The Myocardial Infarction Platelet Transcriptome
RNA-seq (gene expression) · human · 91 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
- 91 samples: 91 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
- Myocardial Infarction Platelet Gene Expression Signatures in WomenJACC. Basic to translational science · 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 GSE272178_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 PRJNA1135265 and SRA study SRP519696. 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 91 sample titles: 91 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.
- GSM8395221 HARP patient 0002, baseline
- GSM8395222 HARP patient 0028, baseline
- GSM8395223 HARP patient 0031, baseline
- GSM8395224 HARP patient 0031, followup
- GSM8395225 HARP patient 0033, baseline
- GSM8395226 HARP patient 0033, followup
- GSM8395227 HARP patient 0034, baseline
- GSM8395228 HARP patient 0035, baseline
- GSM8395229 HARP patient 0036, baseline
- GSM8395230 HARP patient 0039, baseline
- GSM8395231 HARP patient 0043, baseline
- GSM8395232 HARP patient 0049, baseline
- GSM8395233 HARP patient 0052, baseline
- GSM8395234 HARP patient 0052, followup
- GSM8395235 HARP patient 0055, baseline
- GSM8395236 HARP patient 0057, baseline
- GSM8395237 HARP patient 0058, baseline
- GSM8395238 HARP patient 0070, baseline
- GSM8395239 HARP patient 0088, baseline
- GSM8395240 HARP patient 0088, followup
- GSM8395241 HARP patient 0094, baseline
- GSM8395242 HARP patient 0098, baseline
- GSM8395243 HARP patient 0109, baseline
- GSM8395244 HARP patient 0111, baseline
- GSM8395245 HARP patient 0114, baseline
- GSM8395246 HARP patient 0114, followup
- GSM8395247 HARP patient 0128, baseline
- GSM8395248 HARP patient 0128, followup
- GSM8395249 HARP patient 0135, baseline
- GSM8395250 HARP patient 0136, baseline
- GSM8395251 HARP patient 0136, followup
- GSM8395252 HARP patient 0173, baseline
- GSM8395253 HARP patient 0173, followup
- GSM8395254 HARP patient 0174, baseline
- GSM8395255 HARP patient 0174, followup
- GSM8395256 HARP patient 0177, baseline
- GSM8395257 HARP patient 0184, baseline
- GSM8395258 HARP patient 0184, followup
- GSM8395259 HARP patient 0201, baseline
- GSM8395260 HARP patient 0204, baseline
+ 51 more — browse all 91 samples with per-sample file links →
- GSE278026 Platelets induce endothelial cell mitochondrial dysfunction in myocardial infarction 52 samples shares myocardial, infarction, platelet
- GSE313527 Plasma Extracellular Vesicle Modulate Immune Cell Transcriptional Responses Following Acute Myocardial Infarction 74 samples shares myocardial, infarction
- GSE249812 Dynamic molecular signatures of acute myocardial infarction based on transcriptomics 15 samples shares myocardial, infarction
- GSE291736 Extracellular BRICK1 drives heart repair after myocardial infarction 15 samples shares myocardial, infarction
- GSE269269 Single-Cell RNA Sequencing of Peripheral Blood Mononuclear Cells From Acute Myocardial Infarction 10 samples shares myocardial, infarction
- GSE313688 YBX1 Drives Post-Myocardial Infarction Angiogenesis via an m⁶A-IGF2BP1/3 Dependent Stabilization of HIF-1α mRNA 4 samples shares myocardial, infarction
- GSE298270 CRISPR/Cas-based activation of PPARGC1A boosts endogenous mitochondria and enhances cardiac function and recovery after myocardial infarction 4 samples shares myocardial, infarction
- GSE48060 Transcriptome from circulating cells suggests dysregulated pathways associated with long-term recurrent events following first-time myocardial infarction. 52 samples shares myocardial, infarction
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.