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Neural network learning defines glioblastoma features to be of neural crest perivascular or radial glia lineages

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...

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Autores principales: Hu, Yizhou, Jiang, Yiwen, Behnan, Jinan, Ribeiro, Mariana Messias, Kalantzi, Chrysoula, Zhang, Ming-Dong, Lou, Daohua, Häring, Martin, Sharma, Nilesh, Okawa, Satoshi, Del Sol, Antonio, Adameyko, Igor, Svensson, Mikael, Persson, Oscar, Ernfors, Patrik
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Association for the Advancement of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9177076/
https://www.ncbi.nlm.nih.gov/pubmed/35675414
http://dx.doi.org/10.1126/sciadv.abm6340
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author Hu, Yizhou
Jiang, Yiwen
Behnan, Jinan
Ribeiro, Mariana Messias
Kalantzi, Chrysoula
Zhang, Ming-Dong
Lou, Daohua
Häring, Martin
Sharma, Nilesh
Okawa, Satoshi
Del Sol, Antonio
Adameyko, Igor
Svensson, Mikael
Persson, Oscar
Ernfors, Patrik
author_facet Hu, Yizhou
Jiang, Yiwen
Behnan, Jinan
Ribeiro, Mariana Messias
Kalantzi, Chrysoula
Zhang, Ming-Dong
Lou, Daohua
Häring, Martin
Sharma, Nilesh
Okawa, Satoshi
Del Sol, Antonio
Adameyko, Igor
Svensson, Mikael
Persson, Oscar
Ernfors, Patrik
author_sort Hu, Yizhou
collection PubMed
description 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.
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spelling pubmed-91770762022-06-17 Neural network learning defines glioblastoma features to be of neural crest perivascular or radial glia lineages Hu, Yizhou Jiang, Yiwen Behnan, Jinan Ribeiro, Mariana Messias Kalantzi, Chrysoula Zhang, Ming-Dong Lou, Daohua Häring, Martin Sharma, Nilesh Okawa, Satoshi Del Sol, Antonio Adameyko, Igor Svensson, Mikael Persson, Oscar Ernfors, Patrik Sci Adv Biomedicine and Life Sciences 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. American Association for the Advancement of Science 2022-06-08 /pmc/articles/PMC9177076/ /pubmed/35675414 http://dx.doi.org/10.1126/sciadv.abm6340 Text en Copyright © 2022 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC). https://creativecommons.org/licenses/by-nc/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license (https://creativecommons.org/licenses/by-nc/4.0/) , which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited.
spellingShingle Biomedicine and Life Sciences
Hu, Yizhou
Jiang, Yiwen
Behnan, Jinan
Ribeiro, Mariana Messias
Kalantzi, Chrysoula
Zhang, Ming-Dong
Lou, Daohua
Häring, Martin
Sharma, Nilesh
Okawa, Satoshi
Del Sol, Antonio
Adameyko, Igor
Svensson, Mikael
Persson, Oscar
Ernfors, Patrik
Neural network learning defines glioblastoma features to be of neural crest perivascular or radial glia lineages
title Neural network learning defines glioblastoma features to be of neural crest perivascular or radial glia lineages
title_full Neural network learning defines glioblastoma features to be of neural crest perivascular or radial glia lineages
title_fullStr Neural network learning defines glioblastoma features to be of neural crest perivascular or radial glia lineages
title_full_unstemmed Neural network learning defines glioblastoma features to be of neural crest perivascular or radial glia lineages
title_short Neural network learning defines glioblastoma features to be of neural crest perivascular or radial glia lineages
title_sort neural network learning defines glioblastoma features to be of neural crest perivascular or radial glia lineages
topic Biomedicine and Life Sciences
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9177076/
https://www.ncbi.nlm.nih.gov/pubmed/35675414
http://dx.doi.org/10.1126/sciadv.abm6340
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