1,116 research outputs found

    Therapeutic and prognostic strategies in neuroblastoma : exploring nuclear hormone receptors, MYC targets, and DIAPH3

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    Neuroblastoma (NB) is a pediatric cancer derived from the cells of neural crest origin that form the sympathoadrenal system. Typically, the tumor cells migrate along the spinal cord and spread to the chest, neck, and/or abdomen. Different clinical behaviors are observed in this disease: some tumors spontaneously regress without treatment, while others are highly aggressive and resistant to current therapies. Approximately 40% of high-risk NB patients have MYCN amplification while 10% have MYC (i.e. encoding c-MYC) overexpression. These patients have undifferentiated tumors with a poor prognosis. Our group previously found that the expression and activation of nuclear hormone receptors (NHRs) estrogen receptor alpha (ERα) by 17-β-estradiol (E2), and the glucocorticoid receptor (GR) by dexamethasone (DEX), could trigger differentiation by disrupting the regulation of the miR-17 ~ 92 microRNA cluster by MYCN. In paper I, we sought to investigate whether the simultaneous activation of both ERα and GR has a more beneficial effect compared to the activation of either ERα or GR alone. We examined cell survival, alterations in cell shape as indicated by neurite extension, variations in metabolic pathways, accumulation of lipid droplets, and performed xenograft experiments. Our findings revealed that the simultaneous activation of GR and ERα, compared to their single activation, led to reduced viability and a more robust differentiation. This dual activation also caused changes in glycolysis and oxidative phosphorylation, increased lipid droplet accumulation, and decreased aggressiveness in mouse models. The triple activation with an additional activation of the retinoic acid receptor using all trans-retinoic acid (ATRA), amplified the differentiation phenotype. Bulk-sequencing analysis showed that patients with high levels of NHRs are related to favorable survival and clinical outcome. In summary, our data suggest that combination activation of these NHRs could be a potential differentiation induction treatment. Paper II investigates target genes of c-MYC and MYCN to explore if it is possible to obtain a better prognosis prediction using the expression of this group of genes, instead of the expression of MYC and/or MYCN alone. In addition, we analyzed if there are different prediction power capabilities between c-MYC and MYCN target genes, and their different role during sympathoadrenal development. We screened lists of target genes by using comprehensive approaches, including differential expression analysis between clinical risk groups, INSS stages, MYCN amplification status, progression status; Univariate Cox regression analysis to select the target genes related to prognosis prediction power, and protein interaction network analysis to select genes that share a meaningful biology function. Following the training and validation of (LASSO) regression prediction models in three different patient cohorts (SEQC, Kocak, and Versteeg), we found that a risk score computed on c-MYC/MYCN target genes with prognostic value, could effectively classify patients in groups with different survival probabilities. The high-risk group of patients exhibited unfavorable clinical outcomes and low survival rates. Further, single cell RNA sequencing analysis revealed that c-MYC and MYCN targets have different expression patterns during sympathoadrenal development. Notably, genes linked to adverse outcomes were predominantly expressed in sympathoblasts in comparison to chromaffin cells. In summary, our research provides new insights into the importance of c-MYC/MYCN target genes during sympathoadrenal development and their value in predicting patient outcome. In paper III we studied the function of one member of the formin protein family involved in cytoskeleton modulation: Diaphanous Related Formin 3 (DIAPH3). We found that high DIAPH3 expression in NB tumors are associated with MYCN amplification, higher stage, risk, progression and negative clinical outcome. Elevated DIAPH3 expression was also found in specific cells during mouse sympathoadrenal development and in progenitor cells of the post- natal human adrenal gland. Furthermore, the knockdown of DIAPH3 resulted in a slight decrease in cell growth and cell cycle arrest. Our study suggests that DIAPH3 could be a promising target for new therapeutic strategies

    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

    Integrating Experimental and Computational Approaches to Optimize 3D Bioprinting of Cancer Cells

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    A key feature distinguishing 3D bioprinting from other 3D cell culture techniques is its precise control over created structures. This property allows for the high-resolution fabrication of biomimetic structures with controlled structural and mechanical properties such as porosity, permeability, and stiffness. However, for bioprinting to be successful, a comprehensive understanding of cell behavior is essential, yet challenging. This includes the survivability of cells throughout the printing process, their interactions with the printed structures, and their responses to environmental cues after printing. There are numerous variables in bioprinting which influence the cell behavior, so bioprinting quality during and after the procedure. Thus, to achieve desirable results, it is necessary to consider and optimize these influential variables. So far, these optimizations have been accomplished primarily through trial and error and replicating several experiments, a procedure that is not only time-consuming but also costly. This issue motivated the development of computational techniques in the bioprinting process to more precisely predict and elucidate cells’ function within 3D printed structures during and after printing. During printing, we developed predictive machine learning models to determine the effect of different variables such as cell type, bioink formulation, printing settings parameters, and crosslinking condition on cell viability in extrusion-based bioprinting. To do this, we first created a dataset of these parameters for gelatin and alginate-based bioinks and the corresponding cell viability by integrating data obtained in our laboratory and those derived from the literature. Then, we developed regression and classification neural networks to predict cell viability based on these bioprinting variables. Compared to models that have been developed so far, the performance of our models was superior and showed great prediction results. The study further demonstrated that among the variables investigated in bioprinting, cell type, printing pressure, and crosslinker concentration, respectively, had the most significant impact on the survival of cells. Additionally, we introduced a new optimization strategy that employs the Bayesian optimization model based on the developed regression neural network to determine the optimal combination of the selected bioprinting parameters for maximizing cell viability and eliminating trial-and-error experiments. In our study, this strategy enabled us to identify the optimal crosslinking parameters, within a specified range, including those not previously explored, resulting in optimum cell viability. Finally, we experimentally validated the optimization model's performance. After printing, we developed a cellular automata model for the first time to predict and elucidate the post-printing cell behavior within the 3D bioprinted construct. To improve our model, we bioprinted a 3D construct using cell-laden hydrogel and evaluated cellular functions, including viability and proliferation, in 11 days. The results showed that our model successfully simulated the 3D bioprinted structure and captured in-vitro observations. The proposed model is beneficial for demonstrating complex cellular systems, including cellular proliferation, movement, cell interactions with the environment (e.g., extracellular microenvironment and neighboring cells), and cell aggregation within the scaffold. We also demonstrated that this computational model could predict post-printing biological functions for different initial cell numbers in bioink and different bioink formulations with gelatin and alginate without replicating several in-vitro measurements. Taken all together, this thesis introduces novel bioprinting process design strategies by presenting mathematical and computational frameworks for both during and after bioprinting. We believe such frameworks will substantially impact 3D bioprinting's future application and inspire researchers to further realize how computational methods might be utilized to advance in-vitro 3D bioprinting research

    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

    Towards Leveraging Inhibition State of the Kinome for Precision Oncology

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    Protein phosphorylation forms the most common method of regulation in eukaryotes, and kinases are enzymes that chiefly enable its application. Due to their central role in physiology, dysregulation of the kinome is implicated in a myriad of diseases, particularly cancer. This dissertation demonstrates that the measured inhibition of the kinome (the kinome inhibition state) by cancer targeted therapies can be predictive of cell line and patient-derived xenograft (PDX) tumor responses to treatment by that therapy using interpretable machine learning models. The predictive capability of kinome inhibition states with currently used baseline genomics for monotherapy cancer cell line responses across diverse cancer types is demonstrated first using multi-dose kinome inhibition states, and second using multi-assay single-dose data. Then, the predictive value of kinome inhibition states is extended to kinase inhibitor combination therapies, demonstrating that combined kinome inhibition states can accurately predict cancer cell line sensitivity and synergy to combination treatments, providing the basis for rational kinome-informed drug combination selection. Finally, the predictive capacity of kinome inhibition states is demonstrated for PDX tumor responses in five common solid tumor types, confirming the generalizability of kinome inhibition-based prediction models in a preclinical setting, and emphasizing their potential for clinical translation and application in precision oncology. Overall, this dissertation provides compelling evidence that integrating kinome inhibition states in machine learning models can enhance the prediction of cancer cell line and PDX tumor responses. This work shows that kinome inhibition data has potential to be included in precision oncology platforms alongside baseline genomic profiling, aiding in the identification of effective therapeutic strategies and ultimately improving patient outcomes.Doctor of Philosoph

    Computational Approaches to Drug Profiling and Drug-Protein Interactions

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    Despite substantial increases in R&D spending within the pharmaceutical industry, denovo drug design has become a time-consuming endeavour. High attrition rates led to a long period of stagnation in drug approvals. Due to the extreme costs associated with introducing a drug to the market, locating and understanding the reasons for clinical failure is key to future productivity. As part of this PhD, three main contributions were made in this respect. First, the web platform, LigNFam enables users to interactively explore similarity relationships between ‘drug like’ molecules and the proteins they bind. Secondly, two deep-learning-based binding site comparison tools were developed, competing with the state-of-the-art over benchmark datasets. The models have the ability to predict offtarget interactions and potential candidates for target-based drug repurposing. Finally, the open-source ScaffoldGraph software was presented for the analysis of hierarchical scaffold relationships and has already been used in multiple projects, including integration into a virtual screening pipeline to increase the tractability of ultra-large screening experiments. Together, and with existing tools, the contributions made will aid in the understanding of drug-protein relationships, particularly in the fields of off-target prediction and drug repurposing, helping to design better drugs faster

    Single-probe Single Cell Mass Spectrometry Studies: Investigation of Cell Heterogeneity and Quantification of Intracellular Small Molecules

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    Studying cell heterogeneity can provide a deeper understanding of biological activities, but corresponding studies cannot be performed using traditional bulk analysis methods. The development of diverse single cell bioanalysis methods is in urgent need and of great significance. Mass spectrometry (MS) has been recognized as a powerful technique for bioanalysis for its high sensitivity, wide applicability, label-free detection, and capability for quantitative analysis. The paramount significance of single cell mass spectrometry (SCMS) techniques have been recognized, and they are becoming indispensable tools in fundamental research and studies of human diseases such as cancers and infectious disease. My studies consist of two major parts: (1) the development novel method to quantify nitric oxide (NO) using combined chemical reactions and SCMS techniques and (2) the investigation of cell heterogeneity using integrated bioinformatics tools and SCMS methods. In Chapter one, we reviewed the development of single cell mass spectrometry (SCMS) field and summarized multiple existing SCMS techniques. We also included the methods that have been used for quantitative studies of small molecules in single cells. In particular, we further developed the Single-probe, a microscale device that is ideally suited for SCMS study of live single cells under ambient environment, for molecular quantification in single cells. In Chapter two, the single-probe SCMS was coupled with chemical reactions to detect and quantify nitric oxide (NO) in single cells. We then performed detailed data analysis to study the subpopulations of cells based on their NO expression levels. In Chapter three, cellular heterogeneity in infectious disease was revealed using the Single-probe SCMS, and we discovered the bystander effect of cells, which are uninfected cells adjacent to infected cells. In Chapter four, we developed a novel data analysis method for assessing the global metabolomic profiles from the SCMS experiments, allowing us to identify subpopulations and determine the number of subpopulations without prior knowledge. Finally, in Chapter five, a new machine learning method was applied to classify cells with different drug resistant levels

    Segmentation of Pathology Images: A Deep Learning Strategy with Annotated Data

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    Cancer has significantly threatened human life and health for many years. In the clinic, histopathology image segmentation is the golden stand for evaluating the prediction of patient prognosis and treatment outcome. Generally, manually labelling tumour regions in hundreds of high-resolution histopathological images is time-consuming and expensive for pathologists. Recently, the advancements in hardware and computer vision have allowed deep-learning-based methods to become mainstream to segment tumours automatically, significantly reducing the workload of pathologists. However, most current methods rely on large-scale labelled histopathological images. Therefore, this research studies label-effective tumour segmentation methods using deep-learning paradigms to relieve the annotation limitations. Chapter 3 proposes an ensemble framework for fully-supervised tumour segmentation. Usually, the performance of an individual-trained network is limited by significant morphological variances in histopathological images. We propose a fully-supervised learning ensemble fusion model that uses both shallow and deep U-Nets, trained with images of different resolutions and subsets of images, for robust predictions of tumour regions. Noise elimination is achieved with Convolutional Conditional Random Fields. Two open datasets are used to evaluate the proposed method: the ACDC@LungHP challenge at ISBI2019 and the DigestPath challenge at MICCAI2019. With a dice coefficient of 79.7 %, the proposed method takes third place in ACDC@LungHP. In DigestPath 2019, the proposed method achieves a dice coefficient 77.3 %. Well-annotated images are an indispensable part of training fully-supervised segmentation strategies. However, large-scale histopathology images are hardly annotated finely in clinical practice. It is common for labels to be of poor quality or for only a few images to be manually marked by experts. Consequently, fully-supervised methods cannot perform well in these cases. Chapter 4 proposes a self-supervised contrast learning for tumour segmentation. A self-supervised cancer segmentation framework is proposed to reduce label dependency. An innovative contrastive learning scheme is developed to represent tumour features based on unlabelled images. Unlike a normal U-Net, the backbone is a patch-based segmentation network. Additionally, data augmentation and contrastive losses are applied to improve the discriminability of tumour features. A convolutional Conditional Random Field is used to smooth and eliminate noise. Three labelled, and fourteen unlabelled images are collected from a private skin cancer dataset called BSS. Experimental results show that the proposed method achieves better tumour segmentation performance than other popular self-supervised methods. However, by evaluated on the same public dataset as chapter 3, the proposed self-supervised method is hard to handle fine-grained segmentation around tumour boundaries compared to the supervised method we proposed. Chapter 5 proposes a sketch-based weakly-supervised tumour segmentation method. To segment tumour regions precisely with coarse annotations, a sketch-supervised method is proposed, containing a dual CNN-Transformer network and a global normalised class activation map. CNN-Transformer networks simultaneously model global and local tumour features. With the global normalised class activation map, a gradient-based tumour representation can be obtained from the dual network predictions. We invited experts to mark fine and coarse annotations in the private BSS and the public PAIP2019 datasets to facilitate reproducible performance comparisons. Using the BSS dataset, the proposed method achieves 76.686 % IOU and 86.6 % Dice scores, outperforming state-of-the-art methods. Additionally, the proposed method achieves a Dice gain of 8.372 % compared with U-Net on the PAIP2019 dataset. The thesis presents three approaches to segmenting cancers from histology images: fully-supervised, unsupervised, and weakly supervised methods. This research effectively segments tumour regions based on histopathological annotations and well-designed modules. Our studies comprehensively demonstrate label-effective automatic histopathological image segmentation. Experimental results prove that our works achieve state-of-the-art segmentation performances on private and public datasets. In the future, we plan to integrate more tumour feature representation technologies with other medical modalities and apply them to clinical research

    Beam scanning by liquid-crystal biasing in a modified SIW structure

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    A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium
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