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Transcriptome and microbiome profile of nasopharyngeal samples among COVID-19 patients with different levels of severity revealed through RNA-seq analysis

GSE208342 Homo sapiens (human) RNA-seq (gene expression) 20 samples Submitted 2024/06/30 Platform GPL21697
At a glance

RNA-seq (gene expression) · human · 20 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
20 samples: 8 × Nasopharyngeal tissue, severe, patient vs 5 × Nasopharyngeal tissue, mild, patient vs 5 × Nasopharyngeal tissue, negative, sample vs 2 × Nasopharyngeal tissue, asymptomatic, patientGroups 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.
Summary (written by the submitters)
Differences in symptom manifestation among COVID-19 patients are caused mainly by the host factors. Gene expression profiles from nasopharyngeal samples might provide a clue to dysregulated biological processes, causing a variety of severity levels. The metagenomic profile from nasopharyngeal tissue might also provide details of coinfection that might contribute to the severity.
Read the full summary ↓
Hence, we performed total RNA sequencing from asymptomatic patients and patients with mild and severe illnesses. Severe groups were characterized by the upregulation of the complement gene MASP1, inflammatory cytokines CXCL5 and CXCL12, and the fibrosis markers COL1A1 and COL3A1. On the contrary, the lack of activation of the B cell immune response, T cell immune response, and the cell-surface antigen recognition and signaling process were found in this severe group.
This dataset
Open GSE208342 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 GSE208342_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 PRJNA859510 and SRA study SRP386771. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
8 × Nasopharyngeal tissue, severe, patient vs 5 × Nasopharyngeal tissue, mild, patient vs 5 × Nasopharyngeal tissue, negative, sample vs 2 × Nasopharyngeal tissue, asymptomatic, patient

Supports a between-group comparison across 20 samples.

How we read this: 4 replicated groups read from 20 sample titles; they account for 20 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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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.