Triglyceride metabolism controls inflammation and microglial phenotypes associated with APOE4 [RNA-seq]
RNA-seq (gene expression) · human · 26 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
- 26 samples: 8 conditions, mostly in triplicateGroups 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
- Triglyceride metabolism controls inflammation and microglial phenotypes associated with APOE4Cell reports · 2025
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 GSE286874_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 PRJNA1210469 and SRA study SRP557196. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 26 samples.
How we read this: 8 replicated groups read from 26 sample titles; they account for 26 of them. 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.
- GSM8730648 APOE3_DGAT_1_2_inh_LPS_REP1 APOE3_DGAT_1_2_inh_LPS
- GSM8730649 APOE3_DGAT_1_2_inh_LPS_REP2 APOE3_DGAT_1_2_inh_LPS
- GSM8730650 APOE3_DGAT_1_2_inh_LPS_REP3 APOE3_DGAT_1_2_inh_LPS
- GSM8730651 APOE3_DGAT_1_2_inh_REP1 APOE3_DGAT_1_2_inh
- GSM8730652 APOE3_DGAT_1_2_inh_REP2 APOE3_DGAT_1_2_inh
- GSM8730653 APOE3_DGAT_1_2_inh_REP3 APOE3_DGAT_1_2_inh
- GSM8730654 APOE3_LPS_REP1 APOE3_LPS
- GSM8730655 APOE3_LPS_REP2 APOE3_LPS
- GSM8730656 APOE3_LPS_REP3 APOE3_LPS
- GSM8730657 APOE3_Untreated_REP1 APOE3_Untreated
- GSM8730658 APOE3_Untreated_REP2 APOE3_Untreated
- GSM8730659 APOE3_Untreated_REP3 APOE3_Untreated
- GSM8730660 APOE4_DGAT_1_2_inh_LPS_REP1 APOE4_DGAT_1_2_inh_LPS
- GSM8730661 APOE4_DGAT_1_2_inh_LPS_REP2 APOE4_DGAT_1_2_inh_LPS
- GSM8730662 APOE4_DGAT_1_2_inh_LPS_REP3 APOE4_DGAT_1_2_inh_LPS
- GSM8730663 APOE4_DGAT_1_2_inh_REP1 APOE4_DGAT_1_2_inh
- GSM8730664 APOE4_DGAT_1_2_inh_REP2 APOE4_DGAT_1_2_inh
- GSM8730665 APOE4_DGAT_1_2_inh_REP3 APOE4_DGAT_1_2_inh
- GSM8730666 APOE4_DGAT_1_2_inh_REP4 APOE4_DGAT_1_2_inh
- GSM8730667 APOE4_LPS_REP1 APOE4_LPS
- GSM8730668 APOE4_LPS_REP2 APOE4_LPS
- GSM8730669 APOE4_LPS_REP3 APOE4_LPS
- GSM8730670 APOE4_LPS_REP4 APOE4_LPS
- GSM8730671 APOE4_untreated_REP1 APOE4_untreated
- GSM8730672 APOE4_untreated_REP2 APOE4_untreated
- GSM8730673 APOE4_untreated_REP3 APOE4_untreated
- GSE274868 Astrocyte priming enhances microglial Aβ clearance and is compromised by APOE4 36 samples shares apoe4, microglial
- GSE319130 Multi-omic analysis reveals molecular subtypes with distinct immunological phenotypes in SLE [RNA-Seq] 804 samples shares phenotypes, rna-seq
- GSE287297 Fusion oncoproteins and cooperating mutations define disease phenotypes in NUP98-rearranged leukemia [RNA-seq] 70 samples shares phenotypes, rna-seq
- GSE310672 Progesterone receptors drive advanced breast cancer phenotypes including circulating tumor- and stem-like cell expansion in the context of ESR1 mutation [RNA-seq] 48 samples shares phenotypes, rna-seq
- GSE297560 Space radiation induces distinct senescent phenotypes: Implications for space travel [bulk RNA-Seq] 21 samples shares phenotypes, rna-seq
- GSE293119 Integration of Alzheimer’s GWAS, 3D genomics, and single-cell CRISPRi non-coding screen implicates causal variants in a microglial enhancer regulating TSPAN14. [RNA-Seq] 18 samples shares microglial, rna-seq
- GSE333448 A roadmap for enriching rare cell populations from human post-mortem brain, demonstrated by 100% microglial purity (scRNA-Seq) 18 samples shares microglial, rna-seq
- GSE299831 KOLF2.1J iTF-Microglia: A standardized platform to study microglial transcriptional regulatory networks in CNS disease [RNA-seq] 12 samples shares microglial, rna-seq
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.