Clonally expanded CD8 T cells patrol the cerebrospinal fluid in Alzheimer’s disease

Abstract

Alzheimer’s disease is an incurable neurodegenerative disorder in which neuroinflammation has a critical function1. However, little is known about the contribution of the adaptive immune response in Alzheimer’s disease2. Here, using integrated analyses of multiple cohorts, we identify peripheral and central adaptive immune changes in Alzheimer’s disease. First, we performed mass cytometry of peripheral blood mononuclear cells and discovered an immune signature of Alzheimer’s disease that consists of increased numbers of CD8+ T effector memory CD45RA+ (TEMRA) cells. In a second cohort, we found that CD8+ TEMRA cells were negatively associated with cognition. Furthermore, single-cell RNA sequencing revealed that T cell receptor (TCR) signalling was enhanced in these cells. Notably, by using several strategies of single-cell TCR sequencing in a third cohort, we discovered clonally expanded CD8+ TEMRA cells in the cerebrospinal fluid of patients with Alzheimer’s disease. Finally, we used machine learning, cloning and peptide screens to demonstrate the specificity of clonally expanded TCRs in the cerebrospinal fluid of patients with Alzheimer’s disease to two separate Epstein–Barr virus antigens. These results reveal an adaptive immune response in the blood and cerebrospinal fluid in Alzheimer’s disease and provide evidence of clonal, antigen-experienced T cells patrolling the intrathecal space of brains affected by age-related neurodegeneration.

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Fig. 1: Peripheral CD8+ TEMRA cells are increased in AD and are negatively associated with cognition.
Fig. 2: CD8+ T cells enter the brain in patients with AD.
Fig. 3: Clonal expansion of CD8+ TEMRA cells in the CSF of patients with AD.
Fig. 4: Antigen identification of clonally expanded TCRs in the CSF of patients with AD.

Data availability

scRNA-seq and scTCR-seq datasets have been deposited online in the Gene Expression Omnibus (GEO) under accession number GSE134578.

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Acknowledgements

We thank M. Leipold, S. Douglas and H. Maecker from the Stanford Human Immune Monitoring Core for helpful discussion and assistance with mass cytometry experiments; B. Dulken and A. Brunet of Stanford University for sharing related mouse research; B. Carter of the Palo Alto Veterans Affairs FACS facility; V. Henderson and the entire Stanford Alzheimer's disease Reserach Center team; G. Kerchner and S. Sha for CSF collection; G. Deutsch, C. Litovsky and M. Thieu for assistance with cognitive assessments; and V. Carr, S. Guerin, A. Trelle and the Stanford Aging and Memory Study (SAMS) team for MRI data collection. This work was supported by a Glenn/American Federation for Aging Research (AFAR) Postdoctoral Fellowship for the Biology of Aging (D.G.), a National Institutes of Health National Institute on Aging (NIA) F32 Fellowship (AG055255-01A1) (D.G.), an Irene Diamond Fund/AFAR Postdoctoral Transition Award in Aging (D.G.), a National Multiple Sclerosis Society Postdoctoral Fellowship (N.S.), the National Institutes of Health Institute for Allergy, Infectious Diseases and Immunology (U19-AI057229), the Howard Hughes Medical Institute (N.S. and M.M.D.), the Austrian Science Funds Special Research Program F44 (F4413-B23) (M.S.U.), NIA R01 AG048076 (A.D.W.), the Dana Foundation (A.D.W.), the Cure Alzheimer’s Fund (T.W.-C.), the NOMIS Foundation (T.W.-C.), the Stanford Brain Rejuvenation Project (an initiative of the Stanford Neurosciences Institute), NIA R01 AG045034 05 (T.W.-C.) and the NIA funded Stanford Alzheimer’s Disease Research Center (P50AG047366).

Author information

D.G. and T.W.-C. planned the study. D.G. performed the experiments, analysed the data and wrote the manuscript with help from T.W.-C. N.S. performed TCR plate-seq and analysis. M.M.D. guided TCR plate-seq and GLIPH experiments. B.L. independently analysed the dataset and performed TCR network analysis. M.S.U. and L.A. performed mouse histology, 3D rendering and electron microscopy. F.E., M.G., A.D.W. and D.R.G. recruited study subjects and oversaw the acquisition of samples and clinical data. O.L., A.C.Y., J.M., K.C., D.C., M.B.D.L.S., A.M., J.P., G.K.-Y.T. and Y.K. assisted with experiments and/or sample processing.

Correspondence to David Gate or Tony Wyss-Coray.

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Competing interests

D.G., N.S., M.M.D. and T.W.-C. are co-inventors on a patent application related to this work. Patent STDU2-36496/US-1/PRO is for compositions and methods for measuring T cell markers associated with Alzheimer’s disease.

Additional information

Peer review information Nature thanks Michael T. Heneka, Paul Thomas and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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

Extended data figures and tables

Extended Data Fig. 1 Clinical measures of subjects from mass cytometry experiments.

a, Cohort 1 groups were age-matched and included 57 healthy subjects and 23 patients with MCI or AD. b, Cognitive scoring shows significantly decreased cognitive scores in patients with MCI or AD. Note that not all patients with MCI or AD could complete a cognitive exam and these patients are thus not included in this analysis. Unpaired two-sided t-test with Welch’s correction (n = 51 healthy; n = 12 MCI or AD); mean ± s.e.m. c, d, Quantification of CSF Aβ as a ratio to phosphorylated tau (c) or total tau (d) reveals significantly reduced ratios in patients with MCI or AD. Unpaired two-sided t-test with Welch’s correction (n = 18 healthy, n = 7 MCI or AD); mean ± s.e.m. e, Top, representative MRI images show reduction in cortical grey matter in patients with AD. Middle, bottom, representative MRI images of several brain regions as measured by subcortical segmentation and hippocampal segmentation. Right, quantification of MRI images of patients with MCI or AD compared to control individuals shows significant reductions in the percentage of the intracranial volume (ICV) of brain regions classically associated with AD pathology. CA1, cornu ammonis area 1. Unpaired two-sided t-test; mean ± s.e.m. Source data

Extended Data Fig. 2 Gating strategy for identifying immune cell populations from mass cytometry data.

A gating strategy was used to identify populations of immune cells by mass cytometry.

Extended Data Fig. 3 Mass cytometry SPADE and CITRUS clustering shows increased numbers of CD8+ TEMRA cells in MCI or AD.

a, Normalization of mass cytometry data was performed before all analyses. A gating strategy for input into downstream analyses is shown. Live, single leukocytes were selected for analysis. b, Plotting of clusters by P value and fold change of each cluster reveals cluster 63 as the most highly increased cluster among patients with MCI or AD. Clusters are sized according to their percentage of total PBMCs. Unpaired two-sided t-test (n = 57 healthy; n = 23 MCI or AD). c, CITRUS clustering showing significant differentiating populations (top left). Cluster 229992 and its significant daughter populations are outlined. Expression of CD3, CD8 and CD45RA shows that cluster 229992 corresponds to CD8+ TEMRA cells. d, Quantification of cluster 229992 cells as a percentage of total PBMCs for individual subjects. Percentages of this cluster are significantly higher in patients with MCI or AD than healthy control individuals. Unpaired two-sided t-test with Welch’s correction; mean ± s.e.m. e, Marker expression of cluster 229992 shows it to be a CD3+CD8+CD45RA+CD27 TEMRA population. f, The regularized supervised learning algorithm from CITRUS predicts disease group with a 20% error rate (80% positive predictability). The number of model features increases from left to right. The most predictive model is shown as the lowest cross validation error rate (red line) constrained by the false discovery rate (yellow triangle). Source data

Extended Data Fig. 4 Altered CD8+ T cell subsets in PBMCs from patients with MCI or AD.

a, Mass cytometry plots of live PBMCs show increased abundance of CD3+CD8+ T cells in patients with MCI or AD. b, Gating of CD3+ T cells into CD4 and CD8 populations shows increased prevalence of CD8+ T cells in patients with MCI or AD. c, d, Significantly increased effector CD8+ T cells (c) and significantly reduced memory CD8+ T cells (d) in patients with MCI or AD. Multivariate analysis of covariance (MANCOVA) using age as a covariate, followed by post-hoc pairwise comparisons of estimated marginal means by unpaired two-sided t-test. Bonferroni correction for multiple comparisons. Confidence intervals (CI) of 95% are shown. Source data

Extended Data Fig. 5 Correlations of memory T cell populations with cognitive scores.

a, Gating strategy for measuring memory T cell populations. b, Linear regression analysis correlating CD8+ T cell populations with cognitive scores indicates a positive relationship between TEM and TCM CD8+ T cells and no relationship with naive cells. The significance of the difference between datasets was measured by ANCOVA. c, The relationship between the percentage of CD8+ TEMRA cells and cognitive score was not influenced by age. The significance of the difference between the two datasets was measured by analysis of covariance (ANCOVA). Pearson’s correlation r values are shown for each group (b, c). d, Gating strategy for T cell stimulation experiments. e, Increased intracellular TNF cytokine response in PMA–ionomycin-stimulated CD8+ T cells from patients with MCI or AD. Unpaired two-sided t-test with Welch’s correction (n = 10 healthy; n = 14 MCI or AD); mean ± s.e.m.

Extended Data Fig. 6 Analysis of peripheral CD8+ TEMRA cells by scRNA-seq.

a, Gating strategy for sorting peripheral CD8+ TEMRA cells for scRNA-seq. b, Differential expression analysis by scRNA-seq of CD8+ TEMRA cells from patients with MCI or AD versus CD8+ TEMRA cells from healthy individuals shows significantly increased expression of genes that are involved in T cell signalling, including IFITM3, NFKBIA and CD8B2. Violin plots show average log-normalized counts for significantly upregulated genes in MCI or AD CD8+ TEMRA cells by individual patient (n = 7 healthy; n = 6 MCI or AD). c, Violin plots show average log-normalized counts for significantly upregulated genes in MCI or AD CD8+ TEMRA cells by group. GAPDH is shown as a control. Each dot represents a single cell. Source data

Extended Data Fig. 7 Histological analysis of CD8+ T cells in hippocampi from patients with AD and from APP/PS1 mice.

a, A blood vessel in the brain of a control (non-neurological disease) patient shows a lack of extravascular CD8+ T cells. Asterisk indicates the blood vessel lumen. b, A CD8+ T cell within an Aβ plaque. Scale bar, 5 μm. c, CD8+ T cells in the AD-affected hippocampus in close proximity to Aβ plaques. White lines measure the distances from each cell to the nearest plaque centre. Data in a, b were replicated in at least three independent experiments. d, Quantification of the average distance from CD8+ T cells to the nearest Aβ plaque. The dashed red line indicates the average of all cells. e, Representative images of the dentate gyrus that were used to quantify CD3+CD8+ T cells in the hippocampi of control individuals and patients with AD. Inset shows two CD3+CD8+ T cells. Sizes of the area plots used for quantification are shown for each image. Scale bar, 500 μm. f, Association of a CD8+ T cell with NEFH+ neuronal processes by immunohistochemistry and 3D modelling in the APP/PS1 mouse model of cerebral amyloidosis. g, Electron microscopy showing an association of a CD8+ T cell with neuronal processes. Red arrowheads indicate areas in which the CD8+ T cell associates with neuronal processes. Data in eg were replicated in at least two independent experiments. Source data

Extended Data Fig. 8 Clonal expansion of T cells in the CSF.

a, Gating strategy for enumerating T cell subtypes in CSF from healthy elderly individuals. b, Quantification of CSF cells shows that the majority of cells are T cells, with a minority population of CD14+ monocytes and a quantifiably minor number of CD19+ B cells (n = 10 healthy subjects); mean ± s.e.m. c, Quantification of CD8+ T cell subsets shows that the majority of cells are TEM or TEMRA cells (n = 10 healthy subjects); mean ± s.e.m. d, Single-cell sorting of CSF cells shows that CD4 and CD8 T cells were sorted from control individuals and patients with AD. Data were replicated in two independent experiments. e, Donut plots depicting CSF clonality in plate-seq samples. Clones are coloured by their proportion of the total TCRαβ sequences for each subject. f, Gating strategy for drop-seq experiments. Live CSF cells were sorted using a live/dead marker. g, Multidimensional reduction and visualization by t-SNE shows distribution of CSF cells by group, sex and patient (n = 9 healthy, n = 9 MCI or AD). Source data

Extended Data Fig. 9 Clonally expanded CD8+ T cells in CSF from patients with AD and patients with PD.

a, The top individual clones in AD were assessed by combining scRNA-seq and scTCR-seq datasets. A MAST differential expression test with Benjamini–Hochberg correction was conducted to compare CD8+ T cell maximum clones (n = 12–18 cells) from patients with MCI or AD against all CSF T cells. Note the colocalization of AD clones with CD8+ clusters on the t-SNE plots. b, Separate analysis of patients with MCI, AD and PD by percentages of maximum clones revealed an enrichment of highly expanded clones (defined as comprising 3% or more of all TCRαβ sequences; indicated by dotted line) in patients with these diseases. Only one out of ten healthy subjects had a highly expanded clone in their CSF, versus four out of six patients with AD, two out of six patients with PD and one out of five patients with MCI. c, Quantification of overall clonality (defined as the percentage of total TCRαβ sequences that are identical to one or more TCRαβ sequences) in the four groups of cohort 4. Significance was measured by two-way ANOVA followed by Tukey’s multiple comparisons test. Only samples with detectable clones were included in the analysis (n = 11 healthy; n = 5 MCI; n = 5 AD; n = 6 PD). Box plots in b, c Box plots show median and 25th to 75th percentiles, and whiskers indicate the minimum and maximum values. d, Gene-expression analysis was conducted on all 24 samples and clustered by t-SNE. Clusters were composed of a mixture of groups, patients, clonal and non-clonal cells. e, Genes (encoding cytotoxic effector proteins) that showed increased expression in a maximum PD clone (n = 14 cells) were analogous to those observed to be overexpressed in AD clones. MAST differential expression test with Benjamini–Hochberg correction. Source data

Extended Data Fig. 10 EBV BZLF1 antigen identification in the CSF of patients with AD.

a, TCRβ chains derived from GLIPH were used to clone two full TCRαβ TCRs that were introduced into TCR-deficient SKW-3 cells by lentiviral transduction. TCRαβ 1 and TCRαβ 2 cell lines expressed TCRαβ by flow cytometry but controls (no virus and empty lenti viral vector) did not express TCRαβ. Data were replicated in three independent experiments. b, Both TCRαβ 1 and TCRαβ 2 cells upregulate the activation marker CD69 after stimulation with αCD2/CD3/CD28 beads (n = 3 per group). One-way ANOVA (F(3,8) = 204.02, P = 6.78 × 10−8) with Tukey’s test for multiple comparisons; mean ± s.e.m. c, Gating strategy for MHC-I peptide pool experiments. d, TCRαβ 1 and TCRαβ 2 were presented with antigens in a non-autologous fashion (mismatch between fibroblast and TCR). No significant differences in reactivity were detected in either cell line. One-way ANOVA (F(3,8) = 1.16, P = 0.38) with Tukey’s test for multiple comparisons; mean ± s.e.m. e, Individual histograms of autologous candidate peptide stimulations. CD69 expression is shown for control DMSO and each peptide for both cell lines. Note the increased expression of CD69 induced by peptide 7 in TCRαβ 1 cells. Data were replicated in three independent experiments. f, Gating strategy for quantifying HLA-B*08:01 EBV BZLF1 dextramer positivity. Source data

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Gate, D., Saligrama, N., Leventhal, O. et al. Clonally expanded CD8 T cells patrol the cerebrospinal fluid in Alzheimer’s disease. Nature 577, 399–404 (2020). https://doi.org/10.1038/s41586-019-1895-7

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