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RNA-Seq of Single Prostate CTCs Implicates Non-Canonical Wnt Signaling in Antiandrogen Resistance

GSE67980 Homo sapiens (human) Single-cell RNA-seq 169 samples Submitted 2015/09/17 Platform GPL16288
At a glance

Single-cell RNA-seq · human · 169 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
Prostate cancerThis series is part of our curated briefing on it.
Samples
169 samples
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)
Prostate cancer is initially responsive to androgen deprivation, but the effectiveness of androgen receptor (AR) inhibitors in recurrent disease is variable. Biopsy of bone metastases is challenging, hence sampling circulating tumor cells (CTCs) may reveal drug resistance mechanisms. We established single cell RNA-sequencing profiles of 77 intact CTCs isolated from 13 patients (mean 6 CTCs/patient) using microfluidic enrichment.
Read the full summary ↓
Single CTCs from each individual display considerable heterogeneity, including expression of AR gene mutations and splicing variants. Retrospective analysis of CTCs from patients progressing on AR inhibitor, compared with untreated cases indicates activation of noncanonical Wnt signaling (P=0.0064). Ectopic expression of Wnt5a in prostate cancer cells attenuates the antiproliferative effect of AR inhibition, while its suppression in drug-resistant cells restores partial sensitivity, a correlation also evident in an established mouse model. Thus, single cell analysis of prostate CTCs reveals heterogeneity in signaling pathways that could contribute to treatment failure.
This dataset
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.

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 GSE67980_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA281419 and SRA study SRP057275. Searching any of these in the dataset finder brings you back here.

Samples in this study

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