GEO series

A multiplexed single-cell CRISPR screening platform enables systematic dissection of the unfolded protein response

GSE90546 Homo sapiens (human) Single-cell RNA-seq 17 samples Submitted 2016/12/15 Platform GPL20301Platform GPL16791

At a glance

Single-cell RNA-seq · human · 17 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)
Samples: 17 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)

Functional genomics efforts face tradeoffs between number of perturbations examined and complexity of phenotypes measured. We bridge this gap with Perturb-seq, which combines droplet-based single-cell RNA-seq with a strategy for barcoding CRISPR-mediated perturbations, allowing many perturbations to be profiled in pooled format. We applied Perturb-seq to dissect the mammalian unfolded protein response (UPR) using single and combinatorial CRISPR perturbations.

Read the full summary ↓

A genome-scale CRISPR interference (CRISPRi) screen identified genes whose depletion perturbs ER homeostasis. Subjecting ~100 hits to Perturb-seq enabled high-precision functional clustering of genes. Single-cell analyses revealed epistasis among the three UPR branches, bifurcated UPR branch activation between cells subject to the same perturbation, and differential activation of the branches across hits, including a feedback loop between the translocon and the IRE1α branch. These studies provide insight into how the three sensors of ER homeostasis monitor distinct types of stress and highlight the ability of Perturb-seq to dissect complex cellular responses.

This dataset

Open GSE90546 in finder → Find more human RNA-seq datasets →

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 GSE90546_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 PRJNA354963 and SRA study SRP094654. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 27984733) ↗

Samples in this study

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+ 17 more — browse all 17 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.