Living cells transition among many physiologically distinct states during development, for tissue maintenance, and in disease. Dysregulation of transition rates between cell states can lead to pathologies, such as developmental disorders and cancer. In these systems and others, we would like to know which transitions can occur between the cellular states, in what sequence, and at what rates. Single-cell methods have expanded the ability to identify distinct cellular states in many systems. State of the art single-cell techniques can measure the expression levels of thousands of genes, but destroy cells in the process, and provide only static snapshots. No existing method can be used to infer the complex dynamics of state transitions in situ and without perturbations. To overcome this problem, we recently showed that combining the lineage history of a group of related cells with single-cell measurements of their gene expression profiles can be used to infer the dynamic transition rates between the cellular states. Here, I propose a comprehensive interdisciplinary research program to: 1) Use this approach to infer the rates of stochastic and reversible cell state transitions between the distinct pluripotent states in mouse embryonic (ES) cells, and identify the sequence of transitions involved in reprogramming of somatic cells into stem cells. 2) Extend the framework to in vivo systems by developing a synthetic platform for individual cells to autonomously record their lineage history within their DNA. 3) Apply the inference framework and the lineage-recording platform to study the dynamics of neural differentiation and neurodevelopmental diseases. My extensive background in theoretical/computational quantitative biology and training in experimental cell biology puts me in a unique position to accomplish the objectives of this proposal, which requires a seamless integration between computational and experimental approaches. I will use the mentoring phase of the K99 to facilitate my transition from theory to experimental biology and complete my laboratory training in mammalian cell biology and synthetic biology. In addition, I will work closely with collaborator to learn emerging techniques in multiplexed single-molecule imaging, the protocols for reprogramming, and methodology for induced neurogenesis. Together, the research program proposed here and the training during the mentored phase of the award will ensure that I will be well equipped to start an independent research lab and bring a novel approach to fundamental questions in diverse areas of developmental biology.
Dysregulation of the cell state transition rates can lead to pathologies, such as type-2 diabetes, cancer, and developmental disorders. Here, we propose an approach that combines a novel computational framework and a synthetic lineage-recording platform to infer the dynamics of cell state transitions in development and in disease from high-throughput in situ single-cell measurements. We will apply this approach to infer cell state transition dynamics in reprogramming and in neural differentiation, and identify the mechanisms that hinder proper differentiation in certain neurodevelopmental disorders.
| Frieda, Kirsten L; Linton, James M; Hormoz, Sahand et al. (2017) Synthetic recording and in situ readout of lineage information in single cells. Nature 541:107-111 |
| Hormoz, Sahand; Singer, Zakary S; Linton, James M et al. (2016) Inferring Cell-State Transition Dynamics from Lineage Trees and Endpoint Single-Cell Measurements. Cell Syst 3:419-433.e8 |
