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A Combined Cellomics and Proteomics Approach to Uncover Neuronal Pathways to Psychiatric Disorder

  • Martina Rosato
  • Titia Gebuis
  • Iryna Paliukhovich
  • Sven Stringer
  • Patrick F. Sullivan
  • August B. Smit
  • Ronald E. van KesterenEmail author
Protocol
Part of the Neuromethods book series (NM, volume 146)

Abstract

Studying biological mechanisms underlying neuropsychiatric disorders is highly challenging as many risk genes are associated with these disorders. This complexity requires research approaches to reliably dissect the cell biology of the risk genes involved. Here, we describe a combined cellomics–proteomics approach that allows (a) medium-throughput functional screening and unbiased selection of important risk genes, and (b) discovery of common functional pathways and interactome connections of selected risk genes. The overlay of pathway and proteome data from selected genes in a biological context can be used to formulate new testable hypothesis of both the genetics and the biology of the disorders.

Keywords

Cellomics High-content screening Proteomics Psychiatric disorders 

Notes

Acknowledgments

MR was supported by the European grant U-FP7 MC-ITN IN-SENS (#607616) and the Schizophrenia United Network (SUN) project. We gratefully acknowledge support from the Swedish Research Council (Vetenskapsrådet, award D0886501).

References

  1. 1.
    Sullivan PF, Daly MJ, O’Donovan M (2012) Genetic architectures of psychiatric disorders: the emerging picture and its implications. Nat Rev Genet 13(8):537–551CrossRefGoogle Scholar
  2. 2.
    Gejman PV, Sanders AR, Duan J (2010) The role of genetics in the etiology of schizophrenia. Psychiatr Clin North Am 33(1):35–66CrossRefGoogle Scholar
  3. 3.
    Schizophrenia Working Group of the Psychiatric Genomics Consortium (2014) Biological insights from 108 schizophrenia-associated genetic loci. Nature 511(7510):421–427CrossRefGoogle Scholar
  4. 4.
    Pardinas AF, Holmans P, Pocklington AJ, Escott-Price V, Ripke S, Carrera N, Legge SE, Bishop S, Cameron D, Hamshere ML et al (2018) Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection. Nat Genet 50(3):381–389CrossRefGoogle Scholar
  5. 5.
    Failla P, Romano C, Alberti A, Vasta A, Buono S, Castiglia L, Luciano D, Di Benedetto D, Fichera M, Galesi O (2007) Schizophrenia in a patient with subtelomeric duplication of chromosome 22q. Clin Genet 71(6):599–601CrossRefGoogle Scholar
  6. 6.
    Fryland T, Christensen JH, Pallesen J, Mattheisen M, Palmfeldt J, Bak M, Grove J, Demontis D, Blechingberg J, Ooi HS et al (2016) Identification of the BRD1 interaction network and its impact on mental disorder risk. Genome Med 8(1):53CrossRefGoogle Scholar
  7. 7.
    Huang CC, Cheng MC, Tsai HM, Lai CH, Chen CH (2014) Genetic analysis of GABRB3 at 15q12 as a candidate gene of schizophrenia. Psychiatr Genet 24(4):151–157CrossRefGoogle Scholar
  8. 8.
    Stoll G, Pietilainen OPH, Linder B, Suvisaari J, Brosi C, Hennah W, Leppa V, Torniainen M, Ripatti S, Ala-Mello S et al (2013) Deletion of TOP3beta, a component of FMRP-containing mRNPs, contributes to neurodevelopmental disorders. Nat Neurosci 16(9):1228–1237CrossRefGoogle Scholar
  9. 9.
    Vacic V, McCarthy S, Malhotra D, Murray F, Chou HH, Peoples A, Makarov V, Yoon S, Bhandari A, Corominas R et al (2011) Duplications of the neuropeptide receptor gene VIPR2 confer significant risk for schizophrenia. Nature 471(7339):499–503CrossRefGoogle Scholar
  10. 10.
    Harrill JA, Robinette BL, Mundy WR (2011) Use of high content image analysis to detect chemical-induced changes in synaptogenesis in vitro. Toxicol In Vitro 25(1):368–387CrossRefGoogle Scholar
  11. 11.
    Jain S, van Kesteren RE, Heutink P (2012) High content screening in neurodegenerative diseases. J Vis Exp (59):e3452Google Scholar
  12. 12.
    Kozak K (2009) Data mining techniques in high content screening: a survey. J Comput Sci Syst Biol 02(04)Google Scholar
  13. 13.
    Linhoff MW, Lauren J, Cassidy RM, Dobie FA, Takahashi H, Nygaard HB, Airaksinen MS, Strittmatter SM, Craig AM (2009) An unbiased expression screen for synaptogenic proteins identifies the LRRTM protein family as synaptic organizers. Neuron 61(5):734–749CrossRefGoogle Scholar
  14. 14.
    Nieland TJ, Logan DJ, Saulnier J, Lam D, Johnson C, Root DE, Carpenter AE, Sabatini BL (2014) High content image analysis identifies novel regulators of synaptogenesis in a high-throughput RNAi screen of primary neurons. PLoS One 9(3):e91744CrossRefGoogle Scholar
  15. 15.
    Sharma K, Choi SY, Zhang Y, Nieland TJ, Long S, Li M, Huganir RL (2013) High-throughput genetic screen for synaptogenic factors: identification of LRP6 as critical for excitatory synapse development. Cell Rep 5(5):1330–1341CrossRefGoogle Scholar
  16. 16.
    Cohen E, Ivenshitz M, Amor-Baroukh V, Greenberger V, Segal M (2008) Determinants of spontaneous activity in networks of cultured hippocampus. Brain Res 1235:21–30CrossRefGoogle Scholar
  17. 17.
    Chen N, Koopmans F, Gordon A, Paliukhovich I, Klaassen RV, van der Schors RC, Peles E, Verhage M, Smit AB, Li KW (2015) Interaction proteomics of canonical Caspr2 (CNTNAP2) reveals the presence of two Caspr2 isoforms with overlapping interactomes. Biochim Biophys Acta 1854(7):827–833CrossRefGoogle Scholar
  18. 18.
    Pandya NJ, Klaassen RV, van der Schors RC, Slotman JA, Houtsmuller A, Smit AB, Li KW (2016) Group 1 metabotropic glutamate receptors 1 and 5 form a protein complex in mouse hippocampus and cortex. Proteomics 16(20):2698–2705CrossRefGoogle Scholar
  19. 19.
    Pandya NJ, Koopmans F, Slotman JA, Paliukhovich I, Houtsmuller AB, Smit AB, Li KW (2017) Correlation profiling of brain sub-cellular proteomes reveals co-assembly of synaptic proteins and subcellular distribution. Sci Rep 7(1):12107CrossRefGoogle Scholar
  20. 20.
    Daub A, Sharma P, Finkbeiner S (2009) High-content screening of primary neurons: ready for prime time. Curr Opin Neurobiol 19(5):537–543CrossRefGoogle Scholar
  21. 21.
    Twarog NR, Low JA, Currier DG, Miller G, Chen T, Shelat AA (2016) Robust classification of small-molecule mechanism of action using a minimalist high-content microscopy screen and multidimensional phenotypic trajectory analysis. PLoS One 11(2):e0149439CrossRefGoogle Scholar
  22. 22.
    Bates D, Mächler M, Bolker B, Walker S (2015) Fitting linear mixed-effects models using lme4. J Stat Softw 67(1)Google Scholar
  23. 23.
    Lundby A, Rossin EJ, Steffensen AB, Acha MR, Newton-Cheh C, Pfeufer A, Lynch SN, Consortium QTIIG, Olesen SP, Brunak S et al (2014) Annotation of loci from genome-wide association studies using tissue-specific quantitative interaction proteomics. Nat Methods 11(8):868–874CrossRefGoogle Scholar
  24. 24.
    Kamburov A, Lawrence MS, Polak P, Leshchiner I, Lage K, Golub TR, Lander ES, Getz G (2015) Comprehensive assessment of cancer missense mutation clustering in protein structures. Proc Natl Acad Sci U S A 112(40):E5486–E5495CrossRefGoogle Scholar
  25. 25.
    Rosenbluh J, Mercer J, Shrestha Y, Oliver R, Tamayo P, Doench JG, Tirosh I, Piccioni F, Hartenian E, Horn H et al (2016) Genetic and proteomic interrogation of lower confidence candidate genes reveals signaling networks in beta-catenin-active cancers. Cell Syst 3(3):302–316.e4CrossRefGoogle Scholar
  26. 26.
    Li T, Wernersson R, Hansen RB, Horn H, Mercer J, Slodkowicz G, Workman CT, Rigina O, Rapacki K, Staerfeldt HH et al (2017) A scored human protein-protein interaction network to catalyze genomic interpretation. Nat Methods 14(1):61–64CrossRefGoogle Scholar
  27. 27.
    Horn H, Lawrence MS, Chouinard CR, Shrestha Y, Hu JX, Worstell E, Shea E, Ilic N, Kim E, Kamburov A et al (2018) NetSig: network-based discovery from cancer genomes. Nat Methods 15(1):61–66CrossRefGoogle Scholar
  28. 28.
    Li T, Kim A, Rosenbluh J, Horn H, Greenfeld L, An D, Zimmer A, Liberzon A, Bistline J, Natoli T et al (2018) GeNets: a unified web platform for network-based genomic analyses. Nat Methods 15(7):543–546CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Martina Rosato
    • 1
  • Titia Gebuis
    • 1
  • Iryna Paliukhovich
    • 1
  • Sven Stringer
    • 2
  • Patrick F. Sullivan
    • 3
    • 4
  • August B. Smit
    • 5
  • Ronald E. van Kesteren
    • 1
    Email author
  1. 1.Department of Molecular and Cellular Neurobiology, Faculty of Science, Center for Neurogenomics and Cognitive ResearchVrije Universiteit AmsterdamAmsterdamThe Netherlands
  2. 2.Department of Complex Trait Genetics, Faculty of Science, Center for Neurogenomics and Cognitive ResearchVrije Universiteit AmsterdamAmsterdamThe Netherlands
  3. 3.Department of Medical Epidemiology and BiostatisticsKarolinska InstitutetStockholmSweden
  4. 4.Department of GeneticsUniversity of North CarolinaChapel HillUSA
  5. 5.Department of Molecular and Cellular Neurobiology, Faculty of Science, Center for Neurogenomics and Cognitive Research, Amsterdam NeuroscienceVrije Universiteit AmsterdamAmsterdamThe Netherlands

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