Single-Cell RNA-Sequencing-Based CRISPRi Screening Resolves Molecular Drivers of Early Human Endoderm Development

Summary: Studies in vertebrates have outlined conserved molecular control of definitive endoderm (END) development. However, recent work also shows that key molecular aspects of human END regulation differ even from rodents. Differentiation of human embryonic stem cells (ESCs) to END offers a tractable system to study the molecular basis of normal and defective human-specific END development. Here, we interrogated dynamics in chromatin accessibility during differentiation of ESCs to END, predicting DNA-binding proteins that may drive this cell fate transition. We then combined single-cell RNA-seq with parallel CRISPR perturbations to comprehensively define the loss-of-function phenotype of those factors in END development. Following a few candidates, we revealed distinct impairments in the differentiation trajectories for mediators of TGFβ signaling and expose a role for the FOXA2 transcription factor in priming human END competence for human foregut and hepatic END specification. Together, this single-cell functional genomics study provides high-resolution insight on human END development. : Genga et al. utilize a single-cell RNA-sequencing-based CRISPR interference approach to screen transcription factors predicted to have a role in human definitive endoderm differentiation. The perturbation screen identifies an important role of TGFβ signaling-related factors. Follow-up of FOXA2 reveals genome-wide molecular changes and altered differentiation competency in endoderm. Keywords: pluripotent stem cells, endoderm, single-cell RNA-seq, CRISPRi, human development, chromatin accessibility, hepatic endoderm, dCas9-KRAB, stem cell differentiation, perturbation scree

Inferring population dynamics from single-cell RNA-sequencing time series data

Recent single-cell RNA-sequencing studies have suggested that cells follow continuous transcriptomic trajectories in an asynchronous fashion during development. However, observations of cell flux along trajectories are confounded with population size effects in snapshot experiments and are therefore hard to interpret. In particular, changes in proliferation and death rates can be mistaken for cell flux. Here we present pseudodynamics, a mathematical framework that reconciles population dynamics with the concepts underlying developmental trajectories inferred from time-series single-cell data. Pseudodynamics models population distribution shifts across trajectories to quantify selection pressure, population expansion, and developmental potentials. Applying this model to time-resolved single-cell RNA-sequencing of T-cell and pancreatic beta cell maturation, we characterize proliferation and apoptosis rates and identify key developmental checkpoints, data inaccessible to existing approaches