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Single-cell analysis of human PBMCs in healthy and type 2 diabetes populations: Dysregulated immune networks in type 2 diabetes unveiled through single-cell profiling

GSE268210 Homo sapiens (human) Single-cell RNA-seq 30 samples Submitted 2024/06/12 Platform GPL16791
At a glance

Single-cell RNA-seq · human · 30 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
30 samples: 10 × PBMC, Type 2 diabetes, scRNA-seq, vs 10 × PBMC, Type 2 diabetes, scTCR, vs 10 × PBMC, Type 2 diabetes, scBCR,Groups 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
Single-cell analysis of human PBMCs in healthy and type 2 diabetes populations: dysregulated immune networks in type 2 diabetes unveiled through single-cell profilingFrontiers in endocrinology · 2024
Summary (written by the submitters)
Abnormalities in glucose metabolism that precede the onset of type 2 diabetes (T2D) activate immune cells, leading to elevated inflammatory factors and chronic inflammation. However, no single-cell RNA sequencing (scRNA-seq) studies have characterized the properties and networks of individual immune cells in T2D. Here, we analyzed peripheral blood mononuclear cells (PBMCs) from healthy controls and T2D patients by scRNA-seq.
Read the full summary ↓
We found that CD14 monocytes in T2D patients were in a pro-inflammatory state and intermediate monocytes expressed more MHC class II genes. In T2D patients, cytotoxic CD4 T cells, effector memory CD8 T cells, and γδ T cells have increased cytotoxicity and clonal expansion. B cells were characterized by increased differentiation into intermediate B cells, plasma cells, and isotype class switching with increased expression of soluble antibody genes. These results suggest that monocytes, T cells, and B cells could interact to induce chronic inflammation in T2D patients with pro-inflammatory characteristics.
Published in
Single-cell analysis of human PBMCs in healthy and type 2 diabetes populations: dysregulated immune networks in type 2 diabetes unveiled through single-cell profiling
Gu D, Lim J, Han KY et al. · Frontiers in endocrinology 2024 · PMID 39072276 · doi:10.3389/fendo.2024.1397661
This dataset
Open GSE268210 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 GSE268210_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 PRJNA1115130 and SRA study SRP509449. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39072276) ↗
Study design
10 × PBMC, Type 2 diabetes, scRNA-seq, vs 10 × PBMC, Type 2 diabetes, scTCR, vs 10 × PBMC, Type 2 diabetes, scBCR,

Supports a between-group comparison across 30 samples.

How we read this: 3 replicated groups read from 30 sample titles; they account for 30 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
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