MMPs and NETs are detrimental in CNS-tuberculosis with MMP inhibitor doxycycline improving murine CNS-tuberculosis survival (human)
RNA-seq (gene expression) · human · 38 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
- Homo sapiens (human)
- Samples
- 38 samples: 10 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 GSE244085_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 PRJNA1021211 and SRA study SRP463285. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 35 samples.
How we read this: 10 replicated groups read from 38 sample titles; they account for 35 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.
- GSM7806340 CNSTB, S1,inflammatory1
- GSM7806341 CNSTB, S2, granuloma1 CNSTB, S2, granuloma
- GSM7806342 CNSTB, S2, granuloma2 CNSTB, S2, granuloma
- GSM7806343 CNSTB, S2, normal1 CNSTB, S2, normal
- GSM7806344 CNSTB, S2, normal2 CNSTB, S2, normal
- GSM7806345 CNSTB, S2, granuloma3 CNSTB, S2, granuloma
- GSM7806346 CNSTB, S2, granuloma4 CNSTB, S2, granuloma
- GSM7806347 CNSTB, S2, granuloma5 CNSTB, S2, granuloma
- GSM7806348 CNSTB, S2, granuloma6 CNSTB, S2, granuloma
- GSM7806349 CNSTB, S3, granuloma1 CNSTB, S3, granuloma
- GSM7806350 CNSTB, S3, granuloma2 CNSTB, S3, granuloma
- GSM7806351 CNSTB, S3, granuloma3 CNSTB, S3, granuloma
- GSM7806352 CNSTB, S3, inflammatory1
- GSM7806353 CNSTB, S3, granuloma4 CNSTB, S3, granuloma
- GSM7806354 CNSTB, S4, inflammatory1 CNSTB, S4, inflammatory
- GSM7806355 CNSTB, S4, inflammatory2 CNSTB, S4, inflammatory
- GSM7806356 CNSTB, S4, inflammatory3 CNSTB, S4, inflammatory
- GSM7806357 CNSTB, S4, inflammatory4 CNSTB, S4, inflammatory
- GSM7806358 CNSTB, S4, granuloma1 CNSTB, S4, granuloma
- GSM7806359 CNSTB, S4, granuloma2 CNSTB, S4, granuloma
- GSM7806360 CNSTB, S4, granuloma3 CNSTB, S4, granuloma
- GSM7806361 CNSTB, S4, granuloma4 CNSTB, S4, granuloma
- GSM7806362 CNSTB, S4, granuloma5 CNSTB, S4, granuloma
- GSM7806363 CNSTB, S5, granuloma1 CNSTB, S5, granuloma
- GSM7806364 CNSTB, S5, granuloma2 CNSTB, S5, granuloma
- GSM7806365 CNSTB, S5, granuloma3 CNSTB, S5, granuloma
- GSM7806366 CNSTB, S5, granuloma4 CNSTB, S5, granuloma
- GSM7806367 CNSTB, S5, granuloma5 CNSTB, S5, granuloma
- GSM7806368 CNSTB, S6, granuloma1 CNSTB, S6, granuloma
- GSM7806369 CNSTB, S6, granuloma2 CNSTB, S6, granuloma
- GSM7806370 CNSTB, S6, granuloma3 CNSTB, S6, granuloma
- GSM7806371 CNSTB, S6, normal1 CNSTB, S6, normal
- GSM7806372 CNSTB, S6, normal2 CNSTB, S6, normal
- GSM7806373 Demyelination, S2
- GSM7806374 Demyelination, S3, R1 Demyelination, S3,
- GSM7806375 Demyelination, S3, R2 Demyelination, S3,
- GSM7806376 Demyelination, S4, R1 Demyelination, S4,
- GSM7806377 Demyelination, S4, R2 Demyelination, S4,
- GSE296619 HMGA2 interacts with KAT6A to regulate MMPs chromatin architecture and promote triple-negative breast cancer metastasis 6 samples shares mmp, mmps
- GSE312934 Mycobacterial Metallophosphatase MmpE Functions as a Nucleomodulin to Regulate Host Gene Expression and Promote Intracellular Survival 4 samples shares mmp, survival
- GSE243188 Epithelial barrier function and its underlying mechanical regulation affects the biology of Macro-Metastasis Parenchyma Interface (MMPI). 28 samples shares mmp
- GSE275051 Upregulation of MMP3 promotes cisplatin resistance in ovarian cancer 24 samples shares mmp
- GSE232118 Cyclic stretching stimuli promotes HGF and MMP secretion from hESC-derived hepatoblasts and endothelial cells towards liver lobule formation in vitro. 15 samples shares mmp
- GSE286069 MMP12-dependent myofibroblast formation contributes to the nucleus pulposus fibrosis and intervertebral disc degeneration 15 samples shares mmp
- GSE64461 Endothelial MMP14 is required for endothelial dependant growth support of human airway basal cells 10 samples shares mmp
- GSE190361 SPOCK2 controls the proliferation and function of immature pancreatic β-cells through MMP2. 10 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.