← BioTransfer GEO Dataset Finder
GEO series

Interferon-γ signaling in the progression from early to end stage kidney disease in type 2 diabetes

GSE230848 Homo sapiens (human) RNA-seq (gene expression) 12 samples Submitted 2024/05/13 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 12 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
12 samples: 12 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
Interferon-γ induces combined pyroptotic angiopathy and APOL1 expression in human kidney diseaseCell reports · 2024
Summary (written by the submitters)
The effects of circulating pro-inflammatory factors on diabetic kidney disease (DKD) progression remain poorly defined. In this study, transcriptional and proteomic profiling of kidney tissue and blood samples from individuals with type 2 diabetes mellitus and early DKD was used to identify gene expression associated with kidney disease progression.
Read the full summary ↓
Interferon gamma (IFNG) was both identified as an upstream regulator of gene expression and detected in the serum of individuals who progressed. Individual IFNG activation scores (representing summary expression of 72 downstream signaling mediators) early in disease correlated with DKD progression over an average of 9.7 years. IFNG pathway inhibition reduced gene activity associated with DKD progression in both kidney organoids generated from human pluripotent stem cells and mouse DKD models. Thus, expression of genes associated with IFNG activity early in DKD predicted disease progression; individuals demonstrating IFNG activity may benefit from targeted therapeutics early in the disease course.
Published in
Interferon-γ induces combined pyroptotic angiopathy and APOL1 expression in human kidney disease
Juliar BA, Stanaway IB, Sano F et al. · Cell reports 2024 · PMID 38838223 · doi:10.1016/j.celrep.2024.114310
This dataset
Open GSE230848 in finder → Find more human RNA-seq datasets →
Download · which file do I need?

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.

1 · Just have a look

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.

Series matrix ↓
2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE230848_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓
3 · Start from the raw reads

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.

Raw reads on ENA ↗

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 PRJNA962837 and SRA study SRP435016. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 38838223) ↗
Study design
12 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 12 sample titles: 12 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.

Samples in this study
Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

Share this dataset

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.