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Single-cell analysis reveals T cell infiltration in old neurogenic niches

Nature volume 571, pages 205–210 (2019)Cite this article

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Abstract

The mammalian brain contains neurogenic niches that comprise neural stem cells and other cell types. Neurogenic niches become less functional with age, but how they change during ageing remains unclear. Here we perform single-cell RNA sequencing of young and old neurogenic niches in mice. The analysis of 14,685 single-cell transcriptomes reveals a decrease in activated neural stem cells, changes in endothelial cells and microglia, and an infiltration of T cells in old neurogenic niches. T cells in old brains are clonally expanded and are generally distinct from those in old blood, which suggests that they may experience specific antigens. T cells in old brains also express interferon-γ, and the subset of neural stem cells that has a high interferon response shows decreased proliferation in vivo. We find that T cells can inhibit the proliferation of neural stem cells in co-cultures and in vivo, in part by secreting interferon-γ. Our study reveals an interaction between T cells and neural stem cells in old brains, opening potential avenues through which to counteract age-related decline in brain function.

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Fig. 1: Single-cell RNA-seq reveals changes in cell composition in old neurogenic niches, with infiltration of T cells in proximity to neural stem cells.
Fig. 2: T cells invading old brains are clonally expanded and differ from T cells in old blood.
Fig. 3: The neurogenic niche responds to interferon signalling.
Fig. 4: Interferon-γ signalling from T cells negatively affects NSCs.

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Data availability

All raw sequencing reads for single-cell RNA-seq data (10x Genomics, Smart-seq v4, and Fluidigm C1) as well as bulk RNA-seq data can be found under BioProject PRJNA450425. The command and configuration files, in addition to a list of all versioned dependencies present in the running environment, are available in the Github repository for this paper (https://github.com/gitbuckley/SingleCellAgingSVZ).

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Acknowledgements

We thank D. Wagh from the Stanford Functional Genomics Facility for assistance with 10x Genomics libraries and Fluidigm Corporation for help with Fluidigm C1 libraries; the Stanford Shared FACS Facility and C. Carswell-Crumpton for FACS support; and T. Palmer, T. Rando, A. Kundaje, M. Monje-Deisseroth and V. Sebastiano for guidance. This work was supported by NIH P01 AG036695 (A.B.), a generous gift from T. and M. Barakett (A.B.), NIH T32 GM7365 (B.W.D.), the Stanford MSTP program (B.W.D.), an NSF Graduate Research Fellowship (M.T.B.), a Human Frontiers Science Program Long-term Fellowship (P.N.N.), and a Postdoctoral Fellowship and Career Transition Grant from the National Multiple Sclerosis Society (N.S.).

Reviewer information

Nature thanks Burkhard Ludewig, Hongjun Song and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Author information

Author notes

  1. Dena S. Leeman

    Present address: Immunology Discovery, Genentech, South San Francisco, CA, USA

  2. Stéphane C. Boutet

    Present address: 10x Genomics, Pleasanton, CA, USA

  3. Katja Hebestreit

    Present address: Verge Genomics, South San Francisco, CA, USA

  4. These authors contributed equally: Ben W. Dulken, Matthew T. Buckley, Paloma Navarro Negredo

Authors and Affiliations

  1. Department of Genetics, Stanford University, Stanford, CA, USA

    Ben W. Dulken, Matthew T. Buckley, Paloma Navarro Negredo, Dena S. Leeman, Katja Hebestreit & Anne Brunet

  2. Stanford Medical Scientist Training Program, Stanford University, Stanford, CA, USA

    Ben W. Dulken, Benson M. George & John V. Pluvinage

  3. Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA, USA

    Ben W. Dulken, Benson M. George & Irving L. Weissman

  4. Department of Immunology and Microbiology, Stanford University, Stanford, CA, USA

    Naresha Saligrama & Mark M. Davis

  5. Institute for Immunity, Transplantation and Infection, Stanford University School of Medicine, Stanford, CA, USA

    Naresha Saligrama & Mark M. Davis

  6. Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA

    Romain Cayrol & Hannes Vogel

  7. Cancer Biology Program, Stanford University, Stanford, CA, USA

    Dena S. Leeman

  8. Fluidigm Corporation, South San Francisco, CA, USA

    Stéphane C. Boutet

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

    John V. Pluvinage & Tony Wyss-Coray

  10. Glenn Laboratories for the Biology of Aging at Stanford University, Stanford University School of Medicine, Stanford, CA, USA

    Tony Wyss-Coray & Anne Brunet

  11. Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, CA, USA

    Mark M. Davis

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Contributions

B.W.D. and A.B. planned the study. B.W.D. performed and analysed most experiments, except for those indicated below. M.T.B. performed one 10x Genomics and Smart-seq v4 replicate and analysed the combined data. P.N.N. designed and analysed human brain experiments and performed and analysed immunocytochemistry in vivo and in culture. N.S. performed TCR sequencing by nested PCR and MOG injection under the supervision of M.M.D. R.C. and H.V. provided human brain tissues and helped with design. S.C.B. helped with Fluidigm C1 libraries. D.S.L. helped with the NSC FACS protocol. K.H. helped with statistical analysis. B.M.G., J.V.P., T.W.-C., I.L.W. and M.M.D. provided intellectual contribution. B.W.D. and A.B. wrote the initial manuscript, and M.T.B. and P.N.N. wrote the revised version.

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Correspondence to Anne Brunet.

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Extended data figures and tables

Extended Data Fig. 1 Quality control for 10x Genomics single-cell RNA-seq data.

a, Unique gene counts for 14,685 cells, separated by cell type and individual mouse (complete cell count breakdown available in Supplementary Table 3). Mean ± 25th and 75th percentile are shown by central and outer lines of the boxes; upper and lower whiskers extend to 1.5× of the interquartile range. b, Heat map showing the expression of the top five marker genes for each significant cell cluster identified by Seurat. The cell identity assigned to each cluster is indicated on the bottom of each column. c, t-SNE plots of all 14,685 single-cell transcriptomes analysed by the 10x Genomics platform, coloured by replicates (left), individual mouse (centre), and separated by age (two right-hand plots). Clustering was the same as for Fig. 1a. Eight thousand eight hundred and eighty-four cells from young (young 1, 2,306; young 2, 2,675; young 3, 3,903) and 5,801 cells from old (old 1, 1,435; old 2, 2,541; old 3, 1,825).

Extended Data Fig. 2 T cells in old brains are within the brain parenchyma and express Ifng.

a, Immunofluorescence staining of brain sections of the SVZ neurogenic niche from old (24 months old) male mice shows that age-associated T cells do not co-localize with markers of endothelial cells (CD31). Representative of n = 4 old mice showing similar results. White, CD3 (T cells); green, CD31 (endothelial cells); blue, DAPI (DNA). The image on the right is an enlarged view of the area in the white square. White arrows point at T cells. Scale bar, 50 μm. b, Violin plots showing expression of Ifna1 (encoding interferon-α), Ifnb1 (encoding interferon-β) and Ifng (encoding interferon-γ) in single cells from different cell types. The data for Ifng are also presented in Fig. 1h. Cell types were defined by cell clustering as defined in Fig. 1a. Cells were coloured by age as in Fig. 1b. Expression values are represented as normalized log2-transformed counts. c, Schematic of the experimental design for quantifying T cell infiltration in old human brains. Formalin-fixed paraffin-embedded brain tissue blocks of the basal ganglia with an identifiable ependymal lining (left and middle) from young and elderly humans were sectioned and stained with haematoxylin and eosin or antibodies to CD3 or CD8 for T cell quantification (right). Scale bars, 100 µm.

Extended Data Fig. 3 T cells infiltrating old brains are clonally expanded and differ from T cells in old blood.

a, t-SNE projections of 247 CD8+ T cell transcriptomes from old blood and old SVZ (as in Fig. 2a), coloured by experimental replicate and individual mouse. b, Expression of the top 50 differentially expressed genes upregulated for 247 CD8+ T cells isolated from the blood or SVZ of old mice. Heat map of log-normalized counts, with single cells clustered by the expression of the genes shown in the plot. c, Expression of Ifng in T cells from blood or SVZs of two old (24 months old) mice, as measured by nested PCR. Data are mean ± s.e.m. of the percentage of T cells that were positive for Ifng. Nested PCR does not provide a quantitative metric but rather a binary determination of whether the T cell expresses the transcript for the cytokine or not. d, e, Clonality of T cells isolated from the blood and perfused brain of old mice, represented with the same x axis to enable direct comparison of clone sizes from different mice (the data are the same as Fig. 2c, d). TCR sequences were extracted from single-cell RNA-seq data using TraCer (old mouse 1, 2, 3 and 4) (d) or by nested PCR of the TCR transcripts (old mouse 5 and 6) (e). For each mouse, TCR-β sequence clones are ordered from left to right in order of decreasing frequency in the top row. The source of the T cell is indicated in the bottom row. f, Venn diagram showing the lack of overlap between T cell clones from separate mice, for the four mice for which the TCR repertoire was analysed by single-cell RNA-seq via TraCeR. TCR-β sequences were used, and all unique sequences were only counted once. g, h, Venn diagram showing the lack of overlap between T cell clones from the old blood and old SVZ (g) or from separate mice (h), for the two mice for which the TCR repertoire was analysed by nested PCR. TCR-β sequences were used, and all unique sequences were only counted once.

Extended Data Fig. 4 The neurogenic niche responds to interferon-γ.

a, t-SNE plot showing levels of expression of Ifngr1 and Ifngr2 (summed)—which encode the interferon-γ receptor—in 14,685 cells clustered as in Fig. 1a. A darker colour indicates a higher summed expression of Ifngr1 and Ifngr2. Note that there is a lack of correlation between the age-dependent changes in Ifngr1 and Ifngr2 levels and changes in the interferon-γ response (Fig. 3a), possibly because of post-transcriptional changes of the interferon-γ receptor. b, Violin plots showing expression of Ifngr1 and Ifngr2 (summed) by age and cell type. Decrease in microglia is significant at P < 10−15 and others are not significant, two-sided Wilcoxon rank-sum test. Horizontal lines in violin plots denote median summed Ifngr1 and Ifngr2 expression. See Supplementary Table 3 for exact cell counts. c, MSigDB Hallmarks (v.6.1) GSEA results for old compared with young astrocytes/qNSCs, aNSCs/NPCs, neuroblasts, oligodendrocyte progenitor cells, oligodendrocytes, endothelial cells, microglia, macrophages and T cells in the neurogenic niche. The normalized enrichment score is presented for each pathway with FDR < 0.05. Other cell types did not show pathways that met this FDR cutoff. d, Combined log-normalized expression values of genes in interferon-γ response hallmark in various cell types of the SVZ. Single cells were grouped by cell type and age (Supplementary Table 3). e, FACS analysis of STAT1 levels in the young and old NSCs lineage (PROM1+CD45−CD31−CD24−O4−), freshly isolated from the brains of five young (5 months old) and four old (26 months old) male mice. Data are mean ± s.e.m. of the mean STAT1 fluorescence of the approximately 500 cells analysed for each mouse. Each dot represents around 500 cells from 1 mouse. *P = 0.016, two-sided Wilcoxon rank-sum test. Data shown are from one experiment (all experiments are plotted in Extended Data Fig. 5d). f, FACS analysis of STAT1 levels in endothelial cells freshly isolated from the brains of five young (5 months old) and four old (26 months old) male mice. Data are mean ± s.e.m. of the mean STAT1 fluorescence of the approximately 500 cells analysed for each mouse. Each dot represents around 500 cells from 1 mouse. *P = 0.016, two-sided Wilcoxon rank-sum test. g, Immunofluorescence staining of SVZ brain sections from young (4 months old) and old (29 months old) male mice showing BST2 levels in microglia. Green, BST2; red, IBA1 (microglia maker); blue, DAPI. Scale bar, 20 µm. h, t-SNE plot of 14,685 single-cell transcriptomes with points coloured by putative cell-cycle phase (G0/G1, G2/M or S) as predicted using the CellCycleScoring function in Seurat. i, t-SNE plot of 14,685 single-cell transcriptomes clustered as in Fig. 1a showing levels of expression of Bst2 in cells of the SVZ neurogenic niche. Darker colour indicates higher expression of Bst2. j, Violin plots showing Bst2 expression by age and cell type. Horizontal lines in violin plots denote median Bst2 expression. Increase in astrocytes/qNSCs is significant at P = 6.4 × 10−14, increase in oligodendrocytes at P = 0.025, increase in microglia at P < 2.2 × 10−16, and others are not significant, two-sided Wilcoxon rank-sum test. See Supplementary Table 3 for exact cell counts.

Extended Data Fig. 5 The old NSC lineage exhibits a heterogeneous response to interferon-γ.

a, PCA of 562 old cells in the neural stem cell lineage (astrocytes/qNSCs and aNSCs/NPCs) performed only using genes in the interferon-γ response hallmark from MSigDB (Supplementary Table 8). ‘interferon-γ-high’ cells (dark red with black ring) are defined as old cells exhibiting an average expression of genes in the interferon-γ response hallmark pathway in the top 5% of old cells. b, PCA as in a, but with a separate PCA performed for each of three 10x Genomics replicates (n = 162, n = 315 and n = 85 cells) and for the dataset generated with Fluidigm C1 technology (n = 137 cells). c, Average of normalized expression values of genes in interferon-γ response hallmark for young and old cells in cells of the NSC lineage (astrocytes/qNSCs and aNSCs/NPCs). Cells are grouped by age. Interferon-high cells (in black) are defined as cells that exhibit an average expression of genes in the interferon-γ response hallmark pathway in the top 5% of the cells analysed within each 10x Genomics replicate. Note that replicate three contains approximately twofold more young cells than old cells. Horizontal lines in violin plots denote median interferon-γ response pathway expression. See Supplementary Table 3 for exact cell counts. d, FACS analysis of STAT1-positive cells in the young and old NSC lineage (PROM1+CD45−CD31−CD24−O4−). Left, FACS histograms of STAT1 fluorescence in PROM1+ cells isolated from the SVZ from two representative young (3 months old) and old (20 months old) male mice. Right, quantification of the percentage of STAT1-high cells in 15 young (3–5 months old) and 14 old (19–26 months old) mice. Data are mean ± s.e.m. of the percentage of cells that are STAT1-high of the approximately 500 cells analysed for each mouse. Each dot represents approximately 500 cells from 1 mouse. The combined results from five independent experiments are shown (for independent experiments, see Supplementary Table 12). ***P = 5.08 × 10−6, two-sided Wilcoxon rank-sum test. e, The gene encoding the surface marker BST2 is expressed in the old NSC lineage and is correlated with genes that belong to interferon-γ signalling. Data are shown as a heat map with log-normalized expression of Bst2 and other select genes in the interferon-γ response hallmark pathway. Cells are clustered on the basis of expression of this gene set. The ages of the mice from which the cells are isolated is indicated in a bar above the heat map. f, Live FACS analysis for BST2 in cultured NSCs after interferon-γ treatment for 48 h. g, Abundance of total STAT1 protein in BST2-positive compared with BST2-negative aNSCs/NPCs isolated from nine old (25 months old) mice, as measured by intracellular FACS. Data are mean ± s.e.m. of total STAT1 fluorescence. Each dot represents cells from one mouse. The combined results from two independent experiments are shown (for independent experiments, see Supplementary Table 12). *P = 0.04, two-sided Wilcoxon rank-sum test. h, FACS quantification for Ki-67, a marker of cycling cells, in BST2-positive and BST2-negative aNSCs/NPCs from 15 old (23–25 months old) mice. Data are mean ± s.e.m. of percentage of cells that are Ki-67 positive of the approximately 100 cells analysed for each mouse. Each dot represents around 100 cells from 1 mouse. The combined results from three independent experiments are shown (for independent experiments, see Supplementary Table 12). ***P = 7.80 × 10−4, two-sided Wilcoxon rank-sum test.

Extended Data Fig. 6 T cells can influence NSCs in vivo and in co-cultures.

a, Schematic showing the induction of T cell infiltration of the brains of young mice by immunization with recombinant MOG. NSCs were purified 13–15 days after MOG immunization, and BST2 levels and proliferative (cycling) status (as determined by intracellular Ki-67 levels) of NSCs were measured by FACS. b, c, FACS analysis of CD8+ (b) and CD4+ (c) T cells freshly isolated from the brains of five control or five MOG-injected young mice (3 months old). Data are mean ± s.e.m of the percentage of live cells that are defined as CD8+ or CD4+ T cells, defined as CD3+CD45+TCRγ/δ−B220−TER119−CD11b−. *P = 0.016 (b), *P = 0.045 (c), two-sided Wilcoxon rank-sum test. Each dot represents one mouse. d, Percentage of aNSCs/NPCs that are BST2-positive sorted from 30 young (3 months old) male mice injected with adjuvant (control) and 31 young (3 months old) male mice injected with adjuvant with MOG (see ‘MOG injection’ in Methods), combined over five experiments (for individual experiments, see Supplementary Table 12). Data are mean ± s.e.m. of the percentage of cells that are BST2-positive of the approximately 500 cells analysed from each mouse. Each dot represents cells from one mouse. **P = 0.002, two-sided Wilcoxon rank-sum test. e, FACS analysis for Ki-67 in freshly isolated BST2-positive and BST2-negative aNSCs/NPCs sorted from 7 young mice (3 months old) that were injected with MOG. Data are mean ± s.e.m. of the percentage of cells that are Ki-67-positive of the approximately 100 cells analysed from each mouse. Samples were excluded if there were fewer than 30 intact cells analysed in a given sample (resulting in 7 samples for BST2-negative and 5 samples for BST2-positive aNSCs/NPCs). Each dot represents cells from one mouse. *P = 0.018, two-sided Wilcoxon rank-sum test. f, g, BST2 levels and EdU incorporation in cultured NSCs after co-culture with spleen CD8+ T cells incubated with a combination of IL2, along with beads coated with anti-CD3 and anti-CD28 antibodies, which are known to activate CD8+ T cells. f, BST2 levels in NSCs, measured by live FACS analysis, plotted as mean BST2 fluorescence. g, Percentage of NSCs incorporating EdU, a nucleotide analogue, during a 4-h pulse of EdU. The effects of activated T cells on NSCs are reversed by the addition of a neutralizing antibody to interferon-γ. Data are mean ± s.e.m. of the percentage of cells that are BST2-positive or EdU-positive of the approximately 1,000 cells analysed from each NSC culture. Each dot represents an independent culture of NSCs, derived from a separate mouse (n = 5). Data are from one independent experiment (see Supplementary Table 12). **P = 0.008, n.s. = not significant, two-sided Wilcoxon rank-sum test.

Extended Data Fig. 7 FACS gating strategies.

a, FACS scheme for the isolation of PROM1+EGFR+CD45−CD31−O4−CD24a− aNSCs/NPCs from the adult SVZ. The gate shown on each plot is indicated above the plot. Marker and fluorophore are shown on each axis. b, FACS scheme for the isolation of CD8+CD4−CD3+CD45+TCRγ/δ−B220−TER119−CD11b− T cells from adult SVZ, spleen and blood. The gate shown on each plot is indicated above the plot. The marker and the fluorophore are shown on each axis.

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Dulken, B.W., Buckley, M.T., Navarro Negredo, P. et al. Single-cell analysis reveals T cell infiltration in old neurogenic niches. Nature 571, 205–210 (2019). https://doi.org/10.1038/s41586-019-1362-5

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