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Longitudinal multi-omics identifies responses of megakaryocytes, erythroid cells and plasmablasts as hallmarks of severe COVID-19 trajectories [sequencing]

GSE161777 Homo sapiens (human) RNA-seq (gene expression) 101 samples Submitted 2020/12/11 Platform GPL24676Platform GPL15520
At a glance

RNA-seq (gene expression) · human · 101 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
101 samples
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.
Summary (written by the submitters)
In order to characterise the temporal dynamics of host response during COVID-19, we performed a longitudinal multi-omics study using a two centre German cohort of 13 patients. Bulk RNA was extracted from peripheral blood sampled at up to 5 time points per patient. At each sample point, a patient’s disease trajectory, “pseudotime”, was categorised according to clinical parameters.
Read the full summary ↓
Both, whole transcriptome and B cell receptor sequence analysis was used to determine signatures specific to different disease trajectories. We identified coexpression modules that were associated with specific patterns across different COVID-19 disease trajectories. One module identified was related to failing interferon I response at the peak of disease severity. A second module showed biphasic upregulation of transcripts associated with erythropoiesis. Bulk BCR identified expansion of IgA+ and IgG+ cells. In sum, this study demonstrated distinct gene expression dynamics upon SARS-CoV-2 infection.
This dataset
Open GSE161777 in finder → Find more human RNA-seq datasets →
Download · which file do I need?

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.

1 · Just have a look

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.

Series matrix ↓
2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE161777_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓
3 · Start from the raw reads

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.

Raw reads on ENA ↗

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 PRJNA679331 and SRA study SRP293160. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 33296687) ↗
Samples in this study

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