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

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Abstract

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.

Abstract

Neural network defines GBM features to be of neural crest perivascular or radial glia lineage.

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Most cited references 60

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Comprehensive Integration of Single-Cell Data

Tim Stuart, Andrew Butler, Paul Hoffman … (2019)

Single-cell transcriptomics has transformed our ability to characterize cell states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, a key analytical challenge is to integrate these datasets to better understand cellular identity and function. Here, we develop a strategy to "anchor" diverse datasets together, enabling us to integrate single-cell measurements not only across scRNA-seq technologies, but also across different modalities. After demonstrating improvement over existing methods for integrating scRNA-seq data, we anchor scRNA-seq experiments with scATAC-seq to explore chromatin differences in closely related interneuron subsets and project protein expression measurements onto a bone marrow atlas to characterize lymphocyte populations. Lastly, we harmonize in situ gene expression and scRNA-seq datasets, allowing transcriptome-wide imputation of spatial gene expression patterns. Our work presents a strategy for the assembly of harmonized references and transfer of information across datasets.

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RNA velocity of single cells

Gioele La Manno, Ruslan Soldatov, Amit Zeisel … (2018)

RNA abundance is a powerful indicator of the state of individual cells. Single-cell RNA sequencing can reveal RNA abundance with high quantitative accuracy, sensitivity and throughput1. However, this approach captures only a static snapshot at a point in time, posing a challenge for the analysis of time-resolved phenomena, such as embryogenesis or tissue regeneration. Here we show that RNA velocity—the time derivative of the gene expression state—can be directly estimated by distinguishing unspliced and spliced mRNAs in common single-cell RNA sequencing protocols. RNA velocity is a high-dimensional vector that predicts the future state of individual cells on a timescale of hours. We validate its accuracy in the neural crest lineage, demonstrate its use on multiple published datasets and technical platforms, reveal the branching lineage tree of the developing mouse hippocampus, and examine the kinetics of transcription in human embryonic brain. We expect RNA velocity to greatly aid the analysis of developmental lineages and cellular dynamics, particularly in humans.

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The basics of epithelial-mesenchymal transition.

Raghu Kalluri, Robert Weinberg (2009)

The origins of the mesenchymal cells participating in tissue repair and pathological processes, notably tissue fibrosis, tumor invasiveness, and metastasis, are poorly understood. However, emerging evidence suggests that epithelial-mesenchymal transitions (EMTs) represent one important source of these cells. As we discuss here, processes similar to the EMTs associated with embryo implantation, embryogenesis, and organ development are appropriated and subverted by chronically inflamed tissues and neoplasias. The identification of the signaling pathways that lead to activation of EMT programs during these disease processes is providing new insights into the plasticity of cellular phenotypes and possible therapeutic interventions.

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Author and article information

Contributors

Yizhou Hu:

ORCID: https://orcid.org/0000-0002-2635-0258

Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: ResourcesRole: SoftwareRole: ValidationRole: VisualizationRole: Writing - original draftRole: Writing - review & editing

Yiwen Jiang:

ORCID: https://orcid.org/0000-0003-4517-2650

Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: ResourcesRole: ValidationRole: VisualizationRole: Writing - original draftRole: Writing - review & editing

Jinan Behnan:

ORCID: https://orcid.org/0000-0001-5780-0971

Role: ConceptualizationRole: InvestigationRole: MethodologyRole: ValidationRole: Writing - original draftRole: Writing - review & editing

Mariana Messias Ribeiro:

ORCID: https://orcid.org/0000-0003-4808-6643

Role: Formal analysisRole: SoftwareRole: ValidationRole: Writing - original draft

Chrysoula Kalantzi:

ORCID: https://orcid.org/0000-0002-3603-8073

Role: Investigation

Ming-Dong Zhang:

ORCID: https://orcid.org/0000-0002-6348-1994

Role: Investigation

Daohua Lou:

ORCID: https://orcid.org/0000-0001-7031-998X

Role: Investigation

Martin Häring:

ORCID: https://orcid.org/0000-0003-4949-0533

Role: InvestigationRole: Methodology

Nilesh Sharma:

ORCID: https://orcid.org/0000-0001-7777-3210

Role: InvestigationRole: Validation

Satoshi Okawa: Role: Formal analysisRole: Software

Antonio Del Sol:

ORCID: https://orcid.org/0000-0002-9926-617X

Role: ConceptualizationRole: Data curationRole: Formal analysisRole: SoftwareRole: Visualization

Igor Adameyko:

ORCID: https://orcid.org/0000-0001-5471-0356

Role: ConceptualizationRole: ResourcesRole: Writing - original draftRole: Writing - review & editing

Mikael Svensson:

ORCID: https://orcid.org/0000-0003-1179-7003

Role: ConceptualizationRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SupervisionRole: Validation

Oscar Persson:

ORCID: https://orcid.org/0000-0001-5906-7443

Role: ConceptualizationRole: MethodologyRole: ResourcesRole: Supervision

Patrik Ernfors:

ORCID: https://orcid.org/0000-0002-1140-3986

Role: ConceptualizationRole: Funding acquisitionRole: MethodologyRole: Project administrationRole: ResourcesRole: SupervisionRole: ValidationRole: VisualizationRole: Writing - original draftRole: Writing - review & editing

Journal

Journal ID (nlm-ta): Sci Adv

Journal ID (iso-abbrev): Sci Adv

Journal ID (publisher-id): sciadv

Journal ID (hwp): advances

Title: Science Advances

Publisher: American Association for the Advancement of Science

ISSN (Electronic): 2375-2548

Publication date Collection: June 2022

Publication date (Electronic, pub): 08 June 2022

Volume: 8

Issue: 23

Electronic Location Identifier: eabm6340

Affiliations

[1 ]Division of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden.

[2 ]Computational Biology Group, Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 4362 Esch-sur-Alzette, Luxembourg.

[3 ]CIC bioGUNE, Bizkaia Technology Park, 48160 Derio, Spain.

[4 ]IKERBASQUE, Basque Foundation for Science, 48013 Bilbao, Spain.

[5 ]Department of Molecular Neurosciences, Center for Brain Research, Medical University Vienna, Vienna, Austria.

[6 ]Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden.

[7 ]Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.

[8 ]Department of Neurosurgery, Karolinska University Hospital, Stockholm, Sweden.

Author notes

[* ]Corresponding author. Email: patrik.ernfors@ 123456ki.se

[†]

These authors contributed equally to this work.

Author information

Yizhou Hu https://orcid.org/0000-0002-2635-0258

Yiwen Jiang https://orcid.org/0000-0003-4517-2650

Jinan Behnan https://orcid.org/0000-0001-5780-0971

Mariana Messias Ribeiro https://orcid.org/0000-0003-4808-6643

Chrysoula Kalantzi https://orcid.org/0000-0002-3603-8073

Ming-Dong Zhang https://orcid.org/0000-0002-6348-1994

Daohua Lou https://orcid.org/0000-0001-7031-998X

Martin Häring https://orcid.org/0000-0003-4949-0533

Nilesh Sharma https://orcid.org/0000-0001-7777-3210

Antonio Del Sol https://orcid.org/0000-0002-9926-617X

Igor Adameyko https://orcid.org/0000-0001-5471-0356

Mikael Svensson https://orcid.org/0000-0003-1179-7003

Oscar Persson https://orcid.org/0000-0001-5906-7443

Patrik Ernfors https://orcid.org/0000-0002-1140-3986

Article

Publisher ID: abm6340

DOI: 10.1126/sciadv.abm6340

PMC ID: 9177076

PubMed ID: 35675414

SO-VID: 1a37d16f-38dd-421a-b3c5-88559f0f3513

Copyright © 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).

License:

This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license, 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.

History

Date received : 30 September 2021

Date accepted : 20 April 2022

Funding

Funded by: FundRef http://dx.doi.org/10.13039/501100003748, Svenska Sällskapet för Medicinsk Forskning;

Award ID: 18 0635

Funded by: FundRef http://dx.doi.org/10.13039/501100003748, Svenska Sällskapet för Medicinsk Forskning;

Funded by: Swedish Cancer Society;

Award ID: 18 0635

Funded by: Swedish Society for Medical Research (SSMF);

Funded by: Swedish Medical Research Council;

Funded by: Knut and Alice Wallenbergs Foundation;

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Copyeditor Mjoy Azul

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

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Multimodal Learning Material

Most cited references 60

Comprehensive Integration of Single-Cell Data

RNA velocity of single cells

The basics of epithelial-mesenchymal transition.

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Cited by 14

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