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Age-associated microglial transcriptome leads to diminished immunogenicity and dysregulation of MCT4 and P2RY12/P2RY13 related functions

GSE287220 Mus musculus (mouse) RNA-seq (gene expression) 20 samples Submitted 2025/01/17 Platform GPL13112
At a glance

RNA-seq (gene expression) · mouse · 20 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
20 samples: 5 × BV-2 cells, aged, untreated, N vs 5 × BV-2 cells, aged, LPS treated, N vs 5 × BV-2 cells, young, untreated, N vs 5 × BV-2 cells, young, LPS treated, NGroups 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-associated microglial transcriptome leads to diminished immunogenicity and dysregulation of MCT4 and P2RY12/P2RY13 related functionsCell death discovery · 2025
Summary (written by the submitters)
The aging process is marked by a time-dependent deterioration in cellular functions, particularly the immune and neural systems. Understanding the phenotype acquisition of microglia, the sentinel immune cells of the brain, is crucial for understanding the nature of age-related neurological diseases. However, the specific phenotype adopted by microglia during aging remains a subject of debate and is contingent on the chosen experimental model.
Read the full summary ↓
To address these unresolved questions, we employed a novel and highly controlled approach utilizing long-term cultivated BV-2 microglia, exempted of additional external stimuli. Our findings revealed that aged microglial cells, in comparison to their younger counterparts, acquire a distinct gene expression profile, primarily characterized by alterations in microglial immune response. Indeed, pro-inflammatory stimulated aged and young BV-2 microglia exhibited similar transcriptomic profiles, yet the response intensity to stimulus was markedly muted in the aged microglia. Functional neurotoxic assays confirmed diminished neuronal death in coculture with aged, activated microglia, underscoring a compromised immune response. Furthermore, a subsequent comparative analysis of aged BV-2 microglia with established transcriptomic microglial datasets from aged mice and humans identified 13 overlapping genes, laying the foundation for identifying core microglial aging signature. Particularly noteworthy were SLC16A3 and P2RY13, which consistently exhibited upregulation and downregulation, respectively, across all datasets. Additionally, four other genes—CAPG, LGALS3BP, NRIP1, and P2RY12—were found to share regulatory patterns in response to both aging and extrinsic activation. In-depth investigation focused on SLC16A3, encoding the high-affinity lactate transporter MCT4, revealed disruptions in extracellular acidification rate and lactate concentration with age. Microglial purine sensing and motility capacities, regulated by P2RY12/P2RY13, displayed age-related alterations. Remarkably, protein analysis in human brain tissue validated the observed upregulation of MCT4 and downregulation of P2RY12 in aged microglia. In conclusion, our study unveils a distinct phenotype in aged microglia characterized by compromised immune responsiveness. Through the integration of in vitro cultured BV-2 microglia with primary microglia datasets, we identify critical molecular determinants of microglial cellular aging confirmed in human aged brain tissue. This comprehensive approach offers potential insights for understanding and potentially reprogramming aged microglia, with implications for combating age-related neurological disorders.
Published in
Age-associated microglial transcriptome leads to diminished immunogenicity and dysregulation of MCT4 and P2RY12/P2RY13 related functions
Škandík M, Friess L, Vázquez-Cabrera G et al. · Cell death discovery 2025 · PMID 39828750 · doi:10.1038/s41420-025-02295-1
This dataset
Open GSE287220 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 GSE287220_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 PRJNA1211637 and SRA study SRP557862. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39828750) ↗
Study design
5 × BV-2 cells, aged, untreated, N vs 5 × BV-2 cells, aged, LPS treated, N vs 5 × BV-2 cells, young, untreated, N vs 5 × BV-2 cells, young, LPS treated, N

Supports a between-group comparison across 20 samples.

How we read this: 4 replicated groups read from 20 sample titles; they account for 20 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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