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Mass-spectrometry (MS)-based proteomics is a powerful and robust platform for studying the interactions between biological systems during health and disease. Bacterial infections represent a significant threat to global health and drive the pursuit of novel therapeutic strategies to combat emerging and resistant pathogens. During infection, the interplay between a host and pathogen determines the ability of the microbe to survive in a hostile environment and promotes an immune response by the host as a protective measure. It is the protein-level changes from either biological system that define the outcome of infection, and MS-based proteomics provides a rapid and effective platform to identify such changes. In particular, proteomics detects alterations in protein abundance, quantifies protein secretion and (or) release, measures an array of post-translational modifications that influence signaling cascades, and profiles protein–protein interactions through protein complex and (or) network formation. Such information provides new insight into the role of known and novel bacterial effectors, as well as the outcome of host cell activation. In this Review, we highlight the diverse applications of MS-based proteomics in profiling the relationship between bacterial pathogens and the host. Our work identifies a plethora of strategies for exploring mechanisms of infection from dual perspectives (i.e., host and pathogen), and we suggest opportunities to extrapolate the current knowledgebase to other biological systems for applications in therapeutic discovery.

Fast and accurate identifications of pathogenic bacteria along with their associated antibiotic resistance proteins are of paramount importance for patient treatments and public health. While mass spectrometry has become an important, technique for diagnostics of infectious disease, there is a need for mass spectrometry workflows offering this capability. To meet this need, we have augmented the previously published Microorganism Classification and Identification (MiCId) workflow for this capability. To evaluate the performance of the newly augmented MiCId workflow, we have used MS/MS datafiles from samples of 10 antibiotic resistance bacterial strains belonging to three different species: Escherichia coli, Klebsiella pneumoniae, and Pseudomonas aeruginosa. The evaluation results show that MiCId’s workflow has a sensitivity value around 85% (with a lower bound at about 72%) and a precision greater than 95% in the identification of antibiotic resistance proteins. Using MS/MS datasets from samples of two bacterial clonal isolates, one being antibiotic-sensitive while the other (obtained from the same patient at different times) being multidrug-resistant, we applied MiCId’s workflow to investigate possible mechanisms of antibiotic resistance in these pathogenic bacteria; the results showed that MiCId’s conclusions are in agreement with the published study. Furthermore, we show that MiCId’s workflow is fast. It pro-vides microorganismal identifications, protein identifications, sample biomass estimates, and antibiotic resistance protein identifications in 6–17 minutes per MS/MS sample using computing resources that are available in most desktop and laptop computers, making it a highly portable workflow. This study demonstrated that MiCId’s workflow is fast, portable, and with high sensitivity and high precision, making it a valuable tool for rapid identifications of bacteria as well as detection of their antibiotic resistance proteins. The new version of MiCId (v.07.01.2021) is freely available for download at https://www.ncbi.nlm.nih.gov/CBBresearch/Yu/downloads.html.

Research into the basic biology of human health and disease, as well as translational human research and clinical applications, all benefit from the growing accessibility and versatility of mass spectrometry (MS)-based proteomics. Although once limited in throughput and sensitivity, proteomic studies have quickly grown in scope and scale over the last decade due to significant advances in instrumentation, computational approaches, and bio-sample preparation. Here, we review these latest developments in MS and highlight how these techniques are used to study the mechanisms, diagnosis, and treatment of human diseases. We first describe recent groundbreaking technological advancements for MS-based proteomics, including novel data acquisition techniques and protein quantification approaches. Next, we describe innovations that enable the unprecedented depth of coverage in protein signaling and spatiotemporal protein distributions, including studies of post-translational modifications, protein turnover, and single-cell proteomics. Finally, we explore new workflows to investigate protein complexes and structures, and we present new approaches for protein–protein interaction studies and intact protein or top-down MS. While these approaches are only recently incipient, we anticipate that their use in biomedical MS proteomics research will offer actionable discoveries for the improvement of human health.

Mass spectrometry (MS)-based proteomics is the most comprehensive approach for the quantitative profiling of proteins, their interactions and modifications. It is a challenging topic as a firm grasp requires expertise in biochemistry for sample preparation, analytical chemistry for instrumentation and computational biology for data analysis. In this short guide, we highlight the various components of a mass spectrometer, the sample preparation process for conversion of proteins into peptides, and quantification and analysis strategies. The advancing technology of MS-based proteomics now opens up opportunities in clinical applications and single-cell analysis.

The post-genomic era consists of experimental and computational efforts to meet the challenge of clarifying and understanding the function of genes and their products. Proteomic studies play a key role in this endeavour by complementing other functional genomics approaches, encompasses the large-scale analysis of complex mixtures, including the identification and quantification of proteins expressed under different conditions, the determination of their properties, modifications and functions. Understanding how biological processes are regulated at the protein level is crucial to understanding the molecular basis of diseases and often highlights the prevention, diagnosis and treatment of diseases. High-throughput technologies are widely used in proteomics to perform the analysis of thousands of proteins. Specifically, mass spectrometry (MS) is an analytical technique for characterizing biological samples and is increasingly used in protein studies because of its targeted, nontargeted, and high performance abilities. However, as large data sets are created, computational methods such as data mining techniques are required to analyze and interpret the relevant data. More specifically, the application of data mining techniques in large proteomic data sets can assist in many interpretations of data; it can reveal protein-protein interactions, improve protein identification, evaluate the experimental methods used and facilitate the diagnosis and biomarker discovery. With the rapid advances in mass spectrometry devices and experimental methodologies, MS-based proteomics has become a reliable and necessary tool for elucidating biological processes at the protein level. Over the past decade, we have witnessed a great expansion of our knowledge of human diseases with the adoption of proteomic technologies based on MS, which leads to many interesting discoveries. Here, we review recent advances of data mining in MS-based proteomics in biomedical research. Recent research in many fields shows that proteomics goes beyond the simple classification of proteins in biological systems and finally reaches its initial potential – as an essential tool to aid related disciplines, notably biomedical research. From here, there is great potential for data mining in MS-based proteomics to move beyond basic research, into clinical research and diagnostics.

Background: Staphylococcus aureus (S. aureus), an important pathogen, causes a wide range of infections in human starting from food poisoning to septicemia. It affects the host cells with various exotoxins, known as virulence factors, which are synthesized in growth phase-dependent manner of the bacteria. S. aureus has been reported to become resistant to antibiotics rapidly. Among two common clinical isolates, Methicillin-sensitive S. aureus (MSSA) and Methicillin-resistant S. aureus (MRSA), MRSA pose major problems across hospitals around the world. Objective: The objective of the present study was to profile the exoproteins of Methicillin-sensitive S. aureus (ATCC 25293) and subsequently to establish a proteomics-based method of characterization of S. aureus that is crucial in treating hospital-acquired infections. Methods: We used two-dimensional nanoLC/ESI-MS based proteomic platform to characterize and quantify the exoproteins isolated from Methicillin-sensitive S. aureus (ATCC 25293) strain. Results: A total of 69 proteins were identified from extracellular proteome pool of ATCC 25293 strain that includes 18 extracellular proteins, 40 cytoplasmic proteins, 2 membrane proteins, 3 cell wall proteins and 6 uncharacterized proteins. Conclusion: We propose that this mass spectrometry-based proteomics method of characterization of exoproteins might be useful to identify S. aureus strains that are resistant to antibiotics.

Ovarian cancer is one of the leading causes of cancer related deaths affecting United States women. Early-stage detection of ovarian cancer has been linked to increased survival, however, current screening methods, such as biomarker testing, have proven to be ineffective in doing so. Therefore, further developments are necessary to be able to achieve positive patient prognosis. Ongoing efforts are being made in biomarker discovery towards clinical applications in screening for early-stage ovarian cancer. In this perspective, we discuss and provide examples for several workflows employing mass spectrometry-based proteomics towards protein biomarker discovery and characterization in the context of ovarian cancer; workflows include protein identification and characterization as well as intact protein profiling. We also discuss the opportunities to merge these workflows for a multiplexed approach for biomarkers. Lastly, we provide our insight as to future developments that may serve to enhance biomarker discovery workflows while also considering translational potential.

Large-scale multi-omic analysis allows a thorough understanding of different physiological or pathological conditions, particularly cancer. Here, an extraction method simultaneously yielding DNA, RNA and protein (thereby referred to as "triple extraction", TEx) was tested for its suitability to unbiased, system-wide proteomic investigation. Largely proven efficient for transcriptomic and genomic studies, we aimed at exploring TEx compatibility with mass spectrometry-based proteomics and phospho-proteomics, as compared to a standard urea extraction. TEx is suitable for the shotgun investigation of proteomes, providing similar results as urea-based protocol both at the qualitative and quantitative levels. TEx is likewise compatible with the exploration of phosphorylation events, actually providing a higher number of correctly localized sites than urea, although the nature of extracted modifications appears somewhat distinct between both techniques. These results highlight that the presented protocol is well suited for the examination of the proteome and modified proteome of this bladder cancer cell model, as efficiently as other more widely used workflows for mass spectrometry-based analysis. Potentially applicable to other mammalian cell types and tissues, TEx represents an advantageous strategy for multi-omics on scarce and/or heterogenous samples. This article is protected by copyright. All rights reserved.

Background : Oral tongue squamous cell carcinoma (OTSCC) is an aggressive cancer with high morbidity and mortality rates, despite multimodality management. There are currently no clinically relevant molecular markers to help identify patients at a higher risk of recurrence and failure. Methods: 2D-DIGE coupled with tandem mass spectrometry was performed on tissues obtained from early staged OTSCC along with its paired apparently adjacent normal tissue samples (n=10). Top upregulated protein was validated using another independent set of tissue samples by Immunohistochemistry (n=346), comprising of retrospective early stage OTSCC (n=150) and prospective series of oral pre-cancers, normal and oral cancers (n=195). For further validation of protein expression, saliva samples collected from Oral Cancer and pre-cancer samples were analysed by ELISA (n=80). Results: We found vimentin, the mesenchymal protein to be the most upregulated protein in tongue tumour tissues compared to adjacent apparent normal tissues. Vimentin was found to be significantly overexpressed in oral pre-cancers along with cancers compared to normal tissues. Conclusion: Vimentin detection in saliva can be useful diagnostic test to detect oral precancers that may have malignant potential needing closer follow up. Salivary ELISA for vimentin can additionally be useful for disease monitoring in oral cancers.

Mass spectrometry-based proteomics and other technologies have matured to enable routine acquisition of system-wide data sets that describe concentrations, modifications, and interactions of proteins, mRNAs, and other molecules. Productive integrative studies differ from parallel data analysis by quantitative modeling of the relationships between data. We outline steps and considerations towards integromic studies to exploit the synergy between data sets., Graphical Abstract Highlights Technological advances produce many system-wide 'omics data sets. Integrating proteomics with other data needs quantitative modeling of inter-data relationships. Some tools have emerged for considering specific properties of 'omics data. Productive integration of proteomic and other data can provide exciting new insights., Mass spectrometry based proteomics and other technologies have matured to enable routine quantitative, system-wide analysis of concentrations, modifications, and interactions of proteins, mRNAs, and other molecules. These studies have allowed us to move toward a new field concerned with mining information from the combination of these orthogonal data sets, perhaps called “integromics.” We highlight examples of recent studies and tools that aim at relating proteomic information to mRNAs, genetic associations, and changes in small molecules and lipids. We argue that productive data integration differs from parallel acquisition and interpretation and should move toward quantitative modeling of the relationships between the data. These relationships might be expressed by temporal information retrieved from time series experiments, rate equations to model synthesis and degradation, or networks of causal, evolutionary, physical, and other interactions. We outline steps and considerations toward such integromic studies to exploit the synergy between data sets.

The label-free mass spectrometry-based proteomics data inevitably suffer from the problem of missing values. The existence of missing values prevents the downstream analyses which need a complete data matrix. Our motivation is to introduce the state-of-art machine learning algorithm XGboost to realize a method of imputation which can improve the accuracy of imputation. But in practical, XGboost has many parameters need to be tuned to deliver on its potential high performance. Although cross validation may find the best parameters, it is much time-consuming. Alternatively, we empirically determined the parameters to two kinds of base learners of XGboost. To explore the robustness and performance of XGboost based imputation with predetermined parameters, we conducted tests on three benchmark datasets. As a comparative, six common imputation methods were also experimented in terms of normalized root mean squared error and Pearson correlation coefficient. The comparative experimental results indicated that the XGboost based imputation method using the linear base learner is competitive to or out-performs its competitors, including the random forest based imputation, by achieving smaller imputation errors and better structure preservation under the empirical parameters for the three benchmark datasets.

BackgroundAmyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration (FTLD) are progressive neurodegenerative diseases that share clinical and neuropathologic features. Critical to the mission of developing effective therapies for ALS and FTLD is the discovery of biomarkers that can illuminate shared mechanisms of neurodegeneration, which can then be evaluated for diagnostic, prognostic or pharmacodynamic value across the disease spectrums. MethodsHere, we merged unbiased discovery-based approaches and targeted quantitative comparative analyses between ALS and FTLD cerebrospinal fluid (CSF) to identify proteins that are altered in ALS and FTLD. ResultsDiscovery mass spectrometry (MS)-based proteomic approaches combined with tandem mass tags (TMT) quantification methods from 40 CSF samples comprising 20 patients with ALS and 20 healthy control (HC) individuals identified 19 differentially expressed candidate biomarker proteins after CSF fractionation. Notably, these candidate biomarkers included novel and previously identified proteins, thus validating our approach. Candidate biomarkers were subsequently examined using parallel reaction monitoring (PRM) MS methods on 80 unfractionated CSF samples comprising 30 patients with ALS, 19 patients with FTLD, and 31 HC individuals. Two candidate biomarkers (CNTNAP2 and CLSTN1) were downregulated in both ALS and FTLD compared to healthy controls, and 11 further candidate proteins were significantly downregulated in FTLD compared to HC. ConclusionsTaken together, this study identifies multiple novel proteins that are altered in ALS and FTLD, which provides the foundation for their evaluation and development as biomarkers for these diseases.

Background The purpose of this study was to determine whether the residual fixative from a liquid-based Pap test or a swab of the cervix contained proteins that were also found in the primary tumor of a woman with high grade serous ovarian cancer. This study is the first step in determining the feasibility of using the liquid-based Pap test or a cervical swab for the detection of ovarian cancer protein biomarkers. Methods Proteins were concentrated by acetone precipitation from the cell-free supernatant of the liquid-based Pap test fixative or eluted from the cervical swab. Protein was also extracted from the patient’s tumor tissue. The protein samples were digested into peptides with trypsin, then the peptides were run on 2D-liquid chromatography mass spectrometry (2D-LCMS). The data was searched against a human protein database for the identification of peptides and proteins in each biospecimen. The proteins that were identified were classified for cellular localization and molecular function by bioinformatics integration. Results We identified almost 5000 proteins total in the three matched biospecimens. More than 2000 proteins were expressed in each of the three biospecimens, including several known ovarian cancer biomarkers such as CA125, HE4, and mesothelin. By Scaffold analysis of the protein Gene Ontology categories and functional analysis using PANTHER, the proteins were classified by cellular localization and molecular function, demonstrating that the Pap test fluid and cervical swab proteins are similar to each other, and also to the tumor extract. Conclusions Our results suggest that Pap test fixatives and cervical swabs are a rich source of tumor-specific biomarkers for ovarian cancer, which could be developed as a test for ovarian cancer detection.

Background Poly (ADP)-ribose polymerase (PARP) inhibitors have entered routine clinical practice for the treatment of high-grade serous ovarian cancer (HGSOC), yet the molecular mechanisms underlying treatment response to PARP1 inhibition (PARP1i) are not fully understood. Methods Here, we used unbiased mass spectrometry based proteomics with data-driven protein network analysis to systematically characterize how HGSOC cells respond to PARP1i treatment. Results We found that PARP1i leads to pronounced proteomic changes in a diverse set of cellular processes in HGSOC cancer cells, consistent with transcript changes in an independent perturbation dataset. We interpret decreases in the levels of the pro-proliferative transcription factors SP1 and β-catenin and in growth factor signaling as reflecting the anti-proliferative effect of PARP1i; and the strong activation of pro-survival processes NF-κB signaling and lipid metabolism as PARPi-induced adaptive resistance mechanisms. Based on these observations, we nominate several protein targets for therapeutic inhibition in combination with PARP1i. When tested experimentally, the combination of PARPi with an inhibitor of fatty acid synthase (TVB-2640) has a 3-fold synergistic effect and is therefore of particular pre-clinical interest. Conclusion Our study improves the current understanding of PARP1 function, highlights the potential that the anti-tumor efficacy of PARP1i may not only rely on DNA damage repair mechanisms and informs on the rational design of PARP1i combination therapies in ovarian cancer.

Proteins are the main effectors of cellular phenotypes. Aberrant protein functions dictate disease onset and progression. The precise and reproducible quantification of proteins and posttranslational modifications (PTMs), such as phosphorylation, remains a challenge. A number of mass spectrometry (MS) methods allow the high-throughput characterization of the proteome and phosphoproteome in normal and disease patient samples with unprecedented depth, thus showing promise for precision medicine. This chapter reviews currently available MS technologies for protein and PTM quantification and discusses improvements in the preparation of human biological samples for MS analysis. Key publications that advanced the utilization of MS for the molecular profiling of cancer patients' samples are also highlighted. Finally, remaining challenges for integrating MS-based proteomics and phosphoproteomics with other omics, clinical and imaging data to improve precision medicine approaches are discussed.

Large-scale proteomic studies have to deal with unwanted variability, especially when samples originate from different centers and/or multiple analytical batches are needed. Such variability is typically added throughout all the steps of a clinical study, from biological sample collection and storage, sample preparation, spectral data acquisition, to peptide/protein quantification. In order to remove such diverse variability, normalization of the protein data is performed. There are several published works comparing normalization methods in the -omics field, but reports focusing on proteomic data generated with mass spectrometry (MS) are much fewer. Additionally, most of these studies have only dealt with small datasets. As a case study, we focused on the normalization of a large quantitative MS-based proteomic dataset obtained with isobaric tandem-mass tagging (TMT) of plasma samples from an overweight and obese pan-European cohort. Different normalization methods were evaluated, namely, standardization, quantile sample, removal of unwanted variation (RUV), ComBat, mean and median centering, and single standard normalization; some of these methods are generic while others have been specifically created to deal with genomic or metabolomic data. We checked how relationships between proteins and clinical variables were impacted after normalizing the data with the different methods. We compared the normalized datasets using an array of diagnostic plots. Some methods were well adapted for this particular large-scale shotgun proteomic dataset of human plasma samples. In particular, quantile sample normalization, RUV, mean and median centering showed very good performance, while quantile protein normalization provided results of inferior quality than those obtained with unnormalized data.

With the current reproducibility of proteome preparation workflows along with the speed and sensitivity of the mass spectrometers, the transition of the mass spectrometry (MS)-based proteomics technology from biomarker discovery to clinical implementation is under appraisal in the biomedicine community. Therefore, this technology might be implemented soon to detect well-known biomarkers in cancers and other diseases. Acute myeloid leukemia (AML) is an aggressive heterogeneous malignancy that requires intensive treatment to cure the patient. Leukemia relapse is still a major challenge even for patients who have favorable genetic abnormalities. MS-based proteomics could be of great help to both describe the proteome changes of individual patients and identify biomarkers that might encourage specific treatments or clinical strategies. Herein, we will review the advances and availability of the MS-based proteomics strategies that could already be used in clinical proteomics. However, the heterogeneity of complex diseases as AML requires consensus to recognize AML biomarkers and to establish MS-based workflows that allow their unbiased identification and quantification. Although our literature review appears promising towards the utilization of MS-based proteomics in clinical AML in a near future, major efforts are required to validate AML biomarkers and agree on clinically approved workflows.

Pituitary adenoma (PA) is a common intracranial neoplasm, which affects the hypothalamus-pituitary-target organ axis systems, and is hazardous to human health. Post-translational modifications (PTMs), including phosphorylation, ubiquitination, nitration, and sumoylation, are vitally important in the PA pathogenesis. The large-scale analysis of PTMs could provide a global view of molecular mechanisms for PA. Proteoforms, which are used to define various protein structural and functional forms originated from the same gene, are the future direction of proteomics research. The global studies of different proteoforms and PTMs of hypophyseal hormones such as growth hormone (GH) and prolactin (PRL) and the proportion change of different GH proteoforms or PRL proteoforms in human pituitary tissue could provide new insights into the clinical value of pituitary hormones in PAs. Multiple quantitative proteomics methods, including mass spectrometry (MS)-based label-free and stable isotope-labeled strategies in combination with different PTM-peptide enrichment methods such as TiO2 enrichment of tryptic phosphopeptides and antibody enrichment of other PTM-peptides increase the feasibility for researchers to study PA proteomes. This article reviews the research status of PTMs and proteoforms in PAs, including the enrichment method, technical limitation, quantitative proteomics strategies, and the future perspectives, to achieve the goals of in-depth understanding its molecular pathogenesis, and discovering effective biomarkers and clinical therapeutic targets for predictive, preventive, and personalized treatment of PA patients.

Formalin fixation and paraffin-embedding (FFPE) is the most common method to preserve human tissue for clinical diagnosis, and FFPE archives represent an invaluable resource for biomedical research. Proteins in FFPE material are stable over decades but their efficient extraction and streamlined analysis by mass spectrometry (MS)-based proteomics has so far proven challenging. Herein we describe a MS-based proteomic workflow for quantitative profiling of large FFPE tissue cohorts directly from histopathology glass slides. We demonstrate broad applicability of the workflow to clinical pathology specimens and variable sample amounts, including low-input cancer tissue isolated by laser microdissection. Using state-of-the-art data dependent acquisition (DDA) and data independent acquisition (DIA) MS workflows, we consistently quantify a large part of the proteome in 100 min single-run analyses. In an adenoma cohort comprising more than 100 samples, total workup took less than a day. We observed a moderate trend towards lower protein identification in long-term stored samples (>15 years), but clustering into distinct proteomic subtypes was independent of archival time. Our results underscore the great promise of FFPE tissues for patient phenotyping using unbiased proteomics and they prove the feasibility of analyzing large tissue cohorts in a robust, timely, and streamlined manner. (c) 2020 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.

Recent advances in mass spectrometry (MS)-based proteomics have enabled tremendous progress in the understanding of cellular mechanisms, disease progression, and the relationship between genotype and phenotype. Though many popular bioinformatics methods in proteomics are derived from other omics studies, novel analysis strategies are required to deal with the unique characteristics of proteomics data. In this review, we discuss the current developments in the bioinformatics methods used in proteomics and how they facilitate the mechanistic understanding of biological processes. We first introduce bioinformatics software and tools designed for mass spectrometry-based protein identification and quantification, and then we review the different statistical and machine learning methods that have been developed to perform comprehensive analysis in proteomics studies. We conclude with a discussion of how quantitative protein data can be used to reconstruct protein interactions and signaling networks.

SummaryWe describe a method for rapid in silico selection of diagnostic peptides from newly described viral pathogens and applied this approach to SARS-CoV-2/COVID-19. This approach is multi-tiered, beginning with compiling the theoretical protein sequences from genomic derived data. In the case of SARS-CoV-2 we begin with 496 peptides that would be produced by proteolytic digestion of the viral proteins. To eliminate peptides that would cause cross-reactivity and false positives we remove peptides from consideration that have sequence homology or similar chemical characteristics using a progressively larger database of background peptides. Using this pipeline, we can remove 47 peptides from consideration as diagnostic due to the presence of peptides derived from the human proteome. To address the complexity of the human microbiome, we describe a method to create a database of all proteins of relevant abundance in the saliva microbiome. By utilizing a protein-based approach to the microbiome we can more accurately identify peptides that will be problematic in COVID-19 studies which removes 12 peptides from consideration. To identify diagnostic peptides, another 7 peptides are flagged for removal following comparison to the proteome backgrounds of viral and bacterial pathogens of similar clinical presentation. By aligning the protein sequences of SARS-CoV-2 field isolates deposited to date we can identify peptides for removal due to their presence in highly variable regions that may lead to false negatives as the pathogen evolves. We provide maps of these regions and highlight 3 peptides that should be avoided as potential diagnostic or vaccine targets. Finally, we leverage publicly deposited proteomics data from human cells infected with SARS-CoV-2, as well as a second study with the closely related MERS-CoV to identify the two proteins of highest abundance in human infections. The resulting final list contains the 24 peptides most unique and diagnostic of SARS-CoV-2 infections. These peptides represent the best targets for the development of antibodies are clinical diagnostics. To demonstrate one application of this we model peptide fragmentation using a deep learning tool to rapidly generate targeted LCMS assays and data processing method for detecting CoVID-19 infected patient samples.Graphical Abstract

Purpose: Preparation of culture samples for mass spectrometry-based proteomics Principle: Utilizing a membrane-enrichment method of lysing cells and preparing peptides has yielded higher representation of membrane proteins in our mass spectrometry-based proteomic results.Traditional methods do not adequately extract or digest hydrophobic, transmembrane proteins. Particularly, we can now see full expression patterns of proteorhodopsin, something we could not detect using traditional mass spec proteomics prep.This protocol builds on the work of Molloy (2008) Methods Mol Biol (doi:10.1007/978-1-60327-064-9_30), Erde et al. (2014) J. Proteome Res. (doi:10.1021/pr4010019), and Waldbauer, et al. (2017) Anal. Chem. (doi: 10.1021/acs.analchem.7b02752). Summary: Pure culture samples were spun down and flash frozen for proteomics.A carbonate extraction protocol was used for membrane enrichment before eFASP.The membrane fraction was enzymatically digested with both chymotrypsin and trypsin and the cytosolic fraction was digested with just trypsin.These samples were then ready to be processed further by in vitroisotopic peptide labeling (diDO-IPTL).

The investigation of post-translational modifications (PTMs) plays an important role for the study of type 2 diabetes. The importance of PTMs has been realized with the advancement of analytical techniques. The challenging detection and analysis of post-translational modifications is eased by different enrichment methods and by high throughput mass spectrometry based proteomics studies. This technology along with different quantitation methods provide accurate knowledge about the changes happening in disease conditions as well as in normal conditions. In this review, we have discussed PTMs such as phosphorylation, N-glycosylation, O-GlcNAcylation, acetylation and advanced glycation end products in type 2 diabetes which have been characterized by high throughput mass spectrometry based proteomics analysis.

With the advance of mass spectrometry based proteomics techniques, thousands of phosphorylation sites can be identified from a single cell line or a single tissue sample by large scale global analysis. However, such large scale analysis is time consuming. And, due to the high complexity of the phosphoproteome sample, this approach is often unable to identify low abundance regulatory phosphorylation sites. Alternatively, the “divide and conquer” strategy by targeted analysis of a subset of phosphoproteome, i.e. subphosphoproteome, can improve the detection sensitivity of these phosphorylation sites. In this review, we summarized the analytical methods in this field. The enrichment approaches for the analysis of subphosphoproteomes generated by tyrosine kinases and basophilic kinases were introduced. The strategies for the analysis of the subphosphoproteomes composed by endogenous phosphopeptides, multiply phosphorylated peptides, etc., were also reviewed.

Authentication of gelatin, an ingredient in food and drug products, is of interest with respect to some regulations and religion rules. Determination of the gelatin origin is particularly challenging because of the similarity in amino acid sequences of collagen types in different species. In this study, Polymerase Chain Reaction (PCR) technique using species-specific primers and also chemometrics analysis of mass spectral data were investigated to differentiate bovine, porcine and fish source of gelatin in commercial pure gelatin and gelatin-containing food and drug products. The specific PCR primer set was optimized using real-time PCR approach to routinely determine the gelatin source. Real-time PCR is sensitive to identify gelatin origin from traces of species-specific DNA. However, source determination is impossible when DNA is denatured or removed during production process. Alongside, chemometrics methods (PLS/DA and PCA) were used for mining LC/MS data sets to obtain patterns for discrimination of gelatin origins. LC/MS data sets involve MS spectrum of tryptic gelatin peptides generated from gelatin samples. Using PLS/DA method, a discrimination model is developed for identifying origin of the extracted gelatin. The method is practical to determine the halal authenticity of gelatin in the pure and processed products.

SummaryOn behalf of The Human Proteome Organization (HUPO) Proteomics Standards Initiative (PSI), we are here introducing two novel standard data formats, proBAM and proBed, that have been developed to address the current challenges of integrating mass spectrometry based proteomics data with genomics and transcriptomics information in proteogenomics studies. proBAM and proBed are adaptations from the well-defined, widely used file formats SAM/BAM and BED respectively, and both have been extended to meet specific requirements entailed by proteomics data. Therefore, existing popular genomics tools such as SAMtools and Bedtools, and several very popular genome browsers, can be used to manipulate and visualize these formats already out-of-the-box. We also highlight that a number of specific additional software tools, properly supporting the proteomics information available in these formats, are now available providing functionalities such as file generation, file conversion, and data analysis. All the related documentation to the formats, including the detailed file format specifications, and example files are accessible athttp://www.psidev.info/probamandhttp://www.psidev.info/probed.

Mass spectrometry based proteomics is an indispensable tool for almost all research areas relevant for the understanding of proteolytic processing, ranging from the identification of substrates, products and cleavage sites up to the analysis of structural features influencing protease activity. The majority of methods for these studies are based on bottom-up proteomics performing analysis at peptide level. As this approach is characterized by a number of pitfalls, e.g. loss of molecular information, there is an ongoing effort to establish top-down proteomics, performing separation and MS analysis both at intact protein level. We briefly introduce major approaches of bottom-up proteomics used in the field of protease research and highlight the shortcomings of these methods. We then discuss the present state-of-the-art of top-down proteomics. Together with the discussion of known challenges we show the potential of this approach and present a number of successful applications of top-down proteomics in protease research. This article is part of a Special Issue entitled: Proteolysis as a Regulatory Event in Pathophysiology edited by Stefan Rose-John.

Human tissues are an important link between organ-specific spatial molecular information, patient pathology, and patient treatment options. However, patient tissues are uniquely obtained by time and location, and limited in their availability and size. Currently, little knowledge exists about appropriate and simplified protocols for routine MS-based analysis of the various types and sizes of tissues. Following standard procedures used in pathology, we selected small fresh frozen uterine tissue samples to investigate how the tissue preparation protocol affected the subsequent proteomics analysis. First, we observed that protein extraction with 0.1% SDS followed by extraction with a 30% ACN/urea resulted in a decrease in the number of identified proteins, when compared to extraction with 30% ACN/urea only. The decrease in the number of proteins was approximately 55% and 20%, for 10 and 16 μm thick tissue, respectively. Interestingly, the relative abundance of the proteins shared between the two methods was higher when SDS/ACN/urea was used, compared to the 30% ACN/urea extraction, indicating the role of SDS to be beneficial for protein solubility. Second, the influence of tissue thickness was investigated by comparing the results obtained for 10, 16, and 20 μm thick (1 mm2) tissue using urea/30% ACN. We observed an increase in the number of identified proteins and corresponding quantity with an increase in the tissue thickness. Finally, by analyzing very small amounts of tissues (∼0.2 mm2) of 10, 16, and 20 μm thickness, we observed that the increase in tissue thickness resulted in a higher number of protein identifications and corresponding quantitative values.

// Jisook Park 1 , Eunjung Lee 2 , Kyoung-Jin Park 3 , Hyung-Doo Park 3 , Jong-Won Kim 3 , Hye In Woo 4 , Kwang Hyuck Lee 5 , Kyu-Taek Lee 5 , Jong Kyun Lee 5 , Joon-Oh Park 6 , Young Suk Park 6 , Jin Seok Heo 7 , Seong Ho Choi 7 , Dong Wook Choi 7 , Kee-Taek Jang 8 and Soo-Youn Lee 3, 9 1 Samsung Biomedical Research Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea 2 Division of Genetics and Genomics, Boston Children's Hospital and Harvard Medical School, Boston, MA, United States 3 Department of Laboratory Medicine and Genetics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea 4 Department of Laboratory Medicine, Samsung Changwon Hospital, Sungkyunkwan University School of Medicine, Changwon, Korea 5 Division of Gastroenterology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea 6 Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea 7 Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea 8 Department of Pathology and Translational Genomics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea 9 Department of Clinical Pharmacology and Therapeutics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea Correspondence to: Soo-Youn Lee, email: suddenbz@skku.edu Keywords: pancreatic cancer, biomarker, validation, proteomics, mass spectrometry Received: October 06, 2016 Accepted: April 15, 2017 Published: April 27, 2017 ABSTRACT We performed an integrated analysis of proteomic and transcriptomic datasets to develop potential diagnostic markers for early pancreatic cancer. In the discovery phase, a multiple reaction monitoring assay of 90 proteins identified by either gene expression analysis or global serum proteome profiling was established and applied to 182 clinical specimens. Nine proteins ( P < 0.05) were selected for the independent validation phase and quantified using stable isotope dilution-multiple reaction monitoring-mass spectrometry in 456 specimens. Of these proteins, four proteins (apolipoprotein A-IV, apolipoprotein CIII, insulin-like growth factor binding protein 2 and tissue inhibitor of metalloproteinase 1) were significantly altered in pancreatic cancer in both the discovery and validation phase ( P < 0.01). Moreover, a panel including carbohydrate antigen 19-9, apolipoprotein A-IV and tissue inhibitor of metalloproteinase 1 showed better performance for distinguishing early pancreatic cancer from pancreatitis (Area under the curve = 0.934, 86% sensitivity at fixed 90% specificity) than carbohydrate antigen 19-9 alone (71% sensitivity). Overall, we present the panel of robust biomarkers for early pancreatic cancer diagnosis through bioinformatics analysis that combined transcriptomic and proteomic data as well as rigorous validation on a large number of independent clinical samples.

Quantitative mass spectrometry approaches are used for absolute and relative quantification in global proteome studies. To date, relative and absolute quantification techniques are available that differ in quantification accuracy, proteome coverage, complexity and robustness. This review focuses on most common relative or absolute quantification strategies exemplified by three experimental studies. A label-free relative quantification approach was performed for the investigation of the membrane proteome of sensory cilia to the depth of olfactory receptors in Mus musculus. A SILAC-based relative quantification approach was successfully applied for the identification of core components and transient interactors of the peroxisomal importomer in Saccharomyces cerevisiae. Furthermore, AQUA using stable isotopes was exemplified to unraveling the prenylome influenced by novel prenyltransferase inhibitors. Characteristic enrichment and fragmentation strategies for a robust quantification of the prenylome are also summarized.

Microwave based treatment (MWT) of wet wheat kernels induced a striking reduction of gluten, up to