Differentiation rules in 3D tissue
We study how differentiation decisions depend on spatio-temporal patterns of signaling. We do this using embryoid bodies composed of ES cells carrying different markers for differnetiation stage and signal, through live imaging by two-photon microscopy, and by controlling the mechanical and biochemical environment of the EB. Our recent works established reproducible developmental programs in EBs (Boxman et. al. 2016), and analyzed symmetry breaking and mesendoderm onset (Sagy et. al. 2019) as well as definitive endoderm onset and patterning (Pour et. al. 2019).
Early decisions in reprogramming to pluripotency
In this process, a population of identical adult cells is directed to pluripotency by forced activation of four transcription factors, yet only a small fraction ends up in the desired state. We want to understand the dynamics of the process, its different phases, and the different paths cells can take during reprogramming. We performed live imaging of the entire reprogramming process, combining live fluorescent markers and immuno-staining at intermediate points. We have delineated the stages of the process, and demonstrated that repreogramming fates are epigenetically pre-determined before activation of the Yamanaka factors (Smith et. al. 2010, Pour et. al. 2015, Thakurela et. al. 2019)
We leverage stem-cell based 3D models to enable the large-scale production of muscle mass for cultured meat applications.
Control of timing in transcription regulatory networks
During cell state changes, master regulators need to orchestrate the timely activation of many different proteins. How does a master transcriptional regulator control its targets differentially? Can it achieve consistent timing differences in their activation? Does the variability in its levels generate variable onset times or variable levels of its targets? We study the accuracy, reproducibility and temporal structure of a transcriptional response at the single cell level, using the early meiosis transcriptional module as a model system (Goldschmidt et. al. 2015).
Information processing by signaling networks
Cell populations in nature face different cues from the environment that change at different frequencies. For example, a yeast colony in the vineyard senses different levels of heat, humidity, osmolarity and nutrient levels changing at different rates. Effective response to these fluctuating cues raises several challenges. Can the cells distinguish between a fleeting cue and a consistent change? Can they filter out the former to avoid mistaken decisions? How do their signaling and transcriptional networks handle these complex fluctuations? We study the decision responses to signal fluctuations in the yeast meiosis process using live cell microscopy and custom-designed microfluidic devices capable of generating spatial and temporal signal gradients (Nachman 2007).