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Temporal and Clonal Resolution of Cellular Evolution Under Stress

GSE305751 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 42 samples Submitted 2026/08/01 Platform GPL30173Platform GPL18573
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 42 samples

What it is
ChIP-seq / ATAC-seq (genome binding and open chromatin)Where proteins bind the genome, or which regions are open and active.
Organism
Homo sapiens (human)
Samples
42 samples: 14 conditions, mostly in triplicateGroups 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)
Dissecting cellular evolution under transient versus persistent stress is essential for understanding both healthy and pathological processes. Yet cellular stress responses involve nonlinear dynamics and survival bottlenecks, making them challenging to study. We develop a framework that combines single-cell multiomic lineage tracing with statistical modeling to quantify how individual cells contribute to the future population, by explicitly modeling exponential expansion and intra-clonal heterogeneity.
Read the full summary ↓
Applied to a cancer model under short- and long-term treatment, we identify clones primed to endure treatment versus ones that survive by producing diverse progeny, enabling characterization of molecular features enriched in each clonal survival strategy. This framework can quantify treatment-driven selection and adaptation, revealing that their relative contributions to population dynamics vary between treatments. Furthermore, short- and long-term treatment results in expansion of distinct clones characterized by different gene programs and transcription factor activity. Notably, certain pathways such as AP-1 signaling can limit initial cancer cell growth but promote later resistance, and such opposing associations are consistently observed in independent experiments and clinical data. Together, this study introduces a generalizable approach for investigating cellular evolution in a time- and clone-resolved manner, and highlights time-dependent molecular programs that underlie stress responses.
This dataset
Open GSE305751 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 GSE305751_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: Look for peak files (.bed, .narrowPeak) and signal tracks (.bigWig) among the supplementary files; the raw reads are on ENA.

Also filed as BioProject PRJNA1307552 and SRA study SRP609694. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
14 conditions, mostly in triplicate
Day0_scMulti_GEX_2022_04 ×3 Day10_DABTRAM_scMulti_GEX_2022_04 ×3 Day10_COCL2_scMulti_GEX_2022_04 ×3 Day10_CIS_scMulti_GEX_2022_04 ×3 Week5_DABTRAM_scMulti_GEX_2022_04 ×3 Week5_COCL2_scMulti_GEX_2022_04 ×3 Week5_CIS_scMulti_GEX_2022_04 ×3 Day0_scMulti_ATAC_2022_05 ×2 +13 more

Supports a between-group comparison across 35 samples.

How we read this: 14 replicated groups read from 42 sample titles; they account for 35 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

+ 2 more — browse all 42 samples with per-sample file links →

Datasets on a similar topic

Search all human ChIP / ATAC / CUT&Tag datasets in GEO →

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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.