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IntroductionSjögren’s syndrome (SS) is a systemic autoimmune disease, which affects the exocrine glands leading to glandular dysfunction and, particularly, symptoms of oral and ocular dryness. The aetiology of SS remains unclear, and the disease lacks distinctive clinical features. The current diagnostic work-up is complex, invasive and often time-consuming. Thus, there is an emerging need for identifying disease-specific and, ideally, non-invasive immunological and molecular biomarkers that can simplify the diagnostic process, allow stratification of patients, and assist in monitoring the disease course and outcome of therapeutic intervention in SS.MethodsThis systematic review addresses the use of proteomics and miRNA-expression profile analyses in this regard.Results and discussionOut of 272 papers that were identified and 108 reviewed, a total of 42 papers on proteomics and 23 papers on miRNA analyses in saliva, blood and salivary gland tissue were included in this review. Overall, the proteomic and miRNA studies revealed considerable variations with regard to candidate biomarker proteins and miRNAs, most likely due to variation in sample size, processing and analytical methods, but also reflecting the complexity of SS and patient heterogeneity. However, interesting novel knowledge has emerged and further validation is needed to confirm their potential role as biomarkers in SS.

Idiopathic pulmonary fibrosis is a progressive lung disease that causes scarring and loss of lung function. Macrophages play a key role in fibrosis, but their responses to altered morphological and mechanical properties of the extracellular matrix in fibrosis is relatively unexplored. Our previous work showed functional changes in murine fetal liver-derived alveolar macrophages on fibrous or globular collagen morphologies. In this study, we applied differential proteomics to further investigate molecular mechanisms underlying the observed functional changes. Macrophages cultured on uncoated, fibrous, or globular collagen-coated plastic were analyzed by liquid chromatography-mass spectrometry. The presence of collagen affected expression of 77 proteins, while 142 were differentially expressed between macrophages grown on fibrous or globular collagen. Biological process and pathway enrichment analysis revealed that culturing on any type of collagen induced higher expression of enzymes involved in glycolysis. However, this did not lead to a higher rate of glycolysis, probably because of a concomitant decrease in activity of these enzymes. Our data suggest that macrophages sense collagen morphologies and can respond with changes in expression and activity of metabolism-related proteins. These findings suggest intimate interactions between macrophages and their surroundings that may be important in repair or fibrosis of lung tissue.

As novel liquid chromatography-mass spectrometry (LC-MS) technologies for proteomics offer a substantial increase in LC-MS runs per day, robust and reproducible sample preparation emerges as a new bottleneck for throughput. We introduce a novel strategy for positive-pressure 96-well filter-aided sample preparation (PF96) on a commercial positive-pressure solid-phase extraction device. PF96 allows for a five-fold increase in throughput in conjunction with extraordinary reproducibility with Pearson product-moment correlations on the protein level of r = 0.9993, as demonstrated for mouse heart tissue lysate in 40 technical replicates. The targeted quantification of 16 peptides in the presence of stable-isotope-labeled reference peptides confirms that PF96 variance is barely assessable against technical variation from nanoLC-MS instrumentation. We further demonstrate that protein loads of 36-60 μg result in optimal peptide recovery, but lower amounts ≥3 μg can also be processed reproducibly. In summary, the reproducibility, simplicity, and economy of time provide PF96 a promising future in biomedical and clinical research.

Retinal artery occlusion (RAO) is a devastating condition with no effective treatment. The management of RAO could potentially be improved through an in-depth understanding of the molecular alterations in the condition. This study combined advanced proteomic techniques and an experimental model to uncover the retinal large-scale protein profile of RAO. In 13 pigs, RAO was induced with an argon laser and confirmed by fluorescein angiography. Left eyes serving as controls received a sham laser without inducing occlusion. Retinal samples were collected after one, three, or six days and analyzed with liquid chromatography—tandem mass spectrometry. In RAO, 36 proteins were differentially regulated on day one, 86 on day three, and 557 on day six. Upregulated proteins included clusterin, vitronectin, and vimentin, with several proteins increasing over time with a maximum on day six, including clusterin, vimentin, osteopontin, annexin-A, signal transducer, and the activator of transcription 3. On day six, RAO resulted in the upregulation of proteins involved in cellular response to stress, hemostasis, innate immune response, and cytokine signaling. Downregulated proteins were involved in transmission across chemical synapses and visual phototransduction. This study identified the upregulation of multiple inflammatory proteins in RAO and the downregulation of proteins involved in visual pathways. Retinal artery occlusion (RAO) is a devastating condition with no effective treatment. The management of RAO could potentially be improved through an in-depth understanding of the molecular alterations in the condition. This study combined advanced proteomic techniques and an experimental model to uncover the retinal large-scale protein profile of RAO. In 13 pigs, RAO was induced with an argon laser and confirmed by fluorescein angiography. Left eyes serving as controls received a sham laser without inducing occlusion. Retinal samples were collected after one, three, or six days and analyzed with liquid chromatography—tandem mass spectrometry. In RAO, 36 proteins were differentially regulated on day one, 86 on day three, and 557 on day six. Upregulated proteins included clusterin, vitronectin, and vimentin, with several proteins increasing over time with a maximum on day six, including clusterin, vimentin, osteopontin, annexin-A, signal transducer, and the activator of transcription 3. On day six, RAO resulted in the upregulation of proteins involved in cellular response to stress, hemostasis, innate immune response, and cytokine signaling. Downregulated proteins were involved in transmission across chemical synapses and visual phototransduction. This study identified the upregulation of multiple inflammatory proteins in RAO and the downregulation of proteins involved in visual pathways.

Data-independent acquisition (DIA) methods have become increasingly attractive in mass spectrometry (MS)-based proteomics, because they enable high data completeness and a wide dynamic range. Recently, we combined DIA with parallel accumulation – serial fragmentation (dia-PASEF) on a Bruker trapped ion mobility separated (TIMS) quadrupole time-of-flight (TOF) mass spectrometer. This requires alignment of the ion mobility separation with the downstream mass selective quadrupole, leading to a more complex scheme for dia-PASEF window placement compared to DIA. To achieve high data completeness and deep proteome coverage, here we employ variable isolation windows that are placed optimally depending on precursor density in the m/z and ion mobility plane. This Automatic Isolation Design procedure is implemented in the freely available py_diAID package. In combination with in-depth project-specific proteomics libraries and the Evosep LC system, we reproducibly identified over 7,700 proteins in a human cancer cell line in 44 minutes with quadruplicate single-shot injections at high sensitivity. Even at a throughput of 100 samples per day (11 minutes LC gradients), we consistently quantified more than 6,000 proteins in mammalian cell lysates by injecting four replicates. We found that optimal dia-PASEF window placement facilitates in-depth phosphoproteomics with very high sensitivity, quantifying more than 35,000 phosphosites in a human cancer cell line stimulated with an epidermal growth factor (EGF) in triplicate 21 minutes runs. This covers a substantial part of the regulated phosphoproteome with high sensitivity, opening up for extensive systems-biological studies.

Motivation In quantitative bottom-up mass spectrometry (MS)-based proteomics, the reliable estimation of protein concentration changes from peptide quantifications between different biological samples is essential. This estimation is not a single task but comprises the two processes of protein inference and protein abundance summarization. Furthermore, due to the high complexity of proteomics data and associated uncertainty about the performance of these processes, there is a demand for comprehensive visualization methods able to integrate protein with peptide quantitative data including their post-translational modifications. Hence, there is a lack of a suitable tool that provides post-identification quantitative analysis of proteins with simultaneous interactive visualization. Results In this article, we present VIQoR, a user-friendly web service that accepts peptide quantitative data of both labeled and label-free experiments and accomplishes the crucial components protein inference and summarization and interactive visualization modules, including the novel VIQoR plot. We implemented two different parsimonious algorithms to solve the protein inference problem, while protein summarization is facilitated by a well-established factor analysis algorithm called fast-FARMS followed by a weighted average summarization function that minimizes the effect of missing values. In addition, summarization is optimized by the so-called Global Correlation Indicator (GCI). We test the tool on three publicly available ground truth datasets and demonstrate the ability of the protein inference algorithms to handle shared peptides. We furthermore show that GCI increases the accuracy of the quantitative analysis in datasets with replicated design. Availability and implementation VIQoR is accessible at: http://computproteomics.bmb.sdu.dk/Apps/VIQoR/. The source code is available at: https://bitbucket.org/veitveit/viqor/. Supplementary information Supplementary data are available at Bioinformatics online.

Protein phosphorylation dynamically integrates environmental and cellular information to control biological processes. Identifying functional phosphorylation amongst the thousands of phosphosites regulated by a perturbation at a global scale is a major challenge. Here we introduce ‘personalized phosphoproteomics’, a combination of experimental and computational analyses to link signaling with biological function by utilizing human phenotypic variance. We measure individual subject phosphoproteome responses to interventions with corresponding phenotypes measured in parallel. Applying this approach to investigate how exercise potentiates insulin signaling in human skeletal muscle, we identify both known and previously unidentified phosphosites on proteins involved in glucose metabolism. This includes a cooperative relationship between mTOR and AMPK whereby the former directly phosphorylates the latter on S377, for which we find a role in metabolic regulation. These results establish personalized phosphoproteomics as a general approach for investigating the signal transduction underlying complex biology. Functionally relevant phosphorylation sites are detected by integrating phosphoproteomic and phenotypic data.

Spectrum clustering is a powerful strategy to minimize redundant mass spectral data by grouping highly similar mass spectra corresponding to repeatedly measured analytes. Based on spectrum similarity, near-identical spectra are grouped in clusters, after which each cluster can be represented by its so-called consensus spectrum for downstream processing. Although several algorithms for spectrum clustering have been adequately benchmarked and tested, the influence of the consensus spectrum generation step is rarely evaluated. Here, we present an implementation and benchmark of common consensus spectrum algorithms, including spectrum averaging, spectrum binning, the most similar spectrum, and the best-identified spectrum. We have analyzed diverse public datasets using two different clustering algorithms (spectra-cluster and MaRaCluster) to evaluate how the consensus spectrum generation procedure influences downstream peptide identification. The BEST and BIN methods were found the most reliable methods for consensus spectrum generation, including for datasets with post-translational modifications (PTM) such as phosphorylation. All source code and data of the present study are freely available on GitHub at https://github.com/statisticalbiotechnology/representative-spectra-benchmark.

Species determination based on genetic evidence is an indispensable tool in archaeology, forensics, ecology, and food authentication. Most available analytical approaches involve compromises with regard to the number of detectable species, high cost due to low throughput, or a labor-intensive manual process. Here, we introduce “Species by Proteome INvestigation” (SPIN), a shotgun proteomics workflow for analyzing archaeological bone capable of querying over 150 mammalian species by liquid chromatography-tandem mass spectrometry (LC-MS/MS). Rapid peptide chromatography and data-independent acquisition (DIA) with throughput of 200 samples per day reduce expensive MS time, whereas streamlined sample preparation and automated data interpretation save labor costs. We confirm the successful classification of known reference bones, including domestic species and great apes, beyond the taxonomic resolution of the conventional peptide mass fingerprinting (PMF)-based Zooarchaeology by Mass Spectrometry (ZooMS) method. In a blinded study of degraded Iron-Age material from Scandinavia, SPIN produces reproducible results between replicates, which are consistent with morphological analysis. Finally, we demonstrate the high throughput capabilities of the method in a high-degradation context by analyzing more than two hundred Middle and Upper Palaeolithic bones from Southern European sites with late Neanderthal occupation. While this initial study is focused on modern and archaeological mammalian bone, SPIN will be open and expandable to other biological tissues and taxa.

Proteomic profiling of extracellular vesicles (EVs) represents a promising approach for early detection and therapeutic monitoring of diseases such as cancer. The focus of this study was to apply robust EV isolation and subsequent data-independent acquisition mass spectrometry (DIA-MS) for urinary EV proteomics of prostate cancer and prostate inflammation patients. Urinary EVs were isolated by functionalized magnetic beads through chemical affinity on an automatic station, and EV proteins were analyzed by integrating three library-base analyses (Direct-DIA, GPF-DIA, and Fractionated DDA-base DIA) to improve the coverage and quantitation. We assessed the levels of urinary EV-associated proteins based on 40 samples consisting of 20 cases and 20 controls, where 18 EV proteins were identified to be differentiated in prostate cancer outcome, of which three (i.e., SERPINA3, LRG1, and SCGB3A1) were shown to be consistently upregulated. We also observed 6 out of the 18 (33%) EV proteins that had been developed as drug targets, while some of them showed protein-protein interactions. Moreover, the potential mechanistic pathways of 18 significantly different EV proteins were enriched in metabolic, immune, and inflammatory activities. These results showed consistency in an independent cohort with 20 participants. Using a random forest algorithm for classification assessment, including the identified EV proteins, we found that SERPINA3, LRG1, or SCGB3A1 add predictable value in addition to age, prostate size, body mass index (BMI), and prostate-specific antigen (PSA). In summary, the current study demonstrates a translational workflow to identify EV proteins as molecular markers to improve the clinical diagnosis of prostate cancer.

Prognostic characteristics inform risk stratification in intensive care unit (ICU) patients with coronavirus disease 2019 (COVID-19). We obtained blood samples (n = 474) from hospitalized COVID-19 patients (n = 123), non-COVID-19 ICU sepsis patients (n = 25) and healthy controls (n = 30). Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA was detected in plasma or serum (RNAemia) of COVID-19 ICU patients when neutralizing antibody response was low. RNAemia is associated with higher 28-day ICU mortality (hazard ratio [HR], 1.84 [95% CI, 1.22–2.77] adjusted for age and sex). RNAemia is comparable in performance to the best protein predictors. Mannose binding lectin 2 and pentraxin-3 (PTX3), two activators of the complement pathway of the innate immune system, are positively associated with mortality. Machine learning identified ‘Age, RNAemia’ and ‘Age, PTX3’ as the best binary signatures associated with 28-day ICU mortality. In longitudinal comparisons, COVID-19 ICU patients have a distinct proteomic trajectory associated with mortality, with recovery of many liver-derived proteins indicating survival. Finally, proteins of the complement system and galectin-3-binding protein (LGALS3BP) are identified as interaction partners of SARS-CoV-2 spike glycoprotein. LGALS3BP overexpression inhibits spike-pseudoparticle uptake and spike-induced cell-cell fusion in vitro., Here the authors use RT-qPCR and mass spectrometry to analyze longitudinal blood samples from intensive care unit (ICU) COVID-19 patients and controls. They find that viral RNA and pentraxin-3 predict 28-day ICU mortality and that galectin-3-binding protein is an interaction partner of SARS-CoV-2 spike glycoprotein with antiviral properties.

Identifying cancer biomarkers is of great interest for patient prognosis and monitoring. However, tumor-associated factors are diluted in blood and challenging to detect. Broggi et al. show that lymphatic exudate, accessible at the time of lymphadenectomy, is highly enriched in tumor-associated factors and therefore a promising source for biomarker discovery., Liquid biopsies allow monitoring of cancer progression and detection of relapse, but reliable biomarkers in melanoma are lacking. Because secreted factors preferentially drain to lymphatic vessels before dilution in the blood, we hypothesized that lymph should be vastly enriched in cancer biomarkers. We characterized postoperative lymphatic exudate and plasma of metastatic melanoma patients after lymphadenectomy and found a dramatic enrichment in lymphatic exudate of tumor-derived factors and especially extracellular vesicles containing melanoma-associated proteins and miRNAs, with unique protein signatures reflecting early versus advanced metastatic spread. Furthermore, lymphatic exudate was enriched in memory T cells, including tumor-reactive CD137+ and stem cell–like types. In mice, lymph vessels were the major route of extracellular vesicle transport from tumors to the systemic circulation. We suggest that lymphatic exudate provides a rich source of tumor-derived factors for enabling the discovery of novel biomarkers that may reflect disease stage and therapeutic response., Graphical Abstract

Mass spectrometry (MS) is the state-of-the-art methodology for capturing the breadth and depth of the immunopeptidome across human leukocyte antigen (HLA) allotypes and cell types. The majority of studies in the immunopeptidomics field are discovery driven. Hence, data-dependent tandem MS (MS/MS) acquisition (DDA) is widely used, as it generates high-quality references of peptide fingerprints. However, DDA suffers from the stochastic selection of abundant ions that impairs sensitivity and reproducibility. In contrast, in data-independent acquisition (DIA), the systematic fragmentation and acquisition of all fragment ions within given isolation m/z windows yield a comprehensive map for a given sample. However, many DIA approaches commonly require generating comprehensive DDA-based spectrum libraries, which can become impractical for studying noncanonical and personalized neoantigens. Because the amount of HLA peptides eluted from biological samples such as small tissue biopsies is typically not sufficient for acquiring both meaningful DDA data necessary for generating comprehensive spectral libraries and DIA MS measurements, the implementation of DIA in the immunopeptidomics translational research domain has remained limited. We implemented a DIA immunopeptidomics workflow and assessed its sensitivity and accuracy by matching DIA data against libraries with growing complexity—from sample-specific libraries to libraries combining 2 to 40 different immunopeptidomics samples. Analyzing DIA immunopeptidomics data against a complex multi-HLA spectral library resulted in a two-fold increase in peptide identification compared with sample-specific library and in a three-fold increase compared with DDA measurements, yet with no detrimental effect on the specificity. Furthermore, we demonstrated the implementation of DIA for sensitive personalized neoantigen discovery through the analysis of DIA data with predicted MS/MS spectra of clinically relevant HLA ligands. We conclude that a comprehensive multi-HLA library for DIA approach in combination with MS/MS prediction is highly advantageous for clinical immunopeptidomics, especially when low amounts of biological samples are available., Graphical Abstract, Highlights • So far, DIA in the immunopeptidomics translational research has been limited. • DIA immunopeptidomics data were analyzed against a complex multi-HLA spectral library. • This resulted in improved sensitivity with no detrimental effect on the specificity. • We implemented DIA for clinical antigen discovery combined with predicted MS/MS spectra., In Brief The implementation of DIA in the immunopeptidomics translational research domain has remained limited because the amount of HLA peptides eluted from clinical samples is typically not sufficient for acquiring both meaningful DDA data for generating comprehensive spectral libraries and DIA MS measurements. We implemented a DIA immunopeptidomics workflow and assessed its sensitivity and accuracy with libraries of growing complexity and multi-HLA libraries. In addition, we demonstrated the analysis of DIA data with predicted MS/MS spectra of clinically relevant HLA ligands.

Cancer has no borders: Generation and analysis of molecular data across multiple centers worldwide is necessary to gain statistically significant clinical insights for the benefit of patients. Here we conceived and standardized a proteotype data generation and analysis workflow enabling distributed data generation and evaluated the quantitative data generated across laboratories of the international Cancer Moonshot consortium. Using harmonized mass spectrometry (MS) instrument platforms and standardized data acquisition procedures, we demonstrate robust, sensitive, and reproducible data generation across eleven international sites on seven consecutive days in a 24/7 operation mode. The data presented from the high-resolution MS1-based quantitative data-independent acquisition (HRMS1-DIA) workflow shows that coordinated proteotype data acquisition is feasible from clinical specimens using such standardized strategies. This work paves the way for the distributed multi-omic digitization of large clinical specimen cohorts across multiple sites as a prerequisite for turning molecular precision medicine into reality., Distributed multi-omic digitization of clinical specimen across multiple sites is a prerequisite for turning molecular precision medicine into reality. Here, the authors show that coordinated proteotype data acquisition is feasible using standardized MS data acquisition and analysis strategies.

Background In recent times, Plasmodium vivax (P. vivax) has become a serious threat to public health due to its ability to cause severe infection with fatal outcomes. Its unique biology makes it resilient to control measures that are otherwise effective against P. falciparum. A deeper understanding of P. vivax biology and pathogenesis is, therefore, essential for developing the right control strategies. Proteomics of P. falciparum has been helpful in studying disease biology and elucidating molecular mechanisms involved in the development of disease. However, unlike P. falciparum, proteomics data for P. vivax infection is minimal due to the absence of a continuous culture system. The dependence on clinical samples and animal models has drastically limited P. vivax research, creating critical knowledge gaps in our understanding of the disease. This study describes an in-depth proteomics analysis of P. vivax-infected human plasma and parasite isolates, to understand parasite biology, pathogenesis, and to identify new diagnostic targets for P. vivax malaria. Methods A mass-spectrometry- (MS) based proteomics approach (Q Exactive) was applied to analyze human plasma and parasite isolates from vivax malaria patients visiting a primary health centre in India. Additionally, a targeted proteomics assay was standardized for validating unique peptides of most recurring parasite proteins. Results Thirty-eight P. vivax proteins were detected in human plasma with high confidence. Several glycolytic enzymes were found along with hypothetical, cytoskeletal, ribosomal, and nuclear proteins. Additionally, 103 highly abundant P. vivax proteins were detected in parasite isolates. This represents the highest number of parasite proteins to be reported from clinical samples so far. Interestingly, five of these; three Plasmodium exported proteins (PVX_003545, PVX_003555 and PVX_121935), a hypothetical protein (PVX_083555) and Pvstp1 (subtelomeric transmembrane protein 1, PVX_094303) were found in both plasma and parasite isolates. Conclusions A parasite proteomics investigation is essential to understand disease pathobiology and design novel interventions. Control strategies against P. vivax also depend on early diagnosis. This work provides deeper insights into the biology of P. vivax by identifying proteins expressed by the parasite during its complex life-cycle within the human host. The study also reports antigens that may be explored as diagnostic candidates.

Objective: Staphylococcus epidermidis, as a primary colonizer, is strongly associated with infections of (dental) implants (i.e., peri-implantitis), but little is known about the surface proteome of this bacterium. For the identification of bacterial adhesins, this study investigated the surface proteome of S. epidermidis adhering directly to titanium implant substrata.Materials and methods: S. epidermidis strain ATTC 35984 was cultured either planktonically or on titanium implant specimens. The surface proteomes were isolated by mutanolysin digestion, and proteins were separated by 2D gel electrophoreses to reveal highly expressed proteins only. Protein spots were visualized by silver staining and proteins were identified by mass spectrometry. Results: Surface proteome analyses of S. epidermidis on titanium identified six expressed proteins. Three proteins were highly expressed on the titanium implants including accumulation-associated protein Q8CQD9. These specific proteins could be potential pathogenicity factors of bacteria in peri-implant biofilms. Conclusion: For the first time, our study identified S. epidermidis surface proteins, which are expressed after adhesion to titanium implant materials. Clinical relevance: Our study reveals possible candidates for a newly protein-based vaccine against peri-implantitis.

The promising pipeline of therapeutic monoclonal antibodies (mAbs) demands robust bioanalytical methods with swift development times for pharmacokinetic studies. Over the past decades ligand binding assays were the methods of choice for absolute quantification. However, the production of the required anti-idiotypic antibodies and ligands limits high-throughput method development for sensitive, accurate, and reproducible quantification of therapeutic mAbs. In recent years, high-resolution liquid chromatography tandem mass-spectrometry (LC-MS) systems have enabled absolute quantification of therapeutic mAbs with short method development times. These systems have additional benefits, such as a large linear dynamic range, a high specificity and the option of multiplexing. Here, we briefly discuss the current strategies for the quantification of therapeutic mAbs in biological matrices using LC-MS analysis based on top-down and middle-down quantitative proteomics. Then, we present the widely used bottom-up method in a six-step workflow, which can be used as guidance for quantitative LC-MS/MS method development of mAbs. Finally, strengths and weaknesses of the bottom-up method, which currently provides the most benefits, are discussed in detail.

Large-scale single-cell analyses are of fundamental importance in order to capture biological heterogeneity within complex cell systems, but have largely been limited to RNA-based technologies. Here we present a comprehensive benchmarked experimental and computational workflow, which establishes global single-cell mass spectrometry-based proteomics as a tool for large-scale single-cell analyses. By exploiting a primary leukemia model system, we demonstrate both through pre-enrichment of cell populations and through a non-enriched unbiased approach that our workflow enables the exploration of cellular heterogeneity within this aberrant developmental hierarchy. Our approach is capable of consistently quantifying ~1000 proteins per cell across thousands of individual cells using limited instrument time. Furthermore, we develop a computational workflow (SCeptre) that effectively normalizes the data, integrates available FACS data and facilitates downstream analysis. The approach presented here lays a foundation for implementing global single-cell proteomics studies across the world., Single-cell proteomics can provide insights into the molecular basis for cellular heterogeneity. Here, the authors develop a multiplexed single-cell proteomics and computational workflow, and show that their strategy captures the cellular hierarchies in an Acute Myeloid Leukemia culture model.

Background Translational and post-translational control mechanisms in the cell result in widely observable differences between measured gene transcription and protein abundances. Herein, protein complexes are among the most tightly controlled entities by selective degradation of their individual proteins. They furthermore act as control hubs that regulate highly important processes in the cell and exhibit a high functional diversity due to their ability to change their composition and their structure. Better understanding and prediction of these functional states demands methods for the characterization of complex composition, behavior, and abundance across multiple cell states. Mass spectrometry provides an unbiased approach to directly determine protein abundances across different cell populations and thus to profile a comprehensive abundance map of proteins. Results We provide a tool to investigate the behavior of protein subunits in known complexes by comparing their abundance profiles across up to 140 cell types available in ProteomicsDB. Thorough assessment of different randomization methods and statistical scoring algorithms allows determining the significance of concurrent profiles within a complex, therefore providing insights into the conservation of their composition across human cell types as well as the identification of intrinsic structures in complex behavior to determine which proteins orchestrate complex function. This analysis can be extended to investigate common profiles within arbitrary protein groups. CoExpresso can be accessed through http://computproteomics.bmb.sdu.dk/Apps/CoExpresso. Conclusions With the CoExpresso web service, we offer a potent scoring scheme to assess proteins for their co-regulation and thereby offer insight into their potential for forming functional groups like protein complexes. Electronic supplementary material The online version of this article (10.1186/s12859-018-2573-8) contains supplementary material, which is available to authorized users.

Genome-wide localization of chromatin and transcription regulators can be detected by a variety of techniques. Here, we describe a novel method ‘greenCUT&RUN’ for genome-wide profiling of transcription regulators, which has a very high sensitivity, resolution, accuracy and reproducibility, whilst assuring specificity. Our strategy begins with tagging of the protein of interest with GFP and utilizes a GFP-specific nanobody fused to MNase to profile genome-wide binding events. By using a GFP-nanobody the greenCUT&RUN approach eliminates antibody dependency and variability. Robust genomic profiles were obtained with greenCUT&RUN, which are accurate and unbiased towards open chromatin. By integrating greenCUT&RUN with nanobody-based affinity purification mass spectrometry, ‘piggy-back’ DNA binding events can be identified on a genomic scale. The unique design of greenCUT&RUN grants target protein flexibility and yields high resolution footprints. In addition, greenCUT&RUN allows rapid profiling of mutants of chromatin and transcription proteins. In conclusion, greenCUT&RUN is a widely applicable and versatile genome-mapping technique.

Polycomb proteins assemble to form complexes with important roles in epigenetic regulation. The Polycomb Repressive Complex 2 (PRC2) modulates the di- and tri-methylation of lysine 27 on histone H3, each of which are associated with gene repression. Although three subunits, EZH1/2, SUZ12, and EED, form the catalytic core of PRC2, a wider group of proteins associate with low stoichiometry. This raises the question of whether dynamic variation of the PRC2 interactome results in alternative forms of the complex during differentiation. Here we compared the physical interactions of PRC2 in undifferentiated and differentiated states of NTERA2 pluripotent embryonic carcinoma cells. Label-free quantitative proteomics was used to assess endogenous immunoprecipitation of the EZH2 and SUZ12 subunits of PRC2. A high stringency data set reflecting the endogenous state of PRC2 was produced that included all previously reported core and associated PRC2 components, and several novel interacting proteins. Comparison of the interactomes obtained in undifferentiated and differentiated cells revealed candidate proteins that were enriched in complexes isolated from one of the two states. For example, SALL4 and ZNF281 associate with PRC2 in pluripotent cells, whereas PCL1 and SMAD3 preferentially associate with PRC2 in differentiating cells. Analysis of the mRNA and protein levels of these factors revealed that their association with PRC2 correlated with their cell state-specific expression. Taken together, we propose that dynamic changes to the PRC2 interactome during differentiation may contribute to directing its activity during cell fate transitions.

Deregulated cathepsin proteolysis occurs across numerous cancers, but in vivo substrates mediating tumorigenesis remain ill-defined. Applying 8-plex iTRAQ terminal amine isotopic labeling of substrates (TAILS), a systems-level N-terminome degradomics approach, we identified cathepsin B, H, L, S, and Z in vivo substrates and cleavage sites with the use of six different cathepsin knockout genotypes in the Rip1-Tag2 mouse model of pancreatic neuroendocrine tumorigenesis. Among 1,935 proteins and 1,114 N termini identified by TAILS, stable proteolytic products were identified in wild-type tumors compared with one or more different cathepsin knockouts (17%–44% of 139 cleavages). This suggests a lack of compensation at the substrate level by other cathepsins. The majority of neo-N termini (56%–83%) for all cathepsins was consistent with protein degradation. We validated substrates, including the glycolytic enzyme pyruvate kinase M2 associated with the Warburg effect, the ER chaperone GRP78, and the oncoprotein prothymosin-alpha. Thus, the identification of cathepsin substrates in tumorigenesis improves the understanding of cathepsin functions in normal physiology and cancer. ISSN:2666-3864 ISSN:2211-1247

Studies have shown that the process of extracellular vesicles (EVs) secretion and lysosome status are linked. When the lysosome is under stress, the cells would secrete more EVs to maintain cellular homeostasis. However, the process that governs lysosomal activity and EVs secretion remains poorly defined and we postulated that certain proteins essential for EVs biogenesis are constantly synthesized and preferentially sorted to the EVs rather than the lysosome. A pulsed stable isotope labelling of amino acids in cell culture (pSILAC) based quantitative proteomics methodology was employed to study the preferential localization of the newly synthesized proteins into the EVs over lysosome in mHypoA 2/28 hypothalamic cell line. Through proteomic analysis, we found numerous newly synthesized lysosomal enzymes&mdash, such as the cathepsin proteins&mdash, that preferentially localize into the EVs over the lysosome. Chemical inhibition against cathepsin D promoted EVs secretion and a change in the EVs protein composition and therefore indicates its involvement in EVs biogenesis. In conclusion, we applied a heavy isotope pulse/trace proteomic approach to study EVs biogenesis in hypothalamic cells. The results demonstrated the regulation of EVs secretion by the cathepsin proteins that may serve as a potential therapeutic target for a range of neurological disorder associated with energy homeostasis.

Throughout the HIV-1 replication cycle, complex host-pathogen interactions take place in the infected cell, leading to the production of new virions. The virus modulates the host cellular machinery in order to support its life cycle, while counteracting intracellular defense mechanisms. We investigated the dynamic host response to HIV-1 infection by systematically measuring transcriptomic, proteomic, and phosphoproteomic expression changes in infected and uninfected SupT1 CD4+ T cells at five time points of the viral replication process. By means of a Gaussian mixed-effects model implemented in the new R/Bioconductor package TMixClust, we clustered host genes based on their temporal expression patterns. We identified a proteo-transcriptomic gene expression signature of 388 host genes specific for HIV-1 replication. Comprehensive functional analyses of these genes confirmed the previously described roles of some of the genes and revealed novel key virus-host interactions affecting multiple molecular processes within the host cell, including signal transduction, metabolism, cell cycle, and immune system. The results of our analysis are accessible through a freely available, dedicated and user-friendly R/Shiny application, called PEACHi2.0. This resource constitutes a catalogue of dynamic host responses to HIV-1 infection that provides a basis for a more comprehensive understanding of virus-host interactions.

Objectives:Multi-analyte assays with algorithmic analyses (MAAAs) use combinations of circulating and clinical markers including omics-based sources for diagnostic and/or prognostic purposes. Assessing MAAAs is challenging under existing health technology assessment (HTA) methods or practices. We undertook a scoping review to explore the HTA methods used for MAAAs to identify the criteria used for clinical research and reimbursement purposes.Methods:This review included only non-companion (stand-alone) tests that are actionable and that have been evaluated by leading HTA or insurer/reimbursement bodies up to September 2017.Results:Twenty-five reports and articles evaluating seventeen MAAAs were examined, most of which have been developed in oncology. The two main models used were the EUnetHTA Core model and the Evaluation of Genomic Applications in Practice and Prevention ACCE framework. Clinical validity and utility criteria were used, as were economic, ethical, legal, and social aspects. Economic evidence on MAAAs was scarce, and there is no consensus on whether the perspectives used are sufficiently broad to include all relevant stakeholders.Conclusions:Clinical utility and efficiency were the most used criteria, with stronger evidence needed linking the use of the algorithm with the clinical outcomes in real-life practice. HTA bodies must as well consider questions related to the analytical validity of MAAAs or with organizational aspects. The two main models, the EUnetHTA Core model and the ACCE framework, could be adapted to the assessment of MAAAs.

Streptomycetes are multicellular bacteria with complex developmental cycles. They are of biotechnological importance as they produce most bioactive compounds used in biomedicine, e.g. antibiotic, antitumoral and immunosupressor compounds. Streptomyces genomes encode many Ser/Thr/Tyr kinases, making this genus an outstanding model for the study of bacterial protein phosphorylation events. We used mass spectrometry based quantitative proteomics and phosphoproteomics to characterize bacterial differentiation and activation of secondary metabolism of Streptomyces coelicolor. We identified and quantified 3461 proteins corresponding to 44.3% of the S. coelicolor proteome across three developmental stages: vegetative hypha (first mycelium); secondary metabolite producing hyphae (second mycelium); and sporulating hyphae. A total of 1350 proteins exhibited more than 2-fold expression changes during the bacterial differentiation process. These proteins include 136 regulators (transcriptional regulators, transducers, Ser/Thr/Tyr kinases, signaling proteins), as well as 542 putative proteins with no clear homology to known proteins which are likely to play a role in differentiation and secondary metabolism. Phosphoproteomics revealed 85 unique protein phosphorylation sites, 58 of them differentially phosphorylated during differentiation. Computational analysis suggested that these regulated protein phosphorylation events are implicated in important cellular processes, including cell division, differentiation, regulation of secondary metabolism, transcription, protein synthesis, protein folding and stress responses. We discovered a novel regulated phosphorylation site in the key bacterial cell division protein FtsZ (pSer319) that modulates sporulation and regulates actinorhodin antibiotic production. We conclude that manipulation of distinct protein phosphorylation events may improve secondary metabolite production in industrial streptomycetes, including the activation of cryptic pathways during the screening for new secondary metabolites from streptomycetes.

Blood is an ideal body fluid for the discovery or monitoring of diagnostic and prognostic protein biomarkers. However, discovering robust biomarkers requires the analysis of large numbers of samples to appropriately represent interindividual variability. To address this analytical challenge, we established a high-throughput and cost-effective proteomics workflow for accurate and comprehensive proteomics at an analytical depth applicable for clinical studies. For validation, we processed one μL each from 62 plasma samples in 96-well plates and analyzed the product by quantitative data-independent acquisition (DIA) liquid chromatography/mass spectrometry (LC/MS); the data were queried using feature quantification with Spectronaut. To show the applicability of our workflow to serum, we analyzed a unique set of samples from 48 chronic pancreatitis patients, pre- and post Total Pancreatectomy with Islet AutoTransplantation (TPIAT) surgery. We identified 16 serum proteins with a statistically significant abundance alteration, representing a molecular signature distinct from that of chronic pancreatitis. In summary, we established a cost-efficient high throughput workflow for comprehensive proteomics using PVDF-membrane-based digestion that is robust, automatable, and applicable to small plasma- and serum volumes, e.g. fingerstick. Application of this plasma/serum proteomics workflow resulted in the first mapping of the molecular implications of TPIAT on the serum proteome.

The choroid plexus epithelium within the brain ventricles secretes the majority of the cerebrospinal fluid (CSF). The luminal Na+-K+-ATPase acts in concert with a host of other transport proteins to mediate efficient fluid secretion across the epithelium. The CSF contains little protein buffer, but the pH value seems nonetheless maintained within narrow limits, even when faced with acid-base challenges. The involvement of choroid plexus acid-base transporters in CSF pH regulation is highlighted by the expression of several acid-base transporters in the epithelium. The aim of the present study was to identify novel acid-base transporters expressed in the luminal membrane of the choroid plexus epithelium to pave the way for systematic investigations of each candidate transporter in the regulation of CSF pH. Mass spectrometry analysis of proteins from epithelial cells isolated by fluorescence-activated cell sorting identified the Cl−/H+ exchangers ClC-3, -4, -5, and -7 in addition to known choroid plexus acid-base transporters. RT-PCR on FACS isolated epithelial cells confirmed the expression of the corresponding mRNAs, as well as Na+/H+ exchanger NHE6 mRNA. Both NHE6 and ClC-7 were immunolocalized to the luminal plasma membrane domain of the choroid plexus epithelial cells. Dynamic imaging of intracellular pH and membrane potential changes in isolated choroid plexus epithelial cells demonstrated Cl− gradient-driven changes in intracellular pH and membrane potential that are consistent with Cl−/H+ exchange. In conclusion, we have detected for the first time NHE6 and ClC-7 in the choroid plexus, which are potentially involved in pH regulation of the CSF.

Comprehensive knowledge of the human leukocyte antigen (HLA) class-I and class-II peptides presented to T-cells is crucial for designing innovative therapeutics against cancer and other diseases. However methodologies for their purification for mass-spectrometry analysis have been a major limitation. We designed a novel high-throughput, reproducible and sensitive method for sequential immuno-affinity purification of HLA-I and -II peptides from up to 96 samples in a plate format, suitable for both cell lines and tissues. Our methodology drastically reduces sample-handling and can be completed within five hours. We challenged our methodology by extracting HLA peptides from multiple replicates of tissues (n = 7) and cell lines (n = 21, 10 8 cells per replicate), which resulted in unprecedented depth, sensitivity and high reproducibility (Pearson correlations up to 0.98 and 0.97 for HLA-I and HLA-II). Because of the method's achieved sensitivity, even single measurements of peptides purified from 10 7 B-cells resulted in the identification of more than 1700 HLA-I and 2200 HLA-II peptides. We demonstrate the feasibility of performing drug-screening by using ovarian cancer cells treated with interferon gamma (IFNγ). Our analysis revealed an augmented presentation of chymotryptic-like and longer ligands associated with IFNγ induced changes of the antigen processing and presentation machinery. This straightforward method is applicable for basic and clinical applications.

TGFBIp is a constituent of the extracellular matrix in many human tissues including the cornea, where it is one of the most abundant proteins expressed. TGFBIp interacts with Type I, II, IV, VI and XII collagens as well as several members of the integrin family, suggesting it plays an important role in maintaining structural integrity and possibly corneal transparency as well. Significantly, more than 60 point mutations within the TGFBI gene have been reported to result in aberrant TGFBIp folding and aggregation in the cornea, resulting in severe visual impairment and blindness. Several studies have focused on targeting TGFBIp in the cornea as a therapeutic approach to treat TGFBI-linked corneal dystrophies, but the effect of this approach on corneal homeostasis and matrix integrity remained unknown. In the current study, we evaluated the histological and proteomic profiles of corneas from TGFBI-deficient mice as well as potential redundant functions of the paralogous protein POSTN. The absence of TGFBIp in mouse corneas did not grossly affect the collagen scaffold, and POSTN is unable to compensate for loss of TGFBIp. Proteomic comparison of wildtype and TGFBI(-/-) mice revealed 11 proteins were differentially regulated, including Type VI and XII collagens. However, as these alterations did not manifest at the macroscopic and behavioral levels, these data support partial or complete TGFBI knockdown as a potential therapy against TGFBI-linked corneal dystrophies. Lastly, in situ hybridization verified TGFBI mRNA in the epithelial cells but not in other cell types, supportive of a therapy directed specifically at this lineage. This article is protected by copyright. All rights reserved.

In the realm of clinical chemistry, the field of clinical proteomics, that is, the application of proteomic methods for understanding mechanisms and enabling diagnosis, prediction, measurement of activity, and treatment response in disease, is first and foremost a discovery and research tool that feeds assay development downstream. Putative new assay candidates generated by proteomics discovery projects compete with well-established assays with known indications, well-described performance, and of known value in specific clinical settings. Careful attention to the many options available in the design, execution, and interpretation of clinical proteomics studies is thus necessary for translation into clinical practice. We here review and discuss important options associated with clinical proteomics endeavors stretching from the planning phases to the final use in clinical chemistry.

The precise identification of Human Leukocyte Antigen class I (HLA-I) binding motifs plays a central role in our ability to understand and predict (neo-)antigen presentation in infectious diseases and cancer. Here, by exploiting co-occurrence of HLA-I alleles across ten newly generated as well as forty public HLA peptidomics datasets comprising more than 115,000 unique peptides, we show that we can rapidly and accurately identify many HLA-I binding motifs and map them to their corresponding alleles without any a priori knowledge of HLA-I binding specificity. Our approach recapitulates and refines known motifs for 43 of the most frequent alleles, uncovers new motifs for 9 alleles that up to now had less than five known ligands and provides a scalable framework to incorporate additional HLA peptidomics studies in the future. The refined motifs improve neo-antigen and cancer testis antigen predictions, indicating that unbiased HLA peptidomics data are ideal for in silico predictions of neo-antigens from tumor exome sequencing data. The new motifs further reveal distant modulation of the binding specificity at P2 for some HLA-I alleles by residues in the HLA-I binding site but outside of the B-pocket and we unravel the underlying mechanisms by protein structure analysis, mutagenesis and in vitro binding assays., Author summary Predicting the differences between cancer and normal cells that are visible to the immune system is of central importance for cancer immunotherapy. Here we introduce a novel computational framework to harness the wealth of data from in-depth HLA peptidomics studies, including ten novel high quality (