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Journal articles on the topic 'Label-free quantitative mass spectrometry'

Author: Grafiati

Published: 4 June 2021

Last updated: 5 February 2022

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1

Zhu, Wenhong, Jeffrey W. Smith, and Chun-Ming Huang. "Mass Spectrometry-Based Label-Free Quantitative Proteomics." Journal of Biomedicine and Biotechnology 2010 (2010): 1–6. http://dx.doi.org/10.1155/2010/840518.

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In order to study the differential protein expression in complex biological samples, strategies for rapid, highly reproducible and accurate quantification are necessary. Isotope labeling and fluorescent labeling techniques have been widely used in quantitative proteomics research. However, researchers are increasingly turning to label-free shotgun proteomics techniques for faster, cleaner, and simpler results. Mass spectrometry-based label-free quantitative proteomics falls into two general categories. In the first are the measurements of changes in chromatographic ion intensity such as peptide peak areas or peak heights. The second is based on the spectral counting of identified proteins. In this paper, we will discuss the technologies of these label-free quantitative methods, statistics, available computational software, and their applications in complex proteomics studies.

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Neilson, Karlie A., Naveid A. Ali, Sridevi Muralidharan, et al. "Less label, more free: Approaches in label-free quantitative mass spectrometry." PROTEOMICS 11, no. 4 (2011): 535–53. http://dx.doi.org/10.1002/pmic.201000553.

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3

Lai, Xianyin, Lianshui Wang, and Frank A. Witzmann. "Issues and Applications in Label-Free Quantitative Mass Spectrometry." International Journal of Proteomics 2013 (January 16, 2013): 1–13. http://dx.doi.org/10.1155/2013/756039.

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To address the challenges associated with differential expression proteomics, label-free mass spectrometric protein quantification methods have been developed as alternatives to array-based, gel-based, and stable isotope tag or label-based approaches. In this paper, we focus on the issues associated with label-free methods that rely on quantitation based on peptide ion peak area measurement. These issues include chromatographic alignment, peptide qualification for quantitation, and normalization. In addressing these issues, we present various approaches, assembled in a recently developed label-free quantitative mass spectrometry platform, that overcome these difficulties and enable comprehensive, accurate, and reproducible protein quantitation in highly complex protein mixtures from experiments with many sample groups. As examples of the utility of this approach, we present a variety of cases where the platform was applied successfully to assess differential protein expression or abundance in body fluids, in vitro nanotoxicology models, tissue proteomics in genetic knock-in mice, and cell membrane proteomics.

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Kopylov, A. T., V. G. Zgoda, and A. I. Archakov. "Mass spectrometry label-free quantitative analysis of proteins." Biochemistry (Moscow) Supplement Series B: Biomedical Chemistry 4, no. 1 (2010): 49–58. http://dx.doi.org/10.1134/s1990750810010075.

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Unsihuay, Daisy, Daniela Mesa Sanchez, and Julia Laskin. "Quantitative Mass Spectrometry Imaging of Biological Systems." Annual Review of Physical Chemistry 72, no. 1 (2021): 307–29. http://dx.doi.org/10.1146/annurev-physchem-061020-053416.

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Mass spectrometry imaging (MSI) is a powerful, label-free technique that provides detailed maps of hundreds of molecules in complex samples with high sensitivity and subcellular spatial resolution. Accurate quantification in MSI relies on a detailed understanding of matrix effects associated with the ionization process along with evaluation of the extraction efficiency and mass-dependent ion losses occurring in the analysis step. We present a critical summary of approaches developed for quantitative MSI of metabolites, lipids, and proteins in biological tissues and discuss their current and future applications.

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Ankney, J. Astor, Adil Muneer, and Xian Chen. "Relative and Absolute Quantitation in Mass Spectrometry–Based Proteomics." Annual Review of Analytical Chemistry 11, no. 1 (2018): 49–77. http://dx.doi.org/10.1146/annurev-anchem-061516-045357.

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Mass spectrometry–based quantitative proteomics is a powerful tool for gaining insights into function and dynamics of biological systems. However, peptides with different sequences have different ionization efficiencies, and their intensities in a mass spectrum are not correlated with their abundances. Therefore, various label-free or stable isotope label–based quantitation methods have emerged to assist mass spectrometry to perform comparative proteomic experiments, thus enabling nonbiased identification of thousands of proteins differentially expressed in healthy versus diseased cells. Here, we discuss the most widely used label-free and metabolic-, enzymatic-, and chemical labeling–based proteomic strategies for relative and absolute quantitation. We summarize the specific strengths and weaknesses of each technique in terms of quantification accuracy, proteome coverage, multiplexing capability, and robustness. Applications of each strategy for solving specific biological complexities are also presented.

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ZHANG, Wei, Ji-Yang ZHANG, Hui LIU, et al. "Development of Algorithms for Mass Spectrometry-based Label-free Quantitative Proteomics*." PROGRESS IN BIOCHEMISTRY AND BIOPHYSICS 38, no. 6 (2011): 506–18. http://dx.doi.org/10.3724/sp.j.1206.2010.00560.

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HIRABAYASHI, Atsumu, Masafumi FURUKAWA, Mitsuhiro UMEDA, Tomomi BANDO, and Yoshimitsu ORII. "Probe for Label-free Quantitative Analysis in Liquid Chromatography/Mass Spectrometry." Analytical Sciences 25, no. 1 (2009): 67–71. http://dx.doi.org/10.2116/analsci.25.67.

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9

Müller, Fränze, Lutz Fischer, Zhuo Angel Chen, Tania Auchynnikava, and Juri Rappsilber. "On the Reproducibility of Label-Free Quantitative Cross-Linking/Mass Spectrometry." Journal of The American Society for Mass Spectrometry 29, no. 2 (2017): 405–12. http://dx.doi.org/10.1007/s13361-017-1837-2.

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10

Wasinger, Valerie C., Ming Zeng, and Yunki Yau. "Current Status and Advances in Quantitative Proteomic Mass Spectrometry." International Journal of Proteomics 2013 (March 6, 2013): 1–12. http://dx.doi.org/10.1155/2013/180605.

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The accurate quantitation of proteins and peptides in complex biological systems is one of the most challenging areas of proteomics. Mass spectrometry-based approaches have forged significant in-roads allowing accurate and sensitive quantitation and the ability to multiplex vastly complex samples through the application of robust bioinformatic tools. These relative and absolute quantitative measures using label-free, tags, or stable isotope labelling have their own strengths and limitations. The continuous development of these methods is vital for increasing reproducibility in the rapidly expanding application of quantitative proteomics in biomarker discovery and validation. This paper provides a critical overview of the primary mass spectrometry-based quantitative approaches and the current status of quantitative proteomics in biomedical research.

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11

Deng, Ning, Zhenye Li, Chao Pan, and Huilong Duan. "freeQuant: A Mass Spectrometry Label-Free Quantification Software Tool for Complex Proteome Analysis." Scientific World Journal 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/137076.

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Study of complex proteome brings forward higher request for the quantification method using mass spectrometry technology. In this paper, we present a mass spectrometry label-free quantification tool for complex proteomes, called freeQuant, which integrated quantification with functional analysis effectively. freeQuant consists of two well-integrated modules: label-free quantification and functional analysis with biomedical knowledge. freeQuant supports label-free quantitative analysis which makes full use of tandem mass spectrometry (MS/MS) spectral count, protein sequence length, shared peptides, and ion intensity. It adopts spectral count for quantitative analysis and builds a new method for shared peptides to accurately evaluate abundance of isoforms. For proteins with low abundance, MS/MS total ion count coupled with spectral count is included to ensure accurate protein quantification. Furthermore, freeQuant supports the large-scale functional annotations for complex proteomes. Mitochondrial proteomes from the mouse heart, the mouse liver, and the human heart were used to evaluate the usability and performance of freeQuant. The evaluation showed that the quantitative algorithms implemented in freeQuant can improve accuracy of quantification with better dynamic range.

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WANG, HAILONG, YAN LI, LIJUAN YANG, et al. "Mass spectrometry-based, label-free quantitative proteomics of round spermatids in mice." Molecular Medicine Reports 10, no. 4 (2014): 2009–24. http://dx.doi.org/10.3892/mmr.2014.2460.

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13

Schiess, Ralph, Lukas N. Mueller, Alexander Schmidt, Markus Mueller, Bernd Wollscheid, and Ruedi Aebersold. "Analysis of Cell Surface Proteome Changes via Label-free, Quantitative Mass Spectrometry." Molecular & Cellular Proteomics 8, no. 4 (2008): 624–38. http://dx.doi.org/10.1074/mcp.m800172-mcp200.

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14

Danielsen, Heidi N., Susan H. Hansen, Florian-Alexander Herbst, et al. "Direct Identification of Functional Amyloid Proteins by Label-Free Quantitative Mass Spectrometry." Biomolecules 7, no. 4 (2017): 58. http://dx.doi.org/10.3390/biom7030058.

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Kohli, Priyanka, Malte P. Bartram, Sandra Habbig, et al. "Label-free quantitative proteomic analysis of the YAP/TAZ interactome." American Journal of Physiology-Cell Physiology 306, no. 9 (2014): C805—C818. http://dx.doi.org/10.1152/ajpcell.00339.2013.

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The function of an individual protein is typically defined by protein-protein interactions orchestrating the formation of large complexes critical for a wide variety of biological processes. Over the last decade the analysis of purified protein complexes by mass spectrometry became a key technique to identify protein-protein interactions. We present a fast and straightforward approach for analyses of interacting proteins combining a Flp-in single-copy cellular integration system and single-step affinity purification with single-shot mass spectrometry analysis. We applied this protocol to the analysis of the YAP and TAZ interactome. YAP and TAZ are the downstream effectors of the mammalian Hippo tumor suppressor pathway. Our study provides comprehensive interactomes for both YAP and TAZ and does not only confirm the majority of previously described interactors but, strikingly, revealed uncharacterized interaction partners that affect YAP/TAZ TEAD-dependent transcription. Among these newly identified candidates are Rassf8, thymopoetin, and the transcription factors CCAAT/enhancer-binding protein (C/EBP)β/δ and core-binding factor subunit β (Cbfb). In addition, our data allowed insights into complex stoichiometry and uncovered discrepancies between the YAP and TAZ interactomes. Taken together, the stringent approach presented here could help to significantly sharpen the understanding of protein-protein networks.

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Di Tomasso, Geneviève, Lisa M. Miller Jenkins, and Pascale Legault. "ARiBo pull-down for riboproteomic studies based on label-free quantitative mass spectrometry." RNA 22, no. 11 (2016): 1760–70. http://dx.doi.org/10.1261/rna.057513.116.

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17

Cederlund, Andreas, Frank Nylén, Erica Miraglia, Peter Bergman, Gudmundur H. Gudmundsson, and Birgitta Agerberth. "Label-Free Quantitative Mass Spectrometry Reveals Novel Pathways Involved in LL-37 Expression." Journal of Innate Immunity 6, no. 3 (2013): 365–76. http://dx.doi.org/10.1159/000355931.

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18

Negishi, Ayako, Masaya Ono, Yasushi Handa, et al. "Large-scale quantitative clinical proteomics by label-free liquid chromatography and mass spectrometry." Cancer Science 100, no. 3 (2009): 514–19. http://dx.doi.org/10.1111/j.1349-7006.2008.01055.x.

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19

Scheerlinck, E., M. Dhaenens, A. Van Soom, et al. "Minimizing technical variation during sample preparation prior to label-free quantitative mass spectrometry." Analytical Biochemistry 490 (December 2015): 14–19. http://dx.doi.org/10.1016/j.ab.2015.08.018.

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20

Lindemann, Claudia, Nikolas Thomanek, Franziska Hundt, et al. "Strategies in relative and absolute quantitative mass spectrometry based proteomics." Biological Chemistry 398, no. 5-6 (2017): 687–99. http://dx.doi.org/10.1515/hsz-2017-0104.

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Abstract 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.

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21

Xiao, Peng, Fan Zhang, Xinxue Wang, Dewei Song, and Hongmei Li. "Analysis of B-type natriuretic peptide impurities using label-free data-independent acquisition mass spectrometry technology." Clinical Chemistry and Laboratory Medicine (CCLM) 59, no. 1 (2021): 217–26. http://dx.doi.org/10.1515/cclm-2020-0012.

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AbstractObjectivesSynthetic B-type natriuretic peptide (BNP) is employed in most clinical testing platforms as a raw material of calibrator. Characterization of impurities with structures similar (BNPstrimp compounds) to that of BNP is a reasonable way to decrease clinical measurement errors and improve drug safety.MethodsA novel quantitative method targeted towards BNPstrimp compounds was developed. First, the peptide samples were separated and identified using ultra-performance liquid chromatography, coupled with high-resolution mass spectrometry (MS). To evaluate biological activity further, BNPstrimp immunoaffinity was investigated using western blot (WB) assays. Second, a quantitative label-free data-independent acquisition (DIA) MS approach was developed, and the internal standard peptide (ISP) was hydrolyzed. Absolute quantification was performed using an isotope dilution MS (ID-MS) approach. Third, method precision was investigated using the C-peptide reference material.ResultsSeventeen BNPstrimp compounds were identified in synthetic BNP, and 10 of them were successfully sequenced. The immunoassay results indicated that deaminated, oxidized, and isomerized BNPstrimp compounds exhibited weaker immunoaffinity than intact BNP1-32. The mass fraction of the synthetic solid ISP1-16, quantified by ID-MS, was 853.5 (±17.8) mg/g. Validation results indicated that the developed method was effective and accurate for the quantitation of the well-separated BNP impurities.ConclusionsThe developed approach was easy to perform, and it was suitable for the parallel quantification of low-abundance BNPstrimp compounds when they performed a good separation in liquid chromatography. The quantitative results were comparable and traceable. This approach is a promising tool for BNP product quality and safety assessment.

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22

Liangpunsakul, Suthat, Xianyin Lai, and Frank Witzmann. "Mo1982 Potential Serum Biomarkers of Excessive Alcohol Use Discovered by Label-Free Quantitative Mass Spectrometry." Gastroenterology 146, no. 5 (2014): S—708—S—709. http://dx.doi.org/10.1016/s0016-5085(14)62571-3.

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23

Fan, Nai-Jun, Jiang-Ling Gao, Yan Liu, Wei Song, Zhan-Yang Zhang, and Chun-Fang Gao. "Label-Free Quantitative Mass Spectrometry Reveals a Panel of Differentially Expressed Proteins in Colorectal Cancer." BioMed Research International 2015 (2015): 1–13. http://dx.doi.org/10.1155/2015/365068.

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To identify potential biomarkers involved in CRC, a shotgun proteomic method was applied to identify soluble proteins in three CRCs and matched normal mucosal tissues using high-performance liquid chromatography and mass spectrometry. Label-free protein profiling of three CRCs and matched normal mucosal tissues were then conducted to quantify and compare proteins. Results showed that 67 of the 784 identified proteins were linked to CRC (28 upregulated and 39 downregulated). Gene Ontology and DAVID databases were searched to identify the location and function of differential proteins that were related to the biological processes of binding, cell structure, signal transduction, cell adhesion, and so on. Among the differentially expressed proteins, tropomyosin-3 (TPM3), endoplasmic reticulum resident protein 29 (ERp29), 18 kDa cationic antimicrobial protein (CAMP), and heat shock 70 kDa protein 8 (HSPA8) were verified to be upregulated in CRC tissue and seven cell lines through western blot analysis. Furthermore, the upregulation of TPM3, ERp29, CAMP, and HSPA8 was validated in 69 CRCs byimmunohistochemistry (IHC) analysis. Combination of TPM3, ERp29, CAMP, and HSPA8 can identify CRC from matched normal mucosal achieving an accuracy of 73.2% using IHC score. These results suggest that TPM3, ERp29, CAMP, and HSPA8 are great potential IHC diagnostic biomarkers for CRC.

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Tutturen, Astrid E. V., Siri Dørum, Trevor Clancy, et al. "Characterization of the Small Intestinal Lesion in Celiac Disease by Label-Free Quantitative Mass Spectrometry." American Journal of Pathology 188, no. 7 (2018): 1563–79. http://dx.doi.org/10.1016/j.ajpath.2018.03.017.

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25

Smits, Arne H., Pascal W. T. C. Jansen, Ina Poser, Anthony A. Hyman, and Michiel Vermeulen. "Stoichiometry of chromatin-associated protein complexes revealed by label-free quantitative mass spectrometry-based proteomics." Nucleic Acids Research 41, no. 1 (2012): e28-e28. http://dx.doi.org/10.1093/nar/gks941.

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Huang, Qingxia, Hehua Lei, Manyuan Dong, Huiru Tang, and Yulan Wang. "Quantitative analysis of 10 classes of phospholipids by ultrahigh-performance liquid chromatography tandem triple-quadrupole mass spectrometry." Analyst 144, no. 13 (2019): 3980–87. http://dx.doi.org/10.1039/c9an00676a.

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27

Trinh, Hung V., Jonas Grossmann, Peter Gehrig, et al. "iTRAQ-Based and Label-Free Proteomics Approaches for Studies of Human Adenovirus Infections." International Journal of Proteomics 2013 (March 11, 2013): 1–16. http://dx.doi.org/10.1155/2013/581862.

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Both isobaric tags for relative and absolute quantitation (iTRAQ) and label-free methods are widely used for quantitative proteomics. Here, we provide a detailed evaluation of these proteomics approaches based on large datasets from biological samples. iTRAQ-label-based and label-free quantitations were compared using protein lysate samples from noninfected human lung epithelial A549 cells and from cells infected for 24 h with human adenovirus type 3 or type 5. Either iTRAQ-label-based or label-free methods were used, and the resulting samples were analyzed by liquid chromatography (LC) and tandem mass spectrometry (MS/MS). To reduce a possible bias from quantitation software, we applied several software packages for each procedure. ProteinPilot and Scaffold Q+ software were used for iTRAQ-labeled samples, while Progenesis LC-MS and ProgenesisF-T2PQ/T3PQ were employed for label-free analyses. R2 correlation coefficients correlated well between two software packages applied to the same datasets with values between 0.48 and 0.78 for iTRAQ-label-based quantitations and 0.5 and 0.86 for label-free quantitations. Analyses of label-free samples showed higher levels of protein up- or downregulation in comparison to iTRAQ-labeled samples. The concentration differences were further evaluated by Western blotting for four downregulated proteins. These data suggested that the label-free method was more accurate than the iTRAQ method.

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Suvannasankha, Attaya, Colin D. Crean, Heather M. Sahm, Rafat Abonour, Sherif Farag, and Mu Wang. "A Potential Biomarker Panel of Multiple Myeloma Identified Using Label-Free Mass Spectrometry-Based Quantitative Proteomics." Blood 118, no. 21 (2011): 2890. http://dx.doi.org/10.1182/blood.v118.21.2890.2890.

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Abstract Abstract 2890 Background: Multiple myeloma is an incurable and fatal hematologic malignancy. Recent gene microarray studies showed distinct gene expression profiles defining MM subgroups and their association with cytogenetic abnormalities and treatment outcome. However, aside from transcriptional control, a variety of post-transcriptional/post-translational modifications likely play an important role in regulating protein expression and function, and ultimately may prove informative for predicting tumor behavior. Objectives: We hypothesize that the protein profile in MM cells is different than normal plasma cells. Methodology: Normal plasma cells and myeloma cells were isolated using CD138 immune magnetic beads from bone marrow aspirates from healthy volunteers or patients with newly diagnosed MM, respectively. CD138+ cells were frozen and subsequently analyzed in one batch. Proteins were digested by trypsin. Tryptic peptides were injected onto an HPLC system and analyzed on a Thermo-Fisher LTQ mass spectrometer. Peptide identification and quantification were carried out using proprietary algorithms. Identified proteins were categorized into priority groups based on the quality of the peptide identification by tandem mass spectrometry. Proteins with significant changes in expression level were further analyzed by bioinformatics tools for the determination of the biological significance. Results: In the discovery phase of this study, 433 proteins were identified and their expression levels were quantitatively compared. 169 of these proteins demonstrated a significant difference between normal plasma cells and MM cells. Among the significantly changed proteins, 18 were identified and quantified with high confidence, and were therefore chosen for further validation. The identified proteins are known to be involved in the glycolysis/gluconeogenesis pathway, the oxidative phosphorylation pathway, cysteine metabolism and the pentose phosphate pathway. None of these proteins are known to be of prognostic value or being currently targeted for therapy in MM. A high-throughput LC/MS-based multiple-reaction-monitoring (MRM) assay for quantitative validation of these candidates with clinical samples is ongoing. To date, using the MRM assay, we were able to detect MRM peptides for 13 of the 18 targeted proteins in clinical samples. The quantification of these peptides will be further confirmed using a separate set of clinical samples. Conclusion: Significant differences in protein expression were observed between MM and normal plasma cells. The study presents an important step toward using proteomics as a tool to develop diagnostic and/or prognostic biomarkers in the clinical setting. However, both follow-up analytical and clinical validations are required before they can serve as disease-specific biomarkers. Disclosures: Abonour: Celgene: Membership on an entity's Board of Directors or advisory committees.

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Holt, Tom G., Bernard K. Choi, Neil S. Geoghagen, et al. "Label-Free High-Throughput Screening via Mass Spectrometry: A Single Cystathionine Quantitative Method for Multiple Applications." ASSAY and Drug Development Technologies 7, no. 5 (2009): 495–506. http://dx.doi.org/10.1089/adt.2009.0200.

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Manes, Nathan P., Li Dong, Weidong Zhou, et al. "Discovery of Mouse Spleen Signaling Responses to Anthrax using Label-Free Quantitative Phosphoproteomics via Mass Spectrometry." Molecular & Cellular Proteomics 10, no. 3 (2010): M110.000927. http://dx.doi.org/10.1074/mcp.m110.000927.

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Han, Na-Young, Ji-Youn Hong, Jong-Moon Park, et al. "Label-free quantitative proteomic analysis of human periodontal ligament stem cells by high-resolution mass spectrometry." Journal of Periodontal Research 54, no. 1 (2018): 53–62. http://dx.doi.org/10.1111/jre.12604.

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32

HAMM, G., N. DESBENOIT, R. LEGOUFFE, A. BRUNELLE, F. BRIGNOLE-BAUDOUIN, and J. STAUBER. "Quantitative and qualitative label free imaging using mass spectrometry in the context of an ophthalmic application." Acta Ophthalmologica 90 (August 6, 2012): 0. http://dx.doi.org/10.1111/j.1755-3768.2012.4745.x.

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Sessler, Nicole, Karsten Krug, Alfred Nordheim, Benjamin Mordmüller, and Boris Macek. "Analysis of the Plasmodium falciparum proteasome using Blue Native PAGE and label-free quantitative mass spectrometry." Amino Acids 43, no. 3 (2012): 1119–29. http://dx.doi.org/10.1007/s00726-012-1296-9.

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Held, Jason M., Birgit Schilling, Alexandria K. D'Souza, et al. "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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Yu, Xiaolan, Yongsheng Wang, Markus V. Kohnen, et al. "Large Scale Profiling of Protein Isoforms Using Label-Free Quantitative Proteomics Revealed the Regulation of Nonsense-Mediated Decay in Moso Bamboo (Phyllostachys edulis)." Cells 8, no. 7 (2019): 744. http://dx.doi.org/10.3390/cells8070744.

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Moso bamboo is an important forest species with a variety of ecological, economic, and cultural values. However, the gene annotation information of moso bamboo is only based on the transcriptome sequencing, lacking the evidence of proteome. The lignification and fiber in moso bamboo leads to a difficulty in the extraction of protein using conventional methods, which seriously hinders research on the proteomics of moso bamboo. The purpose of this study is to establish efficient methods for extracting the total proteins from moso bamboo for following mass spectrometry-based quantitative proteome identification. Here, we have successfully established a set of efficient methods for extracting total proteins of moso bamboo followed by mass spectrometry-based label-free quantitative proteome identification, which further improved the protein annotation of moso bamboo genes. In this study, 10,376 predicted coding genes were confirmed by quantitative proteomics, accounting for 35.8% of all annotated protein-coding genes. Proteome analysis also revealed the protein-coding potential of 1015 predicted long noncoding RNA (lncRNA), accounting for 51.03% of annotated lncRNAs. Thus, mass spectrometry-based proteomics provides a reliable method for gene annotation. Especially, quantitative proteomics revealed the translation patterns of proteins in moso bamboo. In addition, the 3284 transcript isoforms from 2663 genes identified by Pacific BioSciences (PacBio) single-molecule real-time long-read isoform sequencing (Iso-Seq) was confirmed on the protein level by mass spectrometry. Furthermore, domain analysis of mass spectrometry-identified proteins encoded in the same genomic locus revealed variations in domain composition pointing towards a functional diversification of protein isoform. Finally, we found that part transcripts targeted by nonsense-mediated mRNA decay (NMD) could also be translated into proteins. In summary, proteomic analysis in this study improves the proteomics-assisted genome annotation of moso bamboo and is valuable to the large-scale research of functional genomics in moso bamboo. In summary, this study provided a theoretical basis and technical support for directional gene function analysis at the proteomics level in moso bamboo.

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Rijkers, Maaike, Floris P. van Alphen, Pieter F. van der Meer, et al. "Label Free Quantitative Mass Spectrometry Identifies Processes Linked to Platelet Degranulation As Early Events during Platelet Storage." Blood 128, no. 22 (2016): 2638. http://dx.doi.org/10.1182/blood.v128.22.2638.2638.

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Abstract Background Platelet concentrates (PCs) are stored at room temperature to preserve their biological activity. To minimize the risk of bacterial outgrowth, storage time is limited to 7 days. It is well-established that prolonged storage of PCs results in modifications that result in a decreased hemostatic efficacy. This loss of platelet functionality during storage is commonly referred to as the platelet storage lesion (PSL). Typical events linked to development of the PSL are shape changes, platelet activation and loss of receptors crucial for platelet functionality. Two-dimensional (2D) differential gel electrophoresis (DIGE), isotope tagging and isotope-coded affinity tagging (ICAT) have been previously used to monitor changes in protein composition during storage. These studies have provided valuable insights into the changes associated with the PSL, however, these studies generally focused on a limited set of proteins. Aim We aimed to generate an overview of changes in the platelet proteome during storage using label free quantitative mass spectrometry. Furthermore, we employed Gene ontology (GO) enrichment analysis to identify pathways and biological processes that were linked to development of the PSL. Methods Three independently pooled PCs were stored in plasma under standard blood bank conditions for 16 days. Tryptic peptides were separated by nanoscale C18 reverse phase chromatography coupled on line to an Orbitrap Fusion Tribrid mass spectrometer. The RAW mass spectrometry files were processed with the MaxQuant computational platform. The global changes in protein level during platelet storage were assessed employing the analysis-of-variance functions of PERSEUS. Gene ontology enrichment analysis of biological processes, molecular functions and cellular compartments of the significantly different proteins was performed using the Cytoscape plug-in BiNGO. Results A total number of 2501 proteins was detected in all 3 biological replicates in at least one of the time points analyzed. The analysis showed that 18 proteins were down-regulated over time, whereas the level of 3 proteins was found to increase. CytoScape BinGo analysis of these significantly downregulated proteins revealed that the majority of this set was linked to GO-terms platelet degranulation, secretion and regulated exocytosis. This set of proteins included von Willebrand factor (VWF), serglycin (SRGN), SPARC, amyloid beta A4 protein (APP), multimerin-1 (MMRN1) and platelet factor 4 (PF4). A significant decline in these protein levels was observed at day 5 of storage, suggesting that release of α-granules is a relatively early event during platelet storage. At day 5 also a marked decline in S100A9 was observed. S100A9 has been implicated in degranulation in neutrophils, and may therefore also be linked to platelet granule release. Levels of membrane surface platelet glycoproteins such as glycoprotein Ibα did not significantly change at day 5. Only one single protein, histone H2A, was found to be consistently decreased already at two days of storage, but the significance of this finding is not clear. Upon prolonged storage (13 and 16 days) an increase in the level of α-2-macroglobulin (A2M), immunoglobulin M (IGM) and glycogenin-1 (GYG1) was observed suggesting that platelets acquire an (increased) potential to bind and/or internalize proteins from their environment. Consistent with this notion we also detected significant levels of several serine protease inhibitors, although levels of these proteins did not change upon storage. Conclusions Overall, our findings highlight dynamic changes in protein composition of platelets during storage. Our data provide evidence for sustained release of α-granules over time which becomes significant at day 5. Our data also suggest that during storage, platelets can bind or ingest proteins from their environment which may have impact on the hemostatic properties of stored platelets. Disclosures Leebeek: CSL Behring: Membership on an entity's Board of Directors or advisory committees, Research Funding; Baxalta: Consultancy, Membership on an entity's Board of Directors or advisory committees; Dutch Hemphilia Foundation: Research Funding.

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Heroux, Maxime S., Marla A. Chesnik, Brian D. Halligan, et al. "Comprehensive characterization of glioblastoma tumor tissues for biomarker identification using mass spectrometry-based label-free quantitative proteomics." Physiological Genomics 46, no. 13 (2014): 467–81. http://dx.doi.org/10.1152/physiolgenomics.00034.2014.

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Cancer is a complex disease; glioblastoma (GBM) is no exception. Short survival, poor prognosis, and very limited treatment options make it imperative to unravel the disease pathophysiology. The critically important identification of proteins that mediate various cellular events during disease is made possible with advancements in mass spectrometry (MS)-based proteomics. The objective of our study is to identify and characterize proteins that are differentially expressed in GBM to better understand their interactions and functions that lead to the disease condition. Further identification of upstream regulators will provide new potential therapeutic targets. We analyzed GBM tumors by SDS-PAGE fractionation with internal DNA markers followed by liquid chromatography-tandem mass spectrometry (MS). Brain tissue specimens obtained for clinical purposes during epilepsy surgeries were used as controls, and the quantification of MS data was performed by label-free spectral counting. The differentially expressed proteins were further characterized by Ingenuity Pathway Analysis (IPA) to identify protein interactions, functions, and upstream regulators. Our study identified several important proteins that are involved in GBM progression. The IPA revealed glioma activation with z score 2.236 during unbiased core analysis. Upstream regulators STAT3 and SP1 were activated and CTNNα was inhibited. We verified overexpression of several proteins by immunoblot to complement the MS data. This work represents an important step towards the identification of GBM biomarkers, which could open avenues to identify therapeutic targets for better treatment of GBM patients. The workflow developed represents a powerful and efficient method to identify biomarkers in GBM.

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Nukala, Sarath Babu, Giovanna Baron, Giancarlo Aldini, Marina Carini, and Alfonsina D’Amato. "Mass Spectrometry-based Label-free Quantitative Proteomics To Study the Effect of 3PO Drug at Cellular Level." ACS Medicinal Chemistry Letters 10, no. 4 (2019): 577–83. http://dx.doi.org/10.1021/acsmedchemlett.8b00593.

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Peng, Lifeng, Danyl McLauchlan, Pisana Rawson, Brian Hood, and T. William Jordan. "Quantitative Proteomic Analysis of Bovine Mammary Biopsies Based on Differential Subcellular Fractionation and Label-Free Mass Spectrometry." Journal of Proteomics & Bioinformatics S2, no. 01 (2008): 298. http://dx.doi.org/10.4172/jpb.s1000213.

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Hauck, Stefanie M., Johannes Dietter, Roxane L. Kramer, et al. "Deciphering Membrane-Associated Molecular Processes in Target Tissue of Autoimmune Uveitis by Label-Free Quantitative Mass Spectrometry." Molecular & Cellular Proteomics 9, no. 10 (2010): 2292–305. http://dx.doi.org/10.1074/mcp.m110.001073.

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Gemperline, David C., Mark Scalf, Lloyd M. Smith, and Richard D. Vierstra. "Morpheus Spectral Counter: A computational tool for label-free quantitative mass spectrometry using the Morpheus search engine." PROTEOMICS 16, no. 6 (2016): 920–24. http://dx.doi.org/10.1002/pmic.201500420.

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Ono, Masaya, Miki Shitashige, Kazufumi Honda, et al. "Label-free Quantitative Proteomics Using Large Peptide Data Sets Generated by Nanoflow Liquid Chromatography and Mass Spectrometry." Molecular & Cellular Proteomics 5, no. 7 (2006): 1338–47. http://dx.doi.org/10.1074/mcp.t500039-mcp200.

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Nanni, Paolo, Fredrik Levander, Giulia Roda, Alessandra Caponi, Peter James, and Aldo Roda. "A label-free nano-liquid chromatography–mass spectrometry approach for quantitative serum peptidomics in Crohn's disease patients." Journal of Chromatography B 877, no. 27 (2009): 3127–36. http://dx.doi.org/10.1016/j.jchromb.2009.08.003.

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Noberini, Roberta, Cristina Morales Torres, Evelyn Oliva Savoia, et al. "Label-Free Mass Spectrometry-Based Quantification of Linker Histone H1 Variants in Clinical Samples." International Journal of Molecular Sciences 21, no. 19 (2020): 7330. http://dx.doi.org/10.3390/ijms21197330.

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Epigenetic aberrations have been recognized as important contributors to cancer onset and development, and increasing evidence suggests that linker histone H1 variants may serve as biomarkers useful for patient stratification, as well as play an important role as drivers in cancer. Although traditionally histone H1 levels have been studied using antibody-based methods and RNA expression, these approaches suffer from limitations. Mass spectrometry (MS)-based proteomics represents the ideal tool to accurately quantify relative changes in protein abundance within complex samples. In this study, we used a label-free quantification approach to simultaneously analyze all somatic histone H1 variants in clinical samples and verified its applicability to laser micro-dissected tissue areas containing as low as 1000 cells. We then applied it to breast cancer patient samples, identifying differences in linker histone variants patters in primary triple-negative breast tumors with and without relapse after chemotherapy. This study highlights how label-free quantitation by MS is a valuable option to accurately quantitate histone H1 levels in different types of clinical samples, including very low-abundance patient tissues.

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Ryu, Soyoung, Byron Gallis, Young Ah Goo, Scott A. Shaffer, Dragan Radulovic, and David R. Goodlett. "Comparison of a Label-Free Quantitative Proteomic Method Based on Peptide Ion Current Area to the Isotope Coded Affinity Tag Method." Cancer Informatics 6 (January 2008): CIN.S385. http://dx.doi.org/10.4137/cin.s385.

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Recently, several research groups have published methods for the determination of proteomic expression profiling by mass spectrometry without the use of exogenously added stable isotopes or stable isotope dilution theory. These so-called label-free, methods have the advantage of allowing data on each sample to be acquired independently from all other samples to which they can later be compared in silico for the purpose of measuring changes in protein expression between various biological states. We developed label free software based on direct measurement of peptide ion current area (PICA) and compared it to two other methods, a simpler label free method known as spectral counting and the isotope coded affinity tag (ICAT) method. Data analysis by these methods of a standard mixture containing proteins of known, but varying, concentrations showed that they performed similarly with a mean squared error of 0.09. Additionally, complex bacterial protein mixtures spiked with known concentrations of standard proteins were analyzed using the PICA label-free method. These results indicated that the PICA method detected all levels of standard spiked proteins at the 90% confidence level in this complex biological sample. This finding confirms that label-free methods, based on direct measurement of the area under a single ion current trace, performed as well as the standard ICAT method. Given the fact that the label-free methods provide ease in experimental design well beyond pair-wise comparison, label-free methods such as our PICA method are well suited for proteomic expression profiling of large numbers of samples as is needed in clinical analysis.

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Smith, Josh, Gavin Davey, Karol Polom, Franco Roviello, and Jonathan Bones. "Mining the acidic serum proteome utilizing off-gel isoelectric focusing and label free quantitative liquid chromatography mass spectrometry." Journal of Chromatography A 1566 (September 2018): 32–43. http://dx.doi.org/10.1016/j.chroma.2018.06.044.

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Egelhofer, Volker, Wolfgang Hoehenwarter, David Lyon, Wolfram Weckwerth, and Stefanie Wienkoop. "Using ProtMAX to create high-mass-accuracy precursor alignments from label-free quantitative mass spectrometry data generated in shotgun proteomics experiments." Nature Protocols 8, no. 3 (2013): 595–601. http://dx.doi.org/10.1038/nprot.2013.013.

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Abdallah, Cosette, Eliane Dumas-Gaudot, Jenny Renaut, and Kjell Sergeant. "Gel-Based and Gel-Free Quantitative Proteomics Approaches at a Glance." International Journal of Plant Genomics 2012 (November 20, 2012): 1–17. http://dx.doi.org/10.1155/2012/494572.

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Two-dimensional gel electrophoresis (2-DE) is widely applied and remains the method of choice in proteomics; however, pervasive 2-DE-related concerns undermine its prospects as a dominant separation technique in proteome research. Consequently, the state-of-the-art shotgun techniques are slowly taking over and utilising the rapid expansion and advancement of mass spectrometry (MS) to provide a new toolbox of gel-free quantitative techniques. When coupled to MS, the shotgun proteomic pipeline can fuel new routes in sensitive and high-throughput profiling of proteins, leading to a high accuracy in quantification. Although label-based approaches, either chemical or metabolic, gained popularity in quantitative proteomics because of the multiplexing capacity, these approaches are not without drawbacks. The burgeoning label-free methods are tag independent and suitable for all kinds of samples. The challenges in quantitative proteomics are more prominent in plants due to difficulties in protein extraction, some protein abundance in green tissue, and the absence of well-annotated and completed genome sequences. The goal of this perspective assay is to present the balance between the strengths and weaknesses of the available gel-based and -free methods and their application to plants. The latest trends in peptide fractionation amenable to MS analysis are as well discussed.

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Chen, Zhuo, Lutz Fischer, Salman Tahir, Jimi-Carlo Bukowski-Wills, Paul Barlow, and Juri Rappsilber. "Quantitative cross-linking/mass spectrometry reveals subtle protein conformational changes." Wellcome Open Research 1 (November 15, 2016): 5. http://dx.doi.org/10.12688/wellcomeopenres.9896.1.

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Quantitative cross-linking/mass spectrometry (QCLMS) probes protein structural dynamics in solution by quantitatively comparing the yields of cross-links between different conformational statuses. We have used QCLMS to understand the final maturation step of the proteasome lid and also to elucidate the structure of complement C3(H2O). Here we benchmark our workflow using a structurally well-described reference system, the human complement protein C3 and its activated cleavage product C3b. We found that small local conformational changes affect the yields of cross-linking residues that are near in space while larger conformational changes affect the detectability of cross-links. Distinguishing between minor and major changes required robust analysis based on replica analysis and a label-swapping procedure. By providing workflow, code of practice and a framework for semi-automated data processing, we lay the foundation for QCLMS as a tool to monitor the domain choreography that drives binary switching in many protein-protein interaction networks.

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Costanzo, Michele, Armando Cevenini, Emanuela Marchese, et al. "Label-Free Quantitative Proteomics in a Methylmalonyl-CoA Mutase-Silenced Neuroblastoma Cell Line." International Journal of Molecular Sciences 19, no. 11 (2018): 3580. http://dx.doi.org/10.3390/ijms19113580.

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Methylmalonic acidemias (MMAs) are inborn errors of metabolism due to the deficient activity of methylmalonyl-CoA mutase (MUT). MUT catalyzes the formation of succinyl-CoA from methylmalonyl-CoA, produced from propionyl-CoA catabolism and derived from odd chain fatty acids β-oxidation, cholesterol, and branched-chain amino acids degradation. Increased methylmalonyl-CoA levels allow for the presymptomatic diagnosis of the disease, even though no approved therapies exist. MMA patients show hyperammonemia, ketoacidosis, lethargy, respiratory distress, cognitive impairment, and hepatomegaly. The long-term consequences concern neurologic damage and terminal kidney failure, with little chance of survival. The cellular pathways affected by MUT deficiency were investigated using a quantitative proteomics approach on a cellular model of MUT knockdown. Currently, a consistent reduction of the MUT protein expression was obtained in the neuroblastoma cell line (SH-SY5Y) by using small-interfering RNA (siRNA) directed against an MUT transcript (MUT siRNA). The MUT absence did not affect the cell viability and apoptotic process in SH-SY5Y. In the present study, we evaluate and quantify the alterations in the protein expression profile as a consequence of MUT-silencing by a mass spectrometry-based label-free quantitative analysis, using two different quantitative strategies. Both quantitative methods allowed us to observe that the expression of the proteins involved in mitochondrial oxido-reductive homeostasis balance was affected by MUT deficiency. The alterated functional mitochondrial activity was observed in siRNA_MUT cells cultured with a propionate-supplemented medium. Finally, alterations in the levels of proteins involved in the metabolic pathways, like carbohydrate metabolism and lipid metabolism, were found.

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