Patients
The study was approved by the Institutional Ethics Committee (study approval numbers 108-39/4-2014-UVN and 7/8/2014-25) and conducted in accordance with the Helsinki Declaration. Fully informed consent was obtained from all donors before neurosurgical resection. GBM diagnosis was established according to the 2021 WHO classification. Patients with newly diagnosed GBM (IDH-wild-type, grade 4 glioma, n = 117) undergoing neurosurgical resection at the Department of Neurosurgery, Na Homolce Hospital, Prague or at the Department of Neurosurgery and Neurooncology, First Faculty of Medicine, Charles University and Military University Hospital, Prague, were included in the study.
In silico bioinformatic analysis
TCGA gene expression data from Affymetrix expression array platform HG-U133A were accessed via the GlioVis online portal (http://gliovis.bioinfo.cnio.es/) in September 201920. For in silico analysis, data from newly diagnosed IDH-wild-type GBMs were divided into terciles based on FAP messenger RNA expression (FAP high = upper tercile, n = 119; FAP low = lower tercile, n = 119). To assess the cellular composition of the TME, we estimated the absolute abundance of immune and stromal cell populations in IDH-wild-type TCGA-GBM samples using the MCP-counter algorithm21, implemented via the R package immunedeconv. To evaluate the association between FAP expression and the TME landscape, patients were stratified into terciles based on their FAP expression levels. Abundance scores were calculated according to the MCP-counter framework based on log2-transformed gene expression values. In this study, the MCP-counter ‘cancer-associated fibroblast (CAF)’ category was used and referred to as nonmalignant mesenchymal cells. Ivy Glioblastoma Atlas (http://glioblastoma.alleninstitute.org/) was used to explore the expression of monocyte and macrophage markers in perivascular regions based on FAP mRNA expression. For this purpose, we defined ‘perivascular regions’ as Hyperplastic blood vessels and microvascular proliferations, divided them based on FAP expression into terciles, and compared the expression of monocyte and macrophage markers between ‘FAP-high’ (upper tercile) and ‘FAP-low’ (lower tercile) perivascular regions. To further identify the characteristics of FAP-high perivascular regions, differentially expressed genes (DEGs) between FAP-high and FAP-low regions were identified and subjected to gene set enrichment analysis using the iDEP application (http://bioinformatics.sdstate.edu/idep/).
Isolation and quantification of total RNA, determination of RNA integrity and library preparation for RNA-bulk transcriptome analysis
Total RNA was isolated from 13 GBM tissues using RNeasy Micro Kit (Qiagen) according to the manufacturer’s protocol, including treatment by DNase I. The quantity and quality of isolated RNA were measured using a NanoDrop ND-1000 (Thermo Fisher Scientific) and Agilent 2100 Bioanalyzer (Agilent Technologies). The RNA integrity number ranged between 6.3 and 9.5. A KAPA mRNA HyperPrep kit with poly(A) mRNA selection (Roche) was used to construct the sequencing library, starting with 1 μg of total RNA.
RNA-bulk transcriptome analysis
Libraries were sequenced on a NextSeq 500 platform (Illumina) using the 75-bp single-end configuration. The sequencing yielded an average of 30 million reads per sample. Technical quality control and gene quantification were done using the nf-core/RNAseq v1.4.2 bioinformatics pipeline22, with HISAT2 mapping23 and read counting with featureCounts24. GRCh38 (Ensembl annotation version 95) was chosen as the reference genome25. Gene set enrichment analysis was performed against Gene Ontology (GO) terms26 using the fgsea package27.
Immunohistochemistry
CD45 and CD68 immunopositivity was detected in 4-µm formalin-fixed paraffin-embedded GBM tissue sections using primary monoclonal antibodies: anti-CD45 (clone X16/99, Leica), anti-CD68 (clone KP1, Thermo Fisher Scientific) following antigen retrieval using an EDTA-based pH 9.0 epitope retrieval solution (BOND Epitope Retrieval Solution 2) for 20 min. Bond polymer refine detection (Leica) was used to visualize the primary antibody signal and hematoxylin nuclear counterstaining. Images were captured by an experienced pathologist (Petr Hrabal) on an Axioskop 2 mot plus microscope using the Axiocam ICc1 camera (Zeiss). Using ImageJ28, the percentage of CD45 and CD68 positive area was analyzed in five random fields of view. FAP expression in stromal cells was evaluated by immunohistochemistry in a previous study19. The immunopositivity score for each tumor represents an average of five independent microscopic fields, evaluated using a four-tiered semiquantitative scale (where 0 is negative; 1 is perivascular positivity in sporadic blood vessels; 2 is perivascular positivity in over 2/3 of the visual field; 3 is extensive perivascular positivity and FAP+ trabeculae)19.
Immunofluorescence
Immunofluorescence labeling was performed in 10-µm frozen sections of human GBM and mouse brain tumors. Samples were fixed and permeabilized using either a 1:1 mixture of methanol and acetone (5 minutes, −20 °C) or 4% paraformaldehyde followed by 0.1% Triton-X100. Nonspecific antibody binding was blocked with 10% fetal bovine serum (FBS) and 1% bovine serum albumin in Tris-buffered saline (60 min, room temperature). Human GBM sections were subjected to sequential double immunofluorescence labeling for FAP in combination with CD68, CD163, CD206, αSMA, PDGFRβ, NG2 or GFAP using the following primary antibodies diluted in Tris-buffered saline with 1% bovine serum albumin: anti-FAP (clone D8, Applied DNA Sciences, 1:800, 1 h, room temperature), anti-CD68 (clone D4B9C, Cell Signalling, 1:800, overnight, 4 °C), anti-CD163 antibody (clone GHI/61, Santa Cruz, 1:200, overnight, 4 °C) and anti-CD206 (clone 15-2, Santa Cruz, 1:200, overnight, 4 °C), anti-αSMA (clone 1A7, Abcam; 1:200; overnight, 4 °C), anti-PDGFRβ (clone PR72112, Invitrogen; 1:100; overnight, 4 °C), anti-NG2 (clone E3B3G, Cell Signaling; 1:200; overnight, 4 °C) and anti-GFAP (clone GF-01, Exbio; 1:200; overnight, 4 °C). Mouse tumor sections from the co-implantation model were stained for leukocyte and macrophage markers using anti-CD45 (clone 30-F11, R&D Systems; 1:100; overnight, 4 °C) and anti-F4/80 (clone CI-A3-1, Novus Biologicals; 1:500; overnight, 4 °C). Primary antibodies were visualized using the species-appropriate Alexa Fluor-conjugated secondary antibodies (anti-rat Alexa Fluor 488, anti-rabbit Alexa Fluor 594 or 488, anti-mouse Alexa Fluor 647 or 488, all 1:500, 60 min, room temperature). Images of human GBM sections were acquired using a Leica Stellaris 5 confocal microscope (Leica), whereas mouse tumor sections were imaged using an Olympus IX70 microscope equipped with an ORCA-Flash 4.0 camera (Hamamatsu). For mouse tumors, five representative fields per marker were imaged and positive cells were manually quantified using the Cell Counter plugin in ImageJ (NIH).
Isolation and quantitation of total RNA and determination of RNA integrity for real-time qRT–PCR
Total RNA was isolated with the TRIzol reagent (Thermo Fisher Scientific) according to the manufacturer’s protocol. RNA concentration was assayed with the RiboGreen RNA quantitation Kit (Thermo Fisher Scientific) according to the manufacturer’s protocol. Integrity of the isolated RNA samples was determined using Agilent 2100 Bioanalyzer (Agilent Technologies) and the Agilent RNA 6000 Nano Kit (Agilent Technologies). All samples used for the real-time RT–PCR quantitation of the studied mRNAs displayed RNA integrity number ≥5.
Real-time qRT–PCR
The forward and reverse primers and TaqMan probes (Thermo Fisher Scientific; Supplementary Table 1) were designed with the Primer Express software (Thermo Fisher Scientific). A two-step real-time RT–PCR assay was used to quantitate mRNA expression. SuperScript IV VILO MasterMix (Thermo Fisher Scientific) was used for reverse transcription according to the manufacturer’s instructions. The subsequent PCR step was performed with an aliquot of the RT-mix, corresponding to a PCR input of 50 ng of total RNA. The PCR assays for RT-samples were run in triplicate, along with no-template controls, on the QuantStudio 12K Flex instrument (Thermo Fisher Scientific). Threshold cycle (Ct) values for amplification reactions were determined from plots of background-subtracted fluorescence intensity (ΔFI) of the reporter dye (6-FAM) versus PCR cycle number using the QuantStudio 12K Flex Software. Expression data were normalized to a reference transcript using the ΔCt method (that is, 2−ΔCt).
Single-cell transcriptomics analysis
scRNA-seq data from newly diagnosed GBM tumor tissues analyzed in this study include four datasets: LeBlanc et al.29 (10 patients, GSE173280), Abdelfattah et al.30 (10 patients, GSE182109), Nomura et al.31 (56 patients, GSE274546) and an in-house dataset from Ebert et al.15 (3 patients). Each dataset was individually preprocessed using the standard workflow in the Seurat package (Seurat v5) (https://satijalab.org/seurat/articles/pbmc3k_tutorial). This included quality control, log normalization (scale factor 10,000), data scaling, linear dimensional reduction, cell clustering and uniform manifold approximation and projection (UMAP) reduction. We used cell type annotations for the clusters as reported by the authors in the publications first reporting the generation of each dataset (GSE17328029, GSE18210930 and GSE27454631, Ebert et al.15). High relative expression of established pericyte markers (PDGFRB, THY1, ACTA2 and CSPG4) was used to confirm that pericyte clusters were annotated appropriately. Pericyte clusters from each dataset were subset and reclustered to resolve pericyte subpopulations. Highly variable genes (1000 features) were identified using variance-stabilizing transformation, followed by principal component analysis (PCA). The first 20 principal components were used to construct a shared nearest neighbor graph and perform graph-based clustering (resolution 0.1–0.3), with dimensionality selection guided by elbow plots. A UMAP was performed using 10–35 principal components, selected per dataset to optimize subcluster separation. We further identified specific marker genes for pericyte subpopulations using the ‘FindAllMarkers’ function (minimum percentage of ≥25% of cells in at least one group). The list of pericyte subpopulation markers was further refined based on cluster specificity across the four datasets. The resulting pericyte subpopulation markers (Supplementary Table 2) were then used to annotate pericyte cells in each dataset as follows: pericyte subpopulations for a total number of cells \({\boldsymbol{n}}\) and genes \(m\) having normalized expression \(E\), were annotated by first calculating the scaled gene expression (\({S}_{ij}\)) for gene \(i\) in cell \(j\) as
$${S}_{{ij}}=\,{E}_{{ij}}/\left(\frac{{\sum }_{j=1}^{n}{E}_{{ij}}}{n}\right)\,.$$
From this, we then calculated the subpopulation score for the cell \({\boldsymbol{j}}\) over \(l\) genes in a gene list \(g\) as
$${N}_{{gj}}=\frac{{\sum }_{i\in g}{Sij}}{l}\left/\frac{{\sum }_{i\notin g}{Sij}}{m-l}\right.$$
Each cell is assigned the pericyte subpopulation for which it has a maximum score \({N}_{{gj}}\) greater than 1. Cells with a score less than 1 are not assigned to any subpopulation. To reduce cell-to-cell noise and increase robustness of pericyte annotations, pericyte subpopulation signature scores were smoothed using k-nearest neighbor averaging with the UCell SmoothKNN function. Pericyte subtype annotations were validated against results from an independent single-cell analysis of mural cell populations across different brain tumor disease cohorts including IDH-wild-type GBM32.
Pericyte subpopulation composition was quantified by calculating, within each dataset, the percentage of pericytes in each subpopulation for each patient and then averaging these patient-level percentages to obtain dataset-level means (rescaled to sum to 100%). Similarly, for FAP+ GBM cell type composition, FAP+ cells (FAP counts >0) were identified, and per-patient cell type percentages were quantified and averaged within each dataset (rescaled to sum to 100%).
The expression of marker genes was quantified for each pericyte subpopulation in each dataset. To facilitate comparison between datasets, gene counts for genes of interest were z-score normalized within each dataset. We identified DEGs in each dataset (log2FC >0.5 or <−0.5, and adjusted P value <0.05 as determined by Benjamini–Hochberg false discovery rate (FDR) analysis) between FAP+ (FAP counts >0) and FAP− pericytes (FAP counts equal to 0) using the Seurat function ‘FindMarkers’. Overlapping the significantly up- and downregulated gene lists across the four datasets identified common DEGs. We use the ligand–receptor interaction database in CellChat33 to identify ligands of interest from the common DEG list. The identified ligand of interest was selected and quantified as z-score expression and percentage expressing (percent of cells with counts >0) across GBM cell types in each dataset. Analyses were performed both in FAP+ (FAP counts >0) across all cell type clusters and in the full, unfiltered cell population. Gene expression of the corresponding receptors was also quantified in myeloid subpopulations. These myeloid subpopulations were obtained by selecting the ‘myeloid’ cluster as defined by the dataset’s authors. Previously published lists of markers for myeloid subpopulations34 were used to annotate monocytes, macrophages and microglia using the same signature scoring method used for pericyte subpopulations. The macrophage population was then selected and further annotated as M1 or M2 using previously published markers35.
Spatial transcriptomics analysis
Visium spatial transcriptomics data of newly diagnosed GBM tumor tissues in this study include Ravi et al.36 (20 patients) and Greenwald et al.37 (GSE237183, 13 samples from 6 patients).
For the Ravi dataset, data were downloaded as SPATA2 objects and converted to Seurat objects using the SPATA2 (v2.0.4) function, ‘asSeurat()’. The Spaceranger output was downloaded and used to create the Seurat objects for the Greenwald dataset. The data were individually processed following the standard spatial transcriptomics workflow in the Seurat package (Seurat v5) (https://satijalab.org/seurat/articles/spatial_vignette). Libraries were scaled to 10,000 UMIs per spot and log-normalized with the Seurat function ‘NormalizeData’. To analyze the spatial distribution of cell types in GBM, we used robust cell type decomposition (RCTD)38 analysis using the spacexr package (v2.2.0) to infer cellular composition in spatial transcriptomics data. We used the annotated Abdelfattah scRNA-seq dataset as a reference to identify cell types and subpopulations in the TME. This approach was used for broad cell types (for example, pericytes, endothelial cells, myeloid cells, malignant cells, lymphocytes and oligodendrocytes) as well as their corresponding subpopulations, including pericyte subpopulations (for example, FAP+ pericytes) and myeloid subpopulations. In addition, we annotated the malignant cell cluster in the scRNA-seq data using the GBM cell-state signatures described by Neftel et al.39 (NPC1, NPC2, OPC, AC, MES1 and MES2) and the same signature scoring method used for pericyte and myeloid subtype annotations. To facilitate broader downstream comparisons, related malignant states were subsequently consolidated into broader categories, with NPC1 and NPC2 grouped as NPC, and MES1 and MES2 grouped as MES. This analysis generated a cell type ‘proportion’ composition score (0–1) for each cell type per spot.
To identify tissues enriched in vessels, we summed the endothelial cell and pericyte composition scores to define a ‘vessel’ score. Spots with a vessel score ≥ mean + 1 standard deviation across all spots in the tissue were classified as ‘vessel enriched’. To ensure these enriched spots formed contiguous vessel structures, we computed a spot-aggregation score, that measured the percentage of surrounding ‘neighborhood’ spots (in Visium data, up to six neighbors) that were also vessel enriched. Only tissues with an average vessel aggregation score >0.3 were classified as vessel-enriched and included in further analysis (13/33 tissues). FAP+ pericyte-enriched spots were defined as those with a FAP+ pericyte composition score ≥ mean + 1 s.d. across all spots in the tissue. To enable comparison, vessel structures depleted of FAP+ pericytes were annotated as spots with a vessel score ≥ the vessel enrichment threshold and a FAP+ pericyte score < FAP+ pericyte enrichment threshold. log2FC differences in ligand expression were quantified in FAP+ pericyte-enriched spots versus FAP+ pericyte-depleted vessel spots per tissue. Significance was assessed by paired t-test and Benjamini–Hochberg multiple testing correction with P < 0.05 considered significant.
To estimate the relative contribution of ligand expression from different cell subtypes within FAP+ pericyte-enriched spatial regions, mean cell-type proportions (composition scores) were first quantified in FAP+ pericyte-enriched spots for each vessel-enriched tissue. Expression of ligands of interest was then assessed across four scRNA-seq datasets. For each dataset, average expression of each ligand was calculated within each annotated cell subtype and normalized to the global mean expression of that ligand across all cells within that dataset. This generated a dataset-specific normalized ligand expression score for each ligand–cell type pair. Normalized ligand expression scores were then averaged across datasets to generate an overall mean normalized expression value for each ligand–cell type pair. To account for the relative abundance of each cell subtype within FAP+ pericyte enriched spots, these mean normalized expression values were multiplied by the corresponding average cell-type proportion for each tissue within FAP+ pericyte enriched spots. This produced a contribution-weighted ligand expression score for each ligand–cell subtype combination in each tissue. For statistical comparisons, contribution-weighted expression scores were analyzed using a one-way analysis of variance (ANOVA) with cell subtype as the grouping variable. Ligand–cell subtype combinations were considered significant where Tukey-adjusted P values <0.001. For heat map visualization, mean normalized expression values were multiplied by the average cell-type proportion within FAP+ pericyte enriched spots across all tissues.
To measure log2FC differences in expression of myeloid markers in local neighborhoods surrounding FAP+ pericyte-enriched versus FAP+ pericyte-depleted vessel spots, the region was expanded to include the immediate six neighboring spots (seven-spot local neighborhood). Significance was once again assessed by t-test, with P < 0.05 considered significant.
To quantify the spatial enrichment of myeloid subtypes around FAP+ pericyte spots, we calculated the neighborhood composition score (NCS40). Spots with a FAP+ pericyte composition score ≥ the FAP+ pericyte enrichment threshold were identified, and the proportion of neighboring spots (within the seven-spot local neighborhood) enriched for each myeloid subtype (for example, M1/M2 macrophages, monocytes and microglia) was determined. The NCS was computed as the ratio of enriched myeloid subtype spots to total neighborhood spots, with average NCS values calculated per tissue. This analysis was also performed for other pericyte subpopulations annotated using RCTD analysis.
To observe how this spatial enrichment changes across regions proximal and distal to FAP+ pericyte-enriched spots, we quantified myeloid composition scores at increasing distances from these spots. Spots with a FAP+ pericyte composition score ≥ FAP+ pericyte enrichment threshold were identified, and the average composition score of each myeloid subtype was quantified per binned distance (100 µm). To facilitate cross-tissue comparison, average composition scores were normalized to 0–1. To enable direct comparison between FAP+ pericyte-enriched spots and other pericyte spots not enriched with FAP+ pericyte, we performed the same analysis around FAP− pericyte spots defined as pericyte enriched spots with pericyte composition score ≥ pericyte enrichment threshold and FAP+ pericytes composition score < FAP+ pericyte enrichment threshold. This analysis was also performed for myeloid marker genes.
Derivation of primary cell cultures and cultivation of cell lines
FAP+ pericyte-like cells were derived from fresh human GBM tissue according to a previously described procedure19. In brief, tumor tissue was dissociated mechanically and enzymatically (TrypLE, Gibco) and cultured in complete pericyte media, consisting of pericyte medium (PM) supplemented with 2% fetal bovine serum, pericyte growth supplement, 100 units/ml penicillin G and 100 µg/ml streptomycin (all provided by ScienCell Research Laboratories) on fibronectin-coated (2 µg/cm2, Merck) plastic. After 7 days, the FAP-expressing cells were isolated by magnetic-activated cell sorting using anti-FAP antibody (F11)-coated DynaBeads (Invitrogen). Cells were cultured on fibronectin-coated plastic under standard conditions at 37 °C in a humid atmosphere of 5% CO2 in a complete pericyte medium. Patient-derived GBM stem-like cell cultures (GSCs) were isolated as described previously41 and cultured in complete GSC medium consisting of DMEM/F-12 (Sigma-Aldrich) with 1% GlutaMAX (Thermo Fisher Scientific), 2% B-27 supplement minus vitamin A (Thermo Fisher Scientific), 100 U/ml penicillin, 100 μg/ml streptomycin (Sigma-Aldrich), 20 ng/ml EGF and 20 ng/ml FGF2 (PeproTech EC) in nonadherent cell culture flasks. The GSC cell line NCH644 (Cell Lines Service) was cultured in complete stem cell medium as described above. Cell cultures were tested for mycoplasma using the MycoAlert PLUS Mycoplasma Detection Kit (Lonza, LT07-703) and authenticated by short tandem repeat profiling performed by Generi Biotech.
Monocytes were isolated from healthy donors’ buffy coats by density gradient centrifugation using Ficoll-Paque (Cytiva) and by indirect magnetic-activated cell sorting using a Pan Monocyte Isolation Kit (Miltenyi Biotec) according to the manufacturer’s instructions. THP-1 monocytic cell line was a kind gift from Professor Klener (Institute of Pathological Physiology, First Faculty of Medicine, Charles University). Monocytes and THP-1 cells were cultured in RPMI1640 medium supplemented with 10% heat-inactivated fetal bovine serum (monocyte cultivation medium). The purity of isolated monocytes was verified by flow cytometric assessment of CD14 expression.
Primary mouse pericytes were isolated from adult C57BL/6 mice according to an established protocol42. In brief, six mouse brains were dissociated using the Papain dissociation system (Worthington Biochemical). Cells were initially cultured in endothelial-optimized conditions; after two passages, the medium was replaced with complete pericyte medium. To ensure culture purity, cells were characterized at each passage for the expression of pericyte markers: NG2, αSMA and contaminant markers: endothelial marker CD31, astrocyte marker GFAP and immune cell marker CD45. At the fourth passage, cells were positive for NG2 and α-SMA and negative for CD31, GFAP and CD45. Based on this marker profile, the cells were considered pericytes and were used for downstream in vivo experiment.
GL261 murine GBM cell line was acquired from NCI tumor repository and cultured in RPMI1640 medium supplemented with 10% of fetal bovine serum.
In vivo co-implantation model
All animal experiments were approved by the Commission for Animal Welfare of the First Faculty of Medicine, Charles University, and by the Ministry of Education, Youth and Sports of the Czech Republic (MSMT-3828/2024-5), in accordance with national animal protection legislation. Eight-week-old male C57BL/6J mice (The Jackson Laboratory) were used for intracranial implantation. Surgical procedures were performed under intramuscular anesthesia with ketamine (100 mg/kg) and xylazine (10 mg/kg) to minimize animal suffering. Orthotopic tumors were generated using a stereotactic frame (Stoelting) by co-implantation of 1.7 × 104 pericytes with 5 × 104 GL261 GBM cells in a total volume of 4 µl (n = 7). Control mice received 5 × 104 GL261 cells alone in a total volume of 4 µl (n = 7). Cells were injected at coordinates 1.2 mm anterior to bregma, 2.5 mm lateral to the midline and 3 mm deep into the right hemisphere at a rate of 1 µl/min using a Pump 11 Elite syringe pump (Harvard Apparatus). Mice were monitored and weighed daily. Brain tumors were collected 15 days post implantation, when the first mice in the cohort developed neurological symptoms or 10% body weight loss. Brains were embedded in Tissue Freezing Medium (Leica) and stored at −20 °C until further processing. Brains were sectioned coronally at a thickness of 10 µm using a cryotome (Leica). Sections were stained with hematoxylin and eosin for histological evaluation and volumetric analysis. From the first section showing detectable tumor tissue onward, every tenth section was collected for volumetric analysis. Tumor volume was analyzed using ImageJ software with the Volumest plugin.
Immunocytochemistry
Primary FAP+ pericyte-like cell cultures and primary mouse pericytes growing on fibronectin-coated glass coverslips were fixed with 4% paraformaldehyde for 10 min at room temperature and permeabilized with 0.1% Triton X-100. Cells were incubated overnight at 4 °C with the following primary antibodies: anti-FAP (mouse hybridoma F19, ATCC, 55 µg/ml), anti-fibroblast (clone TE-7, Merck Millipore, 1:200), anti-NG2/MCSP (clone LHM 2, Novus Biologicals, 1:200), anti-αSMA (clone 1A7, Abcam, 1:100), anti-PDGFRβ (polyclonal, LS Bio, 1:100), anti-vWf (polyclonal, Dako, 1:200), anti-GFAP (clone GF-01, Exbio, 1:200) and anti-Sox2 (polyclonal, Abcam, 1:600), anti-FAP (clone AF3715, R&D Systems, 1:40), anti-NG2 (clone 546930, Invitrogen, 1:400), anti-CD31 (clone MEC 7.46, R&D Systems, 1:100), anti-GFAP (polyclonal, Abcam, 1:5000) and anti-CD45 (clone 30-F11, R&D Systems, 1:100). The primary antibodies were visualized using the corresponding secondary antibodies: anti-mouse Alexa Fluor 488 (Invitrogen), anti-rabbit Alexa Fluor 488 (Invitrogen), and nuclei were counterstained with Hoechst 33258 (Invitrogen, final concentration 0.5 ng/ml). Samples were mounted in Aqua-Poly/Mount (Polysciences). Images were acquired using an epifluorescence microscope (IX70, Olympus) with Orca-flash 4.0 camera (Hamamatsu).
FISH
To verify the stromal origin of FAP+ pericyte-like cultures and rule out malignant cell contamination, fluorescence in situ hybridization (FISH) was performed using a dual-color probes (IntellMed) targeting the CDKN2A (9p21) locus (Orange) with the centromere 9 (CEN9) control (green) in cultures 95A and 109A, or the EGFR locus (orange) with the centromere 7 (CEN7) control (green) in cultures 69A and 90A. Cells growing on fibronectin-coated glass coverslips were fixed in a methanol and acetic acid solution (3:1), washed in 50 mM NH4Cl and permeabilized using 0.2% Triton X-100. Following dehydration through a graded ethanol series, the probes were hybridized for 5 min at 75 °C, followed by an overnight incubation at 37 °C in a humidified chamber. Remaining unbound probes were washed away using a washing solution I (0.4× saline-sodium citrate/0.3% NP-40) heated to 73 °C for 2 min and subsequently with washing solution II (2× saline-sodium citrate/0.1% NP-40) for 30 s at room temperature. Nuclei were counterstained with Hoechst 33258 (Invitrogen, final concentration 0.5 ng/ml). At least 100 nuclei per culture were analyzed by two independent observers using an Olympus fluorescence microscope. Images were acquired using an epifluorescence microscope (IX70, Olympus) with Orca-flash 4.0 camera (Hamamatsu). The status of the 9p deletion and EGFR amplification in the corresponding patient tumor tissue was established by the clinical pathology department during routine diagnostic examination.
Preparation of conditioned media
To obtain serum-free conditioned media, 0.6 × 106 FAP+ pericyte-like cells were seeded in 100-mm fibronectin-coated Petri dishes and grown in complete pericyte medium for 72 h. Then, the medium was removed, cells were washed twice with PBS, and 10 ml of serum-free RPMI 1640 medium or pericyte medium without supplements was added. Cells were cultured at 37 °C under standard conditions. After 72 h, the conditioned medium was collected, centrifuged (618g at 4 °C for 10 min), filtered through a 0.22-µm pore filter (Merck) and stored in aliquots at −80 °C. Corresponding media not exposed to cells were used as controls.
For secretome analysis, FAP+ pericyte-like cells and GSCs were seeded at 1 × 104/cm2 in their corresponding complete media, grown for 72 h after which the media were replaced for fresh complete media. After additional 72 h, the conditioned media were collected and processed as described above.
Analysis of conditioned media using xMAP Intelliflex DR-SE multiplex platform
Cytokine and chemokine levels in conditioned media from FAP+ pericyte-like cells and GSCs cultured in their corresponding complete medium were quantified using human cytokine/chemokine/growth factor magnetic bead panels A and B (Merck Life Science, HCYTPAB). Fluorescent signals were acquired on an xMAP Intelliflex DR-SE analyzer (version 2.1, Luminex Corporation), and analyte concentrations were calculated using the Belysa analysis software (version 1.2.2, Merck Life Science). All procedures were performed according to the manufacturer’s instructions.
Monocyte migration
Monocyte migration was evaluated using a Transwell migration assay. For primary human monocytes, freshly isolated cells were resuspended in serum-free RPMI media and 2 × 105 cells were seeded into the upper compartment of polycarbonate inserts with 5-µm pores (Corning). For THP-1 cells, 2 × 10⁵ cells were resuspended in serum-free pericyte medium and seeded into polycarbonate inserts with 8-µm pores (Falcon). In both assays, the lower compartment was filled with serum-free conditioned media from FAP+ pericyte-like cell cultures or the corresponding nonconditioned control media (serum-free RPMI for primary monocytes and pericyte medium without supplements for THP-1 cells). Recombinant CCL2 (100 ng/ml; Miltenyi) was used as a positive control. Cells were allowed to migrate for 2 h. For primary monocytes, nonmigrated cells were removed from the upper surface of the membrane using a cotton swab, and migrated cells were fixed with 4% paraformaldehyde, stained with crystal violet and manually counted. For THP-1 cells, transmigrated cells in the lower compartment were counted using an automated cell counter (Beckman Coulter). Primary monocyte migration assays were performed using monocytes from four different donors and conditioned media from four independent FAP+ pericyte-like cell cultures to account for potential donor-specific and cell culture-specific variations. Two inserts were used per condition, and migrating cells were quantified in five nonoverlapping visual fields per insert at 20× objective. THP-1 migration assays were repeated twice using conditioned media from six independent FAP⁺ pericyte-like cell cultures, with three inserts per condition.
Monocyte polarization experiments, CSF1R inhibition
FAP+ pericyte-like cells (0.1 × 106 per well) were seeded into six-well plates in complete pericyte medium. After 1 day, the medium was removed, cells were washed twice with PBS and 0.4 × 106 freshly isolated monocytes in monocyte cultivation medium were added. For M1/M2 monocyte polarization, 1 × 106 freshly isolated healthy donors’ monocytes per well were plated in a six-well plate in monocyte cultivation medium. To induce M1 macrophage polarization, cells were cultured with GM-CSF (50 ng/ml), and IFN-γ (50 ng/ml) and lipopolysaccharide (10 ng/ml) were added 24 h before collection. To induce M2 macrophage polarization, cells were cultured in M-CSF (50 ng/ml)-containing medium and IL-4 (20 ng/ml) was added 24 h before collection. Monocytes without treatment were cultured in complete monocyte cultivation medium. Media were changed on day 3 and day 5.
For CSF1R inhibition experiments, FAP+ pericyte-like cells (6 × 104 cells/well) were seeded in the upper compartment of a Transwell insert (0.4-µm pore size, 24-well format; VWR). After 24 h, monocytes from healthy donors (n = 2; 2.4 × 105 cells per well) were seeded in the lower compartment. The selective CSF1R inhibitor BLZ945 (MedChemExpress) was added to the medium (0.5 µM and 1 µM) at the onset of the 6-day differentiation period. Fresh medium containing BLZ945 was replenished on days 3 and 5. As positive controls, monocytes were concurrently differentiated into M1 and M2 macrophages over 6 days as described above.
On day 6, cells were collected using Accutase (Merck) and the expression of M1 (CD80, HLA-DR), and M2 (CD14, CD163 and CD206) polarization markers was quantified by flow cytometry.
Flow cytometry
Flow cytometry was used to phenotype freshly isolated monocytes, macrophages generated in co-culture experiments, monocytes without treatment and classically (M1) or alternatively (M2) polarized macrophages. Cells were stained with fluorochrome-conjugated antibodies against CD14 (APC-Cy7, clone MEM-15, Exbio), CD45 (FITC, clone MEM-28, Exbio), HLA-DR (PE, clone L243, Sony), CD80 (APC, clone 2D10, Sony), CD206 (APC, clone 15-2, Sony) and CD163 (PE, clone GH1/61, Sony). Cell viability was assessed using Hoechst 33258 (Invitrogen, final concentration 5 µg/ml). Before antibody staining, cells were incubated with an Fc receptor-blocking solution (Miltenyi Biotec). Data acquisition was performed on a BD FACSVerse flow cytometer with the BD FACSuite software (Becton Dickinson), and data were analyzed using FlowJo software (TreeStar). For experiments involving CSF1R inhibition, a modified antibody panel was used consisting of HLA-DR (PE-CF594, clone L243, BD), CD80 (BV605, clone L307.4, BD), CD206 (BV421, clone 15-2, Sony) and CD163 (APC, clone GHI/61, Exbio). Cell viability was assessed using LIVE/DEAD Fixable Near-IR Dead Cell Stain Kit (Molecular Probes). Data acquisition for these experiments was performed using a NovoCyte Quanteon flow cytometer (Agilent Technologies), and data were analyzed using FlowJo software.
Quantification of IFNγ production by T cells co-cultured with differentiated macrophages
Primary T cells were isolated from healthy donor buffy coats by negative magnetic selection (Pan T Cell Isolation Kit, Miltenyi Biotec). T cells were preactivated for 3 days using anti-CD3/CD2/CD28-coated beads (T Cell Activation/Expansion Kit, human; Miltenyi Biotec) according to the manufacturer’s recommendations. Monocytes (1 × 10⁵ cells/24WP well) were differentiated for 6 days under four conditions: (1) classically activated macrophages (M1: GM-CSF + IFNγ + LPS), (2) alternatively activated macrophages (M2: M-CSF + IL-4), (3) GSC-educated macrophages or (4) FAP⁺ pericyte-like cell-educated macrophages. GSCs or FAP⁺ pericyte-like cells (2.5 × 10⁴ cells) were seeded in the upper compartment of 24-well Transwell insert (0.4-µm pore size; VWR) 24 h before monocyte addition into the lower compartment to allow adherence. After 6 days of differentiation, Transwell inserts were removed, macrophages were thoroughly washed with prewarmed PBS, and activated T cells (5 × 10⁵ cells per well) were added.
After 72 h of co-culture, supernatants were collected, centrifuged to remove cellular debris and IFNγ concentrations were quantified using a human IFNγ Quantikine ELISA kit (R&D Systems) according to the manufacturer’s instructions. Absorbance was measured at 450 nm with wavelength correction at 570 nm using a microplate reader (Tecan). Cytokine concentrations were calculated by interpolation from a standard curve.
Transcriptional signature of FAP+ pericyte-like cells and survival analysis
A transcriptional signature was derived from genes distinguishing FAP⁺ pericyte-like cells from other pericyte populations. To ensure specificity for FAP⁺ pericytes and minimize confounding from genes expressed by malignant, endothelial or other stromal cell types within the TME, genes with high expression in nonpericyte compartments (≥15% expressing cells and z-score ≥0 in more than 50% of dataset per cell type) were excluded. The refined ten-gene signature comprised FAP, LTBP2, CYP1B1, ASAM (CLMP), FBLN2, OGN, FMOD, BICC1, CXCL6 and SFRP2. Gene nomenclature was harmonized across datasets (ASAM/CLMP).
Survival analyses were performed using publicly available GBM cohorts, including TCGA RNA-seq (HiSeqV2), TCGA HT_HG-U133A microarray and CGGA mRNAseq_325 and mRNAseq_693 datasets. Only primary, IDH-wild-type GBMs with available overall survival data were included. Platform availability of signature genes was 10/10 in TCGA RNA-seq and both CGGA RNA-seq cohorts and 7/10 in the TCGA U133A microarray dataset (missing genes: CLMP/ASAM, BICC1 and SFRP2).
Within each cohort, gene expression values were analyzed independently. For RNA-seq datasets, expression values were log2-transformed where required and z-score-normalized per gene across samples within each cohort. A signature score was calculated as the mean z-score across the available signature genes in each cohort.
Overall survival was assessed using Cox proportional hazards regression, with the signature score modeled as both a continuous variable (trend analysis) and a categorical variable after stratifying patients into cohort-specific terciles. Survival differences between the upper and lower terciles were evaluated using Kaplan–Meier analysis with log-rank testing. All analyses were performed in R using the survival and survminer packages.
Statistical analysis
Statistica 12 and GraphPad Prism 8.0 were used. Normality was assessed using the Shapiro–Wilk test. For datasets that did not meet the assumptions of normality, nonparametric tests were applied. When homogeneity of variances was not met, Welch’s correction was used. A two-sided P < 0.05 was considered statistically significant. A linear mixed model (ANOVA) was used to analyze data from monocyte migration experiments. The log2-transformed number of migrating monocytes was used as the dependent variable. For experiments using the THP-1 monocytic cell line, the chemoattractant type (control medium, conditioned media from individual FAP⁺ pericyte-like cell cultures and the positive control CCL2) was modeled as a fixed factor (treatment), and experimental repetition was included as a random factor. For experiments using primary human monocytes, the chemoattractant type was included as a fixed factor (factor A) in the model. Random factors included individual donors (factor B), the type of pericyte-like cell culture used to produce the conditioned medium (factor C), the interaction between these two factors (B × C) and individual inserts used in the modified Boyden chamber assay (factor D). PCA was used to evaluate the expression of M1 and M2 markers. In addition, paired t-tests were used for each marker to determine whether monocytes co-cultured with FAP+ pericyte-like cells more closely resembled the classically activated M1 macrophages or alternatively activated M2 macrophages. The null hypothesis posited that the co-cultured monocytes were as far from classically activated M1 macrophages as from alternatively activated M2 macrophages. Rejection of the null hypothesis was interpreted as resemblance to one of the above phenotypes.
