Analysis of Antigen-Specific T and B Cells for Monitoring Immune Protection Against SARS-CoV-2.

Immunological memory is the basis of protection against most pathogens. Long-living memory T and B cells able to respond to specific stimuli, as well as persistent antibodies in plasma and in other body fluids, are crucial for determining the efficacy of vaccination and for protecting from a second infection by a previously encountered pathogen. Antigen-specific cells are represented at a very low frequency in the blood, and indeed, they can be considered "rare events" present in the memory T-cell pool. Therefore, such events should be analyzed with careful attention. In the last 20 years, different methods, mostly based upon flow cytometry, have been developed to identify such rare antigen-specific cells, and the COVID-19 pandemic has given a dramatic impetus to characterize the immune response against the virus. In this regard, we know that the identification, enumeration, and characterization of SARS-CoV-2-specific T and B cells following infection and/or vaccination require i) the use of specific peptides and adequate co-stimuli, ii) the use of appropriate inhibitors to avoid nonspecific activation, iii) the setting of appropriate timing for stimulation, and iv) the choice of adequate markers and reagents to identify antigen-specific cells. Optimization of these procedures allows not only determination of the magnitude of SARS-CoV-2-specific responses but also a comparison of the effects of different combinations of vaccines or determination of the response provided by so-called "hybrid immunity," resulting from a combination of natural immunity and vaccine-generated immunity. Here, we present two methods that are largely used to monitor the response magnitude and phenotype of SARS-CoV-2-specific T and B cells by polychromatic flow cytometry, along with some tips that can be useful for the quantification of these rare events. © 2023 Wiley Periodicals LLC. Basic Protocol 1: Identification of antigen-specific T cells Basic Protocol 2: Identification of antigen-specific B cells.

Immune Response Failure in Paucisymptomatic Long-Standing SARS-CoV-2 Spreaders.

The coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has rapidly spread throughout the world. This disease has a spectrum of different clinical pictures with different outcomes. Herein, we report all the data from three paucisymptomatic patients during a hospital stay that might represent a paradigmatic example of the method by which SARS-CoV-2 is shed. We demonstrated the lack of an adequate qualitative and quantitative immune response by multiparametric flow cytometry analysis. Our data can provide a new perspective about the method by which SARS-CoV-2 is shed and the clinical weight of viral persistence. In all three cases, the long persistence of the virus and the consistent reduction in both innate and adaptative immune cells are not associated with greater disease severity. These patients might represent at least part of the population. In particular, one patient oscillated between positive and negative swab tests several times without presenting any immune response. In all three cases, the immune response failure was not associated with a clinically significant involvement, indicating that it is not the virus's ability to impair the immune system, as well as its presence and persistence the fundamental mechanism that might causally lead to death. Finally, this kind of immune response in paucisymptomatic patients could pose a considerable danger to public health that questions the quarantine period. It is urgent to quantify the phenomenon with a large sample study.

Immunological Characteristics of Non-Intensive Care Hospitalized COVID-19 Patients: A Preliminary Report.

The outbreak of coronavirus disease 2019 (COVID-19) is posing a threat to global health. This disease has different clinical manifestations and different outcomes. The immune response to the novel 2019 coronavirus is complex and involves both innate and adaptive immunity. In this context, cell-mediated immunity plays a vital role in effective immunity against SARS-CoV-2. Significant differences have been observed when comparing severe and non-severe patients. Since these immunological characteristics have not been fully elucidated, we aimed to use cluster analysis to investigate the immune cell patterns in patients with COVID-19 who required hospitalization but not intensive care. We identified four clusters of different immunological patterns, the worst being characterized by total lymphocytes, T helper lymphocytes CD4+ (CD4+), T cytotoxic lymphocytes CD8+ (CD8+) and natural killer (NK) cells below the normal range, together with natural killer lymphocyte granzyme < 50% (NK granzyme+) and antibody-secreting plasma cells (ASCs) equal to 0 with fatal outcomes. In the worst group, 50% of patients died in the intensive care unit. Moreover, a negative trend was found among four groups regarding total lymphocytes, CD4+, CD8+ and B lymphocytes (p < 0.001, p < 0.005, p < 0.000, p < 0.044, respectively). This detailed analysis of immune changes may have prognostic value. It may provide a new perspective for identifying subsets of COVID-19 patients and selecting novel prospective treatment strategies. Notwithstanding these results, this is a preliminary report with a small sample size, and our data may not be generalizable. Further cohort studies with larger samples are necessary to quantify the prognostic value's weight, according to immunological changes in COVID-19 patients, for predicting prognoses and realizing improvements in clinical conditions.

Computational Analysis of Multiparametric Flow Cytometric Data to Dissect B Cell Subsets in Vaccine Studies.

The generation of the B cell response upon vaccination is characterized by the induction of different functional and phenotypic subpopulations and is strongly dependent on the vaccine formulation, including the adjuvant used. Here, we have profiled the different B cell subsets elicited upon vaccination, using machine learning methods for interpreting high-dimensional flow cytometry data sets. The B cell response elicited by an adjuvanted vaccine formulation, compared to the antigen alone, was characterized using two automated methods based on clustering (FlowSOM) and dimensional reduction (t-SNE) approaches. The clustering method identified, based on multiple marker expression, different B cell populations, including plasmablasts, plasma cells, germinal center B cells and their subsets, while this profiling was more difficult with t-SNE analysis. When undefined phenotypes were detected, their characterization could be improved by integrating the t-SNE spatial visualization of cells with the FlowSOM clusters. The frequency of some cellular subsets, in particular plasma cells, was significantly higher in lymph nodes of mice primed with the adjuvanted formulation compared to antigen alone. Thanks to this automatic data analysis it was possible to identify, in an unbiased way, different B cell populations and also intermediate stages of cell differentiation elicited by immunization, thus providing a signature of B cell recall response that can be hardly obtained with the classical bidimensional gating analysis. © 2019 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.