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Direct contact between iPSC-derived macrophages and hepatocytes drives reciprocal acquisition of Kupffer cell identity and hepatocyte maturation

GSE307755 Homo sapiens (human) RNA-seq (gene expression) 53 samples Submitted 2025/09/30 Platform GPL11154
At a glance

RNA-seq (gene expression) · human · 53 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
53 samples: 53 samples, each with a distinct title — no replicated groupsGroups 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)
As the resident tissue macrophage of the liver, Kupffer Cells (KC) play an important role in homeostasis and tissue support. However, current in-vitro liver models often ignore the contribution of these KCs toward the proper response and function of the tissue. This is especially relevant when we consider the implications of immune-mediated drug injuries.
Read the full summary ↓
To address this issue, we developed an isogenic co-culture system utilizing iPSC-derived macrophages (iMacs) and hepatocytes (iHeps). Directly co-culturing iHeps with iMacs improved the differentiation and maturation of the iHeps, with significant downregulation of fetal hepatocyte markers as well as upregulation of cytochrome genes. Furthermore, the co-culture also imparted stronger KC identity to the iMacs in a contact-dependent manner, with iMacs cultured in iHep conditioned media alone showing weaker expression of key KC markers. Finally, challenging the iHep-iMac co-culture system with 7 paradigm hepatotoxic compounds showed dose-dependent cytokine response in the 5 compounds associated with immune-mediated liver injuries, while no significant changes were observed in the 2 compounds with no reported immune-dependent complications. This effect was also not recapitulated when the co-culture was instead performed with human peripheral blood monocyte-derived macrophages, suggesting that iMacs are essential for liver toxicity response. Taken together, our study shows not only the importance of macrophages in tissue systems, but also that the source of macrophages is critical to the development of accurate in-vitro human models.
This dataset
Open GSE307755 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 GSE307755_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 PRJNA1327270 and SRA study SRP619157. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
53 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 53 sample titles: 53 distinct titles with little repetition. 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

+ 13 more — browse all 53 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

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Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.