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Nature neuroscience
12, 2019;22 (12): 2087-2097.

A single-cell atlas of entorhinal cortex from individuals with Alzheimer's disease reveals cell-type-specific gene expression regulation.

Alexandra Grubman, Gabriel Chew, John F Ouyang, Guizhi Sun, Xin Yi Choo, Catriona McLean, Rebecca K Simmons, Sam Buckberry, Dulce B Vargas-Landin, Daniel Poppe, Jahnvi Pflueger, Ryan Lister, Owen J L Rackham, Enrico Petretto, Jose M Polo
Author Information
  1. Alexandra Grubman: Department of Anatomy and Developmental Biology, Monash University, Clayton, Victoria, Australia. ORCID
  2. Gabriel Chew: Program in Cardiovascular and Metabolic Disorders, Duke-National University of Singapore Medical School, Singapore, Singapore.
  3. John F Ouyang: Program in Cardiovascular and Metabolic Disorders, Duke-National University of Singapore Medical School, Singapore, Singapore. ORCID
  4. Guizhi Sun: Department of Anatomy and Developmental Biology, Monash University, Clayton, Victoria, Australia.
  5. Xin Yi Choo: Department of Anatomy and Developmental Biology, Monash University, Clayton, Victoria, Australia. ORCID
  6. Catriona McLean: Victorian Brain Bank, Florey Institute of Neurosciences, Parkville, Victoria, Australia. ORCID
  7. Rebecca K Simmons: ARC Center of Excellence in Plant Energy Biology, The University of Western Australia, Perth, Western Australia, Australia.
  8. Sam Buckberry: ARC Center of Excellence in Plant Energy Biology, The University of Western Australia, Perth, Western Australia, Australia.
  9. Dulce B Vargas-Landin: ARC Center of Excellence in Plant Energy Biology, The University of Western Australia, Perth, Western Australia, Australia. ORCID
  10. Daniel Poppe: ARC Center of Excellence in Plant Energy Biology, The University of Western Australia, Perth, Western Australia, Australia.
  11. Jahnvi Pflueger: ARC Center of Excellence in Plant Energy Biology, The University of Western Australia, Perth, Western Australia, Australia.
  12. Ryan Lister: ARC Center of Excellence in Plant Energy Biology, The University of Western Australia, Perth, Western Australia, Australia. ORCID
  13. Owen J L Rackham: Program in Cardiovascular and Metabolic Disorders, Duke-National University of Singapore Medical School, Singapore, Singapore. owen.rackham@duke-nus.edu.sg. ORCID
  14. Enrico Petretto: Program in Cardiovascular and Metabolic Disorders, Duke-National University of Singapore Medical School, Singapore, Singapore. enrico.petretto@duke-nus.edu.sg. ORCID
  15. Jose M Polo: Department of Anatomy and Developmental Biology, Monash University, Clayton, Victoria, Australia. jose.polo@monash.edu. ORCID
PMID: 31768052 DOI: 10.1038/s41593-019-0539-4

Abstract

There is currently little information available about how individual cell types contribute to Alzheimer's disease. Here we applied single-nucleus RNA sequencing to entorhinal cortex samples from control and Alzheimer's disease brains (n = 6 per group), yielding a total of 13,214 high-quality nuclei. We detail cell-type-specific gene expression patterns, unveiling how transcriptional changes in specific cell subpopulations are associated with Alzheimer's disease. We report that the Alzheimer's disease risk gene APOE is specifically repressed in Alzheimer's disease oligodendrocyte progenitor cells and astrocyte subpopulations and upregulated in an Alzheimer's disease-specific microglial subopulation. Integrating transcription factor regulatory modules with Alzheimer's disease risk loci revealed drivers of cell-type-specific state transitions towards Alzheimer's disease. For example, transcription factor EB, a master regulator of lysosomal function, regulates multiple disease genes in a specific Alzheimer's disease astrocyte subpopulation. These results provide insights into the coordinated control of Alzheimer's disease risk genes and their cell-type-specific contribution to disease susceptibility. These results are available at http://adsn.ddnetbio.com.

References

  1. Huang, K.-L. et al. A common haplotype lowers PU.1 expression in myeloid cells and delays onset of Alzheimer’s disease. Nat. Neurosci. 20, 1052–1061 (2017). [PMID: 28628103]
  2. Lambert, J. C. et al. Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer’s disease. Nat. Genet. 45, 1452–1458 (2013). [PMID: 24162737]
  3. Zhang, B. et al. Integrated systems approach identifies genetic nodes and networks in late-onset Alzheimer’s disease. Cell 153, 707–720 (2013). [PMID: 23622250]
  4. Füger, P. et al. Microglia turnover with aging and in an Alzheimer’s model via long-term in vivo single-cell imaging. Nat. Neurosci. 20, 1371–1376 (2017). [PMID: 28846081]
  5. Whitehouse, P. J. et al. Alzheimer’s disease and senile dementia: loss of neurons in the basal forebrain. Science 215, 1237–1239 (1982). [PMID: 7058341]
  6. Zhong, S. et al. A single-cell RNA-seq survey of the developmental landscape of the human prefrontal cortex. Nature 555, 524–528 (2018). [PMID: 29539641]
  7. Mathys, H. Single-cell transcriptomic analysis of Alzheimer’s disease.Nature 570, 332–337 (2019). [PMID: 31042697]
  8. Wilhelmsson, U. et al. Injury leads to the appearance of cells with characteristics of both microglia and astrocytes in mouse and human brain. Cereb. Cortex 27, 3360–3377 (2017). [PMID: 28398520]
  9. Zeisel, A. et al. Brain structure. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science 347, 1138–1142 (2015). [PMID: 25700174]
  10. Darmanis, S. et al. A survey of human brain transcriptome diversity at the single cell level. Proc. Natl Acad. Sci. USA 112, 7285–7290 (2015). [PMID: 26060301]
  11. Miceli, F., et al. KCNQ3-Related Disorders. in Gene Reviews (ed. Adam, M. P.) https://www.ncbi.nlm.nih.gov/books/NBK201978/ (Univ. Washington, 2014).
  12. Ebermann, I. et al. GPR98 mutations cause Usher syndrome type 2 in males. J. Med. Genet. 46, 277–280 (2009). [PMID: 19357117]
  13. Grubman, A., Choo, X. Y., Chew, G., Ouyang, J. F. & Sun, G. Mouse and human microglial phenotypes in Alzheimer’s disease are controlled by amyloid plaque phagocytosis through Hif1α. Preprint at biorXiv https://www.biorxiv.org/content/10.1101/639054v1 (2019).
  14. Veereshwarayya, V., Kumar, P., Rosen, K. M., Mestril, R. & Querfurth, H. W. Differential effects of mitochondrial heat shock protein 60 and related molecular chaperones to prevent intracellular β-amyloid-induced inhibition of complex IV and limit apoptosis. J. Biol. Chem. 281, 29468–29478 (2006). [PMID: 16887805]
  15. De Strooper, B. & Karran, E. The cellular phase of Alzheimer’s disease. Cell 164, 603–615 (2016). [PMID: 26871627]
  16. Xie, H. et al. Rapid cell death is preceded by amyloid plaque-mediated oxidative stress. Proc. Natl Acad. Sci. USA 110, 7904–7909 (2013). [PMID: 23610434]
  17. Liddelow, S. A. et al. Neurotoxic reactive astrocytes are induced by activated microglia. Nature 541, 481–487 (2017). [PMID: 28099414]
  18. Krasemann, S. et al. The TREM2-APOE pathway drives the transcriptional phenotype of dysfunctional microglia in neurodegenerative diseases. Immunity 47, 566–581.e9 (2017). [PMID: 28930663]
  19. Keren-Shaul, H. et al. A unique microglia type associated with restricting development of Alzheimer’s disease. Cell 169, 1276–1290.e17 (2017). [PMID: 28602351]
  20. De Rossi, P. et al. Predominant expression of Alzheimer’s disease-associated BIN1 in mature oligodendrocytes and localization to white matter tracts. Mol. Neurodegener. 11, 59 (2016). [PMID: 27488240]
  21. Savvaki, M. et al. The expression of TAG-1 in glial cells is sufficient for the formation of the juxtaparanodal complex and the phenotypic rescue of tag-1 homozygous mutants in the CNS. J. Neurosci. 30, 13943–13954 (2010). [PMID: 20962216]
  22. Bartzokis, G. Age-related myelin breakdown: a developmental model of cognitive decline and Alzheimer’s disease. Neurobiol. Aging 25, 5–18 (2004). [PMID: 14675724]
  23. Behrendt, G. et al. Dynamic changes in myelin aberrations and oligodendrocyte generation in chronic amyloidosis in mice and men. Glia 61, 273–286 (2013). [PMID: 23090919]
  24. Lake, B. B. et al. Neuronal subtypes and diversity revealed by single-nucleus RNA sequencing of the human brain. Science 352, 1586–1590 (2016). [PMID: 27339989]
  25. John Lin, C.-C. et al. Identification of diverse astrocyte populations and their malignant analogs. Nat. Neurosci. 20, 396–405 (2017). [PMID: 28166219]
  26. Mi, S. et al. LINGO-1 negatively regulates myelination by oligodendrocytes. Nat. Neurosci. 8, 745–751 (2005). [PMID: 15895088]
  27. Santoro, M. et al. Expression profile of long non-coding RNAs in serum of patients with multiple sclerosis. J. Mol. Neurosci. 59, 18–23 (2016). [PMID: 27034068]
  28. Fallin, M. D. et al. Bipolar I disorder and schizophrenia: a 440–single-nucleotide polymorphism screen of 64 candidate genes among Ashkenazi Jewish case-parent trios. Am. J. Hum. Genet. 77, 918–936 (2005). [PMID: 16380905]
  29. Hall, C. N., Klein-Flügge, M. C., Howarth, C. & Attwell, D. Oxidative phosphorylation, not glycolysis, powers presynaptic and postsynaptic mechanisms underlying brain information processing. J. Neurosci. 32, 8940–8951 (2012). [PMID: 22745494]
  30. Romito-DiGiacomo, R. R., Menegay, H., Cicero, S. A. & Herrup, K. Effects of Alzheimer’s disease on different cortical layers: the role of intrinsic differences in Abeta susceptibility. J. Neurosci. 27, 8496–8504 (2007). [PMID: 17687027]
  31. Bis, J. C. et al. Whole exome sequencing study identifies novel rare and common Alzheimer’s-Associated variants involved in immune response and transcriptional regulation. Mol. Psychiatry https://doi.org/10.1038/s41380-018-0112-7 (2018).
  32. Sims, R. et al. Rare coding variants in PLCG2, ABI3, and TREM2 implicate microglial-mediated innate immunity in Alzheimer’s disease. Nat. Genet. 49, 1373–1384 (2017). [PMID: 28714976]
  33. Kajiho, H. et al. Characterization of RIN3 as a guanine nucleotide exchange factor for the Rab5 subfamily GTPase Rab31. J. Biol. Chem. 286, 24364–24373 (2011). [PMID: 21586568]
  34. He, Z.-Y., Li, L., Wang, Y.-Z., Liu, X. & Yuan, L.-Y. Associations between thromboxane A synthase 1 gene polymorphisms and the risk of ischemic stroke in a Chinese Han population.Neural Regen. Res. 13, 463 (2018). [PMID: 29623931]
  35. Diener, H. C. et al. European Stroke Prevention Study 2. Dipyridamole and acetylsalicylic acid in the secondary prevention of stroke. J. Neurol. Sci. 143, 1–13 (1996). [PMID: 8981292]
  36. Paris, D. et al. Inhibition of Alzheimer’s beta-amyloid induced vasoactivity and proinflammatory response in microglia by a cGMP-dependent mechanism. Exp. Neurol. 157, 211–221 (1999). [PMID: 10222124]
  37. Alzheimer’s Association. 2018 Alzheimer’s disease facts and figures. Alzheimers Dement. 14, 367–429 (2018).
  38. Raj, T. et al. Integrative transcriptome analyses of the aging brain implicate altered splicing in Alzheimer’s disease susceptibility. Nat. Genet. 50, 1584–1592 (2018). [PMID: 30297968]
  39. 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). [PMID: 19937809]
  40. Blalock, E. M., Buechel, H. M., Popovic, J., Geddes, J. W. & Landfield, P. W. Microarray analyses of laser-captured hippocampus reveal distinct gray and white matter signatures associated with incipient Alzheimer’s disease. J. Chem. Neuroanat. 42, 118–126 (2011). [PMID: 21756998]
  41. Lin, Y.-T. et al. APOE4 causes widespread molecular and cellular alterations associated with Alzheimer’s disease phenotypes in human iPSC-derived brain cell types. Neuron 98, 1294 (2018). [PMID: 29953873]
  42. Bartzokis, G. et al. Apolipoprotein E genotype and age-related myelin breakdown in healthy individuals: implications for cognitive decline and dementia. Arch. Gen. Psychiatry 63, 63–72 (2006). [PMID: 16389198]
  43. Chapuis, J. et al. Increased expression of BIN1 mediates Alzheimer genetic risk by modulating tau pathology. Mol. Psychiatry 18, 1225–1234 (2013). [PMID: 23399914]
  44. Yu, L. et al. Association of brain DNA methylation in SORL1, ABCA7, HLA-DRB5, SLC24A4, and BIN1 with pathological diagnosis of Alzheimer disease. JAMA Neurol. 72, 15–24 (2015). [PMID: 25365775]
  45. Lambert, J.-C. et al. Genome-wide haplotype association study identifies the FRMD4A gene as a risk locus for Alzheimer’s disease. Mol. Psychiatry 18, 461–470 (2013). [PMID: 22430674]
  46. Hokama, M. et al. Altered expression of diabetes-related genes in Alzheimer’s disease brains: the Hisayama study. Cereb. Cortex 24, 2476–2488 (2014). [PMID: 23595620]
  47. Shijo, M. et al. Association of adipocyte enhancer-binding protein 1 with Alzheimer’s disease pathology in human hippocampi. Brain Pathol. 28, 58–71 (2018). [PMID: 27997051]
  48. Maynard, M. A. et al. Human HIF-3α4 is a dominant-negative regulator of HIF-1 and is down-regulated in renal cell carcinoma. FASEB J. 19, 1396–1406 (2005). [PMID: 16126907]
  49. Martini-Stoica, H. et al. TFEB enhances astroglial uptake of extracellular tau species and reduces tau spreading. J. Exp. Med. 215, 2355–2377 (2018). [PMID: 30108137]
  50. de Toledo-Morrell, L., Goncharova, I., Dickerson, B., Wilson, R. S. & Bennett, D. A. From healthy aging to early Alzheimer’s disease: in vivo detection of entorhinal cortex atrophy. Ann. N. Y. Acad. Sci. 911, 240–253 (2000). [PMID: 10911878]
  51. McKenzie, A. T. et al. Brain cell type specific gene expression and co-expression network architectures. Sci. Rep. 8, 8868 (2018). [PMID: 29892006]
  52. Habib, N. et al. Massively parallel single-nucleus RNA-seq with DroNc-seq. Nat. Methods 14, 955–958 (2017). [PMID: 28846088]
  53. Tirosh, I. et al. Single-cell RNA-seq supports a developmental hierarchy in human oligodendroglioma. Nature 539, 309–313 (2016). [PMID: 27806376]
  54. Zhu, Y., Wang, L., Yin, Y. & Yang, E. Systematic analysis of gene expression patterns associated with postmortem interval in human tissues. Sci. Rep. 7, 5435 (2017). [PMID: 28710439]
  55. Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2018). [PMID: 29608179]
  56. Lummertz da Rocha, E. et al. Reconstruction of complex single-cell trajectories using CellRouter. Nat. Commun. 9, 892 (2018). [PMID: 29497036]
  57. van der Maaten, L. & Hinton, G. Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008).
  58. Lambert, S. A. et al. The human transcription factors. Cell 175, 598–599 (2018). [PMID: 30290144]
  59. Kolde, R. Pheatmap: pretty heatmaps. R package version 61, 926 (2012).
  60. Wickham, H. ggplot2: Elegant Graphics for Data Analysis. (Springer: 2016)..
  61. Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003). [PMID: 14597658]
  62. Buniello, A. et al. The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019. Nucleic Acids Res. 47, D1005–D1012 (2019). [PMID: 30445434]
  63. Skene, N. G. & Grant, S. G. N. Identification of vulnerable cell types in major brain disorders using single cell transcriptomes and expression weighted cell type enrichment. Front. Neurosci. 10, 16 (2016). [PMID: 26858593]
  64. McKenzie, A. T., Katsyv, I., Song, W.-M., Wang, M. & Zhang, B. DGCA: a comprehensive R package for differential gene correlation analysis. BMC Syst. Biol. 10, 106 (2016). [PMID: 27846853]
  65. Falcon, S. & Gentleman, R. Using GOstats to test gene lists for GO term association. Bioinformatics 23, 257–258 (2007). [PMID: 17098774]
  66. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B 57, 289–300 (1995).
  67. Li, H. et al. Reference component analysis of single-cell transcriptomes elucidates cellular heterogeneity in human colorectal tumors. Nat. Genet. 49, 708–718 (2017). [PMID: 28319088]
  68. Yu, G., Wang, L.-G., Han, Y. & He, Q.-Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16, 284–287 (2012). [PMID: 22455463]
  69. Conway, J. R., Lex, A. & Gehlenborg, N. UpSetR: an R package for the visualization of intersecting sets and their properties. Bioinformatics 33, 2938–2940 (2017). [PMID: 28645171]

Grants

  1. MC_U120097112/Medical Research Council

MeSH Term

Alzheimer Disease
Apolipoproteins E
Astrocytes
Atlases as Topic
Case-Control Studies
Down-Regulation
Entorhinal Cortex
Female
Gene Expression Regulation
Genetic Predisposition to Disease
Humans
Male
Microglia
Oligodendrocyte Precursor Cells
Sequence Analysis, RNA
Up-Regulation

Chemicals

Apolipoproteins E
Journal Article Research Support, Non-U.S. Gov't

Links to CNCB-NGDC Resources

GEN: GEND000112 (A single-cell atlas of the human cortex reveals drivers of transcriptional changes in Alzheimer’s disease in specific cell subpopulations)

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