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Identifying modulators of inflammation in an in vitro airway epithelial inflammation model using an arrayed CRISPRi screen

GSE271359 Homo sapiens (human) RNA-seq (gene expression) 186 samples Submitted 2024/09/15 Platform GPL18573
At a glance

RNA-seq (gene expression) · human · 186 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
186 samples: 186 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.
Summary (written by the submitters)
To study modulators of inflammatory airway disease, we developed an in vitro bronchial epithelial cell model system based on the BEAS-2B cell line exposed to lipopolysaccharide (LPS) and diesel exhaust particles (DEP). Combined LPS and DEP exposure triggered a molecular inflammatory phenotype, including elevated TSLP and IL-8 mRNA expression, similar to that observed in airway epithelial cells from asthma patients.
Read the full summary ↓
In this model, we performed high-throughput perturbation of 24 mRNA and long non-coding (lncRNA) target genes through an arrayed CRISPR-interference screen, followed by shallow RNA-sequencing to identify modulators of inflammation. Perturbation of individual targets had a significant impact on the BEAS-2B DEP/LPS transcriptome. Some mRNA targets reversed the DEP/LPS gene expression signature upon knockdown and could serve as candidate therapeutic targets. Perturbation of other target genes however further amplified the pro-inflammatory signature and thus may have an anti-inflammatory role in these cells. In conclusion, we present a novel in vitro model system for airway inflammation that exhibited an inflammatory profile relevant for asthmatic patients, and demonstrate the feasibility to perform high-throughput target perturbation and molecular phenotyping by integrating CRISPR-interference and shallow RNA-sequencing.
This dataset
Open GSE271359 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 GSE271359_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 PRJNA1130987 and SRA study SRP517585. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
186 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 186 sample titles: 186 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

+ 146 more — browse all 186 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.