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Single-cell transcriptomic profiling of the aging mouse brain

Abstract

The mammalian brain is complex, with multiple cell types performing a variety of diverse functions, but exactly how each cell type is affected in aging remains largely unknown. Here we performed a single-cell transcriptomic analysis of young and old mouse brains. We provide comprehensive datasets of aging-related genes, pathways and ligand–receptor interactions in nearly all brain cell types. Our analysis identified gene signatures that vary in a coordinated manner across cell types and gene sets that are regulated in a cell-type specific manner, even at times in opposite directions. These data reveal that aging, rather than inducing a universal program, drives a distinct transcriptional course in each cell population, and they highlight key molecular processes, including ribosome biogenesis, underlying brain aging. Overall, these large-scale datasets (accessible online at https://portals.broadinstitute.org/single_cell/study/aging-mouse-brain) provide a resource for the neuroscience community that will facilitate additional discoveries directed towards understanding and modifying the aging process.

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Fig. 1: Identification of cell types.
Fig. 2: Aging-related population shifts and changes in gene expression.
Fig. 3: Validation of shared and cell-type-specific aging-related gene expression changes.
Fig. 4: Validation of bidirectional aging-related gene expression changes.
Fig. 5: Aging-related changes in the expression of ribosomal protein genes.
Fig. 6: Aging-related changes in cellular pathways and processes.
Fig. 7: Aging-related changes in intercellular communication.

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Data availability

The raw single-cell RNA sequencing data are available through NCBI’s Gene Expression Omnibus (GEO) under the accession number GSE129788. The processed datasets can be readily viewed, explored and downloaded through our web-based interactive viewers at https://portals.broadinstitute.org/single_cell/study/aging-mouse-brain and http://shiny.baderlab.org/AgingMouseBrain/.

Code availability

The code used to perform analysis of the sequencing data was an adaptation of standard R packages, such as Seurat and MAST, as described in the Methods section. The code used for the ligand–receptor interaction analyses is available on GitHub at https://github.com/BaderLab/AgingMouseBrainCCInx. More detailed information is available upon request.

References

  1. Lopez-Otin, C., Blasco, M. A., Partridge, L., Serrano, M. & Kroemer, G. The hallmarks of aging. Cell 153, 1194–1217 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Wyss-Coray, T. Ageing, neurodegeneration and brain rejuvenation. Nature 539, 180–186 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Mattson, M. P. & Arumugam, T. V. Hallmarks of brain aging: adaptive and pathological modification by metabolic states. Cell Metab. 27, 1176–1199 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Lee, C. K., Weindruch, R. & Prolla, T. A. Gene-expression profile of the ageing brain in mice. Nat. Genet. 25, 294–297 (2000).

    Article  CAS  PubMed  Google Scholar 

  5. Zahn, J. M. et al. AGEMAP: a gene expression database for aging in mice. PLoS Genet. 3, e201 (2007).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  6. de Magalhaes, J. P., Curado, J. & Church, G. M. Meta-analysis of age-related gene expression profiles identifies common signatures of aging. Bioinformatics 25, 875–881 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  7. Clarke, L. E. et al. Normal aging induces A1-like astrocyte reactivity. Proc. Natl Acad. Sci. USA 115, E1896–E1905 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. 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 (2018).

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  9. Kalamakis, G. et al. Quiescence modulates stem cell maintenance and regenerative capacity in the aging brain. Cell 176, 1407–1419.e1414 (2019).

    Article  CAS  PubMed  Google Scholar 

  10. de Magalhaes, J. P. Programmatic features of aging originating in development: aging mechanisms beyond molecular damage? FASEB J. 26, 4821–4826 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  11. Xu, C. & Su, Z. Identification of cell types from single-cell transcriptomes using a novel clustering method. Bioinformatics 31, 1974–1980 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Zeisel, A. et al. Molecular architecture of the mouse nervous system. Cell 174, 999–1014.e1022 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Saunders, A. et al. Molecular diversity and specializations among the cells of the adult mouse brain. Cell 174, 1015–1030.e1016 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Rosenberg, A. B. et al. Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding. Science 360, 176–182 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Bahar, R. et al. Increased cell-to-cell variation in gene expression in ageing mouse heart. Nature 441, 1011–1014 (2006).

    Article  CAS  PubMed  Google Scholar 

  16. Sim, F. J., Zhao, C., Penderis, J. & Franklin, R. J. The age-related decrease in CNS remyelination efficiency is attributable to an impairment of both oligodendrocyte progenitor recruitment and differentiation. J. Neurosci. 22, 2451–2459 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Walter, J., Keiner, S., Witte, O. W. & Redecker, C. Age-related effects on hippocampal precursor cell subpopulations and neurogenesis. Neurobiol. Aging 32, 1906–1914 (2011).

    Article  PubMed  Google Scholar 

  18. Ben Abdallah, N. M., Slomianka, L., Vyssotski, A. L. & Lipp, H. P. Early age-related changes in adult hippocampal neurogenesis in C57 mice. Neurobiol. Aging 31, 151–161 (2010).

    Article  PubMed  Google Scholar 

  19. Habib, N. et al. Massively parallel single-nucleus RNA-seq with DroNc-seq. Nat. Methods 14, 955–958 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Hrvatin, S. et al. Single-cell analysis of experience-dependent transcriptomic states in the mouse visual cortex. Nat. Neurosci. 21, 120–129 (2018).

    Article  CAS  PubMed  Google Scholar 

  21. Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Baruch, K. et al. Aging. Aging-induced type I interferon response at the choroid plexus negatively affects brain function. Science 346, 89–93 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Li, M., Hale, J. S., Rich, J. N., Ransohoff, R. M. & Lathia, J. D. Chemokine CXCL12 in neurodegenerative diseases: an SOS signal for stem cell-based repair. Trends Neurosci. 35, 619–628 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Mathews, P. M. & Levy, E. Cystatin C in aging and in Alzheimer’s disease. Ageing Res. Rev. 32, 38–50 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Warren, L. A. et al. Transcriptional instability is not a universal attribute of aging. Aging Cell 6, 775–782 (2007).

    Article  CAS  PubMed  Google Scholar 

  26. Martinez-Jimenez, C. P. et al. Aging increases cell-to-cell transcriptional variability upon immune stimulation. Science 355, 1433–1436 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Gonskikh, Y. & Polacek, N. Alterations of the translation apparatus during aging and stress response. Mech. Ageing Dev. 168, 30–36 (2017).

    Article  CAS  PubMed  Google Scholar 

  28. Frenk, S. & Houseley, J. Gene expression hallmarks of cellular ageing. Biogerontology 19, 547–566 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Sun, D. et al. Epigenomic profiling of young and aged HSCs reveals concerted changes during aging that reinforce self-renewal. Cell Stem Cell. 14, 673–688 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Kowalczyk, M. S. et al. Single-cell RNA-seq reveals changes in cell cycle and differentiation programs upon aging of hematopoietic stem cells. Genome Res. 25, 1860–1872 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Peters, M. J. et al. The transcriptional landscape of age in human peripheral blood. Nat. Commun. 6, 8570 (2015).

    Article  CAS  PubMed  Google Scholar 

  32. Enge, M. et al. Single-cell analysis of human pancreas reveals transcriptional signatures of aging and somatic mutation patterns. Cell 171, 321–330.e314 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Davie, K. et al. A single-cell transcriptome atlas of the aging drosophila brain. Cell 174, 982–998.e20 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Angelidis, I. et al. An atlas of the aging lung mapped by single cell transcriptomics and deep tissue proteomics. Nat. Commun. 10, 963 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  35. Zahn, J. M. et al. Transcriptional profiling of aging in human muscle reveals a common aging signature. PLoS Genet. 2, e115 (2006).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  36. Leeman, D. S. et al. Lysosome activation clears aggregates and enhances quiescent neural stem cell activation during aging. Science 359, 1277–1283 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. 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).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  39. Keren-Shaul, H. et al. A unique microglia type associated with restricting development of Alzheimer’s disease. Cell 169, 1276–1290.e1217 (2017).

    Article  CAS  PubMed  Google Scholar 

  40. Mathys, H. et al. Temporal tracking of microglia activation in neurodegeneration at single-cell resolution. Cell Rep. 21, 366–380 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Buchwalter, A. & Hetzer, M. W. Nucleolar expansion and elevated protein translation in premature aging. Nat. Commun. 8, 328 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  42. Steffen, K. K. & Dillin, A. A ribosomal perspective on proteostasis and aging. Cell Metab. 23, 1004–1012 (2016).

    Article  CAS  PubMed  Google Scholar 

  43. Katsimpardi, L. et al. Vascular and neurogenic rejuvenation of the aging mouse brain by young systemic factors. Science 344, 630–634 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Villeda, S. A. et al. Young blood reverses age-related impairments in cognitive function and synaptic plasticity in mice. Nat. Med. 20, 659–663 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Campbell, J. N. et al. A molecular census of arcuate hypothalamus and median eminence cell types. Nat. Neurosci. 20, 484–496 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Wu, Y. E., Pan, L., Zuo, Y., Li, X. & Hong, W. Detecting activated cell populations using single-cell RNA-Seq. Neuron 96, 313–329.e316 (2017).

    Article  CAS  PubMed  Google Scholar 

  47. Dulken, B. W. et al. Single-cell analysis reveals T cell infiltration in old neurogenic niches. Nature 571, 205–210 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Gupta, I. et al. Single-cell isoform RNA sequencing characterizes isoforms in thousands of cerebellar cells. Nat. Biotechnol. 36, 1197–1202 (2018).

    Article  CAS  Google Scholar 

  49. Spitzer, S. O. et al. Oligodendrocyte progenitor cells become regionally diverse and heterogeneous with age. Neuron 101, 459–471.e455 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Rodriques, S. G. et al. Slide-seq: a scalable technology for measuring genome-wide expression at high spatial resolution. Science 363, 1463–1467 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Zhang, R., Lahens, N. F., Ballance, H. I., Hughes, M. E. & Hogenesch, J. B. A circadian gene expression atlas in mammals: implications for biology and medicine. Proc. Natl Acad. Sci. USA 111, 16219–16224 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Saxena, A. et al. Trehalose-enhanced isolation of neuronal sub-types from adult mouse brain. Biotechniques 52, 381–385 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Zheng, G. X. et al. Massively parallel digital transcriptional profiling of single cells. Nat. Commun. 8, 14049 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Satija, R., Farrell, J. A., Gennert, D., Schier, A. F. & Regev, A. Spatial reconstruction of single-cell gene expression data. Nat. Biotechnol. 33, 495–502 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Macosko, E. Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–421 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Marques, S. et al. Oligodendrocyte heterogeneity in the mouse juvenile and adult central nervous system. Science 352, 1326–1329 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Ubink, R. & Hokfelt, T. Expression of neuropeptide Y in olfactory ensheathing cells during prenatal development. J. Comp. Neurol. 423, 13–25 (2000).

    Article  CAS  PubMed  Google Scholar 

  59. Beckervordersandforth, R. et al. In vivo fate mapping and expression analysis reveals molecular hallmarks of prospectively isolated adult neural stem cells. Cell Stem Cell. 7, 744–758 (2010).

    Article  CAS  PubMed  Google Scholar 

  60. Codega, P. et al. Prospective identification and purification of quiescent adult neural stem cells from their in vivo niche. Neuron 82, 545–559 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Llorens-Bobadilla, E. et al. Single-cell transcriptomics reveals a population of dormant neural stem cells that become activated upon brain injury. Cell Stem Cell. 17, 329–340 (2015).

    Article  CAS  PubMed  Google Scholar 

  62. Shah, P. T. et al. Single-cell transcriptomics and fate mapping of ependymal cells reveals an absence of neural stem cell function. Cell 173, 1045–1057.e1049 (2018).

    Article  CAS  PubMed  Google Scholar 

  63. Zywitza, V., Misios, A., Bunatyan, L., Willnow, T. E. & Rajewsky, N. Single-cell transcriptomics characterizes cell types in the subventricular zone and uncovers molecular defects impairing adult. Neurogenesis. Cell Rep. 25, 2457–2469.e2458 (2018).

    Article  CAS  PubMed  Google Scholar 

  64. Liu, Y. et al. CD44 expression identifies astrocyte-restricted precursor cells. Dev. Biol. 276, 31–46 (2004).

    Article  CAS  PubMed  Google Scholar 

  65. Hochgerner, H., Zeisel, A., Lonnerberg, P. & Linnarsson, S. Conserved properties of dentate gyrus neurogenesis across postnatal development revealed by single-cell RNA sequencing. Nat. Neurosci. 21, 290–299 (2018).

    Article  CAS  PubMed  Google Scholar 

  66. Tasic, B. et al. Adult mouse cortical cell taxonomy revealed by single cell transcriptomics. Nat. Neurosci. 19, 335–346 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Zhang, X. et al. BAIAP3, a C2 domain-containing Munc13 protein, controls the fate of dense-core vesicles in neuroendocrine cells. J. Cell Biol. 216, 2151–2166 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Zeisel, A. et al. Brain structure. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science 347, 1138–1142 (2015).

    Article  CAS  PubMed  Google Scholar 

  69. Vanlandewijck, M. et al. A molecular atlas of cell types and zonation in the brain vasculature. Nature 554, 475–480 (2018).

    Article  CAS  PubMed  Google Scholar 

  70. Straub, A. C. et al. Endothelial cell expression of haemoglobin alpha regulates nitric oxide signalling. Nature 491, 473–477 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Han, X. et al. Mapping the mouse cell atlas by Microwell-Seq. Cell 172, 1091–1107.e1017 (2018).

    Article  CAS  PubMed  Google Scholar 

  72. Geijtenbeek, T. B. et al. Identification of DC-SIGN, a novel dendritic cell-specific ICAM-3 receptor that supports primary immune responses. Cell 100, 575–585 (2000).

    Article  CAS  PubMed  Google Scholar 

  73. Artegiani, B. et al. A single-cell RNA sequencing study reveals cellular and molecular dynamics of the hippocampal neurogenic niche. Cell Rep. 21, 3271–3284 (2017).

    Article  CAS  PubMed  Google Scholar 

  74. Romanov, R. A. et al. Molecular interrogation of hypothalamic organization reveals distinct dopamine neuronal subtypes. Nat. Neurosci. 20, 176–188 (2017).

    Article  CAS  PubMed  Google Scholar 

  75. Tepe, B. et al. Single-cell RNA-Seq of mouse olfactory bulb reveals cellular heterogeneity and activity-dependent molecular census of adult-born neurons. Cell Rep. 25, 2689–2703.e2683 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Mrdjen, D. et al. High-dimensional single-cell mapping of central nervous system immune cells reveals distinct myeloid subsets in health, aging, and disease. Immunity 48, 380–395.e386 (2018).

    Article  CAS  PubMed  Google Scholar 

  77. Finak, G. et al. MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data. Genome Biol. 16, 278 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  78. Reimand, J. et al. Pathway enrichment analysis and visualization of omics data using g:Profiler, GSEA, Cytoscape and EnrichmentMap. Nat. Protoc. 14, 482–517 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Kucera, M., Isserlin, R., Arkhangorodsky, A. & Bader, G. D. AutoAnnotate: a Cytoscape app for summarizing networks with semantic annotations. F1000Research 5, 1717 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  80. Kirouac, D. C. et al. Dynamic interaction networks in a hierarchically organized tissue. Mol. Syst. Biol. 6, 417 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  81. Uhlen, M. et al. Proteomics. Tissue-based map of the human proteome. Science 347, 1260419 (2015).

    Article  PubMed  CAS  Google Scholar 

  82. Razick, S., Magklaras, G. & Donaldson, I. M. iRefIndex: a consolidated protein interaction database with provenance. BMC Bioinforma. 9, 405 (2008).

    Article  CAS  Google Scholar 

  83. Cerami, E. G. et al. Pathway Commons, a web resource for biological pathway data. Nucleic Acids Res. 39, D685–D690 (2011).

    Article  CAS  PubMed  Google Scholar 

  84. Stark, C. et al. BioGRID: a general repository for interaction datasets. Nucleic Acids Res. 34, D535–D539 (2006).

    Article  CAS  PubMed  Google Scholar 

  85. Soumillon, M., Cacchiarelli, D., Semrau, S., van Oudenaarden, A. & Mikkelsen, T. S. Characterization of directed differentiation by high-throughput single-cell RNA-seq. Preprint at bioRxiv https://www.biorxiv.org/content/10.1101/003236v1(2014).

  86. Derr, A. et al. End Sequence Analysis Toolkit (ESAT) expands the extractable information from single-cell RNA-seq data. Genome Res. 26, 1397–1410 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    Article  CAS  PubMed  Google Scholar 

  88. Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  89. Anders, S. & Huber, W. Differential expression analysis for sequence count data. Genome Biol. 11, R106 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. 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 

  91. Livak, K. J. & Schmittgen, T. D. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods 25, 402–408 (2001).

    Article  CAS  PubMed  Google Scholar 

  92. Eisenberg, E. & Levanon, E. Y. Human housekeeping genes, revisited. Trends Genet. 29, 569–574 (2013).

    Article  CAS  PubMed  Google Scholar 

  93. Sharma, K. et al. Cell type- and brain region-resolved mouse brain proteome. Nat. Neurosci. 18, 1819–1831 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Tabula Muris, C. et al. Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris. Nature 562, 367–372 (2018).

    Article  CAS  Google Scholar 

  95. Kantzer, C. G. et al. Anti-ACSA-2 defines a novel monoclonal antibody for prospective isolation of living neonatal and adult astrocytes. Glia 65, 990–1004 (2017).

    Article  PubMed  Google Scholar 

  96. Boisvert, M. M., Erikson, G. A., Shokhirev, M. N. & Allen, N. J. The aging astrocyte transcriptome from multiple regions of the mouse brain. Cell Rep. 22, 269–285 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Soreq, L. et al. Major shifts in glial regional identity are a transcriptional hallmark of human brain. Aging Cell Rep. 18, 557–570 (2017).

    Article  CAS  PubMed  Google Scholar 

  98. Lein, E. S. et al. Genome-wide atlas of gene expression in the adult mouse brain. Nature 445, 168–176 (2007).

    Article  CAS  PubMed  Google Scholar 

  99. Carpenter, A. E. et al. CellProfiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biol. 7, R100 (2006).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  100. Xu, H., Chen, M., Manivannan, A., Lois, N. & Forrester, J. V. Age-dependent accumulation of lipofuscin in perivascular and subretinal microglia in experimental mice. Aging Cell 7, 58–68 (2008).

    Article  CAS  PubMed  Google Scholar 

  101. Mann, H. B. & Whitney, D. R. On a test of whether one of two random variables is stochastically larger than the other. Ann. Math. Stat. 18, 50–60 (1947).

    Article  Google Scholar 

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

    Google Scholar 

Download references

Acknowledgments

We acknowledge T. Okino and his team at Ono Pharmaceuticals for fruitful discussions and useful suggestions during the progress of this work. We are grateful to F. Rapino, N. Rodriguez-Muela, K. Pfaff, A. Freeman and J. LaLonde for their helpful advice in different aspects of our work and/or for reviewing the manuscript. We also thank the stuff members of the Harvard Bauer Core Facility, the Harvard Center for Biological Imaging and the Harvard Stem Cell and Regenerative Biology Histology Core for their technical advice and assistance. The work was supported by Ono Pharmaceutical Co., Ltd (L.L.R.), the Stanley Center for Psychiatric Research and the Klarman Cell Observatory (J.Z.L). The funders had no role in the study design, experiments performed, data collection, data analysis and interpretation, or preparation of the manuscript.

Author information

Authors and Affiliations

Authors

Contributions

M.X., S.L.L., S.M.B. and L.L.R. conceived the study. M.X., S.L.L., S.M.B., J.Z.L. and L.L.R. designed the study. M.X., X.A., D.D. and L.N. performed the scRNA-seq experiments. S.L.L., S.K.S. and J.Z.L. processed the scRNA-seq data. M.X. and S.L.L. analyzed the scRNA-seq data. B.T.I. and G.D.B created the ligand–receptor interaction network. M.X. and V.L.B. performed the bulk RNA-seq experiments. S.L.L and V.L.B. processed the bulk RNA-seq data. M.X. and S.L.L. analyzed the bulk RNA-seq data. M.X. and Z.N. performed the flow cytometry experiments. M.X. performed the qRT-PCR experiments. M.X. and B.A.M. performed the RNAscope ISH experiments. M.X. and C.O. performed the IHC experiments. R.I. and G.D.B. provided the cell–cell interaction dataset. M.X., S.L.L., S.S.L., A.R., G.D.B. and J.Z.L. supervised aspects of the study. L.L.R. supervised the whole study. J.Z.L. and L.L.R. secured funding. M.X. wrote the original draft of the manuscript. S.L.L., S.M.B. and L.L.R. edited the manuscript. All authors reviewed the manuscript and approved its submission.

Corresponding authors

Correspondence to Methodios Ximerakis or Lee L. Rubin.

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Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Neuroscience thanks Mark Cookson and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Supplementary information

Supplementary Figs. 1–18.

Reporting Summary

Supplementary Table 1

List of abbreviations for all cell types and subtypes.

Supplementary Table 2

Transcriptional profile of each cell type and subtype by age. This file contains 9 worksheet tabs (1 for the whole brain, 1 for all cell types, 6 for the cell classes and 1 for the additional neuronal subtypes). Each tab contains the gene expression profile of the corresponding population by age in the form of transcripts per million (TPM). ‘Y’ prefix indicates expression in young brains and ‘O’ in old brains.

Supplementary Table 3

List of most discriminating genes per cell type. These cell-type marker genes were calculated by aggregating all young and old cells identified as belonging to a certain cell type and comparing them to all the other brain cells. Each cell type has its own worksheet tab within the file with data showing thus: gene name (Gene); original P value (pval); FDR-adjusted P value (padj); the fold change expression (avg_diff); the fraction of cells transcribing the corresponding gene in the cell type of interest (pct.1); and the fraction of cells transcribing the gene in all the remaining brain cell types (pct.2).

Supplementary Table 4

List of most discriminating genes per neuronal subtype. These cell-type marker genes were calculated by aggregating all young and old cells identified as belonging to a certain major (CHOL, DOPA, GABA, GLUT) or minor (e.g., GABA_1, GLUT_1) neurotransmitter-expressing neuronal subtype and comparing them to all the other cells within the neuronal lineage class. Each major/minor neuronal subtype has its own worksheet tab within the file with data showing thus: gene name (Gene); original P value (pval); the fold change expression (avg_diff); the fraction of cells transcribing the corresponding gene in the subtype of interest (pct.1); and the fraction of cells transcribing the gene in all the remaining cell types/subtypes within the neuronal lineage class (pct.2).

Supplementary Table 5

Cell counts of each cell type and subtype by age and animal. This file contains 8 worksheet tabs (1 for all cell types, 6 for the cell classes and 1 for the additional neuronal subtypes). Each tab contains cell counts split out by cell type/subtype/state (Cell_type), animal of origin (Animal_name), animal age (Animal_type), total number of cells (Total_cells) and the fraction of cells of the specific cell type/subtype/state within the animal of origin (Fraction_per_brain).

Supplementary Table 6

Differential gene expression data between young and old cell types. Each cell type has its own worksheet tab within the file with data as determined by MAST analysis showing thus: gene name (Gene); original P value (pval); FDR-adjusted P value (padj); log fold change from young to old (logFC_Young_to_Old); folds change from young to old, based on the LogFC values (FC_Young_to_Old); folds change from young to old, based on the TPM values (TPM_based_FC_Young_to_Old); gene expression in the form of transcripts per million (TPM); standard deviation for TPM by cell (SD); fraction of cells that are positive for the gene (Percent_Pos_Cells); and fraction of animals with cells expressing the specified gene (Percentage_of_animals_positive_for_gene).

Supplementary Table 7

Differential gene expression data between young and old neuronal subtypes. Each neuronal subtype has its own worksheet tab within the file with data as determined by MAST analysis showing thus: gene name (Gene); original P value (pval); FDR-adjusted P value (padj); log fold change from young to old (logFC_Young_to_Old); folds change from young to old, based on the LogFC values (FC_Young_to_Old); folds change from young to old, based on the TPM values (TPM_based_FC_Young_to_Old); gene expression in the form of transcripts per million (TPM); standard deviation for TPM by cell (SD); fraction of cells that are positive for the gene (Percent_Pos_Cells); and fraction of animals with cells expressing the specified gene (Percentage_of_animals_positive_for_gene).

Supplementary Table 8

Matrix of aging-related genes across cell types and neuronal subtypes. This file contains logFC values for all the identified aging-related genes (FDR < 0.05 and FC > 10%), as determined by MAST analysis, across 15 cell types and 3 major neurotransmitter-expressing neuronal subtypes. Positive values indicate upregulation with aging (in red), while negative values indicate downregulation with aging (in blue).

Supplementary Table 9

List of aging-related pathways and processes by cell type and neuronal subtype. Two rounds of gene set enrichment analysis (GSEA) were performed, using either preranked genes lists with all genes transcribed (‘all genes’) or without the highly abundant mitochondrially encoding genes and ribosomal protein genes (‘no mito & ribo’). Each round has its own worksheet tab within the file with data showing thus: the cell-type identity (Population); the original GSEA term (GSEA term); the direction with aging (Aging direction); the size of gene set (Size); the enrichment score (ES); the normalized enrichment score (NES); the nominal P value (NOM p-val) and the FDR q value (FDR q-val). Only significant GSEA terms are listed (GSEA statistics; P < 0.05, with cutoff q < 0.25). To improve interpretation, GSEA terms overrepresenting the same pathway were grouped together (Node/Pathway), while nodes/pathways belonging to similar biological processes were also grouped together (Process) for easier navigation/exploration (see details in Methods).

Supplementary Table 10

Matrix of aging-related pathways and processes across cell types and neuronal subtypes. This file contains NES values for all GSEA terms (from both rounds of pathway analysis) that were significantly associated with aging (GSEA statistics; P < 0.05, with cutoff q < 0.25) across 18 cell types and 3 major neurotransmitter-expressing neuronal subtypes. Positive values indicate upregulation with aging (in red), while negative values indicate downregulation with aging (in blue).

Supplementary Table 11

List of primers sequences used in qRT-PCR.

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Ximerakis, M., Lipnick, S.L., Innes, B.T. et al. Single-cell transcriptomic profiling of the aging mouse brain. Nat Neurosci 22, 1696–1708 (2019). https://doi.org/10.1038/s41593-019-0491-3

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