Charting the regulatory topography of the cell differentiation landscape with single-cell RNA-Seq.

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 DESCRIPTION (provided by applicant): A single stem cell generates a staggering array of highly specialized adult cell types in response to carefully regulated molecular cues from the surrounding tissue environment. However, recent evidence has challenged the classification of adult cells into discrete types. Immune cells, for example might be better described as inhabitants of a vast, continuous functional "landscape". In order to resolve whether either of these models are correct, we must be able to measure the complete gene expression profile of individual cells and observe them moving between different functional states during differentiation. This proposal aims to answer a fundamental question in biology: how continuous is the gene expression landscape during cell differentiation? I hypothesize that single-cell transcriptome sequencing (RNA-Seq) can be used to directly visualize the landscape traversed by differentiating cells, and that tracking the paths cells take across it will reveal the gene regulatory networks that govern cell differentiation. Many groups have tried to algorithmically infer gene regulatory networks from global transcriptome measurements obtained with microarrays or RNA-Seq. Unfortunately, computational methods for inferring regulatory networks from bulk cell expression data have likely been hamstrung by Simpson's paradox, which destroys the crucial source of variation that an algorithm needs to accurately reconstruct networks from expression data. Simpson's paradox describes how a trend present in two or more groups of individuals changes or disappears entirely when those groups are mixed together. In time series expression analyses of cell differentiation, Simpson's paradox often completely obscures changes in expression that occur during a transition from one state to the next, because each bulk measurement contains a mixture of both states. We recently developed a new algorithm called Monocle that constitutes a major breakthrough in the analysis of gene expression data because it overcomes Simpson's paradox using single- cell RNA-Seq. I will exploit the new sources of regulatory information that Monocle makes accessible to develop an algorithm that reconstructs both the transcriptional landscape and the active regulatory pathways governing cell differentiation. I will then analyze the monocyte-derived cell lineage as a model system for discerning whether cells fall into discrete states and dissecting the pathways regulating transitions between them. Recent analyses of monocyte differentiation have suggested that this lineage is far more plastic and less sharply defined than previously believed, and this plasticity is suspected to contribute to many common diseases, including atherosclerosis, Crohn's disease, and multiple sclerosis. To extend my approach to non-cell- autonomous regulation, I will investigate how mesenchymal stem cells block dendritic cell generation from monocytes through cell-cell signaling. If successful, the research proposed here will provide not just a map of pathways governing monocyte differentiation, but a general strategy useful for illuminating the gene regulatory networks that govern a wide array of dynamic processes in cells of nearly any tissue.