Extracellular vesicles and their RNA cargo facilitate bidirectional cross-kingdom communication between human and bacterial cells [RNA-Seq]
RNA-seq (gene expression) · human · 105 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
- 105 samples: 29 conditions, mostly with about 4 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
- Extracellular vesicles and their RNA cargo facilitate bidirectional cross-kingdom communication between human and bacterial cellsGut microbes · 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 GSE297395_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 PRJNA1263755 and SRA study SRP586026. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 105 samples.
How we read this: 29 replicated groups read from 105 sample titles; they account for 105 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.
- GSM8989977 ANC 5ng 10h Z2 ANC 5ng 10h Z
- GSM8989978 ANC 5ng 10h Z3 ANC 5ng 10h Z
- GSM8989979 ANC 5ng 10h Z5 ANC 5ng 10h Z
- GSM8989980 ANC 100ng 10h Z2 ANC 100ng 10h Z
- GSM8989981 ANC 100ng 10h Z3 ANC 100ng 10h Z
- GSM8989982 ANC 100ng 10h Z4 ANC 100ng 10h Z
- GSM8989983 ANC 100ng 10h Z5 ANC 100ng 10h Z
- GSM8989984 ANC 2ng 10h Z2 ANC 2ng 10h Z
- GSM8989985 ANC 2ng 10h Z3 ANC 2ng 10h Z
- GSM8989986 ANC 2ng 10h Z5 ANC 2ng 10h Z
- GSM8989987 ANC 5ng 24h Z2 ANC 5ng 24h Z
- GSM8989988 ANC 5ng 24h Z3 ANC 5ng 24h Z
- GSM8989989 ANC 5ng 24h Z4 ANC 5ng 24h Z
- GSM8989990 ANC 5ng 24h Z5 ANC 5ng 24h Z
- GSM8989991 ANC 100ng 24h Z2 ANC 100ng 24h Z
- GSM8989992 ANC 100ng 24h Z3 ANC 100ng 24h Z
- GSM8989993 ANC 100ng 24h Z4 ANC 100ng 24h Z
- GSM8989994 ANC 100ng 24h Z5 ANC 100ng 24h Z
- GSM8989995 ANC 2ng 24h Z2 ANC 2ng 24h Z
- GSM8989996 ANC 2ng 24h Z3 ANC 2ng 24h Z
- GSM8989997 ANC 2ng 24h Z4 ANC 2ng 24h Z
- GSM8989998 ANC 2ng 24h Z5 ANC 2ng 24h Z
- GSM8989999 ANC 100ng 48h Z2 ANC 100ng 48h Z
- GSM8990000 ANC 100ng 48h Z3 ANC 100ng 48h Z
- GSM8990001 ANC 100ng 48h Z4 ANC 100ng 48h Z
- GSM8990002 ANC 100ng 48h Z5 ANC 100ng 48h Z
- GSM8990003 Ef MV 10h Z2 Ef MV 10h Z
- GSM8990004 Ef MV 10h Z3 Ef MV 10h Z
- GSM8990005 Ef MV 10h Z4 Ef MV 10h Z
- GSM8990006 Ef MV 10h Z5 Ef MV 10h Z
- GSM8990007 Lc MV 10h Z2 Lc MV 10h Z
- GSM8990008 Lc MV 10h Z3 Lc MV 10h Z
- GSM8990009 Lc MV 10h Z4 Lc MV 10h Z
- GSM8990010 Lc MV 10h Z5 Lc MV 10h Z
- GSM8990011 Ef MV 24h Z2 Ef MV 24h Z
- GSM8990012 Ef MV 24h Z3 Ef MV 24h Z
- GSM8990013 Ef MV 24h Z4 Ef MV 24h Z
- GSM8990014 Lc MV 24h Z2 Lc MV 24h Z
- GSM8990015 Lc MV 24h Z3 Lc MV 24h Z
- GSM8990016 Lc MV 24h Z5 Lc MV 24h Z
+ 65 more — browse all 105 samples with per-sample file links →
- GSE311038 Multi-omics integrative approach of senescent endothelial cells and derived extracellular vesicles in a replicative senescence model [RNA-seq] 12 samples shares extracellular, vesicles, rna-seq
- GSE255775 Transcriptomic Profiling of Plasma Extracellular Vesicles Enables Reliable Annotation of the Cancer-specific Transcriptome and Molecular Subtype 319 samples shares extracellular, vesicles
- GSE259422 Transcriptomic Profiling of Plasma Extracellular Vesicles Enables Reliable Annotation of the Cancer-specific Transcriptome and Molecular Subtype (plasma_cancer) 156 samples shares extracellular, vesicles
- GSE268230 Extracellular vesicles alter trophoblast function in pregnancies complicated by COVID-19 129 samples shares extracellular, vesicles
- GSE208732 Mapping functional to morphological variation reveals the basis of regional extracellular matrix subversion and nerve invasion in pancreatic cancer [RNA-seq] 102 samples shares extracellular, rna-seq
- GSE242856 The RNA content of plasma-derived extracellular vesicles in cerebral malaria 84 samples shares extracellular, vesicles
- GSE247367 Pathogenic TSC2 variants alter developmental trajectories of human cortical organoids and perturb cell-cell communication networks [bulk RNA-seq] 80 samples shares communication, rna-seq
- GSE302211 Extracellular Vesicle Online Knowledge Explorer (EVOKE) - A guiding tool for the selection of methods to prepare extracellular vesicles from human biofluids II 73 samples shares extracellular, vesicles
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.