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Differential Phagocytic Properties of CD45low Microglia and CD45high Brain Mononuclear Phagocytes—Activation and Age-Related Effects
Open accessAbstract
In the central nervous system (CNS), microglia are innate immune mononuclear phagocytes (CNS MPs) that can phagocytose infectious particles, apoptotic cells, neurons and pathological protein aggregates such as Aβ in Alzheimer’s disease (AD). While CD11b+CD45low microglia account for the majority of CNS MPs, a small population of CD11b+CD45high CNS MPs is also recognized in AD that surround Aβ plaques. These transcriptionally-distinct and pathologically-relevant CD45high cells have unclear origin and undefined phagocytic characteristics. We have comprehensively validated rapid flow cytometric assays of bulk-phase and amyloid β fibril (fAβ) phagocytosis and applied these to study acutely isolated CNS MPs. Using these methods, we provide novel insights into differential abilities of CD11b+ CD45low and CD45high CNS MPs to phagocytose macroparticles and fAβ under normal, acute and chronic neuroinflammatory states. CD45high CNS MPs also highly upregulate TREM2, CD11c and several disease-associated-microglia (DAM) signature genes, and have a higher phagocytic capacity for Aβ as compared to CD45low microglia in the 5xFAD mouse model of AD that becomes more apparent with ageing. Our data suggest an overall pro-phagocytic and protective role for CD11b+CD45high CNS MPs in neurodegeneration, which if promoted, could be beneficial.
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![Figure 3 Identification of phagocytic receptors that regulate microsphere and fAb42 phagocytosis in BV2 microglia. (A) Differentially expressed phagocytic proteins identified by quantitative proteomics in BV2 microglia that were exposed to M1 [lipopolysaccharide (LPS)], M2a (IL4), and M2c (IL10 + TGFβ) polarizing stimuli for 48 h. Please refer to Table S1 in Supplementary Material for additional information. (B) Relative expression of BV2 phagocytic proteins at the transcriptomic level in acutely isolated CNS MPs, comparing LPS-treated vs. untreated CD45low CNS MPs and CD45low vs. CD45high CD11b+ CNS MPs. Red indicates relative upregulation, Blue indicates relative downregulation and Gray indicates genes/proteins not identified by RNAseq. These data were obtained from publicly available RNAseq data. (C) Comparison of phycoerythrin (PE)-microsphere and fAβ42 phagocytic profiles of classically activated (LPS), alternatively activated (IL4), and deactivated (TGFβ + IL10) BV2 microglia. (D,E) Effects of functional blockade of selected phagocytic receptors on PE-microsphere (D) and fAβ42 phagocytosis (E) by BV2 microglia. N = 5 independent experiments; *p < 0.05, **p < 0.01, ***p < 0.005.](/paper-figures/288728808e2e4c409f803de8f0c92e15/differential-phagocytic-properties-of-cd45low-microglia-and-cd45high-brain-figure-3.jpg)





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“We have found that Cytochalasin D inhibits the second peak of fluorescence in this assay, suggesting that the first peak represent non-specific binding of Aβ42 to cells while the second peak represents true uptake in an actin-dependent manner [31].”
From Identification and therapeutic modulation of a pro-inflammatory subset of disease-associated-microglia in Alzheimer’s disease (2018) · cites this paper as [31]
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Amyloid oligomers exacerbate tau pathology in a mouse model of tauopathy
Selenica, M.-L. B., Brownlow, M., Jimenez, J. P., Lee, D. C., Pena, G., Dickey, C. A., Gordon, M. N., & Morgan, D. (2013) · Neurodegenerative Diseases · doi:10.1159/000337230 · Cited by 10,359
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
Cox, J., Hein, M. Y., Luber, C. A., Paron, I., Nagaraj, N., & Mann, M. (2014) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m113.031591 · Cited by 5,180
Oakley, H., Cole, S. L., Logan, S., Maus, E., Shao, P., Craft, J., Guillozet-Bongaarts, A., Ohno, M., Disterhoft, J., Van Eldik, L., et al. (2006) · The Journal of Neuroscience · doi:10.1523/jneurosci.1202-06.2006 · Cited by 3,530 · cited 2×
“Therefore, we studied phagocytic profiles of acutely isolated CNS MPs in the 5xFAD mouse model of AD pathology in which rapid accumulation of Aβ plaques begins at 3 months of age followed by neurodegeneration and neurobehavioral deficits by 8–10 months of age (59).”
BackgroundGene Ontology Consortium: going forward
The Gene Ontology Consortium (2015) · Nucleic Acids Research · doi:10.1093/nar/gku1179 · Cited by 3,492
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
Variant of TREM2 Associated with the Risk of Alzheimer's Disease
Jonsson, T., Stefansson, H., Steinberg, S., Jonsdottir, I., Jonsson, P. V., Snaedal, J., Bjornsson, S., Huttenlocher, J., Levey, A. I., Lah, J. J., et al. (2013) · New England Journal of Medicine · doi:10.1056/nejmoa1211103 · Cited by 2,558
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Tang, Y., & Le, W. (2016) · Molecular Neurobiology · doi:10.1007/s12035-014-9070-5 · Cited by 2,190
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Qin, L., Wu, X., Block, M. L., Liu, Y., Breese, G. R., Hong, J., Knapp, D. J., & Crews, F. T. (2007) · Glia · doi:10.1002/glia.20467 · Cited by 2,144
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Orihuela, R., McPherson, C. A., & Harry, G. J. (2016) · British Journal of Pharmacology · doi:10.1111/bph.13139 · Cited by 2,070
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Metadata
- Authors
- Rangaraju, S., Raza, S. A., Li, N. X., Betarbet, R., Dammer, E. B., Duong, D., Lah, J. J., Seyfried, N. T., & Levey, A. I.
- Affiliations
- Emory University (US)
- Corresponding author
- Rangaraju, Srikant
- ORCID
- Venue
- Frontiers in Immunology
- Publisher
- Frontiers Media SA
- Published
- 2018-03-02
- Volume
- 9
- Pages
- 405-405
- ISSN
- 1664-3224 (ISSN-L)
- PMC
- PMC5840283
- OpenAlex
- W2792377614
- Cited by
- 126
- References
- 80
- Citations by year
- 2026: 9 · 2025: 8 · 2024: 12 · 2023: 18 · 2022: 35 · 2021: 13 · 2020: 19 · 2019: 8 · 2018: 3
- FWCI
- 4.82
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Frontiers in Immunology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Institutes of Health (P50 AG025688, NS099474-1, P30 NS055077, 1U01AG046161)
- National Science Foundation (CHE-1507932, NSF/DMR-BSF 1610377)
- American Brain Foundation
- Alzheimer’s Association
- Topics
- Life Sciences › Neuroscience › Neurology › Neuroinflammation and Neurodegeneration Mechanisms
- Life Sciences › Immunology and Microbiology › Immunology › Phagocytosis and Immune Regulation
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Alzheimer's disease research and treatments
- Keywords
- microglia, phagocytosis, amyloid-beta fibrils, Alzheimer's disease, disease-associated microglia, TREM2, CD11c, neuroinflammation, aging, 5xFAD mice, neurodegeneration
- MeSH terms
- Aging, Alzheimer Disease, Animals, Brain, Cell Line, Disease Models, Animal, Humans, Membrane Glycoproteins, Mice, Inbred C57BL, Mice, Mutant Strains, Phagocytes, Phagocytosis, Receptors, Immunologic, Mononuclear Phagocyte System, Amyloid beta-Peptides, Leukocyte Common Antigens, Microglia, Mice
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