Expression data from the transplanted kidney and the native kidney of NEP25 mice with/without the treatment of LMB2
RNA-seq (gene expression) · mouse · 32 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
- 32 samples: 32 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
- A novel glomerulopathy model demonstrates renal counterbalance via local angiotensin II regulationProceedings of the Japan Academy. Series B, Physical and biological sciences · 2025
Read the full summary ↓
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 GSE125840_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 PRJNA517694 and SRA study SRP182826. 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 32 sample titles: 32 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.
- GSM3582991 TK (W to N)_LMB2_#1 (Transplated kidney_LMB2_1-D)
- GSM3582992 TK (W to N)_LMB2_#2 (Transplated kidney_LMB2_2-D)
- GSM3582993 TK (W to N)_LMB2_#3 (Transplated kidney_LMB2_3-D)
- GSM3582994 TK (W to N)_LMB2_#4 (Transplated kidney_LMB2_4-D)
- GSM3582995 TK (W to N)_Vehicle_#1 (Transplated kidney_Vehicle_5-D)
- GSM3582996 TK (W to N)_Vehicle_#2 (Transplated kidney_Vehicle_6-D)
- GSM3582997 TK (W to N)_Vehicle_#3 (Transplated kidney_Vehicle_7-D)
- GSM3582998 TK (W to N)_Vehicle_#4 (Transplated kidney_Vehicle_8-D)
- GSM3582999 TK (N to N)_LMB2_#1 (Transplated kidney_LMB2_9-D)
- GSM3583000 TK (N to N)_LMB2_#2 (Transplated kidney_LMB2_10-D)
- GSM3583001 TK (N to N)_LMB2_#3 (Transplated kidney_LMB2_11-D)
- GSM3583002 TK (N to N)_LMB2_#4 (Transplated kidney_LMB2_12-D)
- GSM3583003 TK (N to N)_Vehicle_#1 (Transplated kidney_Vehicle_13-D)
- GSM3583004 TK (N to N)_Vehicle_#2 (Transplated kidney_Vehicle_14-D)
- GSM3583005 TK (N to N)_Vehicle_#3 (Transplated kidney_Vehicle_15-D)
- GSM3583006 TK (N to N)_Vehicle_#4 (Transplated kidney_Vehicle_16-D)
- GSM3583007 NK (W to N)_LMB2_#1 (Native kidney_LMB2_1-R)
- GSM3583008 NK (W to N)_LMB2_#2 (Native kidney_LMB2_2-R)
- GSM3583009 NK (W to N)_LMB2_#3 (Native kidney_LMB2_3-R)
- GSM3583010 NK (W to N)_LMB2_#4 (Native kidney_LMB2_4-R)
- GSM3583011 NK (W to N)_Vehicle_#1 (Native kidney_Vehicle_5-R)
- GSM3583012 NK (W to N)_Vehicle_#2 (Native kidney_Vehicle_6-R)
- GSM3583013 NK (W to N)_Vehicle_#3 (Native kidney_Vehicle_7-R)
- GSM3583014 NK (W to N)_Vehicle_#4 (Native kidney_Vehicle_8-R)
- GSM3583015 NK (N to N)_LMB2_#1 (Native kidney_LMB2_9-R)
- GSM3583016 NK (N to N)_LMB2_#2 (Native kidney_LMB2_10-R)
- GSM3583017 NK (N to N)_LMB2_#3 (Native kidney_LMB2_11-R)
- GSM3583018 NK (N to N)_LMB2_#4 (Native kidney_LMB2_12-R)
- GSM3583019 NK (N to N)_Vehicle_#1 (Native kidney_Vehicle_13-R)
- GSM3583020 NK (N to N)_Vehicle_#2 (Native kidney_Vehicle_14-R)
- GSM3583021 NK (N to N)_Vehicle_#3 (Native kidney_Vehicle_15-R)
- GSM3583022 NK (N to N)_Vehicle_#4 (Native kidney_Vehicle_16-R)
- GSE298720 Global gene expression profiles were compared among IL1B-treated, Ad-OSCAR-treated, and control mice, with or without subsequent treatment with OSCAR inhibitors (Brivudine or Adefovir). 35 samples shares treatment, without
- GSE262107 Next Generation Sequencing to identify the differential expression genes of murine lung tissues upon the stimulation of LPS with/without recombinant TSLP pretreatment 10 samples shares treatment, without
- GSE256002 Comparison of transcriptional changes in regenerating mice livers at baseline and 24-48 hours post 70% partial hepatectomy with or without etomoxir treatment 27 samples shares treatment, without
- GSE296464 Transcriptomic analysis of pancreas from Elp3 epithelial-specific knockout and wild-type mice with and without caerulein treatment 16 samples shares treatment, without
- GSE324668 Single-nucleus transcriptomic profiling of mouse hearts following angiotensin II-infused with or without bisphenol F (BPF) treatment. 10 samples shares treatment, without
- GSE249630 Modeling T cell temporal response to cancer immunotherapy rationalize development of combinatorial treatments protocols 1372 samples shares treatment
- GSE294630 CDA HFD mice analysed for hepatic gene expression after treatment with semaglutide 97 samples shares treatment
- GSE294629 DIO-MASH mice analysed for hepatic gene expression after treatment with semaglutide 77 samples shares treatment
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.