Whole transcriptome analysis reveals T cell signaling activation in septic patients with thrombocytopenia
RNA-seq (gene expression) · human · 104 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
- 104 samples: 104 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
- Whole transcriptome analysis reveals T cell signaling activation in septic patients with thrombocytopeniaScientific reports · 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 GSE273700_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 PRJNA1142876 and SRA study SRP523568. 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 104 sample titles: 104 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.
- GSM8435561 RICU190827-1_001A [Thrombocytopenia(+)]
- GSM8435562 RICU190903-1_002A [Thrombocytopenia(+)]
- GSM8435563 RICU200410-1_001A [Thrombocytopenia(+)]
- GSM8435564 RICU200417-1_002A [Thrombocytopenia(+)]
- GSM8435565 RICU200824-1_001A [Thrombocytopenia(+)]
- GSM8435566 RICU200901-2_002A [Thrombocytopenia(+)]
- GSM8435567 RICU200902-1_001A [Thrombocytopenia(+)]
- GSM8435568 RICU200909-1_002A [Thrombocytopenia(+)]
- GSM8435569 RICU201021-1_001A [Thrombocytopenia(+)]
- GSM8435570 RICU201028-1_002A [Thrombocytopenia(+)]
- GSM8435571 RICU201029-1_001A [Thrombocytopenia(+)]
- GSM8435572 RICU201105-1_002A [Thrombocytopenia(+)]
- GSM8435573 RICU201201-2_001A [Thrombocytopenia(+)]
- GSM8435574 RICU201208-2_002A [Thrombocytopenia(+)]
- GSM8435575 RICU210429-2_001A [Thrombocytopenia(+)]
- GSM8435576 RICU210504-1_002A [Thrombocytopenia(+)]
- GSM8435577 RICU210520-1_001A [Thrombocytopenia(+)]
- GSM8435578 RICU210527-1_002A [Thrombocytopenia(+)]
- GSM8435579 RICU211026-1_001A [Thrombocytopenia(+)]
- GSM8435580 RICU211102-1_002A [Thrombocytopenia(+)]
- GSM8435581 RICU211124-1_001A [Thrombocytopenia(+)]
- GSM8435582 RICU211130-2_001A [Thrombocytopenia(+)]
- GSM8435583 RICU211201-1_002A [Thrombocytopenia(+)]
- GSM8435584 RICU211201-3_001A [Thrombocytopenia(+)]
- GSM8435585 RICU211208-2_002A [Thrombocytopenia(+)]
- GSM8435586 RICU211208-3_002A [Thrombocytopenia(+)]
- GSM8435587 RICU211214-1_001A [Thrombocytopenia(+)]
- GSM8435588 RICU211221-1_002A [Thrombocytopenia(+)]
- GSM8435589 RICU211227-1_001A [Thrombocytopenia(+)]
- GSM8435590 RICU211228-2_001A [Thrombocytopenia(+)]
- GSM8435591 RICU220103-1_002A [Thrombocytopenia(+)]
- GSM8435592 RICU220104-1_002A [Thrombocytopenia(+)]
- GSM8435593 RICU220121-3_001A [Thrombocytopenia(+)]
- GSM8435594 RICU220126-3_002A [Thrombocytopenia(+)]
- GSM8435595 RICU220210-1_001A [Thrombocytopenia(+)]
- GSM8435596 RICU220216-1_002A [Thrombocytopenia(+)]
- GSM8435597 RICU220225-1_001A [Thrombocytopenia(+)]
- GSM8435598 RICU220301-1_002A [Thrombocytopenia(+)]
- GSM8435599 RICU220316-1_001A [Thrombocytopenia(+)]
- GSM8435600 RICU220324-1_002A [Thrombocytopenia(+)]
+ 64 more — browse all 104 samples with per-sample file links →
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- GSE282668 TGF-β signaling promotes astroglial activation and TDP-43 proteinopathy in organoid models of frontotemporal lobar degeneration 54 samples shares activation
- GSE267625 Whole blood transcriptome in long-COVID patients reveals association with lung function and immune response 111 samples shares whole
- GSE178411 Whole-transcriptome analysis illustrates evolving transcriptional human response to injury in acute wounds and scars 108 samples shares whole
- GSE294985 Transcriptomes of human whole blood cells in sepsis patients 40 samples shares whole
- GSE277709 Whole blood transcriptome profile identifies motor neurone disease (MND) RNA biomarker signatures 84 samples shares whole
- GSE254951 Impact of Hydroxycarbamide Treatment on the Whole Blood Transcriptome in Sickle Cell Disease 50 samples shares whole
- GSE244679 Whole Transcriptome Profiling of Lesional Psoriatic and Adjacent Normal Skin Tissues 48 samples shares whole
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.