Time-lapse microscopy of living cells allows the quantification of changes in gene promoter activity by following the intensity of fluorescent proteins in individual cells over time. Stem cell populations can be highly heterogeneous and can exhibit complex responses. Using quantitative imaging data on large numbers of live cells over time, we can construct potential landscapes for promoter activity based on steady state population distributions and measures of fluctuations in individual cells.  We have previously applied Langevin/Fokker Planck equations to predict rates of relaxation in cell populations. We have shown that such data can provide information about symmetric and asymmetric inheritance and allow prediction of rates of cell state change. We are extending this work to consider multidimensional landscapes. The goal of this research project is to develop models that can be used to evaluate the stability and predict transitions as cell populations progress from pluripotent to differentiated states.  This project involves a team working in live cell imaging, data analysis, and probabilistic model development.

References

Sisan D.R., et al. (2012) Predicting rates of cell state change due to stochastic fluctuations using a data-driven landscape model. PNAS 109, 19262-19267

Bhadriraju K, et al: “Large-scale time-lapse microscopy of Oct4 expression in human embryonic stem cell colonies.” Stem Cell Research (17): 122-129, 2016

key words

Stochastic fluctuations; Gene expression; Fluorescence protein reporters; Single cell analysis; Stem cell; Pluripotency; Differentiation; Fluorescence microscopy; Probabilistic models

Citizenship:  Open to U.S. citizens

Level:  Open to Postdoctoral applicants