SORLA upregulation suppresses global pathological effects in aged tauopathy mouse brain
RNA-seq (gene expression) · mouse · 115 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
- 115 samples: 38 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.
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 GSE278216_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 PRJNA1166103 and SRA study SRP534851. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 113 samples.
How we read this: 38 replicated groups read from 115 sample titles; they account for 113 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.
- GSM8538901 No_Template_Control_rep1 No_Template_Control
- GSM8538902 GFAP_SKO_PS19_1_rep1 GFAP_SKO_PS19_1
- GSM8538903 Iba1_SKO_PS19_1_rep1 Iba1_SKO_PS19_1
- GSM8538904 GFAP_SKO_PS19_1_rep2 GFAP_SKO_PS19_1
- GSM8538905 Iba1_SKO_PS19_1_rep2 Iba1_SKO_PS19_1
- GSM8538906 GFAP_SKO_PS19_1_rep3 GFAP_SKO_PS19_1
- GSM8538907 Iba1_SKO_PS19_1_rep3 Iba1_SKO_PS19_1
- GSM8538908 GFAP_PS19_1_rep1 GFAP_PS19_1
- GSM8538909 Iba1_PS19_1_rep1 Iba1_PS19_1
- GSM8538910 GFAP_PS19_1_rep2 GFAP_PS19_1
- GSM8538911 Iba1_PS19_1_rep2 Iba1_PS19_1
- GSM8538912 GFAP_PS19_1_rep3 GFAP_PS19_1
- GSM8538913 Iba1_PS19_1_rep3 Iba1_PS19_1
- GSM8538914 GFAP_SKO_PS19_2_rep1 GFAP_SKO_PS19_2
- GSM8538915 Iba1_SKO_PS19_2_rep1 Iba1_SKO_PS19_2
- GSM8538916 GFAP_SKO_PS19_2_rep2 GFAP_SKO_PS19_2
- GSM8538917 Iba1_SKO_PS19_2_rep2 Iba1_SKO_PS19_2
- GSM8538918 GFAP_SKO_PS19_2_rep3 GFAP_SKO_PS19_2
- GSM8538919 Iba1_SKO_PS19_2_rep3 Iba1_SKO_PS19_2
- GSM8538920 GFAP_PS19_2_rep1 GFAP_PS19_2
- GSM8538921 Iba1_PS19_2_rep1 Iba1_PS19_2
- GSM8538922 GFAP_PS19_2_rep2 GFAP_PS19_2
- GSM8538923 Iba1_PS19_2_rep2 Iba1_PS19_2
- GSM8538924 GFAP_PS19_2_rep3 GFAP_PS19_2
- GSM8538925 Iba1_PS19_2_rep3 Iba1_PS19_2
- GSM8538926 GFAP_SKO_PS19_3_rep1 GFAP_SKO_PS19_3
- GSM8538927 Iba1_SKO_PS19_3_rep1 Iba1_SKO_PS19_3
- GSM8538928 GFAP_SKO_PS19_3_rep2 GFAP_SKO_PS19_3
- GSM8538929 Iba1_SKO_PS19_3_rep2 Iba1_SKO_PS19_3
- GSM8538930 GFAP_SKO_PS19_3_rep3 GFAP_SKO_PS19_3
- GSM8538931 Iba1_SKO_PS19_3_rep3 Iba1_SKO_PS19_3
- GSM8538932 GFAP_PS19_3_rep1 GFAP_PS19_3
- GSM8538933 Iba1_PS19_3_rep1 Iba1_PS19_3
- GSM8538934 GFAP_PS19_3_rep2 GFAP_PS19_3
- GSM8538935 Iba1_PS19_3_rep2 Iba1_PS19_3
- GSM8538936 GFAP_PS19_3_rep3 GFAP_PS19_3
- GSM8538937 Iba1_PS19_3_rep3 Iba1_PS19_3
- GSM8538938 GFAP_SKO1_rep1 GFAP_SKO1
- GSM8538939 Iba1_SKO1_rep1 Iba1_SKO1
- GSM8538940 GFAP_WT1_rep1 GFAP_WT1
+ 75 more — browse all 115 samples with per-sample file links →
- GSE313209 Hv1 upregulation worsens spinal, spleen, and lung molecular pathology and impairs locomotion after spinal cord injury in aged male mice 123 samples shares upregulation, aged
- GSE237392 Loss of Kmt2c or Kmt2d drives brain metastasis via KDM6A-dependent upregulation of MMP3 81 samples shares upregulation, brain
- GSE237390 Loss of Kmt2c or Kmt2d drives brain metastasis via KDM6A-dependent upregulation of MMP3 [ChIP-seq] 54 samples shares upregulation, brain
- GSE268251 Increased CPT1a expression is a critical cardioprotective response to pathological stress that suppresses gene programs for remodeling and enables rescue by gene transfer 30 samples shares pathological, suppresses
- GSE237287 Loss of Kmt2c or Kmt2d drives brain metastasis via KDM6A-dependent upregulation of MMP3 [RNA-seq] 18 samples shares upregulation, brain
- GSE307584 Aberrant Upregulation of Histamine N-Methyltransferase Exacerbates Pathological Cardiac Hypertrophy and Heart Failure via Disruption of SAM/Frizzled-2/ CaMKII Signaling Pathway [RNA-seq] 12 samples shares upregulation, pathological
- GSE279507 Mapping the Evolution of Acinar cell derived Pancreatic Preneoplastic Lesions in a Mouse Model: A Detailed Transcriptomic and Pathological Characterization 5 samples shares pathological
- GSE254996 ACSS2 upregulation enhances neuronal resilience to aging and neurodegeneration [RNA-seq] 98 samples shares upregulation
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.