← BioTransfer GEO Dataset Finder
GEO series

Widespread transcriptional memory shapes heritable states and functional heterogeneity in cancer and stem cells [scRNA-seq]

GSE329336 Homo sapiens Expression profiling by high throughput sequencing; Other 3 samples Submitted 2026/04/30 Platform GPL24676Platform GPL29480
Summary
Recent studies show that non-genetic heterogeneity, particularly through heritable cell states, shapes cancer evolution and developmental trajectories. However, single-cell snapshots lack temporal information to identify these states. We employ lineage-resolved single-cell transcriptomics to map heritable cell states that persist across divisions, distinguishing them from transient fluctuations. We uncover that heritable states are underpinned by widespread transcriptional memory, whereby heritable gene expression defines two classes of states: clustered states, characterized by clustered gene expression, and latent states, marked by non-clustered gene expression. This memory shows conservation across cell types and conditions and appears to be maintained by robust epigenetic mechanisms resistant to environmental perturbations. Functionally, memory genes predict critical behaviors including metastatic potential and lineage commitment, with latent-state genes often outperforming clustered-state genes. Our findings establish transcriptional memory as a potential basis of heritable cellular heterogeneity, providing a framework for understanding functional cellular variations across biological systems. A record of this paper’s transparent peer review process is included in the supplemental information.
This dataset
Download

Direct links to NCBI, no account and no request form: the whole study as GSE329336_RAW.tar, processed values as the series matrix, the supplementary file directory, and per-sample supplementary files for any of the 3 samples. Raw sequencing reads are also available from ENA.

Also filed as BioProject PRJNA1458673 and SRA study SRP695585. Searching any of these in the dataset finder brings you back here.

Samples in this study

The sample list for this study is not cached yet. Press Sort into groups and it will be fetched from NCBI.

+ 3 more — browse all 3 samples with per-sample file links →

Similar datasets

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.