Histone Lactylation Antagonizes Senescence and Skeletal Muscle Aging by Modulating Aging-Related Pathways
RNA-seq (gene expression) · mouse · 72 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
- 72 samples: 72 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.
- Paper
- Histone Lactylation Antagonizes Senescence and Skeletal Muscle Aging by Modulating Aging-Related PathwaysAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
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.
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.
Supplementary files
The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE226008_RAW.tar (it can run to tens of gigabytes).
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.
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 PRJNA938234 and SRA study SRP476303. Searching any of these in the dataset finder brings you back here.
Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.
How we read this: Read from 72 sample titles: 72 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.
- GSM7061572 IMR90_Senescent_rep1(RNA-seq)
- GSM7061573 IMR90_Senescent_rep2(RNA-seq)
- GSM7061574 IMR90_Young_rep1(RNA-seq)
- GSM7061575 IMR90_Young_rep2(RNA-seq)
- GSM7061576 IMR90_Hypoxia_rep1(RNA-seq)
- GSM7061577 IMR90_Hypoxia_rep2(RNA-seq)
- GSM7061578 IMR90_Normoxia_rep1(RNA-seq)
- GSM7061579 IMR90_Normoxia_rep2(RNA-seq)
- GSM7061592 IMR90_Senescent_rep1(CUT&Tag)
- GSM7061593 IMR90_Senescent_rep2(CUT&Tag)
- GSM7061594 IMR90_Young_rep1(CUT&Tag)
- GSM7061595 IMR90_Young_rep2(CUT&Tag)
- GSM7061596 IMR90_Hypoxia_rep1(CUT&Tag)
- GSM7061597 IMR90_Hypoxia_rep2(CUT&Tag)
- GSM7061598 IMR90_Normoxia_rep1(CUT&Tag)
- GSM7061599 IMR90_Normoxia_rep2(CUT&Tag)
- GSM7329321 IMR90_2-DG_rep1(CUT&Tag)
- GSM7329322 IMR90_2-DG_rep2(CUT&Tag)
- GSM7329323 IMR90_Control1_rep1(CUT&Tag)
- GSM7329324 IMR90_Control1_rep2(CUT&Tag)
- GSM7329325 IMR90_2-DG_rep1(RNA-seq)
- GSM7329326 IMR90_2-DG_rep2(RNA-seq)
- GSM7329327 IMR90_Control1_rep1(RNA-seq)
- GSM7329328 IMR90_Control1_rep2(RNA-seq)
- GSM7950045 IMR90_A485_rep1(RNA-seq)
- GSM7950046 IMR90_A485_rep2(RNA-seq)
- GSM7950047 IMR90_Control2_rep1(RNA-seq)
- GSM7950048 IMR90_Control2_rep2(RNA-seq)
- GSM7950049 IMR90_MS-275_rep1(RNA-seq)
- GSM7950050 IMR90_MS-275_rep2(RNA-seq)
- GSM7950051 IMR90_Control3_rep1(RNA-seq)
- GSM7950052 IMR90_Control3_rep2(RNA-seq)
- GSM7950058 Muscle_MS-275_rep1(RNA-seq)
- GSM7950059 Muscle_MS-275_rep2(RNA-seq)
- GSM7950060 Muscle_Control4_rep1(RNA-seq)
- GSM7950061 Muscle_Control4_rep2(RNA-seq)
- GSM8866191 IMR90_Pyruvate(CUT&Tag)
- GSM8866192 IMR90_Control6(CUT&Tag)
- GSM8866193 IMR90_A485_lac(CUT&Tag)
- GSM8866194 IMR90_MS-275_lac(CUT&Tag)
+ 32 more — browse all 72 samples with per-sample file links →
- GSE277861 Enalapril mitigates senescence and aging-related phenotypes by targeting antioxidative genes via phosphorylated Smad1/5/9 [RNA-seq] 68 samples shares aging-related, senescence
- GSE277684 Premature senescence impairs hematopoietic stem cell function during sickle cell disease in mice and humans 52 samples shares senescence
- GSE296209 Effects of hyperbaric oxygen on tissue-specific aging and senescence in mice and human stromal cells 45 samples shares senescence
- GSE282171 Depression and Anxiety-Induced Metabolic Reprogramming Promotes Hepatocellular Carcinoma Progression through CBX5 Lactylation: Involvement of Taurocholate and PHGDH-Mediated Ferroptosis 28 samples shares lactylation
- GSE302307 PP2A Regulates Senescence and Immunogenicity in Medulloblastoma 24 samples shares senescence
- GSE285138 A phosphorylation switch within the Mediator subunit MED15 controls cellular senescence and cognitive decline 24 samples shares senescence
- GSE220722 Primary cilia as a targetable node between biliary injury, senescence and regeneration 12 samples shares senescence
- GSE275233 Circadian Gene BMAL1 Regulation of Cell Senescence in Thyroid aging [scRNA-Seq] 10 samples shares senescence
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.