Human Microglia Show Unique Transcriptional Changes in Alzheimer's Disease
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Abstract
Microglia, the innate immune cells of the brain, influence Alzheimer’s disease (AD) progression and are potential therapeutic targets. However, microglia exhibit diverse functions, the regulation of which is not fully understood, complicating therapeutics development. To better define the transcriptomic phenotypes and gene regulatory networks associated with AD, we enriched for microglia nuclei from 12 AD and 10 control human dorsolateral prefrontal cortices (7 males and 15 females, all aged >60 years) before single-nucleus RNA sequencing. Here we describe both established and previously unrecognized microglial molecular phenotypes, the inferred gene networks driving observed transcriptomic change, and apply trajectory analysis to reveal the putative relationships between microglial phenotypes. We identify microglial phenotypes more prevalent in AD cases compared with controls. Further, we describe the heterogeneity in microglia subclusters expressing homeostatic markers. Our study demonstrates that deep profiling of microglia in human AD brain can provide insight into microglial transcriptional changes associated with AD.
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This inflammatory cluster showed significant downregulation of INPP5D, IL-1β expression was significantly elevated, and this cluster was enriched for NLR signaling.
“While this cluster showed significant downregulation of INPP5D, IL-1β expression was significantly elevated, and this cluster was enriched for NLR signaling [175].”
From Microglial function, INPP5D/SHIP1 signaling, and NLRP3 inflammasome activation: implications for Alzheimer’s disease (2023) · cites this paper as [175]
In Results, ‘Complexity of microglia states’ (page unavailable), the paper reports increased IL1β in cluster 6 relative to homeostatic microglia and enrichment for NLR signaling. It does not state that the IL1β increase was statistically significant, and a search for INPP5D found no support for its claimed significant downregulation. (LLM, conf=0.86)
Experimental — accuracy not yet measuredRegulatory networks of microglial transcription factors are specific to distinct microglial phenotypes observed in Alzheimer's disease, including the endolysosomal phenotype and inflammatory effector cluster.
“the regulatory networks of these transcription factors are specific to the distinct microglial phenotypes observed in Alzheimer's disease, including the endolysosomal phenotype and inflammatory effector cluster”
From Targeting Microglial Transcriptional Reprogramming as a Therapy Strategy for Alzheimer's Disease (2026) · cites this paper as [86]
A recent single-nucleus RNA sequencing profiling study of microglia from AD and non-AD brains identified an "inflammatory" cluster that was over-represented in the AD brains.
“Furthermore, a recent single-nucleus RNA sequencing profiling study of microglia from AD and non-AD brains identified an “inflammatory” cluster that was over-represented in the AD brains.”
From Microglial function, INPP5D/SHIP1 signaling, and NLRP3 inflammasome activation: implications for Alzheimer’s disease (2023) · cites this paper as [175]
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Scenic: single-cell regulatory network inference and clustering.
Aibar, S., González-Blas, C. B., Moerman, T., Huynh-Thu, V. A., Imrichova, H., Hulselmans, G., Rambow, F., Marine, J.-C., Geurts, P., Aerts, J., et al. (2017) · Nature Methods · doi:10.1038/nmeth.4463 · Cited by 6,520 · see citing works
A Unique Microglia Type Associated with Restricting Development of Alzheimer’s Disease
Keren-Shaul, H., Spinrad, A., Weiner, A., Matcovitch-Natan, O., Dvir-Szternfeld, R., Ulland, T. K., David, E., Baruch, K., Lara-Astaiso, D., Toth, B., et al. (2017) · Cell · doi:10.1016/j.cell.2017.05.018 · Cited by 5,493 · see citing works
Hafemeister, C., & Satija, R. (2019) · Genome Biology · doi:10.1186/s13059-019-1874-1 · Cited by 4,479 · see citing works
Meta-Analysis of 74,046 Individuals Identifies 11 New Susceptibility Loci for Alzheimer’s Disease
(EADI), E. A. D. I., Lambert, J.-C., (GERAD), G. A. E. R. I. A. D., Alzheimer's Disease Genetic Consortium (ADGC), (CHARGE), C. F. H. A. A. R. I. G. E., Ibrahim-Verbaas, C. A., Harold, D., Naj, A. C., Sims, R., Bellenguez, C., et al. (2013) · Nature Genetics · doi:10.1038/ng.2802 · Cited by 4,336 · 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
Identification of a unique TGF-β–dependent molecular and functional signature in microglia
Butovsky, O., Jedrychowski, M. P., Moore, C. S., Cialic, R., Lanser, A. J., Gabriely, G., Koeglsperger, T., Dake, B., Wu, P. M., Doykan, C. E., et al. (2014) · Nature Neuroscience · doi:10.1038/nn.3599 · Cited by 2,543 · see citing works
Elmore, M. R. P., Najafi, A. R., Koike, M. A., Dagher, N. N., Spangenberg, E. E., Rice, R. A., Kitazawa, M., Matusow, B., Nguyen, H., West, B. L., et al. (2014) · Neuron · doi:10.1016/j.neuron.2014.02.040 · Cited by 1,968 · see citing works
Self-citationMicroglia in Alzheimer’s disease
Hansen, D. V., Hanson, J. E., & Sheng, M. (2018) · Journal of Cell Biology · doi:10.1083/jcb.201709069 · Cited by 1,935 · see citing works
Conserved cell types with divergent features in human versus mouse cortex
Hodge, R. D., Bakken, T. E., Miller, J. A., Smith, K. A., Barkan, E. R., Graybuck, L. T., Close, J. L., Long, B., Johansen, N., Penn, O., et al. (2019) · Nature · doi:10.1038/s41586-019-1506-7 · Cited by 1,775 · see citing works
Self-citationSoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data
Young, M. D., & Behjati, S. (2020) · GigaScience · doi:10.1093/gigascience/giaa151 · Cited by 1,619 · see citing works
The P2Y12 receptor regulates microglial activation by extracellular nucleotides
Haynes, S. E., Hollopeter, G., Yang, G., Kurpius, D., Dailey, M. E., Gan, W.-B., & Julius, D. (2006) · Nature Neuroscience · doi:10.1038/nn1805 · Cited by 1,542 · see citing works
Marschallinger, J., Iram, T., Zardeneta, M., Lee, S. E., Lehallier, B., Haney, M. S., Pluvinage, J. V., Mathur, V., Hahn, O., Morgens, D. W., et al. (2020) · Nature Neuroscience · doi:10.1038/s41593-019-0566-1 · Cited by 1,384 · see citing works
The P2X7 Receptor in Infection and Inflammation
Di Virgilio, F., Dal Ben, D., Sarti, A. C., Giuliani, A. L., & Falzoni, S. (2017) · Immunity · doi:10.1016/j.immuni.2017.06.020 · Cited by 1,267 · see citing works
Sala Frigerio, C., Wolfs, L., Fattorelli, N., Thrupp, N., Voytyuk, I., Schmidt, I., Mancuso, R., Chen, W.-T., Woodbury, M. E., Srivastava, G., et al. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.03.099 · Cited by 926 · see citing works
Brain cell type–specific enhancer–promoter interactome maps and disease <b>-</b> risk association
Nott, A., Holtman, I. R., Coufal, N. G., Schlachetzki, J. C. M., Yu, M., Hu, R., Han, C. Z., Pena, M., Xiao, J., Wu, Y., et al. (2019) · Science · doi:10.1126/science.aay0793 · Cited by 823 · see citing works
Single cell RNA sequencing of human microglia uncovers a subset associated with Alzheimer’s disease
Olah, M., Menon, V., Habib, N., Taga, M. F., Ma, Y., Yung, C. J., Cimpean, M., Khairallah, A., Coronas-Samano, G., Sankowski, R., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-19737-2 · Cited by 722 · see citing works
Turnover of resident microglia in the normal adult mouse brain
Lawson, L. J., Perry, V. H., & Gordon, S. (1992) · Neuroscience · doi:10.1016/0306-4522(92)90500-2 · Cited by 709 · see citing works
Friedman, B. A., Srinivasan, K., Ayalon, G., Meilandt, W. J., Lin, H., Huntley, M. A., Cao, Y., Lee, S.-H., Haddick, P. C. G., Ngu, H., et al. (2018) · Cell Reports · doi:10.1016/j.celrep.2017.12.066 · Cited by 662 · see citing works
Huang, Y., Xu, Z., Xiong, S., Sun, F., Qin, G., Hu, G., Wang, J., Zhao, L., Liang, Y.-X., Wu, T., et al. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0090-8 · Cited by 560 · see citing works
A transcriptomic atlas of aged human microglia
Olah, M., Patrick, E., Villani, A.-C., Xu, J., White, C. C., Ryan, K. J., Piehowski, P., Kapasi, A., Nejad, P., Cimpean, M., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-02926-5 · Cited by 482 · see citing works
Distinct amyloid-β and tau-associated microglia profiles in Alzheimer’s disease
Gerrits, E., Brouwer, N., Kooistra, S. M., Woodbury, M. E., Vermeiren, Y., Lambourne, M., Mulder, J., Kummer, M., Möller, T., Biber, K., et al. (2021) · Acta Neuropathologica · doi:10.1007/s00401-021-02263-w · Cited by 441 · see citing works
Alzheimer’s Patient Microglia Exhibit Enhanced Aging and Unique Transcriptional Activation
Srinivasan, K., Friedman, B. A., Etxeberria, A., Huntley, M. A., van der Brug, M. P., Foreman, O., Paw, J. S., Modrusan, Z., Beach, T. G., Serrano, G. E., et al. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.107843 · Cited by 427 · see citing works
Nixon, R. A. (2017) · The FASEB Journal · doi:10.1096/fj.201700359 · Cited by 400 · see citing works
Factors regulating microglia activation
Kierdorf, K., & Prinz, M. (2013) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2013.00044 · Cited by 360 · see citing works
Shi, M., Chu, F., Zhu, F., & Zhu, J. (2022) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2022.870517 · Cited by 275 · see citing works
APOE and TREM2 regulate amyloid-responsive microglia in Alzheimer’s disease
Nguyen, A. T., Wang, K., Hu, G., Wang, X., Miao, Z., Azevedo, J. A., Suh, E., Van Deerlin, V. M., Choi, D., Roeder, K., et al. (2020) · Acta Neuropathologica · doi:10.1007/s00401-020-02200-3 · Cited by 241 · see citing works
Endo-lysosomal dysregulations and late-onset Alzheimer’s disease: impact of genetic risk factors
Van Acker, Z. P., Bretou, M., & Annaert, W. (2019) · Molecular Neurodegeneration · doi:10.1186/s13024-019-0323-7 · Cited by 222 · see citing works
Transcriptional control of microglia phenotypes in health and disease
Holtman, I. R., Skola, D., & Glass, C. K. (2017) · Journal of Clinical Investigation · doi:10.1172/jci90604 · Cited by 218 · see citing works
Microglia and Neuroinflammation: What Place for P2RY12?
Gómez Morillas, A., Besson, V. C., & Lerouet, D. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22041636 · Cited by 214 · see citing works
Microglial Phagocytosis: A Disease-Associated Process Emerging from Alzheimer’s Disease Genetics
Podleśny-Drabiniok, A., Marcora, E., & Goate, A. M. (2020) · Trends in Neurosciences · doi:10.1016/j.tins.2020.10.002 · Cited by 211 · see citing works
Nanoscale Surveillance of the Brain by Microglia via cAMP-Regulated Filopodia
Bernier, L.-P., Bohlen, C. J., York, E. M., Choi, H. B., Kamyabi, A., Dissing-Olesen, L., Hefendehl, J. K., Collins, H. Y., Stevens, B., Barres, B. A., et al. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.05.010 · Cited by 211 · see citing works
Diverse human astrocyte and microglial transcriptional responses to Alzheimer’s pathology
Smith, A. M., Davey, K., Tsartsalis, S., Khozoie, C., Fancy, N., Tang, S. S., Liaptsi, E., Weinert, M., McGarry, A., Muirhead, R. C. J., et al. (2022) · Acta Neuropathologica · doi:10.1007/s00401-021-02372-6 · Cited by 209 · see citing works
Claes, C., Danhash, E. P., Hasselmann, J., Chadarevian, J. P., Shabestari, S. K., England, W. E., Lim, T. E., Hidalgo, J. L. S., Spitale, R. C., Davtyan, H., et al. (2021) · Molecular Neurodegeneration · doi:10.1186/s13024-021-00473-0 · Cited by 197 · see citing works
Yang, H. S., Onos, K. D., Choi, K., Keezer, K. J., Skelly, D. A., Carter, G. W., & Howell, G. R. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.108739 · Cited by 69 · see citing works
Licastro, F., & Porcellini, E. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22147263 · Cited by 33 · see citing works
Flagged first, then by citations
Research works printed but not linked: 52
- [2]Calsolaro V Edison P Neuroinflammation in Alzheimer’s disease: current evidence and future directions Alzheimers Dement. 2016 12 719 732 27179961
- [3]Colonna M Butovsky O Microglia function in the central nervous system during health and neurodegeneration Annu. Rev. Immunol. 2017 35 441 468 28226226 PMC8167938
- [4]McQuade A Blurton-Jones M Microglia in Alzheimer’s disease: exploring how genetics and phenotype influence risk J. Mol. Biol. 2019 431 1805 1817 30738892 PMC6475606
- [5]Milner MT Maddugoda M Gotz J Burgener SS Schroder K The NLRP3 inflammasome triggers sterile neuroinflammation and Alzheimer’s disease Curr. Opin. Immunol. 2021 68 116 124 33181351
- [6]Prinz M Jung S Priller J Microglia biology: one century of evolving concepts Cell 2019 179 292 311 31585077
- [7]Salter MW Stevens B Microglia emerge as central players in brain disease Nat. Med. 2017 23 1018 1027 28886007
- [8]Streit WJ Xue QS Tischer J Bechmann I Microglial pathology Acta Neuropathol. Commun. 2014 2 142 25257319 PMC4180960
- [9]Webers A Heneka MT Gleeson PA The role of innate immune responses and neuroinflammation in amyloid accumulation and progression of Alzheimer’s disease Immunol. Cell Biol. 2020 98 28 41 31654430
- [10]Wolf SA Boddeke HW Kettenmann H Microglia in physiology and disease Annu. Rev. Physiol. 2017 79 619 643 27959620
- [12]Streit WJ Khoshbouei H Bechmann I Dystrophic microglia in late-onset Alzheimer’s disease Glia 2020 68 845 854 31922322
- [13]Hopp SC The role of microglia in processing and spreading of bioactive tau seeds in Alzheimer’s disease J. Neuroinflammation 2018 15 269 30227881 PMC6145371
- [14]Asai H Depletion of microglia and inhibition of exosome synthesis halt tau propagation Nat. Neurosci. 2015 18 1584 1593 26436904 PMC4694577
- [17]Stratoulias V Venero JL Tremblay ME Joseph B Microglial subtypes: diversity within the microglial community EMBO J. 2019 38 e101997 31373067 PMC6717890
- [18]Mathys H Temporal tracking of microglia activation in neurodegeneration at single-cell resolution Cell Rep. 2017 21 366 380 29020624 PMC5642107
- [20]Rexach JE Tau pathology drives dementia risk-associated gene networks toward chronic inflammatory states and immunosuppression Cell Rep. 2020 33 108398 33207193 PMC7842189
- [21]Tan YL Yuan Y Tian L Microglial regional heterogeneity and its role in the brain Mol. Psychiatry 2020 25 351 367 31772305 PMC6974435
- [24]Mathys H Single-cell transcriptomic analysis of Alzheimer’s disease Nature 2019 570 332 337 31042697 PMC6865822
- [27]Alsema AM Profiling microglia from Alzheimer’s disease donors and non-demented elderly in acute human postmortem cortical tissue Front. Mol. Neurosci. 2020 13 134 33192286 PMC7655794
- [29]Patel T Transcriptional landscape of human microglia implicates age, sex, and APOE-related immunometabolic pathway perturbations Aging Cell 2022 21 e13606 35388616 PMC9124307
- [31]Schoggins JW A diverse range of gene products are effectors of the type I interferon antiviral response Nature 2011 472 481 485 21478870 PMC3409588
- [32]Wang D The role of NLRP3-CASP1 in inflammasome-mediated neuroinflammation and autophagy dysfunction in manganese-induced, hippocampal-dependent impairment of learning and memory ability Autophagy 2017 13 914 927 28318352 PMC5446056
- [33]Xu P The landscape of human tissue and cell type specific expression and co-regulation of senescence genes Mol. Neurodegener. 2022 17 5 35000600 PMC8744330
- [34]Angelova DM Brown DR Microglia and the aging brain: are senescent microglia the key to neurodegeneration? J. Neurochem. 2019 151 676 688 31478208
- [35]Reijns MA Enzymatic removal of ribonucleotides from DNA is essential for mammalian genome integrity and development Cell 2012 149 1008 1022 22579044 PMC3383994
- [36]Mjelle R Cell cycle regulation of human DNA repair and chromatin remodeling genes DNA Repair (Amst). 2015 30 53 67 25881042
- [38]Lauro C Limatola C Metabolic reprograming of microglia in the regulation of the innate inflammatory response Front. Immunol. 2020 11 493 32265936 PMC7099404
- [39]Pagani I Poli G Vicenzi E TRIM22. A multitasking antiviral factor Cells 2021 10 1864 34440633 PMC8391480
- [40]Subramanian N Natarajan K Clatworthy MR Wang Z Germain RN The adaptor MAVS promotes NLRP3 mitochondrial localization and inflammasome activation Cell 2013 153 348 361 23582325 PMC3632354
- [42]Drummond RA CARD9+ microglia promote antifungal immunity via IL-1β- and CXCL1-mediated neutrophil recruitment Nat. Immunol. 2019 20 559 570 30996332 PMC6494474
- [43]Li L Thioredoxin-interacting protein (TXNIP) associated NLRP3 inflammasome activation in human Alzheimer’s disease brain J. Alzheimers Dis. 2019 68 255 265 30741672 PMC10947081
- [44]Kunkle BW Genetic meta-analysis of diagnosed Alzheimer’s disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing Nat. Genet. 2019 51 414 430 30820047 PMC6463297
- [45]Cao J The single-cell transcriptional landscape of mammalian organogenesis Nature 2019 566 496 502 30787437 PMC6434952
- [49]Song X DNA repair inhibition leads to active export of repetitive sequences to the cytoplasm triggering an inflammatory response J. Neurosci. 2021 41 9286 9307 34593604 PMC8580148
- [50]Roy ER Type I interferon response drives neuroinflammation and synapse loss in Alzheimer disease J. Clin. Invest. 2020 130 1912 1930 31917687 PMC7108898
- [52]Song X Ma F Herrup K Accumulation of cytoplasmic DNA due to ATM deficiency activates the microglial viral response system with neurotoxic consequences J. Neurosci. 2019 39 6378 6394 31189575 PMC6687895
- [53]Augusto-Oliveira M Plasticity of microglia Biol. Rev. Camb. Philos. Soc. 2022 97 217 250 34549510
- [55]Delhaye S Bardoni B Role of phosphodiesterases in the pathophysiology of neurodevelopmental disorders Mol. Psychiatry 2021 26 4570 4582 33414502 PMC8589663
- [56]Jin SL Lan L Zoudilova M Conti M Specific role of phosphodiesterase 4B in lipopolysaccharide-induced signaling in mouse macrophages J. Immunol. 2005 175 1523 1531 16034090
- [59]Lähnemann D Eleven grand challenges in single-cell data science Genome Biol. 2020 21 31 32033589 PMC7007675
- [60]Koussounadis A Langdon SP Um IH Harrison DJ Smith VA Relationship between differentially expressed mRNA and mRNA–protein correlations in a xenograft model system Sci. Rep. 2015 5 10775 26053859 PMC4459080
- [61]Franceschi C Garagnani P Parini P Giuliani C Santoro A Inflammaging: a new immune-metabolic viewpoint for age-related diseases Nat. Rev. Endocrinol. 2018 14 576 590 30046148
- [62]de Groot CJ Hulshof S Hoozemans JJ Veerhuis R Establishment of microglial cell cultures derived from postmortem human adult brain tissue: immunophenotypical and functional characterization Microsc. Res. Tech. 2001 54 34 39 11526955
- [63]Stuart T Comprehensive integration of single-cell data Cell 2019 177 1888 1902 31178118 PMC6687398
- [68]Traag VA Waltman L van Eck NJ From Louvain to Leiden: guaranteeing well-connected communities Sci. Rep. 2019 9 5233 30914743 PMC6435756
- [69]Wu T clusterProfiler 4.0: a universal enrichment tool for interpreting omics data Innovation (Camb) 2021 2 100141 34557778 PMC8454663
- [70]Mlecnik B Galon J Bindea G Automated exploration of gene ontology term and pathway networks with ClueGO-REST Bioinformatics 2019 35 3864 3866 30847467 PMC6761950
- [71]Bindea G ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks Bioinformatics 2009 25 1091 1093 19237447 PMC2666812
- [72]Zhu G Exploration of the crucial genes and molecular mechanisms mediating atherosclerosis and abnormal endothelial shear stress Dis. Markers 2022 2022 6306845 35990248 PMC9391161
- [73]Verma P Shakya M Machine learning model for predicting major depressive disorder using RNA-seq data: optimization of classification approach Cogn. Neurodyn. 2022 16 443 453 35401859 PMC8934793
- [74]Dong L Construction, bioinformatics analysis, and validation of competitive endogenous RNA networks in ulcerative colitis Front. Genet. 2022 13 951243 36061211 PMC9428148
- [75]Lee HJ Kim JS Moon C Son Y Profiling of gene expression in the brain associated with anxiety-related behaviors in the chronic phase following cranial irradiation Sci. Rep. 2022 12 13162 35915120 PMC9343641
- [77]Van de Sande B A scalable SCENIC workflow for single-cell gene regulatory network analysis Nat. Protoc. 2020 15 2247 2276 32561888
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Kim, B., Wang, S. S., Kim, J. R., & Kim, J. (2026) · BioEssays · doi:10.1002/bies.70186
- Authors
- Prater, K. E., Green, K. J., Mamde, S., Sun, W., Cochoit, A., Smith, C. L., Chiou, K. L., Heath, L., Rose, S. E., Wiley, J., Keene, C. D., Kwon, R. Y., Snyder-Mackler, N., Blue, E. E., Logsdon, B., Young, J. E., Shojaie, A., Garden, G. A., & Jayadev, S.
- Affiliations
- University of Washington (US)
- Fred Hutch Cancer Center (US)
- Arizona State University (US)
- Sage Bionetworks (US)
- University of North Carolina at Chapel Hill (US)
- Corresponding author
- Jayadev, Suman
- ORCID
- Venue
- Nature Aging
- Published
- 2023-05-29
- Volume
- 3
- Issue
- 7
- Pages
- 894-907
- ISSN
- 2662-8465 (ISSN-L)
- PMC
- PMC10353942
- OpenAlex
- W4378714758
- Cited by
- 164
- References
- 35
- Citations by year
- 2026: 46 · 2025: 52 · 2024: 52 · 2023: 13 · 2022: 1
- FWCI
- 17.11
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Nature Aging (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- Research Square (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- U.S. Department of Health & Human Services | NIH | National Institute on Aging (RF1AG063540, P30AG066509, AG05136, 5T32-AG052354-02)
- Ellison Foundation, Weill Neurohub
- Topics
- Life Sciences › Neuroscience › Neurology › Neuroinflammation and Neurodegeneration Mechanisms
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Keywords
- microglia, Alzheimer's disease, microglial transcriptome, single-nucleus RNA sequencing, gene regulatory networks, microglial heterogeneity, microglial phenotype, dorsolateral prefrontal cortex, human brain
- MeSH terms
- Alzheimer Disease, Brain, Female, Humans, Male, Microglia, Gene Expression Profiling, Transcriptome
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