Bioinformatics for Nervous System research

Research into the development, function and pathologies of the nervous system deserves the best bioinformatics support.

Whether you are tracking the development of the brain in mice, studying a neuropathology with iPSC-derived neurons or characterizing the microenvironment of CNS tumors, we ensure you get the most out of your ‘omics data.

Our expertise is particularly strong in studying the tumors of the nervous system as well as the molecular biology of neurodegenerative pathologies.

Below you will find some highlights. Do leave us a message if you, too, would like to have our bioinformatics support!

Leave us a short description of your bioinformatics needs and we will be in touch very soon!

Omics data analysis in CNS research

How does the nervous system develop and maintain itself? How does it not maintain itself, and degenerate? What genes, proteins and pathways can be targeted to treat the CNS diseases?

The full arsenal of modern 'omics measurements is used to answer such questions in neurobiology, neurology and neuro-oncology.

The analysis of DNA-sequencing and SNP arrays enable studying the genetic predisposition to CNS pathologies. Read more about our services and references in genetics. We also analyze whole-genome, exome and gene panel sequencing data to study somatic mutations in the CNS tumors; see cancer research.

RNA-sequencing, proteomic and epigenomic measurements allow

• identifying cellular identities through the development of the nervous system (single-cell approaches in particular),
• discovering the mechanisms in which diseases arise and progress (examples from multiple CNS pathologies in the paper list below), and
• studying the mechanisms of action or drugs (read case stories)

Learn more

• RNA-seq data analysis
• Single-cell RNA-seq data analysis
• DNA-seq data analysis
• Epigenomic data analysis

References and customer cases

• Studying neural circuit development with single-cell RNA-sequencing (Cornell University)
• Investigating a Parkinson's disease treatment's mechanism of action using bulk and spatial transcriptomics (Herantis Pharma)
• Building a neuronal atlas with single-cell technologies (VIB)
• Discovering cell markers and characterizing microglial response to viral infection (University of Fribourg)
• Epigenetics of pain (University of Minnesota)
• Studying transcription factors in neuronal development with CUT&RUN (Ulm University)

All references

Selected publications from our customers

• Fisher, J. et al. (2024). Cortical somatostatin long-range projection neurons and interneurons exhibit divergent developmental trajectories. Neuron, 112(4), 558–573.e8. https://doi.org/10.1016/j.neuron.2023.11.013
• Martins, R. R. et al. (2022). Transcriptomic signatures of telomerase-dependent and -independent ageing, in the zebrafish gut and brain. bioRxiv 2022.05.24.493215; doi: 101677. https://doi.org/10.1101/2022.05.24.493215
• Tiihonen, J. et al. (2020). Neurobiological roots of psychopathy. Molecular psychiatry, 25(12), 3432–3441. https://doi.org/10.1038/s41380-019-0488-z
• Oksanen, M. et al. (2020). NF-E2-related factor 2 activation boosts antioxidant defenses and ameliorates inflammatory and amyloid properties in human Presenilin-1 mutated Alzheimer's disease astrocytes. Glia, 68(3), 589–599. https://doi.org/10.1002/glia.23741
• Tiihonen, J. et al. (2019). Sex-specific transcriptional and proteomic signatures in schizophrenia. Nature communications, 10(1), 3933. https://doi.org/10.1038/s41467-019-11797-3

Selected publications from our team

• Korvenlaita, N. et al. (2023). Dynamic release of neuronal extracellular vesicles containing miR-21a-5p is induced by hypoxia. Journal of extracellular vesicles, 12(1), e12297. https://doi.org/10.1002/jev2.12297https://doi.org/10.1002/jev2.12297
• Tielbeek, J. J. et al. (2022). Uncovering the genetic architecture of broad antisocial behavior through a genome-wide association study meta-analysis. Molecular psychiatry, 10.1038/s41380-022-01793-3. Advance online publication. https://doi.org/10.1038/s41380-022-01793-3
• Smith, C. et al. (2022). A comparative transcriptomic analysis of glucagon-like peptide-1 receptor- and glucose-dependent insulinotropic polypeptide-expressing cells in the hypothalamus. Appetite, 174, 106022. https://doi.org/10.1016/j.appet.2022.106022
• Pekkarinen, M. et al. (2022). Integrative DNA methylation analysis of pediatric brain tumors reveals tumor type-specific developmental trajectories and epigenetic signatures of malignancy. bioRxiv 2022.03.14.483566 doi: https://doi.org/10.1101/2022.03.14.483566
• Kron, N. S. et al. (2022). Aplysia Neurons as a Model of Alzheimer's Disease: Shared Genes and Differential Expression. Journal of molecular neuroscience : MN, 72(2), 287–302. https://doi.org/10.1007/s12031-021-01918-3
• van Heukelum, S. et al. (2021). A central role for anterior cingulate cortex in the control of pathological aggression. Current biology : CB, 31(11), 2321–2333.e5. https://doi.org/10.1016/j.cub.2021.03.062
• Filppu, P. et al. (2021). CD109-GP130 interaction drives glioblastoma stem cell plasticity and chemoresistance through STAT3 activity. JCI insight, 6(9), e141486. https://doi.org/10.1172/jci.insight.141486
• Loppi, S. et al. (2021). Peripheral inflammation preceeding ischemia impairs neuronal survival through mechanisms involving miR-127 in aged animals. Aging cell, 20(1), e13287. https://doi.org/10.1111/acel.13287
• Kron, N. S. et al. (2021). Co-expression analysis identifies neuro-inflammation as a driver of sensory neuron aging in Aplysia californica. PloS one, 16(6), e0252647. https://doi.org/10.1371/journal.pone.0252647
• Viana, J. et al. (2020). Clozapine-induced transcriptional changes in the zebrafish brain. NPJ schizophrenia, 6(1), 3. https://doi.org/10.1038/s41537-019-0092-x
• Morianos, I. et al. (2020). Activin-A limits Th17 pathogenicity and autoimmune neuroinflammation via CD39 and CD73 ectonucleotidases and Hif1-α-dependent pathways. Proceedings of the National Academy of Sciences of the United States of America, 117(22), 12269–12280. https://doi.org/10.1073/pnas.1918196117
• Policicchio, S. et al. (2020). Genome-wide DNA methylation meta-analysis in the brains of suicide completers. Translational psychiatry, 10(1), 69. https://doi.org/10.1038/s41398-020-0752-7
• Polinski, J. M. et al. (2020). Unique age-related transcriptional signature in the nervous system of the long-lived red sea urchin Mesocentrotus franciscanus. Scientific reports, 10(1), 9182. https://doi.org/10.1038/s41598-020-66052-3
• Kron, N. S. et al. (2020). Changes in Metabolism and Proteostasis Drive Aging Phenotype in Aplysia californica Sensory Neurons. Frontiers in aging neuroscience, 12, 573764. https://doi.org/10.3389/fnagi.2020.573764
• Adriaenssens, A. E. et al. (2019). Glucose-Dependent Insulinotropic Polypeptide Receptor-Expressing Cells in the Hypothalamus Regulate Food Intake. Cell metabolism, 30(5), 987–996.e6. https://doi.org/10.1016/j.cmet.2019.07.013
• Moradi, E. et al. (2019). Supervised pathway analysis of blood gene expression profiles in Alzheimer's disease. Neurobiology of aging, 84, 98–108. https://doi.org/10.1016/j.neurobiolaging.2019.07.004
• Pölönen, P. et al. (2019). Nrf2 and SQSTM1/p62 jointly contribute to mesenchymal transition and invasion in glioblastoma. Oncogene, 38(50), 7473–7490. https://doi.org/10.1038/s41388-019-0956-6
• Garcia-Manteiga, J. M. et al. (2021). Identification of differential DNA methylation associated with multiple sclerosis: A family-based study. Journal of neuroimmunology, 356, 577600. https://doi.org/10.1016/j.jneuroim.2021.577600
• Luoto, S. et al. (2018). Computational Characterization of Suppressive Immune Microenvironments in Glioblastoma. Cancer research, 78(19), 5574–5585. https://doi.org/10.1158/0008-5472.CAN-17-3714
• Nordfors, K. et al. (2018). Whole-exome sequencing identifies germline mutation in TP53 and ATRX in a child with genomically aberrant AT/RT and her mother with anaplastic astrocytoma. Cold Spring Harbor molecular case studies, 4(2), a002246. https://doi.org/10.1101/mcs.a002246
• Viana, J. et al. (2017). Schizophrenia-associated methylomic variation: molecular signatures of disease and polygenic risk burden across multiple brain regions. Human molecular genetics, 26(1), 210–225. https://doi.org/10.1093/hmg/ddw373
• Hyysalo, A. et al. (2017). Laminin α5 substrates promote survival, network formation and functional development of human pluripotent stem cell-derived neurons in vitro. Stem cell research, 24, 118–127. https://doi.org/10.1016/j.scr.2017.09.002
• Granberg, K. J. et al. (2017). Strong FGFR3 staining is a marker for FGFR3 fusions in diffuse gliomas. Neuro-oncology, 19(9), 1206–1216. https://doi.org/10.1093/neuonc/nox028
• Hannon, E. et al. (2016). Methylation QTLs in the developing brain and their enrichment in schizophrenia risk loci. Nature neuroscience, 19(1), 48–54. https://doi.org/10.1038/nn.4182
• Hannon, E. et al. (2016). An integrated genetic-epigenetic analysis of schizophrenia: evidence for co-localization of genetic associations and differential DNA methylation. Genome biology, 17(1), 176. https://doi.org/10.1186/s13059-016-1041-x
• Kumsta, R. et al. (2016). Severe psychosocial deprivation in early childhood is associated with increased DNA methylation across a region spanning the transcription start site of CYP2E1. Translational psychiatry, 6(6), e830. https://doi.org/10.1038/tp.2016.95
• Pidsley, R. et al. (2014). Methylomic profiling of human brain tissue supports a neurodevelopmental origin for schizophrenia. Genome biology, 15(10), 483. https://doi.org/10.1186/s13059-014-0483-2
• Viana, J. et al. (2014). Epigenomic and transcriptomic signatures of a Klinefelter syndrome (47,XXY) karyotype in the brain. Epigenetics, 9(4), 587–599. https://doi.org/10.4161/epi.27806

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