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Quantitative Profiling of Proteins in Complex Mixtures Using Liquid Chromatography and Mass Spectrometry

Chelius, Dirk, Bondarenko, Pavel V.·2002·Journal of Proteome Research

Abstract

The objective of this study was to determine if liquid chromatography mass spectrometry (LC/MS) data of tryptic digests of proteins can be used for quantitation. In theory, the peak area of peptides should correlate to their concentration; hence, the peak areas of peptides from one protein should correlate to the concentration of that particular protein. To evaluate this hypothesis, different amounts of tryptic digests of myoglobin were analyzed by LC/MS in a wide range between 10 fmol and 100 pmol. The results show that the peak areas from liquid chromatography mass spectrometry correlate linearly to the concentration of the protein (r2 = 0.991). The method was further evaluated by adding two different concentrations of horse myoglobin to human serum. The results confirm that the quantitation method can also be used for quantitative profiling of proteins in complex mixtures such as human sera. Expected and calculated protein ratios differ by no more than 16%. We describe a new method combining protein identification with accurate profiling of individual proteins. This approach should provide a widely applicable means to compare global protein expression in biological samples.

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Metadata

Authors
Chelius, D., & Bondarenko, P. V.
Venue
Journal of Proteome Research
Published
2002-06-06
Volume
1
Issue
4
Pages
317-323
ISSN
  • 1535-3893 (ISSN-L)
  • 1535-3907
OpenAlex
W2067074165
Cited by
423
References
0
FWCI
5.29
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Quantitative Profiling of Proteins in Complex Mixtures Using Liquid Chromatography and Mass Spectrometry. https://app.cochat.ai/papers/10.1021/pr025517j
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Chelius, D., & Bondarenko, P. V. (2002). Quantitative Profiling of Proteins in Complex Mixtures Using Liquid Chromatography and Mass Spectrometry. Journal of Proteome Research. https://doi.org/10.1021/pr025517j

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.