Cerebrospinal fluid immune dysregulation during healthy brain aging and cognitive impairment

Abstract

Cerebrospinal fluid (CSF) contains a tightly regulated immune system. However, knowledge is lacking about how CSF immunity is altered with aging or neurodegenerative disease. Here, we performed single-cell RNA sequencing on CSF from 45 cognitively normal subjects ranging from 54 to 82 years old. We uncovered an upregulation of lipid transport genes in monocytes with age. We then compared this cohort with 14 cognitively impaired subjects. In cognitively impaired subjects, downregulation of lipid transport genes in monocytes occurred concomitantly with altered cytokine signaling to CD8 T cells. Clonal CD8 T effector memory cells upregulated C-X-C motif chemokine receptor 6 (CXCR6) in cognitively impaired subjects. The CXCR6 ligand, C-X-C motif chemokine ligand 16 (CXCL16), was elevated in the CSF of cognitively impaired subjects, suggesting CXCL16-CXCR6 signaling as a mechanism for antigen-specific T cell entry into the brain. Cumulatively, these results reveal cerebrospinal fluid immune dysregulation during healthy brain aging and cognitive impairment.

Keywords: Alzheimer's disease; T cells; adaptive immunity; aging; cerebrospinal fluid; cognitive impairment.

Figure 1.. Study design and CSF immune cell gene expression changes by linear modeling.

A) Schematic depicting study design. CSF was isolated by lumbar puncture from living individuals. Single cells were loaded into droplets, then libraries were amplified for whole transcriptome or targeted TCR sequencing. B) Study demographics indicating age and sex of each individual. C) MoCA cognitive scores and pTau181 levels in control versus cognitively impaired subjects. Mean ± s.e.m.; Mann Whitney U test. D) UMAP plot showing clusters of CSF immune cells. E) Heatmap of marker genes utilized to annotate cell clusters. F) Donut plot indicating the distribution of CSF immune cell types. G) UpSet plot showing the number of DEGs per CSF immune cell cluster. H) Volcano plots depicting DEGs of the most altered clusters by linear modeling (LM). See also Figure S1 and Table S1.

Figure 2.. Upregulated lipid processing gene expression in activated CSF monocytes with age.

A) LOESS trajectories (upper) and a corresponding heat map (lower) demonstrating wave-like expression patterns of activated monocytes with age. B) Sets of genes ordered by hierarchical clustering and displayed using LOESS trajectories display distinct wave-like patterns with age. C) Volcano plot from MAST differential expression analysis showing downregulation of cytokine genes and upregulation of lipid processing genes. D) LOESS trajectories of APOE, APOC1 and PLTP expression in activated CSF monocytes with age. E) Representative genes JUNB and RGCC displaying distinct non-linear changes with age. DE-SWAN was used to measure the age at which most differential expression occurs. F) Results of DE-SWAN analysis indicating a consistent dysregulation of CSF immune cell types at age 78. G) UpSet plot comparing the number of DEGs for activated CSF monocytes from DE-SWAN and linear modeling. H) Manhattan plot indicating genes that were differentially expressed by each cluster at age 78. I) LOESS trajectories of lipid processing genes comparing healthy controls to cognitively impaired subjects. J) Volcano plot showing reduction of lipid processing genes APOE and APOC1 comparing only advanced aged subjects. See also Figure S2–3 and Table S2–5.

Figure 3.. Cell-cell communication algorithm indicates non-classical monocytes communicate with CD8⁺ T cells via CXCL16-CXCR6 signaling in cognitive impaired CSF.

A) Circle plots of signaling networks of healthy and cognitively impaired CSF immune systems. B) Cell-cell interaction strengths plotted for all cell types indicating incoming and outgoing interactions. C) Dot plot indicating signaling molecules between non-classical monocytes and T cells in cognitively impaired CSF. D) CXCL16-CXCR6 signaling between non-classical monocytes and CD8⁺ T cells is unique to cognitively impaired CSF. E) The signaling network for CXCL16-CXCL6 indicates activated monocytes as the primary source of CXCL16 for CXCR6 on CD8⁺ T cells. F) Violin plots indicating which cell types express CXCR6 and CXCL16 in the CSF. G) UMAP showing expression of CXCR6 by T cells and CXCL16 by myeloid cells. H) Distribution of clonal and nonclonal CSF T cells.

Figure 4.. Clonally expanded T cell disruption in CSF of patients with cognitive impairment.

A) TCR networking plot depicting Levenshtein similarities > 0.9 for all clonal CSF TCRs. Healthy, cognitively normal patients were binned into equal sized groups. B) Quantification of the proportion of TCRs for each age group that had Levenshtein similarity > 0.9. C) UpSet plot showing that clonally expanded CD4⁺ and CD8⁺ T cells have more DEGs that nonclonal T cells. D) Volcano plots showing DEGs of clonal vs. nonclonal CD8⁺ T cells between cognitively impaired and healthy CSF. E) Quantification of average single cell expression of clonal and nonclonal CD8⁺ T cells from cognitively impaired and healthy CSF. P-values are from MAST differential expression. F) Single cell quantification of CXCR6 expression by CD8⁺ T cell subtypes showing increased expression among CD8⁺ T_EM cells. G) UMAP showing distribution of T cell subtypes and clonality using supervised clustering. H) Single cell quantification of CXCR6 expression in clonal CD8⁺ T_EM cells showing higher expression among cognitively impaired subjects. I) PEA assay measurement of CXCL16 protein showing higher levels in cognitively impaired subjects. J) Correlations of CXCL16 with CSF biomarkers. K) Correlations between CSF CXCL16 and NEFL using SOMAmer measurements. See also Figure S4–6 and Table S4–5.

Figure 5.. Differential expression of the top 45 AD GWAS genes across all major CSF immune cell types.

Asterisks denote the most highly altered genes by adjusted p-value. Note that T cells differentially express numerous AD risk genes in CI CSF. See also Figure S7.