Stem cells & development
We model stem cell decision-making on multiple scales to address the following key questions:
- Which regulatory networks underlie developmental dynamics?
- What molecular mechanisms drive differentiation?
- How can we infer model parameters from cellular genealogies?
Gene expression patterns during early mouse embryo development
Main contact at CMB: Florian Büttner, Dominik Lutter
We developed a novel approach for analysing high-dimensional gene expression data from single cells. This allows for assessing differences in gene expression patterns between different developmental stages as well as within developmental stages. We present a novel framework based on Gaussian Process Latent Variable Models (GPLVMs) in order to map the high-dimenasional gene-expression data onto a 2D map. We extend GPLVMs in order to provide interpretability and take the temporal structure of the data into account. When analysing single-cell qPCR expression data of 48 genes from mouse zygote to blastocyst as presented by Guo et al. (2010) we were able to resolve differences in gene expressions for all developmental stages. Furthermore a new sub-population of cells within the 16-cell stage is identified which is significantly more trophectoderm-like than the rest of the population. A Matlab implementation is available upon request.- Gastrulation is one of the first developmental processes during embryogenesis is. During gastrulation the former pluripotent cells of the epiblast start differentiating into the three germ layers endoderm, mesoderm and ectoderm. We here focus on endoderm differentiation in particular the role of two transcription factors (TF) Foxa2 and Sox17. These transcription factors form a gradient along the posterior anterior axis of the embryo. We here were interested in the molecular mechanisms that form the embryo. Thus, we identified and analyse the role of distinct microRNAs during this process and their influence on lineage segregation and TF gradient formation.
Hematopoietic models on multiple scales
Main contact at CMB: Carsten Marr
To study the hierarchical differentiation paradigm in the myeloid branch from a gene regulatory perspective, we constructed Boolean model of 11 transcription factors and demonstrated its predictive value. We observed excellent agreement between the steady states of our model and microarray expression profiles of granulocytes, monocytes, erythrocytes and megakaryocytes. Moreover, in silico perturbations correctly reproduced previously reported knockout phenotypes. - Within a branching process model, we inferred the differentiation probability per generation of GMPs from colony assays under permissive conditions. We compared the predictions with the differentiation probability in genealogies determined from single-cell time-lapse microscopy and found the differentiation probability to rise with the generation of the cell within the genealogy. To study this feature from a molecular perspective, we set up a stochastic toggle switch model, where we execute the intrinsic lineage decision with two antagonistic transcription factors. We inferred parameters for which the model matches experimentally observed differentiation probabilities via approximate Bayesian computing. These parameters suggest different timescales in the dynamics of granulocyte and monocyte differentiation.
Bayesian inference of Boolean regulatory networks
Main contact at CMB: Dominik Lutter
- Based on time-dependent expression profiles of mRNA and miRNA data we developed a Bayesian method to infer Boolean gene regulatory networks. The output of the method is not only a single 'best' model but a full statistical model of the parameter space. By analyzing this parameter space we were able to predict several unknown gene-gene and gene-miRNA interactions, which have in part already been experimentally confirmed by the Götz group.
Collaboration partners
- Magdalena Götz (Neural Stem Cells Group)
- Heiko Lickert (Endoderm Development Group)
- Timm Schroeder (Research Unit Stem Cell Dynamics)
References
- Multi-scale modeling of GMP differentiation based on single-cell genealogies
C. Marr, M. Strasser, M. Schwarzfischer, T. Schroeder, F. J. Theis
FEBS Journal, accepted, 2012. - A novel approach for resolving differences in single-cell gene expression patterns from zygote to blastocyst
F. Buettner and F. J. Theis
Bioinformatics, accepted, 2012. - Stability and multi-attractor dynamics of a toggle switch based on a two-stage model of gene expression
M. Strasser, F. J. Theis, and C. Marr
Biophysical Journal, 102, 19-29, 2012. - Efficient fluorescence image normalization for time lapse movies
M. Schwarzfischer, C. Marr, J. Krumsiek, P. S. Hoppe, T. Schroeder, F. J. Theis
In Proc. Microscopic Image Analysis with Applications in Biology, Heidelberg, Germany, 2011. - Hierarchical Differentiation of Myeloid Progenitors Is Encoded in the Transcription Factor Network
J. Krumsiek, C. Marr, T. Schroeder, F.J. Theis
PLoS ONE, 6:e22649, 2011 - An ensemble approach for inferring semi-quantitative regulatory dynamics for the differentiation of mouse embryonic stem cells using prior knowledge
D. Lutter, P. Bruns, F.J. Theis
In Advances In Systems Biology, Springer 2011