GEO series

High-efficiency small-molecule controlled base editing for in vivo cancer functional genomics

GSE316050 Mus musculus (mouse) RNA-seq (gene expression) 18 samples Submitted 2026/02/17 Platform GPL34290

At a glance

RNA-seq (gene expression) · mouse · 18 samples

What it is: RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism: Mus musculus (mouse)
Samples: 18 samples: 6 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)

Cancer functional genomics using CRISPR-base editors (BEs) to alter targeted DNA bases within genes of interest holds great promise for molecular characterization and new target discovery. However, traditional BEs, utilizing intact DNA deaminases as mutators, often suffer from limitations given limited control and nonspecific toxicities.

Read the full summary ↓

These challenges can dampen variant phenotype strength and pose significant barriers to in vivo screening. Here, we developed a small molecule-controllable platform using split-engineered BEs (seBEs) that enables identification of critical residues in cancer targets for in vivo functional genomics screens. By placing deaminase activity under small molecule control, seBE significantly reduced cellular toxicity, and newly enabled robust and controllable functional genomics screens. High-density seBE genetic screens using ~11,000 sgRNAs in vitro and ~ 3700 sgRNAs in vivo, reveal known and previously unknown loss-of-function and dominant negative mutations in cancer therapeutic targets. A deeper tiling seBE screen against ADAR1, a key mediator in cancer immunotherapy, reveals critical residues within functional domains that show no phenotype in vitro, but distinctively elicit non-cell-autonomous cancer dependencies in vivo. Overall, our seBE platform offers a generalizable, controllable, and highly efficient method to systematically identify key residues in cancer functional genomics.

This dataset

Open GSE316050 in finder → Find more mouse 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 GSE316050_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: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA1400523 and SRA study SRP661001. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

6 conditions, mostly in triplicate

RN2_intact_BE_Veh biol ×3 RN2_intact_BE_Rap biol ×3 RN2_nCas9 biol ×3 RN2_catalytic_mutant biol ×3 RN2_seBE_Veh biol ×3 RN2_seBE_Rap biol ×3

Supports a between-group comparison across 18 samples.

How we read this: 6 replicated groups read from 18 sample titles; they account for 18 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

GSM9443775 RN2_intact_BE_Veh biol rep1 RN2_intact_BE_Veh biol
GSM9443776 RN2_intact_BE_Rap biol rep1 RN2_intact_BE_Rap biol
GSM9443777 RN2_nCas9 biol rep1 RN2_nCas9 biol
GSM9443778 RN2_catalytic_mutant biol rep1 RN2_catalytic_mutant bi…
GSM9443779 RN2_seBE_Veh biol rep1 RN2_seBE_Veh biol
GSM9443780 RN2_seBE_Rap biol rep1 RN2_seBE_Rap biol
GSM9443781 RN2_intact_BE_Veh biol rep2 RN2_intact_BE_Veh biol
GSM9443782 RN2_intact_BE_Rap biol rep2 RN2_intact_BE_Rap biol
GSM9443783 RN2_nCas9 biol rep2 RN2_nCas9 biol
GSM9443784 RN2_catalytic_mutant biol rep2 RN2_catalytic_mutant bi…
GSM9443785 RN2_seBE_Veh biol rep2 RN2_seBE_Veh biol
GSM9443786 RN2_seBE_Rap biol rep2 RN2_seBE_Rap biol
GSM9443787 RN2_intact_BE_Veh biol rep3 RN2_intact_BE_Veh biol
GSM9443788 RN2_intact_BE_Rap biol rep3 RN2_intact_BE_Rap biol
GSM9443789 RN2_nCas9 biol rep3 RN2_nCas9 biol
GSM9443790 RN2_catalytic_mutant biol rep3 RN2_catalytic_mutant bi…
GSM9443791 RN2_seBE_Veh biol rep3 RN2_seBE_Veh biol
GSM9443792 RN2_seBE_Rap biol rep3 RN2_seBE_Rap biol

Datasets on a similar topic

GSE334271 In vivo base editing partially rescues bone dysplasia in a mouse model of Hutchinson-Gilford Progeria Syndrome 36 samples shares editing, base, vivo
GSE261273 Identification of immune-related genes and small-molecule drugs in hypertension-induced left ventricular hypertrophy based on machine learning algorithms and molecular docking 24 samples shares small-molecule, base
GSE268712 CNS-Targeted Base Editing of the Major Late-Onset Tay-Sachs Mutation Alleviates Disease in Mice 12 samples shares editing, base
GSE280072 Editing DNA Methylation in vivo [RNA-Seq] 12 samples shares editing, vivo
GSE227720 CRISPR-Cas9 base editing to render CaMKIIδ phosphoresistant improves survival and cardiac function in heart failure 12 samples shares editing, base
GSE294246 Ablation of PKCα phosphorylation cascades by CRISPR-Cas9 Base Editing Rescues Heart Failure 12 samples shares editing, base
GSE308849 Ablation of PKCα phosphorylation cascades by CRISPR-Cas9 Base Editing Rescues Heart Failure [RNA-Seq] 12 samples shares editing, base
GSE239872 Base editing effectively prevents the early-onset hypertrophic cardiomyopathy in Mybpc3 mutant mice 9 samples shares editing, base

Search all mouse 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.