BioBridge: Bringing Data Exploration to Biologists

Since the completion of the Human Genome Project in 2003, biologists have become exceptionally good at producing data. Indeed, biological data has experienced a sustained exponential growth rate, putting effective and thorough analysis beyond the reach of many biologists. This thesis presents BioBridge, an interactive visualization tool developed to bring intuitive data exploration to biologists. BioBridge is designed to work on omics style tabular data in general and thus has broad applicability. This work describes the design and evaluation of BioBridge\u27s Entity View primary visualization as well the accompanying user interface. The Entity View visualization arranges glyphs representing biological entities (e.g. genes, proteins, metabolites) along with related text mining results to provide biological context. Throughout development the goal has been to maximize accessibility and usability for biologists who are not computationally inclined. Evaluations were done with three informal case studies, one of a metabolome dataset and two of microarray datasets. BioBridge is a proof of concept that there is an underexploited niche in the data analysis ecosystem for tools that prioritize accessibility and usability. The use case studies, while anecdotal, are very encouraging. These studies indicate that BioBridge is well suited for the task of data exploration. With further development, BioBridge could become more flexible and usable as additional use case datasets are explored and more feedback is gathered

Bioinformatics Solutions for Image Data Processing

In recent years, the increasing use of medical devices has led to the generation of large amounts of data, including image data. Bioinformatics solutions provide an effective approach for image data processing in order to retrieve information of interest and to integrate several data sources for knowledge extraction; furthermore, images processing techniques support scientists and physicians in diagnosis and therapies. In addition, bioinformatics image analysis may be extended to support several scenarios, for instance, in cyber-security the biometric recognition systems are applied to unlock devices and restricted areas, as well as to access sensitive data. In medicine, computational platforms generate high amount of data from medical devices such as Computed Tomography (CT), and Magnetic Resonance Imaging (MRI); this chapter will survey on bioinformatics solutions and toolkits for medical imaging in order to suggest an overview of techniques and methods that can be applied for the imaging analysis in medicine

Modelling and analysis of the tumour microenvironment of colorectal cancer

New bioimaging techniques have recently been proposed to visualise the colocation or interaction of several proteins within individual cells, displaying the heterogeneity of neighbouring cells within the same tissue specimen. Such techniques could hold the key to understanding complex biological systems such as the protein interactions involved in cancer. However, there is a need for new algorithmic approaches that analyse the large amounts of multi-tag bioimage data from cancerous and normal tissue specimens in order to begin to infer protein networks and unravel the cellular heterogeneity at a molecular level. In the firrst part of the thesis, we propose an approach to analyses cell phenotypes in normal and cancerous colon tissue imaged using the robotically controlled Toponome Imaging System (TIS) microscope. It involves segmenting the DAPI labelled image into cells and determining the cell phenotypes according to their protein-protein dependence profile. These were analysed using two new measures, Difference in Sums of Weighted cO-dependence/Anti-co-dependence profiles (DiSWOP and DiSWAP) for overall co-expression and anti-co-expression, respectively. This approach enables one to easily identify protein pairs which have significantly higher/lower co-dependence levels in cancerous tissue samples when compared to normal colon tissue. The proposed approach could identify potentially functional protein complexes active in cancer progression and cell differentiation. Due to the lack of ground truth data for bioimages, the objective evaluation of the methods developed for its analysis can be very challenging. To that end, in the second part of the thesis we propose a model of the healthy and cancerous colonic crypt microenvironments. Our model is designed to generate realistic synthetic fluorescence and histology image data with parameters that allow control over differentiation grade of cancer, crypt morphology, cellularity, cell overlap ratio, image resolution, and objective level. The model learns some of its parameters from real histology image data stained with standard Hematoxylin and Eosin (H&E) dyes in order to generate realistic chromatin texture, nuclei morphology, and crypt architecture. To the best of our knowledge, ours is the first model to simulate image data at subcellular level for healthy and cancerous colon tissue, where the cells are organised to mimic the microenvironment of tissue in situ rather than dispersed cells in a cultured environment. The simulated data could be used to validate techniques such as image restoration, cell segmentation, cell phenotyping, crypt segmentation, and differentiation grading, only to name a few. In addition, developing a detailed model of the tumour microenvironment can aid the understanding of the underpinning laws of tumour heterogeneity. In the third part of the thesis, we extend the model to include detailed models of protein expression to generate synthetic multi-tag fluorescence data. As a first step, we have developed models for various cell organelles that have been learned from real immunofluorescence data. We then develop models for five proteins associated with microsatellite instability, namely MLH1, PMS2, MSH2, MSH6 and p53. The protein models include subcellular location, which cells express the protein and under what conditions

Toward the language oscillogenome

Language has been argued to arise, both ontogenetically and phylogenetically, from specific patterns of brain wiring. We argue that it can further be shown that core features of language processing emerge from particular phasal and cross-frequency coupling properties of neural oscillations; what has been referred to as the language 'oscillome.' It is expected that basic aspects of the language oscillome result from genetic guidance, what we will here call the language 'oscillogenome,' for which we will put forward a list of candidate genes. We have considered genes for altered brain rhythmicity in conditions involving language deficits: autism spectrum disorders, schizophrenia, specific language impairment and dyslexia. These selected genes map on to aspects of brain function, particularly on to neurotransmitter function. We stress that caution should be adopted in the construction of any oscillogenome, given the range of potential roles particular localized frequency bands have in cognition. Our aim is to propose a set of genome-to-language linking hypotheses that, given testing, would grant explanatory power to brain rhythms with respect to language processing and evolution.Economic and Social Research Council scholarship 1474910Ministerio de Economía y Competitividad (España) FFI2016-78034-C2-2-

Distinct tissue niches direct lung immunopathology via CCL18 and CCL21 in severe COVID-19

Prolonged lung pathology has been associated with COVID-19, yet the cellular and molecular mechanisms behind this chronic inflammatory disease are poorly understood. In this study, we combine advanced imaging and spatial transcriptomics to shed light on the local immune response in severe COVID-19. We show that activated adventitial niches are crucial microenvironments contributing to the orchestration of prolonged lung immunopathology. Up-regulation of the chemokines CCL21 and CCL18 associates to endothelial-to-mesenchymal transition and tissue fibrosis within these niches. CCL21 over-expression additionally links to the local accumulation of T cells expressing the cognate receptor CCR7. These T cells are imprinted with an exhausted phenotype and form lymphoid aggregates that can organize in ectopic lymphoid structures. Our work proposes immune-stromal interaction mechanisms promoting a self-sustained and non-resolving local immune response that extends beyond active viral infection and perpetuates tissue remodeling

Biospectroscopy towards screening and diagnosis of cancer

Systems biology is an emerging science that combines high throughput investigation techniques to define the dynamic interplay between different biological regulatory systems in response to internal and external cues. Related technologies, genomics, epigenomics, transcriptomics, proteomics, metabolomics and toponomics have been applied to investigate models of carcinogenesis to identify committing initiating events. Vibrational spectroscopy has the potential to play an integral role within systems biology research approaches, as it is able to identify chemical bond alterations within molecules independent of where these molecules reside. Its integration with current “systems biology” methodologies can contribute in the identification of potential biomarkers of carcinogenesis and assist in their incorporation into clinical practice. Breast tissue undergoes cyclical and longitudinal molecular and histological alterations that are influenced by environmental factors. These factors may include diet and lifestyle in addition to parity, lactation and menopausal status and are implicated in carcinogenesis. Breast cancer may appear decades after the initial carcinogenic event. Available research in this area is limited to when early histological changes occur due to the difficulties imposed by the molecular and histological diversity of breast tissue. Vibrational spectroscopy in combination with powerful chemometric techniques has identified spatial and temporal mammary alterations in benign tissue. Prostate cancer is influenced by environmental factors. Its incidence is higher in populations adopting a Westernised lifestyle and diet and has increased over the past generation. This leads to the assumption that prostatic tissue composition may exhibit chronological alterations. Vibrational spectroscopy techniques were applied to matching prostatic tissues with benign prostatic hyperplasia collected from 1983 to 2013. Significant trans-generational segregation was identified. Spectral areas responsible for this segregation pointed towards epigenetic changes. Immunohistochemical studies for DNA methylation and hypomethylation supported these results. Vibrational spectroscopy techniques were also implemented to explore molecular changes between normal ovarian tissue, borderline ovarian tumours and malignant ovarian carcinomas. Different chemometric techniques were applied to discriminate cancers from controls. Similar techniques were able to segregate different types of epithelial ovarian carcinomas. The accurate diagnosis obtained using ATR-FTIR spectroscopy demonstrates its potential for development as an assisting tool for histopathological diagnosis. The endometrial-myometrial junction areas of benign uterine tissues were scrutinised by Synchrotron FTIR and FPA. These techniques in combination with multivariate analysis revealed clear segregation between the functionalis and basalis layers within the uterine crypts. The same techniques illustrated potential areas within these epithelial surfaces where different stem cell types may reside. Targeting the activation/ inactivation of these stem cells may have applications in the diagnosis and treatment of early uterine cancer

Toward the Language Oscillogenome

Language has been argued to arise, both ontogenetically and phylogenetically, from specific patterns of brain wiring. We argue that it can further be shown that core features of language processing emerge from particular phasal and cross-frequency coupling properties of neural oscillations; what has been referred to as the language ‘oscillome.’ It is expected that basic aspects of the language oscillome result from genetic guidance, what we will here call the language ‘oscillogenome,’ for which we will put forward a list of candidate genes. We have considered genes for altered brain rhythmicity in conditions involving language deficits: autism spectrum disorders, schizophrenia, specific language impairment and dyslexia. These selected genes map on to aspects of brain function, particularly on to neurotransmitter function. We stress that caution should be adopted in the construction of any oscillogenome, given the range of potential roles particular localized frequency bands have in cognition. Our aim is to propose a set of genome-to-language linking hypotheses that, given testing, would grant explanatory power to brain rhythms with respect to language processing and evolution

Toward the Language Oscillogenome

Language has been argued to arise, both ontogenetically and phylogenetically, from specific patterns of brain wiring. We argue that it can further be shown that core features of language processing emerge from particular phasal and cross-frequency coupling properties of neural oscillations; what has been referred to as the language ‘oscillome.’ It is expected that basic aspects of the language oscillome result from genetic guidance, what we will here call the language ‘oscillogenome,’ for which we will put forward a list of candidate genes. We have considered genes for altered brain rhythmicity in conditions involving language deficits: autism spectrum disorders, schizophrenia, specific language impairment and dyslexia. These selected genes map on to aspects of brain function, particularly on to neurotransmitter function. We stress that caution should be adopted in the construction of any oscillogenome, given the range of potential roles particular localized frequency bands have in cognition. Our aim is to propose a set of genome-to-language linking hypotheses that, given testing, would grant explanatory power to brain rhythms with respect to language processing and evolution