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Longitudinal transcriptomics define the stages of myeloid activation in the living human brain after intracerebral hemorrhage

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Science Immunology  19 Feb 2021:
Vol. 6, Issue 56, eabd6279
DOI: 10.1126/sciimmunol.abd6279
  • Fig. 1 The myeloid response to ICH is a highly conserved two-stage process.

    (A) Visual summary of sample collection from patients with ICH and cDNA generation for RNA sequencing of myeloid cells from hematoma effluent and peripheral blood. Additional information can be found in Materials and Methods. (B) PCA of transcriptional profiles of CD14+ monocytes/macrophages and neutrophils from patients with ICH. Each point represents a single sample; all time points from each donor are represented. Projections show the first two principal components, which comprise the largest proportion of the variance in overall gene expression. Additional data are provided in fig. S4. (C) Mean expression over time of genes in one of two dynamic transcriptional modules identified in hematoma monocytes/macrophages and neutrophils or in the remaining static genes. Each point represents the mean relative expression level of all genes either up-regulated (red/orange), down-regulated (blue/teal), or unchanged (gray) over time in a single sample; every monocyte/macrophage or neutrophil sample contributes one point of each color. Colored lines represent loess-smoothed regression of the data. Additional information is provided in fig. S5. (D) Venn diagram depicting overlap in genes between the modules portrayed in (C). Gene lists can be found in table S5.

  • Fig. 2 CD14+ macrophages and neutrophils display broad transcriptional remodeling in the hematoma during the acute stage of ICH.

    (A) Median level of expression of monocyte/macrophage and neutrophil genes in blood versus hematoma. Only the first (earliest) blood and hematoma samples from each patient in the dataset were included in this analysis, spanning 23 to 99 hours after ICH; n = 21 patients for monocytes/macrophages; n = 17 patients for neutrophils. Transcriptional profiles that did not meet minimum quality standards were filtered out of the dataset (see the “Initial data processing” section in Materials and Methods), which led to fewer neutrophil profiles in the comparison than monocyte/macrophage profiles. Significantly up-regulated or down-regulated genes in the hematoma (BH-adjusted P < 0.05) are colored red and blue, respectively. (B) ssGSEA of the first blood and hematoma samples from each patient depicting relative enrichment of all 53 Hallmark gene sets to provide an overview of transcriptional differences between blood and hematoma cells. Key immune and metabolic pathways are highlighted in bold. Differential enrichment between blood and hematoma populations was assessed by Student’s t test adjusted for multiple comparisons using the BH method. *P < 0.05, **P< 0.01, and ***P < 0.001. UV, ultraviolet. NFκB, nuclear factor κB; mTORC1, mTOR complex 1.

  • Fig. 3 CD14+ monocytes/macrophages and neutrophils are functionally and metabolically reprogrammed within the hematoma during the acute response to ICH.

    (A) Canonical pathway analysis of differentially expressed genes in CD14+ monocytes/macrophages and neutrophils during the acute stage. After selecting for pathways with significant enrichment (BH-adjusted P < 0.05), the eight pathways with the highest enrichment Z scores in each cell type are presented. (B to G) Differential expression of genes in glucose utilization pathways and immune factor secretion pathways during the acute stage of ICH. Color scale represents median log2 fold change in expression in hematoma compared to blood. Blank spaces represent genes not expressed by neutrophils in either tissue. Only genes with significant differential expression between blood and hematoma in at least one of the two cell types (BH-adjusted P < 0.05) are included. Dashed lines between glycolysis enzymes and schematic denote genes controlling a particular enzymatic step in glycolysis. Heatmaps of expression of these genes by every sample are presented in fig. S7, and additional differential expression data are presented in table S6. FC, fold change. (H) Upstream regulator analysis of predicted transcriptional mediators of the changes to gene expression during the acute stage of the ICH response. After selecting for pathways with significant enrichment (BH-adjusted P < 0.05), the 10 pathways with the highest enrichment Z scores in each cell type are presented. All analyses were performed using the first (earliest) blood and hematoma samples from each patient in the dataset, spanning 23 to 99 hours after ICH (the acute stage); n = 21 patients for CD14+ monocytes/macrophages, 17 patients for neutrophils. FAT10, F adjacent transcript 10; EIF2: eukaryotic translation initiation factor 2; ATM, ATM serine/threonine kinase (no further expansion of ATM according to NCBI website); NFAT5, nuclear factor of activated T cells 5; FOS, FBJ osteosarcoma oncogene; ATF4, activating transcription factor 4; PGR, progesterone receptor; EPAS1, endothelial PAS domain protein 1; HMGB1, high mobility group box 1; EGR1, early growth response 1; TFEB, transcription factor EB; PPRC1, PPARG related coactivator 1.

  • Fig. 4 CD14+ monocytes/macrophages decrease expression of glycolytic and inflammatory genes over time.

    (A) GO analysis of genes decreasing in expression over time in hematoma CD14+ monocytes/macrophages. MAPK, mitogen-activated protein kinase. Number of genes represented in the pathway is presented at the end of each bar. NLR, NOD-like receptor. (B) Gene expression over time in blood and hematoma CD14+ monocytes/macrophages of enzymes controlling rate limiting steps in glycolysis. Gray shading represents the 95% confidence interval of the regression mean. (C) Lactate levels in hematoma effluent and peripheral blood over time after ICH. n = 8 blood samples from 8 patients and 56 hematoma samples from 18 patients. (D) Gene expression over time of secreted inflammatory cytokines by blood and hematoma CD14+ monocytes/macrophages. For all gene expression plots, n = 82 (blood) and 57 (hematoma). P values represent significance of changes to gene expression in hematoma cells or lactate levels over time in hematoma as measured by spline regression, adjusted using the BH method. Additional data are presented in table S5 and fig. S8. G-CSF, granulocyte colony-stimulating factor.

  • Fig. 5 Hematoma CD14+ monocytes/macrophages acquire a transcriptional profile associated with erythrocyte phagocytosis and repair over time after ICH.

    (A) GO analysis of genes decreasing in expression over time in hematoma CD14+ monocytes/macrophages. Number of genes represented in the pathway is presented at the end of each bar. (B) Gene expression over time of genes involved in efferocytosis (MERTK, HAVCR2, and ITGAV) and heme degradation (HMOX1). (C to E) Gene expression over time of genes encoding anabolic metabolism (C), monocyte chemoattractants (D), the anti-inflammatory cytokine IL-10, and PTGES (E). Gray shading represents the 95% confidence interval of the regression mean. For all gene expression plots, n = 82 (blood monocytes), 57 (hematoma CD14+ monocytes/macrophages), 76 (blood neutrophils), and 49 (hematoma neutrophils) samples. (F) PGE2 levels in hematoma effluent and peripheral blood over time after ICH. n = 8 blood samples from 8 patients and 57 hematoma samples from 18 patients. P values represent significance of changes over time as measured by spline regression, adjusted using the BH method. Additional data are presented in table S5 and fig. S9.

  • Fig. 6 PTGES and glycolytic enzyme genes are more highly expressed by CD14+ monocytes/macrophages in patients with good neurological recovery.

    (A) Differential gene expression in hematoma CD14+ monocytes/macrophages during the subacute stage of ICH (>96 hours after ICH). Each column represents one patient; each row represents one gene (561 total genes). The dendrogram represents agglomerative hierarchical clustering by unweighted pair group method with arithmetic mean. Additional data are presented in table S9. (B) GO analysis of differentially expressed genes in hematoma CD14+ monocytes/macrophages during the subacute stage of ICH. Number of genes represented in the pathway is presented at the end of each bar. For enrichment in poor outcome, only the five most significantly enriched pathways are shown; additional pathways are presented in table S10. tRNA, transfer RNA. (C) Differentially expressed genes from metabolic and functional pathways in Fig. 3 to 5 are displayed. (D) Expression level of PTGES by hematoma CD14+ monocytes/macrophages. (E) Expression level of HK2 by hematoma CD14+ monocytes/macrophages. (F) Lactate levels in hematoma during subacute stage of ICH; n = 14 total patients, lactate levels for one patient were not recorded. Lactate levels were compared by linear modeling adjusted for initial hemorrhage severity and subsequent F test for statistical significance. For all gene expression data, statistically significant differential expression was determined by linear modeling adjusted for initial hemorrhage severity (n = 15 total patients); BH-adjusted P < 0.05 significance threshold. **P < 0.01; ***P < 0.001.

  • Fig. 7 HIF-mediated glycolysis by macrophages promotes reparative functions.

    (A) Expression of HK2 by macrophages treated for 8 hours with ICH-associated danger molecules [ICH-DAMP: S100A8 (1 μg/ml), thrombin (10 U/ml), and IL-1β (10 ng/ml)], HIF activator DFO (100 μg/ml), or vehicle control ± HIF signaling antagonist echinomycin (50 nM). Points represent mean values from two replicate wells of n = 3 donors. n.s., not significant. (B) Glycolytic flux of healthy donor human macrophages stimulated with ICH-DAMP or vehicle control in ±echinomycin (50 nM). Points represent mean values from five replicate wells of n = 4 donors. (C) PTGES expression by healthy donor macrophages under conditions described in (A). (D) IL-6 and PGE2 production by healthy donor macrophages treated for 24 hours with ICH-DAMP or vehicle control ± echinomycin or glycolysis inhibitor 2-DG (1 mM). All comparisons are to vehicle + ICH-DAMP. (E) PGE2 and VEGF production by healthy donor macrophages treated for 48 hours with ICH-DAMP in the presence or absence of 100 nM PGE2. Points represent mean values from five replicate wells of n = 6 donors. Additional cytokine data are presented in fig. S12. All error bars represent SD. *P < 0.05, **P < 0.01, and ***P < 0.001.

Supplementary Materials

  • immunology.sciencemag.org/cgi/content/full/6/56/eabd6279/DC1

    Materials and Methods

    Fig. S1. Quantification of myeloid populations by flow cytometry.

    Fig. S2. Gating strategy for sorting myeloid cells.

    Fig. S3. Hematoma cells resemble blood monocytes and neutrophils.

    Fig. S4. Myeloid transcriptional profiles do not cluster by patient or by sequencing batch.

    Fig. S5. Plotting and clustering of individual splines fitting each gene.

    Fig. S6. GAM analysis reveals core changes to glycolysis and eicosanoid synthesis in hematoma CD14+ monocytes/macrophages.

    Fig. S7. Heatmap representation of changes to pathways summarized in Fig. 3.

    Fig. S8. Neutrophils decrease expression of glycolytic and inflammatory genes over time.

    Fig. S9. Changes to phagocytosis and repair pathways in hematoma neutrophils over time.

    Fig. S10. In vivo rtPA exposure does not affect temporal gene expression trends.

    Fig. S11. TMEM51 expression in blood monocytes correlates with neurological outcome after ICH.

    Fig. S12. HIFs control cytokine and PGE2 production by human macrophages via glycolysis.

    Table S1. Patient summary (Excel file).

    Table S2. Sample metadata (Excel file).

    Table S3. Phagocyte gene sets for CD14+ cell identification (Excel file).

    Table S4. ICH patient blood versus healthy donor blood differential gene expression (Excel file).

    Table S5. Spline regression data (Excel file).

    Table S6. Hematoma versus blood differential gene expression (Excel file).

    Table S7. CD14+ monocyte/macrophage activation GSEA (Excel file).

    Table S8. rtPA treatment differential gene expression (Excel file).

    Table S9. Differential gene expression by patient outcome (Excel file).

    Table S10. GO analysis of CD14+ monocyte/macrophage genes associated with outcomes (Excel file).

    Table S11. Differential gene expression by batch (Excel file).

    Table S12. Raw data table (Excel file).

    ICHseq Investigators

    Members of MISTIE III Core Investigative Teams

    MISTIE III Investigators

    References (7175)

  • The PDF file includes:

    • Materials and Methods
    • Fig. S1. Quantification of myeloid populations by flow cytometry.
    • Fig. S2. Gating strategy for sorting myeloid cells.
    • Fig. S3. Hematoma cells resemble blood monocytes and neutrophils.
    • Fig. S4. Myeloid transcriptional profiles do not cluster by patient or by sequencing batch.
    • Fig. S5. Plotting and clustering of individual splines fitting each gene.
    • Fig. S6. GAM analysis reveals core changes to glycolysis and eicosanoid synthesis in hematoma CD14+ monocytes/macrophages.
    • Fig. S7. Heatmap representation of changes to pathways summarized in Fig. 3.
    • Fig. S8. Neutrophils decrease expression of glycolytic and inflammatory genes over time.
    • Fig. S9. Changes to phagocytosis and repair pathways in hematoma neutrophils over time.
    • Fig. S10. In vivo rtPA exposure does not affect temporal gene expression trends.
    • Fig. S11. TMEM51 expression in blood monocytes correlates with neurological outcome after ICH.
    • Fig. S12. HIFs control cytokine and PGE2 production by human macrophages via glycolysis.
    • ICHseq Investigators
    • Members of MISTIE III Core Investigative Teams
    • MISTIE III Investigators
    • Legends for tables S1 to S12
    • References (7175)

    [Download PDF]

    Other Supplementary Material for this manuscript includes the following:

    • Table S1. Patient summary (Excel file).
    • Table S2. Sample metadata (Excel file).
    • Table S3. Phagocyte gene sets for CD14+ cell identification (Excel file).
    • Table S4. ICH patient blood versus healthy donor blood differential gene expression (Excel file).
    • Table S5. Spline regression data (Excel file).
    • Table S6. Hematoma versus blood differential gene expression (Excel file).
    • Table S7. CD14+ monocyte/macrophage activation GSEA (Excel file).
    • Table S8. rtPA treatment differential gene expression (Excel file).
    • Table S9. Differential gene expression by patient outcome (Excel file).
    • Table S10. GO analysis of CD14+ monocyte/macrophage genes associated with outcomes (Excel file).
    • Table S11. Differential gene expression by batch (Excel file).
    • Table S12. Raw data table (Excel file).

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