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Updated: Oct 9, 2025

Single-cell Analysis of Immunophenotype and Cytokine Production in Peripheral Whole Blood via Mass Cytometry
Published on: June 26, 2018
COVID-19 Specific Immune Markers Revealed by Single Cell Phenotypic Profiling
Francesca Sansico1, Mattia Miroballo1, Daniele Salvatore Bianco2
1Institute for Stem Cell Biology, Regenerative Medicine and Innovative Therapies (ISBReMIT), Fondazione IRCCS "Casa Sollievo della Sofferenza", 71013 San Giovanni Rotondo, Italy.
This study reveals unique immune cell changes in COVID-19 patients, identifying specific surface markers and reduced receptor expression linked to disease severity. These findings offer insights into immune responses and potential clinical outcomes for coronavirus disease 2019.
Area of Science:
- Immunology
- Virology
- Genomics
Background:
- COVID-19, caused by SARS-CoV-2, involves complex inflammation and immune alterations.
- While immune responses in respiratory tracts and blood are known, their impact on cell heterogeneity and disease progression remains unclear.
Purpose of the Study:
- To investigate immune cell heterogeneity and its correlation with disease progression in COVID-19 patients.
- To identify novel immune cell surface markers and receptors affected by SARS-CoV-2 infection.
Main Methods:
- Single-cell RNA sequencing was performed on peripheral blood mononuclear cells (PBMCs) from 50 COVID-19 patients and 27 healthy controls.
- Analysis focused on approximately 400 transcripts related to immune cell function, including surface markers.
Main Results:
- COVID-19 patients showed increased expression of COVID-specific surface markers across various immune cell subsets.
- Reduced expression of IFNGR1 and CXCR4 receptors was observed, correlating with inhibited signaling pathways and immune functions.
- These changes were associated with COVID-19 clinical outcomes.
Conclusions:
- Novel immunoreceptors are selectively expressed in COVID-19 patients, impacting immune functionality.
- Understanding these immune alterations is crucial for correlating them with clinical outcomes and disease progression.
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