selection process to a great degree. These tools and knowledgebases were developed primarily to streamline targeted proteomics assay development and include PeptidePicker, PeptidePickerDB, MRMAssayDB, MouseQuaPro, and PeptideTracker. We have used these tools to develop and document thousands of targeted proteomics assays, many of them for plasma proteins with focus on human and mouse. An important aspect in all these resources is the integrative approach on which they are based. Using these tools in the first steps of designing a singleplexed or multiplexed targeted proteomic experiment can reduce the necessary experimental steps tremendously. All the tools and knowledgebases we describe here are Web-based and freely accessible so scientists can query the information conveniently from the browser. This chapter provides an overview of these software tools and knowledgebases, their content, and how to use them for targeted plasma proteomics. We further demonstrate how to use them with the results of the HUPO Human Plasma Proteome Project to produce a new database of 3.8 k targeted assays for known human plasma proteins. Upon experimental validation, these assays should help in the further quantitative characterizing of the plasma proteome.

Proteomics in thrombosis research

A State of the Art lecture titled “Proteomics in Thrombosis Research” was presented at the ISTH Congress in 2021. In clinical practice, there is a need for improved plasma biomarker‐based tools for diagnosis and risk prediction of venous thromboembolism (VTE). Analysis of blood, to identify plasma proteins with potential utility for such tools, could enable an individualized approach to treatment and prevention. Technological advances to study the plasma proteome on a large scale allows broad screening for the identification of novel plasma biomarkers, both by targeted and nontargeted proteomics methods. However, assay limitations need to be considered when interpreting results, with orthogonal validation required before conclusions are drawn. Here, we review and provide perspectives on the application of affinity‐ and mass spectrometry‐based methods for the identification and analysis of plasma protein biomarkers, with potential application in the field of VTE. We also provide a future perspective on discovery strategies and emerging technologies for targeted proteomics in thrombosis research. Finally, we summarize relevant new data on this topic, presented during the 2021 ISTH Congress.

Longitudinal Plasma Proteomics Analysis Reveals Novel Candidate Biomarkers in Acute COVID-19

The host response to COVID-19 pathophysiology over the first few days of infection remains largely unclear, especially the mechanisms in the blood compartment. We report on a longitudinal proteomic analysis of acute-phase COVID-19 patients, for which we used blood plasma, multiple reaction monitoring with internal standards, and data-independent acquisition. We measured samples on admission for 49 patients, of which 21 had additional samples on days 2, 4, 7, and 14 after admission. We also measured 30 externally obtained samples from healthy individuals for comparison at baseline. The 31 proteins differentiated in abundance between acute COVID-19 patients and healthy controls belonged to acute inflammatory response, complement activation, regulation of inflammatory response, and regulation of protein activation cascade. The longitudinal analysis showed distinct profiles revealing increased levels of multiple lipid-associated functions, a rapid decrease followed by recovery for complement activation, humoral immune response, and acute inflammatory response-related proteins, and level fluctuation in the regulation of smooth muscle cell proliferation, secretory mechanisms, and platelet degranulation. Three proteins were differentiated between survivors and nonsurvivors. Finally, increased levels of fructose-bisphosphate aldolase B were determined in patients with exposure to angiotensin receptor blockers versus decreased levels in those exposed to angiotensin-converting enzyme inhibitors. Data are available via ProteomeXchange PXD029437.

Quantitative Detection of Protein Splice Variants by Selected Reaction Monitoring (SRM) Mass Spectrometry

Molecular diversification of the cellular proteome through alternative splicing has emerged as an important biological principle. However, the lack of tools to specifically detect and quantify proteoforms (Smith et al., Nat Methods 10:186-187, 2013) is a major impediment to functional studies. Recently, biological mass spectrometry (MS) has undergone impressive advances (Mann, Nat Rev Mol Cell Biol 17:678, 2016), including the generation of a highly diverse set of biological applications (Aebersold and Mann, Nature 537:347-355, 2016), and has demonstrated to be an essential tool to address many biological questions (Savitski et al., Science 346:1255784, 2014; Rinner et al., Nat Methods 5:315-318, 2008). In particular, targeted LC-MS, with its high selectivity and specificity, is ideally suited for the precise and sensitive quantification of specific proteins and their proteoforms (Picotti and Aebersold, Nat Methods 9:555-566, 2012). We describe in detail the application of this workflow applied to dissect the molecular diversity of the synaptic adhesion proteins and their splicing-derived proteoforms (Schreiner et al., Elife 4:e07794, 2015).

A proteomic survival predictor for COVID-19 patients in intensive care

Global healthcare systems are challenged by the COVID-19 pandemic. There is a need to optimize allocation of treatment and resources in intensive care, as clinically established risk assessments such as SOFA and APACHE II scores show only limited performance for predicting the survival of severely ill COVID-19 patients. Additional tools are also needed to monitor treatment, including experimental therapies in clinical trials. Comprehensively capturing human physiology, we speculated that proteomics in combination with new data-driven analysis strategies could produce a new generation of prognostic discriminators. We studied two independent cohorts of patients with severe COVID-19 who required intensive care and invasive mechanical ventilation. SOFA score, Charlson comorbidity index, and APACHE II score showed limited performance in predicting the COVID-19 outcome. Instead, the quantification of 321 plasma protein groups at 349 timepoints in 50 critically ill patients receiving invasive mechanical ventilation revealed 14 proteins that showed trajectories different between survivors and non-survivors. A predictor trained on proteomic measurements obtained at the first time point at maximum treatment level (i.e. WHO grade 7), which was weeks before the outcome, achieved accurate classification of survivors (AUROC 0.81). We tested the established predictor on an independent validation cohort (AUROC 1.0). The majority of proteins with high relevance in the prediction model belong to the coagulation system and complement cascade. Our study demonstrates that plasma proteomics can give rise to prognostic predictors substantially outperforming current prognostic markers in intensive care.

A time-resolved proteomic and prognostic map of COVID-19

COVID-19 is highly variable in its clinical presentation, ranging from asymptomatic infection to severe organ damage and death. We characterized the time-dependent progression of the disease in 139 COVID-19 inpatients by measuring 86 accredited diagnostic parameters, such as blood cell counts and enzyme activities, as well as untargeted plasma proteomes at 687 sampling points. We report an initial spike in a systemic inflammatory response, which is gradually alleviated and followed by a protein signature indicative of tissue repair, metabolic reconstitution, and immunomodulation. We identify prognostic marker signatures for devising risk-adapted treatment strategies and use machine learning to classify therapeutic needs. We show that the machine learning models based on the proteome are transferable to an independent cohort. Our study presents a map linking routinely used clinical diagnostic parameters to plasma proteomes and their dynamics in an infectious disease.

Targeted mass spectrometry-based assays enable multiplex quantification of receptor tyrosine kinase, MAP Kinase, and AKT signaling

SUMMARY

A primary goal of the US National Cancer Institute's Ras initiative at the Frederick National Laboratory for Cancer Research is to develop methods to quantify RAS signaling to facilitate development of novel cancer therapeutics. We use targeted proteomics technologies to develop a community resource consisting of 256 validated multiple reaction monitoring (MRM)-based, multiplexed assays for quantifying protein expression and phosphorylation through the receptor tyrosine kinase, MAPK, and AKT signaling networks. As proof of concept, we quantify the response of melanoma (A375 and SK-MEL-2) and colorectal cancer (HCT-116 and HT-29) cell lines to BRAF inhibition by PLX-4720. These assays replace over 60 Western blots with quantitative mass spectrometry-based assays of high molecular specificity and quantitative precision, showing the value of these methods for pharmacodynamic measurements and mechanism of action studies. Methods, fit-for-purpose validation, and results are publicly available as a resource for the community at assays.cancer.gov.

MOTIVATION

A lack of quantitative, multiplexable assays for phosphosignaling limits comprehensive investigation of aberrant signaling in cancer and evaluation of novel treatments. To alleviate this limitation, we sought to develop assays using targeted mass spectrometry for quantifying protein expression and phosphorylation through the receptor tyrosine kinase, MAPK, and AKT signaling networks. The resulting assays provide a resource for replacing over 60 Western blots in examining cancer signaling and tumor biology with high molecular specificity and quantitative rigor.

An Update on MRMAssayDB: A Comprehensive Resource for Targeted Proteomics Assays in the Community

Precise multiplexed quantification of proteins in biological samples can be achieved by targeted proteomics using multiple or parallel reaction monitoring (MRM/PRM). Combined with internal standards, the method achieves very good repeatability and reproducibility enabling excellent protein quantification and allowing longitudinal and cohort studies. A laborious part of performing such experiments lies in the preparation steps dedicated to the development and validation of individual protein assays. Several public repositories host information on targeted proteomics assays, including NCI’s Clinical Proteomic Tumor Analysis Consortium assay portals, PeptideAtlas SRM Experiment Library, SRMAtlas, PanoramaWeb, and PeptideTracker, with all offering varying levels of details. We introduced MRMAssayDB in 2018 as an integrated resource for targeted proteomics assays. The Web-based application maps and links the assays from the repositories, includes comprehensive up-to-date protein and sequence annotations, and provides multiple visualization options on the peptide and protein level. We have extended MRMAssayDB with more assays and extensive annotations. Currently it contains >828 000 assays covering >51 000 proteins from 94 organisms, of which >17 000 proteins are present in >2400 biological pathways, and >48 000 mapping to >21 000 Gene Ontology terms. This is an increase of about four times the number of assays since introduction. We have expanded annotations of interaction, biological pathways, and disease associations. A newly added visualization module for coupled molecular structural annotation browsing allows the user to interactively examine peptide sequence and any known PTMs and disease mutations, and map all to available protein 3D structures. Because of its integrative approach, MRMAssayDB enables a holistic view of suitable proteotypic peptides and commonly used transitions in empirical data. Availability: http://mrmassaydb.proteincentre.com.

Absolute quantification of transcription factors in human erythropoiesis using selected reaction monitoring mass spectrometry

Quantitative changes in transcription factor (TF) abundance regulate dynamic cellular processes, including cell fate decisions. Protein copy number provides information about the relative stoichiometry of TFs that can be used to determine how quantitative changes in TF abundance influence gene regulatory networks. In this protocol, we describe a targeted selected reaction monitoring (SRM)-based mass-spectrometry method to systematically measure the absolute protein concentration of nuclear TFs as human hematopoietic stem and progenitor cells differentiate along the erythropoietic lineage.

For complete details on the use and execution of this protocol, please refer to Gillespie et al. (2020).

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Protocol for absolute quantification of TFs in human erythropoiesis

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Selected reaction monitoring mass-spectrometry parameters for each peptide

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Validated SRM assays corresponding to >100 TFs

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Copy number reveals the relative stoichiometries of TFs during erythropoiesis

An Exploratory Pilot Study with Plasma Protein Signatures Associated with Response of Patients with Depression to Antidepressant Treatment for 10 Weeks

Major depressive disorder (MDD) is a leading cause of global disability with a chronic and recurrent course. Recognition of biological markers that could predict and monitor response to drug treatment could personalize clinical decision-making, minimize unnecessary drug exposure, and achieve better outcomes. Four longitudinal plasma samples were collected from each of ten patients with MDD treated with antidepressants for 10 weeks. Plasma proteins were analyzed qualitatively and quantitatively with a nanoflow LC−MS/MS technique. Of 1153 proteins identified in the 40 longitudinal plasma samples, 37 proteins were significantly associated with response/time and clustered into six according to time and response by the linear mixed model. Among them, three early-drug response markers (PHOX2B, SH3BGRL3, and YWHAE) detectable within one week were verified by liquid chromatography-multiple reaction monitoring/mass spectrometry (LC-MRM/MS) in the well-controlled 24 patients. In addition, 11 proteins correlated significantly with two or more psychiatric measurement indices. This pilot study might be useful in finding protein marker candidates that can monitor response to antidepressant treatment during follow-up visits within 10 weeks after the baseline visit.

The intestinal microbiome potentially affects thrombin generation in human subjects

BACKGROUND

The intestinal microbiome plays a versatile role in the etiology of arterial thrombosis. In venous thrombosis, driven chiefly by plasma coagulation, no such role has yet been established. We hypothesized that the intestinal microbiome composition affects coagulation in humans.

METHODS

We used healthy donor fecal microbiota transplant (FMT) to experimentally change the microbiome composition in metabolic syndrome patients. Thirty-five subjects were randomized in a blinded fashion to healthy donor FMT or autologous FMT as a control in a 2:1 ratio. We measured thrombin generation at baseline and after 6 weeks using automated calibrated thrombinography, and we determined plasma abundance of 32 coagulation related proteins using a targeted mass spectrometry-based quantitative proteomics assay with heavy labeled internal standards.

RESULTS

Healthy donor FMT prolonged the thrombinography lag time (median delta 0.0 versus 0.25 minutes, P = .039). The other thrombinography parameters showed no significant difference. Unsupervised cluster analysis suggested overall downregulation of coagulation related plasma proteins in subject clusters containing predominantly subjects that had a metabolic response to healthy donor FMT. FMT treatment status itself showed no clear clustering pattern with up- or downregulation, however, and proteins did not cluster according to an apparent biological grouping.

DISCUSSION

A single healthy donor FMT tends to modestly suppress the onset thrombin generation in metabolic syndrome patients, representing initial proof-of-principle that the intestinal microbiota composition might affect the coagulation system in humans. The findings merit external validation as a role for intestinal microbiota in coagulation can have clinically important implications.

Recent Technological Advances in the Mass Spectrometry-based Nanomedicine Studies: An Insight from Nanoproteomics

Nanoscience becomes one of the most cutting-edge research directions in recent years since it is gradually matured from basic to applied science. Nanoparticles (NPs) and nanomaterials (NMs) play important roles in various aspects of biomedicine science, and their influences on the environment have caused a whole range of uncertainties which require extensive attention. Due to the quantitative and dynamic information provided for human proteome, mass spectrometry (MS)-based quantitative proteomic technique has been a powerful tool for nanomedicine study. In this article, recent trends of progress and development in the nanomedicine of proteomics were discussed from quantification techniques and publicly available resources or tools. First, a variety of popular protein quantification techniques including labeling and label-free strategies applied to nanomedicine studies are overviewed and systematically discussed. Then, numerous protein profiling tools for data processing and postbiological statistical analysis and publicly available data repositories for providing enrichment MS raw data information sources are also discussed.

Clinical potential of mass spectrometry-based proteogenomics

Cancer genomics research aims to advance personalized oncology by finding and targeting specific genetic alterations associated with cancers. In genome-driven oncology, treatments are selected for individual patients on the basis of the findings of tumour genome sequencing. This personalized approach has prolonged the survival of subsets of patients with cancer. However, many patients do not respond to the predicted therapies based on the genomic profiles of their tumours. Furthermore, studies pairing genomic and proteomic analyses of samples from the same tumours have shown that the proteome contains novel information that cannot be discerned through genomic analysis alone. This observation has led to the concept of proteogenomics, in which both types of data are leveraged for a more complete view of tumour biology that might enable patients to be more successfully matched to effective treatments than they would using genomics alone. In this Perspective, we discuss the added value of proteogenomics over the current genome-driven approach to the clinical characterization of cancers and summarize current efforts to incorporate targeted proteomic measurements based on selected/multiple reaction monitoring (SRM/MRM) mass spectrometry into the clinical laboratory to facilitate clinical proteogenomics.

Rapidly Assessing the Quality of Targeted Proteomics Experiments through Monitoring Stable-Isotope Labeled Standards

Targeted proteomics experiments based on selected reaction monitoring (SRM) have gained wide adoption in the use of clinical biomarkers, cellular modeling, and numerous other biological experiments due to their highly accurate and reproducible quantification. The quantitative accuracy in targeted proteomics experiments is reliant on the stable-isotope, heavy-labeled peptide standards that are spiked into a sample and used as a reference when calculating the abundance of endogenous peptides. Therefore, the quality of measurement for these standards is a critical factor in determining whether data acquisition was successful. With improved mass spectrometry (MS) instrumentation that enables the monitoring of hundreds of peptides in hundreds to thousands of samples, quality assessment is increasingly important and cannot be performed manually. We present Q4SRM, a software tool that rapidly checks the signal from all heavy-labeled peptides and flags those that fail quality-control metrics. Using four metrics, the tool detects problems with both individual SRM transitions and the collective group of transitions that monitor a single peptide. The program's speed and simplicity enable its use at the point of data acquisition and can be ideally run immediately upon the completion of a liquid chromatography-SRM-MS analysis.