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Potential landscape decomposes the cell identity conversion in liver regeneration and tumorigenesis [RNA-seq]

GSE244910 Mus musculus (mouse) Single-cell RNA-seq 10 samples Submitted 2024/09/29 Platform GPL21273

At a glance

Single-cell RNA-seq · mouse · 10 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: 10 samples: 3 × Liver-Met vs 3 × Liver-DDC+Met vs 2 × HCC_EP300_KO vs 2 × HCC_EP300_WTGroups 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.

Summary (written by the submitters)

Tumorigenesis, accompanied by tissue regenerative responses, always occurs during mammalian organ injury. In liver, injury context promotes dedifferentiated liver progenitor-like cells (LPLCs) from hepatocytes for regeneration, and also increases the occurrence of hepatocellular carcinoma (HCC). However, little is known about the contribution of injury specific LPLCs to tumorigenesis.

Read the full summary ↓

Here, by using lineage tracing model, we have demonstrated that LPLCs are highly susceptible to oncogene-induced tumorigenesis. By using quantitative landscape of differentiation dynamics (LDD) model, we have constructed potential landscape of liver regeneration and tumorigenesis via scRNA-seq data, in order to predict distinct regulations of cell fate decisions. Applying this landscape with in vivo validation by transgenic mouse model, we have found blocking Wnt activation not only inhibits tumorigenesis, but also decreases LPLCs. Surprisingly, by using least action pathway analysis, we have decomposed regeneration and tumorigenesis routes, and found EP300 is specifically involved in tumorigenesis, which is further supported by in vivo EP300-knockout model and EP300 inhibitor assays. These findings might be applied as a framework to find tumor-specific regulators and develop strategies specifically inhibiting tumor initiation while leaving tissue regeneration unaffected.

This dataset

Open GSE244910 in finder → Find more mouse 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 GSE244910_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 PRJNA1026106 and SRA study SRP465228. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

3 × Liver-Met vs 3 × Liver-DDC+Met vs 2 × HCC_EP300_KO vs 2 × HCC_EP300_WT

Supports a between-group comparison across 10 samples.

How we read this: 4 replicated groups read from 10 sample titles; they account for 10 of them. 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

GSM7831976 HCC_EP300_KO-1 HCC_EP300_KO
GSM7831977 HCC_EP300_KO-2 HCC_EP300_KO
GSM7831978 HCC_EP300_WT-1 HCC_EP300_WT
GSM7831979 HCC_EP300_WT-2 HCC_EP300_WT
GSM7831980 Liver-Met-1 Liver-Met
GSM7831981 Liver-Met-2 Liver-Met
GSM7831982 Liver-Met-3 Liver-Met
GSM7831983 Liver-DDC+Met-1 Liver-DDC+Met
GSM7831984 Liver-DDC+Met-2 Liver-DDC+Met
GSM7831985 Liver-DDC+Met-3 Liver-DDC+Met

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