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RNA-seq of monocyte derived macrophages and dendritic cells in a Peruvian Tuberculosis cohort

GSE269009 Homo sapiens (human) Single-cell RNA-seq 227 samples Submitted 2024/06/04 Platform GPL18573
At a glance

Single-cell RNA-seq · human · 227 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
227 samples: 227 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.
Paper
History of tuberculosis disease is associated with genetic regulatory variation in PeruviansmedRxiv : the preprint server for health sciences · 2023
Summary (written by the submitters)
In this study, we sought to analyze the effects of genetic variants on gene expression levels in relation to the history of infectious disease and cell type. To do this, we focused on a Peruvian human cohort that had been previously genotyped who either progressed to active tuberculosis (TB) disease (cases) or did not progress to TB (controls).
Read the full summary ↓
We isolated monocytes from PBMC samples of these individuals and differentiated in vitro to dendritic cells (DCs) and macrophages (MPs). We performed RNA-seq for each sample using a modified Smart-seq protocol for low-input RNA-sequencing. We measured the impact of genetic variants on gene expression by identifying expression quantitative trait loci (eQTL). We identified cell-type-specific eQTL genes as well as five genes that showed an interaction between eQTL variants and TB progression status in dendritic cells. We found that the top eQTL interaction with disease history was associated with a gene involved in tyrosine catabolism.
Published in
History of tuberculosis disease is associated with genetic regulatory variation in Peruvians
Suliman S, Nieto-Caballero VE, Asgari S et al. · medRxiv : the preprint server for health sciences 2023 · PMID 37425785 · doi:10.1101/2023.06.20.23291558
This dataset
Open GSE269009 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 GSE269009_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 PRJNA1119954. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 37425785) ↗
Study design
227 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 227 sample titles: 227 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.

Samples in this study

+ 187 more — browse all 227 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

Share this dataset

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.