Hou, Y. et al. Ageing as a risk factor for neurodegenerative disease. Nat. Rev. Neurol. 15, 565–581 (2019).
Google Scholar
Bjartmar, C., Wujek, J. R. & Trapp, B. D. Axonal loss in the pathology of MS: consequences for understanding the progressive phase of the disease. J. Neurol. Sci. 206, 165–171 (2003).
Google Scholar
Lassmann, H. Multiple sclerosis pathology. Cold Spring Harb. Perspect. Med. 8, a028936 (2018).
Google Scholar
Tutuncu, M. et al. Onset of progressive phase is an age-dependent clinical milestone in multiple sclerosis. Mult. Scler. 19, 188–198 (2013).
Google Scholar
Ximerakis, M. et al. Single-cell transcriptomic profiling of the aging mouse brain. Nat. Neurosci. 22, 1696–1708 (2019).
Google Scholar
Tabula Muris, C. A single-cell transcriptomic atlas characterizes ageing tissues in the mouse. Nature 583, 590–595 (2020).
Google Scholar
Westlye, L. T. et al. Lifespan changes of the human brain white matter: diffusion tensor imaging and volumetry. Cereb. Cortex 20, 2055–2068 (2010).
Google Scholar
Hasan, K. M. et al. Quantification of the spatiotemporal microstructural organization of the human brain association, projection and commissural pathways across the lifespan using diffusion tensor tractography. Brain Struct. Funct. 214, 361–373 (2010).
Google Scholar
Conway, B. L. et al. Age is a critical determinant in recovery from multiple sclerosis relapses. Mult. Scler. 25, 1754–1763 (2019).
Google Scholar
Haider, L. et al. Oxidative damage in multiple sclerosis lesions. Brain 134, 1914–1924 (2011).
Google Scholar
Butterfield, D. A. & Halliwell, B. Oxidative stress, dysfunctional glucose metabolism and Alzheimer disease. Nat. Rev. Neurosci. 20, 148–160 (2019).
Google Scholar
Dias, V., Junn, E. & Mouradian, M. M. The role of oxidative stress in Parkinson’s disease. J. Parkinsons Dis. 3, 461–491 (2013).
Google Scholar
Dong, Y. et al. Oxidized phosphatidylcholines found in multiple sclerosis lesions mediate neurodegeneration and are neutralized by microglia. Nat. Neurosci. 24, 489–503 (2021).
Google Scholar
Dong, Y. & Yong, V. W. When encephalitogenic T cells collaborate with microglia in multiple sclerosis. Nat. Rev. Neurol. 15, 704–717 (2019).
Google Scholar
Li, Q. & Barres, B. A. Microglia and macrophages in brain homeostasis and disease. Nat. Rev. Immunol. 18, 225–242 (2018).
Google Scholar
Prinz, M., Jung, S. & Priller, J. Microglia biology: one century of evolving concepts. Cell 179, 292–311 (2019).
Google Scholar
Olah, M. et al. A transcriptomic atlas of aged human microglia. Nat. Commun. 9, 539 (2018).
Google Scholar
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).
Google Scholar
Safaiyan, S. et al. White matter aging drives microglial diversity. Neuron 109, 1100–1117 (2021).
Google Scholar
Pluvinage, J. V. et al. CD22 blockade restores homeostatic microglial phagocytosis in ageing brains. Nature 568, 187–192 (2019).
Google Scholar
Hefendehl, J. K. et al. Homeostatic and injury-induced microglia behavior in the aging brain. Aging Cell 13, 60–69 (2014).
Google Scholar
Marschallinger, J. et al. Lipid-droplet-accumulating microglia represent a dysfunctional and proinflammatory state in the aging brain. Nat. Neurosci. 23, 194–208 (2020).
Google Scholar
Cantuti-Castelvetri, L. et al. Defective cholesterol clearance limits remyelination in the aged central nervous system. Science 359, 684–688 (2018).
Google Scholar
Hickman, S. E. et al. The microglial sensome revealed by direct RNA sequencing. Nat. Neurosci. 16, 1896–1905 (2013).
Google Scholar
Agah, E. et al. Osteopontin as a CSF and blood biomarker for multiple sclerosis: a systematic review and meta-analysis. PLoS ONE 13, e0190252 (2018).
Google Scholar
Maetzler, W. et al. Osteopontin is elevated in Parkinson’s disease and its absence leads to reduced neurodegeneration in the MPTP model. Neurobiol. Dis. 25, 473–482 (2007).
Google Scholar
McGrowder, D. A. et al. Cerebrospinal fluid biomarkers of Alzheimer’s disease: current evidence and future perspectives. Brain Sci. 11, 215 (2021).
Google Scholar
Goldmann, T. et al. A new type of microglia gene targeting shows TAK1 to be pivotal in CNS autoimmune inflammation. Nat. Neurosci. 16, 1618–1626 (2013).
Google Scholar
Plemel, J. R. et al. Microglia response following acute demyelination is heterogenous and limits infiltrating macrophage dispersion. Sci. Adv. 6, eaay6324 (2020).
Google Scholar
Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902 (2019).
Google Scholar
Masuda, T. et al. Novel Hexb-based tools for studying microglia in the CNS. Nat. Immunol. 21, 802–815 (2020).
Google Scholar
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).
Google Scholar
Street, K. et al. Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics. BMC Genomics 19, 477 (2018).
Google Scholar
Jordao, M. J. C. et al. Single-cell profiling identifies myeloid cell subsets with distinct fates during neuroinflammation. Science 363, eaat7554 (2019).
Google Scholar
Keren-Shaul, H. et al. A unique microglia type associated with restricting development of Alzheimer’s disease. Cell 169, 1276–1290 (2017).
Google Scholar
Bruce, K. D. et al. Lipoprotein lipase is a feature of alternatively activated microglia and may facilitate lipid uptake in the CNS during demyelination. Front. Mol. Neurosci. 11, 57 (2018).
Google Scholar
Nugent, A. A. et al. TREM2 regulates microglial cholesterol metabolism upon chronic phagocytic challenge. Neuron 105, 837–854 (2020).
Google Scholar
Browaeys, R., Saelens, W. & Saeys, Y. NicheNet: modeling intercellular communication by linking ligands to target genes. Nat. Methods 17, 159–162 (2020).
Google Scholar
Clemente, N. et al. Osteopontin bridging innate and adaptive immunity in autoimmune diseases. J. Immunol. Res. 2016, 7675437 (2016).
Google Scholar
Cappellano, G. et al. The Yin–Yang of osteopontin in nervous system diseases: damage versus repair. Neural Regen. Res. 16, 1131–1137 (2021).
Google Scholar
Selvaraju, R. et al. Osteopontin is upregulated during in vivo demyelination and remyelination and enhances myelin formation in vitro. Mol. Cell. Neurosci. 25, 707–721 (2004).
Google Scholar
Zhao, C., Fancy, S. P., ffrench-Constant, C. & Franklin, R. J. Osteopontin is extensively expressed by macrophages following CNS demyelination but has a redundant role in remyelination. Neurobiol. Dis. 31, 209–217 (2008).
Google Scholar
Dahiya, S. et al. Osteopontin-stimulated expression of matrix metalloproteinase-9 causes cardiomyopathy in the mdx model of Duchenne muscular dystrophy. J. Immunol. 187, 2723–2731 (2011).
Google Scholar
Rosario, A. M. et al. Microglia-specific targeting by novel capsid-modified AAV6 vectors. Mol. Ther. Methods Clin. Dev. 3, 16026 (2016).
Google Scholar
Krasemann, S. et al. The TREM2–APOE pathway drives the transcriptional phenotype of dysfunctional microglia in neurodegenerative diseases. Immunity 47, 566–581 (2017).
Google Scholar
Bellver-Landete, V. et al. Microglia are an essential component of the neuroprotective scar that forms after spinal cord injury. Nat. Commun. 10, 518 (2019).
Google Scholar
Zhao, Q. et al. Knockdown of long noncoding RNA XIST mitigates the apoptosis and inflammatory injury of microglia cells after spinal cord injury through miR-27a–Smurf1 axis. Neurosci. Lett. 715, 134649 (2020).
Google Scholar
Zhou, H. J. et al. Long noncoding RNA MALAT1 contributes to inflammatory response of microglia following spinal cord injury via the modulation of a miR-199b–IKKβ–NF-κB signaling pathway. Am. J. Physiol. Cell Physiol. 315, C52–C61 (2018).
Google Scholar
Villa, A. et al. Sex-specific features of microglia from adult mice. Cell Rep. 23, 3501–3511 (2018).
Google Scholar
Zheng, J. et al. Single-cell RNA-seq analysis reveals compartment-specific heterogeneity and plasticity of microglia. iScience 24, 102186 (2021).
Google Scholar
Comabella, M. et al. Plasma osteopontin levels in multiple sclerosis. J. Neuroimmunol. 158, 231–239 (2005).
Google Scholar
Braitch, M., Nunan, R., Niepel, G., Edwards, L. J. & Constantinescu, C. S. Increased osteopontin levels in the cerebrospinal fluid of patients with multiple sclerosis. Arch. Neurol. 65, 633–635 (2008).
Google Scholar
Clemente, N. et al. Role of anti-osteopontin antibodies in multiple sclerosis and experimental autoimmune encephalomyelitis. Front. Immunol. 8, 321 (2017).
Google Scholar
Murugaiyan, G., Mittal, A. & Weiner, H. L. Identification of an IL-27–osteopontin axis in dendritic cells and its modulation by IFN-γ limits IL-17-mediated autoimmune inflammation. Proc. Natl Acad. Sci. USA 107, 11495–11500 (2010).
Google Scholar
Hur, E. M. et al. Osteopontin-induced relapse and progression of autoimmune brain disease through enhanced survival of activated T cells. Nat. Immunol. 8, 74–83 (2007).
Google Scholar
Chabas, D. et al. The influence of the proinflammatory cytokine, osteopontin, on autoimmune demyelinating disease. Science 294, 1731–1735 (2001).
Google Scholar
Kariya, Y. et al. Increased cerebrospinal fluid osteopontin levels and its involvement in macrophage infiltration in neuromyelitis optica. BBA Clin. 3, 126–134 (2015).
Google Scholar
Sugiyama, Y. et al. Neuronal and microglial localization of secreted phosphoprotein 1 (osteopontin) in intact and damaged motor cortex of macaques. Brain Res. 1714, 52–64 (2019).
Google Scholar
Ikeshima-Kataoka, H., Matsui, Y. & Uede, T. Osteopontin is indispensable for activation of astrocytes in injured mouse brain and primary culture. Neurol. Res. 40, 1071–1079 (2018).
Google Scholar
Riew, T. R. et al. Osteopontin and its spatiotemporal relationship with glial cells in the striatum of rats treated with mitochondrial toxin 3-nitropropionic acid: possible involvement in phagocytosis. J. Neuroinflammation 16, 99 (2019).
Google Scholar
Gliem, M. et al. Macrophage-derived osteopontin induces reactive astrocyte polarization and promotes re-establishment of the blood brain barrier after ischemic stroke. Glia 63, 2198–2207 (2015).
Google Scholar
Yu, H., Liu, X. & Zhong, Y. The effect of osteopontin on microglia. BioMed Res. Int. 2017, 1879437 (2017).
Google Scholar
Polman, C. H. et al. Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald criteria. Ann. Neurol. 69, 292–302 (2011).
Google Scholar
Kuhlmann, T. et al. An updated histological classification system for multiple sclerosis lesions. Acta Neuropathol. 133, 13–24 (2017).
Google Scholar
Dhaeze, T. et al. CD70 defines a subset of proinflammatory and CNS-pathogenic TH1/TH17 lymphocytes and is overexpressed in multiple sclerosis. Cell. Mol. Immunol. 16, 652–665 (2019).
Google Scholar
Keough, M. B. et al. An inhibitor of chondroitin sulfate proteoglycan synthesis promotes central nervous system remyelination. Nat. Commun. 7, 11312 (2016).
Google Scholar
Leik, C. E. et al. GW3965, a synthetic liver X receptor (LXR) agonist, reduces angiotensin II-mediated pressor responses in Sprague-Dawley rats. Br. J. Pharmacol. 151, 450–456 (2007).
Google Scholar
Petrosyan, H. A. et al. Transduction efficiency of neurons and glial cells by AAV-1, -5, -9, -rh10 and -hu11 serotypes in rat spinal cord following contusion injury. Gene Ther. 21, 991–1000 (2014).
Google Scholar
Mishra, M. K. et al. Laquinimod reduces neuroaxonal injury through inhibiting microglial activation. Ann. Clin. Transl. Neurol. 1, 409–422 (2014).
Google Scholar
Cua, R. C. et al. Overcoming neurite-inhibitory chondroitin sulfate proteoglycans in the astrocyte matrix. Glia 61, 972–984 (2013).
Google Scholar
Rubinson, D. A. et al. A lentivirus-based system to functionally silence genes in primary mammalian cells, stem cells and transgenic mice by RNA interference. Nat. Genet. 33, 401–406 (2003).
Google Scholar
Pelossof, R. et al. Prediction of potent shRNAs with a sequential classification algorithm. Nat. Biotechnol. 35, 350–353 (2017).
Google Scholar
Challis, R. C. et al. Systemic AAV vectors for widespread and targeted gene delivery in rodents. Nat. Protoc. 14, 379–414 (2019).
Google Scholar
Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2018).
Google Scholar
Hafemeister, C. & Satija, R. Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. Genome Biol. 20, 296 (2019).
Google Scholar
BUSpaRse: kallisto | BUStools R utilities. R package version 1.4.2. https://github.com/BUStools/BUSpaRse/ (2021).
Martin, L. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 17, 10–12 (2011).
Google Scholar
Bray, N. L., Pimentel, H., Melsted, P. & Pachter, L. Near-optimal probabilistic RNA-seq quantification. Nat. Biotechnol. 34, 525–527 (2016).
Google Scholar
Soneson, C., Love, M. I. & Robinson, M. D. Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences. F1000Res. 4, 1521 (2015).
Google Scholar
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).
Google Scholar
