bioRxivCancer Biology
Glioblastoma embryonic-like stem cells exhibit immune-evasive phenotype
B. Sese, S. Iniguez, M. A. Ensenat, P. Llinas, G. Ramis, J. I. J. Orozco, P. Villalonga, S. Fernandez, D. Marzese
Abstract
Glioma stem cells (GSCs) are a subset of cells with self-renewal and tumor-initiating capacities that are thought to participate in drug resistance and immune evasion mechanisms in glioblastoma (GBM). Given GBM heterogeneity, we hypothesized that GSCs might also display cellular hierarchies associated with different degrees of stemness. We evaluated a single-cell RNA-seq glioblastoma dataset (n = 28) and identified a stem cell population co-expressing high levels of embryonic pluripotency markers, named core glioma stem cells (c-GSCs). This embryonic-like population represents 4.22% {+/-} 0.59 of the tumor cell mass, and pathway analysis revealed an upregulation of stemness and downregulation of immune-associated pathways. Using induced pluripotent stem cell technology, we generated an in vitro model of c-GSCs by reprogramming glioblastoma patient-derived cells into induced c-GSCs (ic-GSCs). Immunostaining of ic-GSCs showed high expression of embryonic pluripotency markers and downregulation of antigen presentation HLA proteins, mimicking its tumoral counterpart. Transcriptomic analysis revealed a strong agreement of enriched biological pathways between tumor c-GSCs and in vitro ic-GSCs ({kappa} = 0.71). Integration of ic-GSC DNA methylation and gene expression with chromatin state analysis of epigenomic maps (n = 833) indicated that polycomb repressive marks downregulate HLA genes in stem-like phenotype. Together, we identified c-GSCs as a GBM cell population with embryonic signatures and poor immunogenicity. Genome-scale transcriptomic and epigenomic profiling provide a valuable resource for studying immune evasion mechanisms governing c-GSCs and identifying potential therapeutic targets for GBM immunotherapy.
- organism: human
- assay: scRNA-seq
- assay: DNA methylation
- research focus: cell type annotation
- research focus: gene regulatory network inference
- research focus: differential expression
- approach: deep learning
- approach: statistical model
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