Project 443845

Biology has entered a new era of precision measurement. Techniques like single-cell RNA sequencing (scRNA-seq) and single cell ATAC-seq have emerged as powerful tools to sample the complexity of large populations of cells and observe biological processes at unprecedented molecular resolution. One of the most exciting prospects associated with this new trove of data is the possibility of studying temporal processes, such as differentiation and development. If we could understand the genetic forces that control embryonic development, we would understand how cell types are stabilized throughout adult life and how they destabilize in diseases like cancer and with age. This would be within reach if we could analyze the dynamic changes in gene expression, as populations develop and subpopulations differentiate. However, this is not possible with current measurement technologies because they are destructive (in the sense that the cell must be lysed before its expression profile is measured). A number of recent research efforts have tackled this by collecting snap-shots of single cell expression profiles along a time-course and then computationally inferring trajectories from the static snap-shots. We argue that this inference problem is easier with more data, and the right way to measure the "size" of a data set is really the number of time-points, not the number of cells. We propose to collect the first single cell RNA-seq time-course with more than one thousand distinct temporal snapshots, and we develop a novel mathematical and conceptual framework to analyze the data. This tremendous temporal resolution will give us unprecedented statistical power to discover the genetic forces controlling development. To the best of our knowledge, no single study has collected more than 50 distinct temporal snapshots. Our proposal represents a technological leap, comparable to the leap from plate-based scRNA-seq to droplet-based methods.