CD36-mediated lipid-accumulation in reactive microglia drives retinal degeneration through Nlrp3-IL-1β pathway
RNA-seq (gene expression) · human · 18 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
- 18 samples: 6 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.
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 GSE333417_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 PRJNA1471047 and SRA study SRP704054. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 18 samples.
How we read this: 6 replicated groups read from 18 sample titles; they account for 18 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.
- GSM9764217 NC_1 NC
- GSM9764218 NC_2 NC
- GSM9764219 NC_3 NC
- GSM9764220 A1_1 A1
- GSM9764221 A1_2 A1
- GSM9764222 A1_3 A1
- GSM9764223 A2.5_1 A2.5
- GSM9764224 A2.5_2 A2.5
- GSM9764225 A2.5_3 A2.5
- GSM9764226 A10_1 A10
- GSM9764227 A10_2 A10
- GSM9764228 A10_3 A10
- GSM9764229 A50_1 A50
- GSM9764230 A50_2 A50
- GSM9764231 A50_3 A50
- GSM9764232 A100_1 A100
- GSM9764233 A100_2 A100
- GSM9764234 A100_3 A100
- GSE281280 Microglia-RPE Cell Communication via the SPP1-ITGAV Axis Drives EMT and Retinal Degeneration in Dry AMD 6 samples shares degeneration, microglia, retinal, drives
- GSE115828 Integrated analysis of genetic variants regulating retinal transcriptome (GREx) identifies genes underlying age-related macular degeneration 523 samples shares degeneration, retinal
- GSE270418 Unraveling the web of nAMD: A transcriptomic analysis of age-related macular degeneration-associated cytokines in primary human retinal endothelial cells. 48 samples shares degeneration, retinal
- GSE289534 A hexamer tandem repeat RNA embedded within an SVA retrotransposon drives R-loop formation and neurodegeneration 41 samples shares degeneration, drives
- GSE284387 DNMT1 RFTS mutation alters RNA splicing and drives neurodegeneration via its RNA-binding function [RNA-Seq] 26 samples shares degeneration, drives
- GSE273766 Pathophysiology of microglial-driven neurodegeneration in Histiocytosis 24 samples shares degeneration, microglia
- GSE289628 EPAS1 induction drives myocardial degeneration in Desmoplakin-cardiomyopathy [RNA-seq] 16 samples shares degeneration, drives
- GSE327431 Halofantrine protects photoreceptors in multiple models of retinal degeneration [organoids] 15 samples shares degeneration, retinal
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.