Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Single-cell and spatial RNA sequencing identify perturbators of microglial functions with aging

Abstract

Microglia are the immune sentinels of the central nervous system with protective roles such as the removal of neurotoxic oxidized phosphatidylcholines (OxPCs). As aging alters microglial function and elevates neurological disability in diseases such as multiple sclerosis, defining aging-associated factors that cause microglia to lose their custodial properties or even become injurious can help to restore their homeostasis. We used single-cell and spatial RNA sequencing in the spinal cord of young (6-week-old) and middle-aged (52-week-old) mice to determine aging-driven microglial reprogramming at homeostasis or after OxPC injury. We identified numerous aging-associated microglial transcripts including osteopontin elevated in OxPC-treated 52-week-old mice, which correlated with greater neurodegeneration. Osteopontin delivery into the spinal cords of 6-week-old mice worsened OxPC lesions, while its knockdown in 52-week-old lesions attenuated microglial inflammation and axon loss. Thus, elevation of osteopontin and other transcripts in aging disorders including multiple sclerosis perturbs microglial functions contributing to aging-associated neurodegeneration.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Aging exacerbates oxidized phosphatidylcholine-induced neurodegeneration.
Fig. 2: Microglial populations are the major cellular responders to the oxidized phosphatidylcholine injury.
Fig. 3: Oxidized phosphatidylcholine induces distinct transcriptomic profiles in microglia localized to the spinal cord lesion.
Fig. 4: Modulating the liver X receptor pathway reduces oxidized phosphatidylcholine-mediated neurodegeneration.
Fig. 5: Spp1 is upregulated in the aging injured spinal cord and in multiple sclerosis brain lesions.
Fig. 6: OPN exacerbates oxidized phosphatidylcholine-mediated neurodegeneration in young mice and in culture.
Fig. 7: Aging-upregulated osteopontin exacerbates oxidized phosphatidylcholine-mediated neurodegeneration and is associated with altered microglial function.

Similar content being viewed by others

Data availability

scRNA-seq and spRNA-seq datasets reported in this paper are available to download from the NCBI Sequence Read Archive (SRA) with BioProject accession numbers PRJNA648663 and PRJNA734097. The bulk microglia RNA-seq dataset is available to download from the SRA under accession number PRJNA733207. All other data are available upon reasonable request.

References

  1. Hou, Y. et al. Ageing as a risk factor for neurodegenerative disease. Nat. Rev. Neurol. 15, 565–581 (2019).

    Article  PubMed  Google Scholar 

  2. 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).

    Article  CAS  PubMed  Google Scholar 

  3. Lassmann, H. Multiple sclerosis pathology. Cold Spring Harb. Perspect. Med. 8, a028936 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  4. Tutuncu, M. et al. Onset of progressive phase is an age-dependent clinical milestone in multiple sclerosis. Mult. Scler. 19, 188–198 (2013).

    Article  PubMed  Google Scholar 

  5. Ximerakis, M. et al. Single-cell transcriptomic profiling of the aging mouse brain. Nat. Neurosci. 22, 1696–1708 (2019).

    Article  CAS  PubMed  Google Scholar 

  6. Tabula Muris, C. A single-cell transcriptomic atlas characterizes ageing tissues in the mouse. Nature 583, 590–595 (2020).

    Article  CAS  Google Scholar 

  7. Westlye, L. T. et al. Lifespan changes of the human brain white matter: diffusion tensor imaging and volumetry. Cereb. Cortex 20, 2055–2068 (2010).

    Article  PubMed  Google Scholar 

  8. 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).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Conway, B. L. et al. Age is a critical determinant in recovery from multiple sclerosis relapses. Mult. Scler. 25, 1754–1763 (2019).

    Article  PubMed  Google Scholar 

  10. Haider, L. et al. Oxidative damage in multiple sclerosis lesions. Brain 134, 1914–1924 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  11. Butterfield, D. A. & Halliwell, B. Oxidative stress, dysfunctional glucose metabolism and Alzheimer disease. Nat. Rev. Neurosci. 20, 148–160 (2019).

    Article  CAS  PubMed  Google Scholar 

  12. Dias, V., Junn, E. & Mouradian, M. M. The role of oxidative stress in Parkinson’s disease. J. Parkinsons Dis. 3, 461–491 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Dong, Y. et al. Oxidized phosphatidylcholines found in multiple sclerosis lesions mediate neurodegeneration and are neutralized by microglia. Nat. Neurosci. 24, 489–503 (2021).

    Article  CAS  PubMed  Google Scholar 

  14. Dong, Y. & Yong, V. W. When encephalitogenic T cells collaborate with microglia in multiple sclerosis. Nat. Rev. Neurol. 15, 704–717 (2019).

    Article  PubMed  Google Scholar 

  15. Li, Q. & Barres, B. A. Microglia and macrophages in brain homeostasis and disease. Nat. Rev. Immunol. 18, 225–242 (2018).

    Article  CAS  PubMed  Google Scholar 

  16. Prinz, M., Jung, S. & Priller, J. Microglia biology: one century of evolving concepts. Cell 179, 292–311 (2019).

    Article  CAS  PubMed  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  18. 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 

  19. Safaiyan, S. et al. White matter aging drives microglial diversity. Neuron 109, 1100–1117 (2021).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Hefendehl, J. K. et al. Homeostatic and injury-induced microglia behavior in the aging brain. Aging Cell 13, 60–69 (2014).

    Article  CAS  PubMed  Google Scholar 

  22. Marschallinger, J. et al. Lipid-droplet-accumulating microglia represent a dysfunctional and proinflammatory state in the aging brain. Nat. Neurosci. 23, 194–208 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  24. Hickman, S. E. et al. The microglial sensome revealed by direct RNA sequencing. Nat. Neurosci. 16, 1896–1905 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. 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).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  26. 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).

    Article  CAS  PubMed  Google Scholar 

  27. McGrowder, D. A. et al. Cerebrospinal fluid biomarkers of Alzheimer’s disease: current evidence and future perspectives. Brain Sci. 11, 215 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. 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).

    Article  CAS  PubMed  Google Scholar 

  29. Plemel, J. R. et al. Microglia response following acute demyelination is heterogenous and limits infiltrating macrophage dispersion. Sci. Adv. 6, eaay6324 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Masuda, T. et al. Novel Hexb-based tools for studying microglia in the CNS. Nat. Immunol. 21, 802–815 (2020).

    Article  CAS  PubMed  Google Scholar 

  32. 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  CAS  Google Scholar 

  33. Street, K. et al. Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics. BMC Genomics 19, 477 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  34. Jordao, M. J. C. et al. Single-cell profiling identifies myeloid cell subsets with distinct fates during neuroinflammation. Science 363, eaat7554 (2019).

    Article  CAS  PubMed  Google Scholar 

  35. 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 

  36. 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).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  37. Nugent, A. A. et al. TREM2 regulates microglial cholesterol metabolism upon chronic phagocytic challenge. Neuron 105, 837–854 (2020).

    Article  CAS  PubMed  Google Scholar 

  38. Browaeys, R., Saelens, W. & Saeys, Y. NicheNet: modeling intercellular communication by linking ligands to target genes. Nat. Methods 17, 159–162 (2020).

    Article  CAS  PubMed  Google Scholar 

  39. Clemente, N. et al. Osteopontin bridging innate and adaptive immunity in autoimmune diseases. J. Immunol. Res. 2016, 7675437 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  40. Cappellano, G. et al. The Yin–Yang of osteopontin in nervous system diseases: damage versus repair. Neural Regen. Res. 16, 1131–1137 (2021).

    Article  PubMed  Google Scholar 

  41. 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).

    Article  CAS  PubMed  Google Scholar 

  42. 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).

    Article  PubMed  CAS  Google Scholar 

  43. 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).

    Article  CAS  PubMed  Google Scholar 

  44. Rosario, A. M. et al. Microglia-specific targeting by novel capsid-modified AAV6 vectors. Mol. Ther. Methods Clin. Dev. 3, 16026 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  45. 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 

  46. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. 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).

    Article  CAS  PubMed  Google Scholar 

  48. 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).

    Article  CAS  PubMed  Google Scholar 

  49. Villa, A. et al. Sex-specific features of microglia from adult mice. Cell Rep. 23, 3501–3511 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Zheng, J. et al. Single-cell RNA-seq analysis reveals compartment-specific heterogeneity and plasticity of microglia. iScience 24, 102186 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Comabella, M. et al. Plasma osteopontin levels in multiple sclerosis. J. Neuroimmunol. 158, 231–239 (2005).

    Article  CAS  PubMed  Google Scholar 

  52. 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).

    Article  PubMed  Google Scholar 

  53. Clemente, N. et al. Role of anti-osteopontin antibodies in multiple sclerosis and experimental autoimmune encephalomyelitis. Front. Immunol. 8, 321 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  54. 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).

    Article  PubMed  PubMed Central  Google Scholar 

  55. 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).

    Article  CAS  PubMed  Google Scholar 

  56. Chabas, D. et al. The influence of the proinflammatory cytokine, osteopontin, on autoimmune demyelinating disease. Science 294, 1731–1735 (2001).

    Article  CAS  PubMed  Google Scholar 

  57. Kariya, Y. et al. Increased cerebrospinal fluid osteopontin levels and its involvement in macrophage infiltration in neuromyelitis optica. BBA Clin. 3, 126–134 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  58. 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).

    Article  CAS  PubMed  Google Scholar 

  59. 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).

    Article  CAS  PubMed  Google Scholar 

  60. 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).

    Article  PubMed  PubMed Central  Google Scholar 

  61. 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).

    Article  PubMed  Google Scholar 

  62. Yu, H., Liu, X. & Zhong, Y. The effect of osteopontin on microglia. BioMed Res. Int. 2017, 1879437 (2017).

    PubMed  PubMed Central  Google Scholar 

  63. Polman, C. H. et al. Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald criteria. Ann. Neurol. 69, 292–302 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  64. Kuhlmann, T. et al. An updated histological classification system for multiple sclerosis lesions. Acta Neuropathol. 133, 13–24 (2017).

    Article  CAS  PubMed  Google Scholar 

  65. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Keough, M. B. et al. An inhibitor of chondroitin sulfate proteoglycan synthesis promotes central nervous system remyelination. Nat. Commun. 7, 11312 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. 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).

    Article  CAS  PubMed  Google Scholar 

  69. Mishra, M. K. et al. Laquinimod reduces neuroaxonal injury through inhibiting microglial activation. Ann. Clin. Transl. Neurol. 1, 409–422 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Cua, R. C. et al. Overcoming neurite-inhibitory chondroitin sulfate proteoglycans in the astrocyte matrix. Glia 61, 972–984 (2013).

    Article  PubMed  Google Scholar 

  71. 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).

    Article  CAS  PubMed  Google Scholar 

  72. Pelossof, R. et al. Prediction of potent shRNAs with a sequential classification algorithm. Nat. Biotechnol. 35, 350–353 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Challis, R. C. et al. Systemic AAV vectors for widespread and targeted gene delivery in rodents. Nat. Protoc. 14, 379–414 (2019).

    Article  CAS  PubMed  Google Scholar 

  74. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Hafemeister, C. & Satija, R. Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. Genome Biol. 20, 296 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. BUSpaRse: kallisto | BUStools R utilities. R package version 1.4.2. https://github.com/BUStools/BUSpaRse/ (2021).

  77. Martin, L. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 17, 10–12 (2011).

    Article  Google Scholar 

  78. Bray, N. L., Pimentel, H., Melsted, P. & Pachter, L. Near-optimal probabilistic RNA-seq quantification. Nat. Biotechnol. 34, 525–527 (2016).

    Article  CAS  PubMed  Google Scholar 

  79. Soneson, C., Love, M. I. & Robinson, M. D. Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences. F1000Res. 4, 1521 (2015).

    Article  PubMed  CAS  Google Scholar 

  80. 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  CAS  Google Scholar 

Download references

Acknowledgements

We thank the Hotchkiss Brain Institute Advanced Microscopy Platform facility for microscopy and image analysis platforms, the Centre for Health Genomics and Informatics for sequencing, and H. Ramay for bioinformatics assistance in the analysis of microglia bulk RNA-seq. We thank the generosity of A. Srivastava from the University of Florida who kindly provided the AAV6 Y705F + Y731F + T492V. This work was funded by operating grants from the MS Society of Canada (MSSOC) and the Canadian Institutes of Health Research (CIHR) to V.W.Y. (grant nos. 3527 and FDN 167270, respectively) and A.P. (grant nos. 3188 and PJT166056, respectively). Y.D. acknowledges postdoctoral fellowship support from the CIHR. S.G. acknowledges postdoctoral fellowship support from the Harley N. Hotchkiss Postdoctoral Fellowship and the CIHR. B.M.L. and D.I.B. gratefully acknowledge studentships from the Alberta Graduate Excellence Scholarship and CIHR Canada Graduate Scholarships, respectively. S.Z. holds a joint fellowship from the Fonds de Recherche en Santé du Québec and the MSSOC. V.W.Y. and A.P. acknowledge salary support from the Canada Research Chair (tier 1) program.

Author information

Authors and Affiliations

Authors

Contributions

Y.D. conceived the project, designed, performed and analyzed experiments, and wrote the first draft of the paper. R.W.J. was key for postmortem human MS brain spRNA-seq. C.D. was key for the scRNA-seq and spRNA-seq experiments and provided support for the related data analysis. F.V. and S.G. were key for OPN KD with AAV. B.M.L. and D.I.B. performed surgeries. S.Z. and A.P. provided MS brain specimens. M.X. helped co-conceptualize the project. V.W.Y. co-conceived the project, provided support and experimental design, supervised the overall study and critically edited the manuscript. All authors reviewed and edited the manuscript.

Corresponding author

Correspondence to V. Wee Yong.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Aging thanks Martin Kerschensteiner and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Microglia/macrophage functions are altered in the ageing exacerbated OxPC induced spinal cord lesions at day 3.

a-c) Graphs comparing spinal cord lesion spread (a), total lesion volume (b), and lesion epicenter area (c) between 6wk and 52wk mice 3 days after PAzePC injection, analyzed from EC and NR-stained tissue sections. d, g, j, m, q) Representative confocal images of the spinal cord lesions of 6wk and 52wk mice 3 days after PAzePC injection. Sections were labeled with DAPI (blue), E06 (green), IBA1 (red) (d); or with MBP (grey), NFH (red) (g); or with DAPI (blue), Tuj1 (green), cleaved caspase-3 (red) (j); or with CD16/32 (grey), IL-1β (green), Arg1 (red) (m); or with iNOS (green), IBA1 (red) (q). e-f, h-i, k-l, n-p, r-t) Graphs comparing the E06+ percent (e), IBA1+ percent (f), the number of NFH+ (h) or Tuj1+ axons (i), the number of cleaved caspase 3+ particles (k), CD16/32+ percent (l), IL-1β+ percent (n), CD16/32+ IL-1β+ over CD16/32+ proportions (o), iNOS+ percent (p), IBA1+ iNOS+ over IBA1+ proportions (r), Arg1+ percent (s), and CD16/32+ Arg1+ over CD16/32+ proportions (t) between 6wk and 52wk spinal cords within the lesion ROI at day 3. Data were acquired from 2 separate experiments, n = 6 per experimental group. Significance indicated as * p< 0.05, ** p< 0.01, two-tailed, unpaired t-test, comparing 6wk and 52wk mice. Data are represented as mean ± SD.

Source data

Extended Data Fig. 2 Microglia/macrophages exhibit altered functional phenotype in the ageing lesion at day 7.

a, d, g) Lesions of 6wk and 52wk mice 7 days after PAzePC injection. Sections were labeled with iNOS (green), IBA1 (red) (a); or with CD16/32 (grey), Arg1 (red) (d); or with CD206 (green), IBA1 (red) (g). b-c, e-f, h-i) Graphs comparing the iNOS+ percent (b), IBA1+ iNOS+ over IBA1+ proportions (c), Arg1+ percent (e), CD16/32+ Arg1+ over CD16/32+ proportions (f), CD16/32+ percent (h), and CD206+ percent (i) between 6wk and 52wk spinal cords within the lesion ROI at day 7. Data were acquired from 2 separate experiments, n = 6 per experimental group. Significance indicated as * p < 0.05, ** p < 0.01, *** p < 0.001, two-tailed, unpaired t-test, comparing 6wk and 52wk mice. Data are represented as mean ± SD. j) Lesions in Cx3cr1CreER x Ai9 mice 7 days after PAzePC injection. The expression of EYFP, tdTomato, and Tmem119 were measured and the percent of EYFP+ tdTomato+ cells is summarized in k). Data were acquired from 1 experiment with 4 mice, summarized as mean ± SD.

Source data

Extended Data Fig. 3 ScRNAseq quality control and cell clustering.

a) Graphs show the distribution of cells from scRNAseq by the number of total unique genes (left), by the number of Unique Molecular Identifiers (middle), and by percent mitochondrial content (right). b) Graph shows the top 2000 variable features with the top 20 labeled. c) UMAP plots of 15411 cells from 4 experimental groups: PBS or OxPC injected spinal cords from 6wk or 52wk old mice. Left plot shows an overlay of cells based on origin, PBS injected 6wk mice (purple), PAzePC injected 6wk mice (blue), PBS injected 52wk mice (green) or PAzePC injected 52wk mice (red). Right plot shows the separation of cells from each experimental group into 20 clusters. d) Graph showing the total number of cells from each experimental group. Significance indicated as * p < 0.05, ** p < 0.01, one-way ANOVA with Tukey’s multiple comparison comparing the treatments against each other. Data are represented as mean ± SD. e) Graphs showing the number (left) and percent (right) of non-mononuclear phagocytes (MPH) cell clusters from each experimental group. f) Dot plot shows the expression of various cell lineage signature genes across the 20 cell clusters. MPH clusters selected for downstream analysis are highlighted by green dashed lines. The size of the dot corresponds to the percentage of cells expressing the gene in each cluster. The colour represents the average gene expression level. Sample size n = 3 per experimental group (each n represents a pool of 4 spinal cords for a total of 12 mice per group) and data are represented as mean ± SD.

Source data

Extended Data Fig. 4 Microglia from OxPC injected spinal cords express gene signatures comparable to other disease and aging associated microglia.

a-b) Dot plot comparing the expression of signatures genes from disease associated microglia (daMG2 in red, daMG3 in green, daMG4 in blue) that associate with experimental autoimmune encephalomyelitis lesions across the 8 MPH clusters identified by scRNAseq (a) or across the 4 different treatment conditions (b). c-d) Dot plot comparing the expression of white matter associated microglia (WAM) signature genes across the 8 MPH clusters (c) or across the 4 different treatment conditions (d). Dashed green lines highlight the MPH clusters that were most like WAMs. d-e) Dot plot comparing the expression of Alzheimer’s disease associated microglia (DAM) signature genes across the 8 MPH clusters (e) or across the 4 different treatment conditions (f). Dashed green lines highlight the MPH clusters that were most like DAMs. Dot plot comparing the expression of 540-day old microglia (OA2 in red, OA3 in blue) signature genes from the spinal cord across the 8 MPH clusters (g) or across the 4 different treatment conditions (h). The size of the dot corresponds to the percentage of cells expressing the gene in each cluster. The colour represents the average gene expression level. Sample size n = 3 per experimental group (each n represents a pool of 4 spinal cords for a total of 12 mice per group) and data are represented as mean ± SD.

Extended Data Fig. 5 SpRNAseq corroborates transcriptomic changes by MPH cell clusters after OxPC mediated neurodegeneration; similar spRNAseq elevation in MS.

a) Volcano plots comparing the significantly differentially upregulated or downregulated genes in the 8 MPH cluster from spinal cords injected with PBS (6wk and 52wk) or PAzePC (6wk and 52wk). b-c) Top activated (orange) or inactivated (blue) casual network regulators (b) or disease and biological functions (c) responding to OxPC as predicted by ingenuity pathway analysis (IPA) using the significantly differentially expressed genes from each MPH cluster. d) Representative images showing spatial expression of IPA predicted causal regulators Stk11, Myc, Csf1, Arnt, Eif4e, Hif1a, Cdc42, and Ppard mRNA in the spinal cord. For ScRNAseq, data in each experimental group acquired from 3 biological replicates, pooled from 4 spinal cords each. For spatial sequencing, n = 2 per experimental group. e) Graph comparing the relative fold change in the expression of various activation and microglia/macrophage associated genes identified by spRNAseq in the Active Rim, Inactive centre, and NAWM of post-mortem MS brain tissue sections. Data representative from 2 healthy control and 2 MS samples.

Source data

Extended Data Fig. 6 LXR agonist injection reduced OxPC mediated neurodegeneration in spinal cords of 6wk mice.

a) Representative serial images of EC and NR labeling of the spinal cord from 6wk mice treated with DMSO or with DMSO + GW3965 LXR agonist 7 days after PAzePC treatment. b) Graph comparing spinal cord total lesion volume with or without LXR agonist treatment. c-e) Representative confocal images of the ventral spinal cord labeled with DAPI (blue), E06 (green), IBA1 (red) (c); or with MBP (grey), OLIG2 (green), NFH (red) (d); or with DAPI (blue), Tuj1 (green), cleaved capase-3 (red) (e). f-k) Graphs comparing the fold difference in percent E06+ (f), the average size of E06+ particles (g), percent IBA1+ (h), OLIG2+ cell density (i), NFH+ axon density (j), and cleaved caspase-3+ density (k) in the lesion ROI with or without LXR agonist treatment. Data acquired from 3 experiments, 8 mice in total per group. Significance indicated as * p < 0.05, two-tailed, unpaired t-test, comparing DMSO and DMSO + GW3965 treated 6wk mice. Data are represented as mean ± SD.

Source data

Extended Data Fig. 7 Ageing associated transcriptomic changes in the homeostatic spinal cord.

a) Top significantly upregulated (green) and downregulated (blue) genes identified by scRNAseq in the MPH clusters from the PBS injected 52wk versus 6wk spinal cord (clusters without significantly differentially expressed gene are not shown). b) Graph comparing the number of DEGs between the age groups found in each MPH cluster. c) Summary of IPA predicted regulatory effects network in steady-state associated MG3 and MG5 clusters from differentially expressed ageing associated genes. d-e) Top activated (orange) or inactivated (blue) casual network regulators (d) or disease and biological functions (e) in 52wk PBS injected spinal cords compared to 6wk PBS injected spinal cords as predicted by IPA using the differentially expressed ageing associated genes from each MPH cluster. Clusters without significantly differentially expressed gene are not shown. f) Histological demarcation of the NAWM and the NAGM used to spatially compare gene expression from homeostatic spinal cord tissue. g) Violin plots comparing the spatial expression of Fth1 (6wk/52wk NAWM: min = 2.6/2.0, max = 10/10, mid = 7.1/8.4, mean = 7.2/7.9, q1 = 5.9/7.7, q3 = 8.7/8.9; for NAGM: min = 5.0/5.8, max = 9.3/9.3, mid = 8.0/8.3, mean = 7.9/8.2; q1 = 7.6/8.0, q3 = 8.3/8.5), Lyz2 (NAWM: min = 0/0, max = 6.0/6.7, mid = 1.0/2.3, mean = 1.3/2.3, q1 = -/1.6, q3 = 2.0/3.3; for NAGM: min = 0/0, max = 5.0/6.6, mid = 2.6/3.6, mean = 2.5/3.5; q1 = 2.0/3.0, q3 = 3.0/4.1), Apoe (NAWM: min = 1.0/0, max = 9.4/9.4, mid = 5.0/5.2, mean = 4.8/5.1, q1 = 3.5/4.5, q3 = 5.9/5.8; for NAGM: min = 3.9/4.6, max = 8.2/8.0, mid = 6.8/6.6, mean = 6.7/6.6; q1 = 6.4/6.3, q3 = 7.2/7.0), H2-D1 (NAWM: min = 0/0, max = 4.2/4.6, mid = 1.0/1.6, mean = 0.9/1.7, q1 = -/1.0, q3 = 1.6/2.3; for NAGM: min = 0/0, max = 4.6/4.5, mid = 2.3/2.6, mean = 2.2/2.7; q1 = 1.6/2.0, q3 = 2.8/3.3), and Ifi27l2a (NAWM: min = 0/0, max = 3.2/5.0, mid = 0/1.0, mean = 0.5/1.1, q1 = -/-, q3 = 1.0/1.7; for NAGM: min = 0/0, max = 3.2/4.1, mid = 1.0/1.0, mean = 0.7/1.1; q1 = -/-, q3 = 0/1.6) mRNA in the NAWM and NAGM of 6wk and 52wk spinal cords. For ScRNAseq, n = 3 per experimental group (each n represents a pool of 4 spinal cords for a total of 12 mice per group). For spatial sequencing, n = 2 per experimental group.

Extended Data Fig. 8 Spatial expression and IPA analysis of ageing associated genes after OxPC mediated neurodegeneration.

a) Histological demarcation of the OxPC induced lesion to spatially compare gene expression between 6wk and 52wk mice during neurodegeneration. b) Violin plots comparing the spatial expression of Lgals3 (6wk/52wk min = 2.0/3.6, max = 6.4/7.3, mid = 4.5/5.6, mean = 4.5/5.5, q1 = 3.7/4.9, q3 = 5.5/6.3), Lyz2 (6wk/52wk min = 4.9/5.1, max = 9.2/10, mid = 7.0/7.9, mean = 7.0/7.8, q1 = 6.2/7.2, q3 = 7.6/8.7), Cd74 (6wk/52wk min = 0/0, max = 4.2/4.9, mid = 1.0/1.6, mean = 1.0/1.9, q1 = -/1.0, q3 = 1.6/2.6), Wfdc17 (6wk/52wk min = 1.0/2.0, max = 5.9/7.4, mid = 3.8/4.6, mean = 3.7/4.6, q1 = 3.0/4.6, q3 = 4.5/5.6), H2-Aa (6wk/52wk min = -/-, max = 3.4/3.7, mid = 0/0, mean = 0.5/1.0, q1 = -/-, q3 = -/2.0), Ccl2 (6wk/52wk min = -/0, max = 2.3/3.0, mid = 0/1.0, mean = 0.4/0.8, q1 = -/-, q3 = 1.0/1.3), Plin2 (6wk/52wk min = 1.6/1.0, max = 5.4/6.5, mid = 3.9/4.8, mean = 3.8/4.4, q1 = 3.2/3.6, q3 = 4.5/5.4), Gas6 (6wk/52wk min = 1.6/0, max = 5.2/4.9, mid = 4.4/3.3, mean = 4.1/3.2, q1 = 3.8/2.5, q3 = 4.6/4.0), Gnas (6wk/52wk min = 3.8/1.0, max = 7.5/6.5, mid = 6.6/5.5, mean = 6.4/5.1, q1 = 6.1/4.6, q3 = 6.9/6.0), Lgmn (6wk/52wk min = 3.7/2.0, max = 7.1/7.3, mid = 6.5/5.7, mean = 6.3/5.4, q1 = 6.0/4.8, q3 = 6.8/6.2), Lpl (6wk/52wk min = 1.6/1.0, max = 5.1/5.2, mid = 4.2/3.7, mean = 4.0/3.5, q1 = 3.8/3.2, q3 = 4.5/4.2), Apoc1 (6wk/52wk min = 1.0/0, max = 6.1/3.0, mid = 3.8/1.0, mean = 3.7/1.0, q1 = 3.0/-, q3 = 4.5/2.0), Qk (6wk/52wk min = 2.0/0, max = 5.4/5.1, mid = 4.5/3.6, mean = 4.4/3.4, q1 = 4.1/2.8, q3 = 5.0/4.0), and Cd9 (6wk/52wk min = 2.8/1.0, max = 6.7/6.2, mid = 6.0/5.2, mean = 5.7/4.7, q1 = 5.3/4.1, q3 = 6.2/5.8) mRNA in the OxPC lesion of 6wk and 52wk spinal cords. c) Top IPA predicted activated or inhibited causal regulator genes (z-score cut off -2 and 2) identified by scRNAseq in the MPH clusters from the PAzePC injected 52wk spinal cord compared with the PAzePC injected 6wk spinal cord. d) Summary of IPA predicted pathway changes in MPH clusters from differentially expressed ageing associated genes during OxPC mediated neurodegeneration. Clusters without sufficient high z-score connectable features are not shown. e) Day 7 lesions from 6wk and 52wk old mice labeled with CD68 (green) and OPN (red). f-h) Graphs comparing the proportion of CD68+ area that is also OPN+ (f), the fold difference in total lesional OPN MFI (g), and fold difference in OPN MFI from CD68+ area (h) between 6wk and 52wk old lesion spinal cords. i) Representative images and violin plots comparing the spatial expression of Itgb1 (6wk/52wk min = 1.6/0, max = 4.6/4.6, mid = 3.2/2.8, mean = 3.3/2.7, q1 = 2.8/2.2, q3 = 3.8/3.5), Itgav (6wk/52wk min = 1.6/1.0, max = 4.1/4.5, mid = 3.1/2.6, mean = 2.9/2.5, q1 = 2.3/1.8, q3 = 3.3/3.2), and Itgb5 (6wk/52wk min = 3.0/1.0, max = 5.9/5.3, mid = 4.9/4.1, mean = 4.0/3.9, q1 = 4.5/3.2, q3 = 5.4/4.8) mRNA in the lesion area of 6wk and 52wk spinal cords. For ScRNAseq, n = 3 per experimental group (each n represents a pool of 4 spinal cords for a total of 12 mice per group) and data are represented as mean ± SD. For spatial sequencing, n = 2 per experimental group. Microscopy data were acquired from 2 separate experiments, n = 6 per experimental group. Significance indicated as * p < 0.05, ** p < 0.01, two-tailed, unpaired t-test, comparing 6wk and 52wk old mice. Data are represented as mean ± SD.

Source data

Extended Data Fig. 9 Additional experiments investigating OPN receptors and OPN KD.

a) Lesions of 6wk and 52wk mice 7 days after PAzePC injection labeled with CD68 (red) and ITGB1 (green) (left), IBA1 (red) and ITGAV (green) (middle), and CD68 (red) and ITGB5 (green) (right). b-d) Graphs comparing the percent of ITGB1 (b), ITGAV (c), and ITGB5 (d) found in the PAzePC lesions of 6wk and 52wk mice. Data from 2 separate experiment, n =6 per experimental group. Significance indicated as *** p < 0.001, two-tailed, unpaired t-test, comparing 6wk and 52wk. Data are represented as mean ± SD. e) Lesions of 52wk mice injected with Ctrl or OPN KD AAV 7 days after PAzePC treatment labeled for CD68 (red) and GFP (green) (left), NFH (red) and GFP (green) (middle), and GFAP (red) and GFP (green) (right). f) Graph comparing the expression of GFP in CD68+ microglia/macrophage, NFH+ axons, and GFAP+ astrocytes from Ctrl or KD treated spinal cords. g) Lesions of 52wk mice injected with Ctrl or OPN KD AAV 7 days after PAzePC treatment labeled for CD68 (green) and MMP9 (red) (left) or CD68 (red) and MMP14 (red) (right). h) Graphs comparing the percent of MMP9 (left) and MMP14 (right) found in the PAzePC lesions of Ctrl and OPN KD mice. Data were acquired from 2 separate experiments, n = 6 per experimental group. Significance indicated as * p < 0.05, unpaired t-test, comparing Ctrl and OPN KD treated 52wk mice after PAzePC injection. Data are represented as mean ± SD. i) Lesions of 52wk mice injected with AAV6 with a CD68 promoter to express scrambled shRNA (CD68 Ctrl) or OPN targeting shRNA (CD68 OPN KD) 7 days after PAzePC treatment labeled with E06 (green), CD16/32 (red), NFH (grey) (left), or with GFP (green) and CD68 (red) (middle left), or with GFP (green) and NFH (red) (middle right), or with GFP (green) and GFAP (right). j) Graph comparing NFH+ axon density in the PAzePC lesions from CD68 Ctrl and CD68 OPN KD treated 52wk mice. k) Graph comparing the expression of GFP in CD68+ microglia/macrophage, NFH+ axons, and GFAP+ astrocytes from CD68 Ctrl or CD68 OPN KD treated spinal cords. Data were acquired from 2 separate experiments, n = 6 per experimental group. Significance indicated as * p < 0.05, unpaired t-test, comparing CD68 Ctrl and CD68 OPN KD treated 52wk mice after PAzePC injection. Data are represented as mean ± SD.

Source data

Supplementary information

Source data

Source Data Fig. 1

Numerical and statistical source data.

Source Data Fig. 2

Numerical and statistical source data.

Source Data Fig. 4

Numerical and statistical source data.

Source Data Fig. 6

Numerical and statistical source data.

Source Data Fig. 7

Numerical and statistical source data.

Source Data Extended Data Fig. 1

Numerical and statistical source data.

Source Data Extended Data Fig. 2

Numerical and statistical source data.

Source Data Extended Data Fig. 3

Numerical and statistical source data.

Source Data Extended Data Fig. 5

Numerical and statistical source data.

Source Data Extended Data Fig. 6

Numerical and statistical source data.

Source Data Extended Data Fig. 8

Numerical and statistical source data.

Source Data Extended Data Fig. 9

Numerical and statistical source data.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dong, Y., Jain, R.W., Lozinski, B.M. et al. Single-cell and spatial RNA sequencing identify perturbators of microglial functions with aging. Nat Aging 2, 508–525 (2022). https://doi.org/10.1038/s43587-022-00205-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s43587-022-00205-z

This article is cited by

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing