Time-resolved analysis of cell colony growth in vitro after irradiation

Here I developed a novel method to investigate the growth of cell colonies in vitro. The method is inspired by and augments the standard in vitro clonogenic assay (IVCA). While the field of application is radiobiological research, the approach can be applied to any domain where colony growth of adherent cells is of interest. The method utilizes high numbers of time-resolved microscopy image series and hence requires largely automated image data acquisition, image processing, quantitative data extraction and single-colony growth characterization. I designed a multi-step analysis framework to implement these steps. This contrasts with traditional approaches relying on visual examination of cell culture containers and manual classification of cell colonies. This new approach allows yet unattained insights into growth behaviors and growth rates of large numbers of individual cell colonies. In applying the new method to five different cell lines (H3122, H460, RENCA, SAT, UTSCC-5) in different experimental settings, the following main results were found: a) For some of the cell lines, the initial seeding density influences the growth dynamics of the resulting colonies in densities commonly used in standard experiments. b) Pre-experimental cell culture conditions influence the growth dynamics in two tested cell lines (SAT, UTSCC-5) without irradiation. c) Exponential growth rates of two tested cell lines (H3122, RENCA) are normally distributed independent of irradiation dose, but the average growth rate decreases linearly across commonly used doses. d) Some colonies growing from photon-irradiated cells exhibit a distinct delayed abortive growth behavior, as observed for the two analysed cell lines (H3122, RENCA). The frequency of this behavior increases with increasing dose. e) Survival rates, as traditionally determined via the standard IVCA, clearly depend on experimental readout choices, namely the time of readout and the size threshold used to score survival of colonies. My analysis indicates that this dependence emerges from observations c) and d). f) The observed influence of readout choices propagates into relative biological effectiveness quantification for carbon irradiation for three examined cell lines (H460, RENCA, UTSCC-5). Hence, I demonstrate that the presented method can be used to inform experimental design decisions in standard IVCA experiments, to perform robustness analyses on these assays, and to find distinct types of growth behavior. Still, the application in its current form is limited to adherently growing cell lines forming contiguous colonies. In addition, due to the multi-step procedure,some underlying assumptions and methodological decisions need to be made which potentially influence the resulting findings. I discuss these aspects in a dedicated chapter. In future work, potential extensions and combinations with quantitative single-cell analysis methods such as FACS, fluorescent live-cell imaging or single cell omics methods can make this method a cornerstone application to build on in order to understand not only how, but also why colonies grow the way they do. In conclusion, the presented method elucidates colony growth in unprecedented detail. The presented results showcase the potential relevance of these details. However, to establish this method as a standard tool for applied research, a unified analysis framework is necessary to standardize the methodological aspects, from image acquisition to colony growth type classification

Computational Modeling of Inflammatory Mediators in Acute Illness: From Networks to Mechanisms

The acute inflammatory response is a complex defense mechanism that has evolved to respond rapidly to injury, infection, and other disruptions in homeostasis. The complex role of inflammation in health and disease has made it difficult to understand comprehensively. With the advent of high throughput technologies and the growth of systems biology, there has been an unprecedented amount of data and –omics analysis aimed at uncovering this complexity. However, there still remains a shortage of translational insights for acute inflammatory diseases from these studies. In this dissertation, we employ a comprehensive systems approach in order to study the coordination of inflammation and identify key control mechanisms, and how these map onto clinical outcomes. This process begins with collection of high-dimensional time course data of inflammatory mediators, followed by data-driven modeling and network inference that finally informs mechanistic computational models for prediction and analysis. In patients with pediatric acute liver failure (PALF), we inferred inflammatory networks and identified key differences between patients that were survivors versus non-survivors when other analyses proved inconclusive. We showed that inflammatory networks can be used both as biomarkers and to generate mechanistic hypotheses for this poorly understood disease. In experimental models of trauma as well as in human trauma patients, we identify a conserved central network motif of cross-regulating chemokines. We develop a logical model based on this hypothesized network, which is able to capture both inflammatory trajectory and clinical outcome differences among patients with differing injury severity. These studies suggest that the hypothesized cross-regulatory interactions among chemokines MIG, IP-10 and MCP-1 represents an important point of control regulating the progression of acute inflammation. We propose that further analysis and validation of this hypothesis will require targeted perturbation studies in cells and animals with iterative rounds of mechanistic model refinement. We explore an example of such a study focused on the anti-inflammatory effects of NAD+, wherein we characterize a signaling pathway that gives rise to a complex dose and time dependent induction of TGF-β1

Tracking and analysis of movement at different scales: from endosomes to humans

Movement is apparent across all spatio-temporal scales in biology and can have a significant effect on the survival of the individual. For this reason, it has been the object of study in a wide range of research fields, i.e. in molecular biology, pharmaceutics, medical research but also in behavioural biology and ecology. The aim of the thesis was to provide methodologies and insight on the movement patterns seen at different spatio-temporal scales in biology; the intra-cellular, the cellular and the organism level. At the intra-cellular level, current thesis studied the compartmental inheritance in Human Osteosarcoma (U2-OS) cells. The inheritance pattern of the endosomal quantum dot fluorescence across two consecutive generations was for first time empirically revealed. In addition, a in silico model was developed to predict the inheritance across multiple generations. At the cellular level, a semi-automated routine was developed that can realize long-term nuclei tracking in U2-OS cell populations labeled with a cell cycle marker in their cytoplasm. A method to extract cell cycle information without the need to explicitly segment the cells was proposed. The movement behaviour of the cellular population and their possible inter-individual differences was also studied. Lastly, at the organism level, the focus of the thesis was to study the emergence of coordination in unfamiliar free-swimming stickleback fish shoals. It was demonstrated that there exist two different phases, the uncoordinated and the coordinated. In addition, the significance of uncoordinated phase to the establishment of the group’s social network was for first time evinced. The adaptation of the stickleback collectives was also studied over time, i.e. the effect of group’s repeated interactions on the emergence of coordination. Findings at the intra-cellular and cellular level can have significant implications on medical and pharmaceutical research. Findings at the organism level can also contribute to the understanding of how social interactions are formed and maintained in animal collectives

Statistical Modelling

The book collects the proceedings of the 19th International Workshop on Statistical Modelling held in Florence on July 2004. Statistical modelling is an important cornerstone in many scientific disciplines, and the workshop has provided a rich environment for cross-fertilization of ideas from different disciplines. It consists in four invited lectures, 48 contributed papers and 47 posters. The contributions are arranged in sessions: Statistical Modelling; Statistical Modelling in Genomics; Semi-parametric Regression Models; Generalized Linear Mixed Models; Correlated Data Modelling; Missing Data, Measurement of Error and Survival Analysis; Spatial Data Modelling and Time Series and Econometrics

Advances in Bioengineering

The technological approach and the high level of innovation make bioengineering extremely dynamic and this forces researchers to continuous updating. It involves the publication of the results of the latest scientific research. This book covers a wide range of aspects and issues related to advances in bioengineering research with a particular focus on innovative technologies and applications. The book consists of 13 scientific contributions divided in four sections: Materials Science; Biosensors. Electronics and Telemetry; Light Therapy; Computing and Analysis Techniques

Disease state prediction using multiscale dynamics

Treatment of many disorders requires clinic visits, with a strong reliance on patient self-reports for filling in the gaps between these in-person assessments. This lack of monitoring leads to biases in the data and a lack of provision for responding to rapid changes in health. Objective tools for monitoring patients in between clinical visits are mainly absent. In recent years, advances in wearable technology have enabled almost real-time monitoring of changes in physiology. These advances have the potential to transform the landscape for monitoring diseases. The objective of this research is to build non-invasive and continuous monitoring methods using wearables in naturalistic settings. In the first part, a novel wearable-based sleep detection approach is developed. The proposed approach uses the variations observed in the physiological data and detects patterns in these change events associated with sleep-wake transitions. The second part focuses on features derived from wearables to separate healthy controls and participants with health disorders in two different applications. In the first application, research watch data is used for monitoring patient post-trauma. Lastly, the second application presents the use of both passive (motion, location, social contact) and active (clinically validated survey) data collected by a smartphone app for heart failure outcome estimation.Ph.D

Applications in Electronics Pervading Industry, Environment and Society

This book features the manuscripts accepted for the Special Issue “Applications in Electronics Pervading Industry, Environment and Society—Sensing Systems and Pervasive Intelligence” of the MDPI journal Sensors. Most of the papers come from a selection of the best papers of the 2019 edition of the “Applications in Electronics Pervading Industry, Environment and Society” (APPLEPIES) Conference, which was held in November 2019. All these papers have been significantly enhanced with novel experimental results. The papers give an overview of the trends in research and development activities concerning the pervasive application of electronics in industry, the environment, and society. The focus of these papers is on cyber physical systems (CPS), with research proposals for new sensor acquisition and ADC (analog to digital converter) methods, high-speed communication systems, cybersecurity, big data management, and data processing including emerging machine learning techniques. Physical implementation aspects are discussed as well as the trade-off found between functional performance and hardware/system costs