MMPs and NETs are detrimental in CNS-tuberculosis with MMP inhibitor doxycycline improving murine CNS-tuberculosis survival (mouse)
RNA-seq (gene expression) · mouse · 69 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
- 69 samples: 17 conditions, mostly in duplicateGroups 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
- MMPs and NETs are detrimental in CNS-tuberculosis with MMP Inhibition in CNS-tuberculosis mice improving survivalJournal of neuroinflammation · 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 GSE244086_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 PRJNA1021212 and SRA study SRP463276. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 59 samples.
How we read this: 17 replicated groups read from 69 sample titles; they account for 59 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.
- GSM7806378 Healthy, S4, normal1 Healthy, S4, normal
- GSM7806379 Healthy, S4, normal2 Healthy, S4, normal
- GSM7806380 CNSTB, S1, granuloma1 CNSTB, S1, granuloma
- GSM7806381 CNSTB, S2, granuloma1 CNSTB, S2, granuloma
- GSM7806382 CNSTB, S2, granuloma2 CNSTB, S2, granuloma
- GSM7806383 CNSTB, S3, granuloma1 CNSTB, S3, granuloma
- GSM7806384 CNSTB, S3, granuloma2 CNSTB, S3, granuloma
- GSM7806385 CNSTB, S3, granuloma3 CNSTB, S3, granuloma
- GSM7806386 CNSTB, S3, granuloma4 CNSTB, S3, granuloma
- GSM7806387 CNSTB, S4, granuloma1 CNSTB, S4, granuloma
- GSM7806388 CNSTB, S4, granuloma2 CNSTB, S4, granuloma
- GSM7806389 CNSTB, S4, granuloma3 CNSTB, S4, granuloma
- GSM7806390 CNSTB, S4, granuloma4 CNSTB, S4, granuloma
- GSM7806391 CNSTB, S1, inflammatory1 CNSTB, S1, inflammatory
- GSM7806392 CNSTB, S1, inflammatory2 CNSTB, S1, inflammatory
- GSM7806393 CNSTB, S2, inflammatory1 CNSTB, S2, inflammatory
- GSM7806394 CNSTB, S2, inflammatory2 CNSTB, S2, inflammatory
- GSM7806395 Healthy, S2, normal1 Healthy, S2, normal
- GSM7806396 Healthy, S3, normal1 Healthy, S3, normal
- GSM7806397 Healthy, S3, normal2 Healthy, S3, normal
- GSM7806398 CNSTB, S1, granuloma2 CNSTB, S1, granuloma
- GSM7806399 CNSTB, S1, granuloma3 CNSTB, S1, granuloma
- GSM7806400 CNSTB, S1, granuloma4 CNSTB, S1, granuloma
- GSM7806401 CNSTB, S1, granuloma5 CNSTB, S1, granuloma
- GSM7806402 CNSTB, S1, inflammatory3 CNSTB, S1, inflammatory
- GSM7806403 CNSTB, S1, inflammatory4 CNSTB, S1, inflammatory
- GSM7806404 CNSTB, S1, normal1 CNSTB, S1, normal
- GSM7806405 CNSTB, S1, normal2 CNSTB, S1, normal
- GSM7806406 CNSTB, S2, granuloma3 CNSTB, S2, granuloma
- GSM7806407 CNSTB, S2, granuloma4 CNSTB, S2, granuloma
- GSM7806408 CNSTB, S2, inflammatory3 CNSTB, S2, inflammatory
- GSM7806409 CNSTB, S2, inflammatory4 CNSTB, S2, inflammatory
- GSM7806410 CNSTB, S2, inflammatory5 CNSTB, S2, inflammatory
- GSM7806411 CNSTB, S2, normal1 CNSTB, S2, normal
- GSM7806412 CNSTB, S2, normal2 CNSTB, S2, normal
- GSM7806413 CNSTB, S3, granuloma5 CNSTB, S3, granuloma
- GSM7806414 CNSTB, S3, granuloma6 CNSTB, S3, granuloma
- GSM7806415 CNSTB, S3, granuloma7 CNSTB, S3, granuloma
- GSM7806416 CNSTB, S3, granuloma8 CNSTB, S3, granuloma
- GSM7806417 CNSTB, S3, normal1 CNSTB, S3, normal
+ 29 more — browse all 69 samples with per-sample file links →
- GSE266914 Mmp14-Dependent Remodeling of the Pericellular-Dermal Collagen Interface Governs Fibroblast Survival 4 samples shares mmp, survival
- GSE237287 Loss of Kmt2c or Kmt2d drives brain metastasis via KDM6A-dependent upregulation of MMP3 [RNA-seq] 18 samples shares mmp
- GSE237549 RNA-seq of mouse tissue samples from animal model: Effects of MMP9 on gene expression. 10 samples shares mmp
- GSE292393 Fibrogenic macrophages drive cardiac fibrosis via MMP12-mediated tissue remodeling 9 samples shares mmp
- GSE294445 Modulation of non-canonical WNT5B-NFATc2 pathway-dependent MMP14 expression in astrocytes ameliorates neuropathology and motor behaviors in Huntington's disease mice 9 samples shares mmp
- GSE237289 Loss of Kmt2c or Kmt2d drives brain metastasis via KDM6A-dependent upregulation of MMP3 [scRNA-seq] 9 samples shares mmp
- GSE287828 MMP19 in vascular smooth muscle cells protects against thoracic aortic aneurysm and dissection via the MMP19/Aggrecan/Wnt/β-Catenin axis 7 samples shares mmp
- GSE335244 Myeloid MMP14 Couples Extracellular Proteolysis to Inflammatory and Metabolic Remodeling During Obesity 6 samples shares mmp
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.