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Attenuated kidney oxidative metabolism in young adults with type 1 diabetes

GSE279086 Homo sapiens (human) Single-cell RNA-seq 40 samples Submitted 2024/11/11 Platform GPL11154
At a glance

Single-cell RNA-seq · human · 40 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
40 samples: 15 × S_2103 vs 12 × S_2007 vs 7 × S_2107 vs 5 × S_1907Groups 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
Attenuated kidney oxidative metabolism in young adults with type 1 diabetesThe Journal of clinical investigation · 2024
Summary (written by the submitters)
In type 1 diabetes (T1D), impaired insulin sensitivity may contribute to the development of diabetic kidney disease (DKD) through alterations in kidney oxidative metabolism. Young adults with T1D and healthy controls underwent single-cell RNA sequencing, and spatial metabolomics to assess this relationship.
Published in
Attenuated kidney oxidative metabolism in young adults with type 1 diabetes
Choi YJ, Richard G, Zhang G et al. · The Journal of clinical investigation 2024 · PMID 39436695 · doi:10.1172/JCI183984
This dataset
Open GSE279086 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 GSE279086_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: 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 PRJNA1170332. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39436695) ↗
Study design
15 × S_2103 vs 12 × S_2007 vs 7 × S_2107 vs 5 × S_1907

Supports a between-group comparison across 39 samples.

How we read this: 4 replicated groups read from 40 sample titles; they account for 39 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.

Samples in this study
Datasets on a similar topic

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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.