The DOI matches its Crossref record.
Proteome Analysis of Low-Abundance Proteins Using Multidimensional Chromatography and Isotope-Coded Affinity Tags
Abstract
The effectiveness of proteome-wide protein identification and quantitative expression profiling is dependent on the ability of the analytical methodologies employed to routinely obtain information on low-abundance proteins, as these are frequently of great biological importance. Two-dimensional gel electrophoresis, the traditional method for proteome analysis, has proven to be biased toward highly expressed proteins. Recently, two-dimensional chromatography of the complex peptide mixtures generated by the digestion of unseparated protein samples has been introduced for the identification of their components, and isotope-coded affinity tags (ICAT) have been introduced to allow for accurate quantification of the components of protein mixtures by mass spectrometry. Here, we demonstrate that the combination of isotope coded affinity protein tags and multidimensional chromatography/mass spectrometry of tryptic peptide mixtures is capable of detecting and quantifying proteins of low abundance in complex samples.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Mass spectrometry-based proteomics
Aebersold, R., & Mann, M. (2003) · Nature · doi:10.1038/nature01511 · Cited by 6,955
Self-citationProteomics in gram‐negative bacterial outer membrane vesicles
Lee, E., Choi, D., Kim, K., & Gho, Y. S. (2008) · Mass Spectrometry Reviews · doi:10.1002/mas.20175 · Cited by 339
Microcolumns with self-assembled particle frits for proteomics
Ishihama, Y., Rappsilber, J., Andersen, J. S., & Mann, M. (2002) · Journal of Chromatography A · doi:10.1016/s0021-9673(02)01402-4 · Cited by 323
Metadata
- Authors
- Gygi, S. P., Rist, B., Griffin, T. J., Eng, J., & Aebersold, R.
- Venue
- Journal of Proteome Research
- Publisher
- American Chemical Society (ACS)
- Published
- 2002-01-18
- Volume
- 1
- Issue
- 1
- Pages
- 47-54
- ISSN
- 1535-3893 (ISSN-L)
- 1535-3907
- OpenAlex
- W2065190847
- Cited by
- 307
- References
- 0
- FWCI
- 18.50
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Proteome Analysis of Low-Abundance Proteins Using Multidimensional Chromatography and Isotope-Coded Affinity Tags. https://app.cochat.ai/papers/10.1021/pr015509n
- The paper
- Gygi, S. P., Rist, B., Griffin, T. J., Eng, J., & Aebersold, R. (2002). Proteome Analysis of Low-Abundance Proteins Using Multidimensional Chromatography and Isotope-Coded Affinity Tags. Journal of Proteome Research. https://doi.org/10.1021/pr015509n
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.