Single-cell sequencing of human midbrain reveals glial activation and a Parkinson-specific neuronal state
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Abstract
Idiopathic Parkinson’s disease is characterized by a progressive loss of dopaminergic neurons, but the exact disease aetiology remains largely unknown. To date, Parkinson’s disease research has mainly focused on nigral dopaminergic neurons, although recent studies suggest disease-related changes also in non-neuronal cells and in midbrain regions beyond the substantia nigra. While there is some evidence for glial involvement in Parkinson’s disease, the molecular mechanisms remain poorly understood. The aim of this study was to characterize the contribution of all cell types of the midbrain to Parkinson’s disease pathology by single-nuclei RNA sequencing and to assess the cell type-specific risk for Parkinson’s disease using the latest genome-wide association study. We profiled >41 000 single-nuclei transcriptomes of post-mortem midbrain from six idiopathic Parkinson’s disease patients and five age-/sex-matched controls. To validate our findings in a spatial context, we utilized immunolabelling of the same tissues. Moreover, we analysed Parkinson’s disease-associated risk enrichment in genes with cell type-specific expression patterns. We discovered a neuronal cell cluster characterized by CADPS2 overexpression and low TH levels, which was exclusively present in idiopathic Parkinson’s disease midbrains. Validation analyses in laser-microdissected neurons suggest that this cluster represents dysfunctional dopaminergic neurons. With regard to glial cells, we observed an increase in nigral microglia in Parkinson’s disease patients. Moreover, nigral idiopathic Parkinson’s disease microglia were more amoeboid, indicating an activated state. We also discovered a reduction in idiopathic Parkinson’s disease oligodendrocyte numbers with the remaining cells being characterized by a stress-induced upregulation of S100B. Parkinson’s disease risk variants were associated with glia- and neuron-specific gene expression patterns in idiopathic Parkinson’s disease cases. Furthermore, astrocytes and microglia presented idiopathic Parkinson’s disease-specific cell proliferation and dysregulation of genes related to unfolded protein response and cytokine signalling. While reactive patient astrocytes showed CD44 overexpression, idiopathic Parkinson’s disease microglia revealed a pro-inflammatory trajectory characterized by elevated levels of IL1B, GPNMB and HSP90AA1. Taken together, we generated the first single-nuclei RNA sequencing dataset from the idiopathic Parkinson’s disease midbrain, which highlights a disease-specific neuronal cell cluster as well as ‘pan-glial’ activation as a central mechanism in the pathology of the movement disorder. This finding warrants further research into inflammatory signalling and immunomodulatory treatments in Parkinson’s disease.
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Astrocytes within the substantia nigra pars compacta can become reactive in Parkinson's disease.
“can also become reactive in PD”
From Mitochondrial DNA Damage in Substantia Nigra Pars Compacta Astrocytes Exacerbates Dopaminergic Neuron Loss in a 6-Hydroxydopamine Mouse Model of Parkinsonism (2026) · cites this paper as Smajic et al. 2022
The Results report that idiopathic Parkinson’s disease astrocytes were enriched at the reactive end of an astrogliosis trajectory, and the Discussion describes reactive astrogliosis in patient cells. The evidence concerns midbrain astrocytes; the passages do not localize that finding specifically to the substantia nigra pars compacta. (LLM, conf=0.84)
Experimental — accuracy not yet measuredPostmortem analysis of ventral midbrains from PD patients revealed a significantly increased number of microglia with an ameboid shape, suggestive of an activated state.
“Postmortem analysis of ventral midbrains from PD patients revealed a significantly increased number of microglia with an ameboid shape, suggestive of an activated state.86”
From Microbiota–gut–brain axis and its therapeutic applications in neurodegenerative diseases (2024) · cites this paper as [86]
Studies have identified a significant association between PD risk variants and microglia.
“Importantly, studies have identified a significant association between PD risk variants and microglia.86,87”
From Microbiota–gut–brain axis and its therapeutic applications in neurodegenerative diseases (2024) · cites this paper as [86]
Neuroinflammation and microglial activation are consistent features across neurodegenerative diseases, including AD, PD, HD, FTD, and ALS.
“Indeed, neuroinflammation and microglial activation are consistent features across neurodegenerative diseases, including AD,126–128 PD,86,129 HD,130,131 FTD,91,132 and ALS.89,133–135”
From Microbiota–gut–brain axis and its therapeutic applications in neurodegenerative diseases (2024) · cites this paper as [86]
In postmortem midbrain samples, PD patients have an increased number of reactive microglia and a reduction in oligodendrocyte numbers compared to controls.
“the researchers observed an increased number in reactive microglia and a reduction in oligodendrocyte numbers in PD patients.”
From Microglia in neurodegenerative diseases: mechanism and potential therapeutic targets (2023) · cites this paper as [215]
Through snRNA-seq of frozen midbrain tissue from PD patients and controls, seven distinct microglia subpopulations were identified based on expression of specific marker genes.
“Through snRNA-seq analysis of frozen midbrain tissue from PD patients and controls, seven distinct microglia subpopulations were identified based on the expression of specific marker genes.”
From Microglia in neurodegenerative diseases: mechanism and potential therapeutic targets (2023) · cites this paper as [215]
Microglia in the SN of PD patients show a higher proportion of amoeboid morphology, indicative of a reactive state.
“Microglia in the SN of PD patients showed a higher proportion of amoeboid morphology, which is indicative of a reactive state.”
From Microglia in neurodegenerative diseases: mechanism and potential therapeutic targets (2023) · cites this paper as [215]
Reactive microglia states in PD are enriched in cytokine secretion and the stress response to unfolded protein pathways.
“The study also revealed that the reactive microglia states are enriched in cytokine secretion and the stress response to unfolded protein pathways.”
From Microglia in neurodegenerative diseases: mechanism and potential therapeutic targets (2023) · cites this paper as [215]
The microglial activation trajectory extends from P2RY12high cells to two activation branches, one with highly expressed HSP90AA1 or IL1B and the other containing GPNMBhigh cells.
“The microglial activation trajectory extends from P2RY12high cells to two activation branches, one with highly expressed HSP90AA1 or IL1B241 cells and the other containing GPNMBhigh cells.”
From Microglia in neurodegenerative diseases: mechanism and potential therapeutic targets (2023) · cites this paper as [215]
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Cites
Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016 · see citing works
Fast unfolding of communities in large networks
Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008) · Journal of Statistical Mechanics: Theory and Experiment · doi:10.1088/1742-5468/2008/10/p10008 · Cited by 21,424 · see citing works
Modern Applied Statistics with S
Venables, W., & Ripley, B. (2010) · Statisctics and computing/Statistics and computing · doi:10.1007/978-0-387-21706-2 · Cited by 16,188 · see citing works
Comprehensive Integration of Single-Cell Data
Stuart, T., Butler, A., Hoffman, P., Hafemeister, C., Papalexi, E., Mauck, W. M., Hao, Y., Stoeckius, M., Smibert, P., & Satija, R. (2019) · Cell · doi:10.1016/j.cell.2019.05.031 · Cited by 15,453 · see citing works
Robust Locally Weighted Regression and Smoothing Scatterplots
Cleveland, W. S. (1979) · Journal of the American Statistical Association · doi:10.1080/01621459.1979.10481038 · Cited by 11,594 · see citing works
Fast, sensitive and accurate integration of single-cell data with Harmony
Korsunsky, I., Millard, N., Fan, J., Slowikowski, K., Zhang, F., Wei, K., Baglaenko, Y., Brenner, M., Loh, P.-R., & Raychaudhuri, S. (2019) · Nature Methods · doi:10.1038/s41592-019-0619-0 · Cited by 10,488 · see citing works
Spillantini, M. G., Schmidt, M. L., Lee, V. M.-Y., Trojanowski, J. Q., Jakes, R., & Goedert, M. (1997) · Nature · doi:10.1038/42166 · Cited by 8,382 · see citing works
Neurotoxic reactive astrocytes are induced by activated microglia
Liddelow, S. A., Guttenplan, K. A., Clarke, L. E., Bennett, F. C., Bohlen, C. J., Schirmer, L., Bennett, M. L., Münch, A. E., Chung, W.-S., Peterson, T. C., et al. (2017) · Nature · doi:10.1038/nature21029 · Cited by 7,971 · see citing works
Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool
Chen, E. Y., Tan, C. M., Kou, Y., Duan, Q., Wang, Z., Meirelles, G. V., Clark, N. R., & Ma’ayan, A. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-128 · Cited by 7,814 · see citing works
Exploring Network Structure, Dynamics, and Function Using NetworkX
Hagberg, A. A., Schult, D. A., & Swart, P. J. (2008) · Proceedings of the Python in Science Conference · doi:10.25080/tcwv9851 · Cited by 7,477 · see citing works
UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
McInnes, L., Healy, J., & Melville, J. (2018) · arXiv · doi:10.48550/arxiv.1802.03426 · Cited by 7,101 · see citing works
The single-cell transcriptional landscape of mammalian organogenesis
Cao, J., Spielmann, M., Qiu, X., Huang, X., Ibrahim, D. M., Hill, A. J., Zhang, F., Mundlos, S., Christiansen, L., Steemers, F. J., et al. (2019) · Nature · doi:10.1038/s41586-019-0969-x · Cited by 4,490 · see citing works
Self-citationHafemeister, C., & Satija, R. (2019) · Genome Biology · doi:10.1186/s13059-019-1874-1 · Cited by 4,479 · see citing works
Next-generation genotype imputation service and methods
Das, S., Forer, L., Schönherr, S., Sidore, C., Locke, A. E., Kwong, A. M., Vrieze, S. I., Chew, E. Y., Levy, S., McGue, M. K., et al. (2016) · Nature Genetics · doi:10.1038/ng.3656 · Cited by 3,722 · see citing works
MAGMA: Generalized Gene-Set Analysis of GWAS Data
de Leeuw, C. A., Mooij, J. M., Heskes, T., & Posthuma, D. (2015) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1004219 · Cited by 3,653 · see citing works
Scrublet: computational identification of cell doublets in single-cell transcriptomic data
Wolock, S. L., Lopez, R., & Klein, A. M. (2019) · Cell Systems · doi:10.1016/j.cels.2018.11.005 · Cited by 2,758 · see citing works
Scrublet: computational identification of cell doublets in single-cell transcriptomic data
Wolock, S. L., Lopez, R., & Klein, A. M. (2018) · doi:10.1101/357368 · Cited by 2,758 · see citing works
Cribari-Neto, F., & Zeileis, A. (2010) · Journal of Statistical Software · doi:10.18637/jss.v034.i02 · Cited by 2,505 · see citing works
Nalls, M. A., Blauwendraat, C., Vallerga, C. L., Heilbron, K., Bandres-Ciga, S., Chang, D., Tan, M., Kia, D. A., Noyce, A. J., Xue, A., et al. (2019) · The Lancet Neurology · doi:10.1016/s1474-4422(19)30320-5 · Cited by 2,454 · see citing works
Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors
Haghverdi, L., Lun, A. T. L., Morgan, M. D., & Marioni, J. C. (2018) · Nature Biotechnology · doi:10.1038/nbt.4091 · Cited by 2,416 · see citing works
International Parkinson's Disease Genomics Consortium (IPDGC), Nalls, M. A., Parkinson's Study Group (PSG) Parkinson's Research: The Organized GENetics Initiative (PROGENI), 23andMe, GenePD, NeuroGenetics Research Consortium (NGRC), Hussman Institute of Human Genomics (HIHG), Investigator, T. A. J. D., (CHARGE), C. F. H. A. A. R. I. G. E., North American Brain Expression Consortium (NABEC), et al. (2014) · Nature Genetics · doi:10.1038/ng.3043 · Cited by 1,895 · see citing works
Bell, R. D., Winkler, E. A., Sagare, A. P., Singh, I., LaRue, B., Deane, R., & Zlokovic, B. V. (2010) · Neuron · doi:10.1016/j.neuron.2010.09.043 · Cited by 1,448 · see citing works
Dong, Y., & Benveniste, E. N. (2001) · Glia · doi:10.1002/glia.1107 · Cited by 1,296 · see citing works
Disease duration and the integrity of the nigrostriatal system in Parkinson’s disease
Kordower, J. H., Olanow, C. W., Dodiya, H. B., Chu, Y., Beach, T. G., Adler, C. H., Halliday, G. M., & Bartus, R. T. (2013) · Brain · doi:10.1093/brain/awt192 · Cited by 1,294 · see citing works
Modulation of neurodegeneration by molecular chaperones
Muchowski, P. J., & Wacker, J. L. (2004) · Nature reviews. Neuroscience · doi:10.1038/nrn1587 · Cited by 1,021 · see citing works
Obeso, J. A., Stamelou, M., Goetz, C. G., Poewe, W., Lang, A. E., Weintraub, D., Burn, D., Halliday, G. M., Bezard, E., Przedborski, S., et al. (2017) · Movement Disorders · doi:10.1002/mds.27115 · Cited by 859 · cited 1× · see citing works
“Approximately 95% of Parkinson’s disease patients do not harbour an interpretable genetic cause; therefore, they are classified as idiopathic Parkinson’s disease.(4)”
Imamura, K., Hishikawa, N., Sawada, M., Nagatsu, T., Yoshida, M., & Hashizume, Y. (2003) · Acta Neuropathologica · doi:10.1007/s00401-003-0766-2 · Cited by 791 · see citing works
Reinhardt, P., Schmid, B., Burbulla, L. F., Schöndorf, D. C., Wagner, L., Glatza, M., Höing, S., Hargus, G., Heck, S. A., Dhingra, A., et al. (2013) · Cell stem cell · doi:10.1016/j.stem.2013.01.008 · Cited by 500 · see citing works
Parkinson’s disease: animal models and dopaminergic cell vulnerability
Blesa, J., & Przedborski, S. (2014) · Frontiers in Neuroanatomy · doi:10.3389/fnana.2014.00155 · Cited by 483 · see citing works
Bryois, J., Skene, N., Hansen, T. F., Kogelman, L. J. A., Watson, H. J., Liu, Z., Adan, R. A. H., Alfredsson, L. S., Ando, T., Andreassen, O. A., et al. (2020) · Nature Genetics · doi:10.1038/s41588-020-0610-9 · Cited by 375 · see citing works
New insights into the complex role of mitochondria in Parkinson’s disease
Grünewald, A., Kumar, K. R., & Sue, C. M. (2018) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2018.09.003 · Cited by 362 · see citing works
Self-citationMicroglial activation and amyloid‐β clearance induced by exogenous heat‐shock proteins
Kakimura, J., Kitamura, Y., Takata, K., Umeki, M., Suzuki, S., Shibagaki, K., Taniguchi, T., Nomura, Y., Gebicke‐Haerter, P. J., Smith, M. A., et al. (2002) · The FASEB Journal · doi:10.1096/fj.01-0530fje · Cited by 331 · see citing works
Agarwal, D., Sandor, C., Volpato, V., Caffrey, T. M., Monzón‐Sandoval, J., Bowden, R. J., Alegre‐Abarrategui, J., Wade‐Martins, R., & Webber, C. (2020) · Nature Communications · doi:10.1038/s41467-020-17876-0 · Cited by 324 · see citing works
The neurochip: a new multielectrode device for stimulating and recording from cultured neurons
Maher, M. P., Pine, J., Wright, J., & Tai, Y. (1999) · Journal of Neuroscience Methods · doi:10.1016/s0165-0270(98)00156-3 · Cited by 322 · see citing works
Morphometric characterization of microglial phenotypes in human cerebral cortex
Torres-Platas, S. G., Comeau, S., Rachalski, A., Bo, G. D., Cruceanu, C., Turecki, G., Bruno Giros, A., & Mechawar, N. (2014) · Journal of Neuroinflammation · doi:10.1186/1742-2094-11-12 · Cited by 313 · see citing works
LRRK2 kinase in Parkinson's disease
Alessi, D. R., & Sammler, E. M. (2018) · Science · doi:10.1126/science.aar5683 · Cited by 313 · see citing works
van der Poel, M., Ulas, T., Mizee, M. R., Hsiao, C.-C., Miedema, S. S. M., Adelia, Schuurman, K. G., Helder, B., Tas, S. W., Schultze, J. L., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-08976-7 · Cited by 306 · see citing works
Microglial phenotypes in Parkinson’s disease and animal models of the disease
Joers, V., Tansey, M. G., Mulas, G., & Carta, A. R. (2017) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2016.04.006 · Cited by 285 · see citing works
Sheng, J. G., Ito, K., Skinner, R. D., Mrak, R. E., Rovnaghi, C. R., van Eldik, L. J., & Griffin, W. S. T. (1996) · Neurobiology of Aging · doi:10.1016/0197-4580(96)00104-2 · Cited by 269 · see citing works
Thompson, L. H., Barraud, P., Andersson, E., Kirik, D., & Björklund, A. (2005) · Journal of Neuroscience · doi:10.1523/jneurosci.1676-05.2005 · Cited by 248 · see citing works
Parajuli, B., Sonobe, Y., Horiuchi, H., Takeuchi, H., Mizuno, T., & Suzumura, A. (2013) · Cell Death & Disease · doi:10.1038/cddis.2013.503 · Cited by 232 · see citing works
Smith, H., Freeman, O. J., Butcher, A. J., Holmqvist, S., Humoud, I., Schätzl, T., Hughes, D. T., Verity, N., Swinden, D., Hayes, J., et al. (2020) · Neuron · doi:10.1016/j.neuron.2019.12.014 · Cited by 223 · see citing works
Shah, P. T., Stratton, J. A., Stykel, M. G., Abbasi, S., Sharma, S., Mayr, K. A., Koblinger, K., Whelan, P. J., & Biernaskie, J. (2018) · Cell · doi:10.1016/j.cell.2018.03.063 · Cited by 221 · see citing works
Neuroinflammation in the living brain of Parkinson's disease
Ouchi, Y., Yagi, S., Yokokura, M., & Sakamoto, M. (2009) · Parkinsonism & Related Disorders · doi:10.1016/s1353-8020(09)70814-4 · Cited by 210 · see citing works
Sathe, K., Maetzler, W., Lang, J. D., Mounsey, R. B., Fleckenstein, C., Martin, H. L., Schulte, C., Mustafa, S., Synofzik, M., Vukovic, Z., et al. (2012) · Brain · doi:10.1093/brain/aws250 · Cited by 202 · see citing works
The multifaceted role of astrocytes in regulating myelination
Kıray, H., Lindsay, S. L., Hosseinzadeh, S., & Barnett, S. C. (2016) · Experimental Neurology · doi:10.1016/j.expneurol.2016.03.009 · Cited by 201 · see citing works
Smith, J. A. (2018) · Frontiers in Immunology · doi:10.3389/fimmu.2018.00422 · Cited by 168 · see citing works
Hüttenrauch, M., Ogorek, I., Klafki, H., Otto, M., Stadelmann, C., Weggen, S., Wiltfang, J., & Wirths, O. (2018) · Acta Neuropathologica Communications · doi:10.1186/s40478-018-0612-3 · Cited by 166 · see citing works
Deloulme, J., Raponi, É., Gentil, B. J., Bertacchi, N., Marks, A., Labourdette, G., & Baudier, J. (2004) · Molecular and Cellular Neuroscience · doi:10.1016/j.mcn.2004.07.008 · Cited by 158 · see citing works
Neuroimmunological Implications of AQP4 in Astrocytes
Ikeshima‐Kataoka, H. (2016) · International Journal of Molecular Sciences · doi:10.3390/ijms17081306 · Cited by 148 · see citing works
Blauwendraat, C., Faghri, F., Pihlstrøm, L., Geiger, J. T., Elbaz, A., Lesage, S., Corvol, J., May, P., Nicolas, A., Abramzon, Y. A., et al. (2017) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2017.05.009 · Cited by 143 · see citing works
Self-citationThe complex relationships between microglia, alpha-synuclein, and LRRK2 in Parkinson's disease
Schapansky, J., Nardozzi, J. D., & LaVoie, M. J. (2015) · Neuroscience · doi:10.1016/j.neuroscience.2014.09.049 · Cited by 132 · see citing works
Walker, D. G., Tang, T. M., Mendsaikhan, A., Tooyama, I., Serrano, G. E., Sue, L. I., Beach, T. G., & Lue, L.-F. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21020678 · Cited by 128 · see citing works
Arenas, E. (2014) · Journal of Molecular Cell Biology · doi:10.1093/jmcb/mju001 · Cited by 122 · see citing works
A review of genome‐wide transcriptomics studies in Parkinson's disease
Borrageiro, G., Haylett, W. L., Seedat, S., Kuivaniemi, H., & Bardien, S. (2017) · European Journal of Neuroscience · doi:10.1111/ejn.13760 · Cited by 117 · see citing works
A guide to genome‐wide association analysis and post‐analytic interrogation
Reed, E. R., Núñez, S., Kulp, D., Qian, J., Reilly, M. P., & Foulkes, A. S. (2015) · Statistics in Medicine · doi:10.1002/sim.6605 · Cited by 109 · see citing works
Mitkus, S. N., Hyde, T. M., Vakkalanka, R., Kolachana, B. S., Weinberger, D. R., Kleinman, J. E., & Lipska, B. K. (2007) · Schizophrenia Research · doi:10.1016/j.schres.2007.09.032 · Cited by 107 · see citing works
Reynolds, R. H., Botía, J. A., Nalls, M. A., Noyce, A. J., Nicolas, A., Cookson, M., Bandrés‐Ciga, S., Gibbs, J. R., Hernandez, D. G., Singleton, A., et al. (2019) · npj Parkinson s Disease · doi:10.1038/s41531-019-0076-6 · Cited by 100 · see citing works
Kodama, T., Guerrero, S., Shin, M., Moghadam, S. D., Faulstich, M., & du Lac, S. (2012) · Journal of Neuroscience · doi:10.1523/jneurosci.0543-12.2012 · Cited by 84 · see citing works
Clément‐Ziza, M., Münnich, A., Lyonnet, S., Jaubert, F., & Besmond, C. (2008) · RNA · doi:10.1261/rna.1261708 · Cited by 83 · see citing works
Wu, L., Ko, S. W., Toyoda, H., Zhao, M., Xu, H., Vadakkan, K. I., Ren, M., Knifed, E., Shum, F. W. F., Quan, J., et al. (2007) · PLoS ONE · doi:10.1371/journal.pone.0000167 · Cited by 82 · see citing works
Burm, S. M., Zuiderwijk-Sick, E. A., ‘t Jong, A. E. J., van der Putten, C., Veth, J., Kondova, I., & Bajramovic, J. J. (2015) · The Journal of Neuroscience · doi:10.1523/jneurosci.2510-14.2015 · Cited by 82 · see citing works
Endothelial Dysfunction and Claudin 5 Regulation during Acrolein-Induced Lung Injury
Jang, A., Concel, V. J., Bein, K., Brant, K. A., Liu, S., Pope-Varsalona, H., Dopico, R. A., Di, Y. P., Knoell, D. L., Barchowsky, A., et al. (2010) · American Journal of Respiratory Cell and Molecular Biology · doi:10.1165/rcmb.2009-0391oc · Cited by 74 · see citing works
Smolders, S., & Broeckhoven, C. V. (2020) · Acta Neuropathologica Communications · doi:10.1186/s40478-020-00935-4 · Cited by 69 · see citing works
White-matter repair: Interaction between oligodendrocytes and the neurovascular unit
Arai, K., Hamanaka, G., Ohtomo, R., Takase, H., & Lok, J. M. (2018) · Brain Circulation · doi:10.4103/bc.bc_15_18 · Cited by 52 · see citing works
Szymański, P., Kajdanowicz, T. J., & Kersting, K. (2016) · Technische Universität Dortmund Eldorado (Technische Universität Dortmund) · doi:10.17877/de290r-20315 · Cited by 50 · see citing works
Merrill, C. B., Friend, L. N., Newton, S. T., Hopkins, Z. H., & Edwards, J. G. (2015) · Scientific Reports · doi:10.1038/srep16176 · Cited by 49 · see citing works
Single-cell transcriptomic analysis of oligodendrocyte lineage cells
van Bruggen, D., Agirre, E., & Castelo‐Branco, G. (2017) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2017.10.005 · Cited by 48 · see citing works
Szymański, P., Kajdanowicz, T., & Kersting, K. (2016) · Entropy · doi:10.3390/e18080282 · Cited by 45 · see citing works
Parkinsons Disease: Genetics and Beyond
Inamdar, N., Arulmozhi, D. K., Tandon, A., & Bodhankar, S. L. (2007) · Current Neuropharmacology · doi:10.2174/157015907780866893 · Cited by 44 · see citing works
Čajánek, L., Ganji, R. S., Henriques-Oliveira, C., Theofilopoulos, S., Koník, P., Bryja, V., & Arenas, E. (2012) · Molecular and Cellular Biology · doi:10.1128/mcb.00745-12 · Cited by 44 · see citing works
Hwang, I. K., Park, J. H., Lee, T.-K., Kim, D. W., Yoo, K., Ahn, J. H., Kim, Y. H., Cho, J. H., Kim, Y., Won, M., et al. (2017) · Molecular Medicine Reports · doi:10.3892/mmr.2017.6525 · Cited by 43 · see citing works
Ca2+-dependent Activator Proteins of Secretion Promote Vesicular Monoamine Uptake
Brunk, I., Blex, C., Speidel, D., et al. (2008) · Journal of Biological Chemistry · doi:10.1074/jbc.m805328200 · Cited by 42 · see citing works
Grünewald, A., Lax, N. Z., Rocha, M. C., et al. (2014) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2014.05.026 · Cited by 32 · see citing works
Self-citationBradford, B. M., Wijaya, C. A. W., & Mabbott, N. A. (2019) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2019.00411 · Cited by 31 · see citing works
Identification of Tmem10 as a Novel Late-stage Oligodendrocytes Marker for Detecting Hypomyelination
Jiang, W., Yang, W., Yang, W., Zhang, J., Pang, D., Gan, L., Luo, L., Fan, Y., Liu, Y., & Chen, M. (2014) · International Journal of Biological Sciences · doi:10.7150/ijbs.7526 · Cited by 29 · see citing works
Maher, T. J., Ren, Y., Li, Q., Braunlin, E., Garry, M. G., Sorrentino, B. P., & Martin, C. M. (2014) · American Journal of Physiology-Heart and Circulatory Physiology · doi:10.1152/ajpheart.00638.2013 · Cited by 28 · see citing works
Wu, K.-C., Liou, H., Kao, Y.-H., Lee, C.-Y., & Lin, C. (2017) · Neurobiology of Disease · doi:10.1016/j.nbd.2017.05.001 · Cited by 18 · see citing works
The accelerated aging model reveals critical mechanisms of late-onset Parkinson’s disease
Li, S., Liu, H., Bian, S., Sha, X., Li, Y., & Wang, Y. (2020) · BioData Mining · doi:10.1186/s13040-020-00215-w · Cited by 9 · see citing works
Regulatory system for the G1‐arrest during neuronal development in Drosophila
Tsuda, L., & Lim, Y. (2014) · Development Growth & Differentiation · doi:10.1111/dgd.12130 · Cited by 7 · see citing works
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Microglia in neurodegenerative diseases: mechanism and potential therapeutic targets
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Martins-Ferreira, R., Calafell-Segura, J., Leal, B., Rodríguez-Ubreva, J., Martínez-Saez, E., Mereu, E., Pinho E Costa, P., Laguna, A., & Ballestar, E. (2025) · Nature Communications · doi:10.1038/s41467-025-56124-1 · Cited by 123 · see citing works
Axonal energy metabolism, and the effects in aging and neurodegenerative diseases
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Ayala, D. A., Hall, G. M., Das, D., Tijerina, E., Villagran, L. L., Khan, Z., Paliwal, D., Anand, A., Rimer, M., & Srinivasan, R. (2026) · Cellular and Molecular Neurobiology · doi:10.1007/s10571-026-01713-x · Cited by 1 · see citing works
- Authors
- Smajić, S., Prada-Medina, C. A., Landoulsi, Z., Ghelfi, J., Delcambre, S., Dietrich, C., Jarazo, J., Henck, J., Balachandran, S., Pachchek, S., Morris, C. M., Antony, P., Timmermann, B., Sauer, S., Pereira, S. L., Schwamborn, J. C., May, P., Grünewald, A., & Spielmann, M.
- Affiliations
- University of Luxembourg (LU)
- Max Planck Institute for Molecular Genetics, D-14195 Berlin, Germany
- Max Planck Institute for Molecular Genetics (DE)
- Christian-Albrechts-Universität zu Kiel (DE)
- Newcastle University (GB)
- Max Delbrück Center (DE)
- Corresponding author
- Grünewald, Anne
- Spielmann, Malte
- ORCID
- Venue
- Brain
- Publisher
- Oxford University Press (OUP)
- Published
- 2021-12-17
- Volume
- 145
- Issue
- 3
- Pages
- 964-978
- ISSN
- 0006-8950 (ISSN-L)
- 1460-2156
- PMC
- PMC9050543
- OpenAlex
- W4200349851
- Cited by
- 590
- References
- 80
- Citations by year
- 2026: 148 · 2025: 147 · 2024: 150 · 2023: 96 · 2022: 43 · 2021: 4 · 2012: 1
- FWCI
- 38.40
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Brain (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- MDC Repository (Max-Delbrueck-Center for Molecular Medicine) (acceptedVersion)
- Open Repository and Bibliography (University of Luxembourg) (submittedVersion, other-oa)
- Newcastle University ePrints (Newcastle Univesity) (cc-by)
- Funders
- Luxembourg National Research Fund (PRIDE17/12244779/PARK-QC)
- Le Foyer Assurances Luxembourg
- National Centre of Excellence in Research on Parkinson’s disease
- ‘MiRisk-Parkinson’s disease’ (C17/BM/11676395)
- ‘ProtectMove’ (INTER/DFG/19/14429377)
- Deutsche Forschungsgemeinschaft (SP1532/3-1, SP1532/4-1, SP1532/5-1)
- Deutsches Zentrum für Luft- und Raumfahrt
- Newcastle Brain Tissue Resource from UK MRC (MR/L016451/1)
- National Institutes for Health Research Biomedical Research Centre Newcastle
- Parkinson’s UK (258197, SC037554)
- Topics
- Health Sciences › Medicine › Neurology › Parkinson's Disease Mechanisms and Treatments
- Life Sciences › Neuroscience › Neurology › Neuroinflammation and Neurodegeneration Mechanisms
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Keywords
- Parkinson's disease, midbrain, single-nucleus RNA sequencing, glial activation, microglia, oligodendrocytes, astrocytes, dopaminergic neurons, Th, S100B, unfolded protein response, cytokine signaling, IL1B, GPNMB, HSP90AA1, CD44
- MeSH terms
- Humans, Membrane Glycoproteins, Mesencephalon, Parkinson Disease, Substantia Nigra, Microglia, Genome-Wide Association Study, Dopaminergic Neurons
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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