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Cited by 128Neuroinflammation and Neurodegeneration MechanismsMultiple Sclerosis Research StudiesBarrier Structure and Function Studies

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Defining blood-induced microglia functions in neurodegeneration through multiomic profiling

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Mendiola, Andrew S., Yan, Zhaoqi, Dixit, Karuna, Johnson, Jeffrey R., Bouhaddou, Mehdi, Meyer-Franke, Anke, Shin, Min-Gyoung, Yong, Yu, Agrawal, Ayushi, MacDonald, Eilidh, Muthukumar, Gayathri, Pearce, Clairice, Arun, Nikhita, Cabriga, Belinda, Meza-Acevedo, Rosa, Alzamora, Maria del Pilar S., Zamvil, Scott S., Pico, Alexander R., Ryu, Jae Kyu, Krogan, Nevan J., Akassoglou, Katerina·2023·Nature Immunology
DOI 10.1038/s41590-023-01522-0PMC PMC10307624
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Abstract

Blood protein extravasation through a disrupted blood–brain barrier and innate immune activation are hallmarks of neurological diseases and emerging therapeutic targets. However, how blood proteins polarize innate immune cells remains largely unknown. Here, we established an unbiased blood-innate immunity multiomic and genetic loss-of-function pipeline to define the transcriptome and global phosphoproteome of blood-induced innate immune polarization and its role in microglia neurotoxicity. Blood induced widespread microglial transcriptional changes, including changes involving oxidative stress and neurodegenerative genes. Comparative functional multiomics showed that blood proteins induce distinct receptor-mediated transcriptional programs in microglia and macrophages, such as redox, type I interferon and lymphocyte recruitment. Deletion of the blood coagulation factor fibrinogen largely reversed blood-induced microglia neurodegenerative signatures. Genetic elimination of the fibrinogen-binding motif to CD11b in Alzheimer’s disease mice reduced microglial lipid metabolism and neurodegenerative signatures that were shared with autoimmune-driven neuroinflammation in multiple sclerosis mice. Our data provide an interactive resource for investigation of the immunology of blood proteins that could support therapeutic targeting of microglia activation by immune and vascular signals.

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Figures

Fig. 1 Transcriptional profiling of ligand-selective activation of blood-induced microglial responses in vivo. a, Schematic of experimental design for transcriptional profiling of blood-induced microglial responses. b,c, Volcano plots of DEGs from RNA-seq analysis of sorted microglia from plasma-injected brains. Comparisons between DEGs in microglia of brains injected with WT plasma versus aCSF or Fga−/− plasma versus aCSF (b) and Fga−/− plasma versus WT plasma, Fggγ390–396A versus WT plasma or Alb−/− versus WT plasma (c) are shown. The log2 FC and −log10 adjusted P value cutoffs were log2 FC > 0.5, adjusted P < 0.1 with Wald test followed by Benjamini–Hochberg (BH) test correction. Top DEGs are shown. Data are from n = 6 Fga−/−, n = 6 WT, n = 6 aCSF and n = 8 Alb−/− mice. d, Coexpression GO networks upregulated in microglia by WT plasma. Adjusted P value <0.1 by hypergeometric test and BH test correction. e, GSEA plots of top upregulated and downregulated pathways in microglia from Fga−/− plasma-injected versus WT plasma-injected brains. Adjusted P value <0.1 by permutation test with BH test correction. f, Overlay of blood microglia GO network with microglial gene expression values from Fga−/− plasma-injected mice. Red shading, genes upregulated in microglia by WT plasma; blue shading, genes downregulated in microglia by Fga−/− plasma; orange border, *P < 0.1 (Wald test followed by BH test correction). g, Coexpression KEGG pathway networks of top upregulated and downregulated pathways in microglia from Fga−/− plasma-injected versus WT plasma-injected brains. Adjusted P < 0.1 by hypergeometric distribution and BH test correction. Padjust, adjusted P value. Created with BioRender.com.
Fig. 1 Transcriptional profiling of ligand-selective activation of blood-induced microglial responses in vivo. a, Schematic of experimental design for transcriptional profiling of blood-induced microglial responses. b,c, Volcano plots of DEGs from RNA-seq analysis of sorted microglia from plasma-injected brains. Comparisons between DEGs in microglia of brains injected with WT plasma versus aCSF or Fga−/− plasma versus aCSF (b) and Fga−/− plasma versus WT plasma, Fggγ390–396A versus WT plasma or Alb−/− versus WT plasma (c) are shown. The log2 FC and −log10 adjusted P value cutoffs were log2 FC > 0.5, adjusted P < 0.1 with Wald test followed by Benjamini–Hochberg (BH) test correction. Top DEGs are shown. Data are from n = 6 Fga−/−, n = 6 WT, n = 6 aCSF and n = 8 Alb−/− mice. d, Coexpression GO networks upregulated in microglia by WT plasma. Adjusted P value <0.1 by hypergeometric test and BH test correction. e, GSEA plots of top upregulated and downregulated pathways in microglia from Fga−/− plasma-injected versus WT plasma-injected brains. Adjusted P value <0.1 by permutation test with BH test correction. f, Overlay of blood microglia GO network with microglial gene expression values from Fga−/− plasma-injected mice. Red shading, genes upregulated in microglia by WT plasma; blue shading, genes downregulated in microglia by Fga−/− plasma; orange border, *P < 0.1 (Wald test followed by BH test correction). g, Coexpression KEGG pathway networks of top upregulated and downregulated pathways in microglia from Fga−/− plasma-injected versus WT plasma-injected brains. Adjusted P < 0.1 by hypergeometric distribution and BH test correction. Padjust, adjusted P value. Created with BioRender.com.CC BY · PubMed Central
Fig. 2 scRNA-seq analysis of ligand-selective activation of microglia. a, UMAP plots of single microglial cells treated with fibrin, iC3b and LPS identified by unsupervised clustering analysis (n = 16,196 cells from two independent experiments). b, Fractions of cells in each Seurat cluster colored based on treatment label. c, UpSet plot showing a matrix layout of DEGs specific to a cluster (single filled circle with no vertical lines) and DEGs shared between clusters (filled circles connected with vertical lines). Vertical bar plot of the unique or overlapping DEGs in clusters (top). Horizontal bar plot of the number of upregulated DEGs for a cluster (left). d, Heat map of top ten DEGs per single-cell cluster. Example GO molecular function terms and genes for a given cluster are shown. Five-hundred cells (maximum) were randomly selected from each cluster, as shown in a, for visualization. Gene expression is depicted as log-normalized scaled expression. e, List of selected top upregulated ligand-induced genes. f, GSEA plots of top GO terms for ligand-induced gene signatures. Adjusted P < 0.05 by Kolmogorov–Smirnov test with BH test correction. Exp., expression; unstim., unstimulated.
Fig. 2 scRNA-seq analysis of ligand-selective activation of microglia. a, UMAP plots of single microglial cells treated with fibrin, iC3b and LPS identified by unsupervised clustering analysis (n = 16,196 cells from two independent experiments). b, Fractions of cells in each Seurat cluster colored based on treatment label. c, UpSet plot showing a matrix layout of DEGs specific to a cluster (single filled circle with no vertical lines) and DEGs shared between clusters (filled circles connected with vertical lines). Vertical bar plot of the unique or overlapping DEGs in clusters (top). Horizontal bar plot of the number of upregulated DEGs for a cluster (left). d, Heat map of top ten DEGs per single-cell cluster. Example GO molecular function terms and genes for a given cluster are shown. Five-hundred cells (maximum) were randomly selected from each cluster, as shown in a, for visualization. Gene expression is depicted as log-normalized scaled expression. e, List of selected top upregulated ligand-induced genes. f, GSEA plots of top GO terms for ligand-induced gene signatures. Adjusted P < 0.05 by Kolmogorov–Smirnov test with BH test correction. Exp., expression; unstim., unstimulated.CC BY · PubMed Central
Fig. 3 Single-cell RNA-seq analysis of ligand-selective activation of macrophages. a, UMAP plots of single BMDMs treated with fibrin, iC3b or LPS identified by unsupervised clustering analysis (n = 17,625 cells from six mice (fibrin), two mice (iC3b), four mice (LPS) and eight mice (unstimulated)). BMDMs are shown colored by treatment, Seurat cluster or both. b, Fraction of cells in each Seurat cluster colored based on treatment label. c, Heat map of top ten DEGs per single-cell cluster. Example GO molecular function terms and genes for a given cluster are shown. Five-hundred cells (maximum) were randomly selected from each cluster, as shown in a, for visualization. Gene expression is depicted as log-normalized scaled expression. d, List of selected top upregulated ligand-induced genes. e, Coexpression GO term networks for iC3b (black-filled nodes), fibrin (red-filled nodes) and LPS (cyan-filled nodes) from scRNA-seq of BMDMs. Upregulated GO terms are shown as colored nodes, and gene coexpression overlap is shown as gray edges. P < 0.10 (fibrin and iC3b) and P < 0.01 (LPS) by GSEA Kolmogorov–Smirnov test without multiple test correction.
Fig. 3 Single-cell RNA-seq analysis of ligand-selective activation of macrophages. a, UMAP plots of single BMDMs treated with fibrin, iC3b or LPS identified by unsupervised clustering analysis (n = 17,625 cells from six mice (fibrin), two mice (iC3b), four mice (LPS) and eight mice (unstimulated)). BMDMs are shown colored by treatment, Seurat cluster or both. b, Fraction of cells in each Seurat cluster colored based on treatment label. c, Heat map of top ten DEGs per single-cell cluster. Example GO molecular function terms and genes for a given cluster are shown. Five-hundred cells (maximum) were randomly selected from each cluster, as shown in a, for visualization. Gene expression is depicted as log-normalized scaled expression. d, List of selected top upregulated ligand-induced genes. e, Coexpression GO term networks for iC3b (black-filled nodes), fibrin (red-filled nodes) and LPS (cyan-filled nodes) from scRNA-seq of BMDMs. Upregulated GO terms are shown as colored nodes, and gene coexpression overlap is shown as gray edges. P < 0.10 (fibrin and iC3b) and P < 0.01 (LPS) by GSEA Kolmogorov–Smirnov test without multiple test correction.CC BY · PubMed Central
Fig. 4 Phosphoproteomics of fibrin and iC3b signaling in innate immune cells. a, Phosphoproteomic interaction networks for fibrin and iC3b. Selected phosphopeptides shown with phosphorylation as blue–red scheme and time points as rings. Adjusted P < 0.05. b, DEPs between fibrin and iC3b at each time point. c, Functional enrichment terms from fibrin or iC3b phosphorylation pathways. d, Kinase activities from the phosphoproteomic dataset. Differentially regulated kinases between fibrin and iC3b are indicated with black bounding. Kinases below the cutoff are in gray. e, Confocal microscopy and quantification of p-MEK2, p-PXN and p-NCF2 staining in BMDMs stimulated with fibrin or unstimulated (US). f,g, Immunoblot of p-PXN, PXN and GAPDH (f) and p-MEK2, MEK1/2, p-NCF2 and GAPDH (g) in primary rat microglia, US or stimulated with fibrin; FC compared with control is indicated. h, Microscopy and quantification of p-MEK2 staining in BMDMs left US or stimulated for 90 min with fibrin alone or in the presence of 5B8 or IgG2b. i, Immunoblot of p-NCF2, NCF2 and GAPDH from 12-month-old 5XFAD or NTG mouse cortex. Signal ratios (delta) for p-NCF2–GAPDH and NCF2–GAPDH are shown. Data are from n = 2 (fibrin 1 h), n = 2 (iC3b 1 h), n = 3 (US 1 h), n = 3 (fibrin 3 h), n = 3 (iC3b 3 h) and n = 3 (US 3 h) independent experiments (a–d); n = 3 independent experiments in duplicate (e), representative of two independent experiments (f,g); n = 4 independent experiments in duplicates (h); or n = 6 (NTG) and n = 5 (5XFAD) mice (i). Statistics: two-sided Student’s t test with BH test correction (a and b), FDR < 0.05 by hypergeometric test with BH correction (c), P < 0.05 by two-sided z test (d), two-tailed unpaired t test (e) or one-way ANOVA with Tukey’s multiple comparisons test (h). Data are mean ± s.e.m., and nuclei are labeled with DAPI (e and h). Scale bars, 10 μm (e); 50 μm (h). US, unstimulated. Source data
Fig. 4 Phosphoproteomics of fibrin and iC3b signaling in innate immune cells. a, Phosphoproteomic interaction networks for fibrin and iC3b. Selected phosphopeptides shown with phosphorylation as blue–red scheme and time points as rings. Adjusted P < 0.05. b, DEPs between fibrin and iC3b at each time point. c, Functional enrichment terms from fibrin or iC3b phosphorylation pathways. d, Kinase activities from the phosphoproteomic dataset. Differentially regulated kinases between fibrin and iC3b are indicated with black bounding. Kinases below the cutoff are in gray. e, Confocal microscopy and quantification of p-MEK2, p-PXN and p-NCF2 staining in BMDMs stimulated with fibrin or unstimulated (US). f,g, Immunoblot of p-PXN, PXN and GAPDH (f) and p-MEK2, MEK1/2, p-NCF2 and GAPDH (g) in primary rat microglia, US or stimulated with fibrin; FC compared with control is indicated. h, Microscopy and quantification of p-MEK2 staining in BMDMs left US or stimulated for 90 min with fibrin alone or in the presence of 5B8 or IgG2b. i, Immunoblot of p-NCF2, NCF2 and GAPDH from 12-month-old 5XFAD or NTG mouse cortex. Signal ratios (delta) for p-NCF2–GAPDH and NCF2–GAPDH are shown. Data are from n = 2 (fibrin 1 h), n = 2 (iC3b 1 h), n = 3 (US 1 h), n = 3 (fibrin 3 h), n = 3 (iC3b 3 h) and n = 3 (US 3 h) independent experiments (a–d); n = 3 independent experiments in duplicate (e), representative of two independent experiments (f,g); n = 4 independent experiments in duplicates (h); or n = 6 (NTG) and n = 5 (5XFAD) mice (i). Statistics: two-sided Student’s t test with BH test correction (a and b), FDR < 0.05 by hypergeometric test with BH correction (c), P < 0.05 by two-sided z test (d), two-tailed unpaired t test (e) or one-way ANOVA with Tukey’s multiple comparisons test (h). Data are mean ± s.e.m., and nuclei are labeled with DAPI (e and h). Scale bars, 10 μm (e); 50 μm (h). US, unstimulated. Source dataCC BY · PubMed Central
Fig. 5 Unbiased overlay of ligand-selective gene signatures with innate immune cell subsets in EAE. a, UMAP of EAE microglia or macrophage Tox-seq clusters31 overlaid with primary microglia (top) or BMDM (bottom) ligand-activation signatures (this study). Expression is depicted as a log-normalized average modular score for each signature. CNS innate immune cell clusters are numerically labeled and outlined to depict healthy and EAE samples (gray and orange, respectively). b, Dot plot of selected gene markers across the scRNA-seq datasets of primary microglia (left panel) and BMDMs (right panel) unstimulated or stimulated with fibrin or iC3b. Average gene expression is depicted as scaled log-normalized expression. Max., maximum; min., minimum.
Fig. 5 Unbiased overlay of ligand-selective gene signatures with innate immune cell subsets in EAE. a, UMAP of EAE microglia or macrophage Tox-seq clusters31 overlaid with primary microglia (top) or BMDM (bottom) ligand-activation signatures (this study). Expression is depicted as a log-normalized average modular score for each signature. CNS innate immune cell clusters are numerically labeled and outlined to depict healthy and EAE samples (gray and orange, respectively). b, Dot plot of selected gene markers across the scRNA-seq datasets of primary microglia (left panel) and BMDMs (right panel) unstimulated or stimulated with fibrin or iC3b. Average gene expression is depicted as scaled log-normalized expression. Max., maximum; min., minimum.CC BY · PubMed Central
Fig. 6 Single-cell oxidative stress transcriptome of microglia in 5XFAD mice. a, UMAP plot of single CD11b+ ROS− and CD11b+ ROS+ cells identified by unsupervised clustering analysis (n = 1,579 cells from brains of three 5XFAD and three NTG mice) (left). UMAP of Tox-seq-labeled cells from 5XFAD or NTG mice. b, Fraction of cells in each Seurat cluster colored based on Tox-seq label. c, Volcano plot of enriched DEGs in CD11b+ ROS+ cells from 5XFAD mice. Dots depict average log2 FC and −log10 adjusted P values (log2 FC > 0.25, adjusted P < 0.05 with MAST statistical test with BH correction). d, Dot plot of selected gene markers from c. Average gene expression and cell population expression is depicted as log-normalized scaled expression and percentage, respectively. e, UMAP plots of functional subcluster analysis of 5XFAD CD11b+ ROS+ and CD11b+ ROS− microglia overlaid with Tox-seq label (left) or in vitro fibrin signature (right). f, Violin plots of fibrin, iC3b–fibrin and LPS gene signature overlays with Tox-seq-labeled microglia. Violin plots depict minimum, maximum and median expression, with points showing single-cell expression levels. Box plots show the first to third quartiles (25–75% box bounds) with median values indicated and upper and lower whiskers extending to 1.5× interquartile range. n = 1,579 cells from brains of three 5XFAD and three NTG mice. P < 0.05 as determined by two-way ANOVA with Tukey’s multiple comparison test. Source data
Fig. 6 Single-cell oxidative stress transcriptome of microglia in 5XFAD mice. a, UMAP plot of single CD11b+ ROS− and CD11b+ ROS+ cells identified by unsupervised clustering analysis (n = 1,579 cells from brains of three 5XFAD and three NTG mice) (left). UMAP of Tox-seq-labeled cells from 5XFAD or NTG mice. b, Fraction of cells in each Seurat cluster colored based on Tox-seq label. c, Volcano plot of enriched DEGs in CD11b+ ROS+ cells from 5XFAD mice. Dots depict average log2 FC and −log10 adjusted P values (log2 FC > 0.25, adjusted P < 0.05 with MAST statistical test with BH correction). d, Dot plot of selected gene markers from c. Average gene expression and cell population expression is depicted as log-normalized scaled expression and percentage, respectively. e, UMAP plots of functional subcluster analysis of 5XFAD CD11b+ ROS+ and CD11b+ ROS− microglia overlaid with Tox-seq label (left) or in vitro fibrin signature (right). f, Violin plots of fibrin, iC3b–fibrin and LPS gene signature overlays with Tox-seq-labeled microglia. Violin plots depict minimum, maximum and median expression, with points showing single-cell expression levels. Box plots show the first to third quartiles (25–75% box bounds) with median values indicated and upper and lower whiskers extending to 1.5× interquartile range. n = 1,579 cells from brains of three 5XFAD and three NTG mice. P < 0.05 as determined by two-way ANOVA with Tukey’s multiple comparison test. Source dataCC BY · PubMed Central
Fig. 7 Fibrin induces a neurodegenerative gene signature via CD11b receptor in 5XFAD mice. a, Schematic of scRNA-seq analysis of 5XFAD, 5XFAD:Fggγ390–396A and control mice. b, Gating strategy for brain CD11b+ scRNA-seq analysis in mice. c, Volcano plot of DEGs in microglia (clusters 1 and 2) between 5XFAD:Fggγ390–396A and 5XFAD mice. Dots depict average log2 FC and −log10 adjusted P values (log2 FC > 0.25, adjusted P < 0.05 with MAST test and BH correction). Data points represent the average of n = 3 mice (5XFAD) and n = 4 mice (5XFAD:Fggγ390–39 6A). d, Dot plots of microglia neurodegenerative, oxidative stress and homeostatic gene signatures in NTG, 5XFAD and 5XFAD:Fggγ390–396A mice. Dot size and color indicate percentage of cells expressing a gene and average expression level, respectively. Data points are averages of n = 3 mice (5XFAD) and n = 4 mice (5XFAD:Fggγ390–396A). e, Violin plots of log expression levels of microglia genes Cst7, Tyrobp, Apoe, Fth1, Ftl1 and Fabp5 across genotypes. Violin plots depict minimum, maximum and median expression, with points showing single-cell expression levels. Box plots show the first to third quartiles (25–75% box bounds) with median values indicated and upper and lower whiskers extending to 1.5× interquartile range. Data are from single cells of n = 3 mice (NTG), n = 4 mice (Fggγ390–396A), n = 3 mice (5XFAD) or n = 4 mice (5XFAD:Fggγ390–396A). f, Confocal microscopy images of brain sections from 12-month-old 5XFAD:Fggγ390–396A and 5XFAD mice immunostained for oxidative stress (GP91phox), microglia (IBA1), DAM (APOE) and plaque (Methoxy-X04) markers. Image quantification is shown from n = 6 mice (5XFAD:Fggγ390–396A) and n = 7 mice (5XFAD). Data are shown as mean ± s.e.m. P < 0.05 by two-tailed Mann–Whitney U test. Scale bar, 10 μm. Source data
Fig. 7 Fibrin induces a neurodegenerative gene signature via CD11b receptor in 5XFAD mice. a, Schematic of scRNA-seq analysis of 5XFAD, 5XFAD:Fggγ390–396A and control mice. b, Gating strategy for brain CD11b+ scRNA-seq analysis in mice. c, Volcano plot of DEGs in microglia (clusters 1 and 2) between 5XFAD:Fggγ390–396A and 5XFAD mice. Dots depict average log2 FC and −log10 adjusted P values (log2 FC > 0.25, adjusted P < 0.05 with MAST test and BH correction). Data points represent the average of n = 3 mice (5XFAD) and n = 4 mice (5XFAD:Fggγ390–39 6A). d, Dot plots of microglia neurodegenerative, oxidative stress and homeostatic gene signatures in NTG, 5XFAD and 5XFAD:Fggγ390–396A mice. Dot size and color indicate percentage of cells expressing a gene and average expression level, respectively. Data points are averages of n = 3 mice (5XFAD) and n = 4 mice (5XFAD:Fggγ390–396A). e, Violin plots of log expression levels of microglia genes Cst7, Tyrobp, Apoe, Fth1, Ftl1 and Fabp5 across genotypes. Violin plots depict minimum, maximum and median expression, with points showing single-cell expression levels. Box plots show the first to third quartiles (25–75% box bounds) with median values indicated and upper and lower whiskers extending to 1.5× interquartile range. Data are from single cells of n = 3 mice (NTG), n = 4 mice (Fggγ390–396A), n = 3 mice (5XFAD) or n = 4 mice (5XFAD:Fggγ390–396A). f, Confocal microscopy images of brain sections from 12-month-old 5XFAD:Fggγ390–396A and 5XFAD mice immunostained for oxidative stress (GP91phox), microglia (IBA1), DAM (APOE) and plaque (Methoxy-X04) markers. Image quantification is shown from n = 6 mice (5XFAD:Fggγ390–396A) and n = 7 mice (5XFAD). Data are shown as mean ± s.e.m. P < 0.05 by two-tailed Mann–Whitney U test. Scale bar, 10 μm. Source dataCC BY · PubMed Central
Fig. 8 Comparison of microglial oxidative stress signature induced by neurodegeneration and autoimmunity. a, UMAP plot of 5XFAD Tox-seq clusters overlaid with EAE microglial oxidative stress genes signature (left). Violin plots of EAE oxidative stress signature enrichment in 5XFAD and NTG mice (right); n = 1,579 cells from brains of three 5XFAD and three NTG mice. Violin plots depict minimum, maximum and median expression, with points showing single-cell expression levels. Box plots show the first to third quartiles (25–75% box bounds) with median values indicated and upper and lower whiskers extending to 1.5× interquartile range. b, Venn diagram of oxidative stress genes in CD11b+ ROS+ microglia from 5XFAD or EAE. c, UMAP plots of shared and unique oxidative stress microglia genes in 5XFAD and EAE. Gene expression overlays for Apoe, Igf1 and Il1b are shown. Gene expression is depicted as log-normalized scaled expression. The red outline demarcates microglial cells in a ROS-enriched cluster. Representative genes shared between or specific to 5XFAD and EAE Tox-seq are shown. d, Metascape analysis of top significant gene pathways shared in ROS+ microglia from 5XFAD and EAE mice. e, Dot plot of gene expression from blood microglia profiles overlaid with the 5XFAD and EAE shared oxidative stress gene signature in microglia as shown in b.
Fig. 8 Comparison of microglial oxidative stress signature induced by neurodegeneration and autoimmunity. a, UMAP plot of 5XFAD Tox-seq clusters overlaid with EAE microglial oxidative stress genes signature (left). Violin plots of EAE oxidative stress signature enrichment in 5XFAD and NTG mice (right); n = 1,579 cells from brains of three 5XFAD and three NTG mice. Violin plots depict minimum, maximum and median expression, with points showing single-cell expression levels. Box plots show the first to third quartiles (25–75% box bounds) with median values indicated and upper and lower whiskers extending to 1.5× interquartile range. b, Venn diagram of oxidative stress genes in CD11b+ ROS+ microglia from 5XFAD or EAE. c, UMAP plots of shared and unique oxidative stress microglia genes in 5XFAD and EAE. Gene expression overlays for Apoe, Igf1 and Il1b are shown. Gene expression is depicted as log-normalized scaled expression. The red outline demarcates microglial cells in a ROS-enriched cluster. Representative genes shared between or specific to 5XFAD and EAE Tox-seq are shown. d, Metascape analysis of top significant gene pathways shared in ROS+ microglia from 5XFAD and EAE mice. e, Dot plot of gene expression from blood microglia profiles overlaid with the 5XFAD and EAE shared oxidative stress gene signature in microglia as shown in b.CC BY · PubMed Central
Extended Data Fig. 1 Blood-innate immunity multiomics pipeline. a, Blood-innate immunity multiomics pipeline for identification of blood-induced immune pathways and in vivo validation studies. b, Gating strategy for blood-induced microglia transcriptomic profiling.
Extended Data Fig. 1 Blood-innate immunity multiomics pipeline. a, Blood-innate immunity multiomics pipeline for identification of blood-induced immune pathways and in vivo validation studies. b, Gating strategy for blood-induced microglia transcriptomic profiling.CC BY · PubMed Central
Extended Data Fig. 2 RNA-seq analysis of microglia from plasma injected brains. a-d, Volcano plots of DEGs from RNA-seq analysis of sorted microglia from plasma or aCSF injected brains. Comparisons between DEGs in microglia from Alb–/– plasma vs aCSF (a), C3–/– plasma vs aCSF (b), Alb–/– plasma vs WT plasma (c) or C3–/– plasma vs WT plasma (d) injected brains are shown. Dots depict average log2FC and -log10 adjusted P values (abs(log2 > 0.5, adjusted P value < 0.1 with Wald test followed by Benjamini-Hochberg multiple test correction). Data are from n = 6 Fga–/–, n = 6 WT, n = 6 aCSF, n = 8 Alb–/– mice.
Extended Data Fig. 2 RNA-seq analysis of microglia from plasma injected brains. a-d, Volcano plots of DEGs from RNA-seq analysis of sorted microglia from plasma or aCSF injected brains. Comparisons between DEGs in microglia from Alb–/– plasma vs aCSF (a), C3–/– plasma vs aCSF (b), Alb–/– plasma vs WT plasma (c) or C3–/– plasma vs WT plasma (d) injected brains are shown. Dots depict average log2FC and -log10 adjusted P values (abs(log2 > 0.5, adjusted P value < 0.1 with Wald test followed by Benjamini-Hochberg multiple test correction). Data are from n = 6 Fga–/–, n = 6 WT, n = 6 aCSF, n = 8 Alb–/– mice.CC BY · PubMed Central
Extended Data Fig. 3 Blood-induced microglial gene co-expression network. Top, Co-expression gene network of 196 DEGs upregulated in microglia by WT plasma compared to aCSF control. Bottom, overlay of blood gene network with microglial gene expression values from Fga–/– plasma-treated mice. Red shading, genes upregulated in microglia; blue shading, genes downregulated in microglia; the orange boarder indicates significance of P < 0.1 (Wald test following by Benjamini-Hochberg multiple test correction). Data are from n = 3 biological replicates per group, with two mice pooled per replicate.
Extended Data Fig. 3 Blood-induced microglial gene co-expression network. Top, Co-expression gene network of 196 DEGs upregulated in microglia by WT plasma compared to aCSF control. Bottom, overlay of blood gene network with microglial gene expression values from Fga–/– plasma-treated mice. Red shading, genes upregulated in microglia; blue shading, genes downregulated in microglia; the orange boarder indicates significance of P < 0.1 (Wald test following by Benjamini-Hochberg multiple test correction). Data are from n = 3 biological replicates per group, with two mice pooled per replicate.CC BY · PubMed Central
Extended Data Fig. 4 Quality control for primary microglia scRNA-seq and BMDM scRNA-seq datasets. a, b, Violin plots of number genes (a) and unique molecular identifiers (UMI, b) per cell post-normalization and shown for each biologically independent sample from scRNA-seq of primary microglia stimulated with fibrin, iC3b, LPS or left unstimulated. Data are from two biologically independent samples of fibrin, two biologically independent samples of iC3b, two biologically independent samples of LPS, and one biologically independent sample of unstimulated primary microglia. c, Elbow plot of top PC used to select for clustering analysis. d, Distribution of cells in each biologically independent sample across each seurat clusters. e, Dot plot of selected microglial gene markers across each cluster from scRNA-seq dataset of primary microglia as shown in Fig. 3a. Average gene expression and cell population expression is depicted as log expression and percent, respectively. f, g, Violin plots of number genes (f) and UMI (g) per cell post-normalization and shown for each biologically independent sample from scRNA-seq of primary BMDMs stimulated with fibrin, iC3b, LPS or left unstimulated. Data are from three biologically independent samples of fibrin, one biologically independent sample of iC3b, two biologically independent samples of LPS and four biologically independent samples of unstimulated BMDMs. h, Elbow plot of top PC used to select for clustering analysis. i, Distribution of cells in each biologically independent sample across each seurat clusters. j, Gene-set enrichment plots of top GO terms for a given cluster (adjusted P value < 0.05 with BH correction). Violin plots depict minimum, maximum, and median expression, with points showing single-cell expression levels (a, b, f, g). Box plots show the 1st to 3rd quartiles (25–75% box bounds) with median values indicated and upper and lower whiskers extending to 1.5*inter-quartile range (a, b, f, g).
Extended Data Fig. 4 Quality control for primary microglia scRNA-seq and BMDM scRNA-seq datasets. a, b, Violin plots of number genes (a) and unique molecular identifiers (UMI, b) per cell post-normalization and shown for each biologically independent sample from scRNA-seq of primary microglia stimulated with fibrin, iC3b, LPS or left unstimulated. Data are from two biologically independent samples of fibrin, two biologically independent samples of iC3b, two biologically independent samples of LPS, and one biologically independent sample of unstimulated primary microglia. c, Elbow plot of top PC used to select for clustering analysis. d, Distribution of cells in each biologically independent sample across each seurat clusters. e, Dot plot of selected microglial gene markers across each cluster from scRNA-seq dataset of primary microglia as shown in Fig. 3a. Average gene expression and cell population expression is depicted as log expression and percent, respectively. f, g, Violin plots of number genes (f) and UMI (g) per cell post-normalization and shown for each biologically independent sample from scRNA-seq of primary BMDMs stimulated with fibrin, iC3b, LPS or left unstimulated. Data are from three biologically independent samples of fibrin, one biologically independent sample of iC3b, two biologically independent samples of LPS and four biologically independent samples of unstimulated BMDMs. h, Elbow plot of top PC used to select for clustering analysis. i, Distribution of cells in each biologically independent sample across each seurat clusters. j, Gene-set enrichment plots of top GO terms for a given cluster (adjusted P value < 0.05 with BH correction). Violin plots depict minimum, maximum, and median expression, with points showing single-cell expression levels (a, b, f, g). Box plots show the 1st to 3rd quartiles (25–75% box bounds) with median values indicated and upper and lower whiskers extending to 1.5*inter-quartile range (a, b, f, g).CC BY · PubMed Central
Extended Data Fig. 5 Pseudotime trajectory analysis of scRNA-seq BMDM profiles. a, UMAP plots of BMDM scRNA-seq profiles colored by treatment (left) or cluster (right) as shown in Fig. 3a, overlaid with pseudotime trajectory analysis with Slingshot. Black arrows indicate inferred trajectories with unstimulated (cluster 3) defined as starting point. b, Heat map of single cell gene expression patterns across pseudotime trajectories of top 50 DEGs. c, Dot plot of selected macrophage gene markers across each cluster from scRNA-seq datasets of primary BMDMs as shown in Fig. 3a. Average gene expression and cell population expression is depicted as log expression and percent, respectively.
Extended Data Fig. 5 Pseudotime trajectory analysis of scRNA-seq BMDM profiles. a, UMAP plots of BMDM scRNA-seq profiles colored by treatment (left) or cluster (right) as shown in Fig. 3a, overlaid with pseudotime trajectory analysis with Slingshot. Black arrows indicate inferred trajectories with unstimulated (cluster 3) defined as starting point. b, Heat map of single cell gene expression patterns across pseudotime trajectories of top 50 DEGs. c, Dot plot of selected macrophage gene markers across each cluster from scRNA-seq datasets of primary BMDMs as shown in Fig. 3a. Average gene expression and cell population expression is depicted as log expression and percent, respectively.CC BY · PubMed Central
Extended Data Fig. 6 Network analysis and functional validation of phoshoproteomic dataset. a, Log2 intensity of phosphorylated peptides (rows) for each biological replicate (columns) across stimulation comparisons at each time point. Cells colored white indicate not detected. Unstimulated, US. Data from n = 2 (Fibrin 1 h), n = 2 (iC3b 1 h), n = 3 (US 1 h), n = 3 (Fibrin 3 h), n = 3 (iC3b 3 h), n = 3 (US 3 h) independent experiments. b, Log2FC of phosphorylated peptides between fibrin vs US and iC3b vs US at each timepoint. c, UpSet plot showing a matrix layout of DEPs specific to a treatment (single filled circle with no vertical lines) or shared between treatment (filled circles connected with vertical lines) comparisons. Bar plots of the unique or overlapping DEPs in treatment comparison (top) and the number DEPs for each treatment comparison (left). Phosphorylation sites considered DEPs (FDR < 0.05 and abs(log2 FC) > 1.5) are shown. Student’s t-test with BH correction. d, Phosphoproteomic GO network for fibrin or iC3b. Phosphorylation changes (log2 FC) depicted as blue-red scheme and timepoint (h) as rings. GO terms indicated by node fill color and protein interaction strength as edge thickness and opacity. e, Confocal microscopy of p-MEK2 staining in BMDMs left unstimulated or stimulated for 90 min with fibrin alone or in the presence of trametinib. Nuclei labeled with DAPI. Scale bar, 50 μm. Quantification of p-MEK2 for n = 3 independent experiments in duplicates. P < 0.05 as determined by two-tailed unpaired t-test. f, Quantitative PCR of Il1b expression in BMDMs unstimulated or stimulated for 6 h with fibrin alone or in the presence of trametinib. Data are from n = 3 independent experiments performed in duplicates. P < 0.05 as determined by one-way ANOVA with Tukey’s multiple comparisons test. Source data
Extended Data Fig. 6 Network analysis and functional validation of phoshoproteomic dataset. a, Log2 intensity of phosphorylated peptides (rows) for each biological replicate (columns) across stimulation comparisons at each time point. Cells colored white indicate not detected. Unstimulated, US. Data from n = 2 (Fibrin 1 h), n = 2 (iC3b 1 h), n = 3 (US 1 h), n = 3 (Fibrin 3 h), n = 3 (iC3b 3 h), n = 3 (US 3 h) independent experiments. b, Log2FC of phosphorylated peptides between fibrin vs US and iC3b vs US at each timepoint. c, UpSet plot showing a matrix layout of DEPs specific to a treatment (single filled circle with no vertical lines) or shared between treatment (filled circles connected with vertical lines) comparisons. Bar plots of the unique or overlapping DEPs in treatment comparison (top) and the number DEPs for each treatment comparison (left). Phosphorylation sites considered DEPs (FDR < 0.05 and abs(log2 FC) > 1.5) are shown. Student’s t-test with BH correction. d, Phosphoproteomic GO network for fibrin or iC3b. Phosphorylation changes (log2 FC) depicted as blue-red scheme and timepoint (h) as rings. GO terms indicated by node fill color and protein interaction strength as edge thickness and opacity. e, Confocal microscopy of p-MEK2 staining in BMDMs left unstimulated or stimulated for 90 min with fibrin alone or in the presence of trametinib. Nuclei labeled with DAPI. Scale bar, 50 μm. Quantification of p-MEK2 for n = 3 independent experiments in duplicates. P < 0.05 as determined by two-tailed unpaired t-test. f, Quantitative PCR of Il1b expression in BMDMs unstimulated or stimulated for 6 h with fibrin alone or in the presence of trametinib. Data are from n = 3 independent experiments performed in duplicates. P < 0.05 as determined by one-way ANOVA with Tukey’s multiple comparisons test. Source dataCC BY · PubMed Central
Extended Data Fig. 7 A model of fibrin-induced signal transduction in macrophages. Fibrin binding to CD11b-CD18 leads to the conversion of the integrin to the high-affinity extended-open (active) conformation that induces signal transduction in macrophages. This outside-in signaling is propagated by the formation of focal adhesions through recruitment and phosphorylation of scaffold proteins and signaling kinases such as paxillin and focal adhesion kinase (FAK) resulting in phosphorylation of PI3K and cytoskeleton organization. In parallel, the MAPK cascade MEK2 and ERK1/2 components are phosphorylated leading to 1) transactivation of NADPH oxidase complex (NOX2) and mitochondria responses to induce ROS release and oxidative stress, 2) phosphorylation of SMARCA5, NUP98, and the ERK1/2 nuclear transporter RANBP3 to regulate nuclear import, 3) phosphorylation of IRF2B2 regulating IFN signaling and 4) transcriptional activation of fibrin-induced genes involved in inflammatory, oxidative stress, and IFN-I responses. Phosphorylation (P); fibrin-induced proteins identified in this study, red filled shapes; fibrin-induced genes identified in this study are shown in box. Created with Biorender.com.
Extended Data Fig. 7 A model of fibrin-induced signal transduction in macrophages. Fibrin binding to CD11b-CD18 leads to the conversion of the integrin to the high-affinity extended-open (active) conformation that induces signal transduction in macrophages. This outside-in signaling is propagated by the formation of focal adhesions through recruitment and phosphorylation of scaffold proteins and signaling kinases such as paxillin and focal adhesion kinase (FAK) resulting in phosphorylation of PI3K and cytoskeleton organization. In parallel, the MAPK cascade MEK2 and ERK1/2 components are phosphorylated leading to 1) transactivation of NADPH oxidase complex (NOX2) and mitochondria responses to induce ROS release and oxidative stress, 2) phosphorylation of SMARCA5, NUP98, and the ERK1/2 nuclear transporter RANBP3 to regulate nuclear import, 3) phosphorylation of IRF2B2 regulating IFN signaling and 4) transcriptional activation of fibrin-induced genes involved in inflammatory, oxidative stress, and IFN-I responses. Phosphorylation (P); fibrin-induced proteins identified in this study, red filled shapes; fibrin-induced genes identified in this study are shown in box. Created with Biorender.com.CC BY · PubMed Central
Extended Data Fig. 8 Ligand-induced profile overlays with EAE innate immune cell signatures, and quality control data related to Fig. 6. a, Dot plot of selected gene markers across scRNA-seq datasets of primary microglia or BMDMs unstimulated or stimulated with fibrin, iC3b, or LPS. Gene expression is depicted as scaled log-normalized expression. b, Violin plot of primary microglia overlaid with microglial homeostatic gene signature from healthy mice as previously identified31. Treatments in x-axis are rank ordered from highest to lowest expression. P < 0.0001 by one-way ANOVA with Tukey’s multiple comparison test. c, Flow cytometry plots of live CD11b+ROS– and live CD11b+ROS+ cells from brains of 12-month-old 5XFAD and NTG mice. Cell population (%) shown inside plot. Data representative of two independent experiments. d, Quantification of total live CD11b+ cells and CD11b+ROS+ cells from brains of 12m 5XFAD and NTG mice. Data from n = 3 mice per genotype shown as mean ± s.e.m. ROS production assessed via DCFDA (c,d). P < 0.05 as determined by two-tailed, unpaired t-test with Welch’s correction. e, f, Violin plots of number genes (e) and UMI (f) per cell post-normalization, shown for each biologically independent sample for 5XFAD and NTG Tox-seq analysis. Data from n = 3 mice per condition. Box plots show the 1st to 3rd quartiles (25–75% box bounds) with median values indicated and upper and lower whiskers extending to 1.5*inter-quartile range. g, UMAP plots as shown in Fig. 6a overlaid with microglial gene marker expression. Expression depicted as log-fold change expression. h, Heat map of top DEGs per single cell cluster from 5XFAD Tox-seq dataset. Gene expression depicted as scaled z-score. i, Volcano plot of DEGs in microglia between CD11b+ROS+ compared to CD11b+ROS– cells in NTG mice. Dots depict average log2FC and -log10 adjusted P values (log2 > 0.25, adjusted P value < 0.05, MAST test with BH correction). Violin plots depict minimum, maximum, and median expression, with points showing single-cell expression levels (b, e, f). Source data
Extended Data Fig. 8 Ligand-induced profile overlays with EAE innate immune cell signatures, and quality control data related to Fig. 6. a, Dot plot of selected gene markers across scRNA-seq datasets of primary microglia or BMDMs unstimulated or stimulated with fibrin, iC3b, or LPS. Gene expression is depicted as scaled log-normalized expression. b, Violin plot of primary microglia overlaid with microglial homeostatic gene signature from healthy mice as previously identified31. Treatments in x-axis are rank ordered from highest to lowest expression. P < 0.0001 by one-way ANOVA with Tukey’s multiple comparison test. c, Flow cytometry plots of live CD11b+ROS– and live CD11b+ROS+ cells from brains of 12-month-old 5XFAD and NTG mice. Cell population (%) shown inside plot. Data representative of two independent experiments. d, Quantification of total live CD11b+ cells and CD11b+ROS+ cells from brains of 12m 5XFAD and NTG mice. Data from n = 3 mice per genotype shown as mean ± s.e.m. ROS production assessed via DCFDA (c,d). P < 0.05 as determined by two-tailed, unpaired t-test with Welch’s correction. e, f, Violin plots of number genes (e) and UMI (f) per cell post-normalization, shown for each biologically independent sample for 5XFAD and NTG Tox-seq analysis. Data from n = 3 mice per condition. Box plots show the 1st to 3rd quartiles (25–75% box bounds) with median values indicated and upper and lower whiskers extending to 1.5*inter-quartile range. g, UMAP plots as shown in Fig. 6a overlaid with microglial gene marker expression. Expression depicted as log-fold change expression. h, Heat map of top DEGs per single cell cluster from 5XFAD Tox-seq dataset. Gene expression depicted as scaled z-score. i, Volcano plot of DEGs in microglia between CD11b+ROS+ compared to CD11b+ROS– cells in NTG mice. Dots depict average log2FC and -log10 adjusted P values (log2 > 0.25, adjusted P value < 0.05, MAST test with BH correction). Violin plots depict minimum, maximum, and median expression, with points showing single-cell expression levels (b, e, f). Source dataCC BY · PubMed Central
Extended Data Fig. 9 Quality control for scRNA-seq data related to Fig. 7. a, Violin plots of gene counts, UMI counts, and %mito for each biologically independent sample. F, female; M, male. b, Violin plots of cell type marker genes used to annotate each cluster. c, Cluster ratio per biological replicate. d, UMAP plot of all single CD11b+ cells identified by clustering analysis (n = 9,286 cells from brains of three 5XFAD, three NTG, four 5XFAD:Fggγ390-396A, and four Fggγ390-396A mice). e, UMAP plots of microglial cells subclustering analysis split by genotype. Ven diagrams depict percentage of cells per cluster.
Extended Data Fig. 9 Quality control for scRNA-seq data related to Fig. 7. a, Violin plots of gene counts, UMI counts, and %mito for each biologically independent sample. F, female; M, male. b, Violin plots of cell type marker genes used to annotate each cluster. c, Cluster ratio per biological replicate. d, UMAP plot of all single CD11b+ cells identified by clustering analysis (n = 9,286 cells from brains of three 5XFAD, three NTG, four 5XFAD:Fggγ390-396A, and four Fggγ390-396A mice). e, UMAP plots of microglial cells subclustering analysis split by genotype. Ven diagrams depict percentage of cells per cluster.CC BY · PubMed Central
Extended Data Fig. 10 Pathway analysis of unique microglia oxidative stress gene signatures and gene signature overlays. a, Metascape analysis showing the top significant gene pathways shared in ROS+ microglia from EAE (top) and 5XFAD (bottom) mice. Data correspond to gene signatures in Fig. 8b. P values calculated by hypergeometric test without multiple test correction. b, c, Dot plot of microglia gene expression from plasma or control injected brains overlaid with microglia fibrin and iC3b/fibrin activation signature genes (b) or in vivo DAM signature (c). Data points represent the average of n = 3 - 4 biological replicates per group. Two mice were pooled per replicate. Significance determined by Wald test following by BH test correction.
Extended Data Fig. 10 Pathway analysis of unique microglia oxidative stress gene signatures and gene signature overlays. a, Metascape analysis showing the top significant gene pathways shared in ROS+ microglia from EAE (top) and 5XFAD (bottom) mice. Data correspond to gene signatures in Fig. 8b. P values calculated by hypergeometric test without multiple test correction. b, c, Dot plot of microglia gene expression from plasma or control injected brains overlaid with microglia fibrin and iC3b/fibrin activation signature genes (b) or in vivo DAM signature (c). Data points represent the average of n = 3 - 4 biological replicates per group. Two mice were pooled per replicate. Significance determined by Wald test following by BH test correction.CC BY · PubMed Central

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Metadata

Authors
Mendiola, A. S., Yan, Z., Dixit, K., Johnson, J. R., Bouhaddou, M., Meyer-Franke, A., Shin, M.-G., Yong, Y., Agrawal, A., MacDonald, E., Muthukumar, G., Pearce, C., Arun, N., Cabriga, B., Meza-Acevedo, R., Alzamora, M. D. P. S., Zamvil, S. S., Pico, A. R., Ryu, J. K., Krogan, N. J., & Akassoglou, K.
Affiliations
  • Gladstone Institutes (US)
  • University of California, Los Angeles (US)
  • University of California, San Francisco (US)
Corresponding author
Akassoglou, Katerina
ORCID
Venue
Nature Immunology
Publisher
Springer Science and Business Media LLC
Published
2023-06-08
Volume
24
Issue
7
Pages
1173-1187
ISSN
  • 1529-2908 (ISSN-L)
  • 1529-2916
DOI
10.1038/s41590-023-01522-0
PMC
PMC10307624
OpenAlex
W4379879277
Cited by
124
References
87
Citations by year
2026: 32 · 2025: 35 · 2024: 43 · 2023: 14
FWCI
12.82
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Immunology (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • eScholarship (California Digital Library) (submittedVersion, cc-by)
Funders
  • U.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and Stroke (R35 NS097976, K99 NS126707)
  • U.S. Department of Health & Human Services | NIH | National Institute on Aging (RF1 AG064926)
  • BrightFocus Foundation (82021019F)
  • U.S. Department of Health & Human Services | NIH | National Institute of Allergy and Infectious Diseases (K99 AI163868)
Topics
  • Life Sciences › Neuroscience › Neurology › Neuroinflammation and Neurodegeneration Mechanisms
  • Health Sciences › Medicine › Clinical Neurology › Multiple Sclerosis Research Studies
  • Life Sciences › Neuroscience › Neurology › Barrier Structure and Function Studies
Keywords
plasma proteins, microglia, neurodegeneration, blood-brain barrier disruption, fibrinogen, CD11b, Alzheimer's disease, multiple sclerosis, type I interferon, oxidative stress, neuroinflammation, macrophages, phosphoproteome, transcriptome
MeSH terms
Multiomics, Alzheimer Disease, Animals, Blood-Brain Barrier, Fibrinogen, Microglia, Mice

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