Cell state transition dynamics at single-cell level (Hood. Ozinskv. Huang) Introduction: In metazoa, the very same genome produces a vast diversity of discrete, robust or metastable cell states, as most prosaically manifest in the various cell types of the human body. The switching between cell states is at the core of multicellular development and is also important in diseases, such as cancer, and in regeneration. In this project, we will determine how gene regulatory networks (GRN) regulate the cell state changes, and hence phenotype changes, that occur in development and cancer. Thus, ultimately we want know how these networks specify cell phenotype. This will be achieved by measuring gene expression dynamics at single cell-resolution during cell phenotype change in two clinically relevant settings: (a) cardiomyocytes reprogramming and {b) breast cancer (stem) cell epithelial-mesenchymal transition. Why are single-cell level studies critical for a systems understanding of cell state transitions? One of the key missions in biology is to decode the functional complexity of the human body beyond the traditional molecular profiling of the presumably >250 distinct """"""""cell types"""""""". This is highlighted by the finding from single cell analysis that protein or mRNA expression varies by 10-1000-fold between cells even in apparently uniform, clonal cell populations, and that the """"""""outliers"""""""" cells are functionally distinct (96). Yet few of the measurement techniques that have informed our network models in the past (Project 1) actually achieve single-cell resolution. Standard techniques typically capture only the averaged state of the cellular population, thus blurring unique features of inherent cellular heterogeneity. Conversely, the much needed analysis at the single-cell resolution poses new challenges.
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