Статті в журналах: "Quantitative proteomics data"

1

Beynon, Rob, Simon Hubbard, and Andy Jones. "Quantitative proteomics and data analysis." Biochemist 34, no. 1 (February 1, 2012): 61–62. http://dx.doi.org/10.1042/bio03401061.

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2

Handler, David C. L., Flora Cheng, Abdulrahman M. Shathili, and Paul A. Haynes. "PeptideWitch–A Software Package to Produce High-Stringency Proteomics Data Visualizations from Label-Free Shotgun Proteomics Data." Proteomes 8, no. 3 (August 21, 2020): 21. http://dx.doi.org/10.3390/proteomes8030021.

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PeptideWitch is a python-based web module that introduces several key graphical and technical improvements to the Scrappy software platform, which is designed for label-free quantitative shotgun proteomics analysis using normalised spectral abundance factors. The program inputs are low stringency protein identification lists output from peptide-to-spectrum matching search engines for ‘control’ and ‘treated’ samples. Through a combination of spectral count summation and inner joins, PeptideWitch processes low stringency data, and outputs high stringency data that are suitable for downstream quantitation. Data quality metrics are generated, and a series of statistical analyses and graphical representations are presented, aimed at defining and presenting the difference between the two sample proteomes.

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3

Held, Jason M., Birgit Schilling, Alexandria K. D'Souza, Tara Srinivasan, Jessica B. Behring, Dylan J. Sorensen, Christopher C. Benz, and Bradford W. Gibson. "Label-Free Quantitation and Mapping of the ErbB2 Tumor Receptor by Multiple Protease Digestion with Data-Dependent (MS1) and Data-Independent (MS2) Acquisitions." International Journal of Proteomics 2013 (April 4, 2013): 1–11. http://dx.doi.org/10.1155/2013/791985.

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The receptor tyrosine kinase ErbB2 is a breast cancer biomarker whose posttranslational modifications (PTMs) are a key indicator of its activation. Quantifying the expression and PTMs of biomarkers such as ErbB2 by selected reaction monitoring (SRM) mass spectrometry has several limitations, including minimal coverage and extensive assay development time. Therefore, we assessed the utility of two high resolution, full scan mass spectrometry approaches, MS1 Filtering and SWATH MS2, for targeted ErbB2 proteomics. Endogenous ErbB2 immunoprecipitated from SK-BR-3 cells was in-gel digested with trypsin, chymotrypsin, Asp-N, or trypsin plus Asp-N in triplicate. Data-dependent acquisition with an AB SCIEX TripleTOF 5600 and MS1 Filtering data processing was used to assess peptide and PTM coverage as well as the reproducibility of enzyme digestion. Data-independent acquisition (SWATH) was also performed for MS2 quantitation. MS1 Filtering and SWATH MS2 allow quantitation of all detected analytes after acquisition, enabling the use of multiple proteases for quantitative assessment of target proteins. Combining high resolution proteomics with multiprotease digestion enabled quantitative mapping of ErbB2 with excellent reproducibility, improved amino acid sequence and PTM coverage, and decreased assay development time compared to typical SRM assays. These results demonstrate that high resolution quantitative proteomic approaches are an effective tool for targeted biomarker quantitation.

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4

Montaño-Gutierrez, Luis F., Shinya Ohta, Georg Kustatscher, William C. Earnshaw, and Juri Rappsilber. "Nano Random Forests to mine protein complexes and their relationships in quantitative proteomics data." Molecular Biology of the Cell 28, no. 5 (March 2017): 673–80. http://dx.doi.org/10.1091/mbc.e16-06-0370.

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Ever-increasing numbers of quantitative proteomics data sets constitute an underexploited resource for investigating protein function. Multiprotein complexes often follow consistent trends in these experiments, which could provide insights about their biology. Yet, as more experiments are considered, a complex’s signature may become conditional and less identifiable. Previously we successfully distinguished the general proteomic signature of genuine chromosomal proteins from hitchhikers using the Random Forests (RF) machine learning algorithm. Here we test whether small protein complexes can define distinguishable signatures of their own, despite the assumption that machine learning needs large training sets. We show, with simulated and real proteomics data, that RF can detect small protein complexes and relationships between them. We identify several complexes in quantitative proteomics results of wild-type and knockout mitotic chromosomes. Other proteins covary strongly with these complexes, suggesting novel functional links for later study. Integrating the RF analysis for several complexes reveals known interdependences among kinetochore subunits and a novel dependence between the inner kinetochore and condensin. Ribosomal proteins, although identified, remained independent of kinetochore subcomplexes. Together these results show that this complex-oriented RF (NanoRF) approach can integrate proteomics data to uncover subtle protein relationships. Our NanoRF pipeline is available online.

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5

Kraus, Milena, Mariet Mathew Stephen, and Matthieu-P. Schapranow. "Eatomics: Shiny Exploration of Quantitative Proteomics Data." Journal of Proteome Research 20, no. 1 (September 21, 2020): 1070–78. http://dx.doi.org/10.1021/acs.jproteome.0c00398.

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6

Chia-Yu Yen, S. M. Helmike, K. J. Cios, M. B. Perryman, and M. W. Duncan. "Quantitative analysis of proteomics using data mining." IEEE Engineering in Medicine and Biology Magazine 24, no. 3 (May 2005): 67–72. http://dx.doi.org/10.1109/memb.2005.1436462.

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7

Handler, David C., Dana Pascovici, Mehdi Mirzaei, Vivek Gupta, Ghasem Hosseini Salekdeh, and Paul A. Haynes. "The Art of Validating Quantitative Proteomics Data." PROTEOMICS 18, no. 23 (November 25, 2018): 1800222. http://dx.doi.org/10.1002/pmic.201800222.

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8

Santos, Marlon D. M., Amanda Caroline Camillo-Andrade, Louise U. Kurt, Milan A. Clasen, Eduardo Lyra, Fabio C. Gozzo, Michel Batista, et al. "Mixed-Data Acquisition: Next-Generation Quantitative Proteomics Data Acquisition." Journal of Proteomics 222 (June 2020): 103803. http://dx.doi.org/10.1016/j.jprot.2020.103803.

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9

Peng, Gang, Rashaun Wilson, Yishuo Tang, TuKiet T. Lam, Angus C. Nairn, Kenneth Williams, and Hongyu Zhao. "ProteomicsBrowser: MS/proteomics data visualization and investigation." Bioinformatics 35, no. 13 (November 21, 2018): 2313–14. http://dx.doi.org/10.1093/bioinformatics/bty958.

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Abstract Summary Large-scale, quantitative proteomics data are being generated at ever increasing rates by high-throughput, mass spectrometry technologies. However, due to the complexity of these large datasets as well as the increasing numbers of post-translational modifications (PTMs) that are being identified, developing effective methods for proteomic visualization has been challenging. ProteomicsBrowser was designed to meet this need for comprehensive data visualization. Using peptide information files exported from mass spectrometry search engines or quantitative tools as input, the peptide sequences are aligned to an internal protein database such as UniProtKB. Each identified peptide ion including those with PTMs is then visualized along the parent protein in the Browser. A unique property of ProteomicsBrowser is the ability to combine overlapping peptides in different ways to focus analysis of sequence coverage, charge state or PTMs. ProteomicsBrowser includes other useful functions, such as a data filtering tool and basic statistical analyses to qualify quantitative data. Availability and implementation ProteomicsBrowser is implemented in Java8 and is available at https://medicine.yale.edu/keck/nida/proteomicsbrowser.aspx and https://github.com/peng-gang/ProteomicsBrowser. Supplementary information Supplementary data are available at Bioinformatics online.

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10

Röst, Hannes L., Lars Malmström, and Ruedi Aebersold. "Reproducible quantitative proteotype data matrices for systems biology." Molecular Biology of the Cell 26, no. 22 (November 5, 2015): 3926–31. http://dx.doi.org/10.1091/mbc.e15-07-0507.

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Historically, many mass spectrometry–based proteomic studies have aimed at compiling an inventory of protein compounds present in a biological sample, with the long-term objective of creating a proteome map of a species. However, to answer fundamental questions about the behavior of biological systems at the protein level, accurate and unbiased quantitative data are required in addition to a list of all protein components. Fueled by advances in mass spectrometry, the proteomics field has thus recently shifted focus toward the reproducible quantification of proteins across a large number of biological samples. This provides the foundation to move away from pure enumeration of identified proteins toward quantitative matrices of many proteins measured across multiple samples. It is argued here that data matrices consisting of highly reproducible, quantitative, and unbiased proteomic measurements across a high number of conditions, referred to here as quantitative proteotype maps, will become the fundamental currency in the field and provide the starting point for downstream biological analysis. Such proteotype data matrices, for example, are generated by the measurement of large patient cohorts, time series, or multiple experimental perturbations. They are expected to have a large effect on systems biology and personalized medicine approaches that investigate the dynamic behavior of biological systems across multiple perturbations, time points, and individuals.

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11

Lefkovits, Ivan. "Quantitative Proteomics of Lymphocytes." Comparative and Functional Genomics 4, no. 5 (2003): 531–36. http://dx.doi.org/10.1002/cfg.322.

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Lymphocytes are the best-studied higher eukaryote cells. In this report, quantitative relationships of the protein components in resting cell, blast cell and plasma cell types are evaluated. The comparison of these cell types leads to the conclusion that resting cells synthesize about one-twentieth of the protein species as compared to blast cells. Blast cells seem to be metabolically the most robust lymphocyte type. Plasma cells are geared towards synthesis of one main product (antibody in B plasma cells), while most of the synthesis of other protein species (including those for housekeeping and repair) decreases as the messages decay. Although the data presented in this communication allow a meaningful comparison of three cell populations, they are far from providing a full picture. Both silver staining and radiofluorography depict only proteins of high or intermediate abundance. Silver staining misses most proteins present at <10 000 copies/cell, while radiofluorography misses all those proteins with slow turnover (and those with no methionine residue in their sequence). The detection of 1100 spots in the blast cell-related radiofluorograph includes visualization of some 97–99% of protein mass, but some 3900 polypeptide species in the remaining 1–3% of protein mass will pass undetected. This protein mass (0.7–2 pg) reflects some 2500–7500 copies of each of those 3900 polypeptide species that are present in the cell below the detection limit. The work emphasizes that full understanding of cellular function can be achieved only if quantitative aspects of cell inventory are considered.

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12

Pursiheimo, Anna, Anni P. Vehmas, Saira Afzal, Tomi Suomi, Thaman Chand, Leena Strauss, Matti Poutanen, Anne Rokka, Garry L. Corthals, and Laura L. Elo. "Optimization of Statistical Methods Impact on Quantitative Proteomics Data." Journal of Proteome Research 14, no. 10 (September 8, 2015): 4118–26. http://dx.doi.org/10.1021/acs.jproteome.5b00183.

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13

Colaert, Niklaas, Christophe Van Huele, Sven Degroeve, An Staes, Joël Vandekerckhove, Kris Gevaert, and Lennart Martens. "Combining quantitative proteomics data processing workflows for greater sensitivity." Nature Methods 8, no. 6 (May 8, 2011): 481–83. http://dx.doi.org/10.1038/nmeth.1604.

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14

Noirel, J., S. Y. Ow, G. Sanguinetti, A. Jaramillo, and P. C. Wright. "Automated extraction of meaningful pathways from quantitative proteomics data." Briefings in Functional Genomics and Proteomics 7, no. 2 (February 12, 2008): 136–46. http://dx.doi.org/10.1093/bfgp/eln011.

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15

Pavelka, Norman, Marjorie L. Fournier, Selene K. Swanson, Mattia Pelizzola, Paola Ricciardi-Castagnoli, Laurence Florens, and Michael P. Washburn. "Statistical Similarities between Transcriptomics and Quantitative Shotgun Proteomics Data." Molecular & Cellular Proteomics 7, no. 4 (November 19, 2007): 631–44. http://dx.doi.org/10.1074/mcp.m700240-mcp200.

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16

Koh, Hiromi W. L., Hannah L. F. Swa, Damian Fermin, Siok Ghee Ler, Jayantha Gunaratne, and Hyungwon Choi. "EBprot: Statistical analysis of labeling-based quantitative proteomics data." PROTEOMICS 15, no. 15 (May 28, 2015): 2580–91. http://dx.doi.org/10.1002/pmic.201400620.

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17

Cox, Jürgen, and Matthias Mann. "Quantitative, High-Resolution Proteomics for Data-Driven Systems Biology." Annual Review of Biochemistry 80, no. 1 (July 7, 2011): 273–99. http://dx.doi.org/10.1146/annurev-biochem-061308-093216.

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18

Storey, Aaron J., Kevin S. Naceanceno, Renny S. Lan, Charity L. Washam, Lisa M. Orr, Samuel G. Mackintosh, Alan J. Tackett, et al. "ProteoViz: a tool for the analysis and interactive visualization of phosphoproteomics data." Molecular Omics 16, no. 4 (2020): 316–26. http://dx.doi.org/10.1039/c9mo00149b.

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19

Chen, Chen, Jie Hou, John J. Tanner, and Jianlin Cheng. "Bioinformatics Methods for Mass Spectrometry-Based Proteomics Data Analysis." International Journal of Molecular Sciences 21, no. 8 (April 20, 2020): 2873. http://dx.doi.org/10.3390/ijms21082873.

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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.

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20

Malmström, Lars, Pontus Nordenfelt, and Johan Malmström. "Business intelligence strategies enables rapid analysis of quantitative proteomics data." journal of Proteome Science and Computational Biology 1, no. 1 (2012): 5. http://dx.doi.org/10.7243/2050-2273-1-5.

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21

Singh, Sukhdeep, Marco Y. Hein, and A. Francis Stewart. "msVolcano: A flexible web application for visualizing quantitative proteomics data." PROTEOMICS 16, no. 18 (September 2016): 2491–94. http://dx.doi.org/10.1002/pmic.201600167.

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22

Dudekula, Khadar, and Thierry Le Bihan. "Data from quantitative label free proteomics analysis of rat spleen." Data in Brief 8 (September 2016): 494–500. http://dx.doi.org/10.1016/j.dib.2016.05.058.

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23

Mohammed, Yassene, Pallab Bhowmick, Sarah A. Michaud, Albert Sickmann, and Christoph H. Borchers. "Mouse Quantitative Proteomics Knowledgebase: reference protein concentration ranges in 20 mouse tissues using 5000 quantitative proteomics assays." Bioinformatics 37, no. 13 (January 23, 2021): 1900–1908. http://dx.doi.org/10.1093/bioinformatics/btab018.

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Abstract MotivationLaboratory mouse is the most used animal model in biological research, largely due to its high conserved synteny with human. Researchers use mice to answer various questions ranging from determining a pathological effect of knocked out/in gene to understanding drug metabolism. Our group developed >5000 quantitative targeted proteomics assays for 20 mouse tissues and determined the concentration ranges of a total of >1600 proteins using heavy labeled internal standards. We describe here MouseQuaPro; a knowledgebase that hosts this collection of carefully curated experimental data. ResultsThe web-based application includes protein concentrations from >700 mouse tissue samples from three common research strains, corresponding to >200k experimentally determined concentrations. The knowledgebase integrates the assay and protein concentration information with their human orthologs, functional and molecular annotations, biological pathways, related human diseases and known gene expressions. At its core are the protein concentration ranges, which provide insights into (dis)similarities between tissues, strains and sexes. MouseQuaPro implements advanced search as well as filtering functionalities with a simple interface and interactive visualization. This information-rich resource provides an initial map of protein absolute concentration in mouse tissues and allows guided design of proteomics phenotyping experiments. The knowledgebase is available on mousequapro.proteincentre.com. Availability and implementation The knowledgebase is available free of charge on http://mousequapro.proteincentre.com. Supplementary information Supplementary data are available at Bioinformatics online.

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Breckels, Lisa M., Claire M. Mulvey, Kathryn S. Lilley, and Laurent Gatto. "A Bioconductor workflow for processing and analysing spatial proteomics data." F1000Research 5 (December 28, 2016): 2926. http://dx.doi.org/10.12688/f1000research.10411.1.

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Spatial proteomics is the systematic study of protein sub-cellular localisation. In this workflow, we describe the analysis of a typical quantitative mass spectrometry-based spatial proteomics experiment using the MSnbase and pRoloc Bioconductor package suite. To walk the user through the computational pipeline, we use a recently published experiment predicting protein sub-cellular localisation in pluripotent embryonic mouse stem cells. We describe the software infrastructure at hand, importing and processing data, quality control, sub-cellular marker definition, visualisation and interactive exploration. We then demonstrate the application and interpretation of statistical learning methods, including novelty detection using semi-supervised learning, classification, clustering and transfer learning and conclude the pipeline with data export. The workflow is aimed at beginners who are familiar with proteomics in general and spatial proteomics in particular.

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Breckels, Lisa M., Claire M. Mulvey, Kathryn S. Lilley, and Laurent Gatto. "A Bioconductor workflow for processing and analysing spatial proteomics data." F1000Research 5 (July 3, 2018): 2926. http://dx.doi.org/10.12688/f1000research.10411.2.

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Spatial proteomics is the systematic study of protein sub-cellular localisation. In this workflow, we describe the analysis of a typical quantitative mass spectrometry-based spatial proteomics experiment using the MSnbase and pRoloc Bioconductor package suite. To walk the user through the computational pipeline, we use a recently published experiment predicting protein sub-cellular localisation in pluripotent embryonic mouse stem cells. We describe the software infrastructure at hand, importing and processing data, quality control, sub-cellular marker definition, visualisation and interactive exploration. We then demonstrate the application and interpretation of statistical learning methods, including novelty detection using semi-supervised learning, classification, clustering and transfer learning and conclude the pipeline with data export. The workflow is aimed at beginners who are familiar with proteomics in general and spatial proteomics in particular.

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Murugesan, Gavuthami, Lindsay Davidson, Linda Jannetti, Paul R. Crocker, and Bernd Weigle. "Quantitative Proteomics of Polarised Macrophages Derived from Induced Pluripotent Stem Cells." Biomedicines 10, no. 2 (January 23, 2022): 239. http://dx.doi.org/10.3390/biomedicines10020239.

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Macrophages (MΦ) are highly heterogenous and versatile innate immune cells involved in homeostatic and immune responses. Activated MΦ can exist in two extreme phenotypes: pro-inflammatory (M1) MΦ and anti-inflammatory (M2) MΦ. These phenotypes can be recapitulated in vitro by using ligands of toll-like receptors (TLRs) and cytokines such as IFNγ and IL-4. In recent years, human induced pluripotent stem cells (iPSC)-derived MΦ have gained major attention, as they are functionally similar to human monocyte-derived MΦ and are receptive to genome editing. In this study, we polarised iPSC-derived MΦ to M1 or M2 and analysed their proteome and secretome profiles using quantitative proteomics. These comprehensive proteomic data sets provide new insights into functions of polarised MΦ.

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Li, Na, Huanni Li, Lanqin Cao, and Xianquan Zhan. "Quantitative analysis of the mitochondrial proteome in human ovarian carcinomas." Endocrine-Related Cancer 25, no. 10 (October 2018): 909–31. http://dx.doi.org/10.1530/erc-18-0243.

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Mitochondria play important roles in growth, signal transduction, division, tumorigenesis and energy metabolism in epithelial ovarian carcinomas (EOCs) without an effective biomarker. To investigate the proteomic profile of EOC mitochondrial proteins, a 6-plex isobaric tag for relative and absolute quantification (iTRAQ) proteomics was used to identify mitochondrial expressed proteins (mtEPs) in EOCs relative to controls, followed by an integrative analysis of the identified mtEPs and the Cancer Genome Atlas (TCGA) data from 419 patients. A total of 5115 quantified proteins were identified from purified mitochondrial samples, and 262 proteins were significantly related to overall survival in EOC patients. Furthermore, 63 proteins were identified as potential biomarkers for the development of an EOC, and our findings were consistent with previous reports on a certain extent. Pathway network analysis identified 70 signaling pathways. Interestingly, the results demonstrated that cancer cells exhibited an increased dependence on mitophagy, such as peroxisome, phagosome, lysosome, valine, leucine and isoleucine degradation and fatty acid degradation pathways, which might play an important role in EOC invasion and metastasis. Five proteins (GLDC, PCK2, IDH2, CPT2 and HMGCS2) located in the mitochondrion and enriched pathways were selected for further analysis in an EOC cell line and tissues, and the results confirmed reliability of iTRAQ proteomics. These findings provide a large-scale mitochondrial proteomic profiling with quantitative information, a certain number of potential protein biomarkers and a novel vision in the mitophagy bio-mechanism of a human ovarian carcinoma.

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28

KOLCH, Walter, Harald MISCHAK, and Andrew R. PITT. "The molecular make-up of a tumour: proteomics in cancer research." Clinical Science 108, no. 5 (April 22, 2005): 369–83. http://dx.doi.org/10.1042/cs20050006.

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The enormous progress in proteomics, enabled by recent advances in MS (mass spectrometry), has brought protein analysis back into the limelight of cancer research, reviving old areas as well as opening new fields of study. In this review, we discuss the basic features of proteomic technologies, including the basics of MS, and we consider the main current applications and challenges of proteomics in cancer research, including (i) protein expression profiling of tumours, tumour fluids and tumour cells; (ii) protein microarrays; (iii) mapping of cancer signalling pathways; (iv) pharmacoproteomics; (v) biomarkers for diagnosis, staging and monitoring of the disease and therapeutic response; and (vi) the immune response to cancer. All these applications continue to benefit from further technological advances, such as the development of quantitative proteomics methods, high-resolution, high-speed and high-sensitivity MS, functional protein assays, and advanced bioinformatics for data handling and interpretation. A major challenge will be the integration of proteomics with genomics and metabolomics data and their functional interpretation in conjunction with clinical results and epidemiology.

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Nigjeh, Eslam N., Ru Chen, Randall E. Brand, Gloria M. Petersen, Suresh T. Chari, Priska D. von Haller, Jimmy K. Eng, et al. "Quantitative Proteomics Based on Optimized Data-Independent Acquisition in Plasma Analysis." Journal of Proteome Research 16, no. 2 (January 3, 2017): 665–76. http://dx.doi.org/10.1021/acs.jproteome.6b00727.

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Locasale, Jason W., and Alejandro Wolf-Yadlin. "Maximum Entropy Reconstructions of Dynamic Signaling Networks from Quantitative Proteomics Data." PLoS ONE 4, no. 8 (August 26, 2009): e6522. http://dx.doi.org/10.1371/journal.pone.0006522.

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Schaab, Christoph, Tamar Geiger, Gabriele Stoehr, Juergen Cox, and Matthias Mann. "Analysis of High Accuracy, Quantitative Proteomics Data in the MaxQB Database." Molecular & Cellular Proteomics 11, no. 3 (February 2, 2012): M111.014068. http://dx.doi.org/10.1074/mcp.m111.014068.

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Chang, Cheng, Mansheng Li, Chaoping Guo, Yuqing Ding, Kaikun Xu, Mingfei Han, Fuchu He, and Yunping Zhu. "PANDA: A comprehensive and flexible tool for quantitative proteomics data analysis." Bioinformatics 35, no. 5 (August 23, 2018): 898–900. http://dx.doi.org/10.1093/bioinformatics/bty727.

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33

de Groot, Joost C. W., Mark W. E. J. Fiers, Roeland C. H. J. van Ham, and Antoine H. P. America. "Post alignment clustering procedure for comparative quantitative proteomics LC-MS Data." PROTEOMICS 8, no. 1 (January 2008): 32–36. http://dx.doi.org/10.1002/pmic.200700707.

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Tsai, Chia-Feng, Rui Zhao, Sarah M. Williams, Ronald J. Moore, Kendall Schultz, William B. Chrisler, Ljiljana Pasa-Tolic, et al. "An Improved Boosting to Amplify Signal with Isobaric Labeling (iBASIL) Strategy for Precise Quantitative Single-cell Proteomics." Molecular & Cellular Proteomics 19, no. 5 (March 3, 2020): 828–38. http://dx.doi.org/10.1074/mcp.ra119.001857.

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Mass spectrometry (MS)-based proteomics has great potential for overcoming the limitations of antibody-based immunoassays for antibody-independent, comprehensive, and quantitative proteomic analysis of single cells. Indeed, recent advances in nanoscale sample preparation have enabled effective processing of single cells. In particular, the concept of using boosting/carrier channels in isobaric labeling to increase the sensitivity in MS detection has also been increasingly used for quantitative proteomic analysis of small-sized samples including single cells. However, the full potential of such boosting/carrier approaches has not been significantly explored, nor has the resulting quantitation quality been carefully evaluated. Herein, we have further evaluated and optimized our recent boosting to amplify signal with isobaric labeling (BASIL) approach, originally developed for quantifying phosphorylation in small number of cells, for highly effective analysis of proteins in single cells. This improved BASIL (iBASIL) approach enables reliable quantitative single-cell proteomics analysis with greater proteome coverage by carefully controlling the boosting-to-sample ratio (e.g. in general <100×) and optimizing MS automatic gain control (AGC) and ion injection time settings in MS/MS analysis (e.g. 5E5 and 300 ms, respectively, which is significantly higher than that used in typical bulk analysis). By coupling with a nanodroplet-based single cell preparation (nanoPOTS) platform, iBASIL enabled identification of ∼2500 proteins and precise quantification of ∼1500 proteins in the analysis of 104 FACS-isolated single cells, with the resulting protein profiles robustly clustering the cells from three different acute myeloid leukemia cell lines. This study highlights the importance of carefully evaluating and optimizing the boosting ratios and MS data acquisition conditions for achieving robust, comprehensive proteomic analysis of single cells.

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Zhang, Yin, Chun-Yuan Li, Wei Ge, and Yi Xiao. "Exploration of the Key Proteins in the Normal-Adenoma-Carcinoma Sequence of Colorectal Cancer Evolution Using In-Depth Quantitative Proteomics." Journal of Oncology 2021 (June 11, 2021): 1–19. http://dx.doi.org/10.1155/2021/5570058.

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Purpose. In most cases, the carcinogenesis of colorectal cancer (CRC) follows the normal-adenoma-carcinoma (N-A-C) sequence. In this study, we aimed to identify the key proteins in the N-A-C sequence. Methods. Differentially expressed proteins (DEPs) in normal, adenoma, and carcinoma tissues were identified using the Tandem Mass Tag- (TMT-) based quantitative proteomics approach. The landscape of proteomic variation in the N-A-C sequence was explored using gene set enrichment analysis (GSEA) and Proteomaps. Key proteins in the N-A-C sequence were identified, verified, and validated based on our proteomic data, external proteomic data, and external transcriptomic data in the ProteomeXchange, CPTAC, GEO, and TCGA databases. The prognostic value of the key proteins in our database was evaluated by univariate and multivariate Cox regression analysis. The effects of the key proteins on adenoma organoids and colorectal cancer cells were explored in functional studies. Results. Based on our proteomic profiles, we identified 1,294 DEPs between the carcinoma (CG) and normal (NG) groups, 919 DEPs between the adenoma group (AG) and NG, and 1,030 DEPs between the CG and AG. Ribosome- and spliceosome-related pathways were mainly enriched in the N-A process. Extracellular matrix- and epithelial-mesenchymal transition- (EMT-) related pathways were mainly enriched in the A-C process. RRP12 and SERPINH1 were identified, verified, and validated as candidate key proteins in the N-A and A-C processes, respectively. Furthermore, RRP12 and SERPINH1 knockdown impeded the viability and proliferation of adenoma organoids. SERPINH1 was validated as a risk factor for disease-free survival (DFS) based on the TCGA and our database, whereas RRP12 did not show prognostic value. SERPINH1 knockdown was accompanied by EMT-related protein variation, increased apoptosis, and reduced proliferation, invasion, and migration of CRC cells in vitro. Conclusions. RRP12 and SERPINH1 may play an important role in the N-A and A-C processes, respectively. Furthermore, SERPINH1 showed favorable prognostic value for DFS in CRC patients. We speculate that SERPINH1 might promote not only the A-C process but also the development of CRC.

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Tang, Jing, Yang Zhang, Jianbo Fu, Yunxia Wang, Yi Li, Qingxia Yang, Lixia Yao, Weiwei Xue, and Feng Zhu. "Computational Advances in the Label-free Quantification of Cancer Proteomics Data." Current Pharmaceutical Design 24, no. 32 (January 15, 2019): 3842–58. http://dx.doi.org/10.2174/1381612824666181102125638.

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Background: Due to its ability to provide quantitative and dynamic information on tumor genesis and development by directly profiling protein expression, the proteomics has become intensely popular for characterizing the functional proteins driving the transformation of malignancy, tracing the large-scale protein alterations induced by anticancer drug, and discovering the innovative targets and first-in-class drugs for oncologic disorders. Objective: To quantify cancer proteomics data, the label-free quantification (LFQ) is frequently employed. However, low precision, poor reproducibility and inaccuracy of the LFQ of proteomics data have been recognized as the key “technical challenge” in the discovery of anticancer targets and drugs. In this paper, the recent advances and development in the computational perspective of LFQ in cancer proteomics were therefore systematically reviewed and analyzed. Methods: PubMed and Web of Science database were searched for label-free quantification approaches, cancer proteomics and computational advances. Results: First, a variety of popular acquisition techniques and state-of-the-art quantification tools are systematically discussed and critically assessed. Then, many processing approaches including transformation, normalization, filtering and imputation are subsequently discussed, and their impacts on improving LFQ performance of cancer proteomics are evaluated. Finally, the future direction for enhancing the computation-based quantification technique for cancer proteomics are also proposed. Conclusion: There is a dramatic increase in LFQ approaches in recent year, which significantly enhance the diversity of the possible quantification strategies for studying cancer proteomics.

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Wang, Shisheng, Wenxue Li, Liqiang Hu, Jingqiu Cheng, Hao Yang, and Yansheng Liu. "NAguideR: performing and prioritizing missing value imputations for consistent bottom-up proteomic analyses." Nucleic Acids Research 48, no. 14 (June 11, 2020): e83-e83. http://dx.doi.org/10.1093/nar/gkaa498.

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Abstract Mass spectrometry (MS)-based quantitative proteomics experiments frequently generate data with missing values, which may profoundly affect downstream analyses. A wide variety of imputation methods have been established to deal with the missing-value issue. To date, however, there is a scarcity of efficient, systematic, and easy-to-handle tools that are tailored for proteomics community. Herein, we developed a user-friendly and powerful stand-alone software, NAguideR, to enable implementation and evaluation of different missing value methods offered by 23 widely used missing-value imputation algorithms. NAguideR further evaluates data imputation results through classic computational criteria and, unprecedentedly, proteomic empirical criteria, such as quantitative consistency between different charge-states of the same peptide, different peptides belonging to the same proteins, and individual proteins participating protein complexes and functional interactions. We applied NAguideR into three label-free proteomic datasets featuring peptide-level, protein-level, and phosphoproteomic variables respectively, all generated by data independent acquisition mass spectrometry (DIA-MS) with substantial biological replicates. The results indicate that NAguideR is able to discriminate the optimal imputation methods that are facilitating DIA-MS experiments over those sub-optimal and low-performance algorithms. NAguideR further provides downloadable tables and figures supporting flexible data analysis and interpretation. NAguideR is freely available at http://www.omicsolution.org/wukong/NAguideR/ and the source code: https://github.com/wangshisheng/NAguideR/.

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Datta, Keshava K., Parthiban Periasamy, Sonali V. Mohan, Rebekah Ziegman, and Harsha Gowda. "Temporal Quantitative Proteomics Reveals Proteomic and Phosphoproteomic Alterations Associated with Adaptive Response to Hypoxia in Melanoma Cells." Cancers 13, no. 9 (April 30, 2021): 2175. http://dx.doi.org/10.3390/cancers13092175.

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Hypoxia is a common feature in various solid tumours, including melanoma. Cancer cells in hypoxic environments are resistant to both chemotherapy and radiation. Hypoxia is also associated with immune suppression. Identification of proteins and pathways that regulate cancer cell survival in hypoxic environments can reveal potential vulnerabilities that can be exploited to improve the efficacy of anticancer therapies. We carried out temporal proteomic and phosphoproteomic profiling in melanoma cell lines to identify hypoxia-induced protein expression and phosphorylation changes. By employing a TMT-based quantitative proteomics strategy, we report the identification and quantitation of >7000 proteins and >10,000 phosphosites in melanoma cell lines grown in hypoxia. Proteomics data show metabolic reprogramming as one of the prominent adaptive responses in hypoxia. We identify several novel hypoxia-mediated phosphorylation changes that have not been reported before. They reveal kinase signalling pathways that are potentially involved in modulating cellular response to hypoxia. In addition to known protein expression changes, we identify several novel proteomic alterations associated with adaptive response to hypoxia. We show that cancer cells require the ubiquitin–proteasome system to survive in both normoxia and hypoxia. Inhibition of proteasome activity affects cell survival and may provide a novel therapeutic avenue to target cancer cells in hypoxia. Our study can serve as a valuable resource to pursue novel candidates to target hypoxia in cancers and improve the efficacy of anticancer therapies.

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Cordido, Adrian, Marta Vizoso-Gonzalez, Laura Nuñez-Gonzalez, Alberto Molares-Vila, Maria del Pilar Chantada-Vazquez, Susana B. Bravo, and Miguel A. Garcia-Gonzalez. "Quantitative Proteomic Study Unmasks Fibrinogen Pathway in Polycystic Liver Disease." Biomedicines 10, no. 2 (January 27, 2022): 290. http://dx.doi.org/10.3390/biomedicines10020290.

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(1) Background: Polycystic liver disease (PLD) is a heterogeneous group of congenital disorders characterized by bile duct dilatation and cyst development derived from cholangiocytes. Nevertheless, the cystogenesis mechanism is currently unknown and the PLD treatment is limited to liver transplantation. Novel and efficient therapeutic approaches are th6us needed. In this context, the present work has a principal aim to find novel molecular pathways, as well as new therapeutic targets, involved in the hepatic cystogenesis process. (2) Methods: Quantitative proteomics based on SWATH–MS technology were performed comparing hepatic proteomes of Wild Type and mutant/polycystic livers in a polycystic kidney disease (PKD) murine model (Pkd1cond/cond;Tam-Cre−/+). (3) Results: We identified several proteins altered in abundance, with two-fold cut-off up-regulation or down-regulation and an adjusted p-value significantly related to hepatic cystogenesis. Then, we performed enrichment and a protein–protein analysis identifying a cluster focused on hepatic fibrinogens. Finally, we validated a selection of targets by RT-qPCR, Western blotting and immunohistochemistry, finding a high correlation with quantitative proteomics data and validating the fibrinogen complex. (4) Conclusions: This work identified a novel molecular pathway in cystic liver disease, highlighting the fibrinogen complex as a possible new therapeutic target for PLD.

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Doytchinova, Irini A., and Paul Taylor. "Proteomics in Vaccinology and Immunobiology: An Informatics Perspective of the Immunone." Journal of Biomedicine and Biotechnology 2003, no. 5 (2003): 267–90. http://dx.doi.org/10.1155/s1110724303209232.

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The postgenomic era, as manifest, inter alia, by proteomics, offers unparalleled opportunities for the efficient discovery of safe, efficacious, and novel subunit vaccines targeting a tranche of modern major diseases. A negative corollary of this opportunity is the risk of becoming overwhelmed by this embarrassment of riches. Informatics techniques, working to address issues of both data management and through prediction to shortcut the experimental process, can be of enormous benefit in leveraging the proteomic revolution. In this disquisition, we evaluate proteomic approaches to the discovery of subunit vaccines, focussing on viral, bacterial, fungal, and parasite systems. We also adumbrate the impact that proteomic analysis of host-pathogen interactions can have. Finally, we review relevant methods to the prediction of immunome, with special emphasis on quantitative methods, and the subcellular localization of proteins within bacteria.

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Arnold, Georg J., and T. Frohlich. "Dynamic proteome signatures in gametes, embryos and their maternal environment." Reproduction, Fertility and Development 23, no. 1 (2011): 81. http://dx.doi.org/10.1071/rd10223.

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Comprehensive molecular analysis at the level of proteins represents a technically demanding, but indispensable, task since several post-transcriptional regulation mechanisms disable a valid prediction of quantitative protein expression profiles from transcriptome analysis. In crucial steps of gamete and early embryo development, de novo transcription is silenced, meaning that almost all macromolecular events take place at the level of proteins. In this review, we describe selected examples of dynamic proteome signatures addressing capacitation of spermatozoa, in vitro maturation of oocytes, effect of oestrous cycle on oviduct epithelial cells and embryo-induced alterations to the maternal environment. We also present details of the experimental strategies applied and the experiments performed to verify quantitative proteomic data. Far from being comprehensive, examples were selected to cover several mammalian species as well as the most powerful proteomic techniques currently applied. To enable non-experts in the field of proteomics to appraise published proteomic data, our examples are preceded by a customised description of quantitative proteomic methods, covering 2D difference gel electrophoresis (2D-DIGE), nano-liquid chromatography combined with tandem mass spectrometry, and label-free as well as stable-isotope labelling strategies for mass spectrometry-based quantifications.

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Kuzniar, Arnold, and Roland Kanaar. "PIQMIe: a web server for semi-quantitative proteomics data management and analysis." Nucleic Acids Research 42, W1 (May 26, 2014): W100—W106. http://dx.doi.org/10.1093/nar/gku478.

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MacCoss, Michael J., Christine C. Wu, Hongbin Liu, Rovshan Sadygov, and John R. Yates. "A Correlation Algorithm for the Automated Quantitative Analysis of Shotgun Proteomics Data." Analytical Chemistry 75, no. 24 (December 2003): 6912–21. http://dx.doi.org/10.1021/ac034790h.

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Ting, Lily, Mark J. Cowley, Seah Lay Hoon, Michael Guilhaus, Mark J. Raftery, and Ricardo Cavicchioli. "Normalization and Statistical Analysis of Quantitative Proteomics Data Generated by Metabolic Labeling." Molecular & Cellular Proteomics 8, no. 10 (July 14, 2009): 2227–42. http://dx.doi.org/10.1074/mcp.m800462-mcp200.

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Walzer, Mathias, Da Qi, Gerhard Mayer, Julian Uszkoreit, Martin Eisenacher, Timo Sachsenberg, Faviel F. Gonzalez-Galarza, et al. "The mzQuantML Data Standard for Mass Spectrometry–based Quantitative Studies in Proteomics." Molecular & Cellular Proteomics 12, no. 8 (April 18, 2013): 2332–40. http://dx.doi.org/10.1074/mcp.o113.028506.

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Teo, Guoshou, Sinae Kim, Chih-Chiang Tsou, Ben Collins, Anne-Claude Gingras, Alexey I. Nesvizhskii, and Hyungwon Choi. "mapDIA: Preprocessing and statistical analysis of quantitative proteomics data from data independent acquisition mass spectrometry." Journal of Proteomics 129 (November 2015): 108–20. http://dx.doi.org/10.1016/j.jprot.2015.09.013.

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Bhawal, Ruchika, Ann L. Oberg, Sheng Zhang, and Manish Kohli. "Challenges and Opportunities in Clinical Applications of Blood-Based Proteomics in Cancer." Cancers 12, no. 9 (August 27, 2020): 2428. http://dx.doi.org/10.3390/cancers12092428.

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Blood is a readily accessible biofluid containing a plethora of important proteins, nucleic acids, and metabolites that can be used as clinical diagnostic tools in diseases, including cancer. Like the on-going efforts for cancer biomarker discovery using the liquid biopsy detection of circulating cell-free and cell-based tumor nucleic acids, the circulatory proteome has been underexplored for clinical cancer biomarker applications. A comprehensive proteome analysis of human serum/plasma with high-quality data and compelling interpretation can potentially provide opportunities for understanding disease mechanisms, although several challenges will have to be met. Serum/plasma proteome biomarkers are present in very low abundance, and there is high complexity involved due to the heterogeneity of cancers, for which there is a compelling need to develop sensitive and specific proteomic technologies and analytical platforms. To date, liquid chromatography mass spectrometry (LC-MS)-based quantitative proteomics has been a dominant analytical workflow to discover new potential cancer biomarkers in serum/plasma. This review will summarize the opportunities of serum proteomics for clinical applications; the challenges in the discovery of novel biomarkers in serum/plasma; and current proteomic strategies in cancer research for the application of serum/plasma proteomics for clinical prognostic, predictive, and diagnostic applications, as well as for monitoring minimal residual disease after treatments. We will highlight some of the recent advances in MS-based proteomics technologies with appropriate sample collection, processing uniformity, study design, and data analysis, focusing on how these integrated workflows can identify novel potential cancer biomarkers for clinical applications.

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Poulsen, T. B. G., J. S. Andersen, M. K. Kristiansen, S. Rasmusen, L. Arent-Nielsen, C. H. Nielsen, and A. Stensballe. "AB1254 PHENOTYPING OF MULTIPLE BIOFLUIDS FOR LIQUID BIOMARKERS FOR DIAGNOSTICS AND PERSONALIZED MEDICINE OF RHEUMATOID ARTHRITIS, SPONDYLOARTHRITIS AND OSTEOARTHRITIS." Annals of the Rheumatic Diseases 79, Suppl 1 (June 2020): 1918.1–1919. http://dx.doi.org/10.1136/annrheumdis-2020-eular.5949.

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Background:Inflammatory and autoimmune diseases include multifactual pathomechanisms and systemic responses. The etiology of the joint diseases rheumatoid arthritis (RA), spondyloarthritis (SpA) in relation to osteoarthritis (OA) remain incomplete and establishing the correct diagnose remains nontrivial. Advances in high-throughput molecular technologies have increased investigations into the utility of transcriptomic, proteomic and high-density protein arrays approaches as diagnostic tools and companion diagnostics for precision medicine. To increase our understanding of the molecular pathogenesis, we extracted synovial fluid from the joints from multiple patient groups and characterized the protein composition in relation to plasma. Basic blood tests include inflammatory markers and autoantibodies, however, now analysis speed and robustness allow more readily clinical insight biofluids.Objectives:We present recent Omics concepts and studies investigating inflammatory state and treatment outcome in different biofluids from plasma to synovial fluid accessing causalities leading to inflammation and pain. Additionally, the aim was to investigate in any proteomic findings in synovial fluid can be correlated to proteomic changes in patient plasma and can be used as biomarkers for treatment effect.Methods:Plasma and synovial fluid were investigated in multiple pathologies before and after treatment in patients (biologics; MTX; intraarticular gold). Deep proteome, PTM and EV profiling were accomplished using quantitative proteomics approaches using quantitative mass spectrometry-based analysis by DIA/PASEF followed by deep datamining. All biological samples were digested according to a Filter Aided Sample Preparation (FASP) protocol before analysis with tandem mass spectrometry (MS/MS). PTM profiling were evaluated by 4D CCS based feature finding.Results:Mass spectrometry based profiling allowed quantitative profiling of up to 480 proteins in matched synovial fluid and plasma. Complementary analysis by Olink proteomics, cytokine profiling and cell-free DNA. Multiple acute inflammatory proteins were more abundant in the RA synovial fluid, including proteins originating from neutrophil granulocytes, whereas SpA patients had a higher concentration of haptoglobin. Complementary analysis by Olink immunoassay identified significantly downregulated inflammation markers out of 96 tested in relation to antiinflammatory treatment.Conclusion:Discovery of biomarkers and/or inflammatory signatures through integration of multi-omic data allowed stratify patients for improved treatment and prognosis. Firstly, our data using next generation proteomics approaches alleviates many pitfalls of missing values and poor proteome coverage including unbiased PTM profiling without enrichment strategies.Disclosure of Interests:None declared

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Raffel, Simon, Daniel Klimmeck, Mattia Falcone, Aykut Demir, Alireza Pouya, Petra Zeisberger, Christoph Lutz, et al. "Quantitative proteomics reveals specific metabolic features of acute myeloid leukemia stem cells." Blood 136, no. 13 (September 24, 2020): 1507–19. http://dx.doi.org/10.1182/blood.2019003654.

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Abstract Acute myeloid leukemia is characterized by the accumulation of clonal myeloid blast cells unable to differentiate into mature leukocytes. Chemotherapy induces remission in the majority of patients, but relapse rates are high and lead to poor clinical outcomes. Because this is primarily caused by chemotherapy-resistant leukemic stem cells (LSCs), it is essential to eradicate LSCs to improve patient survival. LSCs have predominantly been studied at the transcript level, thus information about posttranscriptionally regulated genes and associated networks is lacking. Here, we extend our previous report on LSC proteomes to healthy age-matched hematopoietic stem and progenitor cells (HSPCs) and correlate the proteomes to the corresponding transcriptomes. By comparing LSCs to leukemic blasts and healthy HSPCs, we validate candidate LSC markers and highlight novel and potentially targetable proteins that are absent or only lowly expressed in HSPCs. In addition, our data provide strong evidence that LSCs harbor a characteristic energy metabolism, adhesion molecule composition, as well as RNA-processing properties. Furthermore, correlating proteome and transcript data of the same individual samples highlights the strength of proteome analyses, which are particularly potent in detecting alterations in metabolic pathways. In summary, our study provides a comprehensive proteomic and transcriptomic characterization of functionally validated LSCs, blasts, and healthy HSPCs, representing a valuable resource helping to design LSC-directed therapies.

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Wu, Jinlu, Qingsong Lin, Teck Kwang Lim, Tiefei Liu, and Choy-Leong Hew. "White Spot Syndrome Virus Proteins and Differentially Expressed Host Proteins Identified in Shrimp Epithelium by Shotgun Proteomics and Cleavable Isotope-Coded Affinity Tag." Journal of Virology 81, no. 21 (August 22, 2007): 11681–89. http://dx.doi.org/10.1128/jvi.01006-07.

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ABSTRACT Shrimp subcuticular epithelial cells are the initial and major targets of white spot syndrome virus (WSSV) infection. Proteomic studies of WSSV-infected subcuticular epithelium of Penaeus monodon were performed through two approaches, namely, subcellular fractionation coupled with shotgun proteomics to identify viral and host proteins and a quantitative time course proteomic analysis using cleavable isotope-coded affinity tags (cICATs) to identify differentially expressed cellular proteins. Peptides were analyzed by offline coupling of two-dimensional liquid chromatography with matrix-assisted laser desorption ionization-tandem time of flight mass spectrometry. We identified 27, 20, and 4 WSSV proteins from cytosolic, nuclear, and membrane fractions, respectively. Twenty-eight unique WSSV proteins with high confidence (total ion confidence interval percentage [CI%], >95%) were observed, 11 of which are reported here for the first time, and 3 of these novel proteins were shown to be viral nonstructural proteins by Western blotting analysis. A first shrimp protein data set containing 1,999 peptides (ion score, ≥20) and 429 proteins (total ion score CI%, >95%) was constructed via shotgun proteomics. We also identified 10 down-regulated proteins and 2 up-regulated proteins from the shrimp epithelial lysate via cICAT analysis. This is the first comprehensive study of WSSV-infected epithelia by proteomics. The 11 novel viral proteins represent the latest addition to our knowledge of the WSSV proteome. Three proteomic data sets consisting of WSSV proteins, epithelial cellular proteins, and differentially expressed cellular proteins generated in the course of WSSV infection provide a new resource for further study of WSSV-shrimp interactions.

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