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snRNASeq of Metastatic Urothelial carcinoma samples

GSE302781 Homo sapiens (human) Single-nucleus RNA-seq 15 samples Submitted 2026/07/31 Platform GPL18573
At a glance

Single-nucleus RNA-seq · human · 15 samples

What it is
Single-nucleus RNA-seqGene activity measured in individual nuclei (used when whole cells are hard to isolate, for example from frozen tissue).
Organism
Homo sapiens (human)
Topic
Bladder cancerThis series is part of our curated briefing on it.
Samples
15 samples: 15 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)
Histological variation is a common feature of metastatic cancer but its clonal origins and impact on disease history remains poorly defined. In this study, we developed a first-in-kind metastatic bladder cancer (BLCA) rapid autopsy cohort enriched in rare histological subtypes to extensively profile individuals with terminal disease. By reconstructing the evolutionary history of patient tumors, we determined that metastasis-to-metastasis seeding was the dominant driver of cancer spread and that increased polyclonal migration contributed to poor prognosis.
Read the full summary ↓
The burden, heterogeneity, and timing of acquired genomic alterations were markedly different between histological subtypes. Plasmacytoid and neuroendocrine variants developed early genomic driver alterations that associated with shorter survival. Mutational signature analysis further revealed that plasmacytoid, but not other subtypes, utilized the Fanconi Anemia pathway to shield from chemotherapy-induced genomic scarring. Using experimental models, we demonstrated that Fanconi Anemia pathway activity positively correlated with resistance to cisplatin chemotherapy, demonstrating a molecular basis for therapy resistance in plasmacytoid patients. We report the first single-nucleus profiling of mBLCA histological subtypes, revealing mixed cell states in plasmacytoid tumors and that increased overall transcriptional heterogeneity was associated with longer patient survival. Characterization of the tumor microenvironment uncovered distinct patterns of immune activation across histological subtypes, with plasmacytoid tumors exhibiting an immune-inflamed profile, while squamous subtype was dominated by immunosuppressive interactions. Lastly, we demonstrated that post-mortem cell-free DNA captured both genomic heterogeneity and transcriptional activity of the subtypes, providing a potential strategy to non-invasively assess tumor identity and aggressiveness in patients with mBLCA. Our results provide new principles into how tumor heterogeneity shapes the evolutionary history of cancer progression within subtypes of human bladder cancer
This dataset
Open GSE302781 in finder → Find more human RNA-seq datasets → Bladder 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 GSE302781_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 PRJNA1292161 and SRA study SRP601239. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
15 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 15 sample titles: 15 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
More Bladder cancer datasets

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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.