GEO series

Onco-fetal reprogramming of endothelial cells drives immunosuppressive macrophages in Hepatocellular Carcinoma (scRNA-seq)

GSE156625 Homo sapiens (human) Single-cell RNA-seq 78 samples Submitted 2020/09/24 Platform GPL17021Platform GPL16791

At a glance

Single-cell RNA-seq · human · 78 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: 78 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)

Liver dysfunction is associated with diseases ranging from metabolic disorders to hepatocellular carcinomas (HCC). Here we employed single-cell RNA-sequencing to extensively characterise the cellular landscape of human liver, from development to disease. We analysed ~212,000 cells representing human fetal liver, HCC and mouse liver. Our analysis revealed a remarkable fetal-like reprogramming of the tumor microenvironment (TME).

Read the full summary ↓

Specifically, the HCC ecosystem displayed features reminiscent of fetal development, including the re-emergence of fetal-associated endothelial cells (PLVAP+/VEGFR2+), and fetal-like (FOLR2+) tumor-associated macrophages (TAMs). In a cross-species comparative analysis, we discovered remarkable similarity of gene expression and regulatory networks between mouse embryonically-seeded, fetal-liver and FOLR2+ tumor macrophages. Spatial transcriptomics further corroborated a shared onco-fetal ecosystem between fetal-liver and HCC. Furthermore, gene regulatory analysis, spatial transcriptomics, and in vitro functional assays implicated VEGF and NOTCH signaling in maintaining onco-fetal ecosystem. Taken together, we report a shared immunosuppressive onco-fetal ecosystem between the human fetal-liver and HCC. Our results unravel a previously unexplored onco-fetal reprogramming of tumor ecosystem, provides a novel target for therapeutic interventions in HCC and also opens up avenues for identifying similar paradigms in other cancers and disease states.

This dataset

Open GSE156625 in finder → Find more 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 GSE156625_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 PRJNA658541 and SRA study SRP278381. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 32976798) ↗

Samples in this study

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

+ 78 more — browse all 78 samples with per-sample file links →

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