Skip to main content

Advertisement

Log in

Perspectives for the Use of a Multiomics Approach for Finding New Diagnostic Associations and Therapeutic Targets in Multiple Sclerosis

  • Published:
Neuroscience and Behavioral Physiology Aims and scope Submit manuscript

Multiple sclerosis (MS) is an extremely heterogeneous disease and, despite many studies, selective biomarkers have not been identified for this pathology, so its early diagnosis remains difficult. The multiomics approach provides a powerful tool for studying the pathogenesis of MS, allowing the most detailed and versatile characterization of the cells involved in the development of disease symptoms. This review describes current directions of research in MS using high-performance technologies, and also assesses the relevance of the multiomics approach.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. M. T. Wallin, W. J. Culpepper, E. Nichols, et al., “Global, regional, and national burden of multiple sclerosis 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016,” Lancet Neurol., 18, No. 3, 269–285 (2019), https://doi.org/10.1016/S1474-4422(18)30443-5.

    Article  Google Scholar 

  2. J. M. Frischer, S. Bramow, A. Dal-Bianco, et al., “The relation between inflammation and neurodegeneration in multiple sclerosis Brains,” Brain, 132, No. 5, 1175–1189 (2009), https://doi.org/10.1093/brain/awp070.

    Article  PubMed  PubMed Central  Google Scholar 

  3. A. J. Solomon, E. P. Klein, and D. Bourdette, “’Undiagnosing’ multiple sclerosis: The challenge of misdiagnosis in MS,” Neurology, 78, No. 24, 1986–1991 (2012), https://doi.org/10.1212/WNL.0b013e318259e1b2.

    Article  PubMed  PubMed Central  Google Scholar 

  4. W. J. Brownlee, T. A. Hardy, F. Fazekas, and D. H. Miller, “Diagnosis of multiple sclerosis: progress and challenges,” Lancet, 389, No. 10076, 1336–1346 (2017), https://doi.org/10.1016/S0140-6736(16)30959-X.

    Article  PubMed  Google Scholar 

  5. International Multiple Sclerosis Genetics Consortium et al., “Risk alleles for multiple sclerosis identified by a genome-wide study,” N. Engl. J. Med., 357, No. 9, 851–862 (2007), https://doi.org/10.1056/NEJMoa073493.

  6. I. M. S. G. Consortium, “Multiple sclerosis genomic map implicates peripheral immune cells and microglia in susceptibility,” Science, 365, No. 6460, eaav7188 (2019), https://doi.org/10.1126/science.aav7188.

  7. M. Mitrovič, N. A. Patsopoulos, A. H. Beecham, et al., “Low-frequency and rare-coding variation contributes to multiple sclerosis risk,” Cell, 175, No. 6, 1679–1687.e7 (2018), https://doi.org/10.1016/j.cell.2018.09.049.

    Article  CAS  Google Scholar 

  8. V. V. Bashinskaya, O. G. Kulakova, I. S. Kiselev, et al., “GWA-Sidentified multiple sclerosis risk loci involved in immune response: Validation in Russians,” J. Neuroimmunol., 282, 85–91 (2015), https://doi.org/10.1016/j.jneuroim.2015.03.015.

    Article  CAS  PubMed  Google Scholar 

  9. R. Waller, M. N. Woodroofe, S. B. Wharton, et al., “Gene expression profiling of the astrocyte transcriptome in multiple sclerosis normal appearing white matter reveals a neuroprotective role,” J. Neuroimmunol., 299, 139–146 (2016), https://doi.org/10.1016/j.jneuroim.2016.09.010.

    Article  CAS  PubMed  Google Scholar 

  10. N. Itoh, Y. Itoh, A. Tassoni, et al., “Cell-specific and region-specific transcriptomics in the multiple sclerosis model: Focus on astrocytes,” Proc. Natl. Acad. Sci. USA, 115, No. 2, E302-9 (2018), https://doi.org/10.1073/pnas.1716032115.

    Article  CAS  PubMed  Google Scholar 

  11. L. Schirmer, D. P. Schafer, T. Bartels, et al., “Diversity and function of glial cell types in multiple sclerosis,” Trends Immunol., 42, No. 3, 228–247 (2021), https://doi.org/10.1016/j.it.2021.01.005.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. S. Jäkel, E. Agirre, A. Mendanha Falcão, et al., “Altered human oligodendrocyte heterogeneity in multiple sclerosis,” Nature, 566, No. 7745, 543–547 (2019), https://doi.org/10.1038/s41586-019-0903-2.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. L. Schirmer, D. Velmeshev, S. Holmqvist, et al., “Neuronal vulnerability and multilineage diversity in multiple sclerosis,” Nature, 573, No. 7772, 75–82 (2019), https://doi.org/10.1038/s41586-019-1404-z.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. F. Dachet, J. B. Brown, T. Valyi-Nagy, et al., “Selective time-dependent changes in activity and cell-specific gene expression in human postmortem brain,” Sci. Rep., 11, No. 1, 6078 (2021), https://doi.org/10.1038/s41598-021-85801-6.

  15. M. Acquaviva, R. Menon, M. Di Dario, et al., “Inferring multiple sclerosis stages from the blood transcriptome via machine learning,” Cell Rep. Med., 1, No. 4, 100053 (2020), https://doi.org/10.1016/j.xcrm.2020.100053.

  16. E. Galli, F. J. Hartmann, B. Schreiner, et al., “GM-CSF and CXCR4 define a T helper cell signature in multiple sclerosis,” Nat. Med., 25, No. 8, 1290–1300 (2019), https://doi.org/10.1038/s41591-019-0521-4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. A. Ramesh, R. D. Schubert, A. L. Greenfield, et al., “A pathogenic and clonally expanded B cell transcriptome in active multiple sclerosis,” Proc. Natl. Acad. Sci. USA, 117, No. 37, 22932–22943 (2020), https://doi.org/10.1073/pnas.2008523117.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. V. Annibali, R. Umeton, A. Palermo, et al., “Analysis of coding and non-coding transcriptome of peripheral B cells reveals an altered interferon response factor (IRF)-1 pathway in multiple sclerosis patients,” J. Neuroimmunol., 324, 165–171 (2018), https://doi.org/10.1016/j.jneuroim.2018.09.005.

    Article  CAS  PubMed  Google Scholar 

  19. J. Friess, M. Hecker, L. Roch, et al., “Fingolimod alters the transcriptome profile of circulating CD4+ cells in multiple sclerosis,” Sci. Rep., 7, No. 1, 42087 (2017), https://doi.org/10.1038/srep42087.

  20. K. S. Gandhi, F. C. McKay, M. Cox, et al., “The multiple sclerosis whole blood mRNA transcriptome and genetic associations indicate dysregulation of specific T cell pathways in pathogenesis,” Hum. Mol. Genet., 19, No. 11, 2134–2143 (2010), https://doi.org/10.1093/hmg/ddq090.

    Article  CAS  PubMed  Google Scholar 

  21. D. Nickles, H. P. Chen, M. M. Li, et al., “Blood RNA profiling in a large cohort of multiple sclerosis patients and healthy controls,” Hum. Mol. Genet., 22, No. 20, 4194–4205 (2013), https://doi.org/10.1093/hmg/ddt267.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. D. Schafflick, C. A. Xu, M. Hartlehnert, et al., “Integrated single cell analysis of blood and cerebrospinal fluid leukocytes in multiple sclerosis,” Nat. Commun., 11, No. 1, 247 (2020), https://doi.org/10.1038/s41467-019-14118-w.

  23. K. Kim, A.-K. Pröbstel, R. Baumann, et al., “Cell type-specific transcriptomics identifies neddylation as a novel therapeutic target in multiple sclerosis,” Brain, 144, No. 2, 450–461 (2021), https://doi.org/10.1093/brain/awaa421.

    Article  PubMed  Google Scholar 

  24. P. Mertins, D. R. Mani, K. V. Ruggles, et al., “Proteogenomics connects somatic mutations to signalling in breast cancer,” Nature, 534, No. 7605, 55–62 (2016), https://doi.org/10.1038/nature18003.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Y. Dou, E. A. Kawaler, D. Cui Zhou, et al., “Proteogenomic characterization of endometrial carcinoma,” Cell, 180, No. 4, 729–748.e26 (2020), https://doi.org/10.1016/j.cell.2020.01.026.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. S. Chen, B. B. Lake, and K. Zhang, “High-throughput sequencing of the transcriptome and chromatin accessibility in the same cell,” Nat. Biotechnol, 37, No. 12, 1452–1457 (2019), https://doi.org/10.1038/s41587-019-0290-0.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. I. R. Holtman, M. Noback, M. Bijlsma, et al., “Glia Open Access Database (GOAD): A comprehensive gene expression encyclopedia of glia cells in health and disease,” Glia, 63, No. 9, 1495–1506 (2015), https://doi.org/10.1002/glia.22810.

    Article  PubMed  Google Scholar 

  28. Y. Zhang, K. Chen, S. A. Sloan, et al., “An RNA-sequencing transcriptome and splicing database of glia, neurons, and vascular cells of the cerebral cortex,” J. Neurosci., 34, No. 36, 11929–11947 (2014), https://doi.org/10.1523/JNEUROSCI.1860-14.2014.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. B. O. Mancarci, L. Toker, S. J. Tripathy, et al., “Cross-laboratory analysis of brain cell type transcriptomes with applications to interpretation of bulk tissue data,” eNeuro, 4, No. 6 (2017), https://doi.org/10.1523/ENEURO.0212-17.2017.

  30. C. Erö, M.-O. Gewaltig, D. Keller, and H. Markram, “A cell atlas for the mouse brain,” Front. Neuroinformatics, 12, 34–39 (2018), https://doi.org/10.3389/fninf.2018.00084.

    Article  Google Scholar 

  31. S. M. Sunkin, L. Ng, C. Lau, et al., “Allen Brain Atlas: an integrated spatio-temporal portal for exploring the central nervous system,” Nucleic Acids Res., 41, D1, D996–1008 (2013), https://doi.org/10.1093/nar/gks1042.

    Article  CAS  PubMed  Google Scholar 

  32. D. C. Factor, A. M. Barbeau, K. C. Allan, et al., “Cell type-specific intralocus interactions reveal oligodendrocyte mechanisms in MS,” Cell, 181, No. 2, 382–395.e21 (2020), https://doi.org/10.1016/j.cell.2020.03.002.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to M. V. Saliutina.

Additional information

Translated from Zhurnal Nevrologii i Psikhiatrii imeni S. S. Korsakova, Vol. 122, No. 5, Iss. 1, pp. 57–62, May, 2022.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Saliutina, M.V. Perspectives for the Use of a Multiomics Approach for Finding New Diagnostic Associations and Therapeutic Targets in Multiple Sclerosis. Neurosci Behav Physi 52, 1368–1372 (2022). https://doi.org/10.1007/s11055-023-01368-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11055-023-01368-x

Keywords

Navigation