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Characterisation of human in vitro tumour-associated macrophage models to define translational relevance

GSE297659 Homo sapiens (human) RNA-seq (gene expression) 27 samples Submitted 2025/11/07 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 27 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
27 samples: 27 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.
Paper
Characterisation of human in vitro tumour-associated macrophage models to define translational relevanceScientific reports · 2025
Summary (written by the submitters)
Tumour-associated macrophages (TAMs) are key components of the tumour microenvironment with a demonstrated ability to modulate anti-tumour T-cell responses and immunotherapy outcomes. With increasing realisation that the M1/M2 paradigm does not reflect the complexity of macrophage phenotypes in cancer patients, an urgent need has arisen to develop improved, translatable in vitro models for human TAMs.
Read the full summary ↓
To address this gap, we have screened conditioned media from a panel of tumour cell lines for their ability to induce suppressive marker upregulation on human monocyte-derived macrophages (hMDMs), as well as active T-cell immunosuppression. We performed secretome characterization of these tumour-conditioned media (TCM) to shed light on cancer cell-derived soluble factors that may contribute to TAM polarisation. Furthermore, we characterized the proteomic and transcriptomic signatures of macrophages exposed to either TCM or primary ascites fluid from ovarian cancer patients and performed bioinformatics analysis to determine the most translationally relevant models of TAMs. In summary, our work provides mechanistic insights on tumour-macrophage crosstalk in the context of establishing suppressive TAM phenotypes and addresses the long-standing gap of defining translationally relevant human in vitro TAM models.
Published in
Characterisation of human in vitro tumour-associated macrophage models to define translational relevance
Dyer A, Dudley R, Ahuja S et al. · Scientific reports 2025 · PMID 41309950 · doi:10.1038/s41598-025-30224-w
This dataset
Open GSE297659 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 GSE297659_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 PRJNA1265639 and SRA study SRP586577. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41309950) ↗
Study design
27 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 27 sample titles: 27 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
Datasets on a similar topic

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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.