Postdoc positions in imaging and theoretical modeling of heterogeneity in expression of pluripotency and differentiation


#1

We are looking for postdoctoral candidates who are interested in high quality imaging measurements of mammalian cells and theoretical modeling of cellular heterogeneity. We have recently begun a program to design cell lines that will serve as accurate biological reporters of pluripotency and differentiation. These iPS cell lines will be used in live cell imaging studies to probe single cell phenotype dynamics and population heterogeneity; the data will be used in models of multidimensional steady state landscapes. We have 3 postdoctoral opportunities available through the Research Associateship Program which is administered by the National Academies. This is a highly competitive process that requires submission of a formal proposal. Three relevant opportunities are below (please scroll down). Please have potential candidates contact me directly.

The 2-year position pays a salary of $71,128 per year and is open ONLY to US citizen who have received their PhD within the past 5 years. Our labs are on a lovely 500-acre campus in Gaithersburg MD, approximately 10 miles north of the NIH Bethesda campus. NIST works in diverse scientific disciplines and we collaborate within a great team of bioengineers, physicists, chemists, materials scientists, statisticians and computational scientists.

The next application date for the program is February 1, 2019. Please share this announcement as appropriate.

Anne Plant, Ph.D.
NIST Fellow
Biosystems and Biomaterials Division
National Institute of Standards and Technology
Gaithersburg, MD 20899
anne.plant@nist.gov
301 975 3124

  1. Advanced Imaging Tools to Measure Dynamics of Pluripotency and Differentiation 50.64.41.B8384

http://nrc58.nas.edu/RAPLab10/Opportunity/Opportunity.aspx?LabCode=50&ROPCD=506441&RONum=B8384

Induced pluripotent stem cell technologies are powerful new tools for biomedical research and have the potential to revolutionize medicine. The mechanisms by which cells transition from pluripotent to differentiated states is incompletely understood, and correlating measurable parameters to identify efficient culture conditions and release criteria for safe and effective therapies is imperfect. One challenge is the natural biological variability in cell responses across a population. In order to provide better biomarkers of pluripotency and differentiation, data describing the changes in gene expression at the single cell level are needed. In this project, quantitative live cell imaging and image analysis will be used to follow gene expression dynamics, and other phenotypic characteristics, in single cells. Imaging modes that employ fluorescence, transmitted light, quantitative phase, and/or surface plasmon resonance microscopy may be used to acquire different kinds of images on large numbers of iPS cells in culture; machine learning algorithms and other image analysis strategies may be used to extract and test image features as predictors of cell state . Quantitation of the extent, probability, and dynamics of changes in phenotypic markers over the population will add confidence in the interpretation of biomarkers of pluripotency and differentiation.

References

Halter, Michael, Daniel R. Sisan, Joe Chalfoun, Benjamin L. Stottrup, Antonio Cardone, Alden Dima, Alessandro Tona, Anne L. Plant, John T. Elliott. (2011) Cell cycle dependent TN-C promoter activity determined by live cell imaging. Cytometry Part A. 3A: 192-202.

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

Peterson, Alexander W.; Halter, Michael W.; Tona, Alessandro; Plant, Anne L. (2014) High Resolution Surface Plasmon Resonance Imaging of Cells. BMC Cell Biology. 15:35 DOI: 10.1186/1471-2121-15-35

  1. Mathematical Models for Characterizing Pluripotent Stem Cell Populations 50.64.41.B8169

http://nrc58.nas.edu/RAPLab10/Opportunity/Opportunity.aspx?LabCode=50&ROPCD=506441&RONum=B8169

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

Halter, Michael, Daniel R. Sisan, Joe Chalfoun, Benjamin L. Stottrup, Antonio Cardone, Alden Dima, Alessandro Tona, Anne L. Plant, John T. Elliott. (2011) Cell cycle dependent TN-C promoter activity determined by live cell imaging. Cytometry Part A. 3A: 192-202.

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

Hubbard, J.B. et al. (2013) Boltzmann’s H -Function and Diffusion Processes. J.Phys.Chem. 117, 12836-12843.

Lund SP, Hubbard JB, Halter M. Nonparametric Estimates of Drift and Diffusion Profiles via Fokker-Planck Algebra. J Phys Chem B. 2014 Nov 6;118(44):12743-9.

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

  1. Developing Biomarkers for Pluripotency and Differentiation in Live Cells 50.64.41.B8005

http://nrc58.nas.edu/RAPLab10/Opportunity/Opportunity.aspx?LabCode=50&ROPCD=506441&RONum=B8005

The development of therapies and other products based on pluripotent stem cells requires control of the pluripotent and differentiated states of cells in culture. Pluripotent cells need to be expanded in culture without differentiation, and when appropriate, cells need to be differentiated into appropriate lineages. The efficiency of expansion and the completeness of differentiation are important to both efficient manufacturing and to product safety. Time-lapse microscopy of living cells allows the quantification of changes in dynamic activity of individual cells over time. We have shown that such data can provide information about fluctuations in promoter activity and can be used to predict rates of state change in cell populations. Applying these techniques to induced pluripotent stem cell (iPSC) colonies will provide a better understanding of cellular mechanics and efficiency of differentiation, and will allow systematic quantitative assessment of how conditions such as extracellular matrix and other agents can influence differentiation. Research challenges could include creating iPSC lines with reporter constructs, designing live cell imaging experiments to assess cell response, and developing quantitative image analysis methods.

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. (2016) Large-scale time-lapse microscopy of Oct4 expression in human embryonic stem cell colonies. Stem Cell Research (17): 122-129

Halter et al. (2011) Cell cycle dependent TN-C promoter activity determined by live cell imaging. Cytometry Part A. 3A: 192-202.


Please go to http://sites.nationalacademies.org/pga/rap/ for complete information about the program and opportunities.