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Deciphering the Transcriptomic Landscape of Type 2 Diabetes: Insights from Bulk RNA Sequencing and Single-Cell Analysis [RNA-seq]

GSE280402 Homo sapiens (human) Single-cell RNA-seq 16 samples Submitted 2024/10/30 Platform GPL16791
At a glance

Single-cell RNA-seq · human · 16 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism
Homo sapiens (human)
Samples
16 samples: 8 × Whole blood, RNAseq, T2D, subj vs 8 × Whole blood, RNAseq, Control, subjGroups 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
Cross-Study Meta-Analysis of Blood Transcriptomes in Type 2 DiabetesInternational journal of molecular sciences · 2025
Summary (written by the submitters)
Type 2 diabetes (T2D) is a chronic metabolic disorder characterized by insulin resistance and relative insulin deficiency. It is a significant public health concern worldwide, with an estimated prevalence of over 422 million individuals affected globally. This number is projected to rise, making diabetes one of the leading causes of morbidity and mortality.
Read the full summary ↓
It is associated with numerous severe microvascular and macrovascular complications, including retinopathy, nephropathy, cardiovascular diseases, and neuropathy, which substantially impact patients' quality of life and healthcare systems. The global prevalence of T2D necessitates a deeper understanding of its underlying mechanisms to develop more effective prevention and treatment strategies. Recent advancements in transcriptomics and single-cell technologies have revolutionized our ability to investigate the cellular and molecular landscapes of T2DM. Transcriptomic analysis provides insights into gene expression patterns in T2D, revealing pathways involved in insulin signaling, metabolic regulation, and inflammation. The importance of inflammation driven by immune cells in the pathogenesis of T2D has been repeatedly demonstrated. This makes research focused on the analysis of gene expression in whole blood cells especially relevant. Transcriptomic markers of T2D have been investigated in studies utilizing expression arrays and RNA-seq. While expression arrays allow for the analysis of a gene set limited by the design of microarrays, RNA-seq allows investigating the entire transcriptome. A study focused on the analysis of transcriptomic markers of T2D in whole blood using RNA-seq demonstrated differential expression of genes associated with inflammation, insulin resistance, and mitochondrial dysfunction. However, the heterogeneity of immune cell populations poses significant challenges in understanding the disease's complexity. Single-cell RNA sequencing (scRNA-seq) enables researchers to dissect this cellular diversity, allowing for the identification of distinct cell types and their specific contributions to T2DM progression. By integrating transcriptomic data with single-cell analyses, we can elucidate the dynamic interactions between various cell types and their roles in T2DM. In this study, we aimed to analyze transcriptomic markers of T2D in whole blood cells and conduct single-cell sequencing on isolated PBMCs from T2D patients and healthy controls in order to advance our understanding of the processes involved in the pathogenesis of T2D and to highlight the significance of transcriptomic and single-cell research in T2D studies.
Published in
Cross-Study Meta-Analysis of Blood Transcriptomes in Type 2 Diabetes
Tkachenko AA, Tonyan ZN, Nasykhova YA et al. · International journal of molecular sciences 2025 · PMID 41465472 · doi:10.3390/ijms262412046
This dataset
Open GSE280402 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 GSE280402_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 PRJNA1178318 and SRA study SRP541458. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41465472) ↗
Study design
8 × Whole blood, RNAseq, T2D, subj vs 8 × Whole blood, RNAseq, Control, subj

Supports a between-group comparison across 16 samples.

How we read this: 2 replicated groups read from 16 sample titles; they account for 16 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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