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Lipid-droplet-accumulating microglia represent a dysfunctional and proinflammatory state in the aging brain

Nature Neuroscience volume 23, pages 194–208 (2020)Cite this article

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An Author Correction to this article was published on 27 July 2020

An Author Correction to this article was published on 31 January 2020

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Abstract

Microglia become progressively activated and seemingly dysfunctional with age, and genetic studies have linked these cells to the pathogenesis of a growing number of neurodegenerative diseases. Here we report a striking buildup of lipid droplets in microglia with aging in mouse and human brains. These cells, which we call ‘lipid-droplet-accumulating microglia’ (LDAM), are defective in phagocytosis, produce high levels of reactive oxygen species and secrete proinflammatory cytokines. RNA-sequencing analysis of LDAM revealed a transcriptional profile driven by innate inflammation that is distinct from previously reported microglial states. An unbiased CRISPR–Cas9 screen identified genetic modifiers of lipid droplet formation; surprisingly, variants of several of these genes, including progranulin (GRN), are causes of autosomal-dominant forms of human neurodegenerative diseases. We therefore propose that LDAM contribute to age-related and genetic forms of neurodegeneration.

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Fig. 1: Microglia in the aged brain accumulate lipid droplets.
Fig. 2: RNA-seq of LD-low and LD-high microglia from aged mice reveals transcriptional changes linked to phagocytosis and ROS production.
Fig. 3: LPS treatment induces lipid droplet formation in microglia.
Fig. 4: LDAM and lipid droplets in BV2 cells are associated with impaired phagocytosis.
Fig. 5: LDAM and lipid-droplet-rich BV2 cells show increased ROS production, and LDAM secrete elevated levels of inflammatory cytokines.
Fig. 6: CRISPR–Cas9 screen identifies genetic regulators of lipid droplet formation.
Fig. 7: Grn−/− mice possess high numbers of lipid-droplet-rich microglia that functionally and partially transcriptionally resemble LDAM.

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Data availability

RNA-seq datasets have been deposited online in the Gene Expression Omnibus (GEO) under accession numbers GSE139542, GSE139946, and GSE140009.

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References

  1. Aguzzi, A., Barres, B. A. & Bennett, M. L. Microglia: scapegoat, saboteur, or something else? Science 339, 156–161 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  2. Mosher, K. I. & Wyss-Coray, T. Microglial dysfunction in brain aging and Alzheimer’s disease. Biochem. Pharmacol. 88, 594–604 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Keren-Shaul, H. et al. A unique microglia type associated with restricting development of Alzheimer’s disease. Cell 169, 1276–1290.e17 (2017).

    CAS  PubMed  Google Scholar 

  4. Krasemann, S. et al. The TREM2-APOE pathway drives the transcriptional phenotype of dysfunctional microglia in neurodegenerative diseases. Immunity 47, 566–581.e9 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Li, Q. et al. Developmental heterogeneity of microglia and brain myeloid cells revealed by deep single-cell RNA sequencing. Neuron 101, 207–223.e10 (2019).

    CAS  PubMed  Google Scholar 

  6. Alzheimer, A. Über eine eigenartige Erkrankung der Hirnrinde. Allg. Z. Psychiatr. 64, 146–148 (1907).

    Google Scholar 

  7. Foley, P. Lipids in Alzheimer’s disease: a century-old story. Biochim. Biophys. Acta 1801, 750–753 (2010).

    CAS  PubMed  Google Scholar 

  8. Fowler, S. D., Mayer, E. P. & Greenspan, P. Foam cells and atherogenesis. Ann. NY Acad. Sci. 454, 79–90 (1985).

    CAS  PubMed  Google Scholar 

  9. Bozza, P. T. & Viola, J. P. Lipid droplets in inflammation and cancer. Prostaglandins Leukot. Essent Fatty Acids 82, 243–250 (2010).

    CAS  PubMed  Google Scholar 

  10. den Brok, M. H., Raaijmakers, T. K., Collado-Camps, E. & Adema, G. J. Lipid droplets as immune modulators in myeloid cells. Trends Immunol. 39, 380–392 (2018).

    Google Scholar 

  11. Childs, B. G. et al. Senescent intimal foam cells are deleterious at all stages of atherosclerosis. Science 354, 472–477 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Castejon, O. J., Castellano, A., Arismendi, G. J. & Medina, Z. The inflammatory reaction in human traumatic oedematous cerebral cortex. J. Submicrosc. Cytol. Pathol. 37, 43–52 (2005).

    CAS  PubMed  Google Scholar 

  13. Lee, S. C., Moore, G. R., Golenwsky, G. & Raine, C. S. Multiple sclerosis: a role for astroglia in active demyelination suggested by class II MHC expression and ultrastructural study. J. Neuropathol. Exp. Neurol. 49, 122–136 (1990).

    CAS  PubMed  Google Scholar 

  14. Liu, L. et al. Glial lipid droplets and ROS induced by mitochondrial defects promote neurodegeneration. Cell 160, 177–190 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Shimabukuro, M. K. et al. Lipid-laden cells differentially distributed in the aging brain are functionally active and correspond to distinct phenotypes. Sci. Rep. 6, 23795 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Chang, P. K., Khatchadourian, A., McKinney, R. A. & Maysinger, D. Docosahexaenoic acid (DHA): a modulator of microglia activity and dendritic spine morphology. J. Neuroinflammation 12, 34 (2015).

    PubMed  PubMed Central  Google Scholar 

  17. Khatchadourian, A., Bourque, S. D., Richard, V. R., Titorenko, V. I. & Maysinger, D. Dynamics and regulation of lipid droplet formation in lipopolysaccharide (LPS)-stimulated microglia. Biochim. Biophys. Acta 1821, 607–617 (2012).

    CAS  PubMed  Google Scholar 

  18. Harris, L. A., Skinner, J. R. & Wolins, N. E. Imaging of neutral lipids and neutral lipid associated proteins. Methods Cell. Biol. 116, 213–226 (2013).

    CAS  PubMed  Google Scholar 

  19. Yu, Y., Ramachandran, P. V. & Wang, M. C. Shedding new light on lipid functions with CARS and SRS microscopy. Biochim. Biophys. Acta 1841, 1120–1129 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Bennett, M. L. et al. New tools for studying microglia in the mouse and human CNS. Proc. Natl Acad. Sci. USA 113, E1738–E1746 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Pluvinage, J. V. et al. CD22 blockade restores homeostatic microglial phagocytosis in ageing brains. Nature 568, 187–192 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Holtman, I. R. et al. Induction of a common microglia gene expression signature by aging and neurodegenerative conditions: a co-expression meta-analysis. Acta Neuropathol. Commun. 3, 31 (2015).

    PubMed  PubMed Central  Google Scholar 

  23. Chiu, I. M. et al. A neurodegeneration-specific gene-expression signature of acutely isolated microglia from an amyotrophic lateral sclerosis mouse model. Cell Rep. 4, 385–401 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Wang, Y. et al. TREM2 lipid sensing sustains the microglial response in an Alzheimer’s disease model. Cell 160, 1061–1071 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. 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.e6 (2019).

    CAS  PubMed  Google Scholar 

  26. Namatame, I., Tomoda, H., Arai, H., Inoue, K. & Omura, S. Complete inhibition of mouse macrophage-derived foam cell formation by triacsin C. J. Biochem. 125, 319–327 (1999).

    CAS  PubMed  Google Scholar 

  27. Hahn, O. et al. Dietary restriction protects from age-associated DNA methylation and induces epigenetic reprogramming of lipid metabolism. Genome Biol. 18, 56 (2017).

    PubMed  PubMed Central  Google Scholar 

  28. Chen, Z. et al. Lipopolysaccharide-induced microglial activation and neuroprotection against experimental brain injury is independent of hematogenous TLR4. J. Neurosci. 32, 11706–11715 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Kawabe, K., Takano, K., Moriyama, M. & Nakamura, Y. Lipopolysaccharide-stimulated transglutaminase 2 expression enhances endocytosis activity in the mouse microglial cell line BV-2. Neuroimmunomodulation 22, 243–249 (2015).

    CAS  PubMed  Google Scholar 

  30. Yousef, H. et al. Aged blood impairs hippocampal neural precursor activity and activates microglia via brain endothelial cell VCAM1. Nat. Med. 25, 988–1000 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Morgens, D. W. et al. Genome-scale measurement of off-target activity using Cas9 toxicity in high-throughput screens. Nat. Commun. 8, 15178 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Kamkaew, A. et al. BODIPY dyes in photodynamic therapy. Chem. Soc. Rev. 42, 77–88 (2013).

    CAS  PubMed  Google Scholar 

  33. Lee, J. et al. Adaptor protein sorting nexin 17 regulates amyloid precursor protein trafficking and processing in the early endosomes. J. Biol. Chem. 283, 11501–11508 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Liu, P. et al. S113R mutation in SLC33A1 leads to neurodegeneration and augmented BMP signaling in a mouse model. Dis. Model. Mech. 10, 53–62 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Huey, E. D. et al. Characteristics of frontotemporal dementia patients with a progranulin mutation. Ann. Neurol. 60, 374–380 (2006).

    PubMed  PubMed Central  Google Scholar 

  36. Tsika, E. et al. Parkinson’s disease-linked mutations in VPS35 induce dopaminergic neurodegeneration. Hum. Mol. Genet. 23, 4621–4638 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Yin, F. et al. Behavioral deficits and progressive neuropathology in progranulin-deficient mice: a mouse model of frontotemporal dementia. FASEB J. 24, 4639–4647 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Vieira-de-Abreu, A. et al. Cross-talk between macrophage migration inhibitory factor and eotaxin in allergic eosinophil activation forms leukotriene C(4)-synthesizing lipid bodies. Am. J. Respir. Cell Mol. Biol. 44, 509–516 (2011).

    CAS  PubMed  Google Scholar 

  39. Hu, X., Xu, B. & Ge, W. The role of lipid bodies in the microglial aging process and related diseases. Neurochem. Res. 42, 3140–3148 (2017).

    CAS  PubMed  Google Scholar 

  40. Rambold, A. S., Cohen, S. & Lippincott-Schwartz, J. Fatty acid trafficking in starved cells: regulation by lipid droplet lipolysis, autophagy, and mitochondrial fusion dynamics. Dev. Cell 32, 678–692 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Ghoshal, N., Dearborn, J. T., Wozniak, D. F. & Cairns, N. J. Core features of frontotemporal dementia recapitulated in progranulin knockout mice. Neurobiol. Dis. 45, 395–408 (2012).

    CAS  PubMed  Google Scholar 

  42. Currie, E., Schulze, A., Zechner, R., Walther, T. C. & Farese, R. V. Jr. Cellular fatty acid metabolism and cancer. Cell Metab. 18, 153–161 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Burke, A. C. & Huff, M. W. ATP-citrate lyase: genetics, molecular biology and therapeutic target for dyslipidemia. Curr. Opin. Lipidol. 28, 193–200 (2017).

    CAS  PubMed  Google Scholar 

  44. Cantuti-Castelvetri, L. et al. Defective cholesterol clearance limits remyelination in the aged central nervous system. Science 359, 684–688 (2018).

    CAS  PubMed  Google Scholar 

  45. Lee, S. J., Zhang, J., Choi, A. M. & Kim, H. P. Mitochondrial dysfunction induces formation of lipid droplets as a generalized response to stress. Oxid. Med. Cell Longev. 2013, 327167 (2013).

    PubMed  PubMed Central  Google Scholar 

  46. Nadjar, A. Role of metabolic programming in the modulation of microglia phagocytosis by lipids. Prostaglandins Leukot. Essent. Fatty Acids 135, 63–73 (2018).

    CAS  PubMed  Google Scholar 

  47. Chinetti-Gbaguidi, G. et al. Human atherosclerotic plaque alternative macrophages display low cholesterol handling but high phagocytosis because of distinct activities of the PPARγ and LXRα pathways. Circ. Res. 108, 985–995 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Chandak, P. G. et al. Efficient phagocytosis requires triacylglycerol hydrolysis by adipose triglyceride lipase. J. Biol. Chem. 285, 20192–20201 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Jaitin, D. A. et al. Lipid-associated macrophages control metabolic homeostasis in a Trem2-dependent manner. Cell 178, 686–698.e14 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Nguyen, A. D et al. Progranulin in the hematopoietic compartment protects mice from atherosclerosis. Atherosclerosis 277, 145–154 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Evers, B. M et al. Lipidomic and transcriptomic basis of lysosomal dysfunction in progranulin deficiency . Cell Rep. 20, 2565–2574 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. Gil-Perotin, S., Alvarez-Buylla, A. & Garcia-Verdugo, J. M. Identification and characterization of neural progenitor cells in the adult mammalian brain. Adv. Anat. Embryol. Cell Biol. 203, 1–101 (2009).

    PubMed  Google Scholar 

  53. Bligh, E. G. & Dyer, W. J. A rapid method of total lipid extraction and purification. Can. J. Biochem. Physiol. 37, 911–917 (1959).

    CAS  PubMed  Google Scholar 

  54. 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).

    PubMed  PubMed Central  Google Scholar 

  55. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B Meth. 57, 289–300 (1995).

    Google Scholar 

  56. Bohlen, C. J. et al. Diverse requirements for microglial survival, specification, and function revealed by defined-medium cultures. Neuron 94, 759–773.e8 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. Deans, R. M. et al. Parallel shRNA and CRISPR–Cas9 screens enable antiviral drug target identification. Nat. Chem. Biol. 12, 361–366 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

The authors thank the members of the Wyss-Coray laboratory for feedback and support throughout the study, W. Stoiber (University of Salzburg, Austria) and the Division of Optical and Electron Microscopy, University of Salzburg, Austria, for excellent assistance with EM images, and D. Channappa from the Department of Neurology and E. Plowey from the Department of Pathology, Stanford University, for providing human postmortem brain samples. CARS imaging was performed at the Microscopy Core Facility of the Institute of Molecular Biosciences, University of Graz, Austria. They also thank A. Enejder, the Heilshorn Biomaterials Group, Stanford University, for discussion and for reviewing the manuscript. This work was supported by the FWF Hertha-Firnberg Postdoctoral program no. T736-B24 (to J.M.), the PMU-FFF E-16/23/117-FEA (to T.K.F), the NIH grant R37 GM058867 (to C.R.B), the Stanford Neuroscience Institute Brain Rejuvenation Project Award and NIH Director’s New Innovator Award (1DP2HD084069-01) to M.C.B., the Department of Veterans Affairs (to T.W.-C.), the National Institute on Aging (DP1-AG053015 to T.W.-C.), the NOMIS Foundation (to T.W.-C.), the Glenn Foundation for Medical Research (to T.W.-C.), the Cure Alzheimer’s Fund (to T.W.-C.) and the Nan Fung Life Sciences Aging Research Fund (to T.W.-C.).

Author information

Authors and Affiliations

  1. Department of Neurology and Neurological Sciences, School of Medicine, Stanford University, Stanford, CA, USA

    Julia Marschallinger, Tal Iram, Macy Zardeneta, Song E. Lee, Benoit Lehallier, Michael S. Haney, John V. Pluvinage, Vidhu Mathur, Oliver Hahn & Tony Wyss-Coray

  2. Paul F. Glenn Center for the Biology of Aging, Stanford University School of Medicine, Stanford, CA, USA

    Julia Marschallinger, Tal Iram, Macy Zardeneta, Song E. Lee, Benoit Lehallier, John V. Pluvinage, Vidhu Mathur, Oliver Hahn & Tony Wyss-Coray

  3. Institute of Molecular Regenerative Medicine, Spinal Cord Injury and Tissue Regeneration Center Salzburg (SCI-TReCS), Paracelsus Medical University, Salzburg, Austria

    Julia Marschallinger, Michael C. Bassik & Ludwig Aigner

  4. Department of Genetics, School of Medicine, and Chemistry, Engineering, and Medicine for Human Health (ChEM-H), Stanford University, Stanford, CA, USA

    Michael S. Haney, David W. Morgens & Michael C. Bassik

  5. Medical Scientist Training Program, Stanford University School of Medicine, Stanford, CA, USA

    John V. Pluvinage

  6. Department of Chemistry, Stanford ChEM-H and Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA

    Justin Kim & Carolyn R. Bertozzi

  7. Department of Laboratory Medicine, Paracelsus Medical University, Salzburg, Austria

    Julia Tevini & Thomas K. Felder

  8. Obesity Research Unit, Paracelsus Medical University, Salzburg, Austria

    Thomas K. Felder

  9. Institute of Molecular Biosciences, BioTechMed-Graz, University of Graz, Graz, Austria

    Heimo Wolinski

  10. Stanford Neurosciences Institute, Stanford University, Stanford, CA, USA

    Tony Wyss-Coray

  11. Department of Veterans Affairs, Palo Alto, CA, USA

    Tony Wyss-Coray

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  1. Julia Marschallinger
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Contributions

J.M. and T.W.-C. conceptualized and designed the study, analyzed and interpreted data, and wrote the manuscript. J.M. and S.E.L. designed the figures. J.M., T.I., M.Z., S.E.L. and J.V.P. acquired the data. J.M. performed the electron microscopy experiments. J.M. and M.Z. performed histology and organotypic slice culture experiments. J.M., T.I. and M.Z. performed the cell culture experiments. J.M., T.I. and S.E.L performed the RNA-seq experiments. J.V.P., M.Z. and J.M. performed the stereotactic procedures. V.M. conducted the in vivo LPS injections and provided Grn−/− mouse brain sections. J.M., M.S.H. and D.W.M. generated and analyzed the CRISPR–Cas9 screen data. J.M. and B.L. analyzed the RNA-seq data. J.T., T.K.F. and O.H. performed the mass spectrometry experiments. J.M. and H.W. performed the CARS imaging. J.K. and C.R.B. designed and produced the methylated BODIPY derivatives. M.C.B and L.A. reviewed the manuscript.

Corresponding author

Correspondence to Tony Wyss-Coray.

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Competing interests

T.W.-C., J.M., C.R.B. and M.S.H. are co-inventors on a patent application related to the work published in this paper. All other authors have no competing interests.

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Peer review information Nature Neuroscience thanks Staci Bilbo, Serge Rivest, 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 Lipid droplet accumulating microglia are abundant in the hippocampus but rare in other brain regions of aged mice.

a–d, Representative confocal images of the cortex (a), thalamus (b), corpus callosum (c) and hippocampal dentate gyrus (d) from 20-month old male mice stained for BODIPY+ (lipid droplets) and Iba1+ (microglia). Scale bar: 20 μm. Arrows point towards BODIPY+ lipid droplets. e, Quantification of BODIPY+/Iba1+ cells. n = 4 mice per group. One-way ANOVA followed by Tukey’s post hoc test. Error bars represent mean ± SD. ***P< 0.001.

Extended Data Fig. 2 LDAM have a unique transcriptional signature that minimally overlaps with published gene expression profiles of microglia in aging and neurodegeneration.

a,b, IPA pathway analysis of genes that are significantly upregulated (a) or downregulated (b) in LD-hi microglia in aging. Analysis based on top 100 down- and up-regulated genes (Fisher’s exact test, Benjamini-Hochberg FDR). c-g, Expression plots comparing RNA-Seq data of LDAM (see Fig. 2) with published RNA-Seq data of microglia in aging (c), AD (d), ALS (e), disease-associated microglia (DAM) (f) and neurodegenerative microglia (MGnD) (g). Data are expressed as signed fdr, i.e the product of log2 FC and log10 fdr. h, Paired dot plot showing FPKM values of LD-lo and LD-hi microglia for ApoE (paired Student’s t-test; P= 0.423). Dotted lines connect LD-lo and LD-hi microglia sorted from the same samples. i, Heatmap showing expression changes of LDAM genes (genes differentially expressed in LD-hi microglia in aging) in LD-hi microglia from GRN-/- mice, from LPS treated mice, and in microglia clusters revealed by Li et al. (2019) and Hammond et al. (2019)15,16. Sample size in a,b,h: n = 3 samples per group. Each sample is a pool of microglia from the hippocampi of 3 mice. LD, lipid droplet.

Extended Data Fig. 3 LPS treatment induces lipid droplet formation in microglia and in BV2 cells.

a,b, 3-month-old male mice were given intraperitoneal (i.p.) injections of LPS (1 mg/kg BW) for four days. Representative confocal images of BODIPY+ and Tmem119+ in the hippocampus (a) and of BODIPY and Iba1 staining in the cortex, corpus callosum, and thalamus (b). c-e, Lipidome profiling of lipid droplets from LPS-treated BV2 cells, primary microglia, and liver tissue. c, Pie charts showing that the lipid composition of lipid droplets from young and aged microglia is highly similar, but differs between young and aged liver tissue. d,e, Distribution of MAG chain lengths (d) and TAG saturation levels (e) of lipid droplets isolated from LPS-treated BV2 cells and from microglia and liver tissue from aged mice. young= 5-month-old male mice, old= 20-month-old male mice; n = 4 mice per group. Data in a-b were replicated in at least two independent experiments. Error bars represent mean ± s.e.m. Scale bars, 20 μm.

Extended Data Fig. 4 Aged plasma induces lipid droplet formation in BV2 cells.

a, Representative micrographs of BODIPY+ staining and of phagocytosis of of pHrodo red Zymosan in BV2 cells treated with 5% plasma from young (3-months) and aged (18-months) mice for 12 hours. Scale bars, 5 μm. b, Quantification of BODIPY+ staining in BV2 cells treated with young and aged plasma. c,d, Quantification of Zymosan uptake in BV2 cells treated with young and aged plasma (c), and in aged plasma treated BODIPY-low and BODIPY-high cells (d). Statistical tests: two-sided Student’s t-test. Error bars represent mean ± SD. *P< 0.05, ***P< 0.001.

Extended Data Fig. 5 Lipid droplet containing microglia in the cortex, corpus callosum, and thalamus of GRN-/- mice.

a-c, Representative confocal images of BODIPY+ (lipid droplets) and Iba1+ (microglia) in the cortex (a), corpus callosum (b), (c) and thalamus from 9-month-old male GRN-/- mice. BODIPY+/Iba1+ cells were frequently found in the thalamus and were detected to a lesser extent in cortex and corpus callosum. Data were replicated in at least three independent experiments.

Extended Data Fig. 6 Expression changes of LDAM genes in lipid droplet-rich microglia from normal aging, GRN-/- and LPS-treated mice.

a, Heatmap showing expression changes of LDAM genes (genes differentially expressed in LD-hi microglia in aging; 692 genes) in LD-hi microglia from GRN-/- mice and from LPS treated mice.

Extended Data Fig. 7 LDAM show signs of metabolic alterations.

a, Paired dot plot showing FPKM values of LD-lo and LD-hi microglia for ACLY (data obtained from RNA-Seq analysis, see Fig. 2). Dotted lines connect LD-lo and LD-hi microglia sorted from the same samples. P=b, NAD colorimetric assay showing the NAD+/NADH ratio of primary hippocampal microglia from 3-month old mice (young) and of LD-lo and LD-hi primary microglia from 20-month old male mice. Experiments were performed two times in technical triplicates. n=3 mice per group per experiment. Statistical tests: paired two-sided Student’s t-test (a) one-way ANOVA (b) followed by Tukey’s post hoc test. Horizontal lines in the box plots indicate medians, box limits indicate first and third quantiles, and vertical whisker lines indicate minimum and maximum values. *P< 0.05, ***P< 0.001.

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Marschallinger, J., Iram, T., Zardeneta, M. et al. Lipid-droplet-accumulating microglia represent a dysfunctional and proinflammatory state in the aging brain. Nat Neurosci 23, 194–208 (2020). https://doi.org/10.1038/s41593-019-0566-1

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