{"database":"biostudies-arrayexpress","file_versions":[],"scores":null,"additional":{"submitter":["Oliver Gould"],"organism":["Homo sapiens"],"software":["Cell Ranger ARC (Software Suite)"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/E-MTAB-17183"],"description":["Cancer cells display heterogeneous and dynamic states in glioblastoma, but how these malignant states arise and whether they follow a tractable cellular trajectory across tumours is poorly understood. Here, we generate a deep single cell and spatial multi-region atlas of 12 isocitrate dehydrogenase wild-type (IDH-wt) primary glioblastomas that integrates transcriptomic, epigenomic and genomic analysis to comprehensively characterise their tumour heterogeneity. This submission contains the Cell Ranger ARC processed outputs from single nuclei joint transcriptome- and chromatin accessibility-sequencing (10x Genomics). We also provide an integrated single nuclei transcriptomics dataset, comprised of malignant and tumour microenvironment cell type annotations."],"repository":["biostudies-arrayexpress"],"sample_protocol":["Sequencing - Libraries were paired-end sequenced on a NovaSeq 6000 System (Illumina) using the NovaSeq S4 Flowcell, targeting a minimum coverage of 100,000 read pairs per nucleus per modality. The following sequencing formats apply to GEX and ATAC libraries:  GEX: Read 1: 28 cycles, i7 Index: 10 cycles, i5 Index: 10 cycles, Read 2: 90 cycles.   ATAC: Read 1N: 50 cycles, i7 Index: 8 cycles, i5 Index: 24 cycles, Read 2N: 49 cycles.","Nucleic Acid Extraction - A series of 50 µm thick sections, totalling 500 to 900 µm thick volume depending on the size of each tumour block, were collected in pre-chilled homogenization glass tubes and kept on dry ice until processing.  Nuclei were then extracted from fresh frozen tissue sections that were homogenised using a glass Dounce homogenizer (Sigma) in nuclei isolation buffer (3mM MgCl2, 10mM NaCl, 10mM Tris (buffer pH7.4), 1 mM DTT, 0.1% Tween-20, 0.1% Nonidet P40, 1% BSA and 0.01% Digitonin) in the presence of Protector RNase Inhibitor (Roche) at 0.2 U/μl. Tissue was homogenised using 10 strokes with pestle A and then 10 strokes with pestle B. Nuclei were then filtered through a 40 μM filter, collected at 500 RCF and resuspended in 0.25 ml of storage buffer (PBS containing 1% BSA and Protector RNase Inhibitor (Roche) 1 U/μl). An aliquot of the nuclei suspension was incubated with Trypan Blue (Gibco 15250061) for counting and purified from debris using a Percoll gradient. The cleaned nuclei suspension was stained with Trypan blue and counted.","Sample Collection - Patients with suspected GB were identified pre-operatively and consented for entry into the study. Surgery was performed at Cambridge University Hospitals NHS Foundation Trust. Written and informed consent was obtained in accordance with the guidelines in The Declaration of Helsinki 2000. Ethical approval for the use of these tissues was obtained from the Cambridge Local Research Ethics Committee (REC 18/EE/0172). All patients underwent 5-ALA guided tumour resection as per local protocols. During tumour debulking, regions of high fluorescence were identified, their spatial location recorded and the tissue samples were collected for this study.  Tissue was sampled from multiple sites of each GB tumour, targeting superior, anterior, posterior and middle regions where possible. Each tissue sample was immediately washed in saline buffer and embedded in OCT medium (Scigen OCT Compound, #4586) using a dry ice-cooled bath of isopentane at −75 °C. OCT-embedded samples were sectioned using a cryostat (Leica CX3050S).  The fresh frozen tissue blocks were trimmed until the tissue surface was fully exposed. Two to three 10 µm thick sections were collected to check RNA integrity and another section was processed for Haematoxylin and Eosin (H&E) staining to assess tissue morphology. The RNA quality of each sample was evaluated by Tapestation (Tapestation RNA ScreenTape, Agilent) after isolating RNA (Qiagen EZ2 Automated RNA Extraction using EZ2 RNA/miRNA Tissue Kit Cat. No.959035). Only samples with RNA integrity number (RIN) values >7 were used for omic profiling.","Library Construction - Two to three 10x reactions were prepared per tumour site and loaded onto the 10X chromium controller according to the manufacturer's protocol for the Chromium Next GEM Single Cell Multiome ATAC + Gene Expression assay (CG000338, 10x Genomics). Post-GEM-RT cleanup, cDNA amplification, 3′ gene expression library construction and ATAC library construction were carried out as per the Chromium Next GEM Single Cell Multiome ATAC + Gene Expression User Guide (CG000338, 10x Genomics), targeting 5,000-10,000 nuclei per reaction."],"figure_sub":["MIAME Score","Organization","Assays and Data","Processed Data","MAGE-TAB Files"],"data_protocol":["Data Transformation - Single nuclei RNA-seq and ATAC-seq reads were aligned to a combined reference consisting of the 10x Genomics GRCh38 3.0.0 pre-mRNA reference genome and the Cell Ranger ARC 2.0.1 ATAC genome using the default parameters in the Cell Ranger ARC software (v2.0.1, 10x Genomics).   The resulting per-nucleus gene expression count matrices were subject to additional processing, including ambient RNA correction with CellBender (v0.2.0), quality control filtering with Scanpy (v1.9.3), and doublet detection with Scrublet (v0.2.3). ATAC fragment files were additionally processed according to the ArchR workflow. Barcode sets were then cross-filtered between modalities to ensure symmetry for downstream analysis."],"omics_type":["Unknown","Transcriptomics","Genomics","Proteomics"],"instrument_platform":["Illumina NovaSeq 6000"],"pubmed_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."],"study_type":["single nucleus RNA sequencing"],"species":["Homo sapiens"],"pubmed_title":["A spatiotemporal cancer cell trajectory underlies glioblastoma heterogeneity"],"pubmed_authors":["Oliver Gould","Grant de Jong, Fani Memi, Tannia Gracia, Olga Lazareva, Oliver Gould, Georgios Solomou, Alexander Aivazidis, Manas Dave, Qianqian Zhang, Melanie Jensen, Ahmet Sureyya Rifaioglu, Joao D. Barros-Silva, Sabine Eckert, Di Zhou, Yvette Wood, Elizabeth Tuck, Sezgin Er, Koen Rademaker, Robert Petryszak, Henry Marshall, Kenny Roberts, Andrew L. Trinh, Shreya Rai, Tyler Shaw, Agnes Oszlanczi, Hayden Powell, Pasha Mazin, Stanislaw Makarchuk, Yinshui Chang, Katarzyna Kania, Liying Jin, Katy Tudor, Hon Man Chan, Harriet Johnson, Benjamin Rumney, Holly Anderson, Jasmine Halliwell, Vanessa Brito Pereira, Zoi Katsirea, Irfan Mamun, Ilaria Mulas, Annelies Quaegebeur, Mayen Briggs, Jessica Cox, Jimmy Tsz Hang Lee, Laura Rueda-Gensini, Manu Saraswat, Zaira Seferbevoka, Adam Young, Minal Patel, Tarryn Porter, Moritz Gerstung, Martin Prete, Elena Prigmore, Moritz Mall, Harry Bulstrode, Julio Saez-Rodriguez, James Briscoe, David H. Rowitch, Richard Mair, Sam Behjati, Oliver Stegle, Omer Ali Bayraktar","Fani Memi","Grant de Jong","Tannia Gracia","Tong Li","Omer Bayraktar"],"additional_accession":[]},"is_claimable":false,"name":"GBM-Space: Joint Transcriptome and Chromatin Accessibility Profiling of Glioblastoma (10x Genomics - Multiome)","description":"Cancer cells display heterogeneous and dynamic states in glioblastoma, but how these malignant states arise and whether they follow a tractable cellular trajectory across tumours is poorly understood. Here, we generate a deep single cell and spatial multi-region atlas of 12 isocitrate dehydrogenase wild-type (IDH-wt) primary glioblastomas that integrates transcriptomic, epigenomic and genomic analysis to comprehensively characterise their tumour heterogeneity. This submission contains the Cell Ranger ARC processed outputs from single nuclei joint transcriptome- and chromatin accessibility-sequencing (10x Genomics). We also provide an integrated single nuclei transcriptomics dataset, comprised of malignant and tumour microenvironment cell type annotations.","dates":{"release":"2026-06-12T00:00:00Z","modification":"2026-06-20T06:12:32.143Z","creation":"2026-06-19T09:03:32.759Z"},"accession":"E-MTAB-17183","cross_references":{"EGA":["EGAS00001008083","EGAD00001015526","EGAS00001005800"],"EFO":["EFO_0002944","EFO_0004170","EFO_0009809","EFO_0005518","EFO_0003816","EFO_0004184"],"doi":["10.1101/2025.05.13.653495"]}}