Single cell RNA sequencing (scRNAseq) of urine-derived cells, peripheral blood mononuclear (PBMC), and tumor cells from bladder cancer patients.
Single-cell RNA-seq · human · 33 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)
- Topic
- Bladder cancerThis series is part of our curated briefing on it.
- Samples
- 33 samples: 33 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.
- Paper
- Urine scRNAseq reveals new insights into the bladder tumor immune microenvironmentThe Journal of experimental medicine · 2024
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.
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.
Supplementary files
The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE267718_RAW.tar (it can run to tens of gigabytes).
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.
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 PRJNA1112449 and SRA study SRP508226. Searching any of these in the dataset finder brings you back here.
Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.
How we read this: Read from 33 sample titles: 33 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.
- GSM8273653 Patient 1 Urine 3' scRNAseq
- GSM8273654 Patient 2 Urine 3' scRNAseq
- GSM8273655 Patient 2 Tumor 3' scRNAseq
- GSM8273656 Patient 3 Urine 3' scRNAseq
- GSM8273657 Patient 3 Tumor 3' scRNAseq
- GSM8273658 Patient 4 Urine 5' scRNAseq
- GSM8273659 Patient 4 Tumor 5' scRNAseq
- GSM8273660 Patient 5 Urine 3' scRNAseq
- GSM8273661 Patient 5 Tumor 3' scRNAseq
- GSM8273662 Patient 5 PBMC 3' scRNAseq
- GSM8273663 Patient 6 Urine 5' scRNAseq
- GSM8273664 Patient 6 PBMC 5' scRNAseq
- GSM8273665 Patient 6 Tumor 5' scRNAseq
- GSM8273666 Patient 7A Urine 3' scRNAseq
- GSM8273667 Patient 7A PBMC 3' scRNAseq
- GSM8273668 Patient 7A Tumor 3' scRNAseq
- GSM8273669 Patient 7B Urine 3' scRNAseq
- GSM8273670 Patient 7B Tumor 3' scRNAseq
- GSM8273671 Patient 8 Urine 3' scRNAseq
- GSM8273672 Patient 8 Tumor 3' scRNAseq
- GSM8273673 Patient 8 PBMC 3' scRNAseq
- GSM8273674 Patient 9 Urine 3' scRNAseq
- GSM8273675 Patient 9 Tumor 3' scRNAseq
- GSM8273676 Patient 9 PBMC 3' scRNAseq
- GSM8273677 BCG Week 1 Pre BCG 3' scRNAseq
- GSM8273678 BCG Week 1 Post BCG 3' scRNAseq
- GSM8273679 BCG Week 3 Pre BCG 3' scRNAseq
- GSM8273680 BCG Week 3 Post BCG 3' scRNAseq
- GSM8273681 BCG Week 4 Post BCG 3' scRNAseq
- GSM8273682 BCG Week 5 Pre BCG 3' scRNAseq
- GSM8273683 BCG Week 5 Post BCG 3' scRNAseq
- GSM8273684 BCG Week 6 Pre BCG 3' scRNAseq
- GSM8273685 BCG Week 6 Post BCG 3' scRNAseq
- GSE181389 Transcriptome profiling of HOXA13 and HOXB13 expression knockdown in bladder cancer cell lines. 59 samples same disease briefing · shares bladder
- GSE193755 Human bladder cancer cell line high throughput sequencing - genes regulated by hMSH2 12 samples same disease briefing · shares bladder
- GSE214886 Downregulation of CBX7 induced by EZH2 upregulates FGFR3 expression to reduce sensitivity to cisplatin in bladder cancer 12 samples same disease briefing · shares bladder
- GSE211924 UCHL5 promotes Tumorigenesis of Bladder Cancer by Activating AKT/mTOR signaling pathway and targeting c-myc, SLC25A19, and ICAM5. 9 samples same disease briefing · shares bladder
- GSE172359 Combination of urine exosomal mRNAs and lncRNAs as novel diagnostic biomarkers for bladder cancer 6 samples same disease briefing · shares bladder
- GSE182310 Transcriptome sequence of Human bladder cancer cell line 6 samples same disease briefing · shares bladder
- GSE193753 Human bladder cancer cell line high throughput sequencing - genes regulated by hMSH2 - CDDP therapy 6 samples same disease briefing · shares bladder
- GSE193754 Human bladder cancer cell line high throughput sequencing - genes regulated by hMSH2 - untreated 6 samples same disease briefing · shares bladder
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.