Multi-omics approaches reveal that Diffuse Midline Gliomas are susceptible to replication stress therapy and cholesterol biosynthesis inhibition [RNA-seq]
Single-cell RNA-seq · human · 64 samples
- What it is
- Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
- Organism
- Homo sapiens (human)
- Samples
- 64 samples: 64 samples, each with a distinct title — no replicated groupsGroups are read from the sample names, not from the paper. Check the list below.
- Data you can get
- A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
- Paper
- Multi-omics approaches reveal that diffuse midline gliomas present altered DNA replication and are susceptible to replication stress therapyGenome biology · 2024
Read the full summary ↓
Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.
Processed table
The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.
Supplementary files
The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE268776_RAW.tar (it can run to tens of gigabytes).
Raw sequencing reads
The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.
What the files usually look like: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.
Also filed as BioProject PRJNA1118728 and SRA study SRP511038. Searching any of these in the dataset finder brings you back here.
Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.
How we read this: Read from 64 sample titles: 64 distinct titles with little repetition. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.
- GSM8298899 HA-bs G1, rep1 (RNA-seq)
- GSM8298900 HA-bs G1, rep2 (RNA-seq)
- GSM8298901 HA-bs ES, rep1 (RNA-seq)
- GSM8298902 HA-bs ES, rep2 (RNA-seq)
- GSM8298903 HA-bs LS, rep1 (RNA-seq)
- GSM8298904 HA-bs LS, rep2 (RNA-seq)
- GSM8298905 HA-bs G2, rep1 (RNA-seq)
- GSM8298906 HA-bs G2, rep2 (RNA-seq)
- GSM8298907 NHA G1, rep1 (RNA-seq)
- GSM8298908 NHA G1, rep2 (RNA-seq)
- GSM8298909 NHA ES, rep1 (RNA-seq)
- GSM8298910 NHA ES, rep2 (RNA-seq)
- GSM8298911 NHA LS, rep1 (RNA-seq)
- GSM8298912 NHA LS, rep2 (RNA-seq)
- GSM8298913 NHA G2, rep1 (RNA-seq)
- GSM8298914 NHA G2, rep2 (RNA-seq)
- GSM8298915 DIPG4 G1, rep1 (RNA-seq)
- GSM8298916 DIPG4 G1, rep2 (RNA-seq)
- GSM8298917 DIPG4 ES, rep1 (RNA-seq)
- GSM8298918 DIPG4 ES, rep2 (RNA-seq)
- GSM8298919 DIPG4 LS, rep1 (RNA-seq)
- GSM8298920 DIPG4 LS, rep2 (RNA-seq)
- GSM8298921 DIPG4 G2, rep1 (RNA-seq)
- GSM8298922 DIPG4 G2, rep2 (RNA-seq)
- GSM8298923 DIPG19, G1, rep1 (RNA-seq)
- GSM8298924 DIPG19, G1, rep2 (RNA-seq)
- GSM8298925 DIPG19 ES, rep1 (RNA-seq)
- GSM8298926 DIPG19 ES, rep2 (RNA-seq)
- GSM8298927 DIPG19 LS, rep1 (RNA-seq)
- GSM8298928 DIPG19 LS, rep2 (RNA-seq)
- GSM8298929 DIPG19 G2, rep1 (RNA-seq)
- GSM8298930 DIPG19 G2, rep2 (RNA-seq)
- GSM8298931 DIPG21 G1, rep1 (RNA-seq)
- GSM8298932 DIPG21 G1, rep2 (RNA-seq)
- GSM8298933 DIPG21 ES, rep1 (RNA-seq)
- GSM8298934 DIPG21 ES, rep2 (RNA-seq)
- GSM8298935 DIPG21 LS, rep1 (RNA-seq)
- GSM8298936 DIPG21 LS, rep2 (RNA-seq)
- GSM8298937 DIPG21 G2, rep1 (RNA-seq)
- GSM8298938 DIPG21 G2, rep2 (RNA-seq)
+ 24 more — browse all 64 samples with per-sample file links →
- GSE325471 The CDK4/6 inhibitor dalpiciclib augments the antitumor efficacy of enzalutamide in preclinical models of castration-resistant prostate cancer through inhibition of MCM4-mediated DNA replication 12 samples shares replication, inhibition
- GSE262761 PIN1 isomerase promotes the initiation and progression of bladder cancer through SREBP2-mediated cholesterol biosynthesis pathway 12 samples shares biosynthesis, cholesterol
- GSE256353 Upregulated cholesterol biosynthesis facilitates the survival of methylation-retaining AML cells following decitabine treatment [single_colony_TEM-Seq] 1728 samples shares biosynthesis, cholesterol
- GSE319092 Integrated Multi-Omics and Interactome Analysis of CDK8 Inhibition Reveals Erythroid Differentiation Programs and BET Synergy in AML Stem-like Cells 135 samples shares multi-omics, inhibition
- GSE271689 Enhancing Immunotherapy Outcomes: Spatial Multi-Omics Predictive Models for Non-Small Cell Lung Cancer 586 samples shares multi-omics
- GSE263083 Multi-omics data analysis for the JGOG3025-TR2 ovarian cancer cohort [RNA-seq] 291 samples shares multi-omics
- GSE260603 Dissecting depression symptoms: multi-omics clustering uncovers immune-related subgroups and cell-type specific dysregulation 273 samples shares multi-omics
- GSE325054 Time-Resolved Multi-Omics Identify Biomarkers of Immediate Reactions to mRNA Vaccination [RNA-Seq] 187 samples shares multi-omics
Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.