The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.
Integration of Proteomics and Genomics in Platelets
Open accessAbstract
Platelets, while anucleate, contain RNA, some of which is translated into protein upon activation. Hypothesising that the platelet proteome is reflected in the transcriptome, we identified 82 proteins secreted from activated platelets and compared these, as well as published proteomic data, to the transcriptional profile. We also compared the transcriptome of platelets to other tissues to identify platelet-specific genes and used ontology to determine gene categories over-represented in platelets. RNA was isolated from highly pure platelet preparations for hybridization to Affymetrix oligonucleotide arrays. We identified 2,928 distinct messages as being present in platelets. The platelet transcriptome was compared with the proteome by relating both to UniGene clusters. Platelet proteomic data correlated well with the transcriptome, with 69% of secreted proteins detectable at the mRNA level, and similar concordance was obtained using two published datasets. While many of the most abundant mRNAs are for known platelet proteins, messages were detected for proteins not previously reported in platelets. Some of these may represent residual megakaryocyte messages; however, proteomic analysis confirmed the expression of many previously unreported genes in platelets. Transcripts for well-described platelet proteins are among the most platelet-specific messages. Ontological categories related to signal transduction, receptors, ion channels, and membranes are over-represented in platelets, while categories involved in protein synthesis are depleted. Despite the absence of gene transcription, the platelet proteome is mirrored in the transcriptome. Conversely, transcriptional analysis predicts the presence of novel proteins in the platelet. Transcriptional analysis is relevant to platelet biology, providing insights into platelet function and the mechanisms of platelet disorders.
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
Cites
Eng, J. K., McCormack, A. L., & Yates, J. R. (1994) · Journal of the American Society for Mass Spectrometry · doi:10.1016/1044-0305(94)80016-2 · Cited by 6,484
What's wrong with Bonferroni adjustments
Perneger, T. V. (1998) · BMJ · doi:10.1136/bmj.316.7139.1236 · Cited by 5,900
Large-scale analysis of the yeast proteome by multidimensional protein identification technology
Washburn, M. P., Wolters, D., & Yates, J. R. (2001) · Nature Biotechnology · doi:10.1038/85686 · Cited by 4,752
Correlation between Protein and mRNA Abundance in Yeast
Gygi, S. P., Rochon, Y., Franza, B. R., & Aebersold, R. (1999) · Molecular and Cellular Biology · doi:10.1128/mcb.19.3.1720 · Cited by 3,819
Huber, W., von Heydebreck, A., Sültmann, H., Poustka, A., & Vingron, M. (2002) · Bioinformatics · doi:10.1093/bioinformatics/18.suppl_1.s96 · Cited by 2,440
An Automated Multidimensional Protein Identification Technology for Shotgun Proteomics
Wolters, D. A., Washburn, M. P., & Yates, J. R. (2001) · Analytical Chemistry · doi:10.1021/ac010617e · Cited by 1,819
Comparing protein abundance and mRNA expression levels on a genomic scale
Greenbaum, D. (2003) · Genome biology · doi:10.1186/gb-2003-4-9-117 · Cited by 1,692
Peng, J., Elias, J. E., Thoreen, C. C., Licklider, L. J., & Gygi, S. P. (2003) · Journal of Proteome Research · doi:10.1021/pr025556v · Cited by 1,647
Large-scale analysis of the human and mouse transcriptomes
Su, A. I., Cooke, M. P., Ching, K. A., Hakak, Y., Walker, J. R., Wiltshire, T., Orth, A. P., Vega, R. G., Sapinoso, L. M., Moqrich, A., et al. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.012025199 · Cited by 1,466
A comparison of selected mRNA and protein abundances in human liver
Anderson, L., & Seilhamer, J. (1997) · ELECTROPHORESIS · doi:10.1002/elps.1150180333 · Cited by 1,262
Cited by
Platelet Activation and Atherothrombosis
Davì, G., & Patrono, C. (2007) · New England Journal of Medicine · doi:10.1056/nejmra071014 · Cited by 2,135
Interpretation of Shotgun Proteomic Data
Nesvizhskii, A. I., & Aebersold, R. (2005) · Molecular & Cellular Proteomics · doi:10.1074/mcp.r500012-mcp200 · Cited by 1,025
The complex transcriptional landscape of the anucleate human platelet
Bray, P. F., McKenzie, S. E., Edelstein, L. C., Nagalla, S., Delgrosso, K., Ertel, A., Kupper, J., Jing, Y., Londin, E., Loher, P., et al. (2013) · BMC Genomics · doi:10.1186/1471-2164-14-1 · Cited by 802
Guthikonda, S., Alviar, C. L., Vaduganathan, M., Arikan, M., Tellez, A., DeLao, T., Granada, J. F., Dong, J.-F., Kleiman, N. S., & Lev, E. I. (2008) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2008.05.031 · Cited by 337
Sun, L., Gorospe, J. R., Hoffman, E. P., & Rao, A. K. (2007) · Journal of Thrombosis and Haemostasis · doi:10.1111/j.1538-7836.2006.02271.x · Cited by 88
Metadata
- Authors
- McRedmond, J. P., Park, S. D., Reilly, D. F., Coppinger, J. A., Maguire, P. B., Shields, D. C., & Fitzgerald, D. J.
- Venue
- Molecular & Cellular Proteomics
- Publisher
- Elsevier BV
- Published
- 2004-02
- Volume
- 3
- Issue
- 2
- Pages
- 133-144
- ISSN
- 1535-9476 (ISSN-L)
- OpenAlex
- W2100124283
- Cited by
- 286
- References
- 44
- FWCI
- 8.20
- Type
- journal article
- Language
- en
- Open access
- hybrid
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: Integration of Proteomics and Genomics in Platelets. https://app.cochat.ai/papers/10.1074/mcp.m300063-mcp200
- The paper
- McRedmond, J. P., Park, S. D., Reilly, D. F., Coppinger, J. A., Maguire, P. B., Shields, D. C., & Fitzgerald, D. J. (2004). Integration of Proteomics and Genomics in Platelets. Molecular & Cellular Proteomics. https://doi.org/10.1074/mcp.m300063-mcp200
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.