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Genotype-phenotype single-cell transcriptomics for massive parallel assessment of genetic variants [RNAseq_mutants]

GSE311877 Homo sapiens (human) Single-cell RNA-seq 80 samples Submitted 2026/07/10 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 80 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
80 samples: 20 conditions, mostly with about 4 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)
Predicting the pathogenic effect of rare genetic variants is hampered by the limited cohort of diagnosed patients and the difficulty in evaluating the effect of existing and novel variants. To address this issue, we leveraged a protein Multiplexed Assay of Variants Effect (MAVE) to functionally assess the effect of ~2,300 missense variants of the TP63 gene, some of which cause autosomal dominant developmental disorders.
Read the full summary ↓
The activity of each variant was measured using an optimized fibroblast-to-keratinocyte conversion protocol, and a subset of mutants was validated using a wide range of functional tests. To expand MAVEs to any disease-driving gene without a specific predefined assay, we developed SCRAMseq (Single Cell RNAseq Associated with MAVEs by sequencing), which can retrieve each variant and characterize the functional consequences at the single-cell level through full-length scRNAseq. The dataset generated and validated here reclassified hundreds of variants present in the general population and definitively classified a Variant of Unknown Significance as pathogenic in a patient with ectodermal dysplasia. This work provides a robust and easy-to-use workflow to dissect the effect of gene variants for any possible disease-driving gene.
This dataset
Open GSE311877 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 GSE311877_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 PRJNA1371878 and SRA study SRP650105. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
20 conditions, mostly with about 4 replicates each
iKCs, WT CTR, biol ×4 iKCs, GFP CTR, biol ×4 iKCs, L514F, biol ×4 iKCs, L514D, biol ×4 iKCs, C522G, biol ×4 iKCs, C522D, biol ×4 iKCs, L548N, biol ×4 iKCs, I550D, biol ×4 +12 more

Supports a between-group comparison across 80 samples.

How we read this: 20 replicated groups read from 80 sample titles; they account for 80 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

+ 40 more — browse all 80 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq 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.