Transcriptomic Profiling of Neutrophils and Low-Density Granulocytes in COVID-19 Patients
RNA-seq (gene expression) · human · 36 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
- 36 samples: 15 × Severe COVID-19 PMN, vs 8 × Mild COVID-19 PMN, vs 7 × Healthy control PMN, vs 6 × Severe COVID-19 LDG,Groups 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
- The assembly of neutrophil inflammasomes during COVID-19 is mediated by type I interferonsPLoS pathogens · 2024
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 GSE272381_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 PRJNA1136544 and SRA study SRP520284. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 36 samples.
How we read this: 4 replicated groups read from 36 sample titles; they account for 36 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.
- GSM8399715 Severe COVID-19 PMN, rep1 Severe COVID-19 PMN,
- GSM8399716 Severe COVID-19 PMN, rep2 Severe COVID-19 PMN,
- GSM8399717 Severe COVID-19 PMN, rep3 Severe COVID-19 PMN,
- GSM8399718 Severe COVID-19 PMN, rep4 Severe COVID-19 PMN,
- GSM8399719 Severe COVID-19 PMN, rep5 Severe COVID-19 PMN,
- GSM8399720 Severe COVID-19 PMN, rep6 Severe COVID-19 PMN,
- GSM8399721 Severe COVID-19 PMN, rep7 Severe COVID-19 PMN,
- GSM8399722 Mild COVID-19 PMN, rep1 Mild COVID-19 PMN,
- GSM8399723 Mild COVID-19 PMN, rep2 Mild COVID-19 PMN,
- GSM8399724 Mild COVID-19 PMN, rep3 Mild COVID-19 PMN,
- GSM8399725 Mild COVID-19 PMN, rep4 Mild COVID-19 PMN,
- GSM8399726 Mild COVID-19 PMN, rep5 Mild COVID-19 PMN,
- GSM8399727 Mild COVID-19 PMN, rep6 Mild COVID-19 PMN,
- GSM8399728 Mild COVID-19 PMN, rep7 Mild COVID-19 PMN,
- GSM8399729 Mild COVID-19 PMN, rep8 Mild COVID-19 PMN,
- GSM8399730 Severe COVID-19 LDG, rep1 Severe COVID-19 LDG,
- GSM8399731 Severe COVID-19 LDG, rep2 Severe COVID-19 LDG,
- GSM8399732 Severe COVID-19 LDG, rep3 Severe COVID-19 LDG,
- GSM8399733 Severe COVID-19 PMN, rep8 Severe COVID-19 PMN,
- GSM8399734 Severe COVID-19 PMN, rep9 Severe COVID-19 PMN,
- GSM8399735 Severe COVID-19 PMN, rep10 Severe COVID-19 PMN,
- GSM8399736 Severe COVID-19 PMN, rep11 Severe COVID-19 PMN,
- GSM8399737 Severe COVID-19 LDG, rep4 Severe COVID-19 LDG,
- GSM8399738 Severe COVID-19 PMN, rep12 Severe COVID-19 PMN,
- GSM8399739 Severe COVID-19 LDG, rep5 Severe COVID-19 LDG,
- GSM8399740 Severe COVID-19 LDG, rep6 Severe COVID-19 LDG,
- GSM8399741 Severe COVID-19 PMN, rep13 Severe COVID-19 PMN,
- GSM8399742 Severe COVID-19 PMN, rep14 Severe COVID-19 PMN,
- GSM8399743 Severe COVID-19 PMN, rep15 Severe COVID-19 PMN,
- GSM8399744 Healthy control PMN, rep1 Healthy control PMN,
- GSM8399745 Healthy control PMN, rep2 Healthy control PMN,
- GSM8399746 Healthy control PMN, rep3 Healthy control PMN,
- GSM8399747 Healthy control PMN, rep4 Healthy control PMN,
- GSM8399748 Healthy control PMN, rep5 Healthy control PMN,
- GSM8399749 Healthy control PMN, rep6 Healthy control PMN,
- GSM8399750 Healthy control PMN, rep7 Healthy control PMN,
- GSE234207 The difference between neutrophils of HC, COVID-19 patients admitted in ICU and wards. 35 samples shares covid-19, neutrophils
- GSE269643 Neutrophils restricted contribution of CCRL2 genetic variants to COVID-19 severity 12 samples shares covid-19, neutrophils
- GSE302902 DRUG-seq of high-density neutrophils (HDNs), mature low-density neutrophils (M-LDN), and immature low-density neutrophils (IM-LDN) in patients with colorectal cancer 51 samples shares neutrophils, low-density
- GSE212861 Dynamics of gene expression profiling by RNA-Seq and identification of high-risk patients for severe COVID-19 [RNA-Seq] 93 samples shares covid-19
- GSE202805 Global Transcriptomic analysis of PBMC derived from COVID-19 acute-severe patients and convalescents 80 samples shares covid-19
- GSE281864 Detailed delineation of the immune response to COVID-19 mRNA vaccination via high-temporal frequency blood transcriptomics [third] 193 samples shares covid-19
- GSE336577 Integrated Transcriptomic and Immune Analysis Reveals Distinct Mitochondrial and Immune Signature in COVID-19 ARDS Requiring Invasive Mechanical Ventilation 94 samples shares covid-19
- GSE273149 Longitudinal transcriptomic analysis reveals persistent enrichment of iron homeostasis and erythrocyte function pathways in severe COVID-19 ARDS 49 samples shares covid-19
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.