Faecalibacterium prausnitzii induces an anti-inflammatory response and a metabolic reprogramming in human monocytes
RNA-seq (gene expression) · human · 72 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
- 72 samples: 12 conditions, mostly with about 6 replicates eachGroups 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
- Faecalibacterium prausnitzii Induces an Anti-inflammatory Response and a Metabolic Reprogramming in Human MonocytesGastroenterology · 2026
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 GSE285037_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 PRJNA1201079 and SRA study SRP553038. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 71 samples.
How we read this: 12 replicated groups read from 72 sample titles; they account for 71 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.
- GSM8695563 PBMC Control 2h, rep1 PBMC Control 2h,
- GSM8695564 PBMC Control 2h, rep2 PBMC Control 2h,
- GSM8695565 PBMC Control 2h, rep3 PBMC Control 2h,
- GSM8695566 PBMC Control 2h, rep4 PBMC Control 2h,
- GSM8695567 PBMC Control 2h, rep5 PBMC Control 2h,
- GSM8695568 PBMC Control 2h, rep6 PBMC Control 2h,
- GSM8695569 PBMC Control 4h, rep1 PBMC Control 4h,
- GSM8695570 PBMC Control 4h, rep2 PBMC Control 4h,
- GSM8695571 PBMC Control 4h, rep3 PBMC Control 4h,
- GSM8695572 PBMC Control 4h, rep4 PBMC Control 4h,
- GSM8695573 PBMC Control 4h, rep5 PBMC Control 4h,
- GSM8695574 PBMC Control 4h, rep6 PBMC Control 4h,
- GSM8695575 PBMC Fp10 2h, rep1 PBMC Fp10 2h,
- GSM8695576 PBMC Fp10 2h, rep2 PBMC Fp10 2h,
- GSM8695577 PBMC Fp10 2h, rep3 PBMC Fp10 2h,
- GSM8695578 PBMC Fp10 2h, rep4 PBMC Fp10 2h,
- GSM8695579 PBMC Fp10 2h, rep5 PBMC Fp10 2h,
- GSM8695580 PBMC Fp10 2h, rep6 PBMC Fp10 2h,
- GSM8695581 PBMC Fp10 4h, rep1 PBMC Fp10 4h,
- GSM8695582 PBMC Fp10 4h, rep2 PBMC Fp10 4h,
- GSM8695583 PBMC Fp10 4h, rep3 PBMC Fp10 4h,
- GSM8695584 PBMC Fp10 4h, rep4 PBMC Fp10 4h,
- GSM8695585 PBMC Fp10 4h, rep5 PBMC Fp10 4h,
- GSM8695586 PBMC Fp10 4h, rep6 PBMC Fp10 4h,
- GSM8695587 PBMC Fp100 2h, rep1
- GSM8695588 PBMC FP100 2h, rep2 PBMC FP100 2h,
- GSM8695589 PBMC FP100 2h, rep3 PBMC FP100 2h,
- GSM8695590 PBMC FP100 2h, rep4 PBMC FP100 2h,
- GSM8695591 PBMC FP100 2h, rep5 PBMC FP100 2h,
- GSM8695592 PBMC FP100 2h, rep6 PBMC FP100 2h,
- GSM8695593 PBMC Fp100 4h, rep1 PBMC Fp100 4h,
- GSM8695594 PBMC Fp100 4h, rep2 PBMC Fp100 4h,
- GSM8695595 PBMC Fp100 4h, rep3 PBMC Fp100 4h,
- GSM8695596 PBMC Fp100 4h, rep4 PBMC Fp100 4h,
- GSM8695597 PBMC Fp100 4h, rep5 PBMC Fp100 4h,
- GSM8695598 PBMC Fp100 4h, rep6 PBMC Fp100 4h,
- GSM8695599 PBMC LPS 2h, rep1 PBMC LPS 2h,
- GSM8695600 PBMC LPS 2h, rep2 PBMC LPS 2h,
- GSM8695601 PBMC LPS 2h, rep3 PBMC LPS 2h,
- GSM8695602 PBMC LPS 2h, rep4 PBMC LPS 2h,
+ 32 more — browse all 72 samples with per-sample file links →
- GSE275152 GLUT1 overexpression in CAR-T cells induces metabolic reprogramming and enhances potency 42 samples shares reprogramming, induces
- GSE327577 Faecalibacterium prausnitzii EXL01 for the Maintenance of Steroid-induced Clinical Response or Remission in Patients with Crohn’s Disease: a first in human trial 43 samples shares faecalibacterium, prausnitzii
- GSE261322 NF-κB- and TET2-mediated macrophage reprogramming overrides the anti-inflammatory effects of hypoxia and improves responses in human cancer (RNA-Seq I). 20 samples shares anti-inflammatory, reprogramming
- GSE286057 Ovarian cancer metastasis to the human omentum disrupts organ homeostasis and induces fundamental tissue reprogramming 36 samples shares reprogramming, induces
- GSE268626 Systemic reprogramming of monocytes in Crohn’s disease promotes their intestinal inflammatory function 20 samples shares reprogramming, monocytes
- GSE282387 Temozolomide Induces Glioblastoma Dedifferentiation into Highly Malignant Stem-like Cells Through Senescence-Associated Reprogramming via HIF1α/HIF2α Regulation [RNA-seq] 12 samples shares reprogramming, induces
- GSE280564 Reprogramming of normal fibroblasts into ovarian cancer-associated fibroblasts via non-vesicular paracrine signaling induces an activated fibroblast phenotype 12 samples shares reprogramming, induces
- GSE261321 NF-κB- and TET2-mediated macrophage reprogramming overrides the anti-inflammatory effects of hypoxia and improves responses in human cancer ChIP-Seq). 24 samples shares anti-inflammatory, reprogramming
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.