GEO series

Scalable, compressed phenotypic screening using pooled perturbations (PDAC screens)

GSE267243 Homo sapiens (human) RNA-seq (gene expression) 54 samples Submitted 2024/05/13 Platform GPL18573

At a glance

RNA-seq (gene expression) · human · 54 samples

What it is: RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism: Homo sapiens (human)
Topic: Pancreatic cancerThis series is part of our curated briefing on it.
Samples: 54 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.
Paper: Compressed phenotypic screens for complex multicellular models and high-content assaysbioRxiv : the preprint server for biology · 2023

Summary (written by the submitters)

High-throughput phenotypic screens leveraging biochemical perturbations and high-content readouts are poised to advance therapeutic discovery, yet they remain constrained by limitations of scale. To address this, we establish a method of pooling exogenous perturbations followed by computational deconvolution to compress a screen’s required sample, labor, and financial input.

Read the full summary ↓

We benchmark the approach with a bioactive small molecule library and a high-content imaging readout, demonstrating the feasibility and increased efficiency of compressed experimental designs compared to conventional approaches. To prove generalizability, we apply compressed screening in two different biological discovery campaigns. In the first, we use early-passage pancreatic cancer organoids to map transcriptional responses to alibrary oftumor-microenvironmentrecombinant protein ligands. We uncover reproducible phenotypic shifts induced by specific ligands that are distinct from canonical reference signatures and uniquely correlate with clinical outcome.In the second, we examine the modulatory effects of a known mechanism of action chemical compound library on primary human peripheral blood mononuclear cell immune responses. Through contrastive analyses, we identify molecules that potentiate and/or inhibit cell-type specific transcriptional features, uncover pleiotropic effects for individual compounds across diverse cell types, and realize a systems-level view of drug responses. In sum, our approach empowers phenotypic screens with information-rich readouts to advance drug discovery efforts as well as basic biological inquiry.

Published in

Compressed phenotypic screens for complex multicellular models and high-content assays

Mead BE, Kummerlowe C, Liu N et al. · bioRxiv : the preprint server for biology 2023 · PMID 36747859 · doi:10.1101/2023.01.23.525189

This dataset

Open GSE267243 in finder → Find more human RNA-seq datasets → Pancreatic 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 GSE267243_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: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA1110737 and SRA study SRP507170. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 36747859) ↗

Samples in this study

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

+ 54 more — browse all 54 samples with per-sample file links →

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Search all human RNA-seq datasets in GEO →

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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.