Despite being the object of intense study, embryonic development has been difficult to model due to a number of reasons. First, complex tissues can be comprised of many cell types, of which we... Show moreDespite being the object of intense study, embryonic development has been difficult to model due to a number of reasons. First, complex tissues can be comprised of many cell types, of which we probably only know a subset. Therefore, we first focused on the discovery of cell types by single-cell RNA-sequencing (scRNA-seq). Cell types are routinely identified by clustering scRNA-seq data, however, there was no principled way to determine the right number of clusters. To improve cell type classification, we developed phiclust, a clusterability measure for scRNA-seq. Another challenge in a developing tissue is that many signaling processes and morphogenic events occur simultaneously, which makes it hard to isolate the individual contributions. For this purpose, I looked at stem cell derived in vitro systems, in which a small number of specific cell types can be combined deliberately and studied in isolation. My analysis of different model systems shows that cellular communication causes structural and transcriptional changes in the developing cells. Finally, while tissue organization has been characterized extensively, we lack generative models that can relate specific patterns to the underlying gene regulatory mechanisms. Therefore, I later focused on deep learning-based approaches to infer gene regulatory networks from observed spatial patterns. Show less
The deeper understanding of an organism's pathology is important for developing treatments. Over centuries of systematic research, clinical researchers have demonstrated that the more information... Show moreThe deeper understanding of an organism's pathology is important for developing treatments. Over centuries of systematic research, clinical researchers have demonstrated that the more information they acquire about the cellular properties and their organisation in the tissue, the better they can understand an organism's functionality and disease progression. Over the last years, the advent of high-resolution imaging techniques have provided researchers with novel single-cell information, which empower researchers to precisely characterize the cells and explore how they are distributed in the tissue. However, the extraction of useful biological insights from the analysis of such novel and complex data, where experts do not know the intrinsic characteristics of the data nor the patterns they want to identify, requires an exploratory data analysis approach. Hence, the aim of this dissertation is the development of an end-to-end pipeline for the analysis of these highly multiplexed cellular images; from the preprocessing of the raw data over the exploration of cellular patterns and their association to clinical characteristics. Show less
High-grade osteosarcoma is a primary bone tumor with complex genetic alterations, for which targeted therapy is lacking. The aim of this thesis was to use high-throughput molecular data analysis of... Show moreHigh-grade osteosarcoma is a primary bone tumor with complex genetic alterations, for which targeted therapy is lacking. The aim of this thesis was to use high-throughput molecular data analysis of high-grade osteosarcoma specimens and model systems, in order to learn more on osteosarcomagenesis and to find possible ways to inhibit this process. By analyzing different microarray data types using a systems biology approach, genomic instability was identified as an important driver of osteosarcomagenesis. A protective role of macrophages against metastasis of osteosarcoma was detected. In addition, the IR/IGF1R and PI3K/Akt signaling pathways were discovered as potential targets for treatment. This thesis provides the first steps in unraveling the genomic and transcriptomic landscape of high-grade osteosarcoma, and provides a biological rationale for certain new options for adjuvant treatment of this highly genomica lly unstable tumor. Show less
