Mathematical modeling for single-cell sequencing

University of Oxford

Animal biology emerges from the immensely complicated coordination of huge numbers of individual cells. Inside one organism, distinct cell types use identical DNA sequences to specialise for a wide range of roles, from muscle and blood cells to neurons. They perform their varied functions by transcribing different genes to mRNA and then translating that RNA to proteins. Over the past decade, new technologies have allowed biologists to observe these distinctions in unprecedented detail. Single-cell RNA sequencing (sc-RNAseq) measures the counts of transcribed RNA for each gene, called gene expression, in easily tens of thousands of individual cells with modern techniques. The measurements, however, are complex and noisy, and require appropriate mathematical tools designed to handle the new kinds of data.

Particular challenges for the field center on time dependence: the largest scale measurements of gene expression break apart cells to release their RNA for counting, which cannot be done multiple times to the same cell to create a time course. New experimental technologies offer paths around that obstacle, giving glimpses of cells' past or future. In lineage tracing, for example, ancestral relationships among cells, recorded with heritable CRISPR-edited barcodes, reveal information about the history of a measured population. Such techniques, together with the complementary mathematical modelling that I work on, prepare the community for ambitious scientific programs, from the human cell atlas to targeted analyses of immune response to coronavirus.

This presentation was recorded in conjunction with our 1851 Virtual Alumni Science Evening