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Longitudinal transcriptomic analysis reveals persistent enrichment of iron homeostasis and erythrocyte function pathways in severe COVID-19 ARDS

GSE273149 Homo sapiens (human) RNA-seq (gene expression) 49 samples Submitted 2024/07/29 Platform GPL34284
At a glance

RNA-seq (gene expression) · human · 49 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
49 samples: 13 conditions, mostly with about 4 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
Longitudinal transcriptomic analysis reveals persistent enrichment of iron homeostasis and erythrocyte function pathways in severe COVID-19 ARDSFrontiers in immunology · 2024
Summary (written by the submitters)
The acute respiratory distress syndrome (ARDS) is a common complications of severe COVID-19 and contributes to patient morbidity and mortality. ARDS is a heterogeneous syndrome caused by various insults, and results in acute hypoxemic respiratory failure. Patients with ARDS from COVID-19 may represent a subgroup of ARDS patients with distinct molecular profiles that drive disease outcomes.
Read the full summary ↓
Here, we hypothesized that longitudinal transcriptomic analysis may identify distinct dynamic pathobiological pathways during COVID-19 ARDS. We identified a patient cohort from an existing ICU biorepository and established three groups for comparison: 1) patients with COVID-19 ARDS that survived hospitalization (COVID survivors, n = 4), 2) patients with COVID-19 ARDS that did not survive hospitalization (COVID non-survivors, n = 5), and 3) patients with ARDS from other causes as a control group (ARDS controls, n = 4). RNA was extracted from peripheral blood mononuclear cells (PBMCs) at 4 time points (Days 1, 3, 7, and 10 following ICU admission) and prepared for RNA sequencing with rRNA depletion and library generation for Illumina. An Illumina NovaSeq X Plus instrument was used to generate 150 base pair paired-end reads, which were aligned to the hg GRCh38.96 reference genome using HiSAT2. Differential expression analysis was performed with DESeq2.
Published in
Longitudinal transcriptomic analysis reveals persistent enrichment of iron homeostasis and erythrocyte function pathways in severe COVID-19 ARDS
Eltobgy M, Johns F, Farkas D et al. · Frontiers in immunology 2024 · PMID 39161760 · doi:10.3389/fimmu.2024.1397629
This dataset
Open GSE273149 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 GSE273149_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 PRJNA1140250 and SRA study SRP522282. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39161760) ↗
Study design
13 conditions, mostly with about 4 replicates each
PBMC_ARDS_CON_30_Day ×4 PBMC_ARDS_CON_63_Day ×4 PBMC_ARDS_CON_79_Day ×4 PBMC_ARDS_CON_83_Day ×4 PBMC_COVID_survivor_23_Day ×4 PBMC_COVID_survivor_40_Day ×4 PBMC_COVID_nonsurvivor_11_Day ×4 PBMC_COVID_nonsurvivor_20_Day ×4 +5 more

Supports a between-group comparison across 49 samples.

How we read this: 13 replicated groups read from 49 sample titles; they account for 49 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

+ 9 more — browse all 49 samples with per-sample file links →

Datasets on a similar topic

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