A spatiotemporal cancer cell trajectory underlies glioblastoma heterogeneity

Abstract Cancer cells display highly heterogeneous and plastic states in glioblastoma, an incurable brain tumour. However, how these malignant states arise and whether they follow defined cellular trajectories across tumours is poorly understood. Here, we generated a deep single cell and spatial multi-omic atlas of human glioblastoma that pairs transcriptomic, epigenomic and genomic profiling of 12 tumours across multiple regions. We identify that glioblastoma heterogeneity is driven by spatially-patterned transitions of cancer cells from developmental-like states towards those defined by a glial injury response and hypoxia. This cellular trajectory regionalises tumours into distinct tissue niches and manifests in a molecularly conserved manner across tumours as well as genetically distinct tumour subclones. Moreover, using a new deep learning framework to map cancer cell states jointly with clones in situ , we show that tumour subclones are finely spatially intermixed through glioblastoma tissue niches. Finally, we show that this cancer cell trajectory is intimately linked to myeloid heterogeneity and unfolds across regionalised myeloid signalling environments. Our findings define a stereotyped trajectory of cancer cells in glioblastoma and unify glioblastoma tumour heterogeneity into a tractable cellular and tissue framework.

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Publication Details

Journal
Nature
Published
2026-11-04
DOI
https://doi.org/10.1038/s41586-026-11234-8
Citations
8
Primary Topic
Single-cell and spatial transcriptomics
Type
article
Field-Weighted Citation Impact
8.52

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article

A spatiotemporal cancer cell trajectory underlies glioblastoma heterogeneity

Julio Sáez-Rodríguez, Pavel Mazin, Irfan Mamun, Omer Ali Bayraktar et al.
8 citations
Nature
Single-cell and spatial transcriptomics
8.52
article

A spatiotemporal cancer cell trajectory underlies glioblastoma heterogeneity

Julio Sáez-Rodríguez, Pavel Mazin, Irfan Mamun, Omer Ali Bayraktar, Olga Lazareva, Alexander Aivazidis, Harriet F. Johnson, Catherine L. Tudor, Tannia Gracia, James Briscoe, Minal Patel, Moritz Mall, Manu Saraswat, Yinshui Chang, Fani Memi, Annelies Quaegebeur, Robert Petryszak, Stanislaw Makarchuk, Tarryn Porter, Holly E. Anderson, Elena Prigmore, Ágnes Oszlánczi, David H. Rowitch, Sezgin Er, Kenny S. Roberts, Katarzyna Kania, Manàs Dave, Mayen Briggs, Jimmy Tsz Hang Lee, Laura Rueda-Gensini, Sam Behjati, João Diogo Barros-Silva, Martin Prete, Ahmet Süreyya Rifaioğlu, Koen Rademaker, Georgios Solomou, Richard James Mair, Elizabeth J. Tuck, Oliver Stegle, Andrew L. Trinh, Ilaria Mulas, Jasmine Ellis, Hon Man Chan, Melanie Jensen, Oliver Gould, Grant de Jong, Zoi Katsirea, Di Zhou, Tyler Shaw, Shreya Rai, Henry Marshall, Liying Jin, Adam Young, Hayden Powell, Yvette Wood, Moritz Gerstung, Jessica Cox, Qianqian Zhang, Benjamin Rumney, Vanessa Brito Pereira, Zaira Seferbevoka, Harry Bulstrode, Sabine Eckert
article en
8 citations

Abstract

Abstract Cancer cells display highly heterogeneous and plastic states in glioblastoma, an incurable brain tumour. However, how these malignant states arise and whether they follow defined cellular trajectories across tumours is poorly understood. Here, we generated a deep single cell and spatial multi-omic atlas of human glioblastoma that pairs transcriptomic, epigenomic and genomic profiling of 12 tumours across multiple regions. We identify that glioblastoma heterogeneity is driven by spatially-patterned transitions of cancer cells from developmental-like states towards those defined by a glial injury response and hypoxia. This cellular trajectory regionalises tumours into distinct tissue niches and manifests in a molecularly conserved manner across tumours as well as genetically distinct tumour subclones. Moreover, using a new deep learning framework to map cancer cell states jointly with clones in situ , we show that tumour subclones are finely spatially intermixed through glioblastoma tissue niches. Finally, we show that this cancer cell trajectory is intimately linked to myeloid heterogeneity and unfolds across regionalised myeloid signalling environments. Our findings define a stereotyped trajectory of cancer cells in glioblastoma and unify glioblastoma tumour heterogeneity into a tractable cellular and tissue framework.

Nature
European Bioinformatics Institute (GB), German Cancer Research Center (DE), Wellcome/MRC Cambridge Stem Cell Institute (GB), Heidelberg University (DE), University of Cambridge (GB), The Francis Crick Institute (GB), Cancer Research UK (GB), University Hospital Heidelberg (DE), Cambridge University Hospitals NHS Foundation Trust (GB), Wellcome Sanger Institute (GB), Istanbul Medipol University (TR), Central Institute of Mental Health (DE), European Molecular Biology Laboratory (DE), DKFZ-ZMBH Alliance (DE), Medizinische Fakultät Mannheim (DE)
Wellcome Trust, National Institute for Health and Care Research
Good health and well-being
Openalex Percentile: Top 2%
Single-cell and spatial transcriptomics
8.52
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