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Single-cell multimodal profiling of pan-cancer cell lines uncovers gene regulatory principles underlying intrinsic cell states and environmental features

GSE311521 Homo sapiens (human) Single-cell RNA-seq 15 samples Submitted 2026/03/02 Platform GPL30882
At a glance

Single-cell RNA-seq · human · 15 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)
Samples
15 samples: 8 × Easy-Sci ATAC co-proccessed human cultured cancer cell lines EXP vs 6 × Easy-Sci RNA co-proccessed human cultured cancer cell lines EXPGroups 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
Single-cell multimodal profiling of pan-cancer cell lines uncovers gene regulatory principles underlying intrinsic cell states and environmental featuresbioRxiv : the preprint server for biology · 2026
Summary (written by the submitters)
Cancer arises from somatic mutations whose effects are executed through dysregulated gene-regulatory programs that reshape chromatin, transcription, and malignant phenotypes. To uncover gene regulatory principles underlying heterogeneous cancer cell states and their linked environmental features, here we present a pan-cancer single-cell, multi-omic atlas of human cancer cell lines, including a compendium of 240,957 transcriptomes and 223,347 chromatin-accessibility profiles from primary cancers spanning 20 tumor types.
Read the full summary ↓
We revealed extensive pan-cancer cell-state heterogeneity, core gene-regulatory networks, and consensus epithelial–mesenchymal transition (EMT) trajectories that transcend tissue of origin and are governed by conserved epigenomic and transcriptomic features. In addition, our copy-number variation analysis implicated transcription factor amplification, followed by hyperactive downstream regulation, as a major driver of malignant states. Further focused analysis of acral versus cutaneous melanoma cell lines uncovers a universal inflammation-suppressive program in acral melanoma versus an inflamed regulatory landscape in cutaneous melanoma, highlighting the JAK–STAT axis as a key discriminator. Finally, by integrating single-cell and bulk datasets across models and patient cohorts, we revealed tumor–microenvironment co-adaptation in vivo, and this was associated with immunotherapy responsiveness.
Published in
Single-cell multimodal profiling of pan-cancer cell lines uncovers gene regulatory principles underlying intrinsic cell states and environmental features
Xu Z, Ugurbil A, Kwan J et al. · bioRxiv : the preprint server for biology 2026 · PMID 42282723 · doi:10.64898/2026.05.31.729161
This dataset
Open GSE311521 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 GSE311521_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 PRJNA1354039 and SRA study SRP637415. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42282723) ↗
Study design
8 × Easy-Sci ATAC co-proccessed human cultured cancer cell lines EXP vs 6 × Easy-Sci RNA co-proccessed human cultured cancer cell lines EXP

Supports a between-group comparison across 14 samples.

How we read this: 2 replicated groups read from 15 sample titles; they account for 14 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
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