key: cord-0951554-0p8fpsqj authors: Sibbertsen, Freya; Glau, Laura; Paul, Kevin; Mir, Thomas S.; Gersting, Søren W.; Tolosa, Eva; Dunay, Gabor A. title: Phenotypic analysis of the pediatric immune response to SARS‐CoV‐2 by flow cytometry date: 2022-01-10 journal: Cytometry A DOI: 10.1002/cyto.a.24528 sha: 0991dc5148037f3166da173b4dc29455f154a823 doc_id: 951554 cord_uid: 0p8fpsqj Pediatric SARS‐CoV‐2 infection is often mild or asymptomatic and the immune responses of children are understudied compared to adults. Here, we present and evaluate the performance of a two‐panel (16‐ and 17 parameter) flow cytometry‐based approach for immune phenotypic analysis of cryopreserved PBMC samples from children after SARS‐CoV‐2 infection. The panels were optimized based on previous SARS‐CoV‐2 related studies for the pediatric immune system. PBMC samples from seven SARS‐CoV‐2 seropositive children from early 2020 and five age‐matched healthy controls were stained for analysis of T‐cells (panel T), B and innate immune cells (panel B). Performance of the panels was evaluated in two parallel approaches, namely classical manual gating of known subpopulations and unbiased clustering using the R‐based algorithm PhenoGraph. Using manual gating we clearly identified 14 predefined subpopulations of interest for panel T and 19 populations in panel B in low‐volume pediatric samples. PhenoGraph found 18 clusters within the T‐cell panel and 21 clusters within the innate and B‐cell panel that could be unmistakably annotated. Combining the data of the two panels and analysis approaches, we found expected differentially abundant clusters in SARS‐CoV‐2 seropositive children compared to healthy controls, underscoring the value of these two panels for the analysis of immune response to SARS‐CoV‐2. We established a two‐panel flow cytometry approach that can be used with limited amounts of cryopreserved pediatric samples. Our workflow allowed for a rapid, comprehensive, and robust pediatric immune phenotyping with comparable performance in manual gating and unbiased clustering. These panels may be adapted for large multi‐center cohort studies to investigate the pediatric immune response to emerging virus variants in the ongoing and future pandemics. The current SARS-CoV-2 pandemic has been responsible for over five million deaths by the time of this submission. A total of 10%-20% of adult patients develop a severe or life threatening disease [1] . The majority of children, however, develop mild symptoms, and their contribution to spreading SARS-CoV-2 remains unclear [2, 3] . Immune responses of children to SARS-CoV-2 after illness or asymptomatic infection remain understudied compared to adults, and the understanding of how their immune system clears the virus without resulting in excess inflammation could provide clues for treatment of adult patients [4] . However, children rarely do develop the Multisystem Inflammatory Syndrome in Children (MIS-C) as a secondary consequence of a SARS-CoV-2 infection [5, 6] . Furthermore, the study of long-term immunological changes several months after infection could provide important insights for the rational planning of vaccination efforts for children of different age. Here, we present an approach to rapidly analyze the frequencies of lymphoid and myeloid cell populations, and the differentiation stages of lymphocytes in pediatric samples of peripheral blood mononuclear cells (PBMC) using two multicolor flow cytometry panels. Our aim was to establish and evaluate panels with a robust performance in classical gating as well as state-of-the art flow bioinformatic approaches such as unbiased clustering and visualization by Uniform Manifold Approximation and Projection analysis (UMAP). Since most broadly available cytometers and commercial fluorochromes limit flow cytometry panels to less than 20 markers, two different panels were designed. The goal of panel T was to identify key functional subsets and differentiation stages of T-cells. Panel B included markers for B-cell subpopulations and for innate cell subsets. The panels have been tailored to target subpopulations with higher relevance in children, such as recent thymic emigrants (RTE) and innate lymphoid subsets, while also considering alterations described for SARS-CoV-2 and other respiratory infections [5] [6] [7] [8] [9] [10] . Furthermore, the panels have been developed and optimized for cryopreserved samples, making transport and remote analysis of specimens possible for larger pediatric cohorts in multi-center settings. For analysis, frozen aliquots of PBMC were incubated for 1 min in a 37 C water bath, then rapidly thawed in 37 C RPMI by gentle pipetting. Viability was above 80% in all samples. Bonferroni correction for multiple testing was applied to all statistical analysis. After gating of single lymphocytes and exclusion of dead cells, CD3 positive cells were identified ( Figure 1A ). This was followed by separation of γδ T cells (TGD). During the first SARS epidemic γδ T effector memory cells were expanded approximately 3 months post symptom onset in seroconverted adult patients, and had a protective role by killing SARS-CoV-infected target cells [12] . Within classical T-cells, CD4 and CD8 lineages were gated within the TCR-γδ negative CD3 positive population. Regulatory T-cells (Treg) were defined as CD25+CD127low in the CD4 T-cell compartment, as this phenotype correlates well with intracellular staining for FOXP3 [13] . After the exclusion of Treg, CD45RA and CD27 were sufficient in conventional (non-Treg) CD4+ T-cells to distinguish between central memory (Tcm), effector memory (Tem) and naive (Tn). We used CD27 instead of CCR7, because of the similar expression patterns of the two molecules [14] and instability of CCR7 in cryopreserved samples [15] . The coexpression of CD31 and CD45RA on CD4+ conventional T-cells was used to mark recent thymic emigrants (RTE), a large subpopulation in young children [16] . Panel T allows for the analysis of the coinhibitory receptor PD-1 and the chronic activation marker HLA-DR ( Figure 1A ). Children with ryngeal samples [21] , but their symptoms are much less pronounced [2] , suggesting lower immune activation. PD-1 is associated with more efficient viral clearance and is known to regulate the development of immunologic memory in CD8+ T-cells after respiratory infections [9] . Counter regulation by co-inhibitory receptors, for example, PD-1 among others has been associated with clonal T-cell responses in SARS-CoV-2 infection of adults [8] . and Tfh were not present as a distinct cluster, probably due to small cell numbers in these pediatric samples, even though they were distinctly identified by manual gating ( Figure 1A) . Importantly, CX3CR1 positive cells presented as a distinct cluster among both CD4 and CD8 effector cells and could thus be annotated as cytotoxic CD4+ (cluster 18) and CX3CR1 positive CD8+ cells (cluster 13) (see also these changes could persist long term after COVID-19. In respiratory syncytial virus infection, a reduction in pDC in blood persisted in children for up to 3 months after infection [25] . Here, we present two flow cytometry panels for an efficient and rapid phenotypic and functional characterization of the pediatric immune response to COVID-19. Both panels perform well in either manual gating or unbiased clustering approaches. Importantly, these panels allow for a broad assessment of the innate and adaptive immune sys- Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: A retrospective cohort study Epidemiological characteristics of 2143 pediatric patients with 2019 Coronavirus disease in China Susceptibility to SARS-CoV-2 infection among children and adolescents compared with adults: A systematic review and meta-analysis SARS-CoV-2, the virus that causes COVID-19: Cytometry and the new challenge for Global Health Deep immune profiling of MIS-C demonstrates marked but transient immune activation compared to adult and pediatric COVID-19 Mapping systemic inflammation and antibody responses in multisystem inflammatory syndrome in children (MIS-C) B cell analysis in SARS-CoV-2 versus malaria: increased frequencies of plasmablasts and atypical memory B cells in COVID-19 Next-generation sequencing of T and B cell receptor repertoires from COVID-19 patients showed signatures associated with severity of disease The PD-1 pathway regulates development and function of memory CD8+ T cells following respiratory viral infection Peripheral immunophenotypes in children with multisystem inflammatory syndrome associated with SARS-CoV-2 infection Cytofkit: A bioconductor package for an integrated mass cytometry data analysis pipeline Anti-severe acute respiratory syndrome coronavirus immune responses: the role played by Vγ9Vδ2 T cells Comprehensive analysis of frequency and phenotype of T regulatory cells in HIV infection: CD39 expression of FoxP3+ T regulatory cells correlates with progressive disease Stepwise differentiation of CD4 memory T cells defined by expression of CCR7 and CD27 Standardization of cryopreserved peripheral blood mononuclear cells through a resting process for clinical immunomonitoring-Development of an algorithm Life after the thymus: CD31+ and CD31 human naive CD4+ T-cell subsets Guidelines for the use of flow cytometry and cell sorting in immunological studies (second edition) Regulators of Tfh cell differentiation The who's who of T-cell differentiation: human memory T-cell subsets The chemokine receptor CX3CR1 defines three antigen-experienced CD8 T cell subsets with distinct roles in immune surveillance and homeostasis Estimating infectiousness throughout SARS-CoV-2 infection course OMIP-062: a 14-color, 16-antibody panel for Immunophenotyping human innate lymphoid, myeloid and T cells in small volumes of whole blood and pediatric airway samples Human group 1 innate lymphocytes are negative for surface CD3ε but express CD5 Comprehensive Phenotyping of human dendritic cells and monocytes Lower number of plasmacytoid dendritic cells in peripheral blood of children with bronchiolitis following respiratory syncytial virus infection Phenotypic analysis of the pediatric immune response to SARS-CoV-2 by flow cytometry The authors declare no conflicts of interest. The peer review history for this article is available at https://publons.