GEO series

A 50-gene high-risk profile predictive of COVID-19 and Idiopathic Pulmonary Fibrosis mortality originates from a molecular imbalance in monocyte and T-cell subsets that reverses in survivors with post-COVID-19 Interstitial Lung Disease

GSE264196 Homo sapiens (human) Single-cell RNA-seq 20 samples Submitted 2024/12/05 Platform GPL30173

At a glance

Single-cell RNA-seq · human · 20 samples

What it is: Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism: Homo sapiens (human)
Samples: 20 samples: 7 × PBMCs, Clinic vs 6 × PBMCs, IPF vs 4 × PBMCs, CTRL vs 3 × PBMCs, CovGroups 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)

Background: We previously identified a 50-gene signature that distinguished two groups of patients with increased risk of COVID-19 and IPF mortality. In this study we aimed to validate our previous findings and to study the source and trajectory of circulating immune cells expressing these 50 genes in patients with COVID19, post-COVID-19 Interstitial Lung Disease (ILD) and Idiopathic Pulmonary Fibrosis (IPF).

Read the full summary ↓

Methods: Whole blood and Peripheral Blood Mononuclear cells (PBMC) were obtained from 216 hospitalized patients with COVID-19, five patients with post-COVID-19-ILD, six patients with IPF and four controls from the University of South Florida (USF)/Tampa General Hospital (TGH). We measured the expression of the 50-gene signature using the nCounter analysis system (Nanostring), and the levels of Interleukin 6 (IL6), interferon γ-induced protein (IP10), Secreted Phosphoprotein 1 (SPP1) and transforming growth factor beta (TGF-β) in these patients by Luminex and Elisa tests. A 7-gene PCR assay was generated to validate the identification of COVID-19 endotypes. For single-cell RNA sequencing (scRNA-seq) we used Chromium Single Cell Controller (10X Genomics). For analysis of gene expression, we used the Scoring Algorithm of Molecular Subphenotypes (SAMS), Cell Ranger and Seurat packages. For statistical analysis, we used Kaplan-Meier survival curves, CoxPH models, Two-way ANOVA, T-test, Fisher’s exact and the Wilcoxon rank test. Results: We identified the presence of three genomic risk profiles based on the 50-gene signature, and a subset of seven genes, associated with low, intermediate, or high-risk of mortality in COVID-19. These risk groups had significant differences in IL6, IP10, SPP1 and TGFβ-1 levels. By applying scRNA-seq, we discovered a novel subtype of Monocytic-Myeloid-Derived Suppressive cells (M-MDSCs) expressing CD14+HLA DRlowCD163+ and high levels of the 7-gene signature. We denominated these cells as 7Gene-M-MDSC. These cells were not observed in survivors with post-COVID-19-ILD. The 43-gene signature was mostly expressed in naive CD4 T and memory CD4 T cells, Tregs, memory CD8 T GZMB+, memory CD8 T GZMK+ and naive CD8 T cells. While increased expression of genes in the 43 gene signature was seen in T cell subsets from survivors with post-COVID-19 ILD, the expression of these genes remained low in IPF. Conclusion: A 50-gene, high-risk genomic profile in peripheral blood of COVID-19 can accurately predict COVID-19 mortality. This profile is characterized by a simultaneous increase in expression of seven genes originating from 7Gene-M-MDSCs and decreased expression of 43 genes originating primarily in T cell subsets. While increased expression of these 43 genes can be seen in survivors with post-COVID-19 ILD, their expression remains low in IPF.

This dataset

Open GSE264196 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 GSE264196_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 PRJNA1101439 and SRA study SRP502307. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

7 × PBMCs, Clinic vs 6 × PBMCs, IPF vs 4 × PBMCs, CTRL vs 3 × PBMCs, Cov

Supports a between-group comparison across 20 samples.

How we read this: 4 replicated groups read from 20 sample titles; they account for 20 of them. 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

GSM8213881 PBMCs, CTRL-1 PBMCs, CTRL
GSM8213882 PBMCs, CTRL-2 PBMCs, CTRL
GSM8213883 PBMCs, CTRL-3 PBMCs, CTRL
GSM8213884 PBMCs, CTRL-4 PBMCs, CTRL
GSM8213885 PBMCs, IPF-1 PBMCs, IPF
GSM8213886 PBMCs, IPF-3 PBMCs, IPF
GSM8213887 PBMCs, IPF-6 PBMCs, IPF
GSM8213888 PBMCs, IPF-8 PBMCs, IPF
GSM8213889 PBMCs, IPF-12 PBMCs, IPF
GSM8213890 PBMCs, IPF-13 PBMCs, IPF
GSM8213891 PBMCs, Clinic-10 PBMCs, Clinic
GSM8213892 PBMCs, Clinic-12 PBMCs, Clinic
GSM8213893 PBMCs, Clinic-13 PBMCs, Clinic
GSM8213894 PBMCs, Clinic-15 PBMCs, Clinic
GSM8213895 PBMCs, Clinic-1 PBMCs, Clinic
GSM8213896 PBMCs, Clinic-5 PBMCs, Clinic
GSM8213897 PBMCs, Clinic-8 PBMCs, Clinic
GSM8213898 PBMCs, Cov-14 PBMCs, Cov
GSM8213899 PBMCs, Cov-43 PBMCs, Cov
GSM8213900 PBMCs, Cov-55 PBMCs, Cov

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