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Lysosomal sequestration of PARP inhibitors drives heterogeneous accumulation in ovarian cancer to increase efficacy [RNA-seq]

GSE281517 Homo sapiens (human) Single-cell RNA-seq 72 samples Submitted 2026/01/05 Platform GPL30173
At a glance

Single-cell RNA-seq · human · 72 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism
Homo sapiens (human)
Topic
Ovarian cancerThis series is part of our curated briefing on it.
Samples
72 samples: 72 samples, each with a distinct title — no replicated groupsGroups 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)
PARP inhibitors have had a transformative impact in the treatment of high-grade serous ovarian cancer (HGSOC), but resistance remains a clinical hurdle. Increased expression of ABC drug efflux transporters has been identified as a resistance mechanism in patients but whether intracellular concentrations of these drugs is heterogeneous within tumours remains unclear.
Read the full summary ↓
We developed a patient-derived explant multi-modal imaging pipeline which demonstrated significant cell-intrinsic heterogeneity of PARP inhibitor accumulation, both between patients and within tumours. Spatial transcriptomics revealed enrichment of apoptotic signatures in high-drug compared to low-drug regions, as well as metabolic and ECM-related differences. While some cases demonstrated a clear anticorrelation of PARP inhibitor levels with MDR1(ABCB1), this was not universal. Rucaparib, an intrinsically fluorescent PARP inhibitor accumulates heterogeneously at the single cell-level in established cell lines, and omics approaches revealed that this is driven by lysosomal sequestration, a pattern also observed in patient samples. Perturbation of lysosomal content altered intracellular levels of rucaparib and niraparib and impacted efficacy suggesting that lysosomes act as a drug reservoir to improve drug response.
This dataset
Open GSE281517 in finder → Find more human RNA-seq datasets → Ovarian cancer landscape →
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 GSE281517_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 PRJNA1184173 and SRA study SRP544246. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
72 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 72 sample titles: 72 distinct titles with little repetition. 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

+ 32 more — browse all 72 samples with per-sample file links →

More Ovarian cancer 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.