SGLT2 inhibition reverses metabolic and inflammatory kidney signatures in youth with type 1 diabetes
Single-cell RNA-seq · human · 27 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
- 27 samples: 27 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 GSE316742_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: 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 PRJNA1404116. 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 27 sample titles: 27 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.
- GSM9459239 S_2107_023529_PB
- GSM9459240 S_2107_023637_PB
- GSM9459241 S_2107_023682_PB
- GSM9459242 S_2203_000804_PB
- GSM9459243 S_2203_000822_PB
- GSM9459244 S_2203_000846_PB
- GSM9459245 S_2203_000892_PB
- GSM9459246 S_2203_000916_PB
- GSM9459247 S_2209_007500_PB
- GSM9459248 S_2209_007554_PB
- GSM9459249 S_2209_007572_PB
- GSM9459250 S_2209_007581_PB
- GSM9459251 S_2209_007599_PB
- GSM9459252 S_2302_015924_PB
- GSM9459253 S_2304_009089_PB
- GSM9459254 S_2304_009134_PB
- GSM9459255 S_2304_009152_PB
- GSM9459256 S_2304_009161_PB
- GSM9459257 S_2307_009688_PB
- GSM9459258 S_2307_009733_PB
- GSM9459259 S_2307_009769_PB
- GSM9459260 S_2307_009778_PB
- GSM9459261 S_2307_009796_PB
- GSM9459262 S_2310_001680_PB
- GSM9459263 S_2310_001725_PB
- GSM9459264 S_2310_001743_PB
- GSM9459265 S_2312_000139_PB
- GSE272294 Decoding the immune checkpoint signatures in human atherosclerosis: implications of type 2 diabetes and lipid-lowering intervention [human PBMC scRNA-Seq] 24 samples shares signatures, diabetes, type
- GSE255566 Single-cell RNA sequencing of peripheral blood mononuclear cells reveals complex cellular signalling signatures of type 2 diabetes mellitus 8 samples shares signatures, diabetes, type
- GSE290695 Identification of Crohn’s disease subtypes in single cell RNA sequencing signatures of treatment naïve samples across the gastrointestinal tract 95 samples shares signatures, type
- GSE249311 Discrete placental gene expression signatures of diabetes during pregnancy 54 samples shares signatures, diabetes
- GSE315442 Transcriptomic profiling reveals distinct molecular signatures among lesion types in hidradenitis suppurativa 48 samples shares signatures, type
- GSE243625 Inflammatory transcriptomic signatures and cell type compositions in inflamed and non-inflamed colonic mucosa of ulcerative colitis 45 samples shares signatures, type
- GSE295260 CDK4/6 inhibition induces a senescence-associated secretory phenotype via delayed NF-κB activation 40 samples shares inhibition, type
- GSE323965 Transcriptional signatures indicating RB function may predict response to CDK4/6 inhibition in endometrial cancer [RNA-Seq] 39 samples shares inhibition, signatures
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.