1,761 research outputs found

    The development of bioinformatics workflows to explore single-cell multi-omics data from T and B lymphocytes

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    The adaptive immune response is responsible for recognising, containing and eliminating viral infection, and protecting from further reinfection. This antigen-specific response is driven by T and B cells, which recognise antigenic epitopes via highly specific heterodimeric surface receptors, termed T-cell receptors (TCRs) and B cell receptors (BCRs). The theoretical diversity of the receptor repertoire that can be generated via homologous recombination of V, D and J genes is large enough (>1015 unique sequences) that virtually any antigen can be recognised. However, only a subset of these are generated within the human body, and how they succeed in specifically recognising any pathogen(s) and distinguishing these from self-proteins remains largely unresolved. The recent advances in applying single-cell genomics technologies to simultaneously measure the clonality, surface phenotype and transcriptomic signature of pathogen- specific immune cells have significantly improved understanding of these questions. Single-cell multi-omics permits the accurate identification of clonally expanded populations, their differentiation trajectories, the level of immune receptor repertoire diversity involved in the response and the phenotypic and molecular heterogeneity. This thesis aims to develop a bioinformatic workflow utilising single-cell multi-omics data to explore, quantify and predict the clonal and transcriptomic signatures of the human T-cell response during and following viral infection. In the first aim, a web application, VDJView, was developed to facilitate the simultaneous analysis and visualisation of clonal, transcriptomic and clinical metadata of T and B cell multi-omics data. The application permits non-bioinformaticians to perform quality control and common analyses of single-cell genomics data integrated with other metadata, thus permitting the identification of biologically and clinically relevant parameters. The second aim pertains to analysing the functional, molecular and immune receptor profiles of CD8+ T cells in the acute phase of primary hepatitis C virus (HCV) infection. This analysis identified a novel population of progenitors of exhausted T cells, and lineage tracing revealed distinct trajectories with multiple fates and evolutionary plasticity. Furthermore, it was observed that high-magnitude IFN-γ CD8+ T-cell response is associated with the increased probability of viral escape and chronic infection. Finally, in the third aim, a novel analysis is presented based on the topological characteristics of a network generated on pathogen-specific, paired-chain, CD8+ TCRs. This analysis revealed how some cross-reactivity between TCRs can be explained via the sequence similarity between TCRs and that this property is not uniformly distributed across all pathogen-specific TCR repertoires. Strong correlations between the topological properties of the network and the biological properties of the TCR sequences were identified and highlighted. The suite of workflows and methods presented in this thesis are designed to be adaptable to various T and B cell multi-omic datasets. The associated analyses contribute to understanding the role of T and B cells in the adaptive immune response to viral-infection and cancer

    T Follicular Helper cell dynamics in response to vaccination

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    High quality long lived humoral immune responses require significant help from T follicular helper (Tfh) cells located within the germinal centres (GC) of lymph nodes (LN). Cognate interactions established between Tfh cells and GC B cells regulates somatic hypermutation and affinity maturation, determining the quality of antibodies produced. However, the anatomically protected location of Tfh cells, within the LN, poses a significant logistical and ethical obstacle to in vivo interrogation in humans. This study utilised the fine needle biopsy (FNB) technique to directly probe the GCs of human axillary LNs pre- and post- seasonal influenza vaccination, with the aim to interrogate the commitment of CD4+ T cells to the Tfh cell lineage. In this study, peripheral blood and draining and contralateral LN FNBs were collected prior to and 5 days post vaccination. Ex vivo phenotyping of LN FNB samples revealed significant expansion of GC Tfh cells was restricted to draining LNs. This early expansion of GC Tfh cells was characterised by an increase in highly activated, motile, and proliferating cells, measured by CD38, ICOS and Ki67 expression. Further, although no significant increase in the absolute number of Pre Tfh cells was observed, there was an increase in CD38+ICOS+ Pre-Tfh cells post vaccination, implicating this population in the immune response and highlighting the changes in cellular profile. Characterisation of cellular subsets by traditional flow cytometry techniques is limited by the number of parameters available on the instrument. Therefore, we leveraged Smart-Seq2 single cell RNA-sequencing (scRNA-seq) to further examine the heterogeneity within GC Tfh and Pre-Tfh cells. In 3 participants, we identified 7 functionally distinct clusters of cells based on differentially expressed (DE) genes. A proliferating cluster and a motile cluster were observed in all participants. The proliferating cluster exhibited an activated, proinflammatory gene signature and was enriched for Tfh differentiation gene pathways, whereas the motile cluster was enriched for pathways involved in cellular migration and motility, critical for rapid reorganisation of GCs to support dynamic interactions and cellular reactivation. To explore functional flexibility and plasticity of LN GC Tfh and Pre-Tfh, we integrated scRNAseq post vaccination data from 5 participants. Based on DE genes, we identified 5 distinct clusters; Resting, Activated migrating, B cell interacting Tfh, Proliferating and Cytotoxic. Trajectory analysis using inferred pseudotime revealed the transition of cells through activation states and the gain/loss of different CD4+ T cell lineage attributes and effector functions. Using the T cell receptor as a natural cellular barcode, we were able to identify divergent differentiation into different fate lineages from a common precursor cell. Overall, the work presented in this thesis is the first to quantify the selective activation of GC Tfh and Pre-Tfh and provides exciting and promising initial evidence of the functional heterogeneity and plastic potential with the Tfh lineage in vivo in human axillary LNs in response to vaccination, that could be leveraged to develop more effective vaccines

    Stochastic compartmental models and CD8+ T cell exhaustion

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    In this PhD thesis, mathematical models for cell differentiation are presented. Cell differentiation is a widely observed process in cellular biology allowing a small pool of not specialised cells to develop and maintain a bigger population of cells with a specific function. Different mathematical techniques are employed in this thesis, to study cell differentiation process. We propose a time-independent stochastic mathematical model to represent a general differentiation process via a sequence of compartments. Since we are interested in the ultimate fate of the system, we define a discrete-time branching processes and consider the impact, on the final population, of cells passing through only one or multiple compartments. Further, we include time dependency and define a continuous-time Markov chain to analyse cells dynamics along the sequence of compartments over time. Also, we focus on the journey of a single cell over time and compute a number of summary statistics of interest. Moreover, the impact of different types of differentiation events is considered and numerical results inspired by biological applications, mainly related to immunology, are summarised to illustrate our theoretical approach and methods. In the last Chapter, we focus on a specific cell differentiation process: cells of the immune system have been observed to differentiate towards a dysfunctional state, called exhaustion, during a chronic infection or cancer. One of the aims of this PhD thesis is to shed light into the exhaustion-differentiation process of CD8+ T cells and its reversibility which is a topic of interest for the current and future development of immunotherapies. In particular, based on data collected by the Kaech Lab, several deterministic mathematical models are defined to investigate cells’ trajectory towards the exhausted state as well as the duration of the antigen signal at early time point of stimulation

    Towards a holistic understanding of the role of green infrastructure in improving urban air quality

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    Air pollution has been identified as a major problem in modern societies, threatening urban population health. Pedestrians, in particular, are directly exposed to one of the main sources of air pollutants: road transport, which is concentrated in proximity to the road, worsening the air. Green infrastructure (GI) has been promoted as a natural method for reducing exposure to local street air pollutants and providing additional Ecosystem Services with a range of environmental, social and economic benefits for citizens. The effectiveness of GI for improving air quality depends on the spatio-temporal context and the species-specific characteristics of the GI. Urban planting could maximise this benefit by a holistic understanding of the effects of GI in cities, balancing its benefits and constraints. However, little is currently known about the application of GI design and planning with regard to air pollution mitigation. Moreover, there is little agreement on the quantifiable effectiveness of GI in improving street air quality as its effectiveness is highly context dependent. Holistic guidance is therefore needed to inform practitioners of site- and species- specifics, trade-offs, and GI maintenance considerations for successful urban planting. This research reviews the academic literature addressing GI-related characteristics in streets, creating a holistic framework to help guide decision-makers on using GI solutions to improve air quality. Additionally, this research aims to understand how and which GI, along with other local characteristics, influence pedestrian air quality and how these characteristics are considered in real-world practice within the United Kingdom. This research progresses through three stages: First, the mechanisms by which GI is considered to influence air quality were identified through literature reviews. A specific literature review was then conducted for each mechanism to extract the associated GI and spatial characteristics that affect the potential for GI to mitigate urban air pollution. In the second stage, this list of characteristics, together with other Ecosystem Services, was discussed in consultation with practitioners in the UK. A survey was conducted to explore and evaluate the recommendations and resources available for planning plantings, as well as the practitioners’ knowledge about the characteristics associated with mitigating air pollution. Supported by results from the survey and the literature reviews, the third stage evaluated (validated) an easy-to-use computational model for its potential use in improving planting decisions for air pollution mitigation. Green infrastructure influences air quality by providing surfaces for pollutant deposition and absorption, effects on airflow and dispersion, and biogenic emissions. The relationship between the specific GI and the spatio-temporal context also influences air quality. Street structure, weather variables, and the type, shape and size of GI influence the dispersion of pollutants, with micro-and macro-morphological traits additionally influencing particulate deposition and gas absorption. In addition, maintaining GI lessens air quality deterioration by controlling biogenic emissions. According to participants in the survey, aesthetics were the principal drivers of urban planting, followed by improving well-being and increasing biodiversity and air pollution mitigation as a lesser priority. Characteristics such as airflow manipulation, leaf surface traits, and biogenic emissions were the less important influences in planting decisions in the UK, despite the fact that these characteristics influence air quality. Perhaps, a lack of communication of current information and low confidence about which specific characteristics have a tangible effect on air quality reduces the incorporation of GI for air pollution mitigation purposes. Uncertainties exist about the quantification of pollutants removed by GI. Field campaigns and computational models still need improvement to address the effectiveness of GI in real-world environments adequately and also to understand whether GI can exert a significant effect on pollutant levels under real-world conditions. This research showed that a promising and easy-to-use model used to evaluate the effectiveness of trees in removing particles was not an acceptable model to study the effect of GI on streets. The validation results showed a poor agreement between wind tunnel data and the model results. More effort is needed to develop better modelling tools that can quantify the actual effect of GI on improving street air quality. This research contributes to the air pollution mitigation field, explicitly helping to inform decision-making for more health-promoting urban settings by optimising the expected benefits of GI through a holistic understanding of their impacts. Facilitating the communication of current evidence through a holistic guide that considers both the benefits and trade-offs of planting decisions for air quality improvement. Improving information on air pollution mitigation to feed the decision-making process might maximise the benefits of GI planting for air pollution mitigation in streets.Open Acces

    Identifying therapeutic targets against viral hepatitis and liver cancer

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    Optimizing T Cell Manufacturing and Quality Using Functionalized Degradable Microscaffolds

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    Adoptive cell therapy using chimeric antigen receptor (CAR) T cells have shown promise in treating cancer, but manufacturing large numbers of high quality cells remains challenging. Currently approved T cell expansion technologies involve anti-CD3 and anti-CD28 antibodies, usually mounted on magnetic beads. This method fails to recapitulate many key signals found in vivo and is also heavily licensed by a few companies, limiting its long-term usefulness to manufactures and clinicians. Furthermore, highly potent, anti-tumor T cells are generally less-differentiated subtypes such as central memory and stem memory T cells. Despite this understanding, little has been done to optimize T cell expansion for generating these subtypes, including measurement and feedback control strategies that are necessary for any modern manufacturing process. The goal of this dissertation was to develop a microcarrier-based degradable microscaffold (DMS) T cell expansion system and determine biologically-meaningful critical quality attitudes and critical process parameters that could be used to optimize for highly-potent T cells. We developed and characterized the DMS system, including quality control steps. We also demonstrated the feasibility of expanding high-quality T cells. We used Design of Experiments methodology to optimize the DMS platform, and we developed a computational pipeline to identify and model the effects of measurable critical quality attributes and critical process parameters on the final product. Finally, we demonstrated the effectiveness of the DMS platform in vivo. This thesis lays the groundwork for a novel T cell expansion method which can be utilized at scale for clinical trials and beyond.Ph.D
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