CXCL10 and IFN-γ Mediate Myocarditis Post-COVID19 mRNA Vaccination.
RNA-seq (gene expression) · human · 42 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
- 42 samples: 42 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.
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 GSE254539_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 PRJNA1070987 and SRA study SRP486716. 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 42 sample titles: 42 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.
- GSM8045643 Macro_control_line15 [A015]
- GSM8045644 Macro_control_line114 [A114]
- GSM8045645 Macro_control_line477 [A477]
- GSM8045646 Macro_control_line632 [A632]
- GSM8045647 Macro_Pfizer_line15 [D015]
- GSM8045648 Macro_Pfizer_line114 [D114]
- GSM8045649 Macro_Pfizer_line477 [D477]
- GSM8045650 Macro_Pfizer_line632 [D632]
- GSM8045651 Macro_Moderna_line15 [F015]
- GSM8045652 Macro_Moderna_line114 [F114]
- GSM8045653 Macro_Moderna_line477 [F477]
- GSM8045654 Macro_Moderna_line632 [F632]
- GSM8045655 CM_Control_line15 [C015]
- GSM8045656 CM_Control_line114 [C114]
- GSM8045657 CM_Control_line477 [C477]
- GSM8045658 CM_Control_line632 [C632]
- GSM8045659 CM_Low_line15 [L015]
- GSM8045660 CM_Low_line114 [L114]
- GSM8045661 CM_Low_line477 [L477]
- GSM8045662 CM_Low_line632 [L632]
- GSM8045663 CM_High_line15 [H015]
- GSM8045664 CM_High_line114 [H114]
- GSM8045665 CM_High_line477 [H477]
- GSM8045666 CM_High_line632 [H632]
- GSM8045667 Mouse_heart_Cntrol_1 [CON1]
- GSM8045668 Mouse_heart_Cntrol_2 [CON2]
- GSM8045669 Mouse_heart_Cntrol_3 [CON3]
- GSM8045670 Mouse_heart_Cntrol_4 [CON4]
- GSM8045671 Mouse_heart_Cntrol_5 [CON5]
- GSM8045672 Mouse_heart_Cntrol_6 [CON6]
- GSM8045673 Mouse_heart_Cytokine_1 [CYT1]
- GSM8045674 Mouse_heart_Cytokine_2 [CYT2]
- GSM8045675 Mouse_heart_Cytokine_3 [CYT3]
- GSM8045676 Mouse_heart_Cytokine_4 [CYT4]
- GSM8045677 Mouse_heart_Cytokine_5 [CYT5]
- GSM8045678 Mouse_heart_Cytokine_6 [CYT6]
- GSM8045679 Mouse_heart_Genistein_1 [GEN1]
- GSM8045680 Mouse_heart_Genistein_2 [GEN2]
- GSM8045681 Mouse_heart_Genistein_3 [GEN3]
- GSM8045682 Mouse_heart_Genistein_4 [GEN4]
+ 2 more — browse all 42 samples with per-sample file links →
- GSE293066 MAVS Orchestrates a Potent IFN-independent Antiviral Immunity by 2 Preserving Mitochondrial Integrity 54 samples shares ifn
- GSE263817 Comparative single-cell analysis reveals IFN-g as a driver of respiratory sequelae post COVID-19 33 samples shares ifn
- GSE290306 ZBTB7B Inhibits Glioma Tumorigenicity by Upregulating GPR17 and CXCL10 4 samples shares cxcl10
- GSE292586 SUMOylation orchestrates a metastable heterochromatin state on a MORC3-responsive element to silence IFNB1 at a distance 151 samples shares ifn
- GSE292571 SUMOylation orchestrates a metastable heterochromatin state on a MORC3-responsive element to silence IFNB1 at a distance [ChIP-seq] 116 samples shares ifn
- GSE253096 Targeting the FOXP3–T-bet interaction to restore Treg stability in IFN-γ–driven autoimmunity 20 samples shares ifn
- GSE180045 Identification of Pathogenic Immune Cell Subsets in Checkpoint Inhibitor-induced Myocarditis 65 samples shares myocarditis
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.