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Integrative transcriptome and proteome profiling of insulin-resistant kidney cell models and patient biopsies reveals common and cell-type-specific mechanisms underpinning Diabetic Kidney Disease

GSE262793 Homo sapiens (human) Single-cell RNA-seq 80 samples Submitted 2024/04/02 Platform GPL20301
At a glance

Single-cell RNA-seq · human · 80 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
80 samples: 16 conditions, mostly with about 5 replicates eachGroups 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
Profiling of insulin-resistant kidney models and human biopsies reveals common and cell-type-specific mechanisms underpinning Diabetic Kidney DiseaseNature communications · 2024
Summary (written by the submitters)
Diabetic kidney disease (DKD) is the leading cause of end stage kidney failure worldwide. It is now clear that cellular insulin resistance is a major driver of this disease. Using established human conditionally immortalised podocytes (Pods), glomerular endothelial cells (GECs), mesangial cells (MCs), and proximal tubular cells (PTCs), we modelled both insulin sensitivity and insulin resistance and performed simultaneous transcriptomics and proteomics for integrated analysis.
Read the full summary ↓
Our data was further compared with bulk- and single-cell transcriptomic kidney biopsy data from early- and advanced-stage DKD patient cohorts. We identified several consistent changes (individual genes, proteins, and molecular pathways) occurring across all insulin-resistant kidney cell types, which were replicated in human early- and/or advanced-stage DKD biopsies. These included the genes CTSS, NRBF2, C3, CXCL1, TFPI2 and PFKFB3, and pathways related to the inflammatory response, ER stress and glycoprotein metabolism. We further identified several cell-line-specific molecular changes occurring in response to insulin resistance, which were replicated in single-cell sequencing data from DKD, together with a selective reduction in mitochondrial function in Pods, MCs and PTC, but not GECs. This study provides a rich data resource to direct future studies in elucidating underlying kidney signalling pathways and potential therapeutic targets in DKD.
Published in
Profiling of insulin-resistant kidney models and human biopsies reveals common and cell-type-specific mechanisms underpinning Diabetic Kidney Disease
Lay AC, Tran VDT, Nair V et al. · Nature communications 2024 · PMID 39562547 · doi:10.1038/s41467-024-54089-1
This dataset
Open GSE262793 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 GSE262793_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 PRJNA905899 and SRA study SRP484908. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39562547) ↗
Study design
16 conditions, mostly with about 5 replicates each
ABN__Basal_GTG_IMI ×5 ABN__InsulinResistant_GTG_IMI ×5 ABN_IR_Basal_GTG_IMI ×5 ABN_IR_InsulinResistant_GTG_IMI ×5 GEnC__Basal_GTG_IMI ×5 GEnC__InsulinResistant_GTG_IMI ×5 GEnC_IR_Basal_GTG_IMI ×5 GEnC_IR_InsulinResistant_GTG_IMI ×5 +8 more

Supports a between-group comparison across 80 samples.

How we read this: 16 replicated groups read from 80 sample titles; they account for 80 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

+ 40 more — browse all 80 samples with per-sample file links →

Datasets on a similar topic

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