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March 13, 2026SHILAP Revista de lepidopterología0 citationsOpen Access

Imbalanced immune cell network and suboptimal cell activation: signatures associated with disease severity in vaccine-naïve COVID-19 patients

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RORanferi Ocaña-GuzmanInstituto Nacional de Enfermedades RespiratoriasEPElvira Pitén-IsidroInstituto Nacional de Enfermedades RespiratoriasJFJulio Flores-GonzalezInstituto Nacional de Enfermedades Respiratorias

Key Points

  • This research aims to identify immune signatures associated with disease severity in vaccine-naïve COVID-19 patients needing invasive mechanical ventilation.
  • Classified peripheral blood immune cells from vaccine-naïve COVID-19 patients into IMV and No-IMV groups.
  • Used multiparametric flow cytometry for high-dimensional immune phenotyping.
  • Applied FlowSOM clustering and UMAP for dimensionality reduction.
  • Evaluated T-cell activation efficiency after polyclonal stimulation in vitro.
  • IMV patients showed disrupted immune cell networks and loss of PD-1- and LAG-3-expressing T-cell subsets.
  • Increased frequency of T cells co-expressing TNF/TNFR and CD95L pathways was observed in IMV patients.
  • Reduced CD8+ T-cell activation capacity was noted, along with a rise of non-conventional cytotoxic CD4+ T-cell subsets.
  • B-cell compartment in IMV patients displayed CCR7+ subset depletion and lower PD-1 expression.
  • Altered immune checkpoint expression was seen in non-classical monocyte subsets, while classical monocytes remained stable.

Abstract

Background COVID-19 vaccination has significantly reduced mortality and morbidity. Recent studies in unvaccinated people indicate a more complex immune response beyond just the cytokine storm. Understanding changes in the immune cell network is crucial for identifying vaccine-independent immune imbalances, especially in vaccine-naïve patients needing invasive mechanical ventilation (IMV). This knowledge could help improve vaccine development and find biomarkers linked to severe COVID-19. Methods Peripheral blood immune cells from vaccine-naïve COVID-19 patients from the first pandemic wave were classified into those who required IMV and those who did not (No-IMV). High-dimensional immune phenotyping was performed using multiparametric flow cytometry combined with FlowSOM clustering and UMAP for dimensionality reduction. Additionally, T-cell activation efficiency after polyclonal stimulation was evaluated in vitro . Results IMV patients, but not No-IMV, exhibited a marked disruption of immune cell networks, characterized by a loss of immune checkpoint (IC)-expressing T-cell subsets, particularly PD-1- and LAG-3-expressing T cells. Conversely, there was an increase in the frequency of T cells co-expressing molecules linked to inflammatory pathways (TNF/TNFR) and cell death (CD95L). These changes were also associated with reduced CD8 + T-cell activation capacity and the rise of non-conventional cytotoxic CD4 + T-cell subsets. In the B-cell compartment, IMV patients displayed depletion of CCR7+ subsets and decreased PD-1 expression. Additionally, higher frequencies of NK and NKT cells expressing TNF pathway-related molecules were observed. While classical monocyte subsets expressing ICs such as PD-L1, PD-L2, and TIM-3 remained stable, non-classical monocyte subsets showed altered IC expression. In contrast, No-IMV patients maintained a relatively balanced immune architecture. Conclusions Vaccine-naïve COVID-19 patients requiring IMV display an immune landscape distinct from that of No-IMV patients. IMV exhibits a profound imbalance in innate and adaptive immune cell networks, characterized by inflammatory skewing, loss of regulatory subsets, and impaired cytotoxic T-cell functionality, features not observed in No-IMV. These findings reveal coordinated immune alterations beyond cytokine hyperinflammation and identify cellular immune signatures associated with severe COVID-19.

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Cite This Study

Ocaña-Guzman et al. (2026) studied this question.

synapsesocial.com/papers/69b3ab0002a1e69014ccbad3https://doi.org/10.3389/fimmu.2026.1794103
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