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Gene expression profiling of sorted primary human bronchial epithelial cell populations

GSE263356 Homo sapiens (human) RNA-seq (gene expression) 18 samples Submitted 2024/06/26 Platform GPL34284
At a glance

RNA-seq (gene expression) · human · 18 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
18 samples: 18 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
Tropism for ciliated cells is the dominant driver of influenza viral burst size in the human airwayProceedings of the National Academy of Sciences of the United States of America · 2024
Summary (written by the submitters)
Influenza viruses pose a significant burden on global human health. Influenza has a broad cellular tropism in the airway, but how infection of different epithelial cell types impacts replication kinetics and burden in the airways is not fully understood. Using primary human airway cultures, which recapitulate the diverse epithelial cell landscape of the human airways, we investigated the impact of cell type composition on virus tropism and replication kinetics.
Read the full summary ↓
Cultures were highly diverse across multiple donors and 30 independent differentiation conditions and supported a range of influenza replication. Although many cell types were susceptible to influenza, ciliated and secretory cells were predominantly infected. Despite the strong tropism preference for secretory and ciliated cells, which consistently make up 75% or more of infected cells, only ciliated cells were associated with increased virus production. Surprisingly, infected secretory cells were associated with overall reduced virus output. These data highlight the heterogeneous outcomes of influenza virus infections in primary human airway cultures and the disparate impacts of infected cell identity on burst size, even among preferentially infected cell types.
Published in
Tropism for ciliated cells is the dominant driver of influenza viral burst size in the human airway
Roach SN, Shepherd FK, Mickelson CK et al. · Proceedings of the National Academy of Sciences of the United States of America 2024 · PMID 39008691 · doi:10.1073/pnas.2320303121
This dataset
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.

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 GSE263356_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA1010235 and SRA study SRP457312. Searching any of these in the dataset finder brings you back here.

Study design
18 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 18 sample titles: 18 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
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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.