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Context-aware single-cell multiomics approach identifies cell-type specific lung cancer susceptibility genes

GSE241468 Homo sapiens (human) Single-cell RNA-seq 32 samples Submitted 2024/08/14 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 32 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism
Homo sapiens (human)
Topic
Lung cancerThis series is part of our curated briefing on it.
Samples
32 samples: 8 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
Context-aware single-cell multiomics approach identifies cell-type-specific lung cancer susceptibility genesNature communications · 2024
Summary (written by the submitters)
Genome-wide association studies (GWAS) identified over fifty genomic loci associated with lung cancer risk. However, the genetic mechanisms and target genes underlying these loci are largely unknown, as most risk-associated-variants might regulate gene expression in a context-specific manner. Here, we generated a barcode-shared multiome (transcriptome and chromatin accessibility map) of 117,911 human lung cells from ever- and never-smokers to profile context-specific gene regulation.
Read the full summary ↓
We observed that most of differentially expressed genes based on smoking status (smoking-responsive genes) were cell-type specific, and inter-cellular communication strength for Major Histocompatibility Complex-I and -II pathways were inverted between ever- and never-smokers. Accessible chromatin peak detection identified candidate cis-regulatory elements (cCREs) from each lung cell type, and 37% of them were cell-type specific. Colocalization of lung cancer candidate causal variants (CCVs) with these cCREs prioritized the variants for 68% of the GWAS loci, a subset of which was also supported by transcription factor footprinting. cCRE colocalization and single-cell based trait relevance score nominated epithelial and immune cells as the main cell groups contributing to lung cancer susceptibility. Notably, cCREs of rare epithelial cell types, including AT2-proliferating (0.13%) and basal cells (1.8%), overlapped with CCVs from multiple GWAS loci. A multi-level cCRE-gene linking system identified candidate susceptibility genes from 57% of lung cancer loci, including smoking-responsive genes. Our multiome dataset identified lung cancer susceptibility genes that were not detected in previous tissue- or cell-line-based approaches and further revealed the cell types and contexts where the susceptibility genes are functional, including the interplay of epithelial and immune cell types even in a single locus.
Published in
Context-aware single-cell multiomics approach identifies cell-type-specific lung cancer susceptibility genes
Long E, Yin J, Shin JH et al. · Nature communications 2024 · PMID 39266564 · doi:10.1038/s41467-024-52356-9
This dataset
Open GSE241468 in finder → Find more human RNA-seq datasets → Lung cancer landscape →
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 GSE241468_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: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA1008237 and SRA study SRP456549. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39266564) ↗
Study design
8 conditions, mostly with about 4 replicates each
Male, smoker, GEX, subject ×4 Male, never-smoker, GEX, subject ×4 Female, smoker, GEX, subject ×4 Female, never-smoker, GEX, subject ×4 Male, smoker, ATAC, subject ×4 Male, never-smoker, ATAC, subject ×4 Female, smoker, ATAC, subject ×4 Female, never-smoker, ATAC, subject ×4

Supports a between-group comparison across 32 samples.

How we read this: 8 replicated groups read from 32 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
More Lung cancer datasets

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