Glioblastoma is the most lethal primary malignant brain tumor in adults. Simplified two-dimensional (2D) cell culture and neurospheres in vitro models fail to recapitulate the complexity of the tumor microenvironment, limiting its ability to predict therapeutic response. Three-dimensional (3D) scaffold-based models have emerged as a promising alternative for addressing these concerns. One such 3D system is gelatin methacrylate (GelMA) hydrogels, and we aimed to understand the suitability of using this system to mimic treatment-resistant glioblastoma cells that reside in specific niches. We characterized the phenotype of patient-derived glioma cells cultured in GelMA hydrogels (3D-GMH) for their tumorigenic properties using invasion and chemoresponse assays. In addition, we used integrated single-cell and spatial transcriptome analysis to compare cells cultured in 3D-GMH to neoplastic cells in vivo. Finally, we assessed tumor-immune cell interactions with a macrophage infiltration assay and a cytokine array. We show that the 3D-GMH system enriches treatment-resistant mesenchymal cells that are not represented in neurosphere cultures. Cells cultured in 3D-GMH resemble a mesenchymal-like cellular phenotype found in perivascular and hypoxic regions and recruit macrophages by secreting cytokines, a hallmark of the mesenchymal phenotype. Our 3D-GMH model effectively mimics the phenotype of glioma cells that are found in the perivascular and hypoxic niches of the glioblastoma core in situ, in contrast to the neurosphere cultures that enrich cells of the infiltrative edge of the tumor. This contrast highlights the need for due diligence in selecting an appropriate model when designing a study‘s objectives.

G protein-coupled receptor 56 (GPR56/ADGRG1) is an adhesion GPCR with an essential role in brain development and cancer. Elevated expression of GPR56 was observed in the clinical specimens of Glioblastoma (GBM), a highly invasive primary brain tumor. However, we found the expression to be variable across the specimens, presumably due to the intratumor heterogeneity of GBM. Therefore, we re-examined GPR56 expression in public domain spatial gene expression data and single-cell expression data for GBM, which revealed that GPR56 expression was high in cellular tumors, infiltrating tumor cells, and proliferating cells, low in microvascular proliferation and peri-necrotic areas of the tumor, especially in hypoxic mesenchymal-like cells. To gain a better understanding of the consequences of GPR56 downregulation in tumor cells and other molecular changes associated with it, we generated a sh-RNA-mediated GPR56 knockdown in the GBM cell line U373 and performed transcriptomics, proteomics, and phospho-proteomics analysis. Our analysis revealed enrichment of gene signatures, pathways, and phosphorylation of proteins potentially associated with mesenchymal (MES) transition in the tumor and concurrent increase in cell invasion and migration behavior of the GPR56 knockdown GBM cells. Interestingly, our analysis also showed elevated expression of Transglutaminase 2 (TG2) - a known interactor of GPR56, in the knockdown cells. The inverse expression of GPR56 and TG2 was also observed in intratumoral, spatial gene expression data for GBM and in GBM cell lines cultured in vitro under hypoxic conditions. Integrating all these observations, we propose a putative functional link between the inverse expression of the two proteins, the hypoxic niche and the mesenchymal status in the tumor. Hypoxia-induced downregulation of GPR56 and activation of TG2 may result in a network of molecular events that contribute to the mesenchymal transition of GBM cells, and we propose a putative model to explain this functional and regulatory relationship of the two proteins.

Large-scale transcriptomic data is used by biologists for the discovery of new molecular patterns or cell subpopulations. clustering is one of the most popular methods for dimensionality reduction and data analysis for large scale datasets. the major problem while clustering the data is the selection of the optimal number of clusters (k) for each dataset and to discover new insights from it. We have developed Recursive consensus clustering (Rcc), an unsupervised clustering algorithm for novel subtype discovery from both bulk and single-cell datasets. Rcc is available as an R package and facilitates the generation of new biological insights through intuitive visualization of clustering results. Recent advances in the field of RNA sequencing has resulted in a wealth of data which allows us to classify and study the transcriptomic subtypes/cell types in different biological systems. The clustering of transcriptomic data reduces the dimensionality of the data and allows a researcher to better analyze, visualize and interpret the data for biological insight. Clustering is an unsupervised technique that allows the grouping of similar objects and enables division of data. Though a widely used technique, researchers face the following challenges in performing clustering on transcriptomic data:

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 3 and grade 4 or GBM) 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, IDH-wild type) or grade 3 IDH-mutant gliomas. We observe that neutrophils are highly heterogeneous among individuals with glioma, and are different from healthy controls. We also show that CD163 expressing M2 monocytes are present in greater proportions in GBM tissue when compared to grade 3 IDH-mutant glioma tissue, and a larger proportion of granulocytic myeloid-derived suppressor cells are present in grade 3 IDH-mutant gliomas when compared to GBM. Finally, we demonstrate 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.

Splice variants are known to be important in the pathophysiology of tumors, including the brain cancers. We applied a proteogenomics pipeline to identify splice variants in glioblastoma (GBM, grade IV glioma), a highly malignant brain tumor, using in-house generated mass spectrometric proteomic data and public domain RNASeq dataset. Our analysis led to the identification of a novel exon that maps to the long isoform of Neural cell adhesion molecule 1 (NCAM1), expressed on the surface of glial cells and neurons, important for cell adhesion and cell signaling. The presence of the novel exon is supported with the identification of five peptides spanning it. Additional peptides were also detected in sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) gel separated proteins from GBM patient tissue, underscoring the presence of the novel peptides in the intact brain protein. The novel exon was detected in the RNASeq dataset in 18 of 25 GBM samples and separately validated in additional 10 GBM tumor tissues using quantitative real-time-polymerase chain reaction (qRT-PCR). Both transcriptomic and proteomic data indicate downregulation of NCAM1, including the novel variant, in GBM. Domain analysis of the novel NCAM1 sequence indicates that the insertion of the novel exon contributes extra low-complexity region in the protein that may be important for protein-protein interactions and hence for cell signaling associated with tumor development. Taken together, the novel NCAM1 variant reported in this study exemplifies the importance of future multiomics research and systems biology applications in GBM.