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High-throughput sequencing for analysis of macropahges from old and young mice.

GSE198666 Mus musculus (mouse) Single-cell RNA-seq 25 samples Submitted 2024/05/31 Platform GPL17021Platform GPL21103
At a glance

Single-cell RNA-seq · mouse · 25 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
25 samples: 11 × Callus Macrophages-BulkRNAseq_Young vs 10 × Callus Macrophages-BulkRNAseq_OldGroups 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
Age-related changes to macrophage subpopulations and TREM2 dysregulation characterize attenuated fracture healing in old miceAging cell · 2024
Summary (written by the submitters)
Immune cells are essential for proper fracture healing. The goal of this study was to compare the transcriptional profiles of the CD45+ immune cells isolated from the healing fracture callus of old (24 month) and young (3 month) mice (C57BL/6). Analysis of all CD45+ cells was completed via single cell RNAseq. Additional analysis of the macrophages involved in fracture healing in old and young mice was performed via bulk RNAseq.
Read the full summary ↓
We show that there were multiple subpopulations of macrophages that were transcriptionally heterogenous and present simultaneously within the fracture callus tissue. Attenuated healing in the old mice was associated with a transcriptional profile that shifted towards more pro-inflammatory phenotypes.
Published in
Age-related changes to macrophage subpopulations and TREM2 dysregulation characterize attenuated fracture healing in old mice
Clark D, Brazina S, Miclau T et al. · Aging cell 2024 · PMID 38825965 · doi:10.1111/acel.14212
This dataset
Open GSE198666 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 GSE198666_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 PRJNA816431 and SRA study SRP364134. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 38825965) ↗
Study design
11 × Callus Macrophages-BulkRNAseq_Young vs 10 × Callus Macrophages-BulkRNAseq_Old

Supports a between-group comparison across 21 samples.

How we read this: 2 replicated groups read from 25 sample titles; they account for 21 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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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.