Data from: Engineering a spatiotemporal macrophage circuit via STING phase separation to override immune suppression in pancreatic cancer
Data files
Dec 02, 2025 version files 575.39 MB
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Bulk_RNA-Seq.zip
2.86 MB
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README.md
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scRNA-Seq.zip
572.53 MB
Abstract
Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal malignancies, largely due to its highly immunosuppressive tumor microenvironment (TME), which fuels metastasis and resistance to immunotherapy. Through comprehensive analysis of single-cell RNA sequencing (scRNA-seq) datasets, we identified multiple heterogeneous tumor-associated macrophage (TAMs) subpopulations as key regulators of PDAC progression, which co-express MRC1 and exert their effects by actively suppressing anti-tumor immune responses. To overcome this barrier, we developed a spatiotemporal macrophage reprogramming platform that leverages STING phase separation to reprogram TAM plasticity and reshape the immune landscape. This system, PMMB, integrates a CSF-1R inhibitor and a STING agonist within a macrophage-mimetic nanostructure, enabling sequential, controlled reprogramming of TAMs. By leveraging STING phase separation, PMMB stabilizes TAMs in an anti-tumor CD80⁺ phenotype while preventing excessive inflammation, achieving durable immune activation. In preclinical models, PMMB not only suppresses both primary and metastatic PDAC but also enhances CD8⁺ T cells infiltration, reinvigorates anti-PD-1 therapy responses, and mitigates immune exhaustion. These findings establish spatiotemporal macrophage circuit engineering via STING phase separation as a novel cross-scale strategy to override PDAC’s immune barriers and drive next-generation macrophage-targeted immunotherapy. This study paves the way for rationally designed, precision macrophage modulation strategies in solid tumors.
Dataset DOI: 10.5061/dryad.kkwh70shs
Description of the data and file structure
File: scRNA-Seq.zip
Description: This compressed folder contains raw and processed single-cell RNA sequencing data used in the study.
Six data files are included, representing two experimental groups (control and treatment), with three biological replicates per group:
PBS-1, PBS-2, PBS-3: Control group (phosphate-buffered saline–treated samples)
PMMB-1, PMMB-2, PMMB-3: Experimental group (treated samples)
In each subfolder of this dataset, you will find three essential files that together form the Gene Expression Matrix for a single sample.
Here is a description of what each file contains:
1. ‘barcodes.tsv.gz’：A list of all unique cell barcodes identified in the sample.
Each line represents a single cell (or droplet that contained a cell). The barcode is a short DNA sequence that is unique to each cell's RNA molecules, allowing the sequencing data to be assigned back to its cell of origin.
2. ‘features.tsv.gz’：A list of all genes (features) that were measured in the experiment.
Each line corresponds to a gene and typically includes three pieces of information: Gene ID，Gene Symbol，Feature Type.
3. ‘matrix.mtx.gz’：A sparse matrix containing the actual expression counts.
This file stores the number of RNA transcripts (UMI counts) for each gene in each cell. It uses a "sparse matrix" format to efficiently store data, as the majority of genes in a given cell have a count of zero. The file structure is:
First line: Header with the total number of genes, cells, and non-zero entries.
Subsequent lines: ‘[row] [column] [count]’, where the row corresponds to a gene in ‘features.tsv’, the column corresponds to a cell in ‘barcodes.tsv’, and the count is the UMI count for that gene in that cell.
File: Bulk_RNA-Seq.zip
Description: This compressed folder contains the bulk RNA sequencing (RNA-seq) data used in this study.
Two data tables are included:
Cell sample data table
Animal sample data table
Each table includes two experimental groups (control and treatment), with three biological replicates per group:
PBS-1, PBS-2, PBS-3: Control group (PBS-treated samples)
PMMB-1, PMMB-2, PMMB-3: Treatment group (treated samples)
Single-cell RNA sequencing of PDAC tumors in mice
To create a subcutaneous xenograft PDAC model, 5×106 KPC cells were administered via injection into the right flank of male C57BL/6 mice. PMMB was injected into the tail vein of mice on days 0, 3, 6, 9, and 12. On Day 14, the tumors from the mice were collected for scRNA-seq.
Tumors were enzymatically dissociated into single-cell suspensions. After removal of dead cells, viable cells (>80%) were resuspended in PBS with 0.5% BSA and processed for scRNA-seq. Single-cell libraries were generated using a droplet-based platform according to the manufacturer’s protocol, including reverse transcription, cDNA amplification, and library indexing.
Libraries were sequenced on an Illumina NovaSeq platform, yielding ~200,000 reads per cell with a median detection of ~2,000–2,500 genes. Raw reads were filtered with fastp (4), aligned to the mouse mm10 genome, and processed to generate gene-barcode matrices for downstream analysis in Seurat.
Bulk RNA sequencing
Total RNA was isolated from PMMB- and PBS-treated MRC1⁺ macrophages (n = 3 per group) and PDAC mouse tumor tissues using TRIzol reagent (Invitrogen) following the manufacturer’s instructions. RNA quality was confirmed using NanoDrop and Agilent Bioanalyzer (RIN > 7.0, OD260/280 > 1.8, total RNA > 1 μg). Libraries were constructed with the Illumina Stranded mRNA Prep Kit, including polyA selection, RNA fragmentation, cDNA synthesis, adapter ligation, and PCR amplification. Sequencing was performed on an Illumina NovaSeq 6000 platform (paired-end, 150 bp).
Raw reads were aligned to the mouse genome (mm10) or human genome (GRCh38) using STAR, and gene counts were generated with featureCounts. Differential expression analysis was performed with DESeq2 (|log₂ fold change| > 1, adjusted p < 0.05). DEGs were further subjected to GO, KEGG, and GSEA analyses to evaluate immune-related pathways, TAM-associated functions, and STING signaling.