Immuno-Phenotyping of High-Grade Glioma Infiltrating Immune Cells Reveals Grade Specific Differences in Cells of Myeloid Origin

Gliomas are heavily infiltrated with immune cells of myeloid origin. Past studies have shown that high-grade gliomas have a higher proportion of alternatively activated and suppressive myeloid cells when compared to low-grade gliomas, which correlate with poor prognosis. However, the differences in immune cell phenotypes within high-grade gliomas (between grade III and IV) are relatively less explored, and a correlation of phenotypic characteristics between immune cells in the blood and high grade tumors has not been performed. Additionally, myeloid cells of granulocytic origin present in gliomas remain poorly characterized. Herein, we address these questions through phenotypic characterizations of monocytes and neutrophils present in blood and tumors of individuals with glioblastoma (GBM, grade IV) or grade III gliomas. Our data show that CD163 expressing M2 monocytes are present in greater proportions in GBM tissue when compared to grade III glioma tissue. In addition, we observe that neutrophils are highly heterogeneous among individuals with glioma, and a greater proportion of granulocytic myeloid-derived suppressor cells are present in grade III gliomas when compared to GBM. Finally, we show that the expression levels of CD86 and CD63 showed a high correlation between blood and tumor, and suggest that these may be used as possible markers for prognosis.


Introduction
High-grade gliomas, the most common form of brain tumors, are associated with poor prognosis 1 .
The current standard of care has met with limited success, possibly due to the high adjacent tissue infiltrative capacity 2,3 , treatment resistant cells 4 , low-medium mutational burden 5 , and cellular heterogeneity 6,7 of glioblastoma (GBM). Immunotherapy, such as the use of antibodies against programmed cell death protein 1 (PD)-1 8 that focus on preventing immunosuppression by regulatory T cells, have shown limited success 3 , which may be due to the diversity of immunosuppressive cells present in the GBM microenvironment.
In addition to regulatory T cells, multiple reports have highlighted the increased presence of alternatively activated or suppressive cells of myeloid origin in GBMs [9][10][11] . For example, larger numbers of myeloid-derived suppressor cells (MDSCs) have been observed in the blood [12][13][14][15] , and these cells are also enriched in the GBM microenvironment 5,[16][17][18] . An enrichment of suppressive monocyte/macrophages (M2 or M0 phenotype) 10,[19][20][21] has also been observed. Further, it has been suggested that the monocytes in the GBM microenvironment may be phenotypically and functionally different 22,23 , and the same is possibly true for neutrophils [24][25][26] too. Determining the prevalence of such immuno-modulatory cells of myeloid origin and characterizing their phenotype, could have implications for the development of new therapies.
In this context, herein, we aimed to address a few unanswered questions with regard to myeloid cells in individuals with gliomas: 1) if there are differences in the abundance of suppressive myeloid cells between grade III gliomas and GBM. Most studies suggest that the frequencies of suppressive cells are increased in GBM when compared to lower grade gliomas (grade II), but comparisons between GBM and grade III do not show significant differences 10,17 . 2) the characterization of granulocytes in the tumor microenvironment. Wang et al. 25 demonstrated that cells with neutrophil gene signatures were present in GBM, and Chai et. al. 26 showed that individuals with GBM had a greater proportion of neutrophilic-MDSC in the blood when compared to heathy controls. However, detailed immuno-phenotyping of these neutrophil populations has not yet been reported. And 3) if there is a correlation in the phenotype of suppressive myeloid cells between blood and tumor tissue of the same individual, similar to regulatory T cells 27 .
Identifying phenotypes of immune cells in the blood that are predictive of glioma severity might help in making decisions with regard to clinical treatment and management. To answer these questions, we evaluated the phenotype (surface protein expression) of myeloid cells obtained from tumor resections and blood of individuals with gliomas using multi-color flow cytometry. Institute 1640 culture medium (RPMI, Gibco, USA) media containing 1% antibiotics (Pen-strep, Thermo Fisher Scientific, USA). Samples were washed three times with cold phosphate-buffered saline (PBS) with antibiotics and minced using a scalpel in a 60-mm petri dish. The tissue fragments were digested in 30ug/ml accutase (Gibco) in 5ml of RPMI for 10-15 minutes at 37°C and dissociated by pipetting with a 1 ml-pipette 2-3 times. Dissociation was stopped by adding 10ml of RPMI, and the cell suspension was passed through 70-μm cell strainers (BD Falcon, USA). The single-cell suspension was washed twice with cold PBS, centrifuged, and used for fixable live-dead staining, as described below. About 3ml peripheral venous blood drawn from consented individuals was spun down at 500g for 5 minutes and plasma was saved. The cell pellet was subjected to RBC lysis using ACK lysis buffer (0.15M Ammonium Chloride, 10mM

Methods
Potassium Bicarbonate, 0.1mM EDTA) for 10 minutes at room temperature in 1:10 (blood:lysis buffer) ratio by volume. Lysis was quenched using PBS solution containing 4mM EDTA, and the solution was centrifuged at 400g for 10 minutes at 4 degree. Supernatant was discarded and the pelleted white blood cells were suspended in PBS.
Blood and tumor cell suspensions were labelled with fixable live-dead stain (0.3 µl dye/100 µl volume of 1 million cell suspension) for 20 minutes at room temperature. Staining was quenched using 2 ml PBS containing 1% bovine albumin and 4mM EDTA (flow cytometry buffer), and cells were centrifuged at 400g for 4 minutes at 4ºC. Cell pellet was fixed using 2% paraformaldehyde (prepared in PBS) while subjecting the tube to pulse-vortex. After 30 minutes, cells were washed and suspended in the flow cytometry buffer. Cell suspensions not stained with fixable live-dead dye were also fixed as described above, and used as controls.
Immuno-phenotyping using flow cytometry: Cells were stained with a panel of antibodies (all from BD Biosciences, USA) described in Supplementary Table 1. Antibodies were added to fixed cell suspensions (made up in flow cytometry buffer) as per manufacturer's instructions. Samples were incubated at 4ºC for 30 minutes, and washed once to remove unbound antibodies. After washing, the centrifuged pellet was suspended in 300 µl flow cytometry buffer and run through a flow cytometer (BD FACS Celesta, USA). Single color controls were prepared using compensation beads (BD Biosciences) to which appropriate antibodies were added.
Fluorescence-minus-one (FMO) controls were prepared using live-dead stained cell suspensions by removing one antibody at a time, and replacing it with its isotype during the antibody staining step. Flow cytometry data was analyzed using FlowJo (FlowJo LLC, USA). A minimum of 20,000 CD45 + live events were collected from each tumor and blood sample. A minimum threshold of 100 events was used to report percentage positive and MFI values. Single-cell and tSNE analysis of flow cytometry data are described in supplementary methods.

Immunohistochemistry (IHC):
IHC was performed on formalin-fixed paraffin-embedded 3micron tissue sections for selected proteins. Deparaffinization was carried out in xylene solution followed by rehydration with a series of graded alcohol, and antigen retrieval in Tris-EDTA buffer,

Clinical characteristics: Individuals undergoing craniotomy at the Mazumdar-Shaw Medical
Center were recruited into this study, following informed consent. A total of 25 individuals (including three healthy volunteers from whom blood was drawn) were recruited (Table 1)    Flow cytometry based analysis of percentages of immune cells revealed: (i) intra-tumor variability in immune infiltration when directly compared with histology sections (bottom panels of Figure 1A and 1B, where all CD15 and CD14 expressing cells are grouped together as granulocytes and monocytes, respectively); and (ii) inter-tumor variability in the frequencies of various immune cell subsets ( Figure 1C). Nevertheless, the combination of histology and flow cytometry data are suggestive of an increased presence of myeloid cell subsets in the tumor microenvironment of GBM when compared to grade III gliomas.

Frequencies of Immune Cells in Tumor and
In addition, peripheral venous blood was collected from three, five and eight individuals who were healthy (no tumor), had grade III gliomas, or GBM, respectively. In blood, unlike tumors, a single monocyte subset was observed (CD14 + CD15 neg ), and all the CD15 expressing cells were labeled as neutrophils, as cells expressing medium to low levels of CD15 were not observed (Supplementary Figure 2). Neutrophils were found to be present in significantly higher percentages in both GBM and grade III gliomas when compared to healthy controls ( Figure 1D).
This increase in neutrophil percentage resulted in a drop in monocyte percentages in the blood of individuals with tumors, but it was not significantly lower than that of healthy controls.  Figure 4). However, these differences were not significant between the two grades of tumor. Similarly, a few surface proteins (CD54, CD282, and HLA-DR) were expressed at lower levels on monocytes from the blood of individuals with tumors, when compared to healthy controls (Supplementary Figure 5).

Phenotyping Immune Cells in Tumor and
Notably, one of the surface proteins, CD163, showed a distinctly different pattern of expression.
CD163 expression levels on both subsets of monocytes, measured as percentage positive cells and MFI, were significantly higher in GBM when compared to grade III tumors (Figure 2A). These differences were observed in IHC sections too, which on quantification showed a higher ratio of CD163 expressing cells among all other cells in GBM when compared to grade III tumors ( Figure   2B). Markedly, the expression level of CD163 on monocytes present in the blood was not significantly different among the two grades of tumor and healthy controls ( Figure 2C). Further, expression levels were not significantly different in the granulocyte subsets in tumor and blood ( Figure 2D), which may not be surprising as granulocytes are not known to express CD163.  Figure 4B). Similar to observations from the manual analysis, we found that compared to healthy controls, the blood of the individuals with gliomas (who were undergoing surgery) have a higher percentage of neutrophils and as a result fewer monocytes ( Figure 4B). This can largely be attributed to the fact that the individuals undergoing surgery are on dexamethasone, which increases the proportion of neutrophils. In addition, we see that among the cell subsets SSC-A -CD14 -CD15 + , SSC-A -CD14 + CD15 + , SSC-A -CD14 + CD15are higher in blood from healthy controls compared to blood from glioma patients, whereas

Myeloid-Derived
NeutrophilsCD14cells are found in higher proportions in blood from individuals with glioma. We did not observe significant differences between blood from Grade III and GBM patients except for SSC-A -CD14 + CD15cells, which are found in higher numbers in Grade III ( Figure 4B and 4C).
With regard to the immune cells infiltrated in GBM and grade III tumors, we observed that the cells in GBM tend to have higher SSC-A indicating higher granularity compared to grade III tumors ( Figure 4A and 4B). We also observe a larger number of cell types in GBM compared to Grade III. We do not see statistically significant differences in different proportions, but we observe a trend for macrophages, monocytes, SSC-A ++ CD14 + CD15 + to be higher in GBM tissue compared to Grade III, whereas SSC-A -CD14 +/++ CD15 ++ , SSC-A -CD14 + CD15 + , SSC-A -CD14 + CD15tend to be higher in Grade III ( Figure 4B and 4C). One of our observations was the variability of CD14 expression on neutrophils (SSC-A +/++ CD15 ++ ). CD14 expression displayed a trimodal distribution with no expression (CD14 -), medium level expression (CD14 + ) and high expression (CD14 ++ ). The majority of neutrophils from healthy donors' blood as well as from a patient with benign tumor (meningioma) expressed a medium level of CD14 expression (CD14 + ), which we have labeled as neutrophils. The percentage of NeutrophilsCD14and NeutrophilsCD14 ++ was significantly increased in the blood of glioma patients ( Figure 4C and 4D). Also, these neutrophil subsets were found to be infiltrated in glioma tissue ( Figure 4D). A subset of patients with GBM had a high percentage of NeutrophilsCD14 -(5/7) among all neutrophils in their tumor tissue as well as blood, in contrast to Grade III patients where we saw only one patient with a high percentage of NeutrophilsCD14 -(blood was not analyzed for this patient).

Discussion
There is a growing appreciation for myeloid cells in tumor microenvironments, especially those that are alternately activated or have suppressive functions. This is especially true for brain tumors, where cells of myeloid origin make up a large percentage of cells in the tumor [28][29][30][31] .
Characterizing the number, phenotype and function of these myeloid cells has the potential to enhance current treatment strategies, or help develop new therapeutic approaches. Through the data presented here, we add to the current knowledge of myeloid cell phenotypes in high-grade gliomas.
First, we report that about one-fourth of the glioma cell count comprises of immune cells identified based on the expression of CD45, and about half of these cells could be classified as granulocytes or monocytes based on the expression of CD15 (or MPO in histology) and CD14, respectively.
These numbers are lower than what has been reported historically 32,33 (reviewed in 28,29 ), which could be due to heterogeneity in the tumor or possibly due to population level variations.
Additionally, in the glioma microenvironment, we observe an additional monocyte sub-population that we characterize as CD14 + CD15 med through manual analysis of flow cytometry data. Their overall proportions are rather low, and they phenotypically resemble the traditional monocytes (CD14 + CD15 neg ) present in the tumor microenvironment. This specific cell subset is not observed in the blood, and hence we speculate that they arise from traditional monocytes in the tumor microenvironment.
Phenotyping of the monocyte subsets in the tumor specifically revealed an enrichment of CD163 expressing cells in GBM compared to grade III gliomas. However, we did not observe any differences in the CD163 expression among the monocytes present in the blood of the same individuals. In fact, we did not observe a higher proportion of CD163 expressing monocytes in the blood of individuals with glioma when compared to healthy controls too, which appears to be contrary to previous reports by Heimberger and colleagues 19 and Agrewala and colleagues 21 . But it is important to note the following differences: the former study compared the expression levels of CD163 at the RNA level, while the latter study used a different gating strategy (CD11b vs. CD14 used by us). Additionally, if we specifically look at the data related to the percentage expression of CD163 among monocytes in blood, we observe an increase (not statistically significant) in both tumor grades when compared to healthy controls. Importantly, we did not observe a correlation between CD163 expression levels in the blood and in the tumor of the same individuals, which might suggest that the tumor microenvironment plays an important role in either recruiting or converting activated monocyte/macrophage populations to CD163 cells (with possible suppressive function).
With regard to MDSC frequencies, we did not observe differences in the blood of individuals with tumors when compared to healthy individuals. This is in stark contrast to a number of previous reports that show increased numbers of these cells in the blood when compared to healthy controls [12][13][14][15]17,19 . One possible reason for this difference is that our study is underpowered for MDSC analysis, as data has been collected from a relatively low number of individuals (4 in the tumor groups and 3 in healthy controls). Another, and we speculate that the most likely possibility, is that our analysis was performed on whole blood, and not the PBMC fraction. How the use of Finally, by comparing the phenotype of myeloid cells between blood and tumor of the same individual, we were able to determine if blood phenotype is a representation of the tumor phenotype. To a large extent, CD86 and CD63 were the only cell-surface proteins whose expression levels correlated among myeloid cells in the blood and tumor. Given that higher expression of both these proteins in the tumor is associated with poor prognosis, we propose that the expression levels of these proteins on myeloid cells in the blood may be used as a prognostic marker for the progression of gliomas.
The following caveats apply to the data presented here. First, the tumor tissue was randomly CD15 med -other granulocytes (gran.); and CD14 + CD15 neg and CD14 + CD15 med as two monocyte subsets.
D -percentages of immune cell subsets in the blood. CD15 high -neutrophils and CD14 + CD15 neg -monocytes.
For statistical comparison of data, two-way ANOVA followed by Bonferroni's test was performed. *** indicates p < 0.001. No significant difference was observed, if not indicated. C -Expression among monocytes in blood measured as either percentage positive or MFI.
Significant differences were not observed between the 3 groups -one-way ANOVA.
D -Expression of CD163 among neutrophils and other granulocytes (gran.) in tumor, and neutrophils in blood. Significant differences were not observed.