Human SRMAtlas: A Resource of Targeted Assays to Quantify the Complete Human Proteome

Platelet proteomics: from discovery to diagnosis

INTRODUCTION

Platelets are the smallest cells within the circulating blood with key roles in physiological hemostasis and pathological thrombosis regulated by the onset of activating/inhibiting processes via receptor responses and signaling cascades. Areas covered: Proteomics as well as genomic approaches have been fundamental in identifying and quantifying potential targets for future diagnostic strategies in the prevention of bleeding and thrombosis, and uncovering the complexity of platelet functions in health and disease. In this article, we provide a critical overview on current functional tests used in diagnostics and the future perspectives for platelet proteomics in clinical applications. Expert commentary: Proteomics represents a valuable tool for the identification of patients with diverse platelet associated defects. In-depth validation of identified biomarkers, e.g. receptors, signaling proteins, post-translational modifications, in large cohorts is decisive for translation into routine clinical diagnostics.

Neurogranin as Cerebrospinal Fluid Biomarker for Alzheimer Disease: An Assay Comparison Study

BACKGROUND

Neurogranin in cerebrospinal fluid (CSF) correlates with cognitive decline and is a potential novel biomarker for Alzheimer disease (AD) dementia. We investigated the analytical and diagnostic performance of 3 commonly used neurogranin assays in the same cohort of patients to improve the interpretability of CSF neurogranin test results.

METHODS

The neurogranin Erenna® assay from Washington University, St. Louis, MO (WashU); ELISA from ADx Neurosciences; and ELISA from Gothenburg University, Mölndal, Sweden (UGot), were compared using silver staining and Western blot after gel electrophoresis. Clinical performance of the 3 assays was compared in samples from individuals diagnosed with subjective cognitive decline (n = 22), and in patients with AD (n = 22), frontotemporal dementia (n = 22), dementia with Lewy bodies (n = 22), or vascular dementia (n = 20), adjusted for sex and age.

RESULTS

The assays detected different epitopes of neurogranin: the WashU assay detected the N-terminal part of neurogranin (S10-D23) and a C-terminal part (G49-G60), the ADx assay detected C-terminal neurogranin truncated at P75, and the UGot assay detected the C-terminal neurogranin with intact ending (D78). Spearman ρ was 0.95 between ADx and WashU, 0.87 between UGot and WashU, and 0.81 between UGot and ADx. ANCOVA (analysis of covariance) showed group differences for ranked neurogranin concentrations in each assay (all P < 0.05), with specific increases in AD.

CONCLUSIONS

Although the 3 assays target different epitopes on neurogranin and have different calibrators, the high correlations and the similar group differences suggest that the different forms of neurogranin in CSF carry similar diagnostic information, at least in the context of neurodegenerative diseases.

The building blocks of successful translation of proteomics to the clinic

Recently, the first two multiplexed tests using selective reaction monitoring (SRM-MS) mass spectrometry have entered clinical practice. Despite different areas of indication, risk stratification in lung cancer and preterm birth, they share multiple steps in their development strategies. Here we review these strategies and their implications for successful translation of biomarkers to clinical practice. We believe that the identification of blood protein panels for the identification of disease phenotypes is now a reproducible and standard (albeit complex) process.

Spectral Libraries for SWATH-MS Assays for Drosophila melanogaster and Solanum lycopersicum

Quantitative proteomics methods have emerged as powerful tools for measuring protein expression changes at the proteome level. Using MS-based approaches, it is now possible to routinely quantify thousands of proteins. However, prefractionation of the samples at the protein or peptide level is usually necessary to go deep into the proteome, increasing both MS analysis time and technical variability. Recently, a new MS acquisition method named SWATH is introduced with the potential to provide good coverage of the proteome as well as a good measurement precision without prior sample fractionation. In contrast to shotgun-based MS however, a library containing experimental acquired spectra is necessary for the bioinformatics analysis of SWATH data. In this study, spectral libraries for two widely used models are built to study crop ripening or animal embryogenesis, Solanum lycopersicum (tomato) and Drosophila melanogaster, respectively. The spectral libraries comprise fragments for 5197 and 6040 proteins for S. lycopersicum and D. melanogaster, respectively, and allow reproducible quantification for thousands of peptides per MS analysis. The spectral libraries and all MS data are available in the MassIVE repository with the dataset identifiers MSV000081074 and MSV000081075 and the PRIDE repository with the dataset identifiers PXD006493 and PXD006495.

Quantification of Von Willebrand Factor Cleavage by adamts-13 in Patients Supported by Left Ventricular Assist Devices

Patients supported by left ventricular assist devices (LVADs) often present with the loss of large von Willebrand factor (VWF) multimers. This VWF deficiency is believed to contribute to the bleeding diathesis of patients on LVAD support and is caused by excessive VWF cleavage by the metalloprotease ADAMTS-13 under high shear stress. However, only a small percentage of patients who have suffered the loss of large VWF multimers bleed. The actual rates of VWF cleavage in these patients have not been reported, primarily because of the lack of reliable detection methods. We have developed and validated a selected reaction monitoring (SRM) mass spectrometry method to quantify VWF cleavage as the ratio of the ADAMTS-13-cleaved peptide MVTGNPASDEIK to the ILAGPAGDSNVVK peptide. The rate of VWF cleavage was found to be 1.26% ± 0.36% in normal plasma. It varied significantly in patient samples, ranging from 0.23% to 2.5% of total VWF antigen, even though all patients had the loss of large VWF multimers. Von Willebrand factor cleavage was greater in post-LVAD samples from patients in whom bleeding had developed, but was mostly reduced in patients in whom thrombosis had developed. This SRM method is reliable to quantify the rate of VWF cleavage in patients on LVAD support.

Selected reaction monitoring mass spectrometry of mastitis milk reveals pathogen-specific regulation of bovine host response proteins

Mastitis is a major challenge to bovine health. The detection of sensitive markers for mastitis in dairy herds is of great demand. Suitable biomarkers should be measurable in milk and should report pathogen-specific changes at an early stage to support earlier diagnosis and more efficient treatment. However, the identification of sensitive biomarkers in milk has remained a challenge, in part due to their relatively low concentration in milk. In the present study, we used a selected reaction monitoring (SRM) mass spectrometry approach, which allowed the absolute quantitation of 13 host response proteins in milk for the first time. These proteins were measured over a 54-h period upon an in vivo challenge with cell wall components from either gram-negative (lipopolysaccharide from Escherichia coli; LPS) or gram-positive bacteria (peptidoglycan from Staphylococcus aureus; PGN). Whereas our data clearly demonstrate that all challenged animals have consistent upregulation of innate immune response proteins after both LPS and PGN challenge, the data also reveal clearly that LPS challenge unleashes faster and shows a more intense host response compared with PGN challenge. Biomarker candidates that may distinguish between gram-negative and gram-positive bacteria include α-2 macroglobulin, α-1 antitrypsin, haptoglobin, serum amyloid A3, cluster of differentiation 14, calgranulin B, cathepsin C, vanin-1, galectin 1, galectin 3, and IL-8. Our approach can support further studies of large cohorts of animals with natural occurring mastitis, to validate the relevance of these suggested biomarkers in dairy production.

Targeted Proteomics Guided by Label-free Quantitative Proteome Analysis in Saliva Reveal Transition Signatures from Health to Periodontal Disease

Periodontal diseases are among the most prevalent worldwide, but largely silent, chronic diseases. They affect the tooth-supporting tissues with multiple ramifications on life quality. Their early diagnosis is still challenging, due to lack of appropriate molecular diagnostic methods. Saliva offers a non-invasively collectable reservoir of clinically relevant biomarkers, which, if utilized efficiently, could facilitate early diagnosis and monitoring of ongoing disease. Despite several novel protein markers being recently enlisted by discovery proteomics, their routine diagnostic application is hampered by the lack of validation platforms that allow for rapid, accurate and simultaneous quantification of multiple proteins in large cohorts. Here we carried out a pipeline of two proteomic platforms; firstly, we applied open ended label-free quantitative (LFQ) proteomics for discovery in saliva (n = 67, including individuals with health, gingivitis, and periodontitis), followed by selected-reaction monitoring (SRM)-targeted proteomics for validation in an independent cohort (n = 82). The LFQ platform led to the discovery of 119 proteins with at least 2-fold significant difference between health and disease. The 65 proteins chosen for the subsequent SRM platform included 50 functionally related proteins derived from the significantly enriched processes of the LFQ data, 11 from literature-mining, and four house-keeping ones. Among those, 60 were reproducibly quantifiable proteins (92% success rate), represented by a total of 143 peptides. Machine-learning modeling led to a narrowed-down panel of five proteins of high predictive value for periodontal diseases with maximum area under the receiver operating curve >0.97 (higher in disease: Matrix metalloproteinase-9, Ras-related protein-1, Actin-related protein 2/3 complex subunit 5; lower in disease: Clusterin, Deleted in Malignant Brain Tumors 1). This panel enriches the pool of credible clinical biomarker candidates for diagnostic assay development. Yet, the quantum leap brought into the field of periodontal diagnostics by this study is the application of the biomarker discovery-through-verification pipeline, which can be used for validation in further cohorts.

The promise of targeted proteomics for quantitative network biology

Proteomics is a powerful tool for obtaining information on a large number of proteins with regard to their expression levels, interactions with other molecules, and posttranslational modifications. Whereas nontargeted, discovery proteomics uncovers differences in the proteomic landscape under different conditions, targeted proteomics has been developed to overcome the limitations of this approach with regard to quantitation. In addition to technical advances in instruments and informatics tools, the advent of the synthetic proteome composed of synthetic peptides or recombinant proteins has advanced the adoption of targeted proteomics across a wide range of research fields. Targeted proteomics can now be applied to measurement of the dynamics of any proteins of interest under a variety of conditions as well as to estimation of the absolute abundance or stoichiometry of proteins in a given network. Multiplexed targeted proteomics assays of high reproducibility and accuracy can provide insight at the quantitative level into entire networks that govern biological phenomena or diseases. Such assays will establish a new paradigm for data-driven science.

Global quantitative analysis of the human brain proteome in Alzheimer's and Parkinson's Disease

Patients with Alzheimer's disease (AD) and Parkinson's disease (PD) often have overlap in clinical presentation and brain neuropathology suggesting that these two diseases share common underlying mechanisms. Currently, the molecular pathways linking AD and PD are incompletely understood. Utilizing Tandem Mass Tag (TMT) isobaric labeling and synchronous precursor selection-based MS3 (SPS-MS3) mass spectrometry, we performed an unbiased quantitative proteomic analysis of post-mortem human brain tissues (n=80) from four different groups defined as controls, AD, PD, and co-morbid AD/PD cases across two brain regions (frontal cortex and anterior cingulate gyrus). In total, we identified 11 840 protein groups representing 10 230 gene symbols, which map to ~65% of the protein coding genes in brain. The utility of including two reference standards in each TMT 10-plex assay to assess intra- and inter-batch variance is also described. Ultimately, this comprehensive human brain proteomic dataset serves as a valuable resource for various research endeavors including, but not limited to, the identification of disease-specific protein signatures and molecular pathways that are common in AD and PD.

Systematic Protein Prioritization for Targeted Proteomics Studies through Literature Mining

There are more than 3.7 million published articles on the biological functions or disease implications of proteins, constituting an important resource of proteomics knowledge. However, it is difficult to summarize the millions of proteomics findings in the literature manually and quantify their relevance to the biology and diseases of interest. We developed a fully automated bioinformatics framework to identify and prioritize proteins associated with any biological entity. We used the 22 targeted areas of the Biology/Disease-driven (B/D)-Human Proteome Project (HPP) as examples, prioritized the relevant proteins through their Protein Universal Reference Publication-Originated Search Engine (PURPOSE) scores, validated the relevance of the score by comparing the protein prioritization results with a curated database, computed the scores of proteins across the topics of B/D-HPP, and characterized the top proteins in the common model organisms. We further extended the bioinformatics workflow to identify the relevant proteins in all organ systems and human diseases and deployed a cloud-based tool to prioritize proteins related to any custom search terms in real time. Our tool can facilitate the prioritization of proteins for any organ system or disease of interest and can contribute to the development of targeted proteomic studies for precision medicine.

Data-Independent Acquisition Coupled to Visible Laser-Induced Dissociation at 473 nm (DIA-LID) for Peptide-Centric Specific Analysis of Cysteine-Containing Peptide Subset

Thanks to comprehensive and unbiased sampling of all precursor ions, the interest to move toward bottom-up proteomic with data-independent acquisition (DIA) is continuously growing. DIA offers precision and reproducibility performances comparable to true targeted methods but has the advantage of enabling retrospective data testing with the hypothetical presence of new proteins of interest. Nonetheless, the chimeric nature of DIA MS/MS spectra inherent to concomitant transmission of a multiplicity of precursor ions makes the confident identification of peptides often challenging, even with spectral library-based extraction strategy. The introduction of specificity at the fragmentation step upon ultraviolet or visible laser-induced dissociation (LID) range targeting only the subset of cysteine-containing peptides (Cys-peptide) has been proposed as an option to streamline and reduce the search space. Here, we describe the first coupling between DIA and visible LID at 473 nm to test for the presence of Cys-peptides with a peptide-centric approach. As a test run, a spectral library was built for a pool of Cys-synthetic peptides used as surrogates of human kinases (1 peptide per protein). By extracting ion chromatograms of query standard and kinase peptides spiked at different concentration levels in an Escherichia coli proteome lysate, DIA-LID demonstrates a dynamic range of detection of at least 3 decades and coefficients of precision better than 20%. Finally, the spectral library was used to search for endogenous kinases in human cellular extract.

Application of targeted mass spectrometry in bottom-up proteomics for systems biology research

The enormous diversity of proteoforms produces tremendous complexity within cellular proteomes, facilitates intricate networks of molecular interactions, and constitutes a formidable analytical challenge for biomedical researchers. Currently, quantitative whole-proteome profiling often relies on non-targeted liquid chromatography-mass spectrometry (LC-MS), which samples proteoforms broadly, but can suffer from lower accuracy, sensitivity, and reproducibility compared with targeted LC-MS. Recent advances in bottom-up proteomics using targeted LC-MS have enabled previously unachievable identification and quantification of target proteins and posttranslational modifications within complex samples. Consequently, targeted LC-MS is rapidly advancing biomedical research, especially systems biology research in diverse areas that include proteogenomics, interactomics, kinomics, and biological pathway modeling. With the recent development of targeted LC-MS assays for nearly the entire human proteome, targeted LC-MS is positioned to enable quantitative proteomic profiling of unprecedented quality and accessibility to support fundamental and clinical research. Here we review recent applications of bottom-up proteomics using targeted LC-MS for systems biology research. SIGNIFICANCE: Advances in targeted proteomics are rapidly advancing systems biology research. Recent applications include systems-level investigations focused on posttranslational modifications (such as phosphoproteomics), protein conformation, protein-protein interaction, kinomics, proteogenomics, and metabolic and signaling pathways. Notably, absolute quantification of metabolic and signaling pathway proteins has enabled accurate pathway modeling and engineering. Integration of targeted proteomics with other technologies, such as RNA-seq, has facilitated diverse research such as the identification of hundreds of "missing" human proteins (genes and transcripts that appear to encode proteins but direct experimental evidence was lacking).

Advances in analytical tools for high throughput strain engineering

The emergence of inexpensive, base-perfect genome editing is revolutionising biology. Modern industrial biotechnology exploits the advances in genome editing in combination with automation, analytics and data integration to build high-throughput automated strain engineering pipelines also known as biofoundries. Biofoundries replace the slow and inconsistent artisanal processes used to build microbial cell factories with an automated design-build-test cycle, considerably reducing the time needed to deliver commercially viable strains. Testing and hence learning remains relatively shallow, but recent advances in analytical chemistry promise to increase the depth of characterization possible. Analytics combined with models of cellular physiology in automated systems biology pipelines should enable deeper learning and hence a steeper pitch of the learning cycle. This review explores the progress, advances and remaining bottlenecks of analytical tools for high throughput strain engineering.

Quantitative Peptidomics: General Considerations

Peptidomics is the detection and identification of the peptides present in a sample, while quantitative peptidomics provides additional information about the amounts of these peptides. Comparison of peptide levels among two or more samples is termed relative quantitation. It is also possible to perform absolute quantitation of peptide levels in which the biological sample is compared to synthetic standards, which requires a separate standard for each peptide. In contrast, relative quantitation can compare levels of all peptides that are detectable in a sample, which can exceed 1000 peptides in a complex sample. In this chapter, various techniques used for quantitative peptidomics are described along with discussion of the advantages and disadvantages of each approach. A guide to selecting the optimal quantitative approach is provided, based on the goals of the experiment and the resources that are available.

Tissue and plasma proteomics for early stage cancer detection

The pursuit of novel and effective biomarkers is essential in the struggle against cancer, which is a leading cause of mortality worldwide. Here we discuss the relative advantages and disadvantages of the most frequently used proteomics techniques, concentrating on the latest advances and application of tissue and plasma proteomics for novel cancer biomarker discovery.

What is targeted proteomics? A concise revision of targeted acquisition and targeted data analysis in mass spectrometry

Targeted proteomics has gained significant popularity in mass spectrometry-based protein quantification as a method to detect proteins of interest with high sensitivity, quantitative accuracy and reproducibility. However, with the emergence of a wide variety of targeted proteomics methods, some of them with high-throughput capabilities, it is easy to overlook the essence of each method and to determine what makes each of them a targeted proteomics method. In this viewpoint, we revisit the main targeted proteomics methods and classify them in four categories differentiating those methods that perform targeted data acquisition from targeted data analysis, and those methods that are based on peptide ion data (MS1 targeted methods) from those that rely on the peptide fragments (MS2 targeted methods).

A 14-Protein Signature for Rapid Identification of Poor Prognosis Stage III Metastatic Melanoma

PURPOSE

To validate differences in protein levels between good and poor prognosis American Joint Committee on Cancer (AJCC) stage III melanoma patients and compile a protein panel to stratify patient risk.

EXPERIMENTAL DESIGN

Protein extracts from melanoma metastases within lymph nodes in patients with stage III disease with good (n = 16, >4 years survival) and poor survival (n = 14, <2 years survival) were analyzed by selected reaction monitoring (SRM). Diagonal Linear Discriminant Analysis (DLDA) was performed to generate a protein biomarker panel.

RESULTS

SRM analysis identified ten proteins that were differentially abundant between good and poor prognosis stage III melanoma patients. The ten differential proteins were combined with 22 proteins identified in our previous work. A panel of 14 proteins was selected by DLDA that was able to accurately classify patients into prognostic groups based on levels of these proteins.

CONCLUSIONS AND CLINICAL RELEVANCE

The ten differential proteins identified by SRM have biological significance in cancer progression. The final signature of 14 proteins identified by SRM could be used to identify AJCC stage III melanoma patients likely to have poor outcomes who may benefit from adjuvant systemic therapy.

Targeted degradomics in protein terminomics and protease substrate discovery

Targeted degradomics integrates positional information into mass spectrometry (MS)-based targeted proteomics workflows and thereby enables analysis of proteolytic cleavage events with unprecedented specificity and sensitivity. Rapid progress in the establishment of protease-substrate relations provides extensive degradomics target lists that now can be tested with help of selected and parallel reaction monitoring (S/PRM) in complex biological systems, where proteases act in physiological environments. In this minireview, we describe the general principles of targeted degradomics, outline the generic experimental workflow of the methodology and highlight recent and future applications in protease research.

Systems Pharmacology Dissection of Cholesterol Regulation Reveals Determinants of Large Pharmacodynamic Variability between Cell Lines

In individuals, heterogeneous drug-response phenotypes result from a complex interplay of dose, drug specificity, genetic background, and environmental factors, thus challenging our understanding of the underlying processes and optimal use of drugs in the clinical setting. Here, we use mass-spectrometry-based quantification of molecular response phenotypes and logic modeling to explain drug-response differences in a panel of cell lines. We apply this approach to cellular cholesterol regulation, a biological process with high clinical relevance. From the quantified molecular phenotypes elicited by various targeted pharmacologic or genetic treatments, we generated cell-line-specific models that quantified the processes beneath the idiotypic intracellular drug responses. The models revealed that, in addition to drug uptake and metabolism, further cellular processes displayed significant pharmacodynamic response variability between the cell lines, resulting in cell-line-specific drug-response phenotypes. This study demonstrates the importance of integrating different types of quantitative systems-level molecular measurements with modeling to understand the effect of pharmacological perturbations on complex biological processes.

The Human Plasma Proteome Draft of 2017: Building on the Human Plasma PeptideAtlas from Mass Spectrometry and Complementary Assays

Human blood plasma provides a highly accessible window to the proteome of any individual in health and disease. Since its inception in 2002, the Human Proteome Organization's Human Plasma Proteome Project (HPPP) has been promoting advances in the study and understanding of the full protein complement of human plasma and on determining the abundance and modifications of its components. In 2017, we review the history of the HPPP and the advances of human plasma proteomics in general, including several recent achievements. We then present the latest 2017-04 build of Human Plasma PeptideAtlas, which yields ∼43 million peptide-spectrum matches and 122,730 distinct peptide sequences from 178 individual experiments at a 1% protein-level FDR globally across all experiments. Applying the latest Human Proteome Project Data Interpretation Guidelines, we catalog 3509 proteins that have at least two non-nested uniquely mapping peptides of nine amino acids or more and >1300 additional proteins with ambiguous evidence. We apply the same two-peptide guideline to historical PeptideAtlas builds going back to 2006 and examine the progress made in the past ten years in plasma proteome coverage. We also compare the distribution of proteins in historical PeptideAtlas builds in various RNA abundance and cellular localization categories. We then discuss advances in plasma proteomics based on targeted mass spectrometry as well as affinity assays, which during early 2017 target ∼2000 proteins. Finally, we describe considerations about sample handling and study design, concluding with an outlook for future advances in deciphering the human plasma proteome.

Proteomics and the human microbiome: where we are today and where we would like to be

What are all these hundreds of different bacterial species doing in and on us? What interactions occur between the host and the microbes, and between the microbes themselves? By studying proteins, metaproteomics tries to find preliminary answers to these questions. There is daunting complexity around this; in fact, many of these proteins have never been studied before. This article is an introduction to the field of metaproteomics in the context of the human microbiome. It summarizes where we are and what we have learnt so far. The focus will be on faecal proteomics as most metaproteomics research has been conducted on that sample type. Metaproteomics has made major advances in the past decade, but new sample preparation strategies, improved mass spectrometric analysis and, most importantly, data analysis and interpretation have the potential to pave the way for large-cohort metaproteomics.

Protein lipoylation: an evolutionarily conserved metabolic regulator of health and disease

Lipoylation is a rare, but highly conserved lysine posttranslational modification. To date, it is known to occur on only four multimeric metabolic enzymes in mammals, yet these proteins are staples in the core metabolic landscape. The dysregulation of these mitochondrial proteins is linked to a range of human metabolic disorders. Perhaps most striking is that lipoylation itself, the proteins that add or remove the modification, as well as the proteins it decorates are all evolutionarily conserved from bacteria to humans, highlighting the importance of this essential cofactor. Here, we discuss the biological significance of protein lipoylation, the importance of understanding its regulation in health and disease states, and the advances in mass spectrometry-based proteomic technologies that can aid these studies.

Composition and Regulation of the Cellular Repertoire of SCF Ubiquitin Ligases

SCF (Skp1-Cullin-F-box) ubiquitin ligases comprise several dozen modular enzymes that have diverse roles in biological regulation. SCF enzymes share a common catalytic core containing Cul1⋅Rbx1, which is directed toward different substrates by a variable substrate receptor (SR) module comprising 1 of 69 F-box proteins bound to Skp1. Despite the broad cellular impact of SCF enzymes, important questions remain about the architecture and regulation of the SCF repertoire, including whether SRs compete for Cul1 and, if so, how this competition is managed. Here, we devise methods that preserve the in vivo assemblages of SCF complexes and apply quantitative mass spectrometry to perform a census of these complexes (the "SCFome") in various states. We show that Nedd8 conjugation and the SR exchange factor Cand1 have a profound effect on shaping the SCFome. Together, these factors enable rapid remodeling of SCF complexes to promote biased assembly of SR modules bound to substrate.

Identification and Validation of Human Missing Proteins and Peptides in Public Proteome Databases: Data Mining Strategy

In an attempt to complete human proteome project (HPP), Chromosome-Centric Human Proteome Project (C-HPP) launched the journey of missing protein (MP) investigation in 2012. However, 2579 and 572 protein entries in the neXtProt (2017-1) are still considered as missing and uncertain proteins, respectively. Thus, in this study, we proposed a pipeline to analyze, identify, and validate human missing and uncertain proteins in open-access transcriptomics and proteomics databases. Analysis of RNA expression pattern for missing proteins in Human protein Atlas showed that 28% of them, such as Olfactory receptor 1I1 ( O60431 ), had no RNA expression, suggesting the necessity to consider uncommon tissues for transcriptomic and proteomic studies. Interestingly, 21% had elevated expression level in a particular tissue (tissue-enriched proteins), indicating the importance of targeting such proteins in their elevated tissues. Additionally, the analysis of RNA expression level for missing proteins showed that 95% had no or low expression level (0-10 transcripts per million), indicating that low abundance is one of the major obstacles facing the detection of missing proteins. Moreover, missing proteins are predicted to generate fewer predicted unique tryptic peptides than the identified proteins. Searching for these predicted unique tryptic peptides that correspond to missing and uncertain proteins in the experimental peptide list of open-access MS-based databases (PA, GPM) resulted in the detection of 402 missing and 19 uncertain proteins with at least two unique peptides (≥9 aa) at <(5 × 10^-4)% FDR. Finally, matching the native spectra for the experimentally detected peptides with their SRMAtlas synthetic counterparts at three transition sources (QQQ, QTOF, QTRAP) gave us an opportunity to validate 41 missing proteins by ≥2 proteotypic peptides.

MEERCAT: Multiplexed Efficient Cell Free Expression of Recombinant QconCATs For Large Scale Absolute Proteome Quantification

A major challenge in proteomics is the absolute accurate quantification of large numbers of proteins. QconCATs, artificial proteins that are concatenations of multiple standard peptides, are well established as an efficient means to generate standards for proteome quantification. Previously, QconCATs have been expressed in bacteria, but we now describe QconCAT expression in a robust, cell-free system. The new expression approach rescues QconCATs that previously were unable to be expressed in bacteria and can reduce the incidence of proteolytic damage to QconCATs. Moreover, it is possible to cosynthesize QconCATs in a highly-multiplexed translation reaction, coexpressing tens or hundreds of QconCATs simultaneously. By obviating bacterial culture and through the gain of high level multiplexing, it is now possible to generate tens of thousands of standard peptides in a matter of weeks, rendering absolute quantification of a complex proteome highly achievable in a reproducible, broadly deployable system.

Data acquisition workflows in liquid chromatography coupled to high resolution mass spectrometry-based metabolomics: Where do we stand?

Typical mass spectrometry (MS) based untargeted metabolomics protocols are tedious as well as time- and sample-consuming. In particular, they often rely on "full-scan-only" analyses using liquid chromatography (LC) coupled to high resolution mass spectrometry (HRMS) from which metabolites of interest are first highlighted, and then tentatively identified by using targeted MS/MS experiments. However, this situation is evolving with the emergence of integrated HRMS based-data acquisition protocols able to perform multi-event acquisitions. Most of these protocols, referring to as data dependent and data independent acquisition (DDA and DIA, respectively), have been initially developed for proteomic applications and have recently demonstrated their applicability to biomedical studies. In this context, the aim of this article is to take stock of the progress made in the field of DDA- and DIA-based protocols, and evaluate their ability to change conventional metabolomic and lipidomic data acquisition workflows, through a review of HRMS instrumentation, DDA and DIA workflows, and also associated informatics tools.

Proteomic analysis of human lacrimal and tear fluid in dry eye disease

To understand the pathophysiology of dry eye disease (DED), it is necessary to characterize proteins in the ocular surface fluids, including tear fluid (TF) and lacrimal fluid (LF). There have been several reports of TF proteomes, but few proteomic studies have examined LF secreted from the lacrimal gland (LG). Therefore, we characterized the proteins constituting TF and LF by liquid chromatography mass spectrometry. TF and LF were collected from patients with non-Sjögren syndrome DED and from healthy subjects. Through protein profiling and label-free quantification, 1165 proteins from TF and 1448 from LF were identified. In total, 849 proteins were present in both TF and LF. Next, candidate biomarkers were verified using the multiple reaction monitoring assay in both TF and LF of 17 DED patients and 17 healthy controls. As a result, 16 marker proteins were identified (fold-change > 1.5, p-value < 0.05), of which 3 were upregulated in TF and 8 up- and 5 down-regulated in LF. In conclusion, this study revealed novel DED markers originating from the LG and tears by in-depth proteomic analysis and comparison of TF and LF proteins.

Enhanced Missing Proteins Detection in NCI60 Cell Lines Using an Integrative Search Engine Approach

The Human Proteome Project (HPP) aims deciphering the complete map of the human proteome. In the past few years, significant efforts of the HPP teams have been dedicated to the experimental detection of the missing proteins, which lack reliable mass spectrometry evidence of their existence. In this endeavor, an in depth analysis of shotgun experiments might represent a valuable resource to select a biological matrix in design validation experiments. In this work, we used all the proteomic experiments from the NCI60 cell lines and applied an integrative approach based on the results obtained from Comet, Mascot, OMSSA, and X!Tandem. This workflow benefits from the complementarity of these search engines to increase the proteome coverage. Five missing proteins C-HPP guidelines compliant were identified, although further validation is needed. Moreover, 165 missing proteins were detected with only one unique peptide, and their functional analysis supported their participation in cellular pathways as was also proposed in other studies. Finally, we performed a combined analysis of the gene expression levels and the proteomic identifications from the common cell lines between the NCI60 and the CCLE project to suggest alternatives for further validation of missing protein observations.

Progress on the HUPO Draft Human Proteome: 2017 Metrics of the Human Proteome Project

The Human Proteome Organization (HUPO) Human Proteome Project (HPP) continues to make progress on its two overall goals: (1) completing the protein parts list, with an annual update of the HUPO draft human proteome, and (2) making proteomics an integrated complement to genomics and transcriptomics throughout biomedical and life sciences research. neXtProt version 2017-01-23 has 17 008 confident protein identifications (Protein Existence [PE] level 1) that are compliant with the HPP Guidelines v2.1 ( https://hupo.org/Guidelines ), up from 13 664 in 2012-12 and 16 518 in 2016-04. Remaining to be found by mass spectrometry and other methods are 2579 "missing proteins" (PE2+3+4), down from 2949 in 2016. PeptideAtlas 2017-01 has 15 173 canonical proteins, accounting for nearly all of the 15 290 PE1 proteins based on MS data. These resources have extensive data on PTMs, single amino acid variants, and splice isoforms. The Human Protein Atlas v16 has 10 492 highly curated protein entries with tissue and subcellular spatial localization of proteins and transcript expression. Organ-specific popular protein lists have been generated for broad use in quantitative targeted proteomics using SRM-MS or DIA-SWATH-MS studies of biology and disease.

Targeted proteomics reveals promising biomarkers of disease activity and organ involvement in antineutrophil cytoplasmic antibody-associated vasculitis

BACKGROUND

Targeted proteomics, which involves quantitative analysis of targeted proteins using selected reaction monitoring (SRM) mass spectrometry, has emerged as a new methodology for discovery of clinical biomarkers. In this study, we used targeted serum proteomics to identify circulating biomarkers for prediction of disease activity and organ involvement in antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV).

METHODS

A large-scale SRM assay targeting 135 biomarker candidates was established using a triple-quadrupole mass spectrometer coupled with nanoflow liquid chromatography. Target proteins in serum samples from patients in the active and remission (6 months after treatment) stages were quantified using the established assays. Identified marker candidates were further validated by enzyme-linked immunosorbent assay using serum samples (n = 169) collected in a large-cohort Japanese study (the RemIT-JAV-RPGN study).

RESULTS

Our proteomic analysis identified the following proteins as biomarkers for discriminating patients with highly active AAV from those in remission or healthy control subjects: tenascin C (TNC), C-reactive protein (CRP), tissue inhibitor of metalloproteinase 1 (TIMP1), leucine-rich alpha-2-glycoprotein 1, S100A8/A9, CD93, matrix metalloproteinase 9, and transketolase (TKT). Of these, TIMP1 was the best-performing marker of disease activity, allowing distinction between mildly active AAV and remission. Moreover, in contrast to CRP, serum levels of TIMP1 in patients with active AAV were significantly higher than those in patients with infectious diseases. The serum levels of TKT and CD93 were higher in patients with renal involvement than in those without, and they predicted kidney outcome. The level of circulating TNC was elevated significantly in patients with lung infiltration. AAV severity was associated with markers reflecting organ involvement (TKT, CD93, and TNC) rather than inflammation. The eight markers and myeloperoxidase (MPO)-ANCA were clustered into three groups: MPO-ANCA, renal involvement (TKT and CD93), and inflammation (the other six markers).

CONCLUSIONS

We have identified promising biomarkers of disease activity, disease severity, and organ involvement in AAV with a targeted proteomics approach using serum samples obtained from a large-cohort Japanese study. Especially, our analysis demonstrated the effectiveness of TIMP1 as a marker of AAV activity. In addition, we identified TKT and CD93 as novel markers for evaluation of renal involvement and kidney outcome in AAV.

A new mass spectrometry-based method for the quantification of histones in plasma from septic shock patients

The aim of this study was to develop a novel method to detect circulating histones H3 and H2B in plasma based on multiple reaction monitoring targeted mass spectrometry and a multiple reaction monitoring approach (MRM-MS) for its clinical application in critical bacteriaemic septic shock patients. Plasma samples from 17 septic shock patients with confirmed bacteraemia and 10 healthy controls were analysed by an MRM-MS method, which specifically detects presence of histones H3 and H2B. By an internal standard, it was possible to quantify the concentration of circulating histones in plasma, which were significantly higher in patients, and thus confirmed their potential as biomarkers for diagnosing septic shock. After comparing surviving patients and non-survivors, a correlation was found between higher levels of circulating histones and unfavourable outcome. Indeed, histone H3 proved a more efficient and sensitive biomarker for septic shock prognosis. In conclusion, these findings suggest the accuracy of the MRM-MS technique and stable isotope labelled peptides to detect and quantify circulating plasma histones H2B and H3. This method may be used for early septic shock diagnoses and for the prognosis of fatal outcomes.

Current and Emerging Technologies for Probing Molecular Signatures of Traumatic Brain Injury

Traumatic brain injury (TBI) is understood as an interplay between the initial injury, subsequent secondary injuries, and a complex host response all of which are highly heterogeneous. An understanding of the underlying biology suggests a number of windows where mechanistically inspired interventions could be targeted. Unfortunately, biologically plausible therapies have to-date failed to translate into clinical practice. While a number of stereotypical pathways are now understood to be involved, current clinical characterization is too crude for it to be possible to characterize the biological phenotype in a truly mechanistically meaningful way. In this review, we examine current and emerging technologies for fuller biochemical characterization by the simultaneous measurement of multiple, diverse biomarkers. We describe how clinically available techniques such as cerebral microdialysis can be leveraged to give mechanistic insights into TBI pathobiology and how multiplex proteomic and metabolomic techniques can give a more complete description of the underlying biology. We also describe spatially resolved label-free multiplex techniques capable of probing structural differences in chemical signatures. Finally, we touch on the bioinformatics challenges that result from the acquisition of such large amounts of chemical data in the search for a more mechanistically complete description of the TBI phenotype.

Multi-laboratory assessment of reproducibility, qualitative and quantitative performance of SWATH-mass spectrometry

Quantitative proteomics employing mass spectrometry is an indispensable tool in life science research. Targeted proteomics has emerged as a powerful approach for reproducible quantification but is limited in the number of proteins quantified. SWATH-mass spectrometry consists of data-independent acquisition and a targeted data analysis strategy that aims to maintain the favorable quantitative characteristics (accuracy, sensitivity, and selectivity) of targeted proteomics at large scale. While previous SWATH-mass spectrometry studies have shown high intra-lab reproducibility, this has not been evaluated between labs. In this multi-laboratory evaluation study including 11 sites worldwide, we demonstrate that using SWATH-mass spectrometry data acquisition we can consistently detect and reproducibly quantify >4000 proteins from HEK293 cells. Using synthetic peptide dilution series, we show that the sensitivity, dynamic range and reproducibility established with SWATH-mass spectrometry are uniformly achieved. This study demonstrates that the acquisition of reproducible quantitative proteomics data by multiple labs is achievable, and broadly serves to increase confidence in SWATH-mass spectrometry data acquisition as a reproducible method for large-scale protein quantification.SWATH-mass spectrometry consists of a data-independent acquisition and a targeted data analysis strategy that aims to maintain the favorable quantitative characteristics on the scale of thousands of proteins. Here, using data generated by eleven groups worldwide, the authors show that SWATH-MS is capable of generating highly reproducible data across different laboratories.

Targeted proteomic assays for quantitation of proteins identified by proteogenomic analysis of ovarian cancer

Mass spectrometry (MS) based targeted proteomic methods such as selected reaction monitoring (SRM) are emerging as a promising tool for verification of candidate proteins in biological and biomedical applications. The Clinical Proteomic Tumor Analysis Consortium (CPTAC) of the National Cancer Institute has investigated the standardization and analytical validation of the SRM assays and demonstrated robust analytical performance on different instruments across different laboratories. An Assay Portal has also been established by CPTAC to provide the research community a resource consisting of large sets of targeted MS-based assays, and a depository to share assays publicly. Herein, we report the development of 98 SRM assays that have been thoroughly characterized according to the CPTAC Assay Characterization Guidance Document; 37 of these passed all five experimental tests. The assays cover 70 proteins previously identified at the protein level in ovarian tumors. The experiments, methods and results for characterizing these SRM assays for their MS response, repeatability, selectivity, stability, and endogenous detection are described in detail. Data are available via PeptideAtlas, Panorama and the CPTAC Assay Portal.

A wellness study of 108 individuals using personal, dense, dynamic data clouds

Personal data for 108 individuals were collected during a 9-month period, including whole genome sequences; clinical tests, metabolomes, proteomes, and microbiomes at three time points; and daily activity tracking. Using all of these data, we generated a correlation network that revealed communities of related analytes associated with physiology and disease. Connectivity within analyte communities enabled the identification of known and candidate biomarkers (e.g., gamma-glutamyltyrosine was densely interconnected with clinical analytes for cardiometabolic disease). We calculated polygenic scores from genome-wide association studies (GWAS) for 127 traits and diseases, and used these to discover molecular correlates of polygenic risk (e.g., genetic risk for inflammatory bowel disease was negatively correlated with plasma cystine). Finally, behavioral coaching informed by personal data helped participants to improve clinical biomarkers. Our results show that measurement of personal data clouds over time can improve our understanding of health and disease, including early transitions to disease states.

The maternal plasma proteome changes as a function of gestational age in normal pregnancy: a longitudinal study

OBJECTIVE

Pregnancy is accompanied by dramatic physiological changes in maternal plasma proteins. Characterization of the maternal plasma proteome in normal pregnancy is an essential step for understanding changes to predict pregnancy outcome. The objective of this study was to describe maternal plasma proteins that change in abundance with advancing gestational age and determine biological processes that are perturbed in normal pregnancy.

STUDY DESIGN

A longitudinal study included 43 normal pregnancies that had a term delivery of an infant who was appropriate for gestational age without maternal or neonatal complications. For each pregnancy, 3 to 6 maternal plasma samples (median, 5) were profiled to measure the abundance of 1125 proteins using multiplex assays. Linear mixed-effects models with polynomial splines were used to model protein abundance as a function of gestational age, and the significance of the association was inferred via likelihood ratio tests. Proteins considered to be significantly changed were defined as having the following: (1) >1.5-fold change between 8 and 40 weeks of gestation; and (2) a false discovery rate-adjusted value of P < .1. Gene ontology enrichment analysis was used to identify biological processes overrepresented among the proteins that changed with advancing gestation.

RESULTS

The following results were found: (1) Ten percent (112 of 1125) of the profiled proteins changed in abundance as a function of gestational age; (2) of the 1125 proteins analyzed, glypican-3, sialic acid-binding immunoglobulin-type lectin-6, placental growth factor, C-C motif-28, carbonic anhydrase 6, prolactin, interleukin-1 receptor 4, dual-specificity mitogen-activated protein kinase 4, and pregnancy-associated plasma protein-A had more than a 5-fold change in abundance across gestation (these 9 proteins are known to be involved in a wide range of both physiological and pathological processes, such as growth regulation, embryogenesis, angiogenesis immunoregulation, inflammation etc); and (3) biological processes associated with protein changes in normal pregnancy included defense response, defense response to bacteria, proteolysis, and leukocyte migration (false discovery rate, 10%).

CONCLUSION

The plasma proteome of normal pregnancy demonstrates dramatic changes in both the magnitude of changes and the fraction of the proteins involved. Such information is important to understand the physiology of pregnancy and the development of biomarkers to differentiate normal vs abnormal pregnancy and determine the response to interventions.

Scaling Proteome-Wide Reactions of Activity-Based Probes

Unified analysis of complex reactions of an activity-based probe with proteins in a proteome remains an unsolved challenge. We propose a power expression, rate = k^obs[Probe]^α, for scaling the progress of proteome-wide reactions and use the scaling factor (0 ≤ α ≤ 1) as an apparent, partial order with respect to the probe to measure the "enzyme-likeness" for a protein in reaction acceleration. Thus, α reports the intrinsic reactivity of the protein with the probe. When α = 0, the involved protein expedites the reaction to the maximal degree; when α = 1, the protein reacts with the probe via an unaccelerated, bimolecular reaction. The selectivity (β) of the probe reacting with two proteins is calculated as a ratio of conversion factors (k^obs values) for corresponding power equations. A combination of α and β provides a tiered system for quantitatively assessing the probe efficacy; an ideal probe exhibits high reactivity with its protein targets (low in α) and is highly selective (high in β) in forming the probe-protein adducts. The scaling analysis was demonstrated using proteome-wide reactions of HT-29 cell lysates with a model probe of threonine β-lactone.

The cellular ceramide transport protein CERT promotes Chlamydia psittaci infection and controls bacterial sphingolipid uptake

Chlamydiaceae are bacterial pathogens that cause diverse diseases in humans and animals. Despite their broad host and tissue tropism, all Chlamydia species share an obligate intracellular cycle of development and have evolved sophisticated mechanisms to interact with their eukaryotic host cells. Here, we have analysed interactions of the zoonotic pathogen Chlamydia psittaci with a human epithelial cell line. We found that C. psittaci recruits the ceramide transport protein (CERT) to its inclusion. Chemical inhibition and CRISPR/Cas9-mediated knockout of CERT showed that CERT is a crucial factor for C. psittaci infections thereby affecting different stages of the infection including inclusion growth and infectious progeny formation. Interestingly, the uptake of fluorescently labelled sphingolipids in bacteria inside the inclusion was accelerated in CERT-knockout cells indicating that C. psittaci can exploit CERT-independent sphingolipid uptake pathways. Moreover, the CERT-specific inhibitor HPA-12 strongly diminished sphingolipid transport to inclusions of infected CERT-knockout cells, suggesting that other HPA-12-sensitive factors are involved in sphingolipid trafficking to C. psittaci. Further analysis is required to decipher these interactions and to understand their contributions to bacterial development, host range, tissue tropism, and disease outcome.

An Optimized Shotgun Strategy for the Rapid Generation of Comprehensive Human Proteomes

This study investigates the challenge of comprehensively cataloging the complete human proteome from a single-cell type using mass spectrometry (MS)-based shotgun proteomics. We modify a classical two-dimensional high-resolution reversed-phase peptide fractionation scheme and optimize a protocol that provides sufficient peak capacity to saturate the sequencing speed of modern MS instruments. This strategy enables the deepest proteome of a human single-cell type to date, with the HeLa proteome sequenced to a depth of ∼584,000 unique peptide sequences and ∼14,200 protein isoforms (∼12,200 protein-coding genes). This depth is comparable with next-generation RNA sequencing and enables the identification of post-translational modifications, including ∼7,000 N-acetylation sites and ∼10,000 phosphorylation sites, without the need for enrichment. We further demonstrate the general applicability and clinical potential of this proteomics strategy by comprehensively quantifying global proteome expression in several different human cancer cell lines and patient tissue samples.

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Multi-shot proteomics quantifies the protein levels of 12,200+ genes in HeLa cells

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This essentially complete HeLa proteome has coverage similar to next-gen RNA-seq

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Deep coverage of major PTMs is achieved without specific enrichment

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The approach is extendable to other human cell lines and patient samples