A central questions in systems biology is how to link observed phenotypical behaviour to molecular state changes. Here, we are interested in the cellular phenotype and its change over differentiation in the context of somatic and embryonic stem cells (SCs). The challenge lies in identifying and quantifying regulatory mechanisms on the transcriptional level that cause a SC to differentiate into a particular cell type, potentially over many generations and dependent on its environment. Population heterogeneities as well as putative stochastic differentiation events require single-cell observations. In collaborations with our local SC Institute, we have therefore imaged live hematopoietic and embryonic SCs and their progeny in vitro, and quantified transcription factor levels using fluorescence-tagged fusion proteins. We now ask which cellular properties at which stage in the genealogy are sufficient to predict its progeny. For this we will determine morphological cell properties by quantifying brightfield and phase-contrast movies in addition to the fluorescence images. We will then train a classifier on the phenotypical as well as the molecular data to see which features are sufficient to predict cell fate. Methodologically, we need to advance functional data classification and take into account dependence of samples from adjacent generations; we will evaluate models on a generative genealogy model to be created in this proposal. Eventually we will apply the predictors to existing differentiation trees to determine the time-point of cellular decision on the single-cell level. Once we can do this in real-time, this will open up novel experimental approaches such as omics-analyses of cells as well as molecular perturbations at the time point of fate decision.

On the translational side, we are aiming in the long run at improving the efficiency of differentiation protocols, thereby contributing to the perspective of stem cells in the treatment of severe diseases such as dementia or leukemia.

Timm Schroeder

Stem Cell Dynamics, Helmholtz Zentrum Munich

Buggenthin F, Marr C, Schwarzfischer M, Hoppe PS, Hilsenbeck O, Schroeder T, Theis FJ (2013). An automatic method for robust and fast cell detection in bright field images from high-throughput microscopy. BMC Bioinformatics 14:297.

Rinck A, Preusse M, Laggerbauer B, Lickert H, Engelhardt S, Theis FJ (2013). The human transcriptome is enriched for miRNA-binding sites located in cooperativity-permitting distance. RNA Biol 10(7):1125-35.

Krumsiek, J., Marr, C., Schroeder, T., and Theis, F. (2011). Hierarchical differentiation of myeloid progenitors is encoded in the transcription factor network. PLoS ONE, 6(8):e22649.

Krumsiek, J., Suhre, K., Illig, T., Adamski, J., and Theis, F. (2011). Gaussian graphical modeling reconstructs pathway reactions from high-throughput metabolomics data. BMC Systems Biology, 5(21).

Mittelstrass, K., Ried, J., Yu, Z., Krumsiek, J., Gieger, C., Prehn, C., Roemisch-Margl, W., Polonikov, A., Peters, A., Theis, F., Meitinger, T., Kronenberg, F., Weidinger, S., Wichmann, H.-E., Suhre, K., Wang-Sattler, R., Adamski, J., and Illig, T. (2011). Discovery of sexual dimorphisms in metabolic and genetic biomarkers. PLoS Genetics, 7(8):e1002215.

Neher, R., Mitkovski, M., Kirchhoff, F., Neher, E., Theis, F., and Zeug, A. (2009). Blind source separation techniques for the decomposition of multiply labeled fluorescence images. Biophysical Journal, 96(9):3791–3800.

Schwarzfischer, M., Marr, C., Krumsiek, J., Hoppe, P., Schroeder, T., and Theis, F. (2011). Efficient fluorescence image normalization for time lapse movies. In Proc. Microscopic Image Analysis with Applications in Biology, Heidelberg, Germany.

Strasser, M., Theis, F., and Marr, C. (2012). Stability and multi-attractor dynamics of a gene switch based on a two-stage model of gene expression. Biophysical Journal, 102:19–29.