GEO series

Bacterial infections shape cardiac macrophages’ response to ischemia

GSE301781 Mus musculus (mouse) Single-cell RNA-seq 12 samples Submitted 2025/09/30 Platform GPL24247

At a glance

Single-cell RNA-seq · mouse · 12 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: 12 samples: 6 × control vs 6 × CLP 6 weeksGroups 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)

Background: Patients with bacterial infections are at increased risk for subsequent cardiovascular events. Whether infections’ effects on innate immune cells within the cardiovascular system influence subsequent pathologies remains unclear. Here, we explore cardiac myeloid cells’ chronic adaptations to a preceding bacterial insult and implications for subsequent myocardial ischemia.

Read the full summary ↓

Methods: We used various flow cytometry protocols to assess cardiac immune cells, peripheral leukocytes and hematopoietic stem and progenitor cells in bone marrow and spleen. A genetic fate mapping model was used to determine cardiac macrophages’ origin after bacteremia. Cardiac leukocytes were analyzed using scRNAseq. Nanoparticle-mediated RNAi was used to target macrophages in-vivo. Results: Cardiac macrophage numbers increased sharply, and numerical changes alongside subset alterations persisted over time. Fate mapping pointed towards local origin as the primary macrophage source after infection. Profiling macrophage heterogeneity using scRNAseq, we identified two previously unknown subpopulations remaining after resolution of infection. Whereas heightened metabolic activity was one subset’s primary feature, the other displayed excessive chemotactic properties, which amplified cardiac leukocyte recruitment and inflammation after a subsequent ischemic injury. Targeting cardiac macrophages’ surplus inflammatory activity after infection using nanoparticle-enabled, macrophage-directed RNAi kept disproportionate subsequent ischemic inflammation at bay. Conclusions: Bacteremia induces long-lasting changes in the cardiovascular system’s innate immune cells’ composition. This may amplify myocardial inflammation after a subsequent ischemic injury.

This dataset

Open GSE301781 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 GSE301781_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 PRJNA1287563 and SRA study SRP599049. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

6 × control vs 6 × CLP 6 weeks

Supports a between-group comparison across 12 samples.

How we read this: 2 replicated groups read from 12 sample titles; they account for 12 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

GSM9089582 control 1 control
GSM9089583 control 2 control
GSM9089584 control 3 control
GSM9089585 control 4 control
GSM9089586 CLP 6 weeks 1 CLP 6 weeks
GSM9089587 CLP 6 weeks 2 CLP 6 weeks
GSM9089588 CLP 6 weeks 3 CLP 6 weeks
GSM9089589 CLP 6 weeks 4 CLP 6 weeks
GSM9089590 control 5 control
GSM9089591 control 6 control
GSM9089592 CLP 6 weeks 5 CLP 6 weeks
GSM9089593 CLP 6 weeks 6 CLP 6 weeks

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