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Distinct amyloid-β and tau-associated microglia profiles in Alzheimer’s disease
Open accessAbstract
Alzheimer’s disease (AD) is the most prevalent form of dementia and is characterized by abnormal extracellular aggregates of amyloid-β and intraneuronal hyperphosphorylated tau tangles and neuropil threads. Microglia, the tissue-resident macrophages of the central nervous system (CNS), are important for CNS homeostasis and implicated in AD pathology. In amyloid mouse models, a phagocytic/activated microglia phenotype has been identified. How increasing levels of amyloid-β and tau pathology affect human microglia transcriptional profiles is unknown. Here, we performed snRNAseq on 482,472 nuclei from non-demented control brains and AD brains containing only amyloid-β plaques or both amyloid-β plaques and tau pathology. Within the microglia population, distinct expression profiles were identified of which two were AD pathology-associated. The phagocytic/activated AD1-microglia population abundance strongly correlated with tissue amyloid-β load and localized to amyloid-β plaques. The AD2-microglia abundance strongly correlated with tissue phospho-tau load and these microglia were more abundant in samples with overt tau pathology. This full characterization of human disease-associated microglia phenotypes provides new insights in the pathophysiological role of microglia in AD and offers new targets for microglia-state-specific therapeutic strategies.
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“a population of clusters, namely AD1, closely reassembled the mouse DAM/MGnD phenotype, it was only abundant in human samples containing Aβ and not tau”
From Microglia and monocytes in inflammatory CNS disease: integrating phenotype and function (2022) · cites this paper as [119]
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“Together this work demonstrates, firstly, that there exist other microglial subsets in AD besides the DAM/MGnD phenotype, and secondly, that human microglia are heterogenous, thus limiting the translation of the DAM signature from animal models.”
From Microglia and monocytes in inflammatory CNS disease: integrating phenotype and function (2022) · cites this paper as [119]
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“These findings were not formally published, however two recent reports suggest the presence of B cells in the brains of patients with AD [81,82].”
From Is there a role for B cells in Alzheimer's disease? (2026) · cites this paper as [82]
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“A study using singlenucleus RNA sequencing identified a cluster of lymphocytes in AD brains enriched for the expression of IL-7R and MS4A1 genes, which encode the interleukin-7 receptor and CD20 respectively, both estab lished markers of B lymphocytes [82].”
From Is there a role for B cells in Alzheimer's disease? (2026) · cites this paper as [82]
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“There was considerable overlap between the Micro_D profile and previously reported microglial states, such as the MIMS-foamy state reported in ref. 11, amyloid-associated microglia in Alzheimer’s disease43,46,47 and lipid-associated macrophages (LAMs) in obesity48 (Extended Data Fig. 8).”
From Foamy microglia link oxylipins to disease progression in multiple sclerosis (2026) · cites this paper as [46]
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“The foamy, GPNMB+ microglial phenotype in MS lesions bears striking transcriptional similarity to LAMs in obesity, amyloid-responsive microglia in Alzheimer’s disease and foam cells in atherosclerosis18,46,48,50,51.”
From Foamy microglia link oxylipins to disease progression in multiple sclerosis (2026) · cites this paper as [46]
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“single nuclei RNA sequencing on AD brains showed that out of seven cell types, gene expression changes were shown only in microglia [119]”
From Microglia and monocytes in inflammatory CNS disease: integrating phenotype and function (2022) · cites this paper as [119]
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“AD2 did not overlap with AD1 and was more frequently observed in human brain samples showing both tau and amyloid pathology [119]”
From Microglia and monocytes in inflammatory CNS disease: integrating phenotype and function (2022) · cites this paper as [119]
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“single cell nuclei extraction from frozen human brain tissue revealed 13 distinct microglial clusters [119]”
From Microglia and monocytes in inflammatory CNS disease: integrating phenotype and function (2022) · cites this paper as [119]
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“Moreover, distinct Aβ and tau-associated microglia signatures have been discovered in AD patients.”
From Microglia in neurodegenerative diseases: mechanism and potential therapeutic targets (2023) · cites this paper as [15]
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Citations
Cites
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
Love, M. I., Huber, W., & Anders, S. (2014) · Genome Biology · doi:10.1186/s13059-014-0550-8 · Cited by 95,106
Robinson, M. D., McCarthy, D. J., & Smyth, G. K. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btp616 · Cited by 41,364
HTSeq—a Python framework to work with high-throughput sequencing data
Anders, S., Pyl, P. T., & Huber, W. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btu638 · Cited by 21,464
Neuropathological stageing of Alzheimer-related changes.
Braak, H., & Braak, E. (1991) · Acta Neuropathologica · doi:10.1007/bf00308809 · Cited by 16,123
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
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
Reversed graph embedding resolves complex single-cell trajectories
Qiu, X., Mao, Q., Tang, Y., Wang, L., Chawla, R., Pliner, H. A., & Trapnell, C. (2017) · Nature Methods · doi:10.1038/nmeth.4402 · Cited by 5,070
Buniello, A., MacArthur, J. A. L., Cerezo, M., Harris, L. W., Hayhurst, J., Malangone, C., McMahon, A., Morales, J., Mountjoy, E., Sollis, E., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gky1120 · Cited by 4,000
Complement and microglia mediate early synapse loss in Alzheimer mouse models
Hong, S., Beja-Glasser, V. F., Nfonoyim, B. M., Frouin, A., Li, S., Ramakrishnan, S., Merry, K. M., Shi, Q., Rosenthal, A., Barres, B. A., et al. (2016) · Science · doi:10.1126/science.aad8373 · Cited by 3,343
Braak, H., Alafuzoff, I., Arzberger, T., Kretzschmar, H., & Del Tredici, K. (2006) · Acta Neuropathologica · doi:10.1007/s00401-006-0127-z · Cited by 3,172
Hyman, B. T., Phelps, C. H., Beach, T. G., Bigio, E. H., Cairns, N. J., Carrillo, M. C., Dickson, D. W., Duyckaerts, C., Frosch, M. P., Masliah, E., et al. (2012) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2011.10.007 · Cited by 3,092
Krasemann, S., Madore, C., Cialic, R., Baufeld, C., Calcagno, N., El Fatimy, R., Beckers, L., O’Loughlin, E., Xu, Y., Fanek, Z., et al. (2017) · Immunity · doi:10.1016/j.immuni.2017.08.008 · Cited by 2,979
Montine, T. J., Phelps, C. H., Beach, T. G., Bigio, E. H., Cairns, N. J., Dickson, D. W., Duyckaerts, C., Frosch, M. P., Masliah, E., Mirra, S. S., et al. (2012) · Acta Neuropathologica · doi:10.1007/s00401-011-0910-3 · Cited by 2,894
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
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
Single-cell transcriptomic analysis of Alzheimer’s disease
Mathys, H., Davila-Velderrain, J., Peng, Z., Gao, F., Mohammadi, S., Young, J. Z., Menon, M., He, L., Abdurrob, F., Jiang, X., et al. (2019) · Nature · doi:10.1038/s41586-019-1195-2 · Cited by 2,568
Jansen, I. E., Savage, J. E., Watanabe, K., Bryois, J., Williams, D. M., Steinberg, S., Sealock, J., Karlsson, I. K., Hägg, S., Athanasiu, L., et al. (2019) · Nature Genetics · doi:10.1038/s41588-018-0311-9 · Cited by 2,277
Nelson, P. T., Alafuzoff, I., Bigio, E. H., Bouras, C., Braak, H., Cairns, N. J., Castellani, R. J., Crain, B. J., Davies, P., Tredici, K. D., et al. (2012) · Journal of Neuropathology & Experimental Neurology · doi:10.1097/nen.0b013e31825018f7 · Cited by 2,050
The Cellular Phase of Alzheimer’s Disease
De Strooper, B., & Karran, E. (2016) · Cell · doi:10.1016/j.cell.2015.12.056 · Cited by 1,990
Ancient DNA and the rewriting of human history: be sparing with Occam’s razor
Haber, M., Mezzavilla, M., Xue, Y., & Tyler-Smith, C. (2016) · Genome Biology · doi:10.1186/s13059-015-0866-z · Cited by 1,924
A molecular atlas of cell types and zonation in the brain vasculature
Vanlandewijck, M., He, L., Mäe, M. A., Andrae, J., Ando, K., Del Gaudio, F., Nahar, K., Lebouvier, T., Laviña, B., Gouveia, L., et al. (2018) · Nature · doi:10.1038/nature25739 · Cited by 1,892
Spatial and temporal heterogeneity of mouse and human microglia at single-cell resolution
Masuda, T., Sankowski, R., Staszewski, O., Böttcher, C., Amann, L., Sagar, Scheiwe, C., Nessler, S., Kunz, P., van Loo, G., et al. (2019) · Nature · doi:10.1038/s41586-019-0924-x · Cited by 1,310
Microglial signatures and their role in health and disease
Butovsky, O., & Weiner, H. L. (2018) · Nature Reviews Neuroscience · doi:10.1038/s41583-018-0057-5 · Cited by 1,046
Disease-associated astrocytes in Alzheimer’s disease and aging
Habib, N., McCabe, C., Medina, S., Varshavsky, M., Kitsberg, D., Dvir-Szternfeld, R., Green, G., Dionne, D., Nguyen, L., Marshall, J. L., et al. (2020) · Nature Neuroscience · doi:10.1038/s41593-020-0624-8 · Cited by 1,009
Grubman, A., Chew, G., Ouyang, J. F., Sun, G., Choo, X. Y., McLean, C., Simmons, R. K., Buckberry, S., Vargas-Landin, D. B., Poppe, D., et al. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0539-4 · Cited by 956
Single-cell genomics identifies cell type–specific molecular changes in autism
Velmeshev, D., Schirmer, L., Jung, D., Haeussler, M., Perez, Y., Mayer, S., Bhaduri, A., Goyal, N., Rowitch, D. H., & Kriegstein, A. R. (2019) · Science · doi:10.1126/science.aav8130 · Cited by 927
Single-cell profiling identifies myeloid cell subsets with distinct fates during neuroinflammation
Jordão, M. J. C., Sankowski, R., Brendecke, S. M., Sagar, Locatelli, G., Tai, Y.-H., Tay, T. L., Schramm, E., Armbruster, S., Hagemeyer, N., et al. (2019) · Science · doi:10.1126/science.aat7554 · Cited by 843
Transcriptomic analysis of purified human cortical microglia reveals age-associated changes
Galatro, T. F., Holtman, I. R., Lerario, A. M., Vainchtein, I. D., Brouwer, N., Sola, P. R., Veras, M. M., Pereira, T. F., Leite, R. E. P., Möller, T., et al. (2017) · Nature Neuroscience · doi:10.1038/nn.4597 · Cited by 741
Self-citationSingle 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
Neuronal vulnerability and multilineage diversity in multiple sclerosis
Schirmer, L., Velmeshev, D., Holmqvist, S., Kaufmann, M., Werneburg, S., Jung, D., Vistnes, S., Stockley, J. H., Young, A., Steindel, M., et al. (2019) · Nature · doi:10.1038/s41586-019-1404-z · Cited by 638
Sannerud, R., Esselens, C., Ejsmont, P., Mattera, R., Rochin, L., Tharkeshwar, A. K., De Baets, G., De Wever, V., Habets, R., Baert, V., et al. (2016) · Cell · doi:10.1016/j.cell.2016.05.020 · Cited by 299
Selective Expression of the Glutamate Receptor Channel δ2 Subunit in Cerebellar Purkinje Cells
Araki, K., Meguro, H., Kushiya, E., Takayama, C., Inoue, Y., & Mishina, M. (1993) · Biochemical and Biophysical Research Communications · doi:10.1006/bbrc.1993.2614 · Cited by 293
Novel Alzheimer risk genes determine the microglia response to amyloid-β but not to TAU pathology
Sierksma, A., Lu, A., Mancuso, R., Fattorelli, N., Thrupp, N., Salta, E., Zoco, J., Blum, D., Buée, L., De Strooper, B., et al. (2020) · EMBO Molecular Medicine · doi:10.15252/emmm.201910606 · Cited by 291
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
Neuropathology of Alzheimer’s disease: a critical update
Jellinger, K. A., & Bancher, C. (1998) · Journal of neural transmission. Supplementum · doi:10.1007/978-3-7091-7508-8_8 · Cited by 233
Kampf, C., Olsson, I., Ryberg, U., Sjöstedt, E., & Pontén, F. (2012) · Journal of Visualized Experiments · doi:10.3791/3620 · Cited by 197
Transcriptional profiling of microglia; current state of the art and future perspectives
Gerrits, E., Heng, Y., Boddeke, E. W. G. M., & Eggen, B. J. L. (2020) · Glia · doi:10.1002/glia.23767 · Cited by 140
Self-citationvan den Bos, H., Spierings, D. C. J., Taudt, A., Bakker, B., Porubskỳ, D., Falconer, E., Novoa, C. A., Halsema, N., Kazemier, H. G., Hoekstra-Wakker, K., et al. (2016) · Genome biology · doi:10.1186/s13059-016-0976-2 · Cited by 139
Self-citationToward a Common Coordinate Framework for the Human Body
Rood, J., Stuart, T., Ghazanfar, S., Biancalani, T., Fisher, E., Butler, A., Hupalowska, A., Gaffney, L., Mauck, W. M., Eraslan, G., et al. (2019) · Cell · doi:10.1016/j.cell.2019.11.019 · Cited by 117
Alsema, A. M., Jiang, Q., Kracht, L., Gerrits, E., Dubbelaar, M. L., Miedema, A., Brouwer, N., Hol, E. M., Middeldorp, J., van Dijk, R. E., et al. (2020) · Frontiers in Molecular Neuroscience · doi:10.3389/fnmol.2020.00134 · Cited by 83
Self-citationWeidner, L. D., Kannan, P., Mitsios, N., Kang, S. J., Hall, M. D., Theodore, W. H., Innis, R. B., & Mulder, J. (2018) · Epilepsia · doi:10.1111/epi.14505 · Cited by 68
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DiFeliceantonio, A. G., & Small, D. M. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0304-0 · Cited by 59
Alsema, A. M., Jiang, Q., Kracht, L., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.03.18.995332 · Cited by 10
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Xi, H. S., Gibilisco, L., Kummer, M. P., Biber, K., Wachter, A., & Woodbury, M. E. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.11.13.381624 · Cited by 7
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Gao, C., Jiang, J., Tan, Y., & Chen, S. (2023) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-023-01588-0 · Cited by 1,616
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Loh, J. S., Mak, W. Q., Tan, L. K. S., Ng, C. X., Chan, H. H., Yeow, S. H., Foo, J. B., Ong, Y. S., How, C. W., & Khaw, K. Y. (2024) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-024-01743-1 · Cited by 1,193
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Morabito, S., Reese, F., Rahimzadeh, N., Miyoshi, E., & Swarup, V. (2023) · Cell Reports Methods · doi:10.1016/j.crmeth.2023.100498 · Cited by 773
Metadata
- Authors
- Gerrits, E., Brouwer, N., Kooistra, S. M., Woodbury, M. E., Vermeiren, Y., Lambourne, M., Mulder, J., Kummer, M., Möller, T., Biber, K., Dunnen, W. F. A. D., De Deyn, P. P., Eggen, B. J. L., & Boddeke, E. W. G. M.
- Affiliations
- University Medical Center Groningen (NL)
- AbbVie (United States) (US)
- Karolinska Institutet (SE)
- AbbVie (Germany) (DE)
- University of Copenhagen (DK)
- Corresponding author
- Boddeke, Erik W. G. M.
- ORCID
- Venue
- Acta Neuropathologica
- Published
- 2021-02-20
- Volume
- 141
- Issue
- 5
- Pages
- 681-696
- ISSN
- 0001-6322 (ISSN-L)
- 1432-0533
- PMC
- PMC8043951
- OpenAlex
- W3129453843
- Cited by
- 441
- References
- 42
- Citations by year
- 2026: 69 · 2025: 80 · 2024: 124 · 2023: 53 · 2022: 87 · 2021: 27
- FWCI
- 30.05
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Acta Neuropathologica (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Research at the University of Copenhagen (University of Copenhagen) (publishedVersion, cc-by)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (publishedVersion, cc-by)
- Socio-Environmental Systems Modeling (acceptedVersion, cc-by)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion, cc-by)
- Data Archiving and Networked Services (DANS) (submittedVersion, cc-by)
- Funders
- Rijksuniversiteit Groningen
- Stichting MS Research
- Fonds Wetenschappelijk Onderzoek (12Z1620N)
- Universitair Medisch Centrum Groningen
- Vlaamse Instelling voor Technologisch Onderzoek
- Alzheimer Nederland
- Vlaamse regering
- Topics
- Life Sciences › Neuroscience › Neurology › Neuroinflammation and Neurodegeneration Mechanisms
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Alzheimer's disease research and treatments
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Keywords
- Alzheimer's disease, microglia, amyloid plaques, tau pathology, microglial activation, microglial phenotype, single-nucleus RNA sequencing, phosphorylated tau, disease-associated microglia, microglia-targeted therapy
- MeSH terms
- Aged, Aged, 80 and over, Alzheimer Disease, Brain, Female, Humans, Male, Amyloid beta-Peptides, tau Proteins, Microglia
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