Longitudinal multi-omics identifies responses of megakaryocytes, erythroid cells and plasmablasts as hallmarks of severe COVID-19 trajectories [sequencing]
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. Both, whole transcriptome and B cell receptor sequence analysis was used to determine signatures specific to differe...
Provenance — who produced it, who reused it
Linked to 4 papers in the literature. Roles are inferred factual signals (who deposited the data vs who reused it), with counts — never a judgement about any author.
3 further papers cite this accession but reuse could not be confirmed.
Deep data QC
94/100 · AStandardized, field-standard QC computed by touching the data — every metric states how it was obtained · evidence: measured
The A grade is a transparent weighted average. Each metric below scored from 0–100% against the published bulk-RNA-seq thresholds, weighted by its importance; nothing is hidden or subjective.
measured = computed from the data · extrapolated/reported = derived or from the repository · dq-1.0