Analysing complex datasets while maintaining the interpretability and explainability of outcomes for clinicians and patients is challenging, not only in viral infections. These datasets often include a variety of heterogeneous clinical, demographic, laboratory, and personal data, and it is not a single factor but a combination of multiple factors that contribute to patient characterisation and host response. Therefore, multivariate approaches are needed to analyse these complex patient datasets, which are impossible to analyse with univariate comparisons (e.g., one immune cell subset versus one clinical factor). Using a SARS-CoV-2 infection as an example, we employed a patient similarity network (PSN) approach to assess the relationship between host immune factors and the clinical course of infection and performed visualisation and data interpretation. A PSN analysis of ~85 immunological (cellular and humoral) and ~70 clinical factors in 250 recruited patients with coronavirus disease (COVID-19) who were sampled four to eight weeks after a PCR-confirmed SARS-CoV-2 infection identified a minimal immune signature, as well as clinical and laboratory factors strongly associated with disease severity. Our study demonstrates the benefits of implementing multivariate network approaches to identify relevant factors and visualise their relationships in a SARS-CoV-2 infection, but the model is generally applicable to any complex dataset.

• Klíčová slova
• COVID-19 severity, IgM and IgG levels, data visualisation, minimal immune signature, multivariate data analysis, patient similarity network,
• MeSH
• COVID-19 * MeSH
• lidé MeSH
• protilátky virové MeSH
• SARS-CoV-2 * MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Názvy látek
• protilátky virové MeSH

Chronic lymphocytic leukaemia (CLL) is a genetically, morphologically and phenotypically heterogeneous chronic disease with clinical variability between patients. Whether the significant heterogeneity of cell size within the CLL population contributes to the heterogeneous features of this disease has not been investigated. The present study aimed to characterise the phenotypic and functional properties of two subpopulations of typical CLL cells that differ in cell size: small (s-CLL) and large (l-CLL) CLL cells delineated by forward scatter cytometry. The s-CLL cells were characterised by the CD5lowCXCR4hi phenotype, while the l-CLL cells were characterised by the CD5hiCXCR4dim phenotype and indicated a higher expression of CXCR3, CD20, CD38 and HLA-DR. The l-CLL cells displayed higher migration activity towards CXCL12, a tendency towards a higher proliferation rate and an increased capacity to produce IgM in the presence of CpG compared with s-CLL cells. When stimulated with CpG and CXCL12, l-CLL cells were characterised by a higher polarisation phenotype and motility than s-CLL cells. Our study revealed that the differences in CLL cell size reflected their activation status, polarisation and migratory abilities. Our data provide evidence of the importance of cell-size heterogeneity within a CLL pool and the dynamics of cell-size changes for disease pathogenesis, thus deserving further investigation.

• Klíčová slova
• cell-size heterogeneity, chronic lymphocytic leukaemia, migration, polarisation, pool of leukemic cells,
• Publikační typ
• časopisecké články MeSH