Dr Alexander Stewart

Mononuclear Phagocytes defend tissues, present antigens and mediate recovery and healing. To date we lack a marker to unify mononuclear phagocytes in humans or that informs us about their origin. Here, we reassess Mononuclear Phagocyte ontogeny in human blood through the lineage receptor CSF1R, in the steady state and in COVID-19. We define CSF1R as the first sensitive and reproducible pan-phagocyte lineage marker, to identify and enumerate all conventional monocytes, and the myeloid dendritic cells. In the steady state CSF1R is sufficient for sorting and immuno-magnetic isolation. In pathology, changes in CSF1R are more sensitive than CD14 and CD16. In COVID-19, a significant drop in membrane CSF1R is useful for stratifying patients, beyond the power of cell categories published thus far, which fail to capture COVID-19 specific events. Importantly, CSF1R defines cells which are neither conventional monocytes nor DCs, which are missed in published analysis. CSF1R decrease can be linked ex vivo to high CSF1 levels. Blood assessment of CSF1R+ cells opens a developmental window to the Mononuclear Phagocyte System in transit from bone marrow to tissues, supports isolation and phenotypic characterisation, identifies novel cell types, and singles out CSF1R inhibition as therapeutic target in COVID-19 and other diseases.

Antibody repertoire analysis by high throughput sequencing is now widely used, but a persisting challenge is enabling immunologists to explore their data to discover discriminating repertoire features for their own particular investigations. Computational methods are necessary for large-scale evaluation of antibody properties. We have developed BRepertoire, a suite of user-friendly web-based software tools for large-scale statistical analyses of repertoire data. The software is able to use data preprocessed by IMGT, and performs statistical and comparative analyses with versatile plotting options. BRepertoire has been designed to operate in various modes, for example analysing sequence-specific V(D)J gene usage, discerning physico-chemical properties of the CDR regions and clustering of clonotypes. Those analyses are performed on the fly by a number of R packages and are deployed by a shiny web platform. The user can download the analysed data in different table formats and save the generated plots as image files ready for publication.We believe BRepertoire to be a versatile analytical tool that complements experimental studies of immune repertoires. To illustrate the server’s functionality, we show use cases including differential gene usage in a vaccination dataset and analysis of CDR3H properties in old and young individuals. The server is accessible under http://mabra.biomed.kcl.ac.uk/BRepertoire.

Older people have reduced immune responses to infection and vaccination. B cell activation is key for the efficacy of the vaccine response, but there are several age-related changes in B cells which may contribute to the loss of vaccine efficacy. Different subpopulations of B cells contain have different functions and phenotypes. These populations can change as we age; older people have been shown to have fewer “IgM memory” cells, regulatory B cells and plasma cells and more IgD-CD27- “double negative” and “Age-related B cells”. While the overall quantity of antibody in the blood does not change, the quality of the B cell response changes; producing less specific antibodies upon challenge and more autoreactive antibodies. This could be due to changes in selection pressures, as has been demonstrated by repertoire sequencing of different subsets of B cells at different ages. Other changes in antibody repertoire are seen, including: greater levels of IgG2 in older people, and altered IgG1 IGHV gene usage. Since B cells rely on their environment for efficient responses, some of these changes may be due to age-related changes in accessory cells/signals. Other changes appear to be intrinsic to older/aged B cells themselves, such as their tendency to produce greater levels of inflammatory cytokines.

The human immunoglobulin repertoire is a hugely diverse set of sequences that are formed by processes of gene rearrangement, heavy and light chain gene assortment, class switching and somatic hypermutation. Early B cell development produces diverse IgM and IgD B cell receptors on the B cell surface, resulting in a repertoire that can bind many foreign antigens but which has had self-reactive B cells removed. Later antigen-dependent development processes adjust the antigen affinity of the receptor by somatic hypermutation. The effector mechanism of the antibody is also adjusted, by switching the class of the antibody from IgM to one of seven other classes depending on the required function. There are many instances in human biology where positive and negative selection forces can act to shape the immunoglobulin repertoire and therefore repertoire analysis can provide useful information on infection control, vaccination efficacy, autoimmune diseases and cancer. It can also be used to identify antigen-specific sequences that may be of use in therapeutics. The juxtaposition of lymphocyte development and numerical evaluation of immune repertoires has resulted in the growth of a new sub-speciality in immunology where immunologists and computer scientists/physicists collaborate to assess immune repertoires and develop models of immune action.

Immunoglobulin gene heterogeneity reflects the diversity and focus of the humoral immune response towards different infections, enabling inference of B cell development processes. Detailed compositional and lineage analysis of long read IGH repertoire sequencing, combining examples of pandemic, epidemic and endemic viral infections with control and vaccination samples, demonstrates general responses including increased use of IGHV4-39 in both Zaire Ebolavirus (EBOV) and COVID-19 patient cohorts. We also show unique characteristics absent in Respiratory Syncytial Virus or yellow fever vaccine samples: EBOV survivors show unprecedented high levels of class switching events while COVID-19 repertoires from acute disease appear underdeveloped. Despite the high levels of clonal expansion in COVID-19 IgG1 repertoires there is a striking lack of evidence of germinal centre mutation and selection. Given the differences in COVID-19 morbidity and mortality with age, it is also pertinent that we find significant differences in repertoire characteristics between young and old patients. Our data supports the hypothesis that a primary viral challenge can result in a strong but immature humoral response where failures in selection of the repertoire risk off-target effects.

Abstract The majority of metabolomics studies to date have utilised blood serum or plasma, biofluids that do not necessarily address the full range of patient pathologies. Here, correlations between serum metabolites, salivary metabolites and sebum lipids are studied for the first time. 83 COVID-19 positive and negative hospitalised participants provided blood serum alongside saliva and sebum samples for analysis by liquid chromatography mass spectrometry. Widespread alterations to serum-sebum lipid relationships were observed in COVID-19 positive participants versus negative controls. There was also a marked correlation between sebum lipids and the immunostimulatory hormone dehydroepiandrosterone sulphate in the COVID-19 positive cohort. The biofluids analysed herein were also compared in terms of their ability to differentiate COVID-19 positive participants from controls; serum performed best by multivariate analysis (sensitivity and specificity of 0.97), with the dominant changes in triglyceride and bile acid levels, concordant with other studies identifying dyslipidemia as a hallmark of COVID-19 infection. Sebum performed well (sensitivity 0.92; specificity 0.84), with saliva performing worst (sensitivity 0.78; specificity 0.83). These findings show that alterations to skin lipid profiles coincide with dyslipidaemia in serum. The work also signposts the potential for integrated biofluid analyses to provide insight into the whole-body atlas of pathophysiological conditions.

The effect of COVID-19 infection on the human metabolome has been widely reported, but to date all such studies have focused on a single wave of infection. COVID-19 has generated numerous waves of disease with different clinical presentations, and therefore it is pertinent to explore whether metabolic disturbance changes accordingly, to gain a better understanding of its impact on host metabolism and enable better treatments. This work used a targeted metabolomics platform (Biocrates Life Sciences) to analyze the serum of 164 hospitalized patients, 123 with confirmed positive COVID-19 RT-PCR tests and 41 providing negative tests, across two waves of infection. Seven COVID-19-positive patients also provided longitudinal samples 2–7 months after infection. Changes to metabolites and lipids between positive and negative patients were found to be dependent on collection wave. A machine learning model identified six metabolites that were robust in diagnosing positive patients across both waves of infection: TG (22:1_32:5), TG (18:0_36:3), glutamic acid (Glu), glycolithocholic acid (GLCA), aspartic acid (Asp) and methionine sulfoxide (Met-SO), with an accuracy of 91%. Although some metabolites (TG (18:0_36:3) and Asp) returned to normal after infection, glutamic acid was still dysregulated in the longitudinal samples. This work demonstrates, for the first time, that metabolic dysregulation has partially changed over the course of the pandemic, reflecting changes in variants, clinical presentation and treatment regimes. It also shows that some metabolic changes are robust across waves, and these can differentiate COVID-19-positive individuals from controls in a hospital setting. This research also supports the hypothesis that some metabolic pathways are disrupted several months after COVID-19 infection.

Separation of B cells into different subsets has been useful to understand their different functions in various immune scenarios. In some instances, the subsets defined by phenotypic FACS separation are relatively homogeneous and so establishing the functions associated with them is straightforward. Other subsets, such as the “Double negative” (DN, CD19+CD27-IgD-) population, are more complex with reports of differing functionality which could indicate a heterogeneous population. Recent advances in single-cell techniques enable an alternative route to characterize cells based on their transcriptome. To maximize immunological insight, we need to match prior data from phenotype-based studies with the finer granularity of the single-cell transcriptomic signatures. We also need to be able to define meaningful B cell subsets from single cell analyses performed on PBMCs, where the relative paucity of a B cell signature means that defining B cell subsets within the whole is challenging. Here we provide a reference single-cell dataset based on phenotypically sorted B cells and an unbiased procedure to better classify functional B cell subsets in the peripheral blood, particularly useful in establishing a baseline cellular landscape and in extracting significant changes with respect to this baseline from single-cell datasets. We find 10 different clusters of B cells and applied a novel, geometry-inspired, method to RNA velocity estimates in order to evaluate the dynamic transitions between B cell clusters. This indicated the presence of two main developmental branches of memory B cells. A T-independent branch that involves IgM memory cells and two DN subpopulations, culminating in a population thought to be associated with Age related B cells and the extrafollicular response. The other, T-dependent, branch involves a third DN cluster which appears to be a precursor of classical memory cells. In addition, we identify a novel DN4 population, which is IgE rich and closely linked to the classical/precursor memory branch suggesting an IgE specific T-dependent cell population.

Treatments for COVID-19 infections have improved dramatically since the beginning of the pandemic, and glucocorticoids have been a key tool in improving mortality rates. The UK’s National Institute for Health and Care Excellence guidance is for treatment to be targeted only at those requiring oxygen supplementation, however, and the interactions between glucocorticoids and COVID-19 are not completely understood. In this work, a multi-omic analysis of 98 inpatient-recruited participants was performed by quantitative metabolomics (using targeted liquid chromatography-mass spectrometry) and data-independent acquisition proteomics. Both ‘omics datasets were analysed for statistically significant features and pathways differentiating participants whose treatment regimens did or did not include glucocorticoids. Metabolomic differences in glucocorticoid-treated patients included the modulation of cortisol and bile acid concentrations in serum, but no alleviation of serum dyslipidemia or increased amino acid concentrations (including tyrosine and arginine) in the glucocorticoid-treated cohort relative to the untreated cohort. Proteomic pathway analysis indicated neutrophil and platelet degranulation as influenced by glucocorticoid treatment. These results are in keeping with the key role of platelet-associated pathways and neutrophils in COVID-19 pathogenesis and provide opportunity for further understanding of glucocorticoid action. The findings also, however, highlight that glucocorticoids are not fully effective across the wide range of ‘omics dysregulation caused by COVID-19 infections.