Neural network learning defines glioblastoma features to be of neural crest perivascular or radial glia lineages.
Yizhou HuYiwen JiangJinan BehnanMariana Messias RibeiroChrysoula KalantziMing-Dong ZhangDaohua LouMartin HäringNilesh SharmaSatoshi OkawaAntonio Del SolIgor AdameykoMikael SvenssonOscar PerssonPatrik ErnforsPublished in: Science advances (2022)
Glioblastoma is believed to originate from nervous system cells; however, a putative origin from vessel-associated progenitor cells has not been considered. We deeply single-cell RNA-sequenced glioblastoma progenitor cells of 18 patients and integrated 710 bulk tumors and 73,495 glioma single cells of 100 patients to determine the relation of glioblastoma cells to normal brain cell types. A novel neural network-based projection of the developmental trajectory of normal brain cells uncovered two principal cell-lineage features of glioblastoma, neural crest perivascular and radial glia, carrying defining methylation patterns and survival differences. Consistently, introducing tumorigenic alterations in naïve human brain perivascular cells resulted in brain tumors. Thus, our results suggest that glioblastoma can arise from the brains' vasculature, and patients with such glioblastoma have a significantly poorer outcome.
Keyphrases
- induced apoptosis
- single cell
- cell cycle arrest
- neural network
- end stage renal disease
- newly diagnosed
- chronic kidney disease
- ejection fraction
- stem cells
- white matter
- magnetic resonance imaging
- cell death
- multiple sclerosis
- rna seq
- functional connectivity
- resting state
- patient reported outcomes
- peritoneal dialysis
- brain injury
- bone marrow
- subarachnoid hemorrhage
- patient reported