GEO series

CD55 upregulation in T cells of COVID-19 patients suppresses type-I interferon responses [bulkRNA-seq]

GSE293708 Homo sapiens (human) Single-cell RNA-seq 16 samples Submitted 2025/04/04 Platform GPL17303

At a glance

Single-cell RNA-seq · human · 16 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: 16 samples: 4 × PBMC_Healthy_Patient vs 4 × PBMC_Mild_Patient vs 4 × PBMC_Severe_Patient vs 4 × PBMC_Critical_PatientGroups 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: CD55 upregulation in T cells of COVID-19 patients suppresses type-I interferon responsesCommunications biology · 2025

Summary (written by the submitters)

Complement overactivation, has been verified in COVID-19 patients. Complement regulatory proteins, including CD55, control complement overactivation thus eliminating complement deposition and cell lysis. We investigated complement regulatory protein expression in COVID-19 for potential deregulated expression patterns driving disease pathogenesis.

Read the full summary ↓

Single-cell RNA-seq revealed increased PBMCs CD55 expression in severely and critically ill patients. This increase was also detected upon integrated subclustering analysis of monocyte, T cell and B cell populations. FACS analysis confirmed the significant upregulation of CD55 expression in CD4+ and CD8+ T cell and monocyte populations of severely and critically ill COVID-19 patients. This upregulation was associated with decreased expression of type-I IFN-stimulated genes (ISGs) in patients with severe and critical COVID-19, indicating a suppressor effect of CD55. Silencing of CD55 in T cells from COVID-19 severely ill patients in-vitro and sensitization with SARS-CoV-2 peptides resulted in significantly augmented expression of ISGs and a reversal of their expression to levels similar to control or higher. The present study uncovers, to the best of our knowledge, a novel regulatory effect of CD55 on type-I IFN responses of severely ill COVID-19 patients, thus indicating its contribution to COVID-19 pathogenesis, and identifies a novel mechanistic pathway in the COVID-19 immune response.

Published in

CD55 upregulation in T cells of COVID-19 patients suppresses type-I interferon responses

Detsika MG, Sakkou M, Triantafyllidou V et al. · Communications biology 2025 · PMID 40316776 · doi:10.1038/s42003-025-08066-z

This dataset

Open GSE293708 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 GSE293708_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 PRJNA1246073 and SRA study SRP576261. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40316776) ↗

Study design

4 × PBMC_Healthy_Patient vs 4 × PBMC_Mild_Patient vs 4 × PBMC_Severe_Patient vs 4 × PBMC_Critical_Patient

Supports a between-group comparison across 16 samples.

How we read this: 4 replicated groups read from 16 sample titles; they account for 16 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

GSM8888967 PBMC_Healthy_Patient1 PBMC_Healthy_Patient
GSM8888968 PBMC_Healthy_Patient2 PBMC_Healthy_Patient
GSM8888969 PBMC_Healthy_Patient3 PBMC_Healthy_Patient
GSM8888970 PBMC_Healthy_Patient4 PBMC_Healthy_Patient
GSM8888971 PBMC_Mild_Patient1 PBMC_Mild_Patient
GSM8888972 PBMC_Mild_Patient2 PBMC_Mild_Patient
GSM8888973 PBMC_Mild_Patient3 PBMC_Mild_Patient
GSM8888974 PBMC_Mild_Patient4 PBMC_Mild_Patient
GSM8888975 PBMC_Severe_Patient1 PBMC_Severe_Patient
GSM8888976 PBMC_Severe_Patient2 PBMC_Severe_Patient
GSM8888977 PBMC_Severe_Patient3 PBMC_Severe_Patient
GSM8888978 PBMC_Severe_Patient4 PBMC_Severe_Patient
GSM8888979 PBMC_Critical_Patient1 PBMC_Critical_Patient
GSM8888980 PBMC_Critical_Patient2 PBMC_Critical_Patient
GSM8888981 PBMC_Critical_Patient3 PBMC_Critical_Patient
GSM8888982 PBMC_Critical_Patient4 PBMC_Critical_Patient

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