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Whole-course Taming of Tumor-associated Macrophages from Central to Peripheral using Engineered Outer Membrane Vesicle Nanohybrids for Improved Immunotherapy [part 1: RNA-seq]

GSE212259 Mus musculus (mouse) RNA-seq (gene expression) 36 samples Submitted 2025/08/01 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 36 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
Mus musculus (mouse)
Samples
36 samples: 12 conditions, mostly in triplicateGroups 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)
Only regulation of tumor-associated macrophages (TAMs) in tumor microenvironment (TME) is always insufficient to achieve satisfactory clinical outcome, and training of central progenitor cells of TAMs in bone marrow is a potential approach. To achieve the whole-course taming of TAMs from central to peripheral, we developed nanohybrids containing bacterial outer membrane vesicle (OMV), GM-CSF and SIRPα-Fc fusion protein, which were coated with calcium phosphate (CaP) shell.
Read the full summary ↓
The surface CaP coating endowed nanohybrids with safe circulation, allowing them to reach both bone marrow and TME. In bone marrow, OMV and GM-CSF together induced trained immunity of central progenitor cells, indicating by transcriptional alterations and epigenetic remodeling, which was inherited to peripheral TAMs. In peripheral TME, OMV and SIRPα-Fc triggered M1-like TAM phagocytosis with subsequent CD8+ T cell activation.
This dataset
Open GSE212259 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 GSE212259_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 PRJNA869176 and SRA study SRP394448. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
12 conditions, mostly in triplicate
RNA_B16_F10_PBS ×3 RNA_B16_F10_OMV-SIRP ×3 RNA_B16_F10_OMV-SIRP-G ×3 RNA_MC38_PBS ×3 RNA_MC38_OMV-SIRP ×3 RNA_MC38_OMV-SIRP-G ×3 RNA_GMPs_PBS ×3 RNA_GMPs_OMV-SIRP ×3 +4 more

Supports a between-group comparison across 36 samples.

How we read this: 12 replicated groups read from 36 sample titles; they account for 36 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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