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SARS-CoV-2 infection response in iPSC-derived alveolar epithelial cell type-2

GSE250377 Homo sapiens (human) RNA-seq (gene expression) 40 samples Submitted 2024/03/20 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 40 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
40 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.
Paper
Pre-Infection Innate Immunity Attenuates SARS-CoV-2 Infection and Viral Load in iPSC-Derived Alveolar Epithelial Type 2 CellsCells · 2024
Summary (written by the submitters)
The coronavirus disease 2019 (COVID-19) pandemic has affected nearly 700 million and claimed more than 6 million lives. However, the large heterogeneity in disease susceptibility and severity of illness (SOI) remains poorly understood.
Read the full summary ↓
A growing body of evidence suggests that alveolar epithelial cell type 2 (AT2), which constitute 10-15% of all alveolar cells and are critical for alveolar homeostasis, are the primary targets of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection and damage to AT2s may directly contribute to disease severity and poor prognosis in COVID-19 patients. Our in-vitro modeling of SARS-CoV-2 infection, in well-characterized, highly uniform, (r2 at 95% CI = 0.92 ± 0.03), induced pluripotent stem cell (iPSC) derived AT2s from 10 participants of our Mexican American Family Study, showed interindividual variability in infection susceptibility and the post-infection cellular viral load. To understand the underlying mechanism of the AT2’s capacity to regulate SARS-CoV-2 infection and cellular viral load, we performed a systematic characterization of the pre- and post-SARS-CoV-2 challenged AT2 transcriptomes. A total of 1,393 genes were found significantly (Oneway ANOVA FDR corrected p ≤ 0.05; FC abs ≥ 2.0) differentially expressed (DE) between pre- and post-SARS-CoV-2 infection-challenged AT2 and suggest significant upregulation of viral infection-related cellular innate immune response pathways (p-value ≤ 0.05; activation z-score ≥ 3.5), whilst the cholesterol and xenobiotic related metabolic pathways were significantly downregulated (p-value ≤ 0.05; activation z-score ≤ - 3.5). Interestingly, pre-infection expression of 238 DE genes showed a high correlation with the post-infection SARS-CoV-2 viral load (FDR corrected p-value ≤ 0.05, and r2-absolute ≥ 0.57). The 85 genes whose expression was negatively correlated with the viral load showed significant enrichment in viral recognition and cytokine-mediated innate immune GO biological processes (p-value range: 4.65x10-10 to 2.24x10-6). The 153 genes whose expression was positively correlated with the viral load showed significant enrichment in cholesterol homeostasis, extracellular matrix, and MAPK/ERK pathway-related GO biological processes (p-value range: 5.06x10-5 to 6.53x10-4). Overall, our results strongly suggest that AT2s pre-infection innate immunity and metabolic state affects their susceptibility to SARS-CoV-2 infection and viral load.
Published in
Pre-Infection Innate Immunity Attenuates SARS-CoV-2 Infection and Viral Load in iPSC-Derived Alveolar Epithelial Type 2 Cells
Kumar S, Granados J, Aceves M et al. · Cells 2024 · PMID 38474333 · doi:10.3390/cells13050369
This dataset
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.

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 GSE250377_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA1053780 and SRA study SRP478463. Searching any of these in the dataset finder brings you back here.

Samples in this study

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