Astrocyte-derived cholesterol drives synaptic gene expression in developing neurons and reciprocal astrocytic transcriptional programs
RNA-seq (gene expression) · human · 416 samples
- What it is
- RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
- Organism
- Homo sapiens (human)
- Samples
- 416 samples: 416 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.
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 GSE289420_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: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.
Also filed as BioProject PRJNA1222887 and SRA study SRP563483. 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 the first 400 of 416 sample titles: 400 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.
- GSM8790755 1_HKM7VBGXT.1.TACGCTGC_ATAGAGAG
- GSM8790756 2_HKM7VBGXT.2.TACGCTGC_ATAGAGAG
- GSM8790757 3_HKM7VBGXT.3.TACGCTGC_ATAGAGAG
- GSM8790758 4_HKM7VBGXT.4.TACGCTGC_ATAGAGAG
- GSM8790759 1_HKM7VBGXT.1.ATGCGCAG_ATAGAGAG
- GSM8790760 2_HKM7VBGXT.2.ATGCGCAG_ATAGAGAG
- GSM8790761 3_HKM7VBGXT.3.ATGCGCAG_ATAGAGAG
- GSM8790762 4_HKM7VBGXT.4.ATGCGCAG_ATAGAGAG
- GSM8790763 1_HKM7VBGXT.1.TAGCGCTC_ATAGAGAG
- GSM8790764 2_HKM7VBGXT.2.TAGCGCTC_ATAGAGAG
- GSM8790765 3_HKM7VBGXT.3.TAGCGCTC_ATAGAGAG
- GSM8790766 4_HKM7VBGXT.4.TAGCGCTC_ATAGAGAG
- GSM8790767 1_HKM7VBGXT.1.ACTGAGCG_ATAGAGAG
- GSM8790768 2_HKM7VBGXT.2.ACTGAGCG_ATAGAGAG
- GSM8790769 3_HKM7VBGXT.3.ACTGAGCG_ATAGAGAG
- GSM8790770 4_HKM7VBGXT.4.ACTGAGCG_ATAGAGAG
- GSM8790771 1_HKM7VBGXT.1.CCTAAGAC_ATAGAGAG
- GSM8790772 2_HKM7VBGXT.2.CCTAAGAC_ATAGAGAG
- GSM8790773 3_HKM7VBGXT.3.CCTAAGAC_ATAGAGAG
- GSM8790774 4_HKM7VBGXT.4.CCTAAGAC_ATAGAGAG
- GSM8790775 1_HKM7VBGXT.1.CGATCAGT_ATAGAGAG
- GSM8790776 2_HKM7VBGXT.2.CGATCAGT_ATAGAGAG
- GSM8790777 3_HKM7VBGXT.3.CGATCAGT_ATAGAGAG
- GSM8790778 4_HKM7VBGXT.4.CGATCAGT_ATAGAGAG
- GSM8790779 1_HKM7VBGXT.1.TGCAGCTA_ATAGAGAG
- GSM8790780 2_HKM7VBGXT.2.TGCAGCTA_ATAGAGAG
- GSM8790781 3_HKM7VBGXT.3.TGCAGCTA_ATAGAGAG
- GSM8790782 4_HKM7VBGXT.4.TGCAGCTA_ATAGAGAG
- GSM8790783 1_HKM7VBGXT.1.TCGACGTC_ATAGAGAG
- GSM8790784 2_HKM7VBGXT.2.TCGACGTC_ATAGAGAG
- GSM8790785 3_HKM7VBGXT.3.TCGACGTC_ATAGAGAG
- GSM8790786 4_HKM7VBGXT.4.TCGACGTC_ATAGAGAG
- GSM8790787 1_HKM7VBGXT.1.ACTCGCTA_AGAGGATA
- GSM8790788 2_HKM7VBGXT.2.ACTCGCTA_AGAGGATA
- GSM8790789 3_HKM7VBGXT.3.ACTCGCTA_AGAGGATA
- GSM8790790 4_HKM7VBGXT.4.ACTCGCTA_AGAGGATA
- GSM8790791 1_HKM7VBGXT.1.GGAGCTAC_AGAGGATA
- GSM8790792 2_HKM7VBGXT.2.GGAGCTAC_AGAGGATA
- GSM8790793 3_HKM7VBGXT.3.GGAGCTAC_AGAGGATA
- GSM8790794 4_HKM7VBGXT.4.GGAGCTAC_AGAGGATA
+ 376 more — browse all 416 samples with per-sample file links →
- GSE287688 Sex-specific KDM6A-HNF4A-CREBH network controls lipoprotein cholesterol metabolism and atherosclerosis via epigenetic reprograming of hepatocytes [RNA-seq] 68 samples shares cholesterol
- GSE330327 Developing a pan cancer therapy based on DISE inducing short RNAs 17 samples shares developing
- GSE264145 Inhibition of DUSP18 impairs cholesterol biosynthesis and promotes anti-tumor immunity in colorectal cancer 12 samples shares cholesterol
- GSE287736 Sex-specific KDM6A-HNF4A-CREBH network controls lipoprotein cholesterol metabolism and atherosclerosis via epigenetic reprograming of hepatocytes 138 samples shares cholesterol
- GSE287355 An EZH2—SREBP2 axis promotes cholesterol biosynthesis and represents a noncanonical vulnerability in tumorigenesis 79 samples shares cholesterol
- GSE282436 Development of a potent epigenetic editor targeting human PCSK9 with durable reduction of cholesterol in mice and non-human primates [hybrid capture methylseq] 66 samples shares cholesterol
- GSE243871 Single-Cell multi-omics reveals disrupted gene regulatory landscape and cell differentiation by Wilms tumor-associated ENL mutation in the developing kidney 28 samples shares developing
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.