← BioTransfer GEO Dataset Finder
GEO series

Lentiviral single-cell MPRA of synthetic enhancers reveals motif affinity-based encoding of cell state specificity [sc-lentiMPRA 2]

GSE331030 Mus musculus (mouse) Single-cell RNA-seq 66 samples Submitted 2026/05/29 Platform GPL30173Platform GPL21626Platform GPL30172
At a glance

Single-cell RNA-seq · mouse · 66 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism
Mus musculus (mouse)
Samples
66 samples: 6 conditions, mostly with about 12 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.
Summary (written by the submitters)
Cell state-specific gene expression programs emerge from the interplay between cis-regulatory elements (CREs), such as enhancers, and transcription factors (TFs). Massively parallel reporter assays (MPRAs) have enabled large-scale dissection of CRE function, but bulk approaches cannot resolve cell state specificity on continuous trajectories of cellular differentiation, and existing single-cell MPRAs are not readily applicable to primary cell differentiation models.
Read the full summary ↓
Here, we developed a single-cell lentiviral Massively Parallel Reporter Assay (sc-lentiMPRA) that overcomes these limitations and enables parallel quantification of enhancer activity and cellular transcriptome. Applying sc-lentiMPRA in blood stem differentiation, we profiled the activity and specificity of ~160 fully synthetic enhancers with controlled motif composition and affinities across ~190,000 single cells. Focusing on Trp53 and Cebpa, we show that enhancers with high and low affinity motifs differ qualitatively and quantitatively in their responses to TF expression gradients. For Trp53, low-affinity motifs exhibited near-linear correlation with TF expression, whereas high-affinity motifs showed reduced sensitivity to TF levels and a potential contribution of cofactor availability. In contrast, Cebpa-associated enhancers displayed non-linear behaviors. Together, sc-lentiMPRA establishes a powerful framework for systematically relating enhancer architecture and TF expression to regulatory output at single-cell resolution during cellular differentiation.
This dataset
Open GSE331030 in finder → Find more 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 GSE331030_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 PRJNA1466561 and SRA study SRP700696. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
6 conditions, mostly with about 12 replicates each
mHSC sc-lentiMPRA Library Beta, GFP quantitat… ×12 mHSC sc-lentiMPRA Library Beta, guide presenc… ×12 mHSC sc-lentiMPRA Library Beta, TAP gene expr… ×12 mHSC sc-lentiMPRA Library Alpha, GFP quantita… ×8 mHSC sc-lentiMPRA Library Alpha, guide presen… ×8 mHSC sc-lentiMPRA Library Alpha, TAP gene exp… ×8 K562 cell line sc-lentiMPRA Library PoC, GFP … ×1 K562 cell line sc-lentiMPRA Library PoC, gui… ×1 +4 more

Supports a between-group comparison across 60 samples.

How we read this: 6 replicated groups read from 66 sample titles; they account for 60 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

+ 26 more — browse all 66 samples with per-sample file links →

Datasets on a similar topic

Search all 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.