Fate decisions in developing tissues involve cells transitioning between a set of discrete cell states. Geometric models, often referred to as Waddington landscapes, are an appealing way to describe differentiation dynamics and developmental decisions. We consider the differentiation of neural and mesodermal cells from pluripotent mouse embryonic stem cells exposed to different combinations and durations of signalling factors. We developed a principled statistical approach using flow cytometry data to quantify differentiating cell states. Then, using a framework based on Catastrophe Theory and approximate Bayesian computation, we constructed the corresponding dynamical landscape. The result was a quantitative model that accurately predicted the proportions of neural and mesodermal cells differentiating in response to specific signalling regimes. Taken together, the approach we describe is broadly applicable for the quantitative analysis of differentiation dynamics and for determining the logic of developmental cell fate decisions.

Dr. Gupta is carrying out his post doctoral studies in the Buter Laboratory in UCLA. He is applying his his knowledge of embryology to stem cells for developing regenerative therapies to treat spinal cord injuries. In this talk, he will discuss how understanding the logic for spinal cord development led us to derive diverse sensory neuronal classes from embryonic stem cells. The spinal sensory neurons enableus to perceive our environment through modalities that are lost in spinal injury patients. These ESC derived senory neurons can help regain sensation in spina cord injury patients through regenerative therapies.