NNMT promotes tubular senescence and fibrosis in chronic kidney disease
RNA-seq (gene expression) · mouse · 92 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
- 92 samples: 17 conditions, mostly in triplicateGroups 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
- NNMT inhibition counteracts tubular senescence and fibrosis in early stages of chronic kidney diseaseCell reports · 2026
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 GSE280554_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 PRJNA1179379 and SRA study SRP541775. Searching any of these in the dataset finder brings you back here.
Supports a case/control comparison: 8 samples read as cases, 15 as controls.
How we read this: 17 replicated groups read from 92 sample titles; they account for 81 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.
- GSM8600322 im-TEC, control_rep1 im-TEC, control
- GSM8600323 im-TEC, control_rep2 im-TEC, control
- GSM8600324 im-TEC, control_rep3 im-TEC, control
- GSM8600325 im-TEC, NNMTi_rep1 im-TEC, NNMTi
- GSM8600326 im-TEC, NNMTi_rep2 im-TEC, NNMTi
- GSM8600327 im-TEC, NNMTi_rep3 im-TEC, NNMTi
- GSM8600328 im-TEC, TGFβ_rep1 im-TEC, TGFβ
- GSM8600329 im-TEC, TGFβ_rep2 im-TEC, TGFβ
- GSM8600330 im-TEC, TGFβ_rep3 im-TEC, TGFβ
- GSM8600331 im-TEC, TGFβ_NNMTi_rep1 im-TEC, TGFβ_NNMTi
- GSM8600332 im-TEC, TGFβ_NNMTi_rep2 im-TEC, TGFβ_NNMTi
- GSM8600333 im-TEC, TGFβ_NNMTi_rep3 im-TEC, TGFβ_NNMTi
- GSM8600570 im-TEC, control_rep1 im-TEC, control
- GSM8600571 im-TEC, control_rep2 im-TEC, control
- GSM8600572 im-TEC, control_rep3 im-TEC, control
- GSM8600573 im-TEC, TGFβ_rep1 im-TEC, TGFβ
- GSM8600574 im-TEC, TGFβ_rep2 im-TEC, TGFβ
- GSM8600575 im-TEC, TGFβ_rep3 im-TEC, TGFβ
- GSM8600576 im-TEC, NAM_rep1 im-TEC, NAM
- GSM8600577 im-TEC, NAM_rep2 im-TEC, NAM
- GSM8600578 im-TEC, NAM_rep3 im-TEC, NAM
- GSM8600579 im-TEC, TGFβ_NAM_rep1 im-TEC, TGFβ_NAM
- GSM8600580 im-TEC, TGFβ_NAM_rep2 im-TEC, TGFβ_NAM
- GSM8600581 im-TEC, TGFβ_NAM_rep3 im-TEC, TGFβ_NAM
- GSM8600582 im-TEC, SAM_rep1 im-TEC, SAM
- GSM8600583 im-TEC, SAM_rep2 im-TEC, SAM
- GSM8600584 im-TEC, SAM_rep3 im-TEC, SAM
- GSM8600585 im-TEC, TGFβ_SAM_rep1 im-TEC, TGFβ_SAM
- GSM8600586 im-TEC, TGFβ_SAM_rep2 im-TEC, TGFβ_SAM
- GSM8600587 im-TEC, TGFβ_SAM_rep3 im-TEC, TGFβ_SAM
- GSM8600588 im_TEC, mock transduced_rep1 im_TEC, mock transduced
- GSM8600589 im_TEC, mock transduced_rep2 im_TEC, mock transduced
- GSM8600590 im_TEC, mock transduced_rep3 im_TEC, mock transduced
- GSM8600591 im_TEC, mock transduced_TGFβ_rep1 im_TEC, mock transduced…
- GSM8600592 im_TEC, mock transduced_TGFβ_rep2 im_TEC, mock transduced…
- GSM8600593 im_TEC, mock transduced_TGFβ_rep3 im_TEC, mock transduced…
- GSM8600594 im_TEC, Nnmt-OE_rep1 im_TEC, Nnmt-OE
- GSM8600595 im_TEC, Nnmt-OE_rep2 im_TEC, Nnmt-OE
- GSM8600596 im_TEC, Nnmt-OE_rep3 im_TEC, Nnmt-OE
- GSM8600597 im_TEC, Nnmt-OE_TGFβ_rep1 im_TEC, Nnmt-OE_TGFβ
+ 52 more — browse all 92 samples with per-sample file links →
- GSE280553 NNMT overexpression promotes tubular senescence and fibrosis in human chronic kidney disease (young, old and NAM-supplemented old mice kidney) [Series_4] 22 samples shares nnmt, senescence, promotes, fibrosis, tubular, chronic, kidney
- GSE300299 Chikungunya virus persists in joint-associated macrophages and promotes chronic disease in mice [scRNA-seq] 12 samples shares promotes, chronic
- GSE301216 Distinct lung functional, histological and cell senescence signatures in the single vs repetitive bleomycin mouse model of idiopathic pulmonary fibrosis 53 samples shares senescence, fibrosis
- GSE284505 Chronic sleep deprivation promotes experimental autoimmune uveitis through STAT1 phosphorylation, ISG15 expression and enhanced pathogenicity of macrophages 12 samples shares promotes, chronic
- GSE274819 Single-cell RNA sequencing of murine kidneys for ischemia-reperfusion induced renal injury and fibrosis 10 samples shares fibrosis, kidney
- GSE232327 Activation of imprinted gene Pw1 promotes cardiac fibrosis after ischemic injury 41 samples shares promotes, fibrosis
- GSE303699 Autoimmune Disease Risk Gene ANKRD55 Promotes Th17 Effector Function Through Metabolic Modulation [RNA-Seq] 60 samples shares promotes
- GSE273690 Adhesion G protein-coupled receptor ADGRG1 promotes protective microglial response in Alzheimer’s disease 48 samples shares promotes
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.