Single-cell expression profiling by RNA-Seq promises to exploit cell-to-cell variation in gene expression to reveal regulatory circuitry governing cell differentiation and other biological processes. Here, we describe Monocle, a novel unsupervised algorithm for ordering cells by progress through differentiation that dramatically increases temporal resolution of expression measurements in a model of skeletal muscle differentiation. This reordering unmasks switch-like changes in expression of key regulatory factors, reveals sequentially organized waves of gene regulation, and exposes novel regulators of cell differentiation. A loss-of function screen revealed that many of these inhibitors act through regulatory elements also used by pro-myogenic factors to activate downstream genes. This study demonstrates that single-cell expression analysis by Monocle can uncover novel regulatory interactions governing differentiation.

Cell differentiation is governed by a vast and complex gene regulatory program. During differentiation, each cell makes fate decisions independently by integrating a wide array of signals from other cells, executing a complex choreography of gene regulatory changes. Recently, several studies carried out at single-cell resolution have revealed high cell-to-cell variation in most genes during differentiation1–5, even among key developmental regulators. Although high variability complicates analysis of such experiments6, it might define