← BioTransfer GEO Dataset Finder
GEO series

Gene expression dynamics of branching morphogenesis in pancreatic cancer organoids (2)

GSE261159 Homo sapiens (human) RNA-seq (gene expression) 181 samples Submitted 2024/03/08 Platform GPL19057Platform GPL18573
At a glance

RNA-seq (gene expression) · human · 181 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
181 samples: 181 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
Heterogeneity-driven phenotypic plasticity and treatment response in branched-organoid models of pancreatic ductal adenocarcinomaNature biomedical engineering · 2025
Summary (written by the submitters)
The transcriptional dynamics occurring at the self-organizing glandular epithelial during branching morphogenesis are pivotal for deciphering various processes ranging from normal tissue growth to cancer formation. For the past decade matrigel© has been widely used as extracellular matrix for the propagation of organoids, here we report that only in the presence of a higher than 70% collagen matrix symmetry breaking can be observed.
Read the full summary ↓
Comparing the transcriptomic profile of collagen and matrigel grown-organoids we identified that matrigel organoids are more basal-like, have a higher EMT signature (GSEA) compared to the more classical collagen grown organoids. In this study we used single primary murine pancreatic ductal adenocarcinoma cells (PDAC) cells embedded in floating collagen gels and adapted the media supplementation, thus allowing organoids to self-organize into highly complex branched structures, replicating the in vivo tumour architecture. During organoid development we identified four distinct morphogenetic phases, each characterized by a unique pattern of cell proliferation, invasion, matrix deformation and protein expression. In our search for novel molecular drivers of branching morphogenesis we compared the transcriptome of two distinct phases, an early development (day 7) vs a late mature time point (day 13). We compared these two distinct developmental time points in using PCA, GSEA and respective heat maps of major processes illustrating the overexpression of genes associated with: proliferation, ECM interaction, signalling by Rho GTPases and EMT in day-7 organoids and genes associated with ion channel transport at day-13 organoids. An important aspect raised during our investigation was the effect of different genotypes (Ptf1aCre/+; KrasG12D/+, Pdx1Cre/+; KrasG12D/+; TP53fl/fl) on the branch formation of organoids. To this end we generated organoids derived from KC and KPC tumours, which interestingly gave rise to strikingly similar branching organoids. Although the morphology was not significantly altered the transcriptional profiling showed higher proliferation (Myc targets, E2F targets), EMT score for the KPC organoids. We propose that the spatiotemporal synchronized processes of cell proliferation, matrix remodeling, contraction and ion channel flux are key-events of the different morphogenic phases and lead to the formation of these complex structures. Importantly, these dynamic processes are accompanied by strong transcriptional profile changes, with the organoids undergoing de-differentiation during branch elongation and later on activating an epithelial gene expression program upon maturation. Taken together, these results illustrate the ability of tumour cells to self-organize in multicellular complex structures and provide with a novel system to study branching morphogenesis and tumour biology in vitro. In the updated submission we include new data sets from our recent publication. More specifically we focus on how we can capture inter- and intratumoral heterogeneity using our previously published organoid system (Randriamanantsoa, Papargyriou et al. 2022) of pancreatic cancer and how these morphologically diverse organoids respond to targeted therapy. We aim to understand the following four biological questions: First, we aim to address intertumoral heterogeneity by culturing classical and basal-like PDAC cells in 2D culture conditions and 3D floating collagen gels. Indeed, in both cases we observe a clear separation between mesenchymal and epithelial organoids and describe pivotal pathways (f.e. TGF pathway) that are differentially regulated between the two culture conditions (2D vs 3D). In detail, we culture 3 epithelial (ID: 8442, 9591, 53631) and 3 mesenchymal (ID: 8028, 9091, 16992) murine pancreatic cancer lines in established 2D conditions and in 3D floating collagen gels. Next, we focus on understanding the transcriptional background of distinct organoid morphologies derived from the same parental cell lines of either epithelial or mesenchymal origin. In more detail we transcriptionally analyze specific epithelial (line ID 9591) as a bulk population and their subclones: TEBBO, cystic branched, thick-branched and tree-like and mesenchymal pancreatic cancer line (line ID 16992) as a bulk population and their subclones: branched mesenchymal, firework and star-like. After performing a 102-drug screening on these clones we identified potent target therapy drugs acting in specific phenotypes. To validate the effects on the transcriptional level we treated all the epithelial organoid phenotypes with the combination of AZD5153 (BET/BRD4 inhibitor)+Poziotinib (pan-HER inhibitor) and the mesenchymal phenotypes with monotreatments of the following drugs: Birinapant (SMAC antagonist) Poziotinib (pan-HER inhibitor), Saracatinib (Src inhibitor) and RO5126766 (RAF/MEK inhibitor). Furthermore, to translate our findings in a human setting we developed a new media composition which enables the formation of branching organoids in pancreatic cancer patient-derived organoids (PDOs). We examined the effect of our newly developed media, the Basal Branching PDO Media on the transcriptome of 3 PDOs (PDO line ID: B211, B250, B320) compared to previously established media conditions such as the Full PDO Media.
Published in
Heterogeneity-driven phenotypic plasticity and treatment response in branched-organoid models of pancreatic ductal adenocarcinoma
Papargyriou A, Najajreh M, Cook DP et al. · Nature biomedical engineering 2025 · PMID 39658630 · doi:10.1038/s41551-024-01273-9
This dataset
Open GSE261159 in finder → Find more 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 GSE261159_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 PRJNA1085645 and SRA study SRP494124. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39658630) ↗
Study design
181 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 181 sample titles: 181 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.

Samples in this study

+ 141 more — browse all 181 samples with per-sample file links →

More Pancreatic cancer datasets

Search all RNA-seq datasets in GEO →

Share this dataset

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.