Marioni group

Computational biology

Mouse spatial genomics (EMBL-EBI)

Preliminary data displaying a first-of-a-kind spatial genomics seqFISH dataset for mouse embryogenesis, and joint integration with a highly curated single cell RNA sequencing reference atlas. (Lohoff*, Ghazanfar* et al, manuscript submitted)

The ultimate goal of my group is to use computational models to understand the molecular mechanisms underlying cell fate decisions. Since cell fate decisions are made at the single cell level, they can only be properly understood by profiling molecular features at single cell resolution. The Marioni group has been a driver of the single cell genomics revolution, pioneering the development of robust and widely-used statistical approaches for all aspects of data analysis, from normalisation through to data integration and interpretation. Moreover, we have demonstrated how single cell genomics can be used to create molecular atlases and, critically, how these can facilitate novel and unexpected discoveries about how cells function in a variety of contexts including early development, ageing, immunology and cancer.

Previously, molecular fingerprints were generated by profiling gene expression levels from bulk populations of millions of input cells. These ensemble-based approaches meant that the expression value for each gene was an average of its expression across a population of input cells. However, there are many biological questions where bulk measures of gene expression are insufficient. During early development, for example, there are only a small number of cells, each having a distinct function and role.

Recent experimental advances, particularly in single cell RNA-sequencing (scRNAseq), have greatly improved the high-throughput generation of cDNA libraries from the poly-adenylated fraction of mRNA molecules within a single cell. scRNAseq can be applied to assay the individual transcriptomes of large numbers of cells. The combination of large numbers of cells with high-throughput profiling of gene expression (and other ‘omics’ measurements) at the single cell level allows us to answer many new biological questions.

From a methodological perspective, my group ensures these new data can be fully exploited by developing the required sophisticated and rigorously tested statistical models. We ensure that our code is well-documented and accessible to the wider scientific community, thereby facilitating use of these tools as well as establishing building blocks for further methodological development. Once established, we apply these tools, together with outstanding experimental collaborators, to understand fundamental biological questions, focusing on early mammalian development, immunology and cancer.  

Moving forward, the group will increasingly focus on modeling cell fate decisions in space and in real time. Although powerful, almost all single-cell genomics techniques to date require cells to be dissociated, with a loss of spatial context and a subsequent inability to fully understand a cell’s ecosystem. While new technologies are emerging to resolve this problem, there is a lack of robust and appropriate computational tools for making sense of the resulting data. Using statistical models, motivated by both classical and machine learning strategies, we will develop such a toolkit, in close collaboration with experimental colleagues. This will enable construction of a comprehensive map of how a cell’s spatial position, its movement and its molecular profile impact its ultimate fate. We will pioneer these approaches using early mammalian development before moving on to apply them in the context of disease, especially cancer.

John Marioni is jointly employed by the Cancer Research UK Cambridge Institute, which is part of the University of Cambridge.