Quantitative Single Cell Dynamics

Single cell data analysis

We design quantitative tools for the analysis of single cell data and provide it for the scientific community.

Analysis of early myeloid lineage choice

Transcription factor expression over time. Source: HMGU
  • We analyse individual haematopoietic stem cells throughout differentiation into megakaryocytic–erythroid and granulocytic–monocytic lineages. The expression dynamics of PU.1 and GATA1 in time-lapse movies are incompatible with the assumption that stochastic switching of these factors precedes and initiates lineage decision-making. Rather, our findings suggest that these transcription factors are only reinforcing lineage choice.
  • Collaboration partner: Timm Schroeder
  • Original publication: Nature 2016

Network plasticity of pluripotency transcription factors in embryonic stem cells

Nanog concentration over 8 generations. Source: HMGU
  • We quantify protein dynamics of the pluripotency factors Nanog and Oct4 in mouse embryoinic stem cells over many generations. Nanog shows strong fluctuations and seems to be less tightly regulated by other core pluripotency factors than previously assumed.
  • Collaboration partner: Timm Schroeder
  • Original publication: Nature Cell Biology 2015

Multiresolution Correlation Analysis

MCA plot. Source: HMGU
  • We present Multiresolution Correlation Analysis (MCA), a method for visually identifying subpopulations based on the local pairwise correlation between covariates. We demonstrate the potential of MCA on a previously published single-cell qPCR data from mouse embryonic stem cells.
  • Orignial publication: BMC Bioinformatics 2014

destiny - diffusion maps for large-scale single-cell data in R

Modeling cellular decisions

We develop and apply data-driven models to investigate cellular heterogeneities and analyze stem cell decision making.

Model driven analysis of cell lineage trees

Measured and simulated protein expression. Source: HMGU
  • STILT (Stochastic Inference on Lineage Trees) allows to fit stochastic models to time-resolved protein expression data and to perform model selection.
  • Applied to single cell trajectories of the pluripotency factor Nanog, we identify an autorepressive feedback loop.
  • The software is freely available and can be downloaded here
  • Collaboration partner: Manfred Claassen
  • Original publication: Cell Systems 2016

Inference of cell fate transitions from time-lapse microscopy data

Inference of cell fate transitions. Source: HMGU
  • We develop a method to analyze cell fate transitions observed in single-cell time lapse microscopy experiments that allows one to infer the impact of external factors such as cell-cell communication on cell fate.
  • Original publication: BMC Systems Biology 2015

A stochastic toggle switch model for differentiation

Quasipotential of the toggle switch. Source: HMGU
  • We study a stochastic model of a genetic toggle switch in the context of drive binary cell fate decisions, specifically taking into account small mRNA numbers. We discover novel dynamics and attractor states not present in previously studied systems.
  • Original publication: Biophysical Journal 2012

A branching process model of granulocyte monocyte progenitor (GMP) differentiation

Differentiation probability. Source: HMGU
  • We infer the differentiation probability per generation of GMPs from colony assays and single-cell time lapse microscopy experiments.  A stochastic toggle switch model fitted to the data explains the discrepancy observed between the datasets and suggests different timescales in the dynamics of granulocyte and monocyte differentiation.
  • Collaboration partner: Timm Schroeder
  • Original publication: FEBS Journal 2012

A Boolean model of myeloid blood differentiation

A regulatory model of myeloid differentiation. Source: HMGU
  • To study the hierarchical differentiation paradigm in the myeloid branch of blood differentiation from a gene regulatory perspective, we constructed a Boolean model of 11 transcription factors. We observe excellent agreement between the steady states of our model and microarray expression profiles.
  • Original publication: PLoS One 2011

 

Combinatorial Histone Acetylation Patterns Are Generated by Motif-Specific Reactions

  • Theoretical framework describes histone acetylation pattern abundances
  • More than 109 computational models were evaluated and compared
  • Motif-specific reaction rates are essential for the histone H4 acetylation network
  • Enzymatic pathways contribute to combinatorial acetylation patterns
  • Original publication: Cell Systems 2016

Bioimage informatics

We process microscopy images and analyse single cell properties in close collaboration with our experimental partners.

Tools for single-cell tracking and quantification

Software tools for quantification of protein expression. Source: HGMU
  • We have developed software tools that allow observing cells for weeks while also measuring molecular properties. The  tools can be used for robust and efficient analysis of large volumes of continuous time-lapse imaging data and are not limited to specific cell or image types.
  • The software is freely available and can be downloaded here
  • Collaboration partner: Timm Schroeder
  • Original publication: Nature Biotechnology 2016

Analysis of single neural cell morphology and motility in 3D confocal image stacks

Registered 3D image stack. Source: HMGU
  • The registration of 3D image stacks acquired at different time points indicates no astrocyte migration to the wound site. We find that hypertrophic reaction of astrocytes is indicated by an increase in the mean volume of cell somata.
  • Collaboration partner: Magdalena Götz
  • Original publication: Nature Neuroscience 2013

Robust and fast cell detection in bright field images from high-throughput microscopy

Identification of differentiated blood cells. Source: HMGU
  • We present a fully automated image processing pipeline that is able to robustly segment and analyze cells with ellipsoid morphology from bright field microscopy in a high-throughput, yet time efficient manner. We apply the method to a time-lapse movie of ∼315,000 images of differentiating hematopoietic stem cells over 6 days.
  • Collaboration partner: Timm Schroeder
  • Original publication: BMC Bioinformatics 2013

Efficient fluorescence image normalization for time lapse movies

Efficient fluorescence image normalization. Source: HMGU
  • We infer the time-dependent background signal and the image gain using information contained in the bleaching background of  fluorescence time-lapse movies.
  • Collaboration partner: Timm Schroeder
  • Original publication: ICSB workshop 2011

We use cookies to improve your experience on our Website. We need cookies to continuously improve the services, to enable certain features and when embedding services or content of third parties, such as video player. By using our website, you agree to the use of cookies. We use different types of cookies. You can personalize your cookie settings here:

Show detail settings
Please find more information in our privacy statement.

There you may also change your settings later.