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Bacterial immunotherapy leveraging IL-10R hysteresis for both phagocytosis evasion and tumor immunity revitalization [CUT&Tag 2]

GSE284220 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 24 samples Submitted 2025/03/03 Platform GPL24676
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 24 samples

What it is
ChIP-seq / ATAC-seq (genome binding and open chromatin)Where proteins bind the genome, or which regions are open and active.
Organism
Homo sapiens (human)
Samples
24 samples: 12 conditions, mostly in duplicateGroups 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)
Bacterial immunotherapy holds promising cancer-fighting potential. However, unlocking its power requires a mechanistic understanding of how bacteria both evade antimicrobial immune defenses and stimulate antitumor immune responses within the tumor microenvironment (TME). Here, by harnessing an engineered Salmonella enterica strain with this dual proficiency, we unveiled a singular mechanism underlying.
Read the full summary ↓
Specifically, the hysteretic nonlinearity of interleukin-10 receptor (IL-10R) expression drives tumor-infiltrated immune cells into a tumor-specific IL-10Rhi state. Bacteria leverage this to enhance tumor-associated macrophages producing IL-10, evade phagocytosis by tumor-associated neutrophils, and coincidently expand and stimulate the preexisting exhausted tumor-resident CD8+ T cells. This effective combination eliminated tumors, prevented recurrence, and inhibited metastasis across multiple tumor types. Analysis of human samples suggested that IL-10Rhi state might be a ubiquitous trait across human tumor types. Our study unveils the unsolved mechanism behind bacterial immunotherapy's dual challenge in solid tumors and provides a framework for intratumor immunomodulation.
This dataset
Open GSE284220 in finder → Find more human ChIP / ATAC / CUT&Tag 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 GSE284220_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: Look for peak files (.bed, .narrowPeak) and signal tracks (.bigWig) among the supplementary files; the raw reads are on ENA.

Also filed as BioProject PRJNA1198012 and SRA study SRP551319. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
12 conditions, mostly in duplicate
H3K4me3-stimu ×2 H3K4me3-interval ×2 H3K4me3-re_stimu ×2 H3K4me3-control ×2 H3K27ac-stimu ×2 H3K27ac-interval ×2 H3K27ac-re_stimu ×2 H3K27ac-control ×2 +4 more

Supports a between-group comparison across 24 samples.

How we read this: 12 replicated groups read from 24 sample titles; they account for 24 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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