Abstract
OBJECTIVE
Owing to recent advances in understanding of the active functional states exhibited within glioblastoma (GBM), intra-tumoral cellular signaling has moved into focus of neuro-oncology. In this study, we aim to explore the diversity of transcellular signaling and investigate correlations between transcriptional dynamics and functional signaling.
METHODS
Electrophysiological characterization of GBM was carried out using planar microelectrodes and Ca2+ imaging, in both 2D cell culture as well as in our novel human cortical GBM model. Exposure to physiologically relevant conditions present within the tumor was carried out to identify specific signaling cells of interest and capture the signaling diversity in response to environmental conditions. Transcriptional dynamics and plasticity were examined by means of scRNA-sequencing with CRISPR based perturbation, spatial transcriptomics and deep long-read RNA-sequencing.
RESULTS
Electrophysiological profiles of multiple primary GBM cell lines revealed characteristics of scale-free networks (R2=0.875), confirmed in both 2D culture as well as a human neocortical GBM model. When GBM was cultured in a “in-vivo” like environment, basal activity was significantly higher (50%, p=0.01). Cellular signaling was directly correlated to changes in the environment, like hypoxia or glutamatergic activation, and total inhibition of electrical signaling required the usage of synaptic inhibitors. Using single-cell RNA sequencing and proteomics, several synaptogenesis related genes were identified to play a crucial role in the lineage states present in GBM. CRISPR based perturbation of these genes resulted in alterations in cellular morphology and decreased cellular connectivity (p< 0.01), with loss of scale free features (R2=0.35), and transcriptomic loss of developmental lineages (FDR< 0.01), leading to significant inhibition of GBM stress response.
CONCLUSION
Our findings highlight the role of electrical signaling in glioblastoma. Cellular stressors induce intercellular signaling, leading to transcriptional adaptation suggesting that there exists a highly complex and powerful mechanism for dynamic transcriptional state adaptation.
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