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Exploring host cellular responses to SARS-CoV-2 variants using RNA sequencing of infected human airway epithelial cells

GSE271378 Homo sapiens (human) RNA-seq (gene expression) 44 samples Submitted 2025/07/01 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 44 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
44 samples: 16 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.
Summary (written by the submitters)
This study explores the interaction between various SARS-CoV-2 variants and host cells by using RNA sequencing (RNA-seq) of human airway epithelial cells. The primary objective was to elucidate the host cellular responses, including metabolic and immune pathways, to different SARS-CoV-2 variants. Human airway epithelial cells were infected with SARS-CoV-2 variants (IC19, Alpha, Beta, Delta, Omicron BA.1, and BA.5) and compared with mock samples at time 0.
Read the full summary ↓
Samples were collected at 24 and 72 hours post-infection for RNA isolation and subsequent bulk RNA-sequencing. The RNA-seq data processing involved quality checks, mapping reads to a reference genome, and identifying differentially expressed genes to determine changes in gene expression in response to viral infection. This analysis aims to provide a deeper understanding of the molecular interactions between SARS-CoV-2 variants and host cells, contributing to the broader knowledge of viral pathogenicity and host-pathogen interactions.
This dataset
Open GSE271378 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 GSE271378_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 PRJNA1131104 and SRA study SRP517657. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
16 conditions, mostly in duplicate
A_IC19 ×2 B_IC19 ×2 C_IC19 ×2 D_IC19 ×2 A_Alpha ×2 B_Alpha ×2 A_Beta ×2 B_Beta ×2 +20 more

Supports a between-group comparison across 32 samples.

How we read this: 16 replicated groups read from 44 sample titles; they account for 32 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

+ 4 more — browse all 44 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.