Bringing Models to the Domain: Deploying Gaussian Processes in the Biological Sciences

Recent developments in single cell sequencing allow us to elucidate processes of individual cells in unprecedented detail. This detail provides new insights into the progress of cells during cell type differentiation. Cell type heterogeneity shows the complexity of cells working together to produce organ function on a macro level. The understanding of single cell transcriptomics promises to lead to the ultimate goal of understanding the function of individual cells and their contribution to higher level function in their environment. Characterizing the transcriptome of single cells requires us to understand and be able to model the latent processes of cell functions that explain biological variance and richness of gene expression measurements. In this thesis, we describe ways of jointly modelling biological function and unwanted technical and biological confounding variation using Gaussian process latent variable models. In addition to mathematical modelling of latent processes, we provide insights into the understanding of research code and the significance of computer science in development of techniques for single cell experiments. We will describe the process of understanding complex machine learning algorithms and translating them into usable software. We then proceed to applying these algorithms. We show how proper research software design underlying the implementation can lead to a large user base in other areas of expertise, such as single cell gene expression. To show the worth of properly designed software underlying a research project, we show other software packages built upon the software developed during this thesis and how they can be applied to single cell gene expression experiments. Understanding the underlying function of cells seems within reach through these new techniques that allow us to unravel the transcriptome of single cells. We describe probabilistic techniques of identifying the latent functions of cells, while focusing on the software and ease-of-use aspects of supplying proper research code to be applied by other researchers

Single-cell RNA-seq and computational analysis using temporal mixture modelling resolves Th1/Tfh fate bifurcation in malaria.

Differentiation of naïve CD4+ T cells into functionally distinct T helper subsets is crucial for the orchestration of immune responses. Due to extensive heterogeneity and multiple overlapping transcriptional programs in differentiating T cell populations, this process has remained a challenge for systematic dissection in vivo. By using single-cell transcriptomics and computational analysis using a temporal mixtures of Gaussian processes model, termed GPfates, we reconstructed the developmental trajectories of Th1 and Tfh cells during blood-stage Plasmodium infection in mice. By tracking clonality using endogenous TCR sequences, we first demonstrated that Th1/Tfh bifurcation had occurred at both population and single-clone levels. Next, we identified genes whose expression was associated with Th1 or Tfh fates, and demonstrated a T-cell intrinsic role for Galectin-1 in supporting a Th1 differentiation. We also revealed the close molecular relationship between Th1 and IL-10-producing Tr1 cells in this infection. Th1 and Tfh fates emerged from a highly proliferative precursor that upregulated aerobic glycolysis and accelerated cell cycling as cytokine expression began. Dynamic gene expression of chemokine receptors around bifurcation predicted roles for cell-cell in driving Th1/Tfh fates. In particular, we found that precursor Th cells were coached towards a Th1 but not a Tfh fate by inflammatory monocytes. Thus, by integrating genomic and computational approaches, our study has provided two unique resources, a database www.PlasmoTH.org, which facilitates discovery of novel factors controlling Th1/Tfh fate commitment, and more generally, GPfates, a modelling framework for characterizing cell differentiation towards multiple fates