Using single-cell transcriptomics to understand cellular heterogeneity
With recent technological developments it has become possible to characterize a single cell’s genome, epigenome and transcriptome. However, to take advantage of such data, which can provide, amongst other things, key insights into cell fate decisions, it is critical that appropriate computational methods are applied and developed.
In this presentation, I will begin by outlining some of the computational tools that we have developed to address challenges in normalization, identification of highly variable genes and obtaining spatially resolved measurements of gene expression.
Subsequently, I will focus on using such techniques to better understand a key developmental time point: gastrulation. During gastrulation the three germ layers and the basic body plan are specified. However, molecular analyses of these processes have been limited due to the small number of cells present in gastrulating embryos. With recent developments in the field of single-cell biology however, it is now possible to overcome these limitations and to characterize, for the first time at the single-cell level, how cell fate decisions are made. I will show how we obtained a map of cell types during this developmental stage and how we combined this atlas with genetic knock out data to obtain insights into cell fate choices.
John Marioni obtained his PhD in Applied Mathematics in the University of Cambridge in 2008 and did his postdoctoral research in the Department of Human Genetics, University of Chicago. In 2010 John Marioni joined the EMBL-EBI, Hinxton (UK) as Group Leader of Computational and Evolutionary Genomics.
His research focusses on developing the computational and statistical tools necessary to exploit high-throughput genomics data in order to understand the regulation of gene expression and to model developmental and evolutionary processes. Within this context, he focuses on (1) understanding how the divergence of gene expression levels is regulated, (2) using gene expression as a definition of the molecular fingerprint of individual cells to study the evolution of cell types, and (3) modeling spatial variability in gene expression levels within a tissue or organism. These three strands of research are brought together by single-cell sequencing technologies.