The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Transcript profiling of human platelets using microarray and serial analysis of gene expression
Abstract
Human platelets are anucleate blood cells that retain cytoplasmic mRNA and maintain functionally intact protein translational capabilities. We have adapted complementary techniques of microarray and serial analysis of gene expression (SAGE) for genetic profiling of highly purified human blood platelets. Microarray analysis using the Affymetrix HG-U95Av2 approximately 12 600-probe set maximally identified the expression of 2147 (range, 13%-17%) platelet-expressed transcripts, with approximately 22% collectively involved in metabolism and receptor/signaling, and an overrepresentation of genes with unassigned function (32%). In contrast, a modified SAGE protocol using the Type IIS restriction enzyme MmeI (generating 21–base pair [bp] or 22-bp tags) demonstrated that 89% of tags represented mitochondrial (mt) transcripts (enriched in 16S and 12S ribosomal RNAs), presumably related to persistent mt-transcription in the absence of nuclear-derived transcripts. The frequency of non-mt SAGE tags paralleled average difference values (relative expression) for the most “abundant” transcripts as determined by microarray analysis, establishing the concordance of both techniques for platelet profiling. Quantitative reverse transcription-polymerase chain reaction (PCR) confirmed the highest frequency of mt-derived transcripts, along with the mRNAs for neurogranin (NGN, a protein kinase C substrate) and the complement lysis inhibitor clusterin among the top 5 most abundant transcripts. For confirmatory characterization, immunoblots and flow cytometric analyses were performed, establishing abundant cell-surface expression of clusterin and intracellular expression of NGN. These observations demonstrate a strong correlation between high transcript abundance and protein expression, and they establish the validity of transcript analysis as a tool for identifying novel platelet proteins that may regulate normal and pathologic platelet (and/or megakaryocyte) functions.
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
Quantitative analysis of complex protein mixtures using isotope-coded affinity tags
Gygi, S. P., Rist, B., Gerber, S. A., Turecek, F., Gelb, M. H., & Aebersold, R. (1999) · Nature Biotechnology · doi:10.1038/13690 · Cited by 4,683
Mitochondria, oxygen free radicals, and apoptosis
Raha, S., & Robinson, B. H. (2001) · American Journal of Medical Genetics · doi:10.1002/ajmg.1398 · Cited by 306
Benecke, B.-J., Ben-Ze'ev, A., & Penman, S. (1978) · Cell · doi:10.1016/0092-8674(78)90347-1 · Cited by 291
Cited by
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
Noerholm, M., Balaj, L., Limperg, T., Salehi, A., Zhu, L. D., Hochberg, F. H., Breakefield, X. O., Carter, B. S., & Skog, J. (2012) · BMC Cancer · doi:10.1186/1471-2407-12-22 · Cited by 190
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
Next Generation Sequencing Analysis of Human Platelet PolyA+ mRNAs and rRNA-Depleted Total RNA
Kissopoulou, A., Jonasson, J., Lindahl, T. L., & Osman, A. (2013) · PLoS ONE · doi:10.1371/journal.pone.0081809 · Cited by 69
Kawai, T., Morita, K., Masuda, K., Nishida, K., Shikishima, M., Ohta, M., Saito, T., & Rokutan, K. (2007) · Biological Psychology · doi:10.1016/j.biopsycho.2007.07.008 · Cited by 36
Metadata
- Authors
- Gnatenko, D. V., Dunn, J. J., McCorkle, S. R., Weissmann, D., Perrotta, P. L., & Bahou, W. F.
- Venue
- Blood
- Publisher
- American Society of Hematology
- Published
- 2003-03-15
- Volume
- 101
- Issue
- 6
- Pages
- 2285-2293
- ISSN
- 0006-4971 (ISSN-L)
- 1528-0020
- OpenAlex
- W2167551295
- Cited by
- 372
- References
- 3
- FWCI
- 13.24
- 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
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.