Blood platelet RNA enables the detection of multiple sclerosis
Open accessAbstract
Background: In multiple sclerosis (MS), clinical assessment, MRI and cerebrospinal fluid are important in the diagnostic process. However, no blood biomarker has been confirmed as a useful tool in the diagnostic work-up. Objectives: Blood platelets contain a rich spliced mRNA repertoire that can alter during megakaryocyte development but also during platelet formation and platelet circulation. In this proof of concept study, we evaluate the diagnostic potential of spliced blood platelet RNA for the detection of MS. Methods: We isolated and sequenced platelet RNA of blood samples obtained from 57 MS patients and 66 age- and gender-matched healthy controls (HCs). 60% was used to develop a particle swarm-optimized (PSO) support vector machine classification algorithm. The remaining 40% served as an independent validation series. Results: In total, 1249 RNAs with differential spliced junction expression levels were identified between platelets of MS patients as compared to HCs, including EPSTI1, IFI6, and RPS6KA3, in line with reported inflammatory signatures in the blood of MS patients. The RNAs were subsequently used as input for a MS classifier, capable of detecting MS with 80% accuracy in the independent validation series. Conclusions: Spliced platelet RNA may enable the blood-based diagnosis of MS, warranting large-scale validation.
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Blood platelet mRNA analysis has shown potential for multiple sclerosis.
“Blood platelet mRNA analysis has shown potential for multiple sclerosis. 140”
From New developments of biofluid‐based biomarkers for routine diagnosis and disease trajectories in frontotemporal dementia (2022) · cites this paper as [140]
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Cites
Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria
Thompson, A. J., Banwell, B. L., Barkhof, F., Carroll, W. M., Coetzee, T., Comi, G., Correale, J., Fazekas, F., Filippi, M., Freedman, M. S., et al. (2018) · The Lancet Neurology · doi:10.1016/s1474-4422(17)30470-2 · Cited by 8,167 · see citing works
Best, M. G., Sol, N., Kooi, I. E., Tannous, J. M., Westerman, B. A., Rustenburg, F., Schellen, P., Verschueren, H., Post, E., Köster, J., et al. (2015) · Cancer Cell · doi:10.1016/j.ccell.2015.09.018 · Cited by 926 · see citing works
Self-citationEscaping the Nuclear Confines: Signal-Dependent Pre-mRNA Splicing in Anucleate Platelets
Denis, M. M., Tolley, N. D., Bunting, M., Schwertz, H., Jiang, H., Lindemann, S., Yost, C. C., Rubner, F. J., Albertine, K. H., Swoboda, K. J., et al. (2005) · Cell · doi:10.1016/j.cell.2005.06.015 · Cited by 652 · see citing works
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Best, M. G., Sol, N., Veld, S. G. J. G. I. ‘., Vancura, A., Muller, M., Niemeijer, A.-L. N., Fejes, A. V., Tjon‐Kon‐Fat, L., Veld, A. E. H. I. ‘., Leurs, C. E., et al. (2017) · Cancer Cell · doi:10.1016/j.ccell.2017.07.004 · Cited by 339 · see citing works
Self-citationPlatelets Contribute to the Pathogenesis of Experimental Autoimmune Encephalomyelitis
Langer, H. F., Choi, E. Y., Zhou, H. Y., Schleicher, R., Chung, K., Tang, Z., Göbel, K., Bdeir, K., Chatzigeorgiou, A., Wong, C. H. Y., et al. (2012) · Circulation Research · doi:10.1161/circresaha.111.256370 · Cited by 204 · see citing works
Evidence of platelet activation in multiple sclerosis
Sheremata, W. A., Jy, W., Horstman, L. L., Ahn, Y. S., Alexander, J. S., & Minagar, A. (2008) · Journal of Neuroinflammation · doi:10.1186/1742-2094-5-27 · Cited by 178 · see citing works
Cecchetti, L., Tolley, N. D., Michetti, N., Bury, L., Weyrich, A. S., & Gresele, P. (2011) · Blood · doi:10.1182/blood-2010-12-324517 · Cited by 150 · see citing works
Best, M. G., Veld, S. G. J. G. I. ‘., Sol, N., & Wurdinger, T. D. (2019) · Nature Protocols · doi:10.1038/s41596-019-0139-5 · Cited by 138 · see citing works
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Schubert, S., Weyrich, A. S., & Rowley, J. W. (2014) · Blood · doi:10.1182/blood-2014-04-512756 · Cited by 128 · see citing works
Body fluid biomarkers for multiple sclerosis—the long road to clinical application
Teunissen, C. E., Malekzadeh, A., Leurs, C., Bridel, C., & Killestein, J. (2015) · Nature Reviews Neurology · doi:10.1038/nrneurol.2015.173 · Cited by 106 · see citing works
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Koenen, R. R. (2016) · Thrombosis and Haemostasis · doi:10.1160/th16-04-0300 · Cited by 76 · see citing works
Role of platelets in neurodegenerative diseases: a universal pathophysiology
Behari, M., & Shrivastava, M. (2013) · International Journal of Neuroscience · doi:10.3109/00207454.2012.751534 · Cited by 68 · see citing works
Morel, A., Rywaniak, J., Bijak, M., Miller, E. D., Niwald, M., & Saluk, J. (2017) · Molecular and Cellular Biochemistry · doi:10.1007/s11010-017-2955-7 · Cited by 59 · see citing works
The physiology of blood platelets and changes of their biological activities in multiple sclerosis
Wachowicz, B., Morel, A., Miller, E. D., & Saluk, J. (2016) · Acta Neurobiologiae Experimentalis · doi:10.21307/ane-2017-026 · Cited by 53 · see citing works
Kim, Y.-H., Lee, J.-R., & Hahn, M. (2017) · Biochemical and Biophysical Research Communications · doi:10.1016/j.bbrc.2017.12.014 · Cited by 49 · see citing works
Plantone, D., Inglese, M., Salvetti, M., & Koudriavtseva, T. (2019) · Frontiers in Neurology · doi:10.3389/fneur.2018.01175 · Cited by 32 · see citing works
Pankratz, S., Bittner, S., Kehrel, B. E., Langer, H. F., Kleinschnitz, C., Meuth, S. G., & Göbel, K. (2016) · International Journal of Molecular Sciences · doi:10.3390/ijms17101723 · Cited by 31 · see citing works
Integrative analysis of Multiple Sclerosis using a systems biology approach
Cervantes-Gracia, K., & Husi, H. (2018) · Scientific Reports · doi:10.1038/s41598-018-24032-8 · Cited by 29 · see citing works
Interactions between platelets and leukocytes in pathogenesis of multiple sclerosis
Dziedzic, A., & Bijak, M. (2018) · Advances in Clinical and Experimental Medicine · doi:10.17219/acem/83588 · Cited by 28 · see citing works
Polymerase-1 pathway activation in acute multiple sclerosis relapse
Achiron, A., Zilkha‐Falb, R., Feldman, A., Bovim, M., Rozen, O., Sarova-Pinhas, I., Magalashvili, D., Dolev, M., Menascu, S., & Gurevich, M. (2018) · Autoimmunity Reviews · doi:10.1016/j.autrev.2018.07.006 · Cited by 9 · see citing works
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A kaleidoscopic view of extracellular vesicles in lysosomal storage disorders
Hegeman, C. V., de Jong, O. G., & Lorenowicz, M. J. (2022) · Extracellular Vesicles and Circulating Nucleic Acids · doi:10.20517/evcna.2022.41 · Cited by 8 · see citing works
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- Authors
- Sol, N., Leurs, C. E., Veld, S. G. I. ’., Strijbis, E. M., Vancura, A., Schweiger, M. W., Teunissen, C. E., Mateen, F. J., Tannous, B. A., Best, M. G., Würdinger, T., & Killestein, J.
- Affiliations
- Amsterdam University Medical Centers (NL)
- Brain Tumor Center Amsterdam, Amsterdam UMC, VU University Medical Center, Amsterdam, the Netherlands
- Harvard University (US)
- Amsterdam Neuroscience (NL)
- ORCID
- Venue
- Multiple Sclerosis Journal - Experimental, Translational and Clinical
- Publisher
- SAGE Publications
- Published
- 2020-07
- Volume
- 6
- Issue
- 3
- Pages
- 2055217320946784-2055217320946784
- ISSN
- 2055-2173 (ISSN-L)
- PMC
- PMC7418262
- OpenAlex
- W3046137862
- Cited by
- 21
- References
- 20
- Citations by year
- 2025: 2 · 2024: 3 · 2023: 2 · 2022: 10 · 2021: 3 · 2020: 1
- FWCI
- 2.63
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Multiple Sclerosis Journal - Experimental Translational and Clinical (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Pure Amsterdam UMC (submittedVersion, other-oa)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Funders
- stichting STOPhersentumoren.nl
- KWF Kankerbestrijding
- European research council (336540, 713727)
- Stichting MS Research
- Dutch organisation of scientific research (91711366)
- Topics
- Health Sciences › Medicine › Pathology and Forensic Medicine › Multiple Sclerosis Research Studies
- Health Sciences › Medicine › Hematology › Platelet Disorders and Treatments
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Extracellular vesicles in disease
- Keywords
- multiple sclerosis, platelet RNA, spliced mRNA, blood-based diagnosis, blood biomarkers, particle swarm optimization, support vector machine classification, EPSTI1, IFI6, RPS6KA3, inflammatory signature, megakaryocyte development
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