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  • Review Article
  • Published:

Gene expression profiling in human neurodegenerative disease

Nature Reviews Neurology volume 8, pages 518–530 (2012)Cite this article

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Abstract

Transcriptome study in neurodegenerative disease has advanced considerably in the past 5 years. Increasing scientific rigour and improved analytical tools have led to more-reproducible data. Many transcriptome analysis platforms assay the expression of the entire genome, enabling a complete biological context to be captured. Gene expression profiling (GEP) is, therefore, uniquely placed to discover pathways of disease pathogenesis, potential therapeutic targets, and biomarkers. This Review summarizes microarray human GEP studies in the common neurodegenerative diseases amyotrophic lateral sclerosis (ALS), Parkinson disease (PD) and Alzheimer disease (AD). Several interesting reports have compared pathological gene expression in different patient groups, disease stages and anatomical areas. In all three diseases, GEP has revealed dysregulation of genes related to neuroinflammation. In ALS and PD, gene expression related to RNA splicing and protein turnover is disrupted, and several studies in ALS support involvement of the cytoskeleton. GEP studies have implicated the ubiquitin–proteasome system in PD pathogenesis, and have provided evidence of mitochondrial dysfunction in PD and AD. Lastly, in AD, a possible role for dysregulation of intracellular signalling pathways, including calcium signalling, has been highlighted. This Review also provides a discussion of methodological considerations in microarray sample preparation and data analysis.

Key Points

  • Gene expression profiling (GEP) has advanced considerably over the past 5 years, and has provided important insight into mechanisms underlying neurodegenerative disease

  • In amyotrophic lateral sclerosis, GEP studies have consistently implicated certain biological structures and pathways, including the cytoskeleton, inflammation, protein turnover and RNA splicing

  • GEP studies in Parkinson disease have highlighted dysfunction of the ubiquitin–proteasome system, RNA splicing, mitochondrial function and inflammation

  • In Alzheimer disease, affected pathways identified by GEP analysis include neuroinflammation, mitochondrial function and calcium signalling

  • GEP studies have investigated selective vulnerability to neurodegeneration between patients and in different anatomical areas of the CNS, in order to characterize disease mechanisms, identify therapeutic targets and potentially inform development of individualized treatments

  • Technical aspects of GEP, including sample preparation, data analysis and validation, require careful consideration to optimize assays and yield reliable results

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Figure 1
Figure 2: Summary of the biological pathways that are consistently identified by gene expression profiling of human tissue samples from patients with amyotrophic lateral sclerosis, Alzheimer disease or Parkinson disease.

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References

  1. Schena, M., Shalon, D., Davis, R. W. & Brown, P. O. Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 270, 467–470 (1995).

    CAS  PubMed  Google Scholar 

  2. Saris, C. G. et al. Weighted gene co-expression network analysis of the peripheral blood from amyotrophic lateral sclerosis patients. BMC Genomics 10, 405 (2009).

    PubMed  PubMed Central  Google Scholar 

  3. Offen, D. et al. Spinal cord mRNA profile in patients with ALS: comparison with transgenic mice expressing the human SOD-1 mutant. J. Mol. Neurosci. 38, 85–93 (2009).

    CAS  PubMed  Google Scholar 

  4. Wang, X. S., Simmons, Z., Liu, W., Boyer, P. J. & Connor, J. R. Differential expression of genes in amyotrophic lateral sclerosis revealed by profiling the post mortem cortex. Amyotroph. Lateral Scler. 7, 201–210 (2006).

    CAS  PubMed  Google Scholar 

  5. Lederer, C. W., Torrisi, A., Pantelidou, M., Santama, N. & Cavallaro, S. Pathways and genes differentially expressed in the motor cortex of patients with sporadic amyotrophic lateral sclerosis. BMC Genomics 8, 26 (2007).

    PubMed  PubMed Central  Google Scholar 

  6. Ferraiuolo, L., Kirby, J., Grierson, A. J., Sendtner, M. & Shaw, P. J. Molecular pathways of motor neuron injury in amyotrophic lateral sclerosis. Nat. Rev. Neurol. 7, 616–630 (2011).

    CAS  PubMed  Google Scholar 

  7. Kirby, J. et al. Phosphatase and tensin homologue/protein kinase B pathway linked to motor neuron survival in human superoxide dismutase 1-related amyotrophic lateral sclerosis. Brain 134, 506–517 (2011).

    PubMed  PubMed Central  Google Scholar 

  8. Jiang, Y. M. et al. Gene expression profile of spinal motor neurons in sporadic amyotrophic lateral sclerosis. Ann. Neurol. 57, 236–251 (2005).

    CAS  PubMed  Google Scholar 

  9. Rabin, S. J. et al. Sporadic ALS has compartment-specific aberrant exon splicing and altered cell-matrix adhesion biology. Hum. Mol. Genet. 19, 313–328 (2010).

    CAS  PubMed  Google Scholar 

  10. Cox, L. E. et al. Mutations in CHMP2B in lower motor neuron predominant amyotrophic lateral sclerosis (ALS). PLoS ONE 5, e9872 (2010).

    PubMed  PubMed Central  Google Scholar 

  11. Hewitt, C. et al. Novel FUS/TLS mutations and pathology in familial and sporadic amyotrophic lateral sclerosis. Arch. Neurol. 67, 455–461 (2010).

    PubMed  Google Scholar 

  12. Kirby, J. et al. Broad clinical phenotypes associated with TAR-DNA binding protein (TARDBP) mutations in amyotrophic lateral sclerosis. Neurogenetics 11, 217–225 (2010).

    CAS  PubMed  Google Scholar 

  13. Kirby, J. et al. Mutant SOD1 alters the motor neuronal transcriptome: implications for familial ALS. Brain 128, 1686–1706 (2005).

    PubMed  Google Scholar 

  14. Ferraiuolo, L. et al. Microarray analysis of the cellular pathways involved in the adaptation to and progression of motor neuron injury in the SOD1 G93A mouse model of familial ALS. J. Neurosci. 27, 9201–9219 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Zhang, R. et al. Gene expression profiling in peripheral blood mononuclear cells from patients with sporadic amyotrophic lateral sclerosis (sALS). J. Neuroimmunol. 230, 114–123 (2011).

    CAS  PubMed  Google Scholar 

  16. Mougeot, J.-L., Li, Z., Price, A., Wright, F. & Brooks, B. Microarray analysis of peripheral blood lymphocytes from ALS patients and the SAFE detection of the KEGG ALS pathway. BMC Med. Genomics 4, 74 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Pradat, P. F. et al. Muscle gene expression is a marker of amyotrophic lateral sclerosis severity. Neurodegen. Dis. 9, 38–52 (2012).

    Google Scholar 

  18. Shtilbans, A. et al. Differential gene expression in patients with amyotrophic lateral sclerosis. Amyotroph. Lateral Scler. 12, 250–256 (2011).

    CAS  PubMed  Google Scholar 

  19. Scherzer, C. R. et al. Molecular markers of early Parkinson's disease based on gene expression in blood. Proc. Natl Acad. Sci. USA 104, 955–960 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Zheng, B. et al. PGC-1α, a potential therapeutic target for early intervention in Parkinson's disease. Sci. Transl. Med. 2, 52ra73 (2010).

    PubMed  PubMed Central  Google Scholar 

  21. Elstner, M. et al. Expression analysis of dopaminergic neurons in Parkinson's disease and aging links transcriptional dysregulation of energy metabolism to cell death. Acta Neuropathol. 122, 75–86 (2011).

    CAS  PubMed  Google Scholar 

  22. Shin, J.-H. et al. PARIS (ZNF746) repression of PGC-1α contributes to neurodegeneration in Parkinson's disease. Cell 144, 689–702 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Papapetropoulos, S. et al. Multiregional gene expression profiling identifies MRPS6 as a possible candidate gene for Parkinson's disease. Gene Expr. 13, 205–215 (2006).

    CAS  PubMed  Google Scholar 

  24. Moran, L. et al. Whole genome expression profiling of the medial and lateral substantia nigra in Parkinson's disease. Neurogenetics 7, 1–11 (2006).

    CAS  PubMed  Google Scholar 

  25. Hauser, M. A. et al. Expression profiling of substantia nigra in Parkinson disease, progressive supranuclear palsy, and frontotemporal dementia with parkinsonism. Arch. Neurol. 62, 917–921 (2005).

    PubMed  Google Scholar 

  26. Zhang, Y., James, M., Middleton, F. A. & Davis, R. L. Transcriptional analysis of multiple brain regions in Parkinson's disease supports the involvement of specific protein processing, energy metabolism, and signaling pathways, and suggests novel disease mechanisms. Am. J. Med. Genet. B Neuropsychiatr. Genet. 137B, 5–16 (2005).

    PubMed  Google Scholar 

  27. Vogt, I. R. et al. Transcriptional changes in multiple system atrophy and Parkinson's disease putamen. Exp. Neurol. 199, 465–478 (2006).

    CAS  PubMed  Google Scholar 

  28. Naydenov, A., Vassoler, F., Luksik, A., Kaczmarska, J. & Konradi, C. Mitochondrial abnormalities in the putamen in Parkinson's disease dyskinesia. Acta Neuropathol. 120, 623–631 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Botta-Orfila, T. et al. Microarray expression analysis in idiopathic and LRRK2-associated Parkinson's disease. Neurobiol. Dis. 45, 462–468 (2012).

    CAS  PubMed  Google Scholar 

  30. Durrenberger, P. et al. Inflammatory pathways in Parkinson's disease; a BNE microarray study. Parkinson Dis. 2012, 214714 (2012).

    Google Scholar 

  31. Bossers, K. et al. Analysis of gene expression in Parkinson's disease: possible involvement of neurotrophic support and axon guidance in dopaminergic cell death. Brain Pathol. 19, 91–107 (2009).

    CAS  PubMed  Google Scholar 

  32. Miller, R. M. et al. Robust dysregulation of gene expression in substantia nigra and striatum in Parkinson's disease. Neurobiol. Dis. 21, 305–313 (2006).

    CAS  PubMed  Google Scholar 

  33. Duke, D., Moran, L., Pearce, R. & Graeber, M. The medial and lateral substantia nigra in Parkinson's disease: mRNA profiles associated with higher brain tissue vulnerability. Neurogenetics 8, 83–94 (2007).

    CAS  PubMed  Google Scholar 

  34. Fearnley, J. M. & Lees, A. J. Ageing and Parkinson's disease: substantia nigra regional selectivity. Brain 114, 2283–2301 (1991).

    PubMed  Google Scholar 

  35. Zhou, C., Huang, Y. & Przedborski, S. Oxidative stress in Parkinson's disease. Ann. NY Acad. Sci. 1147, 93–104 (2008).

    CAS  PubMed  Google Scholar 

  36. Rideout, H. J., Larsen, K. E., Sulzer, D. & Stefanis, L. Proteasomal inhibition leads to formation of ubiquitin/α-synuclein-immunoreactive inclusions in PC12 cells. J. Neurochem. 78, 899–908 (2001).

    CAS  PubMed  Google Scholar 

  37. Vila, M. & Przedborski, S. Targeting programmed cell death in neurodegenerative diseases. Nat. Rev. Neurosci. 4, 365–375 (2003).

    CAS  PubMed  Google Scholar 

  38. Ved, R. et al. Similar patterns of mitochondrial vulnerability and rescue induced by genetic modification of α-synuclein, parkin, and DJ-1 in Caenorhabditis elegans. J. Biol. Chem. 280, 42655–42668 (2005).

    CAS  PubMed  Google Scholar 

  39. Dawson, T. M. & Dawson, V. L. Molecular pathways of neurodegeneration in Parkinson's disease. Science 302, 819–822 (2003).

    CAS  PubMed  Google Scholar 

  40. Quigley, P. M., Korotkov, K., Baneyx, F. & Hol, W. G. The 1.6-Å crystal structure of the class of chaperones represented by Escherichia coli Hsp31 reveals a putative catalytic triad. Proc. Natl Acad. Sci. USA 100, 3137–3142 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Moran, L. et al. Analysis of alpha-synuclein, dopamine and parkin pathways in neuropathologically confirmed parkinsonian nigra. Acta Neuropathol. 113, 253–263 (2007).

    CAS  PubMed  Google Scholar 

  42. Duke, D. et al. Transcriptome analysis reveals link between proteasomal and mitochondrial pathways in Parkinson's disease. Neurogenetics 7, 139–148 (2006).

    CAS  PubMed  Google Scholar 

  43. Mortiboys, H., Johansen, K., Aasly, J. & Bandmann, O. Mitochondrial impairment in patients with Parkinson disease with the G2019S mutation in LRRK2. Neurology 75, 2017–2020 (2010).

    CAS  PubMed  Google Scholar 

  44. Cantuti-Castelvetri, I. et al. Effects of gender on nigral gene expression and Parkinson disease. Neurobiol. Dis. 26, 606–614 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Simunovic, F. et al. Gene expression profiling of substantia nigra dopamine neurons: further insights into Parkinson's disease pathology. Brain 132, 1795–1809 (2009).

    PubMed  Google Scholar 

  46. Stamper, C. et al. Neuronal gene expression correlates of Parkinson's disease with dementia. Mov. Disord. 23, 1588–1595 (2008).

    PubMed  PubMed Central  Google Scholar 

  47. Simunovic, F., Yi, M., Wang, Y., Stephens, R. & Sonntag, K. C. Evidence for gender-specific transcriptional profiles of nigral dopamine neurons in Parkinson disease. PLoS ONE 5, e8856 (2010).

    PubMed  PubMed Central  Google Scholar 

  48. Surmeier, D. J. Calcium, ageing, and neuronal vulnerability in Parkinson's disease. Lancet Neurol. 6, 933–938 (2007).

    CAS  PubMed  Google Scholar 

  49. Shehadeh, L. A. et al. SRRM2, a potential blood biomarker revealing high alternative splicing in Parkinson's disease. PLoS ONE 5, e9104 (2010).

    PubMed  PubMed Central  Google Scholar 

  50. Mar, J. C. et al. Variance of gene expression identifies altered network constraints in neurological disease. PLoS Genet. 7, e1002207 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Matigian, N. et al. Disease-specific, neurosphere-derived cells as models for brain disorders. Dis. Model. Mech. 3, 785–798 (2010).

    CAS  PubMed  Google Scholar 

  52. Mutez, E. et al. Transcriptional profile of Parkinson blood mononuclear cells with LRRK2 mutation. Neurobiol. Aging 32, 1839–1848 (2011).

    CAS  PubMed  Google Scholar 

  53. Parachikova, A. et al. Inflammatory changes parallel the early stages of Alzheimer disease. Neurobiol. Aging 28, 1821–1833 (2007).

    CAS  PubMed  Google Scholar 

  54. Tan, M. G. et al. Genome wide profiling of altered gene expression in the neocortex of Alzheimer's disease. J. Neurosci. Res. 88, 1157–1169 (2010).

    CAS  PubMed  Google Scholar 

  55. Williams, C. et al. Transcriptome analysis of synaptoneurosomes identifies neuroplasticity genes overexpressed in incipient Alzheimer's disease. PLoS ONE 4, e4936 (2009).

    PubMed  PubMed Central  Google Scholar 

  56. Xu, P.-T. et al. Differences in apolipoprotein E3/3 and E4/4 allele-specific gene expression in hippocampus in Alzheimer disease. Neurobiol. Dis. 21, 256–275 (2006).

    CAS  PubMed  Google Scholar 

  57. Emilsson, L., Saetre, P. & Jazin, E. Alzheimer's disease: mRNA expression profiles of multiple patients show alterations of genes involved with calcium signaling. Neurobiol. Dis. 21, 618–625 (2006).

    CAS  PubMed  Google Scholar 

  58. Haroutunian, V., Katsel, P. & Schmeidler, J. Transcriptional vulnerability of brain regions in Alzheimer's disease and dementia. Neurobiol. Aging 30, 561–573 (2009).

    CAS  PubMed  Google Scholar 

  59. Katsel, P., Li, C. & Haroutunian, V. Gene expression alterations in the sphingolipid metabolism pathways during progression of dementia and Alzheimer's disease: a shift toward ceramide accumulation at the earliest recognizable stages of Alzheimer's disease? Neurochem. Res. 32, 845–856 (2007).

    CAS  PubMed  Google Scholar 

  60. Katsel, P., Tan, W. & Haroutunian, V. Gain in brain immunity in the oldest-old differentiates cognitively normal from demented individuals. PLoS ONE 4, e7642 (2009).

    PubMed  PubMed Central  Google Scholar 

  61. Umemura, K. et al. Autotaxin expression is enhanced in frontal cortex of Alzheimer-type dementia patients. Neurosci. Lett. 400, 97–100 (2006).

    CAS  PubMed  Google Scholar 

  62. Weeraratna, A. T. et al. Alterations in immunological and neurological gene expression patterns in Alzheimer's disease tissues. Exp. Cell Res. 313, 450–461 (2007).

    CAS  PubMed  Google Scholar 

  63. Bronner, I. F. et al. Comprehensive mRNA expression profiling distinguishes tauopathies and identifies shared molecular pathways. PLoS ONE 4, e6826 (2009).

    PubMed  PubMed Central  Google Scholar 

  64. Bossers, K. et al. Concerted changes in transcripts in the prefrontal cortex precede neuropathology in Alzheimer's disease. Brain 133, 3699–3723 (2010).

    PubMed  Google Scholar 

  65. Horesh, Y., Katsel, P., Haroutunian, V. & Domany, E. Gene expression signature is shared by patients with Alzheimer's disease and schizophrenia at the superior temporal gyrus. Eur. J. Neurol. 18, 410–424 (2011).

    CAS  PubMed  Google Scholar 

  66. Youn, H. et al. Kalirin is under-expressed in Alzheimer's disease hippocampus. J. Alzheimers Dis. 11, 385–397 (2007).

    CAS  PubMed  Google Scholar 

  67. Wang, S., Qaisar, U., Yin, X. & Grammas, P. Gene expression profiling in Alzheimer's disease brain microvessels. J. Alzheimers Dis. http://dx.doi.org/10.3233/JAD-2012-120454.

  68. Tollervey, J. R. et al. Analysis of alternative splicing associated with aging and neurodegeneration in the human brain. Genome Res. 21, 1572–1582 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  69. Kuchibhotla, K. V. et al. Aβ plaques lead to aberrant regulation of calcium homeostasis in vivo resulting in structural and functional disruption of neuronal networks. Neuron 59, 214–225 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  70. Guo, Q. et al. Alzheimer's PS-1 mutation perturbs calcium homeostasis and sensitizes PC12 cells to death induced by amyloid peptide. Neuroreport 8, 379–383 (1996).

    CAS  PubMed  Google Scholar 

  71. Camandola, S. & Mattson, M. P. Aberrant subcellular neuronal calcium regulation in aging and Alzheimer's disease. Biochim. Biophys. Acta 1813, 965–973 (2011).

    CAS  PubMed  Google Scholar 

  72. Berchtold, N. C. et al. Gene expression changes in the course of normal brain aging are sexually dimorphic. Proc. Natl Acad. Sci. USA 105, 15605–15610 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  73. Saetre, P., Jazin, E. & Emilsson, L. Age-related changes in gene expression are accelerated in Alzheimer's disease. Synapse 65, 971–974 (2011).

    CAS  PubMed  Google Scholar 

  74. Masliah, E. et al. Altered expression of synaptic proteins occurs early during progression of Alzheimer's disease. Neurology 56, 127–129 (2001).

    CAS  PubMed  Google Scholar 

  75. Giuditta, A. et al. Local gene expression in axons and nerve endings: the glia–neuron unit. Physiol. Rev. 88, 515–555 (2008).

    CAS  PubMed  Google Scholar 

  76. Liang, W. S. et al. Altered neuronal gene expression in brain regions differentially affected by Alzheimer's disease: a reference data set. Physiol. Genomics 33, 240–256 (2008).

    CAS  PubMed  Google Scholar 

  77. Liang, W. S. et al. Alzheimer's disease is associated with reduced expression of energy metabolism genes in posterior cingulate neurons. Proc. Natl Acad. Sci. USA 105, 4441–4446 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  78. Dunckley, T. et al. Gene expression correlates of neurofibrillary tangles in Alzheimer's disease. Neurobiol. Aging 27, 1359–1371 (2006).

    CAS  PubMed  Google Scholar 

  79. Alexander, G. E., Chen, K., Pietrini, P., Rapoport, S. I. & Reiman, E. M. Longitudinal PET evaluation of cerebral metabolic decline in dementia: a potential outcome measure in Alzheimer's disease treatment studies. Am. J. Psychiatry 159, 738–745 (2002).

    PubMed  Google Scholar 

  80. Minoshima, S. et al. Metabolic reduction in the posterior cingulate cortex in very early Alzheimer's disease. Ann. Neurol. 42, 85–94 (1997).

    CAS  PubMed  Google Scholar 

  81. Ray, M. & Zhang, W. Analysis of Alzheimer's disease severity across brain regions by topological analysis of gene co-expression networks. BMC Syst. Biol. 4, 136 (2010).

    PubMed  PubMed Central  Google Scholar 

  82. Liu, Z.-P., Wang, Y., Zhang, X.-S. & Chen, L. Identifying dysfunctional crosstalk of pathways in various regions of Alzheimer's disease brains. BMC Syst. Biol. 4, S11 (2010).

    PubMed  PubMed Central  Google Scholar 

  83. Ray, M., Ruan, J. & Zhang, W. Variations in the transcriptome of Alzheimer's disease reveal molecular networks involved in cardiovascular diseases. Genome Biol. 9, R148 (2008).

    PubMed  PubMed Central  Google Scholar 

  84. Gorelick, P. B. et al. Vascular contributions to cognitive impairment and dementia. Stroke 42, 2672–2713 (2011).

    PubMed  PubMed Central  Google Scholar 

  85. Helbecque, N. & Amouyel, P. Commonalities between genetics of cardiovascular disease and neurodegenerative disorders. Curr. Opin. Lipidol. 15, 121–127 (2004).

    CAS  PubMed  Google Scholar 

  86. Simpson, J. E. et al. Microarray analysis of the astrocyte transcriptome in the aging brain: relationship to Alzheimer's pathology and APOE genotype. Neurobiol. Aging 32, 1795–1807 (2011).

    CAS  PubMed  Google Scholar 

  87. Strittmatter, W. J. et al. Apolipoprotein E: high-avidity binding to β-amyloid and increased frequency of type 4 allele in late-onset familial Alzheimer disease. Proc. Natl Acad. Sci. USA 90, 1977–1981 (1993).

    CAS  PubMed  PubMed Central  Google Scholar 

  88. Maes, O. C. et al. Transcriptional profiling of Alzheimer blood mononuclear cells by microarray. Neurobiol. Aging 28, 1795–1809 (2007).

    CAS  PubMed  Google Scholar 

  89. Nagasaka, Y. et al. A unique gene expression signature discriminates familial Alzheimer's disease mutation carriers from their wild-type siblings. Proc. Natl Acad. Sci. USA 102, 14854–14859 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  90. Calciano, M. A., Zhou, W., Snyder, P. J. & Einstein, R. Drug treatment of Alzheimer's disease patients leads to expression changes in peripheral blood cells. Alzheimers Dement. 6, 386–393 (2010).

    CAS  PubMed  Google Scholar 

  91. Booij, B. B. et al. A gene expression pattern in blood for the early detection of Alzheimer's disease. J. Alzheimers Dis. 23, 109–119 (2011).

    CAS  PubMed  Google Scholar 

  92. Kálmán, J. et al. Gene expression profile analysis of lymphocytes from Alzheimer's patients. Psychiatr. Genet. 15, 1–6 (2005).

    PubMed  Google Scholar 

  93. Fehlbaum-Beurdeley, P. et al. Toward an Alzheimer's disease diagnosis via high-resolution blood gene expression. Alzheimers Dement. 6, 25–38 (2010).

    CAS  PubMed  Google Scholar 

  94. Chen, K.-D. et al. Gene expression profiling of peripheral blood leukocytes identifies and validates ABCB1 as a novel biomarker for Alzheimer's disease. Neurobiol. Dis. 43, 698–705 (2011).

    CAS  PubMed  Google Scholar 

  95. Rye, P. D. et al. A novel blood test for the early detection of Alzheimer's disease. J. Alzheimers Dis. 23, 121–129 (2011).

    CAS  PubMed  Google Scholar 

  96. Scherzer, C. et al. Loss of apolipoprotein E receptor LR11 in Alzheimer disease. Arch. Neurol. 61, 1200–1205 (2004).

    PubMed  Google Scholar 

  97. Lee, J., Barral, S. & Reitz, C. The neuronal sortilin-related receptor gene SORL1 and late-onset Alzheimer's disease. Curr. Neurol. Neurosci. Rep. 8, 384–391 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  98. Sutherland, G. T., Janitz, M. & Kril, J. J. Understanding the pathogenesis of Alzheimer's disease: will RNA-Seq realize the promise of transcriptomics? J. Neurochem. 116, 937–946 (2011).

    CAS  PubMed  Google Scholar 

  99. Emmert-Buck, M. R. et al. Laser capture microdissection. Science 274, 998–1001 (1996).

    CAS  PubMed  Google Scholar 

  100. Sutherland, G. T. et al. A cross-study transcriptional analysis of Parkinson's disease. PLoS ONE 4, e4955 (2009).

    PubMed  PubMed Central  Google Scholar 

  101. Casale, V., Oneda, R., Lavezzi, A. M. & Matturri, L. Optimisation of postmortem tissue preservation and alternative protocol for serotonin transporter gene polymorphisms amplification in SIDS and SIUD cases. Exp. Mol. Pathol. 88, 202–205 (2010).

    CAS  PubMed  Google Scholar 

  102. Durrenberger, P. F. et al. Effects of antemortem and postmortem variables on human brain mRNA quality: a BrainNet Europe study. J. Neuropathol. Exp. Neurol. 69, 70–81 (2010).

    PubMed  Google Scholar 

  103. Strand, C., Enell, J., Hedenfalk, I. & Ferno, M. RNA quality in frozen breast cancer samples and the influence on gene expression analysis—a comparison of three evaluation methods using microcapillary electrophoresis traces. BMC Mol. Biol. 8, 38 (2007).

    PubMed  PubMed Central  Google Scholar 

  104. Schroeder, A. et al. The RIN: an RNA integrity number for assigning integrity values to RNA measurements. BMC Mol. Biol. 7, 3 (2006).

    PubMed  PubMed Central  Google Scholar 

  105. Copois, V. et al. Impact of RNA degradation on gene expression profiles: assessment of different methods to reliably determine RNA quality. J. Biotechnol. 127, 549–559 (2007).

    CAS  PubMed  Google Scholar 

  106. Tan, P. K., et al. Evaluation of gene expression measurements from commercial microarray platforms. Nucleic Acids Res. 31, 5676–5684 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  107. MAQC Consortium et al. The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements. Nat. Biotech. 24, 1151–1161 (2006).

  108. Posekany, A., Felsenstein, K. & Sykacek, P. Biological assessment of robust noise models in microarray data analysis. Bioinformatics 27, 807–814 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  109. Stekel, D. Microarray Bioinformatics 110–138 (Cambridge University Press, Cambridge, UK, 2003).

    Google Scholar 

  110. Storey, J. D., Dai, J. Y. & Leek, J. T. The optimal discovery procedure for large-scale significance testing, with applications to comparative microarray experiments. Biostatistics 8, 414–432 (2007).

    PubMed  Google Scholar 

  111. Hosack, D., Dennis, G., Sherman, B., Lane, H. & Lempicki, R. Identifying biological themes within lists of genes with EASE. Genome Biol. 4, R70 (2003).

    PubMed  PubMed Central  Google Scholar 

  112. Smyth, G. K. Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat. Appl. Genet. Mol. Biol. 3, Article 3 (2004).

    Google Scholar 

  113. Liu, X., Milo, M., Lawrence, N. D. & Rattray, M. Probe-level measurement error improves accuracy in detecting differential gene expression. Bioinformatics 22, 2107–2113 (2006).

    CAS  PubMed  Google Scholar 

  114. Blencowe, B. J., Ahmad, S. & Lee, L. J. Current-generation high-throughput sequencing: deepening insights into mammalian transcriptomes. Genes Dev. 23, 1379–1386 (2009).

    CAS  PubMed  Google Scholar 

  115. Yamamoto, M. L. et al. Alternative pre-mRNA splicing switches modulate gene expression in late erythropoiesis. Blood 113, 3363–3370 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  116. Sultan, M. et al. A global view of gene activity and alternative splicing by deep sequencing of the human transcriptome. Science 321, 956–960 (2008).

    CAS  PubMed  Google Scholar 

  117. Mortazavi, A., Williams, B. A., McCue, K., Schaeffer, L. & Wold, B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat. Methods 5, 621–628 (2008).

    CAS  PubMed  Google Scholar 

  118. Metzker, M. L. Sequencing technologies—the next generation. Nat. Rev. Genet. 11, 31–46 (2010).

    CAS  PubMed  Google Scholar 

  119. Tariq, M. A., Kim, H. J., Jejelowo, O. & Pourmand, N. Whole-transcriptome RNAseq analysis from minute amount of total RNA. Nucleic Acids Res. http://dx.doi.org/10.1093/nar/gkr547.

  120. Łabaj, P. P. et al. Characterization and improvement of RNA-Seq precision in quantitative transcript expression profiling. Bioinformatics 27, i383–i391 (2011).

    PubMed  PubMed Central  Google Scholar 

  121. Pascale, F.-B. et al. Toward an Alzheimer's disease diagnosis via high-resolution blood gene expression. Alzheimers Dement. 6, 25–38 (2010).

    Google Scholar 

  122. Kaklamani, V. A genetic signature can predict prognosis and response to therapy in breast cancer: oncotype DX. Expert Rev. Mol. Diagn. 6, 803–809 (2006).

    CAS  PubMed  Google Scholar 

  123. Shi, L. et al. The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models. Nat. Biotechnol. 28, 827–838 (2010).

    CAS  PubMed  Google Scholar 

  124. Rottenberg, S. et al. Impact of intertumoral heterogeneity on predicting chemotherapy response of BRCA1-deficient mammary tumors. Cancer Res. 72, 2350–2361 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  125. Ein-Dor, L., Kela, I., Getz, G., Givol, D. & Domany, E. Outcome signature genes in breast cancer: is there a unique set? Bioinformatics 21, 171–178 (2005).

    CAS  PubMed  Google Scholar 

  126. Ein-Dor, L., Zuk, O. & Domany, E. Thousands of samples are needed to generate a robust gene list for predicting outcome in cancer. Proc. Natl Acad. Sci. USA 103, 5923–5928 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  127. Cabeza-Arvelaiz, Y. et al. Analysis of striatal transcriptome in mice overexpressing human wild-type alpha-synuclein supports synaptic dysfunction and suggests mechanisms of neuroprotection for striatal neurons. Mol. Neurodegen. 6, 83 (2011).

    CAS  Google Scholar 

  128. Bredesen, D. E., Rao, R. V. & Mehlen, P. Cell death in the nervous system. Nature 443, 796–802 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  129. Grünblatt, E. Commonalities in the genetics of Alzheimer's disease and Parkinson's disease. Expert Rev. Neurother. 8, 1865–1877 (2008).

    PubMed  Google Scholar 

  130. van Es, M. A. et al. Angiogenin variants in Parkinson disease and amyotrophic lateral sclerosis. Ann. Neurol. 70, 964–973 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

The work of this group is supported by the Motor Neurone Disease Association, the Wellcome Trust, the Medical Research Council, and by a European Community 7th Framework Programme (FP7/2007-2013) under grant agreement number 259867 Euromotor to P. J. Shaw and J. Kirby; by a David Peake fellowship awarded to J. Cooper-Knock; and by BBSRC and EPSRC funding awarded to M. Rattray.

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Authors and Affiliations

  1. Academic Unit of Neurology, Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, 385A Glossop Road, S10 2HQ, Sheffield, UK

    Johnathan Cooper-Knock, Janine Kirby, Laura Ferraiuolo, Paul R. Heath, Magnus Rattray & Pamela J. Shaw

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  1. Johnathan Cooper-Knock
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  2. Janine Kirby
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  3. Laura Ferraiuolo
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  4. Paul R. Heath
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  5. Magnus Rattray
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  6. Pamela J. Shaw
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Contributions

J. Cooper-Knock researched the data for the article. J. Cooper-Knock, J. Kirby, P. R. Heath, M. Rattray and P. J. Shaw made substantial contributions to discussion of the article content. J. Cooper-Knock and L. Ferraiuolo wrote the article. J. Kirby, P. R. Heath, M. Rattray and P. J. Shaw contributed to review and/or editing of the manuscript before submission.

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Correspondence to Pamela J. Shaw.

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The authors declare no competing financial interests.

Supplementary information

Supplementary Table 1

Summary of gene expression profiling studies conducted on human biosamples from patients with ALS (DOC 96 kb)

Supplementary Table 2

Summary of gene expression profiling studies conducted on human biosamples from patients with PD (DOC 110 kb)

Supplementary Table 3

Summary of gene expression profiling studies conducted on human biosamples from patients with AD (DOC 116 kb)

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Cooper-Knock, J., Kirby, J., Ferraiuolo, L. et al. Gene expression profiling in human neurodegenerative disease. Nat Rev Neurol 8, 518–530 (2012). https://doi.org/10.1038/nrneurol.2012.156

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