Epigenomic analysis of Parkinson's disease neurons identifies Tet2 loss as neuroprotective.
Animals
Cell Line, Tumor
DNA Methylation
DNA-Binding Proteins
/ genetics
Dioxygenases
Epigenesis, Genetic
Epigenomics
Female
Gene Expression Regulation
Humans
Male
Mice, Inbred C57BL
Mice, Knockout
Neurons
/ metabolism
Neuroprotection
Parkinson Disease
/ genetics
Prefrontal Cortex
/ metabolism
Proto-Oncogene Proteins
/ genetics
Journal
Nature neuroscience
ISSN: 1546-1726
Titre abrégé: Nat Neurosci
Pays: United States
ID NLM: 9809671
Informations de publication
Date de publication:
10 2020
10 2020
Historique:
received:
16
09
2019
accepted:
07
07
2020
pubmed:
19
8
2020
medline:
15
12
2020
entrez:
19
8
2020
Statut:
ppublish
Résumé
Parkinson's disease (PD) pathogenesis may involve the epigenetic control of enhancers that modify neuronal functions. Here, we comprehensively examine DNA methylation at enhancers, genome-wide, in neurons of patients with PD and of control individuals. We find a widespread increase in cytosine modifications at enhancers in PD neurons, which is partly explained by elevated hydroxymethylation levels. In particular, patients with PD exhibit an epigenetic and transcriptional upregulation of TET2, a master-regulator of cytosine modification status. TET2 depletion in a neuronal cell model results in cytosine modification changes that are reciprocal to those observed in PD neurons. Moreover, Tet2 inactivation in mice fully prevents nigral dopaminergic neuronal loss induced by previous inflammation. Tet2 loss also attenuates transcriptional immune responses to an inflammatory trigger. Thus, widespread epigenetic dysregulation of enhancers in PD neurons may, in part, be mediated by increased TET2 expression. Decreased Tet2 activity is neuroprotective, in vivo, and may be a new therapeutic target for PD.
Identifiants
pubmed: 32807949
doi: 10.1038/s41593-020-0690-y
pii: 10.1038/s41593-020-0690-y
doi:
Substances chimiques
DNA-Binding Proteins
0
Proto-Oncogene Proteins
0
Dioxygenases
EC 1.13.11.-
TET2 protein, human
EC 1.13.11.-
Tet2 protein, mouse
EC 1.13.11.-
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
1203-1214Subventions
Organisme : NINDS NIH HHS
ID : R01 NS114409
Pays : United States
Commentaires et corrections
Type : CommentIn
Références
GBD 2016 Parkinson’s Disease Collaborators. Global, regional, and national burden of Parkinson’s disease, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet Neurol. 17, 939–953 (2018).
doi: 10.1016/S1474-4422(18)30295-3
Wirdefeldt, K., Gatz, M., Reynolds, C. A., Prescott, C. A. & Pedersen, N. L. Heritability of Parkinson disease in Swedish twins: a longitudinal study. Neurobiol. Aging 32, 1923.e1–1923.e8 (2011).
doi: 10.1016/j.neurobiolaging.2011.02.017
Gomez-Esteban, J. C. et al. Factors influencing the symmetry of Parkinson’s disease symptoms. Clin. Neurol. Neurosurg. 112, 302–305 (2010).
pubmed: 20083340
doi: 10.1016/j.clineuro.2009.12.017
pmcid: 20083340
Baldereschi, M. et al. Parkinson’s disease and parkinsonism in a longitudinal study: two-fold higher incidence in men. ILSA Working Group. Italian Longitudinal Study on Aging. Neurology 55, 1358–1363 (2000).
pubmed: 11087781
doi: 10.1212/WNL.55.9.1358
pmcid: 11087781
Labbe, C., Lorenzo-Betancor, O. & Ross, O. A. Epigenetic regulation in Parkinson’s disease. Acta Neuropathol. 132, 515–530 (2016).
pubmed: 27358065
pmcid: 5026906
doi: 10.1007/s00401-016-1590-9
Jakubowski, J. L. & Labrie, V. Epigenetic biomarkers for Parkinson’s disease: from diagnostics to therapeutics. J. Parkinsons Dis. 7, 1–12 (2017).
pubmed: 27792016
pmcid: 5302044
doi: 10.3233/JPD-160914
Masliah, E., Dumaop, W., Galasko, D. & Desplats, P. Distinctive patterns of DNA methylation associated with Parkinson disease: identification of concordant epigenetic changes in brain and peripheral blood leukocytes. Epigenetics 8, 1030–1038 (2013).
pubmed: 23907097
pmcid: 3891683
doi: 10.4161/epi.25865
Kaut, O., Schmitt, I. & Wullner, U. Genome-scale methylation analysis of Parkinson’s disease patients’ brains reveals DNA hypomethylation and increased mRNA expression of cytochrome P450 2E1. Neurogenetics 13, 87–91 (2012).
pubmed: 22238121
doi: 10.1007/s10048-011-0308-3
pmcid: 22238121
Young, J. I. et al. Genome-wide brain DNA methylation analysis suggests epigenetic reprogramming in Parkinson disease. Neurol. Genet. 5, e342 (2019).
pubmed: 31403079
pmcid: 6659138
doi: 10.1212/NXG.0000000000000342
Braak, H. et al. Staging of brain pathology related to sporadic Parkinson’s disease. Neurobiol. Aging 24, 197–211 (2003).
doi: 10.1016/S0197-4580(02)00065-9
Guo, J. U. et al. Neuronal activity modifies the DNA methylation landscape in the adult brain. Nat. Neurosci. 14, 1345–1351 (2011).
pubmed: 21874013
pmcid: 3183401
doi: 10.1038/nn.2900
Feng, J. et al. Dnmt1 and Dnmt3a maintain DNA methylation and regulate synaptic function in adult forebrain neurons. Nat. Neurosci. 13, 423–430 (2010).
pubmed: 20228804
pmcid: 3060772
doi: 10.1038/nn.2514
Li, X. et al. Ten-eleven translocation 2 interacts with forkhead box O3 and regulates adult neurogenesis. Nat. Commun. 8, 15903 (2017).
pubmed: 28660881
pmcid: 5493768
doi: 10.1038/ncomms15903
Hon, G. C. et al. 5mC oxidation by Tet2 modulates enhancer activity and timing of transcriptome reprogramming during differentiation. Mol. Cell 56, 286–297 (2014).
pubmed: 25263596
pmcid: 25263596
doi: 10.1016/j.molcel.2014.08.026
Lister, R. et al. Global epigenomic reconfiguration during mammalian brain development. Science 341, 1237905 (2013).
pubmed: 23828890
pmcid: 3785061
doi: 10.1126/science.1237905
Price, A. J. et al. Divergent neuronal DNA methylation patterns across human cortical development reveal critical periods and a unique role of CpH methylation. Genome Biol. 20, 196 (2019).
pubmed: 31554518
pmcid: 6761727
doi: 10.1186/s13059-019-1805-1
Roadmap Epigenomics Consortium et al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).
Dao, L. T. M. et al. Genome-wide characterization of mammalian promoters with distal enhancer functions. Nat. Genet. 49, 1073–1081 (2017).
pubmed: 28581502
doi: 10.1038/ng.3884
pmcid: 28581502
Dong, X. et al. Enhancers active in dopamine neurons are a primary link between genetic variation and neuropsychiatric disease. Nat. Neurosci. 21, 1482–1492 (2018).
pubmed: 30224808
pmcid: 6334654
doi: 10.1038/s41593-018-0223-0
Soldner, F. et al. Parkinson-associated risk variant in distal enhancer of ɑ-synuclein modulates target gene expression. Nature 533, 95–99 (2016).
pubmed: 27096366
pmcid: 27096366
doi: 10.1038/nature17939
Fernandez-Santiago, R. et al. Aberrant epigenome in iPSC-derived dopaminergic neurons from Parkinson’s disease patients. EMBO Mol. Med. 7, 1529–1546 (2015).
pubmed: 26516212
pmcid: 4693505
doi: 10.15252/emmm.201505439
Li, P. et al. Hemispheric asymmetry in the human brain and in Parkinson’s disease is linked to divergent epigenetic patterns in neurons. Genome Biol. 21, 61 (2020).
pubmed: 32151270
pmcid: 7063821
doi: 10.1186/s13059-020-01960-1
Narayanan, N. S., Rodnitzky, R. L. & Uc, E. Y. Prefrontal dopamine signaling and cognitive symptoms of Parkinson’s disease. Rev. Neurosci. 24, 267–278 (2013).
pubmed: 23729617
doi: 10.1515/revneuro-2013-0004
pmcid: 23729617
Luo, C. et al. Single-cell methylomes identify neuronal subtypes and regulatory elements in mammalian cortex. Science 357, 600–604 (2017).
pubmed: 28798132
pmcid: 5570439
doi: 10.1126/science.aan3351
Farley, J. E. et al. Transcription factor Pebbled/RREB1 regulates injury-induced axon degeneration. Proc. Natl Acad. Sci. USA 115, 1358–1363 (2018).
pubmed: 29295933
doi: 10.1073/pnas.1715837115
pmcid: 29295933
Schmitt, A. D. et al. A compendium of chromatin contact maps reveals spatially active regions in the human genome. Cell Rep. 17, 2042–2059 (2016).
pubmed: 27851967
pmcid: 5478386
doi: 10.1016/j.celrep.2016.10.061
Lio, C. J. & Rao, A. TET enzymes and 5hmC in adaptive and innate immune systems. Front. Immunol. 10, 210 (2019).
pubmed: 30809228
pmcid: 6379312
doi: 10.3389/fimmu.2019.00210
Nalls, M. A. et al. Identification of novel risk loci, causal insights, and heritable risk for Parkinson’s disease: a meta-analysis of genome-wide association studies. Lancet Neurol. 18, 1091–1102 (2019).
doi: 10.1016/S1474-4422(19)30320-5
pubmed: 31701892
Figge, D. A., Eskow Jaunarajs, K. L. & Standaert, D. G. Dynamic DNA methylation regulates levodopa-induced dyskinesia. J. Neurosci. 36, 6514–6524 (2016).
pubmed: 27307239
pmcid: 5015786
doi: 10.1523/JNEUROSCI.0683-16.2016
Risso, D., Ngai, J., Speed, T. P. & Dudoit, S. Normalization of RNA-seq data using factor analysis of control genes or samples. Nat. Biotechnol. 32, 896–902 (2014).
pubmed: 4404308
pmcid: 4404308
doi: 10.1038/nbt.2931
Donega, V. et al. Transcriptome and proteome profiling of neural stem cells from the human subventricular zone in Parkinson’s disease. Acta Neuropathol. Commun. 7, 84 (2019).
pubmed: 31159890
doi: 10.1186/s40478-019-0736-0
pmcid: 31159890
Johnson, M. E., Stecher, B., Labrie, V., Brundin, L. & Brundin, P. Triggers, facilitators, and aggravators: redefining Parkinson’s disease pathogenesis. Trends Neurosci. 42, 4–13 (2019).
pubmed: 30342839
doi: 10.1016/j.tins.2018.09.007
pmcid: 30342839
Qin, L. et al. Systemic LPS causes chronic neuroinflammation and progressive neurodegeneration. Glia 55, 453–462 (2007).
pubmed: 17203472
pmcid: 2871685
doi: 10.1002/glia.20467
Brichta, L. et al. Identification of neurodegenerative factors using translatome-regulatory network analysis. Nat. Neurosci. 18, 1325–1333 (2015).
pubmed: 26214373
pmcid: 4763340
doi: 10.1038/nn.4070
Sodersten, E. et al. A comprehensive map coupling histone modifications with gene regulation in adult dopaminergic and serotonergic neurons. Nat. Commun. 9, 1226 (2018).
pubmed: 29581424
pmcid: 5964330
doi: 10.1038/s41467-018-03538-9
Wu, T. T. et al. TET2-mediated Cdkn2A DNA hydroxymethylation in midbrain dopaminergic neuron injury of Parkinson’s disease. Hum. Mol. Genet. 29, 1239–1252 (2020).
pubmed: 32037456
doi: 10.1093/hmg/ddaa022
pmcid: 32037456
Kriaucionis, S. & Heintz, N. The nuclear DNA base 5-hydroxymethylcytosine is present in Purkinje neurons and the brain. Science 324, 929–930 (2009).
pubmed: 3263819
pmcid: 3263819
doi: 10.1126/science.1169786
Kozlenkov, A. et al. A unique role for DNA (hydroxy)methylation in epigenetic regulation of human inhibitory neurons. Sci. Adv. 4, eaau6190 (2018).
pubmed: 30263963
pmcid: 6157969
doi: 10.1126/sciadv.aau6190
Szulwach, K. E. et al. 5-hmC-mediated epigenetic dynamics during postnatal neurodevelopment and aging. Nat. Neurosci. 14, 1607–1616 (2011).
pubmed: 22037496
pmcid: 3292193
doi: 10.1038/nn.2959
Herrup, K. & Yang, Y. Cell cycle regulation in the postmitotic neuron: oxymoron or new biology? Nat. Rev. Neurosci. 8, 368–378 (2007).
pubmed: 17453017
doi: 10.1038/nrn2124
pmcid: 17453017
Ellison, E. M., Abner, E. L. & Lovell, M. A. Multiregional analysis of global 5-methylcytosine and 5-hydroxymethylcytosine throughout the progression of Alzheimer’s disease. J. Neurochem. 140, 383–394 (2017).
pubmed: 27889911
pmcid: 5250541
doi: 10.1111/jnc.13912
Stoger, R., Scaife, P. J., Shephard, F. & Chakrabarti, L. Elevated 5hmC levels characterize DNA of the cerebellum in Parkinson’s disease. NPJ Parkinsons Dis. 3, 6 (2017).
pubmed: 28649606
pmcid: 5460211
doi: 10.1038/s41531-017-0007-3
Carrillo-Jimenez, A. et al. TET2 regulates the neuroinflammatory response in microglia. Cell Rep. 29, e698 (2019).
doi: 10.1016/j.celrep.2019.09.013
Jain, N. et al. Global modulation in DNA epigenetics during pro-inflammatory macrophage activation. Epigenetics 14, 1183–1193 (2019).
pubmed: 31262215
pmcid: 6791700
doi: 10.1080/15592294.2019.1638700
Zhang, Q. et al. Tet2 is required to resolve inflammation by recruiting Hdac2 to specifically repress IL-6. Nature 525, 389–393 (2015).
pubmed: 26287468
pmcid: 4697747
doi: 10.1038/nature15252
Pronier, E. et al. Inhibition of TET2-mediated conversion of 5-methylcytosine to 5-hydroxymethylcytosine disturbs erythroid and granulomonocytic differentiation of human hematopoietic progenitors. Blood 118, 2551–2555 (2011).
pubmed: 21734233
pmcid: 3292425
doi: 10.1182/blood-2010-12-324707
Ichiyama, K. et al. The methylcytosine dioxygenase Tet2 promotes DNA demethylation and activation of cytokine gene expression in T cells. Immunity 42, 613–626 (2015).
pubmed: 25862091
pmcid: 4956728
doi: 10.1016/j.immuni.2015.03.005
Izzo, F. et al. DNA methylation disruption reshapes the hematopoietic differentiation landscape. Nat. Genet. 52, 378–387 (2020).
pubmed: 32203468
pmcid: 7216752
doi: 10.1038/s41588-020-0595-4
Gagne, J. J. & Power, M. C. Anti-inflammatory drugs and risk of Parkinson disease: a meta-analysis. Neurology 74, 995–1002 (2010).
pubmed: 20308684
pmcid: 2848103
doi: 10.1212/WNL.0b013e3181d5a4a3
Peter, I. et al. Anti-tumor necrosis factor therapy and incidence of Parkinson disease among patients with inflammatory bowel disease. JAMA Neurol. 75, 939–946 (2018).
pubmed: 29710331
pmcid: 6142934
doi: 10.1001/jamaneurol.2018.0605
Caligiore, D. et al. Parkinson’s disease as a system-level disorder. NPJ Parkinsons Dis. 2, 16025 (2016).
pubmed: 28725705
pmcid: 5516580
doi: 10.1038/npjparkd.2016.25
Weintraub, D. et al. Neurodegeneration across stages of cognitive decline in Parkinson disease. Arch. Neurol. 68, 1562–1568 (2011).
pubmed: 22159053
pmcid: 3290902
doi: 10.1001/archneurol.2011.725
Kordower, J. H. et al. Disease duration and the integrity of the nigrostriatal system in Parkinson’s disease. Brain 136, 2419–2431 (2013).
pubmed: 23884810
pmcid: 3722357
doi: 10.1093/brain/awt192
Li, P. et al. Epigenetic dysregulation of enhancers in neurons is associated with Alzheimer’s disease pathology and cognitive symptoms. Nat. Commun. 10, 2246 (2019).
pubmed: 31113950
pmcid: 6529540
doi: 10.1038/s41467-019-10101-7
Pai, S. et al. Differential methylation of enhancer at IGF2 is associated with abnormal dopamine synthesis in major psychosis. Nat. Commun. 10, 2046 (2019).
pubmed: 31053723
pmcid: 6499808
doi: 10.1038/s41467-019-09786-7
He, Y. & Wang, T. EpiCompare: an online tool to define and explore genomic regions with tissue or cell type-specific epigenomic features. Bioinformatics 33, 3268–3275 (2017).
pubmed: 28605501
pmcid: 5860030
doi: 10.1093/bioinformatics/btx371
Ernst, J. & Kellis, M. Chromatin-state discovery and genome annotation with ChromHMM. Nat. Protoc. 12, 2478–2492 (2017).
pubmed: 29120462
pmcid: 5945550
doi: 10.1038/nprot.2017.124
Labrie, V. et al. Lactase nonpersistence is directed by DNA-variation-dependent epigenetic aging. Nat. Struct. Mol. Biol. 23, 566–573 (2016).
pubmed: 27159559
pmcid: 4899171
doi: 10.1038/nsmb.3227
Diep, D. et al. Library-free methylation sequencing with bisulfite padlock probes. Nat. Methods 9, 270–272 (2012).
pubmed: 22306810
pmcid: 3461232
doi: 10.1038/nmeth.1871
Krueger, F. & Andrews, S. R. Bismark: a flexible aligner and methylation caller for Bisulfite-seq applications. Bioinformatics 27, 1571–1572 (2011).
pubmed: 21493656
pmcid: 3102221
doi: 10.1093/bioinformatics/btr167
The 1000 Genomes Project Consortium et al. A global reference for human genetic variation. Nature 526, 68–74 (2015).
pmcid: 4750478
doi: 10.1038/nature15393
Mo, A. et al. Epigenomic signatures of neuronal diversity in the mammalian brain. Neuron 86, 1369–1384 (2015).
pubmed: 26087164
pmcid: 4499463
doi: 10.1016/j.neuron.2015.05.018
Chen, B., Khodadoust, M. S., Liu, C. L., Newman, A. M. & Alizadeh, A. A. Profiling tumor infiltrating immune cells with CIBERSORT. Methods Mol. Biol. 1711, 243–259 (2018).
pubmed: 29344893
pmcid: 29344893
doi: 10.1007/978-1-4939-7493-1_12
Akalin, A. et al. methylKit: a comprehensive R package for the analysis of genome-wide DNA methylation profiles. Genome Biol. 13, R87 (2012).
pubmed: 3491415
pmcid: 3491415
doi: 10.1186/gb-2012-13-10-r87
Kwon, A. T., Arenillas, D. J., Worsley Hunt, R. & Wasserman, W. W. oPOSSUM-3: advanced analysis of regulatory motif over-representation across genes or ChIP-seq datasets. G3 (Bethesda) 2, 987–1002 (2012).
doi: 10.1534/g3.112.003202
Rao, S. S. et al. A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping. Cell 159, 1665–1680 (2014).
pubmed: 25497547
pmcid: 25497547
doi: 10.1016/j.cell.2014.11.021
Mishra, A. & Hawkins, R. D. Three-dimensional genome architecture and emerging technologies: looping in disease. Genome Med. 9, 87 (2017).
pubmed: 28964259
pmcid: 5623062
doi: 10.1186/s13073-017-0477-2
Wingett, S. et al. HiCUP: pipeline for mapping and processing Hi-C data. F1000Res. 4, 1310 (2015).
pubmed: 26835000
pmcid: 4706059
doi: 10.12688/f1000research.7334.1
Forcato, M. et al. Comparison of computational methods for Hi-C data analysis. Nat. Methods 14, 679–685 (2017).
pubmed: 28604721
pmcid: 5493985
doi: 10.1038/nmeth.4325
Levy-Leduc, C., Delattre, M., Mary-Huard, T. & Robin, S. Two-dimensional segmentation for analyzing Hi-C data. Bioinformatics 30, i386–i392 (2014).
pubmed: 25161224
pmcid: 4147896
doi: 10.1093/bioinformatics/btu443
McLean, C. Y. et al. GREAT improves functional interpretation of cis-regulatory regions. Nat. Biotechnol. 28, 495–501 (2010).
pubmed: 20436461
pmcid: 4840234
doi: 10.1038/nbt.1630
Reimand, J., Kull, M., Peterson, H., Hansen, J. & Vilo, J. g:Profiler—a web-based toolset for functional profiling of gene lists from large-scale experiments. Nucleic Acids Res. 35, W193–W200 (2007).
pubmed: 17478515
pmcid: 1933153
doi: 10.1093/nar/gkm226
Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).
pubmed: 16199517
doi: 10.1073/pnas.0506580102
pmcid: 16199517
Reimand, J. et al. Pathway enrichment analysis and visualization of omics data using g:Profiler, GSEA, Cytoscape and EnrichmentMap. Nat. Protoc. 14, 482–517 (2019).
pubmed: 30664679
pmcid: 6607905
doi: 10.1038/s41596-018-0103-9
Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).
pubmed: 3322381
pmcid: 3322381
doi: 10.1038/nmeth.1923
Ramirez, F., Dundar, F., Diehl, S., Gruning, B. A. & Manke, T. deepTools: a flexible platform for exploring deep-sequencing data. Nucleic Acids Res. 42, W187–W191 (2014).
pubmed: 24799436
pmcid: 4086134
doi: 10.1093/nar/gku365
Diaz, A., Park, K., Lim, D. A. & Song, J. S. Normalization, bias correction, and peak calling for ChIP-seq. Stat. Appl. Genet. Mol. Biol. 11, 9 (2012).
doi: 10.1515/1544-6115.1750
Zhang, Y. et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 9, R137 (2008).
pubmed: 18798982
pmcid: 2592715
doi: 10.1186/gb-2008-9-9-r137
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
doi: 10.1093/bioinformatics/bts635
Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).
doi: 10.1093/bioinformatics/btp616
pubmed: 19910308
Yu, Q. & He, Z. Comprehensive investigation of temporal and autism-associated cell type composition-dependent and independent gene expression changes in human brains. Sci. Rep. 7, 4121 (2017).
pubmed: 28646201
pmcid: 5482876
doi: 10.1038/s41598-017-04356-7
Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).
pubmed: 4402510
pmcid: 4402510
doi: 10.1093/nar/gkv007
Konnova, E. A. & Swanberg, M. Animal models of Parkinson’s disease. in Parkinson’s Disease: Pathogenesis and Clinical Aspects (eds Stoker, T. B. & Greenland, J. C.) (Codon Publications, 2018).
Caiazzo, M. et al. Direct generation of functional dopaminergic neurons from mouse and human fibroblasts. Nature 476, 224–227 (2011).
pubmed: 21725324
doi: 10.1038/nature10284
pmcid: 21725324
Cheng, L. et al. Gene dysregulation is restored in the Parkinson’s disease MPTP neurotoxic mice model upon treatment of the therapeutic drug Cu(II)(atsm). Sci. Rep. 6, 22398 (2016).
pubmed: 26928495
pmcid: 4772163
doi: 10.1038/srep22398
Chen, X. et al. Parkinson’s disease-linked D620N VPS35 knockin mice manifest tau neuropathology and dopaminergic neurodegeneration. Proc. Natl Acad. Sci. USA 116, 5765–5774 (2019).
pubmed: 30842285
doi: 10.1073/pnas.1814909116
pmcid: 30842285
Maco, B. et al. Semiautomated correlative 3D electron microscopy of in vivo-imaged axons and dendrites. Nat. Protoc. 9, 1354–1366 (2014).
pubmed: 24833174
doi: 10.1038/nprot.2014.101
pmcid: 24833174
McQuin, C. et al. CellProfiler 3.0: next-generation image processing for biology. PLoS Biol. 16, e2005970 (2018).
pubmed: 29969450
pmcid: 6029841
doi: 10.1371/journal.pbio.2005970