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Integrated single-cell profiling of RNA and DNA interactomes reveals targetable chromatin architectures in cancer [ChIP-Seq]

GSE337829 Homo sapiens (human) Single-cell RNA-seq 32 samples Submitted 2026/07/07 Platform GPL16791
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)
Samples
32 samples: 12 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)
The human genome is pervasively transcribed into protein-coding and regulatory non-coding RNAs whose functions are coordinated within higher-order nuclear architectures. However, direct mapping of RNA–RNA and DNA–DNA interaction networks in complex human tissues at single-cell resolution has remained a major challenge. Here, we present SCIENCE-seq, a multimodal single-cell interactomics platform enabling simultaneous detection of RNA–RNA interactions and chromatin DNA–DNA contacts within individual cells.
Read the full summary ↓
Applied to primary glioma specimens, SCIENCE-seq reveals cancer-specific interactions organized by lncRNAs and centered on key oncogenic drivers, including EGFR, hTERT, SOX2, CDK6, CDC42, CD47, and HOX loci. These datasets uncover previously unrecognized molecular relationships between premature lncRNAs and pre-mRNAs that regulate transcription, alternative splicing, and polyadenylation. Notably, we identify a glioma-specific trans-chromosomal HOX hub driven by five interacting lncRNAs. Targeting these RNA and DNA interactions with steric antisense oligonucleotides (ASOs) or CRISPRi enables selective inhibition of oncogenic programs in malignant cells while sparing normal tissue, establishing chromatin interactomes as actionable therapeutic targets.
This dataset
Open GSE337829 in finder → Find more human ChIP / ATAC / CUT&Tag 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 GSE337829_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 PRJNA1491292 and SRA study SRP715947. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
12 conditions, mostly in duplicate
GBM8_ChIPseq_H3K27Ac_after_CRISPRi ×2 GBM8_ChIPseq_H3K27Me3_after_CRISPRi ×2 GBM8_ChIPseq_H3K27Ac_before_CRISPRi ×2 GBM8_ChIPseq_H3K27Me3_before_CRISPRi ×2 GBM8_ChIPseq_PPARD ×2 NHA_ChIPseq_H3K27AC_after_CRISPRi ×2 NHA_ChIPseq_H3K27Me3_after_CRISPRi ×2 NHA_ChIPseq_H3K27AC_before_CRISPRi ×2 +12 more

Supports a between-group comparison across 24 samples.

How we read this: 12 replicated groups read from 32 sample titles; they account for 24 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
Datasets on a similar topic

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