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Longitudinal trajectories of mild/moderate SARS-CoV2 infection reveals key precursors and predictors of convalescent virus-specific immunity

GSE173590 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 201 samples Submitted 2024/05/30 Platform GPL24676Platform GPL27644
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 201 samples

What it is
ChIP-seq / ATAC-seq (genome binding and open chromatin)Where proteins bind the genome, or which regions are open and active.
Organism
Homo sapiens (human)
Samples
201 samples: 201 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.
Summary (written by the submitters)
SARS-CoV2 infection results in clinically heterogeneous manifestations that are partially driven by complex longitudinal interactions with the immune system. The public health burden of mild COVID-19 is massive given the >100 million infected worldwide, but most research has focused on severe and fatal COVID-19. We recruited a mild COVID-19 cohort for multimodal immunophenotyping of single immune cells (scRNAseq, scATACseq, flow cytometry), serum proteins, virus-specific cellular and humoral immune responses, and clinical annotation from early acute infection to convalescence.
Read the full summary ↓
Samples were acquired longitudinally from early acute infection before 15 days to >100 days post-symptom onset. Comparison to uninfected controls revealed marked immune activation consistent with acute response to viral infection at 15 days post-symptom onset with stronger inflammatory responses in older (>=40) compared to younger (<40) participants. Type I, II, and III interferon responses were most consistently upregulated in nearly all PBMC subsets. Plasmablasts were expanded in early infection with signatures of active IFNL and IFNG signaling, linking early inflammation and IFN responses to control of viral replication via antibodies. Longitudinal models of scRNAseq confirmed most inflammatory pathways decreased over time, including type I and II IFN signaling, as well as signaling by innate danger sensors (TLR, RIG-I) that detect viral replication. By contrast, genes with increasing expression over time were enriched for EMT and wound healing pathways. Most participants showed consistent decay of inflammatory signatures by convalescence at >d 30 post-symptom onset except those presenting with PASC. Despite similar virus-specific adaptive immune responses between post-acute sequelae PASC and recovered convalescent participants, PASC participants showed persistent serum cytokine and chemokine differences including elevated IL5, TNF, IL-12p70, and soluble CD28 at >30 d post-symptom onset. scRNAseq, scATACseq, and network analyses provided more signs of persistent inflammatory activation in multiple cell types, with multimodal evidence pointing to increased activity of innate cells (CD14 monocytes, DCs) and effector T cells (CD4 TEM, CD8 TEMRA). Cytokines at day 7 PSO were highly correlated with and predictive of both virus-specific IgG and neutralizing antibody titers. Integrative network analyses defined the most enriched ligand-receptor pathways across PBMC subsets including LTA/TNF , TNF, and IFNg in early infection, and these also showed signs of persistent activation in PASC. Common downstream targets of these pathways in PASC point to novel therapeutic targets and mechanistic hypotheses, including IL-1Ꞵ, AP-1, ZFP36/TTP, and CEBP/ꞵ. These findings revealed dynamics of immune activation and critical early signals correlated with adaptive immune responses. Serum protein signatures predicting antibody responses and correlating with PASC may provide opportunities for enhanced immunomonitoring and personalized treatment.
This dataset
Open GSE173590 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 GSE173590_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: Look for peak files (.bed, .narrowPeak) and signal tracks (.bigWig) among the supplementary files; the raw reads are on ENA.

Also filed as BioProject PRJNA726234. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
201 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 201 sample titles: 201 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.

Samples in this study

+ 161 more — browse all 201 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

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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.