GEO series

Cell therapy using ex vivo reprogrammed macrophages enhances antitumor immune responses in melanoma

GSE278699 Mus musculus (mouse) Single-cell RNA-seq 18 samples Submitted 2025/09/30 Platform GPL21103

At a glance

Single-cell RNA-seq · mouse · 18 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: 18 samples: 18 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: Cell therapy using ex vivo reprogrammed macrophages enhances antitumor immune responses in melanomaJournal of experimental & clinical cancer research : CR · 2024

Summary (written by the submitters)

Background: Macrophage-based cell therapies have shown modest success in clinical trials, which can be attributed to their phenotypic plasticity, where transplanted macrophages get reprogrammed towards a pro-tumor phenotype. In most tumor types, including melanoma, the balance between antitumor M1-like and tumor-promoting M2-like macrophages is critical in defining the local immune response with a higher M1/M2 ratio favoring antitumor immunity.

Read the full summary ↓

Therefore, designing novel strategies to increase the M1/M2 ratio in the TME has high clinical significance and benefits macrophage-based cell therapies. Methods: In this study, we reprogrammed antitumor and proinflammatory macrophages ex-vivo with HDAC6 inhibitors (HDAC6i). We administered the reprogrammed macrophages intratumorally as an adoptive cell therapy (ACT) in the syngeneic SM1 murine melanoma model and patient-derived xenograft bearing NSG-SGM3 humanized mouse models. We phenotyped the tumor-infiltrated immune cells by flow cytometry and histological analysis of tumor sections for macrophage markers. We performed bulk RNA-seq profiling of murine bone marrow-derived macrophages treated with vehicle or HDAC6i and single-cell RNA-seq profiling of SM1 tumor-infiltrated immune cells to determine the effect of intratumor macrophage ACT on the tumor microenvironment (TME). We further analyzed the single-cell data to identify key cell-cell interactions and trajectory analysis to determine the fate of tumor-associated macrophages post-ACT. Results: Macrophage ACT resulted in diminished tumor growth in both mouse models. We also demonstrated that HDAC6 inhibition in macrophages suppressed the polarization toward tumor-promoting phenotype by attenuating STAT3-mediated M2 reprogramming. Two weeks post-transplantation, ACT macrophages were viable, and inhibition of HDAC6 rendered intratumor transplanted M1 macrophages resistant to repolarization towards protumor M2 phenotype in-vivo. Further characterization of tumors by flow cytometry, single-cell transcriptomics, and single-cell secretome analyses revealed a significant enrichment of antitumor M1-like macrophages, resulting in increased M1/M2 ratio and infiltration of CD8 effector T-cells. Computational analysis of single-cell RNA-seq data for cell-cell interactions and trajectory analyses indicated activation of monocytes and T-cells in the TME. Conclusions: In summary, for the first time, we demonstrated the potential of reprogramming macrophages ex-vivo with HDAC6 inhibitors as a viable macrophage cell therapy to treat solid tumors.

Published in

Cell therapy using ex vivo reprogrammed macrophages enhances antitumor immune responses in melanoma

Noonepalle SKR, Gracia-Hernandez M, Aghdam N et al. · Journal of experimental & clinical cancer research : CR 2024 · PMID 39272209 · doi:10.1186/s13046-024-03182-w

This dataset

Open GSE278699 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 GSE278699_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 PRJNA1168480 and SRA study SRP536395. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39272209) ↗

Study design

18 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 18 sample titles: 18 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

GSM8553172 1V1
GSM8553173 2V2
GSM8553174 3V3
GSM8553175 4V4
GSM8553176 5V5
GSM8553177 6V6
GSM8553178 7V7
GSM8553179 8V8
GSM8553180 9V9
GSM8553181 11NA1
GSM8553182 12NA2
GSM8553183 13NA3
GSM8553184 14NA4
GSM8553185 15NA5
GSM8553186 16NA6
GSM8553187 17NA7
GSM8553188 18NA8
GSM8553189 19NA9

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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.