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Multilineage communication regulates human liver bud self-organization from pluripotency II

GSE96981 Mus musculus (mouse) Single-cell RNA-seq 741 samples Submitted 2017/06/21 Platform GPL16791Platform GPL17021
At a glance

Single-cell RNA-seq · mouse · 741 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
Mus musculus (mouse)
Samples
741 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)
Conventional 2-D differentiation from pluripotency fails to recapitulate cell interactions occurring during organogenesis. 3-D organoids generate complex organ-like tissues, however it is unclear how heterotypic interactions impact lineage identity. Here we use single-cell RNA-seq to reconstruct hepatocyte-like lineage progression from pluripotency in 2-D culture.
Read the full summary ↓
We then derive 3-D liver bud (LB) organoids by reconstituting hepatic, stromal, and endothelial interactions, and deconstruct heterogeneity during LB development. We find that LB hepatoblasts diverge from the 2-D lineage, and express epithelial migration signatures characteristic of organ budding. We benchmark 3-D LBs against fetal and adult human liver scRNA-seq data, and find a striking correspondence between the 3-D LB and fetal liver cells. We use a receptor-ligand pairing analysis and a high-throughput inhibitor assay to interrogate signaling in LBs, and show that VEGF crosstalk potentiates endothelial network formation and hepatoblast differentiation. Our molecular dissection reveals interlineage communication regulating organoid development, and illuminates previously inaccessible aspects of human liver development.
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 GSE96981_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 PRJNA380268 and SRA study SRP102427. Searching any of these in the dataset finder brings you back here.

Samples in this study

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