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Genome engineering of primary and pluripotent stem cell-derived hepatocytes for modeling liver tumor formation

GSE275241 Homo sapiens (human) RNA-seq (gene expression) 18 samples Submitted 2024/08/25 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 18 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)
Samples
18 samples: 9 conditions, mostly in duplicateGroups 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)
Genome editing has demonstrated its utility in generating isogenic cell-based disease models, enabling the precise introduction of genetic alterations into wildtype cells to mimic disease phenotypes and explore underlying mechanisms. However, its application in liver-related diseases has been limited by challenges in genetic modification of mature hepatocytes in a dish.
Read the full summary ↓
Here we conducted a systematic comparison of various methods for primary hepatocyte culture and gene delivery to achieve robust genome editing of hepatocytes ex vivo. Our efforts yielded editing efficiencies of up to 80% in primary murine hepatocytes cultured in monolayer and 20% in organoids. To model human hepatic tumorigenesis, we utilized hepatocytes differentiated from human pluripotent stem cells (hPSCs) as an alternative human hepatocyte source. We developed a series of cellular models by introducing various single or combined oncogenic alterations into hPSC-derived hepatocytes. Our findings demonstrated that distinct mutational patterns led to phenotypic variances, affecting both overgrowth and transcriptional profiles. Notably, we discovered that the PI3KCA E542K mutant, whether alone or in combination with exogenous c-MYC, significantly impaired hepatocyte functions and facilitated cancer metabolic reprogramming. In conclusion, our study demonstrates genome-engineered hepatocytes as valuable cellular models for understanding hepatocarcinoma (HCC), especially highlighting PIK3CA and c-MYC—key oncogenes often amplified or upregulated in HCC—and providing insights into early tumorigenesis mechanisms.
This dataset
Open GSE275241 in finder → Find more human 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 GSE275241_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 PRJNA1150085 and SRA study SRP527489. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
9 conditions, mostly in duplicate
hPSC-hepatocytes, nontargeting control, ×2 hPSC-hepatocytes, APC knockout, ×2 hPSC-hepatocytes, ARID2 knockout, ×2 hPSC-hepatocytes, PIK3CA and MYC overexpressi… ×2 hPSC-hepatocytes, APC and TP53 knockout, ×2 hPSC-hepatocytes, TP53 knockout, ×2 hPSC-hepatocytes, ARID1A and AXIN1 knockout, ×2 hPSC-hepatocytes, PIK3CA overexpression, ×2 +1 more

Supports a case/control comparison: 16 samples read as cases, 2 as controls.

How we read this: 9 replicated groups read from 18 sample titles; they account for 18 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
Datasets on a similar topic

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