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Circadian Gene BMAL1 Regulation of Cell Senescence in Thyroid aging [scRNA-Seq]

GSE275233 Mus musculus (mouse) Single-cell RNA-seq 10 samples Submitted 2025/08/01 Platform GPL24247Platform GPL24676
At a glance

Single-cell RNA-seq · mouse · 10 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism
Mus musculus (mouse)
Samples
10 samples: 10 samples, each with a distinct title — no replicated groupsGroups 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)
As the global aging society intensifies, the incidence of thyroid diseases among the elderly is increasing annually, with hypothyroidism being particularly common. Studies have shown that in healthy elderly individuals, thyroid-stimulating hormone (TSH) levels are significantly elevated, while thyroid hormone (TH) levels are notably normal or decreased.
Read the full summary ↓
However, the molecular mechanisms underlying thyroid aging and function remain unclear. To understand thyroid aging, it is essential to study the molecular and functional characteristics of various thyroid cell types during the aging process. We conducted single-cell sequencing of thyroids from different age groups and identified eight typical cell types and five thyroid epithelial cell subtypes (EPI). During aging, the expression levels of thyroid hormone synthesis-related genes (TSHR, TG, TPO) increased, suggesting that EPI cells adjust their gene expression patterns in response to elevated TSH. With advancing age, rhythmic disruption and cellular senescence signals accumulated in thyroid. Further analysis indicated that the senescent EPI cell subpopulation (CDKN1A_EPI) exhibited functional decline. Additionally, we unexpectedly found that the expression of the core circadian gene BMAL1 (ARNTL) was downregulated in the CDKN1A_EPI cells of elderly individuals. To verify this finding, we used a thyroid-specific knockout mouse model and found that BMAL1 downregulation inhibited NFKBIA expression, exacerbating cellular senescence. Finally, cell line knockout experiments and transcriptome sequencing further confirmed that BMAL1 knockout led to decreased expression of NF-kB Inhibitor Alpha (NFKBIA), a key mechanism driving thyroid cell senescence. This discovery not only reveals the relationship between thyroid cell aging and circadian rhythm disruption but also enriches our understanding of the mechanisms underlying thyroid aging.
This dataset
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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 GSE275233_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 PRJNA1150072 and SRA study SRP527481. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
10 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 10 sample titles: 10 distinct titles with little repetition. 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.