Microglia play a critical role in brain homeostasis and disease progression. In neurodegenerative conditions, microglia acquire the neurodegenerative phenotype (MGnD), whose function is poorly understood. MicroRNA-155 (miR-155), enriched in immune cells, critically regulates MGnD. However, its role in Alzheimer’s disease (AD) pathogenesis remains unclear. Here, we report that microglial deletion of miR-155 induces a pre-MGnD activation state via interferon-γ (IFN-γ) signaling, and blocking IFN-γ signaling attenuates MGnD induction and microglial phagocytosis. Single-cell RNA-sequencing analysis of microglia from an AD mouse model identifies Stat1 and Clec2d as pre-MGnD markers. This phenotypic transition enhances amyloid plaque compaction, reduces dystrophic neurites, attenuates plaque-associated synaptic degradation and improves cognition. Our study demonstrates a miR-155-mediated regulatory mechanism of MGnD and the beneficial role of IFN-γ-responsive pre-MGnD in restricting neurodegenerative pathology and preserving cognitive function in an AD mouse model, highlighting miR-155 and IFN-γ as potential therapeutic targets for AD.
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Single-cell and bulk RNA-seq data were analyzed using customized code available at https://github.com/The-Butovsky-Lab/Yin-Herron-et-al.-miR-155-deletion-study/.
Efthymiou, A. G. & Goate, A. M. Late-onset Alzheimer’s disease genetics implicates microglial pathways in disease risk. Mol. Neurodegener. 12, 43 (2017).
Lambert, J. C. et al. Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer’s disease. Nat. Genet. 45, 1452–1458 (2013).
Kunkle, B. W. et al. Genetic meta-analysis of diagnosed Alzheimer’s disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing. Nat. Genet. 51, 414–430 (2019).
Colonna, M. & Butovsky, O. Microglia function in the central nervous system during health and neurodegeneration. Annu. Rev. Immunol. 35, 441–468 (2017).
Heneka, M. T. et al. Neuroinflammation in Alzheimer’s disease. Lancet Neurol. 14, 388–405 (2015).
Butovsky, O. et al. Identification of a unique TGF-β-dependent molecular and functional signature in microglia. Nat. Neurosci. 17, 131–143 (2014).
Krasemann, S. et al. The TREM2–APOE pathway drives the transcriptional phenotype of dysfunctional microglia in neurodegenerative diseases. Immunity 47, 566–581 (2017).
Keren-Shaul, H. et al. A unique microglia type associated with restricting development of Alzheimer’s disease. Cell 169, 1276–1290 (2017).
Gosselin, D. et al. Environment drives selection and function of enhancers controlling tissue-specific macrophage identities. Cell 159, 1327–1340 (2014).
Wang, Y. et al. TREM2 lipid sensing sustains the microglial response in an Alzheimer’s disease model. Cell 160, 1061–1071 (2015).
Butovsky, O. & Weiner, H. L. Microglial signatures and their role in health and disease. Nat. Rev. Neurosci. 19, 622–635 (2018).
Wang, Y. et al. TREM2-mediated early microglial response limits diffusion and toxicity of amyloid plaques. J. Exp. Med. 213, 667–675 (2016).
Yuan, P. et al. TREM2 haplodeficiency in mice and humans impairs the microglia barrier function leading to decreased amyloid compaction and severe axonal dystrophy. Neuron 90, 724–739 (2016).
Cheng, Q. et al. TREM2-activating antibodies abrogate the negative pleiotropic effects of the Alzheimer’s disease variant Trem2R47H on murine myeloid cell function. J. Biol. Chem. 293, 12620–12633 (2018).
Cignarella, F. et al. TREM2 activation on microglia promotes myelin debris clearance and remyelination in a model of multiple sclerosis. Acta Neuropathol. 140, 513–534 (2020).
Ellwanger, D. C. et al. Prior activation state shapes the microglia response to antihuman TREM2 in a mouse model of Alzheimer’s disease. Proc. Natl Acad. Sci. USA 118, e2017742118 (2021).
Fassler, M., Rappaport, M. S., Cuño, C. B. & George, J. Engagement of TREM2 by a novel monoclonal antibody induces activation of microglia and improves cognitive function in Alzheimer’s disease models. J. Neuroinflammation 18, 19 (2021).
Schlepckow, K. et al. Enhancing protective microglial activities with a dual function TREM 2 antibody to the stalk region. EMBO Mol. Med. 12, e11227 (2020).
Jain, N., Lewis, C. A., Ulrich, J. D. & Holtzman, D. M. Chronic TREM2 activation exacerbates Aβ-associated tau seeding and spreading. J. Exp. Med. 220, e20220654 (2023).
Clayton, K. et al. Plaque-associated microglia hyper-secrete extracellular vesicles and accelerate tau propagation in a humanized APP mouse model. Mol. Neurodegener. 16, 18 (2021).
Schafer, D. P. et al. Microglia sculpt postnatal neural circuits in an activity and complement-dependent manner. Neuron 74, 691–705 (2012).
Stevens, B. et al. The classical complement cascade mediates CNS synapse elimination. Cell 131, 1164–1178 (2007).
Hong, S. et al. Complement and microglia mediate early synapse loss in Alzheimer mouse models. Science 352, 712–716 (2016).
Faraoni, I., Antonetti, F. R., Cardone, J. & Bonmassar, E. miR-155 gene: a typical multifunctional microRNA. Biochim. Biophys. Acta 1792, 497–505 (2009).
O’Connell, R. M., Taganov, K. D., Boldin, M. P., Cheng, G. & Baltimore, D. MicroRNA-155 is induced during the macrophage inflammatory response. Proc. Natl Acad. Sci. USA 104, 1604–1609 (2007).
Butovsky, O. et al. Targeting miR-155 restores abnormal microglia and attenuates disease in SOD1 mice. Ann. Neurol. 77, 75–99 (2015).
Koval, E. D. et al. Method for widespread microRNA-155 inhibition prolongs survival in ALS-model mice. Hum. Mol. Genet 22, 4127–4135 (2013).
Readhead, B. et al. miR-155 regulation of behavior, neuropathology, and cortical transcriptomics in Alzheimer’s disease. Acta Neuropathol. 140, 295–315 (2020).
Aloi, M. S. et al. The pro-inflammatory microRNA miR-155 influences fibrillar β-amyloid1–42 catabolism by microglia. Glia 69, 1736–1748 (2021).
Hu, R. et al. miR-155 promotes T follicular helper cell accumulation during chronic, low-grade inflammation. Immunity 41, 605–619 (2014).
Parkhurst, C. N. et al. Microglia promote learning-dependent synapse formation through brain-derived neurotrophic factor. Cell 155, 1596–1609 (2013).
Radde, R. et al. Aβ42-driven cerebral amyloidosis in transgenic mice reveals early and robust pathology. EMBO Rep. 7, 940–946 (2006).
Goldmann, T. et al. Origin, fate and dynamics of macrophages at central nervous system interfaces. Nat. Immunol. 17, 797–805 (2016).
Roy, E. R. et al. Type I interferon response drives neuroinflammation and synapse loss in Alzheimer disease. J. Clin. Invest. 130, 1912–1930 (2020).
Sala Frigerio, C. et al. The major risk factors for Alzheimer’s disease: age, sex, and genes modulate the microglia response to Aβ plaques. Cell Rep. 27, 1293–1306 (2019).
Lai, J. J., Cruz, F. M. & Rock, K. L. Immune sensing of cell death through recognition of histone sequences by C-type lectin-receptor-2d causes inflammation and tissue injury. Immunity 52, 123–135 (2020).
Gracias, D. T. et al. The microRNA miR-155 controls CD8+ T cell responses by regulating interferon signaling. Nat. Immunol. 14, 593–602 (2013).
Li, H., Gade, P., Xiao, W. & Kalvakolanu, D. V. The interferon signaling network and transcription factor C/EBP-β. Cell. Mol. Immunol. 4, 407 (2007).
He, M., Xu, Z., Ding, T., Kuang, D.-M. & Zheng, L. MicroRNA-155 regulates inflammatory cytokine production in tumor-associated macrophages via targeting C/EBPβ. Cell. Mol. Immunol. 6, 343–352 (2009).
Prencipe, G. et al. Neutralization of IFN-γ reverts clinical and laboratory features in a mouse model of macrophage activation syndrome. J. Allergy Clin. Immunol. 141, 1439–1449 (2018).
Parhizkar, S. et al. Loss of TREM2 function increases amyloid seeding but reduces plaque-associated ApoE. Nat. Neurosci. 22, 191–204 (2019).
Serneels, L. et al. γ-Secretase heterogeneity in the Aph1 subunit: relevance for Alzheimer’s disease. Science 324, 639–642 (2009).
Hammond, T. R. et al. Single-cell RNA sequencing of microglia throughout the mouse lifespan and in the injured brain reveals complex cell-state changes. Immunity 50, 253–271 (2019).
Gerrits, E. et al. Distinct amyloid-β and tau-associated microglia profiles in Alzheimer’s disease. Acta Neuropathol. 141, 681–696 (2021).
Mathys, H. et al. Single-cell transcriptomic analysis of Alzheimer’s disease. Nature 570, 332–337 (2019).
Zhou, Y. et al. Human and mouse single-nucleus transcriptomics reveal TREM2-dependent and TREM2-independent cellular responses in Alzheimer’s disease. Nat. Med. 26, 131–142 (2020).
Sierksma, A. et al. Deregulation of neuronal miRNAs induced by amyloid-β or TAU pathology. Mol. Neurodegener. 13, 54 (2018).
Culpan, D., Kehoe, P. G. & Love, S. Tumour necrosis factor-α (TNF-α) and miRNA expression in frontal and temporal neocortex in Alzheimer’s disease and the effect of TNF-α on miRNA expression in vitro. Int. J. Mol. Epidemiol. Genet. 2, 156–162 (2011).
Patrick, E. et al. Dissecting the role of non-coding RNAs in the accumulation of amyloid and tau neuropathologies in Alzheimer’s disease. Mol. Neurodegener. 12, 51 (2017).
Lau, P. et al. Alteration of the microRNA network during the progression of Alzheimer’s disease. EMBO Mol. Med. 5, 1613–1634 (2013).
Landgraf, P. et al. A mammalian microRNA expression atlas based on small RNA library sequencing. Cell 129, 1401–1414 (2007).
Korotkov, A. et al. Increased expression of miR142 and miR-155 in glial and immune cells after traumatic brain injury may contribute to neuroinflammation via astrocyte activation. Brain Pathol. 30, 897–912 (2020).
Lee, S. H. et al. Identifying the initiating events of anti-Listeria responses using mice with conditional loss of IFN-γ receptor subunit 1 (IFNGR1). J. Immunol. 191, 4223–4234 (2013).
Picelli, S. et al. Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nat. Methods 10, 1096–1098 (2013).
Garcia-Alonso, L., Holland, C. H., Ibrahim, M. M., Turei, D. & Saez-Rodriguez, J. Benchmark and integration of resources for the estimation of human transcription factor activities. Genome Res. 29, 1363–1375 (2019).
Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587 (2021).
Germain, P. L., Lun, A., Garcia Meixide, C., Macnair, W. & Robinson, M. D. Doublet identification in single-cell sequencing data using scDblFinder. F1000Res 10, 979 (2021).
Zappia, L. & Oshlack, A. Clustering trees: a visualization for evaluating clusterings at multiple resolutions. Gigascience 7, giy083 (2018).
Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16, 1289–1296 (2019).
Subramanian, A. Obesity-instructed TREM2high macrophages identified by comparative analysis of diabetic mouse and human kidney at single cell resolution. Preprint at bioRxiv https://doi.org/10.1101/2021.05.30.446342 (2021).
Gabriely, G. et al. Myeloid cell subsets that express latency-associated peptide promote cancer growth by modulating T cells. iScience 24, 103347 (2021).
Mantri, M. et al. Spatiotemporal single-cell RNA sequencing of developing chicken hearts identifies interplay between cellular differentiation and morphogenesis. Nat. Commun. 12, 1771 (2021).
Mootha, V. K. et al. PGC-1α-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat. Genet. 34, 267–273 (2003).
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).
Griffiths-Jones, S., Grocock, R. J., van Dongen, S., Bateman, A. & Enright, A. J. miRBase: microRNA sequences, targets and gene nomenclature. Nucleic Acids Res. 34, D140–D144 (2006).
Wolf, A., Bauer, B., Abner, E. L., Ashkenazy-Frolinger, T. & Hartz, A. M. A comprehensive behavioral test battery to assess learning and memory in 129S6/Tg2576 mice. PLoS ONE 11, e0147733 (2016).
Lalonde, R. The neurobiological basis of spontaneous alternation. Neurosci. Biobehav. Rev. 26, 91–104 (2002).
We thank R. Krishnan (Ann Romney Center for Neurologic Diseases Flow Cytometry Core facility) and B. Tilton (Boston University Flow Cytometry Core facility) for FACS sorting; Y. Alekseyev, Boston University Single Cell Sequencing Core facility and Boston University Microarray and Sequencing Resource Core Facility for single-cell sequencing; the Single Cell Core at Harvard Medical School for performing the single-cell RNA-seq sample preparation; L. Ding and NeuroTechnology Studio at Brigham and Women’s Hospital for consultation on data acquisition and data analysis; G. Garden for sharing miR-155-flox mice; S. Garamszegi and Genomic Platform of Broad Institute of MIT and Harvard for Smart-seq2 RNA-seq; D. Holtzman for sharing the anti-Apoe and anti-Aβ antibodies. This work was supported by grants from the following organizations to the indicated authors. O.B. and T.I.: NIH-NIA (R01AG054672); O.B.: NIH-NIA (R01AG051812, R01AG075509, R01AG080992, R21AG076982 and R41AG073059), NIH-NINDS (R01NS088137, R21NS104609, R21NS101673), NIH-NEI (R01EY027921), NIH-NIGMS (R01GM132668), Cure Alzheimer’s Fund (ApoE Consortium) and BrightFocus Foundation (2020A016806); T.I. and S.I.: Cure Alzheimer’s Fund, T.I.: NIH-NIA (RF1AG054199, R01AG066429, R01AG072719 and R01AG067763); S.I.: NIH-NIA (R01AG079859), S.H.: NIH-NIA (F31AG071106) and NIH-NIGMS (T32GM008541). M.A.M: NIH-NEI (K12 EY016335, K08 EY030160) and Research to Prevent Blindness Career Development Award.
O.B. is an inventor of a patent for the use of miR-155 inhibitors for the treatment of neurodegenerative diseases. O.B. collaborated with GSK and Regulus Therapeutics, received research funding from Sanofi and GSK, has received honoraria for lectures from and has provided consultancy services to Camp4 and Ono Pharma USA. T.I. consults Takeda. The remaining authors declare no competing interests.
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a, Gating strategy for sorting Ly6C–CD11b+Fcrls+ microglia. b, Confocal microscopy images of Iba1 or CD206 immunoreactivity and detection of miR-155 gene expression using miRNAscope in 8-month-old APP/PS1 mice, scale bar: 50 µm. c, Quantification of fluorescence intensity of miR-155 in Iba1+ cells, Iba1– cells, and CD206+ cells per ROI using one-way ANOVA with Tukey’s post hoc analysis, P < 0.0001 (n = 8 ROIs for Iba1+ group, n = 12 ROIs for Iba1– group, n = 5 ROIs for CD206+ group). Data were presented as mean values ± s.e.m.
Extended Data Fig. 2 Targeting microglial miR-155 causes subtle changes in APP/PS1 mice at 8 months old.
a, qPCR of miR-155 expression in microglia from 8-month-old mice showing the efficiency of tamoxifen treatment, P = 0.0003 using one-way ANOVA with Tukey’s post hoc test (n = 5 mice for WT and APP/PS1:miR-155 cKO group, n = 3 mice for miR-155 cKO group, n = 4 mice for APP/PS1 group). b, Heatmap of DEGs identified in microglia of APP/PS1:miR-155 cKO group at 8 months of age, compared to APP/PS1 group using DESeq2 analysis (Two-sided LRT, FDR-corrected P < 0.05, n = 5-6 per sex/group). c, PCA analysis showing no separation among all the groups at 8 months old. d, Venn diagram showing the difference of DEGs between 4- and 8-month-old groups. Data were presented as mean values ± s.e.m. See also Supplementary Table 1.
a, Experimental design. b, Number of DEGs comparing miR-155 cKO vs. WT at 4- and 8 months old (n = 5-6 mice per sex/group). Heat map of DEGs identified in miR-155 cKO microglia, compared to WT microglia for male (c) and female (d) groups using DESeq2 analysis, two-sided Wald test, FDR-corrected P < 0.05 (n = 5-6 mice per sex/group). e, Top-affected pathways based on the comparison of female miR-155 cKO microglia vs. WT microglia at 8 months of age using IPA (DEGs were identified with two-sided Wald test, FDR-corrected P < 0.05). See also Supplementary Table 1.
Venn diagram (a) and violin plot (b) showing the difference of up-regulated DEGs or down-regulated DEGs by comparing APP/PS1:miR-155 cKO vs. APP/PS1 (P < 0.05) between male and female samples at 4 months of age. c, Sex-specific top-15 upregulated and downregulated DEGs with the Log2 fold changes (FC) between APP/PS1 vs. WT group and APP/PS1:miR-155 cKO vs. WT group. DEGs were identified using DESeq2 analysis with FDR-corrected P < 0.05. d, Volcano plots using common DEGs of sex-specific APP/PS1:miR-155 cKO microglia vs. APP/PS1 microglia and non-plaque-associated microglia or plaque-associated microglia, compared to WT microglia (Two-sided LRT, P < 0.05). See also Supplementary Table 1.
a, GSEA analysis of interferon alpha response and interferon gamma response made using bulk RNA sequencing data from isolated microglia. b, DEG Heatmaps of genes involved in gene ontology terms for phagocytosis, antigen presentation and cellular response to IFN-γ derived from bulk RNAseq data (Two-sided LRT, FDR-corrected P < 0.05, n = 4-6 mice per sex/group). c, Transcription factor functional enrichment analysis in microglia comparing APP/PS1:miR-155 cKO vs. APP/PS1 mice at 4 months old (R_Dorothea, FDR-corrected P < 0.05). d, Workflow of generating miR-155 targetome. e, Volcano plots showing common genes (highlighted) between miR-155 targetome and DEGs from four different comparisons: male and female miR-155 cKO vs. WT at the age of 8 months, APP/PS1:miR-155 cKO vs. APP/PS1 at the age of 4 and 8 months (Two-sided Wald test, P < 0.05, n = 5-13 mice per group). f, Table of common miR-155 targeted genes among four different comparisons in e. See also Supplementary Table 1.
Extended Data Fig. 6 miR-155 ablation sensitizes microglial transition to the pre-MGnD cluster through interferon-γ signaling.
a, Gating strategy for sorting CD11b+CD45+ myeloid cells. b, Unbiased UMAP plots analysis of female APP/PS1 and APP/PS1:miR-155 cKO microglia from single cell sequencing. Initial clustering generated 17 sub-clusters (n = 2 mice per group, 15,429 cells). c, Cell cycle phase analysis for each cluster (clusters 15 and 16 removed). d, Percentage of cell cycle phase within each sub-cluster. e, Volcano plots of DEGs in the proliferating microglia clusters 8 and 9 comparing APP/PS1:miR-155 cKO microglia and APP/PS1 microglia (Two-sided Wald test, P < 0.05). f, Effect-size of differences in the proportion of microglia clusters between genotypes in female APP/PS1:miR-155 cKO mice determined by Poisson regression. The color bar indicates the P value, while the size of the points indicates the effect size. g, UMAP plots analysis of three major microglia clusters identified in male APP/PS1 and APP/PS1:miR-155 cKO microglia from scRNA sequencing (n = 3 mice per group, 43,667 cells). h, Bar charts showing relative percentage of each major microglia cluster in APP/PS1 and APP/PS1:miR-155 cKO mice. i, Violin plots showing the mean z-score of pre-MGnD markers in APP/PS1 and APP/PS1:miR-155 cKO male mice. P = 3.54507e-91 by Kruska-Wallis test, FDR corrected using Benjamini Hochberg. j, Top-affected pathways from IPA analysis of all three clusters based on the comparison of APP/PS1:miR-155 cKO microglia vs. APP/PS1 microglia at 4 months of age (DEGs were identified with two-sided Wilcox FDR-corrected P < 0.05). k, UMAP plots analysis of microglia clusters identified in male APP/PS1 and APP/PS1:miR-155 cKO microglia including subclusters of pre-MGnD. l, Dot plot showing top markers for four microglia clusters (M0, early IFN-responsive pre-MGnD, late IFN-responsive pre-MGnD and MGnD). m, Trajectory analysis showing the transition from M0 to MGnD via IFN-responsive pre-MGnD clusters. n, Dynamic plots showing the expression of selected genes in all four major microglia clusters. o, Effect-size of differences in the proportion of microglia clusters between genotypes in male APP/PS1:miR-155 cKO mice determined by Poisson regression. The color bar indicates the P value, while the size of the points indicates the effect size. Standard error bars were shown. See also Supplementary Table 2.
a, Confocal microscopy images of Stat1+ and Iba1+ cells in the brain of APP/PS1 and APP/PS1:miR-155 cKO mice at 4 months of age. Scale bar: 50 µm. b, Quantification of Stat1positive are in Iba1+ cells per ROI, P = 0.0001 by two-tailed Student’s unpaired t-test (n = 12 ROIs from 3 mice for APP/PS1 group, n = 18 ROIs from 6 mice for APP/PS1:miR-155 cKO group). c, Comparison of transcript per million (TPM) level of Clec2d between WT and APP/PS1 mice, P = 0.0458 by two-tailed Student’s unpaired t-test (n = 11 APP/PS1, n = 9 APP/PS1:miR-155 cKO). d, FACS plots showing four clusters in WT, 2- and 8-month-old APP/PS1 mice, which are Clec7a–Clec2d– (M0), Clec7a–Clec2d+ (early pre-MGnD), Clec7a+Clec2d+ (late pre-MGnD), and Clec7a+Clec2d– (MGnD). e, Quantification of percentage of four clusters mentioned in d from WT, 2- and 8-month-old APP/PS1 mice, P < 0.0001 for M0 and late pre-MGnD, P = 0.0011 (WT vs. 8-month-old APP/PS1) and P = 0.0084 (2-month-old APP/PS1 vs. 8-month-old APP/PS1) for early pre-MGnD, P = 0.0017 (WT vs. 8-month-old APP/PS1) and P = 0.0014 (2-month-old APP/PS1 vs. 8-month-old APP/PS1) for MGnD as determined by one-way ANOVA (n = 3 WT, n = 4 APP/PS1). f, qPCR Quantification of gene expression of P2ry12, Clec2d, and Clec7a in M0, early pre-MGnD, late pre-MGnD, and MGnD clusters, P < 0.0001 for P2ry12, P = 0.0046 (early pre-MGnD vs. MGnD) and P = 0.0103 (late pre-MGnD vs. MGnD) for Clec2d, P = 0.0136 (M0 vs. early pre-MGnD), P = 0.0060 (M0 vs. late pre-MGnD), and P = 0.0023 (M0 vs. MGnD) for Clec7a as determined by one-way ANOVA (n = 5 mice/group). Data were presented as mean values ± s.e.m.
a, Illustration of 3’UTR of Stat1 Gaussia luciferase assay co-transfected with miR-155 mimic or miR-155 antagomir into N9 microglial cells; Created with Biorender.com. b, Control plasmid activity in miR-155 mimic and miR-155 antagomir transfected cells. Fold changes were calculated as compared to control mimic or antagomir using two-tailed Student’s unpaired t-test (n = 3). c, Fold changes of luciferase activity after co-transfection of mutant STAT1 3’UTR and control mimic or miR-155 mimic to check the specific binding of miR-155 to Stat1, P = 0.0234 by two-way ANOVA with Holm-Sidak post hoc analysis (n = 3). d, Fold changes of luciferase activity after non-mutated Stat1 3’UTR co-transfected with the non-relevant miRNA mimic (miR-18), P = 0.0072 by one-way ANOVA with post hoc Tukey’s test (n = 3). Data are representative of 2 independent experiments and show biologically independent replicates (b-d). e, Venn diagram depicting overlap of Ifngr1 cKO downregulated genes and APP/PS1:miR-155 cKO upregulated genes. f, Heat map showing expression of genes which were downregulated by Ifngr1 cKO phagocytic microglia and upregulated in APP/PS1:miR-155 cKO microglia. Data were presented as mean values ± s.e.m. See also Supplementary Tables 1, 3.
Extended Data Fig. 9 Decreased 6E10+ amyloid plaque burden and enhanced phagocytosis function in APP/PS1:miR-155 cKO mice.
a, Representative images of 6E10 staining in APP/PS1 and APP/PS1:miR-155 cKO brains at the age of 4 months. Scale bar: 500 µm. b, 6E10 plaque counts normalized to area and percent area 6E10 positivity in 4-month-old mice. P values were determined using two-tailed Student’s unpaired t-test, P = 0.0084 and P = 0.0104, respectively (n = 10 APP/PS1, n = 9 APP/PS1:miR-155 cKO). c, 6E10 plaque counts normalized to area and percent area 6E10 positivity in 8-month-old mice (n = 14 APP/PS1, n = 16 APP/PS1:miR-155 cKO). P values were determined using two-tailed Student’s unpaired t-test. d, Representative images of Thioflavin-S in the cortex and hippocampus of APP/PS1 and APP/PS1:miR-155 cKO mice at 4 months old. Scale bar: 500 µm. P values were determined using two-tailed Student’s unpaired t-test. e, Thioflavin-S plaque counts normalized to area and percent area Thioflavin-S positivity in 4-month-old mice. P values were determined using two-tailed Student’s unpaired t-test (n = 10 APP/PS1, n = 9 APP/PS1:miR-155 cKO, P = 0.09). f, Thioflavin-S plaque counts normalized to area and percent area Thioflavin-S positivity in 8-month-old mice (n = 14 APP/PS1, n = 16 APP/PS1:miR-155 cKO). P values were determined using two-tailed Student’s unpaired t-test. g, Representative images of Thioflavin-S in the cortex and hippocampus of APP/PS1 and APP/PS1:miR-155 cKO mice at 2.5 months of age. Scale bar: 500 µm. h, Quantification of Thioflavin-S+ plaque area in the cortical region at 2.5 months of age using two-tailed Student’s unpaired t-test (n = 6 APP/PS1, n = 5 APP/PS1:miR-155 cKO). i, Representative images of HJ3.4B staining in APP/PS1 and APP/PS1:miR-155 cKO brains. Scale bar: 500 µm. j, Quantification of HJ3.4B+ plaque area normalized to cortical area at 2.5 months of age using two-tailed Student’s unpaired t-test (n = 6 APP/PS1, n = 5 APP/PS1:miR-155 cKO). k, Apoptotic neuron injection scheme. l, Mean fluorescence intensity (MFI) of phagocytic microglia sorted from WT and miR-155 cKO mice following apoptotic neuron injection, P = 0.0042 using two-tailed Student’s unpaired t-test (n = 7 WT, n = 14 miR-155 cKO). Data were combined from two independent experiments and normalized to control group (l). Data were presented as mean values ± s.e.m.
Extended Data Fig. 10 Proteomics analysis of whole-brain tissue showed enhanced synaptic functions in APP/PS1:miR-155 cKO mice at 4 and 8 months of age.
a, Heat maps of significantly changed proteins between male APP/PS1:miR-155 cKO and APP/PS1 whole brain tissue at 4 and 8 months of age (one-way ANOVA with Student’s t-test between APP/PS1 and APP/PS1:miR-155 cKO mice, P < 0.05, n = 3-4 male mice per group). See also Supplementary Table 4.
RNA-seq data for Fig. 1 and related Extended Data figures.
scRNA-seq data for Fig. 2 and related Extended Data figures.
RNA-seq data for Fig. 3.
Proteomics data for Fig. 5.
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Yin, Z., Herron, S., Silveira, S. et al. Identification of a protective microglial state mediated by miR-155 and interferon-γ signaling in a mouse model of Alzheimer’s disease. Nat Neurosci 26, 1196–1207 (2023). https://doi.org/10.1038/s41593-023-01355-y
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