Multi-omic analysis reveals distinct endothelial gene expression profiles induced by pathological flow
Non-coding RNA profiling · human · 18 samples
- What it is
- Non-coding RNA profilingLevels of RNAs that do not code for proteins, such as miRNA or lncRNA.
- Organism
- Homo sapiens (human)
- Samples
- 18 samples: 18 samples, each with a distinct title — no replicated groupsGroups 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
- Multiomic Reference Map of Endothelial Mechanosensitive Pathways Under Athero- and Erosion-Prone FlowJournal of the American Heart Association · 2026
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 GSE300941_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.
Also filed as BioProject PRJNA1282695 and SRA study SRP594902. Searching any of these in the dataset finder brings you back here.
Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.
How we read this: Read from 18 sample titles: 18 distinct titles with little repetition. 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.
- GSM9072493 Donor_1_OSS
- GSM9072494 Donor_1_LSS
- GSM9072495 Donor_1_ESS
- GSM9072496 Donor_2_OSS
- GSM9072497 Donor_2_LSS
- GSM9072498 Donor_2_ESS
- GSM9072499 Donor_3_OSS
- GSM9072500 Donor_3_LSS
- GSM9072501 Donor_3_ESS
- GSM9072502 [RNA-Seq] Donor_1_OSS
- GSM9072503 [RNA-Seq] Donor_1_LSS
- GSM9072504 [RNA-Seq] Donor_1_ESS
- GSM9072505 [RNA-Seq] Donor_2_OSS
- GSM9072506 [RNA-Seq] Donor_2_LSS
- GSM9072507 [RNA-Seq] Donor_2_ESS
- GSM9072508 [RNA-Seq] Donor_3_OSS
- GSM9072509 [RNA-Seq] Donor_3_LSS
- GSM9072510 [RNA-Seq] Donor_3_ESS
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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.