SinCity

How do cells decipher the information from their environment, process this information through their internal molecular network, and reach a decision is one of the overarching questions in modern biology. Classical explanations for the molecular mechanisms controlling such cell decision-making processes are generally based on population-aggregate measurements by assuming that the average population response is typical for most, if not all, of the cells in the population. In the last few years it has become clear that gene expression variability among cells cannot and should not be ignored since such variability reflects the intrinsic stochastic molecular processes and provides information on the underlying molecular network. Cell-to-cell variability has even been shown to play an important functional role in the cell decision-making process, and beyond. The measurement of gene expression in single cells has the promise of revolutionizing our understanding of gene regulation and resolving many longstanding debates in biology. The main goal of this project is to address the overarching question: how inheritance, stochasticity and regulatory networks contribute to decision-making in metazoan cells during a differentiation process? For this purpose, we will couple phenotypic and molecular interrogation of single cells with known lineages undergoing a physiological differentiation process in two complementary cellular systems derived from primary non-genetically modified cells, namely avian erythrocytic progenitor cells and human CD34+ cells. By using two distinct differentiation systems, we will be able to discern generalizable as well as system specific strategies in the decision-making. The most ambitious goal of the project is to be able, at the end of the project, to deconvolute the heterogeneous fate decision-making process into specific contributions from stochastic fluctuations from various sources (unequal repartition at division, bursting regime), coordinated changes (including those resulting from network interactions) and epigenetic memory. This project will bring together an interdisciplinary consortium with the following objectives: 1. To create a novel microfluidic device that allows for tracking cell lineages at the single cell level and to couple the device to single cell transcriptomics assay; 2. To assess and establish the role of inheritance in cell decision-making processes during cell differentiation; 3. To establish a state-of-the-art method for dynamical network inference from single cell transcriptomics, including the functional role of noise; 4. To perform validation experiments for the most relevant model predictions describing the molecular events that control the differentiation processes. Ultimately, the project will create a holistic framework for integrating single cell experiments and observations (single cells transcriptomics during a differentiation process, coupled with real-time observations using dedicated microfluidic devices) with advanced data analysis including the creation of models of molecular processes underlying cell differentiation.