Speaker
Description
Modern single-cell RNA sequencing techniques have transformed molecular biology by enabling genome-wide molecular profiling across thousands to millions of cells. However, these technologies fail to preserve the natural spatial organisation of individual cells, which is vital to understand how cell-to-cell interactions drive the development of multicellular organisms or shape the collective behaviour of bacteria. The emerging field of spatial transcriptomics addresses this limitation by providing precise localisation of cells at cellular and even sub-cellular resolution. As such data becomes increasingly available, we must develop novel modelling and inference approaches to effectively incorporate spatial information and enable biophysically meaningful insights into spatial dynamics of gene expression. Here we integrate the positional context into mechanistic models of gene expression that capture transcriptional bursting dynamics, as parametrised by two intuitive parameters: burst size and burst frequency. By fitting these models to spatially-resolved single-cell datasets, we infer burst parameters for thousands of genes, quantify the extent of spatial transcriptional heterogeneity across cell populations and explore the underlying intra- and inter-cellular gene regulatory processes.
