MMPs and NETs are detrimental in CNS-tuberculosis with MMP inhibitor doxycycline improving murine CNS-tuberculosis survival
RNA-seq (gene expression) · human · 107 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
- 107 samples: 23 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
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 GSE244089_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 PRJNA1021208 and SRA study SRP463276. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 95 samples.
How we read this: 23 replicated groups read from 107 sample titles; they account for 95 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,
- GSM7806378 Healthy, S4, normal1 Healthy, S4, normal
- GSM7806379 Healthy, S4, normal2 Healthy, S4, normal
+ 67 more — browse all 107 samples with per-sample file links →
No close match found for this topic, so these are simply the most-viewed datasets of the same kind.
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- GSE320162 Minimizing far-extending chromatin perturbation in genome editing preserves stem cell identity [RNA-Seq] 347 samples
- GSE317764 High-Fat Diet Causes Rapid Loss of Intestinal Group 3 Innate Lymphoid Cells Through Microbiota-Driven Inflammation 270 samples
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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.