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Autophagy enables microglia to engage amyloid plaques and prevents microglial senescence

Nature Cell Biology volume 25, pages 963–974 (2023)Cite this article

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Abstract

Dysfunctional autophagy has been implicated in the pathogenesis of Alzheimer’s disease (AD). Previous evidence suggested disruptions of multiple stages of the autophagy-lysosomal pathway in affected neurons. However, whether and how deregulated autophagy in microglia, a cell type with an important link to AD, contributes to AD progression remains elusive. Here we report that autophagy is activated in microglia, particularly of disease-associated microglia surrounding amyloid plaques in AD mouse models. Inhibition of microglial autophagy causes disengagement of microglia from amyloid plaques, suppression of disease-associated microglia, and aggravation of neuropathology in AD mice. Mechanistically, autophagy deficiency promotes senescence-associated microglia as evidenced by reduced proliferation, increased Cdkn1a/p21Cip1, dystrophic morphologies and senescence-associated secretory phenotype. Pharmacological treatment removes autophagy-deficient senescent microglia and alleviates neuropathology in AD mice. Our study demonstrates the protective role of microglial autophagy in regulating the homeostasis of amyloid plaques and preventing senescence; removal of senescent microglia is a promising therapeutic strategy.

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Fig. 1: Analysis of microglial autophagy associated with DAM in AD mouse models.
Fig. 2: Examination of neuropathology and microglia associated with amyloid plaques in AD mice lacking microglial autophagy.
Fig. 3: Molecular profiling of microglia from Atg7cKO mice.
Fig. 4: Characterization of cellular senescence in Atg7-deficient microglia in vivo.
Fig. 5: Characterization of autophagy-deficient microglia and cellular senescence in vitro.
Fig. 6: Examination of DAM and cellular senescence in autophagy-deficient microglia from Atg7cKO; 5xFAD mouse brains.
Fig. 7: The effect of senolytic drug treatment in altering neuropathology of Atg7cKO; 5xFAD mice.

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Data availability

The RNA sequencing data used in this publication have been deposited in NCBI’s Gene Expression Omnibus74 and are accessible through GEO Series accession number GSE192964. The proteomics data have been deposited in PRIDE (https://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD041588). Source data are provided with this paper. All other data supporting the findings of this study are available from the corresponding author on reasonable request.

Code availability

All custom code used for analysis in this paper is available from the corresponding author upon request.

References

  1. Yamamoto, A. & Yue, Z. Autophagy and its normal and pathogenic states in the brain. Annu. Rev. Neurosci. 37, 55–78 (2014).

    Article  CAS  PubMed  Google Scholar 

  2. Van Acker, Z. P., Bretou, M. & Annaert, W. Endo-lysosomal dysregulations and late-onset Alzheimer’s disease: impact of genetic risk factors. Mol. Neurodegener. 14, 20 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  3. Pickford, F. et al. The autophagy-related protein beclin 1 shows reduced expression in early Alzheimer disease and regulates amyloid β accumulation in mice. J. Clin. Invest. 118, 2190–2199 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. Lachance, V. et al. Autophagy protein NRBF2 has reduced expression in Alzheimer’s brains and modulates memory and amyloid-β homeostasis in mice. Mol. Neurodegener. 14, 43 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Nixon, R. A. et al. Extensive involvement of autophagy in Alzheimer disease: an immuno-electron microscopy study. J. Neuropathol. Exp. Neurol. 64, 113–122 (2005).

    Article  PubMed  Google Scholar 

  6. Lee, J. H. et al. Faulty autolysosome acidification in Alzheimer’s disease mouse models induces autophagic build-up of Aβ in neurons, yielding senile plaques. Nat. Neurosci. 25, 688–701 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Tammineni, P., Ye, X., Feng, T., Aikal, D. & Cai, Q. Impaired retrograde transport of axonal autophagosomes contributes to autophagic stress in Alzheimer’s disease neurons. eLife 6, e21776 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  8. Xu, Y., Propson, N. E., Du, S., Xiong, W. & Zheng, H. Autophagy deficiency modulates microglial lipid homeostasis and aggravates tau pathology and spreading. Proc. Natl Acad. Sci. USA 118, e2023418118 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Choi, I. et al. Microglia clear neuron-released alpha-synuclein via selective autophagy and prevent neurodegeneration. Nat. Commun. 11, 1386 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Cho, M. H. et al. Autophagy in microglia degrades extracellular beta-amyloid fibrils and regulates the NLRP3 inflammasome. Autophagy 10, 1761–1775 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Ulland, T. K. et al. TREM2 maintains microglial metabolic fitness in Alzheimer’s disease. Cell 170, 649–663 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Heckmann, B. L. et al. LC3-associated endocytosis facilitates beta-amyloid clearance and mitigates neurodegeneration in murine alzheimer’s disease. Cell 178, 536–551 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Berglund, R. et al. Microglial autophagy-associated phagocytosis is essential for recovery from neuroinflammation. Sci. Immunol. 5, eabb5077 (2020).

    Article  CAS  PubMed  Google Scholar 

  14. Srinivasan, K. et al. Alzheimer’s patient microglia exhibit enhanced aging and unique transcriptional activation. Cell Rep. 31, 107843 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Olah, M. et al. Single cell RNA sequencing of human microglia uncovers a subset associated with Alzheimer’s disease. Nat. Commun. 11, 6129 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Gerrits, E. et al. Distinct amyloid-beta and tau-associated microglia profiles in Alzheimer’s disease. Acta Neuropathol. 141, 681–696 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Nguyen, A. T. et al. APOE and TREM2 regulate amyloid-responsive microglia in Alzheimer’s disease. Acta Neuropathol. 140, 477–493 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Sobue, A. et al. Microglial gene signature reveals loss of homeostatic microglia associated with neurodegeneration of Alzheimer’s disease. Acta Neuropathol. Commun. 9, 1 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Krasemann, S. et al. The TREM2-APOE pathway drives the transcriptional phenotype of dysfunctional microglia in neurodegenerative diseases. Immunity 47, 566–581 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Keren-Shaul, H. et al. A Unique microglia type associated with restricting development of alzheimer’s disease. Cell 169, 1276–1290 (2017).

    Article  CAS  PubMed  Google Scholar 

  21. Oakley, H. et al. Intraneuronal beta-amyloid aggregates, neurodegeneration, and neuron loss in transgenic mice with five familial Alzheimer’s disease mutations: potential factors in amyloid plaque formation. J. Neurosci. 26, 10129–10140 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Kim, J., Kundu, M., Viollet, B. & Guan, K. L. AMPK and mTOR regulate autophagy through direct phosphorylation of Ulk1. Nat. Cell Biol. 13, 132–141 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Mizushima, N., Yamamoto, A., Matsui, M., Yoshimori, T. & Ohsumi, Y. In vivo analysis of autophagy in response to nutrient starvation using transgenic mice expressing a fluorescent autophagosome marker. Mol. Biol. Cell 15, 1101–1111 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Mizushima, N. et al. A protein conjugation system essential for autophagy. Nature 395, 395–398 (1998).

    Article  CAS  PubMed  Google Scholar 

  25. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Spangenberg, E. et al. Sustained microglial depletion with CSF1R inhibitor impairs parenchymal plaque development in an Alzheimer’s disease model. Nat. Commun. 10, 3758 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  27. Shankar, G. M. et al. Amyloid-β protein dimers isolated directly from Alzheimer’s brains impair synaptic plasticity and memory. Nat. Med. 14, 837–842 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Wang, Y. et al. TREM2-mediated early microglial response limits diffusion and toxicity of amyloid plaques. J. Exp. Med. 213, 667–675 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Van Hove, H. et al. A single-cell atlas of mouse brain macrophages reveals unique transcriptional identities shaped by ontogeny and tissue environment. Nat. Neurosci. 22, 1021–1035 (2019).

    Article  PubMed  Google Scholar 

  30. Liberzon, A. et al. The molecular signatures database (MSigDB) hallmark gene set collection. Cell Syst. 1, 417–425 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. 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).

    Article  CAS  PubMed  Google Scholar 

  32. Olah, M. et al. A transcriptomic atlas of aged human microglia. Nat. Commun. 9, 539 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Sankowski, R. et al. Mapping microglia states in the human brain through the integration of high-dimensional techniques. Nat. Neurosci. 22, 2098–2110 (2019).

    Article  CAS  PubMed  Google Scholar 

  34. Shahidehpour, R. K. et al. Dystrophic microglia are associated with neurodegenerative disease and not healthy aging in the human brain. Neurobiol. Aging 99, 19–27 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Xu, P. et al. The landscape of human tissue and cell type specific expression and co-regulation of senescence genes. Mol. Neurodegener. 17, 5 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Chatsirisupachai, K., Palmer, D., Ferreira, S. & de Magalhaes, J. P. A human tissue-specific transcriptomic analysis reveals a complex relationship between aging, cancer, and cellular senescence. Aging Cell 18, e13041 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Avelar, R. A. et al. A multidimensional systems biology analysis of cellular senescence in aging and disease. Genome Biol. 21, 91 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Fuger, P. et al. Microglia turnover with aging and in an Alzheimer’s model via long-term in vivo single-cell imaging. Nat. Neurosci. 20, 1371–1376 (2017).

    Article  PubMed  Google Scholar 

  39. Bertolo, A., Baur, M., Guerrero, J., Potzel, T. & Stoyanov, J. Autofluorescence is a reliable in vitro marker of cellular senescence in human mesenchymal stromal cells. Sci. Rep. 9, 2074 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  40. Streit, W. J., Xue, Q. S., Tischer, J. & Bechmann, I. Microglial pathology. Acta Neuropathol. Commun. 2, 142 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  41. Zhang, P. et al. Senolytic therapy alleviates Aβ-associated oligodendrocyte progenitor cell senescence and cognitive deficits in an Alzheimer’s disease model. Nat. Neurosci. 22, 719–728 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Bjorkoy, G. et al. p62/SQSTM1 forms protein aggregates degraded by autophagy and has a protective effect on huntingtin-induced cell death. J. Cell Biol. 171, 603–614 (2005).

    Article  PubMed  PubMed Central  Google Scholar 

  43. Pankiv, S. et al. p62/SQSTM1 binds directly to Atg8/LC3 to facilitate degradation of ubiquitinated protein aggregates by autophagy. J. Biol. Chem. 282, 24131–24145 (2007).

    Article  CAS  PubMed  Google Scholar 

  44. Itakura, E., Kishi, C., Inoue, K. & Mizushima, N. Beclin 1 forms two distinct phosphatidylinositol 3-kinase complexes with mammalian Atg14 and UVRAG. Mol. Biol. Cell 19, 5360–5372 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Zhong, Y. et al. Distinct regulation of autophagic activity by Atg14L and Rubicon associated with Beclin 1-phosphatidylinositol-3-kinase complex. Nat. Cell Biol. 11, 468–476 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Basisty, N. et al. A proteomic atlas of senescence-associated secretomes for aging biomarker development. PLoS Biol. 18, e3000599 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  47. Bai, B. et al. Proteomic landscape of Alzheimer’s disease: novel insights into pathogenesis and biomarker discovery. Mol. Neurodegener. 16, 55 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Aman, Y. et al. Autophagy in healthy aging and disease. Nat. Aging 1, 634–650 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  49. Gao, S., Casey, A. E., Sargeant, T. J. & Makinen, V. P. Genetic variation within endolysosomal system is associated with late-onset Alzheimer’s disease. Brain 141, 2711–2720 (2018).

    Article  PubMed  Google Scholar 

  50. Chen, H. et al. A review of APOE genotype-dependent autophagic flux regulation in alzheimer’s disease. J. Alzheimers Dis. 84, 535–555 (2021).

    Article  CAS  PubMed  Google Scholar 

  51. Bussian, T. J. et al. Clearance of senescent glial cells prevents tau-dependent pathology and cognitive decline. Nature 562, 578–582 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Saez-Atienzar, S. & Masliah, E. Cellular senescence and Alzheimer disease: the egg and the chicken scenario. Nat. Rev. Neurosci. 21, 433–444 (2020).

    Article  CAS  PubMed  Google Scholar 

  53. Kenkhuis, B. et al. Iron loading is a prominent feature of activated microglia in Alzheimer’s disease patients. Acta Neuropathol. Commun. 9, 27 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Streit, W. J., Braak, H., Xue, Q. S. & Bechmann, I. Dystrophic (senescent) rather than activated microglial cells are associated with tau pathology and likely precede neurodegeneration in Alzheimer’s disease. Acta Neuropathol. 118, 475–485 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  55. Xu, C. et al. SIRT1 is downregulated by autophagy in senescence and ageing. Nat. Cell Biol. 22, 1170–1179 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Dou, Z. et al. Autophagy mediates degradation of nuclear lamina. Nature 527, 105–109 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Garcia-Prat, L. et al. Autophagy maintains stemness by preventing senescence. Nature 529, 37–42 (2016).

    Article  CAS  PubMed  Google Scholar 

  58. Kang, H. T., Lee, K. B., Kim, S. Y., Choi, H. R. & Park, S. C. Autophagy impairment induces premature senescence in primary human fibroblasts. PLoS ONE 6, e23367 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Martinez, J. et al. Molecular characterization of LC3-associated phagocytosis reveals distinct roles for Rubicon, NOX2 and autophagy proteins. Nat. Cell Biol. 17, 893–906 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Gonzales, M. M. et al. Senolytic therapy to modulate the progression of Alzheimer’s disease (SToMP-AD): a pilot clinical trial. J. Prev. Alzheimers Dis. 9, 22–29 (2022).

    CAS  PubMed  Google Scholar 

  61. Hernandez-Silva, D. et al. Senescence-independent anti-inflammatory activity of the senolytic drugs dasatinib, navitoclax, and venetoclax in zebrafish models of chronic inflammation. Int. J. Mol. Sci. 23, 10468 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Khan, A. et al. Neuroprotective effect of quercetin against the detrimental effects of LPS in the adult mouse brain. Front. Pharm. 9, 1383 (2018).

    Article  CAS  Google Scholar 

  63. Ryu, K. Y. et al. Dasatinib regulates LPS-induced microglial and astrocytic neuroinflammatory responses by inhibiting AKT/STAT3 signaling. J. Neuroinflamm. 16, 190 (2019).

    Article  Google Scholar 

  64. Veremeyko, T., Starossom, S. C., Weiner, H. L. & Ponomarev, E. D. Detection of microRNAs in microglia by real-time PCR in normal CNS and during neuroinflammation. J. Vis. Exp. 23, 4097 (2012).

    Google Scholar 

  65. Butovsky, O. et al. Identification of a unique TGF-beta-dependent molecular and functional signature in microglia. Nat. Neurosci. 17, 131–143 (2014).

    Article  CAS  PubMed  Google Scholar 

  66. Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Mukherjee, S. et al. Molecular estimation of neurodegeneration pseudotime in older brains. Nat. Commun. 11, 5781 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Trapnell, C. et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol. 32, 381–386 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Goedhart, J. & Luijsterburg, M. S. VolcaNoseR is a web app for creating, exploring, labeling and sharing volcano plots. Sci. Rep. 10, 20560 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Kim, D., Paggi, J. M., Park, C., Bennett, C. & Salzberg, S. L. Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat. Biotechnol. 37, 907–915 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).

    Article  CAS  PubMed  Google Scholar 

  72. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  73. Pagala, V. R. et al. Quantitative protein analysis by mass spectrometry. Methods Mol. Biol. 1278, 281–305 (2015).

    Article  CAS  PubMed  Google Scholar 

  74. Edgar, R., Domrachev, M. & Lash, A. E. Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 30, 207–210 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

This work was supported by R01AG072520 (Z.Y.), U01AG046170 and R01AG057907 (B.Z.), RF1AG068581 (J.P.) and a Research Education Component (I.C.) of Alzheimer’s Disease Research Center (P30AG066514) of NIH. We thank the assistance of members in the core facilities for Flow Cytometry, Microscopy and Genomics at the Icahn School of Medicine at Mount Sinai. We thank G. Heaton for critical reading and editing.

Author information

Authors and Affiliations

  1. Department of Neurology and Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Insup Choi, Steven P. Seegobin, Xianting Li & Zhenyu Yue

  2. Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Minghui Wang, Peng Xu, Qian Wang & Bin Zhang

  3. Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Minghui Wang, Peng Xu, Qian Wang & Bin Zhang

  4. Sema4, Stamford, CT, USA

    Seungyeul Yoo

  5. Departments of Structural Biology and Developmental Neurobiology, Saint Jude Children’s Research Hospital, Memphis, TN, USA

    Xian Han & Junmin Peng

  6. Integrated Biomedical Sciences Program, University of Tennessee Health Science Center, Memphis, TN, USA

    Xian Han

  7. Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Bin Zhang

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Contributions

Z.Y. conceived the project, supervised the entire study and wrote the paper; I.C. designed, performed and analysed most of the experiments and wrote the paper; M.W., S.Y., P.X., Q.W. and B.Z. analysed scRNA-seq data and proteomic data; S.P.S. helped with SASP and autophagosome analysis; X.H. and J.P. performed proteomics; X.L. participated in animal model establishment.

Corresponding author

Correspondence to Zhenyu Yue.

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S.Y. is an employee of Sema4, a for-profit organization that promotes personalized patient care through information-driven insights. All other authors declare no competing interests.

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Nature Cell Biology thanks Hui Zheng, Li Gan and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Purity test for microglia in Percoll-enriched fraction from the brain.

Brains from Cx3cr1CreER mice (constitutively express EYFP, 3 female mice) and WT mice (negative control for EYFP signals) were processed to obtain microglia-enriched fraction (see in method). ~80% of cells were EYFP+ microglia. Scale bar, 10 µm. Values are reported as mean ± SEM. Source numerical data are available in source data.

Source data

Extended Data Fig. 2 Altered p-ACC and autophagy gene expression in the DAM of 5xFAD mice.

(a) The staining intensities of p-ACC in EYFP+ microglia were determined using Imaris software (see method) from Cx3cr1CreER; 5xFAD brains (3 female mice, 178 cells) and Cx3cr1CreER brains (3 female mice, 268 cells). No tamoxifen injection. p < 0.0001. Scale bar, 20 µm. (b) Clec7a+ and Clec7a−microglia (CD11b+ and CD45Low) from 5xFAD mice (2 male and 4 female mice) were collected and processed for RT-qPCR using primers detecting autophagy-essential genes. The level of Apoe was used for validating Clec7a+ and Clec7a−microglia populations. p = 0.0017 for Becn1; p = 0.0017 for Apoe. p-values were calculated by unpaired two-tailed Student’s t-test. All values are reported as mean ± SEM. (c) Heatmap showing the expression of essential autophagy genes through re-analysis of bulk-RNA sequencing data of Clec7a+ and Clec7a−microglia collected at 24-months-old APP/PS1 mice. p-values were calculated by unpaired two-tailed Student’s t-test (b) or two-tailed Mann-Whitney U test (a). p-values were calculated from Deseq2 analysis pipeline (c). All values are reported as mean ± SEM. Source numerical data are available in source data.

Source data

Extended Data Fig. 3 Analysis of levels of full-length APP, C-terminal fragments (CTF), and Aβ, tau, and the number and size of amyloid plaques.

(a) The size of X-34+ amyloid plaques was quantified from 489 plaques for Atg7cKO; 5xFAD (7 male mice) and 763 plaques for Atg7WT; 5xFAD (4 male mice). Scale bar, 10 µm. (b) The number of X-34+ amyloid plaques was quantified from Atg7cKO; 5xFAD (8 male mice) and Atg7WT; 5xFAD (7 male mice). Scale bar, 500 µm. (c, d) Brains from 5xFAD mice (3 female, c) and littermate controls (3 female) or Atg7cKO; 5xFAD mice (4 male mice, d) and Atg7WT; 5xFAD mice (4 male mice) were homogenized, fractionated into 1% Triton x-100 soluble and insoluble fractions, and processed for Western blot using antibodies against amyloid-beta (c) and APP (Y188 clone) (d). (e) Brains from Atg7cKO; 5xFAD (7 male mice) and Atg7WT; 5xFAD mice (6 male mice) were homogenized in 1% Triton x-100. Levels of amyloid-beta, p-tau (AT8), and tau were examined through Western blot. EYFP indicates the presence of Cx3crCreER in the mice that co-express EYFP and CreER. Actin was used as a loading control. p = 0.008. p-values were calculated by unpaired two-tailed Student’s t-test. All values are reported as mean ± SEM. Source numerical data and unprocessed blots are available in source data.

Source data

Extended Data Fig. 4 Alteration of presynaptic markers.

Levels of several presynaptic markers, including SV2A, SV2B, VGLUT1, VGLUT2, Synaptophysin, and Synaptogyrin, were determined in 8-months-old mice brains from 5xFAD and WT (a, 5 male and 3 female mice per group), Atg7cKO; 5xFAD (7 male mice) and Atg7WT; 5xFAD (b, 6 male mice), and Atg7cKO (4 male and 4 female mice) and Atg7WT (c, 4 male and 3 female mice). (d) Immunostaining of Lamp1 and VGLUT2 along with APP. Representative image from at least 3 different experiments. Scale bar, 50 µm. p = 0.02 for SV2B (a); p = 0.03 for VGLUT1 (a); p = 0.003 for VGLUT2 (a); p = 0.0042 for VGLUT2 (b). p-values were calculated by unpaired two-tailed Student’s t-test. All values are reported as mean ± SEM. Source numerical data and unprocessed blots are available in source data.

Source data

Extended Data Fig. 5 The whole flow of single-cell RNA sequencing.

CD11b+/CD45Low microglia were collected and processed for single-cell RNA sequencing through the 10X Genomics pipeline. The detailed process is described in the Method.

Extended Data Fig. 6 Representative cluster marker genes.

Marker genes from each cluster were shown.

Extended Data Fig. 7 Analysis of single-cell RNA sequencing.

(a) Violin plot for well-known cell-specific markers including Slc17a7 (excitatory neurons), Aqp4 (astrocytes), Mog (oligodendrocytes), Flt1 (endothelial cells), P2ry12 (microglia) and Hexb (microglia), and Ifitm3 and Irf7 (border-associated macrophages). (b) Heatmap for gene set enrichment analysis using HALLMARK 50 database and upregulated genes (adjusted p-values < 0.05, Log2FC > 0.5) of each cluster. (c) Volcano plot for showing upregulated DEGs (adjusted p-values < 0.05, Log2FC > 0.5) of cluster 2 and 5. (d) Heatmap for the expression of Ftl1 and Fth1, markers for dystrophic microglia. (e) Heatmap for gene enrichment test between each cluster DEGs (adjusted p-values < 0.05, Log2FC > 0.5) and human AD microglia clusters. (f) Heatmap for mouse microglia clusters enriched for canonical senescence signatures. p-values (e, f) were calculated by one-sided Fisher’s exact test.

Extended Data Fig. 8 Examination of heterogenous microglial populations in Atg7cKO brain.

(a) Percoll-enriched fractions of microglia from Atg7cKO (2 male and 3 female mice) and Atg7WT (2 male and 3 female mice) mice brains were processed for Western blot using antibodies against p62 and p21Cip1. Actin was used as a loading control. p < 0.0001 for p62; p = 0.0015 for p21Cip1. (b) The staining intensities of p16Ink4a (149 cells for Atg7WT, 97 cells for Atg7cKO) were determined by Imaris software (see method). 2 male and 2 female mice for each genotype. p < 0.0001. Scale bar, 50 µm; 10 µm for magnified images. (c) Volcano plot of upregulated genes of cluster 4 (C4 vs. other clusters; adjusted p-values < 0.05, Log2FC > 0.5). (d) GO term analysis for upregulated genes of cluster 4. (e) Volcano plot of DEGs of cluster 4 (Atg7cKO vs. Atg7WT; adjusted p-values < 0.05, Log2FC > 0.5 and <-0.5). (f) GO term analysis for downregulated DEGs in Atg7cKO compared to Atg7WT of cluster 4. (g) The staining intensity of p21Cip1 in nuclei of CD11b+ microglia was quantified from Atg7cKO; 5xFAD mice (2 male and 2 female mice, 96 cells) and Atg7WT; 5xFAD mice (2 male and 2 female mice, 111 cells). p < 0.0001. Scale bar, 10 µm. (h) The volumes of Lipofuscin were measured from Atg7cKO; 5xFAD mice (2 male and 1 female mice, 50 cells) and Atg7WT; 5xFAD mice (2 male and 1 female mice, 25cells) were analysed. Yellow asterisks (g, h) indicate DAPI-stained amyloid plaques. p < 0.0001. Scale bar, 10 µm. p-values were calculated by unpaired two-tailed Student’s t-test (a), two-tailed Mann-Whitney U test (b, g, h), or Wilcox rank-sum test (c, e). p-values (d, f) were provided by Metascape (default setting, see method). All values are reported as mean ± SEM. Source numerical data and unprocessed blots are available in source data.

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Extended Data Fig. 9 Additional cellular senescence phenotypes in Atg7cKO microglia.

(a) At 1-year post-tamoxifen or corn-oil injection, the number of EYFP+ microglia was quantified from Atg7cKO (tamoxifen, 3 male and 4 female mice, Cx3cr1CreER; Atg7f/f) and Atg7WT (corn-oil, 2 male and 3 female mice, Cx3cr1CreER; Atg7f/f) brain slices. The number of microglia was quantified. p = 0.0004. Scale bar, 500 µm. (b-d) The staining intensities of LaminB1 (b, 106 cells for Atg7WT, 97 cells for Atg7cKO), HMGB1 (c, 113 cells for Atg7WT, 108 cells for Atg7cKO), and γH2A.X (d, 95 cells for Atg7WT, 78 cells for Atg7cKO) were measured in microglia using Imaris software (see method). p = 0.0147 for LaminB1; p = 0.0532 for HMGB1; p = 0.0043 for γH2A.X. Scale bar, 10 µm. p-values were calculated by unpaired two-tailed Student’s t-test (a) or two-tailed Mann-Whitney U test (b, c, d). All values are reported as mean ± SEM. Source numerical data are available in source data.

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Extended Data Fig. 10 The effect of senolytic drug treatment in 5xFAD mice.

(a) Following chronic D + Q (5 female, once a week, 11-weeks) or vehicle treatment (3 female) of 5xFAD mice, the number of X-34+ amyloid plaques was quantified from the vehicle or D + Q-treated mice. Scale bar, 1 mm. (b) The size, circularities, and the number of Lamp1+ dystrophic neurites were quantified from mice treated with either vehicle (128 plaques) or D + Q (169 plaques). Scale bar, 20 µm. (c) The number of Iba-1+ microglia around amyloid plaques (15 µm radius of amyloid plaques) from 5xFAD mice treated with either vehicle (70 plaques) or D + Q (87 plaques) and the staining intensity of Clec7a in microglia from 5xFAD mice treated with either vehicle (139 plaques) or D + Q (149 plaques) were measured. p = 0.0032 for Clec7a. p-values were calculated by two-tailed Mann-Whitney U test. Scale bar, 20 µm. (d) A proposed model for roles of autophagy-deficient senescent microglia in AD pathophysiology. Source numerical data are available in source data.

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Supplementary information

Reporting Summary

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Choi, I., Wang, M., Yoo, S. et al. Autophagy enables microglia to engage amyloid plaques and prevents microglial senescence. Nat Cell Biol 25, 963–974 (2023). https://doi.org/10.1038/s41556-023-01158-0

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