Multi-scale stochastic modelling for single-cell characterizations of pluripotency
Final Report Abstract
Within this SPP project we developed and applied software tools for the quantification of protein dynamics in single in close collaboration with other SPP members. For mouse embryonic stem cells, we found large heterogeneity in protein expression of the NanogVENUS fusion protein. Protein expression is found to be relatively stable in all intensity compartments, while changes of intensity happen on a variety of time scales. We inferred pluripotency transcription factor correlations from immunostainings, revealing significant differences in partial correlations for different colony types. To evaluate if subpopulations within the negative and mosaic colonies exist, we developed multiresolution correlation analysis, a visual tool for the inference of subpopulations. Statistical analysis of sister cell fates revealed high plasticity in the regulatory interactions, challenging the current view of a stable regulatory network responsible for the maintenance and exit from pluripotency. Taken together, we adapted our work program during the SPP and focus on the single-cell quantification and analysis, revealing a high kinetic heterogeneity, novel factor correlations, and an unprecedented plasticity in mutual regulation of pluripotency transcription factors. The developed tools and methods will be used in follow-up projects and allow the scientific community to progress on single-cell analysis.
Publications
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Efficient fluorescence image normalization for time lapse movies. Microscopic Image Analysis with Applications in Biology (2011)
Schwarzfischer, M., Marr, C., Krumsiek, J., Hoppe, P.S., Schroeder, T., and Theis, F.J.
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Hierarchical differentiation of myeloid progenitors is encoded in the transcription factor network. PLoS ONE 6, e22649 (2011)
Krumsiek, J., Marr, C., Schroeder, T. & Theis, F. J.
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The Sox17-mCherry fusion mouse line allows visualization of endoderm and vascular endothelial development. Genesis 50, 496-505 (2011)
Burtscher, I., Barkey, W., Schwarzfischer, M., Theis, F. J. & Lickert, H.
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Multi-scale modeling of GMP differentiation based on single-cell genealogies. FEBS J. 279, 3488–3500 (2012)
Marr, C., Strasser, M., Schwarzfischer, M., Schroeder, T. & Theis, F. J.
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Stability and Multiattractor Dynamics of a Toggle Switch Based on a Two-Stage Model of Stochastic Gene Expression. Biophys. J. 102, 19–29 (2012)
Strasser, M., Theis, F. J. & Marr, C.
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An automatic method for robust and fast cell detection in bright field images from high-throughput microscopy. BMC Bioinformatics 14, 297 (2013)
Buggenthin, F. et al.
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Live imaging of astrocyte responses to acute injury reveals selective juxtavascular proliferation. Nat Neurosci 16, 580–586 (2013)
Bardehle, S., Theis, F.J., Krüger, M., Buggenthin, F., Schwausch, J., Ninkovic, J., Clevers, H., Snippert, H.J., Meyer-Luehmann, M., Bechmann, I., et al.
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Centroid clustering of cellular lineage trees. In Information Technology in Bio-and Medical Informatics, Springer, 15-29 (2014)
Khakhutsky, V., Schwarzfischer, M., Hubig, N., Plant, C., Marr, C., Rieger, M.A., Schroeder, T., and Theis, F.J.
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MCA: Multiresolution Correlation Analysis, a tool for subpopulation identification in single-cell gene expression data. BMC Bioinformatics, 15, 240 (2014)
Feigelman, J., Theis, F. J. & Marr, C.
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Quantification and analysis of single-cell protein dynamics in stem cells using time-lapse microscopy. Doctoral Thesis, Technische Universität München (2014)
Schwarzfischer, M.
