Multiplexed genetic profiling of human blood platelets using fluorescent microspheres

Dmitri V. Gnatenko; Wei Zhu; Wadie F. Bahou

doi:10.1160/TH08-05-0305

Thrombosis and Haemostasis, Table of Contents

Thromb Haemost 2008; 100(05): 929-936
DOI: 10.1160/TH08-05-0305

New Technologies, Diagnostic Tools and Drugs

Schattauer GmbH

Multiplexed genetic profiling of human blood platelets using fluorescent microspheres

Authors

Dmitri V. Gnatenko

¹Department of Medicine, State University of New York, Stony Brook, New York, USA
Wei Zhu

²Department of Applied Mathematics and Statistics, State University of New York, Stony Brook, New York, USA
Wadie F. Bahou

¹Department of Medicine, State University of New York, Stony Brook, New York, USA

³Program in Genetics, State University of New York, Stony Brook, New York, USA

Abstract

Summary

Human platelets have unique and reproducible mRNA profiles, with evidence for distinct profiles in haematopoietic stem cell disorders associated with thrombocytosis. Platelet transcript profiling is traditionally studied by microarray analysis, quantitative reverse transcription-PCR or serial analysis of gene ex-pression,techniques that are labor- and technically-intensive.We have now applied a novel multiplex-based platform for quantitative transcript profiling of human platelets.Simultaneous quantification of 17 platelet transcripts was assayed using intact platelet-rich plasma or gel-filtered platelets lysed in vitro.Accurate and reproducible profiles could be obtained from as few as 5 X 10⁷ platelets (a platelet mass corresponding to ∼100 µl of whole blood), even for the low-abundant platelet transcripts. Correlation coefficients of this 17-member gene set to platelet Affymetrix microarrays were excellent (r² = 0.949, p < 1 X 10^–10), with no correlation to in kind-derived leukocyte profiles, highlighting the cell-specificity of the platform.These data demonstrate that transcript multiplexing using fluorescent micro-spheres can be adapted for rapid molecular profiling using intact platelets (bypassing the need for RNA isolation methods), with potential applicability irrespective of baseline platelet counts.

Keywords

Platelets - transcript profiling - fluorescent microspheres - Luminex - mRNA

Full Text

References

References
1 Gnatenko DV, Dunn JJ, McCorkle SR. et al. Transcript profiling of human platelets using microarray and serial analysis of gene expression. Blood 2003; 101: 2285-2293.
2 Bugert P, Dugrillon A, Gunaydin A. et al. Messenger RNA profiling of human platelets by microarray hybridization. Thromb Haemost 2003; 90: 738-748.
3 McRedmond JP, Park SD, Reilly DF. et al. Integration of proteomics and genomics in platelets: a profile of platelet proteins and platelet-specific genes. Mol Cell Proteomics 2004; 03: 133-144.
4 Dittrich M, Birschmann I, Pfrang J. et al. Analysis of SAGE data in human platelets: features of the transcriptome in an anucleate cell. Thromb Haemost 2006; 95: 643-651.
5 Denis MM, Tolley ND, Bunting M. et al. Escaping the nuclear confines: signal-dependent pre-mRNA splicing in anucleate platelets. Cell 2005; 122: 379-391.
6 Gnatenko DV, Cupit LD, Huang EC. et al. Platelets express steroidogenic 17beta-hydroxysteroid dehydrogenases. Distinct profiles predict the essential thrombocythemic phenotype. Thromb Haemost 2005; 94: 412-421.
7 Sun L, Gorospe JR, Hoffman EP. et al. Decreased platelet expression of myosin regulatory light chain polypeptide (MYL9) and other genes with platelet dysfunction and CBFA2/RUNX1 mutation: insights from platelet expression profiling. J Thromb Haemost 2007; 05: 146-154.
8 Raghavachari N, Xu X, Harris A. et al. Amplified expression profiling of platelet transcriptome reveals changes in arginine metabolic pathways in patients with sickle cell disease. Circulation 2007; 115: 1551-1562.
9 Healy AM, Pickard MD, Pradhan AD. et al. Platelet expression profiling and clinical validation of myeloid-related protein-14 as a novel determinant of cardiovascular events. Circulation 2006; 113: 2278-2284.
10 Gnatenko D, Zhu W, Dhundale A. et al. Class prediction model of thrombocytosis using gene profiling. Blood 2007; 110: 38A.
11 Gnatenko D, Perrotta P, Ji C. et al. Platelet gene/ protein expression analyses using an integrated platform. Blood 2007; 110: 1065A.
12 Gnatenko DV, Bahou WF. Recent advances in platelet transcriptomics. Transfus Med Hemother 2006; 33: 217-226.
13 Gnatenko DV, Perrotta PL, Bahou WF. Proteomic approaches to dissect platelet function: Half the story. Blood 2006; 108: 3983-3991.
14 Senzel L, Gnatenko D, Bahou W. Platelet transcriptome and cardiovascular disease. Future Cardiol 2007; 03: 391-398.
15 Zheng Z, Luo Y, McMaster GK. Sensitive and quantitative measurement of gene expression directly from a small amount of whole blood. Clin Chem 2006; 52: 1294-1302.
16 Flagella M, Bui S, Zheng Z. et al. A multiplex branched DNA assay for parallel quantitative gene expression profiling. Anal Biochem 2006; 352: 50-60.
17 Yang L, Tran DK, Wang X. BADGE, Beads Array for the Detection of Gene Expression, a high-throughput diagnostic bioassay. Genome Res 2001; 11: 1888-1898.
18 Macaulay IC, Tijssen MR, Thijssen-Timmer DC. et al. Comparative gene expression profiling of in vitro differentiated megakaryocytes and erythroblasts identifies novel activatory and inhibitory platelet membrane proteins. Blood 2007; 109: 3260-3269.
19 Bustin SA, Nolan T. Pitfalls of quantitative realtime reverse-transcription polymerase chain reaction. J Biomol Tech 2004; 15: 155-166.
20 Golub TR, Slonim DK, Tamayo P. et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 1999; 286: 531-537.
21 Paik S, Shak S, Tang G. et al. A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med 2004; 351: 2817-2826.
22 Schafer AI. Thrombocytosis. N Engl J Med 2004; 350: 1211-1219.