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Time-course characterization of whole-transcriptome dynamics of HepG2/C3A spheroid and its toxicological implications

GSE263674 Homo sapiens (human) RNA-seq (gene expression) 21 samples Submitted 2025/05/05 Platform GPL16791
At a glance

RNA-seq (gene expression) · human · 21 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
21 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.
Paper
Time-course characterization of whole-transcriptome dynamics of HepG2/C3A spheroids and its toxicological implicationsToxicology letters · 2024
Summary (written by the submitters)
Physiologically relevant in vitro models are a priority in predictive toxicology in order to replace and/or reduce animal experiments. The compromised toxicant metabolism of many immortalized human liver cell lines grown as monolayers as compared to in vivo metabolism limits their physiological relevance. However, recent efforts to culture liver cells in a 3D environment, such as spheroids, to better mimic the in vivo conditions, may enhance the toxicant metabolism of human liver cell lines.
Read the full summary ↓
In this study, we characterized the dynamic changes in the transcriptome of HepG2/C3A hepatocarcinoma cell spheroids maintained in a clinostat system (CelVivo) to gain insight into the metabolic capacity of this model as a function of spheroid size and culture time. We assessed morphological changes (size, necrotic core), cell health, and proliferation rate from initial spheroid seeding to 35 days of continuous culture in conjunction with a time-course (0, 3, 7, 10, 14, 21, 28 days) of the transcriptome (TempO-Seq, BioSpyder). The phenotypic characteristics of HepG2/C3A growing in spheroids was comparable to monolayer growth until ~Day 12 (Day 10-14) when a significant decrease in cell doubling rate was noted which was concurrent with down regulation of cell proliferation and cell cycle pathways over this time period. Principal Component Analysis and hierarchal clustering of the transcriptome data suggests that the Day 3, 7, and 10 spheroids are pronouncedly different from the Day 14, 21, and 28 spheroid in support of a biological transition time point during the long-term 3D spheroid cultures. Unexpectedly, the expression of genes encoding toxicant metabolism and transport rapidly increased during the early time points of spheroids to peak at Day 7 or Day 10 as compared to monolayer cultures with gradual decrease in expression with further culture, suggesting the most metabolically responsive time window for exposure studies. Overall, here we provide baseline information on the cellular and molecular characterization, with a particular focus on toxicant metabolic capacity dynamics and cell growth of HepG2/C3A 3D spheroid cultures over the time course.
Published in
Time-course characterization of whole-transcriptome dynamics of HepG2/C3A spheroids and its toxicological implications
Kim C, Zhu Z, Barbazuk WB et al. · Toxicology letters 2024 · PMID 39368564 · doi:10.1016/j.toxlet.2024.10.004
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 GSE263674_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: 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 PRJNA1098723 and SRA study SRP501241. Searching any of these in the dataset finder brings you back here.

Samples in this study

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