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Whole-blood transcriptome profile in children with sickle cell anemia before and after exercise

GSE244401 Homo sapiens (human) RNA-seq (gene expression) 80 samples Submitted 2024/05/24 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 80 samples

What it is
RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism
Homo sapiens (human)
Samples
80 samples: 40 conditions, mostly in duplicateGroups 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
Whole-blood transcriptome analysis reveals distinct gene expression signatures in paediatric patients with sickle cell anaemia before and after exerciseBritish journal of haematology · 2024
Summary (written by the submitters)
Patients with sickle cell anemia (SCA) show higher levels of circulating pro-inflammatory cytokines and endothelial activation markers compared to healthy peers and altered blood transcriptome profiles. However, the characterization of the whole-blood transcriptome profile in response to exercise has not been evaluated in children with SCA.
Read the full summary ↓
Twenty-three children/adolescents with SCA (10-19 years old) and 17 healthy controls (10-22 years old) performed eight 2-min bouts of cycle ergometry interspersed with 1-min rest intervals (50% or 70% of the peak workload achieved during the cardiopulmonary exercise test). Whole-blood transcriptome profile (RNA-seq) was performed before and after the exercise protocol. Following exercise, gene pathways associated with innate immunity were altered in both children with SCA and controls. Our results shed light on the common and different immune responses to acute exercise at the molecular level in children with SCA and healthy controls.
Published in
Whole-blood transcriptome analysis reveals distinct gene expression signatures in paediatric patients with sickle cell anaemia before and after exercise
Plaza-Florido A, Liem RI, Haddad F et al. · British journal of haematology 2024 · PMID 38768976 · doi:10.1111/bjh.19533
This dataset
Open GSE244401 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 GSE244401_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: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA1022793 and SRA study SRP468142. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 38768976) ↗
Study design
40 conditions, mostly in duplicate
CON02_T ×2 CON05_T ×2 CON06_T ×2 CON07_T ×2 CON08_T ×2 CON09_T ×2 CON10_T ×2 CON11_T ×2 +32 more

Supports a between-group comparison across 80 samples.

How we read this: 40 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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