Thomas Zerjatke

The bulk of the hematopoietic stem cell population is dispensable for murine steady-state and stress hematopoiesis.

Long-term repopulating (LT) hematopoietic stem cells (HSCs) are the most undifferentiated cells at the top of the hematopoietic hierarchy. The regulation of HSC pool size and its contribution to hematopoiesis are incompletely understood. We depleted hematopoietic stem and progenitor cells (HSPCs) in adult mice in situ and found that LT-HSCs recovered from initially very low levels (<1%) to below 10% of normal numbers but not more, whereas progenitor cells substantially recovered shortly after depletion. In spite of the persistent and massive reduction of LT-HSCs, steady-state hematopoiesis was unaffected and residual HSCs remained quiescent. Hematopoietic stress, although reported to recruit quiescent HSCs into cycle, was well tolerated by HSPC-depleted mice and did not induce expansion of the small LT-HSC compartment. Only upon 5-fluorouracil treatment was HSPC-depleted bone marrow compromised in reconstituting hematopoiesis, demonstrating that HSCs and early progenitors are crucial to compensate myeloablation. Hence, a contracted HSC compartment cannot recover in situ to its original size, and normal steady-state blood cell generation is sustained with <10% of normal LT-HSC numbers without increased contribution of the few residual cells.Long-term repopulating (LT) hematopoietic stem cells (HSCs) are the most undifferentiated cells at the top of the hematopoietic hierarchy. The regulation of HSC pool size and its contribution to hematopoiesis are incompletely understood. We depleted hematopoietic stem and progenitor cells (HSPCs) in adult mice in situ and found that LT-HSCs recovered from initially very low levels (<1%) to below 10% of normal numbers but not more, whereas progenitor cells substantially recovered shortly after depletion. In spite of the persistent and massive reduction of LT-HSCs, steady-state hematopoiesis was unaffected and residual HSCs remained quiescent. Hematopoietic stress, although reported to recruit quiescent HSCs into cycle, was well tolerated by HSPC-depleted mice and did not induce expansion of the small LT-HSC compartment. Only upon 5-fluorouracil treatment was HSPC-depleted bone marrow compromised in reconstituting hematopoiesis, demonstrating that HSCs and early progenitors are crucial to compensate myeloablation. Hence, a contracted HSC compartment cannot recover in situ to its original size, and normal steady-state blood cell generation is sustained with <10% of normal LT-HSC numbers without increased contribution of the few residual cells.

Imaging, quantification and visualization of spatio-temporal patterning in mESC colonies under different culture conditions

Motivation: Mouse embryonic stem cells (mESCs) have developed into a prime system to study the regulation of pluripotency in stable cell lines. It is well recognized that different, established protocols for the maintenance of mESC pluripotency support morphologically and functionally different cell cultures. However, it is unclear how characteristic properties of cell colonies develop over time and how they are re-established after cell passage depending on the culture conditions. Furthermore, it appears that cell colonies have an internal structure with respect to cell size, marker expression or biomechanical properties, which is not sufficiently understood. The analysis of these phenotypic properties is essential for a comprehensive understanding of mESC development and ultimately requires a bioinformatics approach to guarantee reproducibility and high-throughput data analysis. Results: We developed an automated image analysis and colony tracking framework to obtain an objective and reproducible quantification of structural properties of cell colonies as they evolve in space and time. In particular, we established a method that quantifies changes in colony shape and (internal) motion using fluid image registration and image segmentation. The methodology also allows to robustly track motion, splitting and merging of colonies over a sequence of images. Our results provide a first quantitative assessment of temporal mESC colony formation and estimates of structural differences between colony growth under different culture conditions. Furthermore, we provide a stream-based visualization of structural features of individual colonies over time for the whole experiment, facilitating visual comprehension of differences between experimental conditions. Thus, the presented method establishes the basis for the model-based analysis of mESC colony development. It can be easily extended to integrate further functional information using fluorescence signals and differentiation markers. Availability: The analysis tool is implemented C++ and Mathematica 8.0 (Wolfram Research Inc., Champaign, IL, USA). The tool is freely available from the authors. We will also provide the source code upon request. Contact: nico.scherf@tu-dresden.de

Quantitative Cell Cycle Analysis Based on an Endogenous All-in-One Reporter for Cell Tracking and Classification

Summary Cell cycle kinetics are crucial to cell fate decisions. Although live imaging has provided extensive insights into this relationship at the single-cell level, the limited number of fluorescent markers that can be used in a single experiment has hindered efforts to link the dynamics of individual proteins responsible for decision making directly to cell cycle progression. Here, we present fluorescently tagged endogenous proliferating cell nuclear antigen (PCNA) as an all-in-one cell cycle reporter that allows simultaneous analysis of cell cycle progression, including the transition into quiescence, and the dynamics of individual fate determinants. We also provide an image analysis pipeline for automated segmentation, tracking, and classification of all cell cycle phases. Combining the all-in-one reporter with labeled endogenous cyclin D1 and p21 as prime examples of cell-cycle-regulated fate determinants, we show how cell cycle and quantitative protein dynamics can be simultaneously extracted to gain insights into G1 phase regulation and responses to perturbations.

Assisting the Machine Paradigms for Human-Machine Interaction in Single Cell Tracking

Single cell tracking emerged as one of the fundamental experimental techniques over the past years in basic life science research. Though a large number of automated tracking methods has been introduced, they are still lacking the accuracy to reliably track complete cellular genealogies over many generations. Manual tracking on the other hand is tedious and slow. Semi-automated approaches to cell tracking are a good compromise to obtain comprehensive information in feasible amounts of time. In this work, we investigate the efficacy of different interaction paradigms for manual correction and processing of precomputed tracking results and present a respective tool that implements those strategies.

Metabolism is the Tie: The Bertalanffy-type Cancer Growth Model as Common Denominator of Various Modelling Approaches

Cancer or tumour growth has been addressed from a variety of mathematical modelling perspectives in the past. Examples are single variable growth models, reaction diffusion models, compartment models, individual cell-based models, clonal competition models, to name only a few. In this paper, we show that the so called Bertalanffy-type growth model is a macroscopic model variant that can be conceived as an optimal condensed modelling approach that to a high degree preserves complexity with respect to the aforementioned more complex modelling variants. The derivation of the Bertalanffy-type model is crucially based on features of metabolism. Therefore, this model contains a shape parameter that can be interpreted as a resource utilisation efficiency. This shape parameter reflects features that are usually captured in much more complex models. To be specific, the shape parameter is related to morphological structures of tumours, which in turn depend on metabolic conditions. We, furthermore, show that a single variable variant of the Bertalanffy-type model can straightforwardly be extended to a multiclonal competition model. Since competition is crucially based on available shared or clone-specific resources, the metabolism-based approach is an obvious candidate to capture clonal competition. Depending on the specific context, metabolic reprogramming or other oncogene driven changes either lead to a suppression of cancer cells or to an improved competition resulting in outgrowth of tumours. The parametrisation of the Bertalanffy-type growth model allows to account for this observed variety of cancer characteristics. The shape parameter, conceived as a classifier for healthy and oncogenic phenotypes, supplies a link to survival and evolutionary stability concepts discussed in demographic studies, such as opportunistic versus equilibrium strategies.

Beyond genealogies: Mutual information of causal paths to analyse single cell tracking data

Single cell tracking, based on the computerised analysis of time-lapse movies, is a sophisticated experimental technique to quantify single cell dynamics in time and space. Although the resulting cellular genealogies comprehensively describe the divisional history of each cell, there are many open questions regarding the statistical analysis of this type of data. In particular, it is unclear, how tracking uncertainties or spatial information of cellular development can correctly be incorporated into the analysis. Here we propose a generalised description of single cell tracking data by spatiotemporal networks that can account for ambiguities in cell assignment as well as for spatial relations between cells. We present a way to measure correlations among cell states by analysing the mutual information in state space considering causal (time-respecting) paths and illustrate our approach by a corresponding example. We conclude that a comprehensive spatiotemporal description of single cell tracking data is ultimately necessary to fully exploit the information obtained by time-lapse imaging.

Solving a PSPACE-complete problem by gene assembly

Gene assembly is a natural process of genome re-arrangement that occurs during sexual reproduction of unicellular organisms called ciliates. Two computational models adapting this process of gene assembly have been proposed: the intramolecular, e.g. (Ehrenfeucht et al., 2004, Computation in Living Cells: Gene Assembly in Ciliates), and the intermolecular model, e.g. (Landweber and Kari, 2001, Evolution as Computation). A context sensitive version of the intramolecular model introduced by Ishdorj and Petre (2007, Proceedings of the 6th International Conference on Unconventional Computation) was shown to be computationally universal and efficient for solving NP-complete problems. In this article we show that within this model PSPACE-complete problems can also be solved in linear time.

Paracrine mechanisms in early differentiation of human pluripotent stem cells: Insights from a mathematical model

With their capability to self-renew and differentiate into derivatives of all three germ layers, human pluripotent stem cells (hPSCs) offer a unique model to study aspects of human development in vitro. Directed differentiation towards mesendodermal lineages is a complex process, involving transition through a primitive streak (PS)-like stage. We have recently shown PS-like patterning from hPSCs into definitive endoderm, cardiac as well as presomitic mesoderm by only modulating the bulk cell density and the concentration of the GSK3 inhibitor CHIR99021, a potent activator of the WNT pathway. The patterning process is modulated by a complex paracrine network, whose identity and mechanistic consequences are poorly understood. To study the underlying dynamics, we here applied mathematical modeling based on ordinary differential equations. We compared time-course data of early hPSC differentiation to increasingly complex model structures with incremental numbers of paracrine factors. Model simulations suggest at least three paracrine factors being required to recapitulate the experimentally observed differentiation kinetics. Feedback mechanisms from both undifferentiated and differentiated cells turned out to be crucial. Evidence from double knock-down experiments and secreted protein enrichment allowed us to hypothesize on the identity of two of the three predicted factors. From a practical perspective, the mathematical model predicts optimal settings for directing lineage-specific differentiation. This opens new avenues for rational stem cell bioprocessing in more advanced culture systems, e.g. in perfusion-fed bioreactors enabling cell therapies.