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    Date Issued2019 (2)Author
    Kernfeld, Eric M. (2)
    Maehr, Rene (2)Bakhti, Mostafa (1)Bastidas-Ponce, Aimee (1)Fiedler, Anna K. (1)View MoreUMass Chan AffiliationDiabetes Center of Excellence (2)Program in Molecular Medicine (2)Graduate School of Biomedical Sciences, Interdisciplinary Graduate Program (1)Document TypeJournal Article (2)KeywordCell Biology (2)Cells (2)Amino Acids, Peptides, and Proteins (1)Bioinformatics (1)Biotechnology (1)View MoreJournalCell reports (1)Nature biotechnology (1)

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

    Genga, Ryan M.; Kernfeld, Eric M.; Parsi, Krishna M.; Parsons, Teagan J.; Ziller, Michael J.; Maehr, Rene (2019-04-16)
    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 TGFbeta 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.
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    Inferring population dynamics from single-cell RNA-sequencing time series data

    Fischer, David S.; Fiedler, Anna K.; Kernfeld, Eric M.; Genga, Ryan; Bastidas-Ponce, Aimee; Bakhti, Mostafa; Lickert, Heiko; Hasenauer, Jan; Maehr, Rene; Theis, Fabian J. (2019-04-01)
    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.
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