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A Workflow for Transcriptome-wide Assessment of Antisense Oligonucleotide Selectivity

GSE293987 Homo sapiens (human) RNA-seq (gene expression) 53 samples Submitted 2025/11/24 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 53 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
Homo sapiens (human)
Samples
53 samples: 20 × EXP2_CONTROL_ASO_sample vs 17 × EXP1_ACTN_ASO_sample vs 8 × EXP1_UTC vs 8 × EXP2_UTCGroups 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.
Paper
A Workflow for Transcriptome-Wide Assessment of Antisense Oligonucleotide SelectivityNucleic acid therapeutics · 2025
Summary (written by the submitters)
Antisense oligonucleotides (ASOs) designed to recruit RNase H1 (gapmer ASOs) have been used successfully to downregulate the expression of therapeutic targets. Gapmer ASOs can be identified that selectively reduce the expression of transcripts containing the perfectly complementary intended ASO target site without affecting the expression of unintended transcripts (selective ASOs).
Read the full summary ↓
However, ASOs can also be identified that reduce the expression of unintended transcripts with target sites that are not perfectly complementary to the ASO (non-selective ASOs). Currently, the understanding of in silico rules for predicting off-targets is suboptimal. In order to determine the selectivity of gapmer ASOs, we therefore developed an experimental workflow called Concentration-Response Digital Gene Expression (CR-DGE). In CR-DGE, ASO treatment is performed at increasing concentrations and the effect on the transcriptome is measured using 3’Tag-Seq. Expression data are then analyzed to identify genes with concentration-responsive knockdown. We demonstrate that CR-DGE identifies ASO concentration-responsive genes with high reproducibility and greater sensitivity than conventional single-concentration assays. Applying CR-DGE to a panel of gapmer ASOs identifies ASOs with a range of selectivity. These results demonstrate that CR-DGE can be used effectively to assess the selectivity of gapmer ASOs, offering a valuable tool for research and therapeutic development.
Published in
A Workflow for Transcriptome-Wide Assessment of Antisense Oligonucleotide Selectivity
Damle SS, Watt A, Kuntz S et al. · Nucleic acid therapeutics 2025 · PMID 41021289 · doi:10.1177/21593337251378141
This dataset
Open GSE293987 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 GSE293987_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 PRJNA1247536 and SRA study SRP576978. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41021289) ↗
Study design
20 × EXP2_CONTROL_ASO_sample vs 17 × EXP1_ACTN_ASO_sample vs 8 × EXP1_UTC vs 8 × EXP2_UTC

Supports a between-group comparison across 53 samples.

How we read this: 4 replicated groups read from 53 sample titles; they account for 53 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

+ 13 more — browse all 53 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

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