Chemokine-mediated cell migration into the central nervous system in progressive multifocal leukoencephalopathy

Abstract

Progressive multifocal leukoencephalopathy (PML) has been associated with different forms of immune compromise. This study analyzes the chemokine signals and attracted immune cells in cerebrospinal fluid (CSF) during PML to define immune cell subpopulations relevant for the PML immune response. In addition to chemokines that indicate a general state of inflammation, like CCL5 and CXCL10, the CSF of PML patients specifically contains CCL2 and CCL4. Single-cell transcriptomics of CSF cells suggests an enrichment of distinct CD4⁺ and CD8⁺ T cells expressing chemokine receptors CCR2, CCR5, and CXCR3, in addition to ITGA4 and the genetic PML risk genes STXBP2 and LY9. This suggests that specific immune cell subpopulations migrate into the central nervous system to mitigate PML, and their absence might coincide with PML development. Monitoring them might hold clues for PML risk, and boosting their recruitment or function before therapeutic immune reconstitution might improve its risk-benefit ratio.

Keywords: HIV; central nervous system; cerebrospinal fluid; chemokines; immune cell migration; multiple sclerosis; natalizumab; progressive multifocal leukoencephalopathy; single-cell RNA sequencing; viral encephalitis.

Graphical abstract

Figure 1

CCL3, CCL5, CXCL10, and CXCL13 are general inflammatory chemokines in CSF, while CCL2 is more specific for O-PML (A) Chemokine levels in CSF supernatant of RRMS (n = 8), RRMS-Nat (n = 6), Nat-PML (n = 6), and O-PML (n = 9) patients relative to CTRL (n = 8), computed by linear regressions of log2-transformed mean fluorescence intensity (MFI) from multiplex-bead assay. Colors indicate beta coefficients. (B) Chemokine levels in CSF supernatant of Nat-PML and O-PML patients relative to RRMS, computed by linear regressions of log2-transformed MFI from multiplex-bead assay. Colors represent beta coefficients. (C) Chemokine levels in CSF supernatant of Nat-PML patients relative to RRMS-Nat, computed by linear regressions of log2-transformed MFI from multiplex-bead assay. Colors indicate beta coefficients. (D) Venn diagram showing overlap of significantly increased chemokines in CSF of RRMS, RRMS-Nat, Nat-PML, and O-PML compared to CTRL. Linear regression models are described in detail in the methods. ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001. CTRL, patients with somatic disorder; RRMS, treatment-naive RRMS patients; RRMS-Nat, natalizumab-treated RRMS patients; Nat-PML, RRMS patients at time of natalizumab-associated PML diagnosis; O-PML, non-MS patients at time of non-natalizumab-associated PML diagnosis.

Figure 2

CSF chemokine levels correlate with counts of immune cell subpopulations in CSF during inflammation (A–C, E, and F) Correlation heatmaps showing Spearman correlation coefficients between CSF leukocyte subpopulation counts and CSF chemokine levels in CTRL (A, n = 8), RRMS (B, n = 8), RRMS-Nat (C, n = 5), Nat-PML (E, n = 5), and O-PML (F, n = 7). (D) The scatterplot depicts the correlation between days since last natalizumab infusion and sVCAM-1 concentration in the CSF of RRMS-Nat patients. Spearman rhos for pairwise complete observations are depicted as colors. ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001. CTRL, patients with somatic disorder; RRMS, treatment-naïve RRMS patients; RRMS-Nat, natalizumab-treated RRMS patients; Nat-PML, RRMS patients at time of natalizumab-associated PML diagnosis; O-PML, non-MS patients at time of non-natalizumab-associated PML diagnosis.

Figure 3

Single-cell RNA sequencing detects increased proportions of CD8⁺ T cells and NK cells in O-PML CSF Manifold plots of lineage cluster annotations (A) and lineage subcluster annotations (B) from combined scRNA-seq datasets are shown. Annotations and dimensional reductions were generated using aCSF. Quantifications of lineage clusters (C) and lineage subclusters (D) as a percentage of mononuclear cells (MNCs) in the CSF of IIH (n = 9), RRMS (n = 12), RRMS-Nat (n = 5), O-PML (n = 3), and VE (n = 5) patients are shown. Boxplots represent the interquartile range (IQR), with whiskers indicating 1.5 times the IQR, median denoted by a line, and mean indicated by “+.” Statistical differences between IIH and O-PML were computed using Wilcoxon tests. In case of significant differences, statistical difference between IIH and RRMS and IIH and VE was also computed. ∗p < 0.05, ∗∗p < 0.01. IIH, idiopathic intracranial hypertension; RRMS, treatment-naïve RRMS patients; RRMS-Nat, natalizumab-treated RRMS patients; O-PML, non-MS patients at time of non-natalizumab-associated PML diagnosis; VE, viral encephalitis patients.

Figure 4

Increased cellular expression of chemokine receptors CCR2, CCR5, and CXCR3 in O-PML CSF (A) Feature plots depict expression level and distribution of CCL2, CCL3, CCL4, CCL5, CCL20, CXCL10, CXCL13, and CXCL16 in CSF of combined scRNA-seq datasets. (B) Quantifications of the proportion of cells positive for CCL2, CCL3, CCL4, CCL5, CCL20, CXCL10, CXCL13, and CXCL16 as percentage of mononuclear cells (MNCs) per sample in the CSF of IIH (n = 9), RRMS (n = 12), RRMS-Nat (n = 5), O-PML (n = 3), and VE (n = 5) patients are shown. (C) Feature plots depict expression level and distribution of CCR2, CCR5, CCR20, CXCR3, CXCR5, and CXCR6 in CSF of combined scRNA-seq datasets. (D) Quantifications of the proportion of cells positive for CCR2, CCR5, CCR20, CXCR3, CXCR5, and CXCR6 as percentage of cell in the lineage cluster per lineage and sample in the CSF of IIH patients, RRMS patients, RRMS-Nat patients, O-PML patients, and VE patients are shown. Boxplots show the IQR, with whiskers indicating IQR x 1.5, line denoting the median, and “+” indicating the mean. Statistical differences between IIH and O-PML were computed using Wilcoxon tests. In case of significant differences, statistical difference between IIH and RRMS and IIH and VE was also computed. ∗p < 0.05, ∗∗p < 0.01. IIH, idiopathic intracranial hypertension; RRMS, treatment-naïve RRMS patients; RRMS-Nat, natalizumab-treated RRMS patients; O-PML, non-MS patients at time of non-natalizumab-associated PML diagnosis; VE, viral encephalitis patients.

Figure 5

O-PML CSF contains distinct CD4⁺ and CD8⁺ T cell cluster expressing CCR2, CCR5, and CXCR3 (A) Manifold plots display Seurat cluster for all combined scRNA-seq datasets and for combined scRNA-seq datasets of each individual patient group (IIH [n = 9], RRMS [n = 12], RRMS-Nat [n = 5], O-PML [n = 3], and VE [n = 5]) after shared nearest neighbor clustering using Seurat. Dimensional reductions were generated using aCSF. (B) Quantification of Seurat clusters within CD4⁺ T cells as percentage of CD4⁺ T cells in CSF of IIH, RRMS, RRMS-Nat, O-PML, and VE patients. (C) Quantification of Seurat clusters within CD8⁺ T cells as percentage of CD8⁺ T cells in CSF of IIH, RRMS, RRMS-Nat, O-PML, and VE patients. (D) Heatmaps show average expression of selection of significantly upregulated genes in CD4-PML cluster compared to all other CD4⁺ T cells within CSF cells of all combined scRNA-seq datasets. (E) Heatmaps show average expression of selection of significantly upregulated genes in CD8-PML cluster compared to all other CD8⁺ T cells within CSF cells of all combined scRNA-seq datasets. Boxplots show the IQR, with whiskers indicating IQR x 1.5, line denoting the median, and “+” indicating the mean. IIH, idiopathic intracranial hypertension; RRMS, treatment-naïve RRMS patients; RRMS-Nat, natalizumab-treated RRMS patients; O-PML, non-MS patients at time of non-natalizumab-associated PML diagnosis; VE, viral encephalitis patients.