Probabilistic modelling of cellular development from single-cell gene expression

The recent technology of single-cell RNA sequencing can be used to investigate molecular and transcriptional changes in cells as they develop. I reviewed the literature on the technology and made a large scale quantitative comparison of the different implementations of single cell RNA sequencing to identify their technical limitations. I investigated how to model transcriptional changes during cellular development. The patterns that gene expression levels can show over development have unknown shape. This leads to the use of non-parametric regression, using Gaussian process models. I used Gaussian process models to investigate expression patterns in early embryonic development, and compared the development of mice and humans. When using in vivo systems, ground truth developmental stage or time for each cell cannot be known. Only a snapshot of cells, all being in different stages of development, can be obtained. In an experiment measuring the transcriptome of zebrafish blood precursor cells, undergoing development from hematopoietic stem cells to thrombocytes, I used a Gaussian process latent variable model (GPLVM) to align the cells according to the developmental trajectory. This way I could investigate which genes were driving the development and characterise the different patterns of expression. With the latent variable strategy in mind, I designed an experiment to study the rare event of murine embryonic stem cells entering a state similar to very early embryos. The GPLVM can take advantage of the nonlinear expression patterns involved in this process. The results showed multiple activation events of genes as cells progress towards the rare state. viii An essential feature of developmental biology is the ability of precursor cells to give rise to multiple types of differentiated (daughter) cells. In the immune system, naive T-helper cells differentiate to different sub-types depending on the infection. In an experiment where mice were infected by malaria, T-helper cells develop into two cell types, Th1 and Tfh. I model this branching development using an Overlapping Mixture of Gaussian Processes, which allowed me to identify which cells belong to which branch, and learn which genes are involved with the different branches. Researchers have recently started to perform high-throughput experiments where spatial context of gene expression is recorded. Similar to the methods I have developed to identify temporal expression patterns, spatial expression patterns can be identified nonparametrically. To enable researchers to make use of this technique, I developed a very fast method to perform a statistical test for spatial dependence and applied the resulting method on multiple data sets. ix Contributions For several studies in this thesis significant work was undertaken by my collaborators. Each chapter presents this in the introduction, but in brief, I list them here. • For Chapter 2, we designed experiments to verify the results of our analysis, and Kedar Natarajan performed these experiments. • In Chapter 3, the original study was designed by Ana Cvejic. The experiments with the zebrafish, including cell sorting, were performed by Charlotte Laballette. Iain Macaulay performed single cell RNAsequencing with these cells. The biological relevance of our results in the light of hematopoiesis was interpreted by Ana Cvejic. • The study presented in Chapter 4 was designed by Tapio Lonnberg and Kylie James, together with Ashraful Haque. They also intepreted the results from the models in relation to immunology.