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Comparative single-cell analysis reveals IFN-g as a driver of respiratory sequelae post COVID-19

GSE263817 Homo sapiens (human) Single-cell RNA-seq 33 samples Submitted 2024/07/16 Platform GPL30209Platform GPL30215Platform GPL24247
At a glance

Single-cell RNA-seq · human · 33 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism
Homo sapiens (human)
Samples
33 samples: 33 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.
Paper
Comparative single-cell analysis reveals IFN-γ as a driver of respiratory sequelae after acute COVID-19Science translational medicine · 2024
Summary (written by the submitters)
Post-acute sequelae of SARS-CoV-2 infection (PASC) represents an urgent public health challenge, with its impact resonating in over 60 million individuals globally. While a growing body of evidence suggests that dysregulated immune reactions may be linked with PASC symptoms, most investigations have primarily centered around blood studies, with few focusing on samples derived from post-COVID affected tissues.
Read the full summary ↓
Further, clinical studies alone often provide correlative insights rather than causation. Thus, it is essential to compare clinical samples with relevant animal models and conduct functional experiments to truly understand the etiology of PASC. In this study, we have made comprehensive comparisons between bronchoalveolar lavage fluid (BAL) single-cell RNA sequencing (scRNAseq) data derived from clinical PASC samples and the relevant PASC mouse model. This revealed a strong pro-fibrotic monocyte-derived macrophage response in respiratory PASC (R-PASC) in both humans and mice, and abnormal interactions between pulmonary macrophages and respiratory resident T cells. IFN-g emerged as a key node mediating the immune anomalies in R-PASC. Strikingly, neutralizing IFN-g post the resolution of acute infection reduced lung inflammation and tissue fibrotic changes in a mouse model of R-PASC. Our study underscores the importance of performing comparative analysis to understand the root cause of PASC for developing effective therapies.
Published in
Comparative single-cell analysis reveals IFN-γ as a driver of respiratory sequelae after acute COVID-19
Li C, Qian W, Wei X et al. · Science translational medicine 2024 · PMID 39018367 · doi:10.1126/scitranslmed.adn0136
This dataset
Open GSE263817 in finder → Find more 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 GSE263817_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: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA1099287 and SRA study SRP501300. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39018367) ↗
Study design
33 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 33 sample titles: 33 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
Datasets on a similar topic

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