GEO series

Gene expression signature of vemurafenib resistance in WM989 and WM983B melanoma cells

GSE97681 Homo sapiens (human) Single-cell RNA-seq 122 samples Submitted 2017/04/13 Platform GPL18573

At a glance

Single-cell RNA-seq · human · 122 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: MelanomaThis series is part of our curated briefing on it.
Samples: 122 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)

Therapies targeting signaling molecules mutated in cancers can often have striking short-term effects, but the emergence of resistant cancer cells is a major barrier to full cures. Resistance can sometimes result from a secondary mutations in rare cells, but other times, there is no clear genetic cause, raising leaving the possibility of non-genetic rare cell variability.

Read the full summary ↓

Here, we show that melanoma cells can display profound transcriptional variability at the single cell level that predicts which cells will ultimately resist drug treatment. This variability involves semi-coordinated transcription of a number of resistance markers at high levels in a very small percentage of cells. The addition of drug then induces an epigenetic reprogramming in these cells, converting the transient transcriptional state to a stably resistant state. This reprogramming begins withis a progressive process consisting of a loss of SOX10-mediated differentiation followed by activation of new signaling pathways, partially mediated by activity of Jun-AP-1 and TEAD. Our work reveals the multistage nature of the acquisition of drug resistance and provides a framework for understanding resistance dynamics. We find that other cell types also exhibit sporadic expression of many of these same marker genes, suggesting the existence of a general rare-cell expression program.

This dataset

Open GSE97681 in finder → Find more human RNA-seq datasets → Melanoma 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 GSE97681_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 PRJNA382674 and SRA study SRP103828. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 28607484) ↗

Samples in this study

The sample list for this study is not cached yet. Press Load samples and it will be fetched from NCBI.

+ 122 more — browse all 122 samples with per-sample file links →

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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.