Peptide quantification by tandem mass spectrometry

Zhu, Xu-Dong; Desiderio, Dominic M.

doi:10.1002/(sici)1098-2787(1996)15:4<213::aid-mas1>3.0.co;2-l

Cited by 29 publications

(9 citation statements)

References 80 publications

(66 reference statements)

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“…The methods defined in Section 1, as peptide level spectral counting and peptide level signal intensity both performed poorly (see Section 4.2) relative to the protein level methods, in terms of quantitative proteome coverage. While matching "heavy" and "light" peptides sharing the same sequence works well in a classic stable isotope quantitation scheme [36], where the peptides are analyzed at the same time, the random scatter in the data inevitably increases when an analogous approach is applied to unlabeled peptides sharing the same sequence but analyzed separately at different times. Thus, the error bars associated with ratios generated from our peptide level data were too large in many cases to distinguish the ratios from 1 on a linear scale or 0 on a log 2 scale, whether any expression change was occurring or not (see .…”

Section: Discussionmentioning

confidence: 99%

Differential quantitative proteomics of Porphyromonas gingivalis by linear ion trap mass spectrometry: Non-label methods comparison, q-values and LOWESS curve fitting

Xia

Wang

Park

et al. 2007

International Journal of Mass Spectrometry

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Differential analysis of whole cell proteomes by mass spectrometry has largely been applied using various forms of stable isotope labeling. While metabolic stable isotope labeling has been the method of choice, it is often not possible to apply such an approach. Four different label free ways of calculating expression ratios in a classic "two-state" experiment are compared: signal intensity at the peptide level, signal intensity at the protein level, spectral counting at the peptide level, and spectral counting at the protein level. The quantitative data were mined from a dataset of 1245 qualitatively identified proteins, about 56% of the protein encoding open reading frames from Porphyromonas gingivalis, a Gram-negative intracellular pathogen being studied under extracellular and intracellular conditions. Two different control populations were compared against P. gingivalis internalized within a model human target cell line. The q-value statistic, a measure of false discovery rate previously applied to transcription microarrays, was applied to proteomics data. For spectral counting, the most logically consistent estimate of random error came from applying the locally weighted scatter plot smoothing procedure (LOWESS) to the most extreme ratios generated from a control technical replicate, thus setting upper and lower bounds for the region of experimentally observed random error.

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Section: Discussionmentioning

confidence: 99%

Differential quantitative proteomics of Porphyromonas gingivalis by linear ion trap mass spectrometry: Non-label methods comparison, q-values and LOWESS curve fitting

Xia

Wang

Park

et al. 2007

International Journal of Mass Spectrometry

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“…Desiderio and colleagues first demonstrated the use of targeted MS for the analysis of bioactive peptides 2 in the 1980s. Application of these methods to the analysis of proteins and post-translational modifications began to accelerate in the late 1990s and has rapidly expanded over the past decade, enabled by improvements in the capabilities of LC-MS systems and in methods to prepare tissue and biofluid samples for MS analysis.…”

Section: Targeted Ms In Biomedicine and Its Adoption In Proteomicsmentioning

confidence: 99%

Quantitative analysis of peptides and proteins in biomedicine by targeted mass spectrometry

2013

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Targeted mass spectrometry (MS) is becoming widely used in academia and in pharmaceutical and biotechnology industries for sensitive and quantitative detection of proteins, peptides and post-translational modifications. Here we describe the increasing importance of targeted MS technologies in clinical proteomics and the potential key roles these techniques will have in bridging biomedical discovery and clinical implementation.

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“…With this procedure, product ions of different kinetic energies (and, hence, different masses) were focused onto the detector. A number of segments (8)(9)(10)(11)(12) that corresponded to different reflectron/acceleration voltage ratios were acquired for each precursor ion. Typically, 256 scans were averaged for each product-ion segment.…”

Section: Maldi-tof Msmentioning

confidence: 99%

“…Processing of these protein precursors is a multistep process that is mediated by a variety of enzymes, including prohormone convertases, aminopeptidases, etc. Thus, in order to gain more insight into the role of neuropeptidergic systems in pituitary tumorigenesis, it is important to investigate the overall metabolic scheme for neuropeptide production [9].…”

Section: Introductionmentioning

confidence: 99%

Analysis of the proteome in the human pituitary

Beranová-Giorgianni

Giorgianni²,

Desiderio

2002

Proteomics

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The pituitary is the master endocrine gland responsible for the regulation of various physiologic and metabolic processes. Proteomics offers an efficient means for a comprehensive analysis of pituitary protein expression. This paper reports on the application of proteomics for the mapping of major proteins in a normal (control) pituitary. Pituitary proteins were separated by two-dimensional gel electrophoresis with immobilized pH 3-10 gradient strips. Major protein spots that were visualized in the two-dimensional gel by silver staining were excised, and the proteins in these spots were digested with trypsin. The tryptic digests were analyzed by mass spectrometry, and the mass spectrometric data were used to identify the proteins through searches of the SWISS-PROT or NCBInr protein sequence databases. The majority of the proteins were identified on the basis of peptide mass fingerprinting data obtained by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. Several proteins were also characterized based on product-ion spectra measured by post-source decay analysis and/or liquid chromatography-electrospray-quadrupole ion trap mass spectrometry. To date, 62 prominent protein spots, corresponding to 38 different proteins, were identified. The identified proteins include important pituitary hormones, structural proteins, enzymes, and other proteins. The protein identification data were used to establish a two-dimensional reference database of the human pituitary, which can be accessed over the Internet (http://www.utmem.edu/proteomics). This database will serve as a tool for further proteomics studies of pituitary protein expression in health and disease.

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Peptide quantification by tandem mass spectrometry

Cited by 29 publications

References 80 publications

Differential quantitative proteomics of Porphyromonas gingivalis by linear ion trap mass spectrometry: Non-label methods comparison, q-values and LOWESS curve fitting

Differential quantitative proteomics of Porphyromonas gingivalis by linear ion trap mass spectrometry: Non-label methods comparison, q-values and LOWESS curve fitting

Quantitative analysis of peptides and proteins in biomedicine by targeted mass spectrometry

Analysis of the proteome in the human pituitary

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