System dynamics and gene network architecture of early T-cell development

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PROJECT SUMMARY Timing of developmental progression is well-studied in early embryos, but cell lineages are generated stochastically from stem-cell precursors in later stages of life, and relatively little is known about the gene networks that control the probabilities that individual cells will initiate development or their rates of developmental progression. Mouse T cell development from multipotent blood precursors is an advantageous model for revealing mechanisms of these kinds of systems. The stages within the T-cell pathway are well defined in gene expression patterns, and cells starting from specific stages along the pathway can be tracked efficiently through development in vitro. Different cohorts of T cell progenitors from earlier or later embryonic and postnatal life have cell-intrinsic differences in the speeds with which they can differentiate. We hypothesize that the earliest cells in this pathway begin with a positively stabilized “Phase 1” gene regulatory network state that intrinsically opposes differentiation, until cumulative responses to signaling can induce a flip to a new network state. The differences in intrinsic differentiation speeds between different T-cell cohorts, and the extents of proliferation they undergo before differentiation is complete, are correlated with the persistence of the phase 1 regulatory state. However, until now it has been difficult to dissect these networks critically because cells in the earliest stages of T-cell development are rare and may have varied kinds of heterogeneity. This proposal is driven by new technological advances that open an exciting opportunity to dissect this mechanism functionally in single cells for the first time, and by a new systems biology collaboration that offers superior analyses of single-cell transcriptional responses to regulatory perturbation, both at the gene and at the cell levels. The new computational methods are optimized for revealing how gene network alterations shift subsets of cells between normal or abnormal developmental states. The experimental tools include recently developed mice with fluorescent reporters that report lineage commitment status of individual cells; imaging conditions that allow tracking living, individual clones through the whole commitment process; and an effective Cas9 transgenic mouse system that allows us to delete genes efficiently in primary T-cell precursors, so that impacts of perturbations on both gene expression and developmental kinetics can be defined. We can both define molecular sub-states in the starting population and monitor the impacts of specific regulatory factor perturbations using single-cell RNA-seq (10Genomics) and a new highly multiplex single-molecule fluorescent in situ hybridization technology for high sensitivity quantitation of low-abundance transcripts. Predictions of key network regulators will be directly tested here by perturbations and time-lapse imaging of clones differentiating from single cells. Finally, the small cell numbers needed allow us to define variances within single clones and to study the earliest ontogenic waves of precursors. We propose to apply these new tools to determine the gene network circuitries that sustain or destabilize the uncommitted state in different waves of early T cells.