Data-driven modeling of inter-organelle dynamic interactions throughout differentiation with multispectral and label free live imaging

We hypothesize that the dynamic communication network of organelles via membrane contact sites is a key feature of cell identity. A dramatic example of changing identity occurs during differentiation of stem cells to various fates. We will use cellular differentiation as a model to explore causal links between organelle contacts and physiology. Live multispectral and quantitative label-free imaging (QLI) will be combined with data modeling of organelle morphology, dynamics, contacts, and interactions to quantitatively map the continuous transition of stem cells to multiple cell types. These representations will be used to identify how stem cells commit to specific differentiation trajectories, and which organelle features are associated with cell fate decisions and the following progression to terminal differentiation. 

• Machine learning inference of organelle communication networks during continuous single-cell state transitions throughout differentiation. 

• Integrating fluorescence and in silico labeling for continuous imaging. 

• Testing causal relationships between organelle contacts and differentiation. 

This project is part of the 2024 Organelle Communication cohort

These researchers will explore a thrilling frontier in cell biology emerging from the discovery that organelles (cellular compartments) can directly connect to each other to exchange materials and information, forming complex and dynamic networks. Much of how these interactions occur remains unknown due to the profound challenges of observing rapid events on a nanometer scale. This cohort will pioneer new tools to directly observe and model the organelle ‘interactome’ across time, space, and cell type. Their findings will expand our understanding of core biological principles, with powerful implications for fields ranging from regenerative medicine to virology.