Fig 1.
Graphical representation of gene expression patterns in the four data sets.
The four GEDI maps show that the gene expression patterns of GAMs and naive microglia are more similar to each other than both macrophage datasets (blue colour indicating low and red colour high mRNA expression levels). The white circle highlights a central cluster of highly expressed genes in GAMs that is different from naive microglia. GAMs: glioma-associated microglia/macrophage microarray data; MG: naive microglia microarray data; Mac1: external data set from Keller, Mazuch et al. 2009; Mac2: external data set from Young, Eksmond et al. 2012.
Fig 2.
WGCNA gene clustering reveals glioma-regulated gene modules.
Each color represents a different module and each module contains genes with similar expression patterns over all four sample sets. The glioma-regulated modules (labeled as red and brown) were further analyzed, as they contained genes that were upregulated in the glioma-associated set when compared to all three control sets. A third module (labeled as green module) contained genes that were highly expressed in GAMs when compared to naïve microglia, but were already highly expressed in peripheral macrophages. The module that is depicted as orange contains genes that were highly expressed in GAMs, and also highly expressed in naïve microglia, however expressed at lower levels in peripheral macrophages. All other clusters contained genes that did not seem to be glioma-regulated.
Table 1.
The 25 highest-upregulated genes in the glioma-associated data set compared to all three control sets.
Table 2.
We used Webgestalt to identify overrepresented GO terms in >2 fold upregulated genes in the glioma-regulated (red and brown) modules.
Table 3.
We used Webgestalt to identify enriched transcription factor binding sites in >2 fold upregulated genes in the glioma-regulated (red and brown) modules.
Table 4.
Expression of known M1, M2, and TAM marker genes in our GAMs data set.
Fig 3.
Comparison of GAMs with data sets of M1, M2a,b,c-stimulated macrophages.
We retrieved data sets from http://www.ebi.ac.uk/arrayexpress (Data set: E-GEOD-32690; [44]), containing data of macrophages that were stimulated for 24 h in vitro into different polarization states (M0 (unstimulated), M1 (IFNγ + LPS), M2a (IL4), M2b (IFNγ + complexed Ig), and M2c (Dexamethasone)) and compared which genes in these data sets significantly overlapped with glioma-regulated genes in our GAMs data set. A: A graphical representation of the overlap of upregulated genes in GAMs and the four macrophage data sets. The GAMs gene expression profile shows the greatest number of overlaps with M1, and M2b polarized macrophages. B: Using Gene Set Enrichment Analysis (GSEA) we found that the 438 upregulated genes in GAMs are significantly enriched in the upregulated genes of all four macrophage polarization sets. C: Only a minority of genes upregulated in GAMs were also induced in the M1 to M2c phenotype, 59.5% of the genes that were upregulated in GAMs were not regulated in any of the four macrophage phenotypes.
Table 5.
M1 and M2a,b,c-specific genes that are >2-fold upregulated in our GAMs data set.
Fig 4.
Flow cytometry isolation strategies for generation of mouse qPCR samples.
A) Plots depicting the strategy for isolation of microglia and macrophages from naïve brains and GL261 glioma-bearing brains using antibody staining for CD11b, CD45, Ly6C, and Ly6G. CD45 staining was used to distinguish between CD11b+/CD45low resident microglia (gate P9) and CD11b+/CD45high/Ly6G-/Ly6Chigh invading macrophages/monocytes (gates P8, P10, and P11), which were mostly absent in naïve brain samples. B) GAMs from RCAS-PDGFb tumors were isolated relying on an antibody-independent approach. Primary tumors from Ntv-a/Ink4a-Arf-/- mice were reimplanted into Cx3cr1GFP/wt Ccr2RFP/wt mice and GAMS were sorted according to GFP and RFP expression. In naïve Cx3cr1GFP/wt Ccr2RFP/wt mice only GFP+ cells were present in the brain. In tumor-bearing mice, resident microglia were GFP+, whereas invading macrophages/monocytes were RFP+/GFPlow or GFP+/RFP+. RFP+/GFPlow naïve blood monocytes were used as peripheral controls.
Fig 5.
qPCR validation of selected M1 and M2a,b,c-specific genes in murine GAMs.
We selected 5 genes that were upregulated in GAMs and specific for either M1 (Il1rn and Isg20), M2a (Clec7a), M2b (Tgfbi), or M2c polarization (Cxcr4) and investigated the expression of these using qRT-PCR. For this we isolated GAMs from GL261 and RCAS-PDGFb tumors using flow cytometry, in order to distinguish between resident microglia and invading macrophages/monocytes and used microglia, and spleen-derived macrophages/monocytes from naïve mice as controls. CTR MG: naïve microglia, CTR Mph: naïve monocytes, Tum MG: glioma-associated microglia, Tum Mph: glioma-associated macrophages/monocytes, GL261: cultured GL261 cells, Tum Mix: cultured RCAS-PDGFb tumor cells. Bar graphs illustrate the absolute number of transcripts normalized to 100,000 transcripts of Actb (n = 4). Analysis was done by students t test. Error bars indicate the Standard Error of Mean (SEM). *, p<0.05; **, p<0.01; ***, p<0.001
Fig 6.
qPCR validation of selected M1 and M2a,b,c-specific genes in human GAMs.
We determined the expression of the M1 (IL1RN and ISG20), M2a (CLEC7A), M2b (TGFBI), and M2c-specific (CXCR4) genes in CD11b+ and CD11b- cells isolated from human GBM (CD11b+ n = 13, CD11b- n = 5), control brain (CD11b+ n = 5, CD11b- n = 2) and blood monocyte samples (n = 2). Bar graphs illustrate the absolute number of transcripts normalized to 100,000 transcripts of ACTB. Analysis was done by students t test. Error bars indicate the SEM. *, p<0.05; **, p<0.01; ***, p<0.001
Fig 7.
Gpnmb and Spp1 expression is upregulated in murine GAMs.
The gene expression of Gpnmb and Spp1 was validated using qRT-PCR. For this we used the same FACS-sorted samples from GL261 and RCAS tumors as in Fig. 5. The upregulation of both genes could be confirmed in both tumor models. Resident microglia and invading macrophages/monocytes show different expression patterns of these genes. CTR MG: naïve microglia, CTR Mph: naïve monocytes, Tum MG: glioma-associated microglia, Tum Mph: glioma-associated macrophages/monocytes, GL261: cultured GL261 cells, Tum Mix: cultured RCAS-PDGFb tumor cells. Bar graphs illustrate the absolute number of transcripts normalized to 100,000 transcripts of Actb (n = 4). Analysis was done by students t test. Error bars indicate the Standard Error of Mean (SEM). *, p<0.05; **, p<0.01; ***, p<0.001
Fig 8.
GPNMB and SPP1 expression is upregulated in human GAMs.
We determined the expression of the genes GPNMB and SPP1 in CD11b+ and CD11b- cells isolated from human GBM (CD11b+ n = 15, CD11b- n = 9), meningioma (CD11b+ n = 5, CD11b- n = 3), grade III anaplastic astrocytoma (CD11b+ n = 2, CD11b- n = 2), control brain (CD11b+ n = 5, CD11b- n = 2) and blood monocyte samples (n = 2). The expression of GPNMB and SPP1 was significantly higher in CD11b+ cells isolated from GBMs compared to CD11b+ cells isolated from control brain, benign meningioma samples, blood monocytes, and CD11b- cells in GBM. Bar graphs illustrate the absolute number of transcripts normalized to 100,000 transcripts of ACTB. Analysis was done by students t test. Error bars indicate the SEM. *, p<0.05; **, p<0.01; ***, p<0.001
Fig 9.
High GPNMB and SPP1 expression is associated with worsened survival prognosis in human GBM patients.
Data taken from the TCGA database, showing survival probability of glioma patients grouped according to high and low expression of our target genes GPNMB (A) and SPP1 (B). High expression of both genes has a negative effect on patient prognosis. Patients were in addition grouped into the four molecular subtypes (proneural, neural, mesenchymal, and classical). Low GPNMB expression seems to have the most severe effect on patient prognosis in the proneural subtype when including G-CIMP+ tumors, but no significant effect in the other subtypes. Low SPP1 expression seems to have the highest effect on patient prognosis in the neural and classical subtypes. Furthermore, both genes are differently regulated in the four subtypes (box plots in A and B) Significances for box plots: GPNMB: proneural GCIMP- (PG-) vs. proneural GCIMP+ (PG+) ***, PG- vs. mesenchymal (M) *** PG- vs. classical (C) **, PG+ vs. neural (N) ***, PG+ vs. M ***, PG+ vs. C **, N vs. M ***, N vs. C *, M vs. C ***. SPP1: PG- vs. M ***, PG+ vs. N ***, PG+ vs. M ***, C vs. M ***.*, p<0.05; **, p<0.01; ***, p<0.001