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SUMMARYLong considered as cytotoxic reagents, reactive oxygen species (ROS) at the right concentration promote cell proliferation in cell culture, stem cells and model organisms. However, how ROS signaling is coordinated with cell cycle progression and integrated into the cell cycle control machinery on the molecular level remains unsolved. Here, we report oscillations of mitochondrial ROS during the cell cycle that target cyclin-dependent kinase 2 (CDK2). Chemical and metabolic interference with ROS production decrease T-loop phosphorylation on CDK2, impeding its full activation and thus efficient DNA replication. ROS regulate CDK2 activity through oxidation of a conserved cysteine residue in close proximity to the T-loop, which prevents binding of the T-loop phosphatase KAP. Together our data reveal how ROS couple mitochondrial metabolism to DNA replication and cell cycle progression, and provide a solution to the longstanding conundrum of how KAP activity towards CDKs can be cell cycle-regulated.

Reactive oxygen species (ROS) at the right concentration promote cell proliferation in cell culture, stem cells, and model organisms. However, the mystery of how ROS signaling is coordinated with cell cycle progression and integrated into the cell cycle control machinery on the molecular level remains unsolved. Here, we report increasing levels of mitochondrial ROS during the cell cycle in human cell lines that target cyclin-dependent kinase 2 (CDK2). Chemical and metabolic interferences with ROS production decrease T-loop phosphorylation on CDK2 and so impede its full activation and thus its efficient DNA replication. ROS regulate CDK2 activity through the oxidation of a conserved cysteine residue near the T-loop, which prevents the binding of the T-loop phosphatase KAP. Together, our data reveal how mitochondrial metabolism is coupled with DNA replication and cell cycle progression via ROS, thereby demonstrating how KAP activity toward CDKs can be cell cycle regulated.

TraCurate is an open-source software tool to curate and manually annotate cell tracking data from time-lapse microscopy. Although many studies of cellular behavior require high-quality, long-term observations across generations of cells, automated cell tracking is often imperfect and typically yields fragmented results that still contain many errors. TraCurate provides the functionality for the curation and correction of cell tracking data with minimal user interaction and expenditure of time and supports the extraction of complete cell tracks and cellular genealogies from experimental data. Source code and binary packages for Linux, macOS and Windows are available at https://tracurate.gitlab.io/, as well as all other complementary tools described herein.

Risk stratification and treatment decisions for leukemia patients are regularly based on clinical markers determined at diagnosis, while measurements on system dynamics are often neglected. However, there is increasing evidence that linking quantitative time-course information to disease outcomes can improve the predictions for patient-specific treatment responses. We designed a synthetic experiment simulating response kinetics of 5,000 patients to compare different computational methods with respect to their ability to accurately predict relapse for chronic and acute myeloid leukemia treatment. Technically, we used clinical reference data to first fit a model and then generate de novo model simulations of individual patients’ time courses for which we can systematically tune data quality (i.e. measurement error) and quantity (i.e. number of measurements). Based hereon, we compared the prediction accuracy of three different computational methods, namely mechanistic models, generalized linear models, and deep neural networks that have been fitted to the reference data. Reaching prediction accuracies between 60 and close to 100%, our results indicate that data quality has a higher impact on prediction accuracy than the specific choice of the particular method. We further show that adapted treatment and measurement schemes can considerably improve the prediction accuracy by 10 to 20%. Our proof-of-principle study highlights how computational methods and optimized data acquisition strategies can improve risk assessment and treatment of leukemia patients.

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., Graphical Abstract, Highlights • Endogenous mRuby-PCNA as an all-in-one cell cycle reporter • Automated image analysis pipeline to segment, track, and classify based on PCNA • Quantitative analysis of cyclin oscillations during a complete cell cycle • Cyclin D1 maintains G1 phase and prevents the transition into quiescence, Zerjatke et al. present endogenously tagged PCNA as an all-in-one cell cycle reporter for living cells to classify all cell cycle phases and quiescence using a single fluorescent channel. Visualizing endogenous cyclin D1 in living cells, they show that cyclin D1 maintains G1 phase and prevents the transition into quiescence.

Risk stratification and treatment decisions for leukaemia patients are regularly based on clinical markers determined at diagnosis, while measurements on system dynamics are often neglected. However, there is increasing evidence that linking quantitative time-course information to disease outcomes can improving the predictions for patient-specific treatment response.We analyzed the potential of different computational methods to accurately predict relapse for chronic and acute myeloid leukaemia, particularly focusing on the influence of data quality and quantity. Technically, we used clinical reference data to generate in-silico patients with varying levels of data quality. Based hereon, we compared the performance of mechanistic models, generalized linear models, and neural networks with respect to their accuracy for relapse prediction. We found that data quality has a higher impact on prediction accuracy than the specific choice of the method. We further show that adapted treatment and measurement schemes can considerably improve prediction accuracy. Our proof-of-principle study highlights how computational methods and optimized data acquisition strategies can improve risk assessment and treatment of leukaemia patients.

The proliferative activity of adult hematopoietic stem cells (HSCs) is controversially discussed. Inducible fluorescent histone 2B fusion protein (H2B-FP) transgenic mice are important tools for tracking the mitotic history of murine HSCs in label dilution experiments. A recent study proposed that the most primitive HSCs divide only four times, to then enter permanent quiescence. We observed that background fluorescence due to leaky H2B-FP expression, occurring in all H2B-FP transgenes independent of label induction, accumulated with age in primitive HSCs with high repopulation potential. We argue that this background had been misinterpreted as retention of induced label and permanent quiescence. We found cell division-independent half-lives of H2B-FPs to be short, which had led to overestimation of HSC divisional activity. Our data do not support HSC mitotic memory and entry into permanent quiescence after few divisions, but show that primitive HSCs of adult mice continue to cycle rarely.nnO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/745729v1_ufig1.gif" ALT="Figure 1">nView larger version (30K):norg.highwire.dtl.DTLVardef@fb47a4org.highwire.dtl.DTLVardef@11c8ea6org.highwire.dtl.DTLVardef@1d94859org.highwire.dtl.DTLVardef@1ceddb_HPS_FORMAT_FIGEXP M_FIG C_FIG

Morcos et al. show that there is no evidence for abrupt entry of aging hematopoietic stem cells into quiescence after four divisions as previously suggested. They examine factors confounding pulse-chase experiments in H2B-fusion protein transgenic mice that can lead to misinterpretations of HSC proliferative behavior., The proliferative activity of aging hematopoietic stem cells (HSCs) is controversially discussed. Inducible fluorescent histone 2B fusion protein (H2B-FP) transgenic mice are important tools for tracking the mitotic history of murine HSCs in label dilution experiments. A recent study proposed that primitive HSCs symmetrically divide only four times to then enter permanent quiescence. We observed that background fluorescence due to leaky H2B-FP expression, occurring in all H2B-FP transgenes independent of label induction, accumulated with age in HSCs with high repopulation potential. We argue that this background had been misinterpreted as stable retention of induced label. We found cell division–independent half-lives of H2B-FPs to be short, which had led to overestimation of HSC divisional activity. Our data do not support abrupt entry of HSCs into permanent quiescence or sudden loss of regeneration potential after four divisions, but show that primitive HSCs of adult mice continue to cycle rarely., Graphical Abstract

Statistical and mathematical modeling are crucial to describe, interpret, compare, and predict the behavior of complex biological systems including the organization of hematopoietic stem and progenitor cells in the bone marrow environment. The current prominence of high-resolution and live-cell imaging data provides an unprecedented opportunity to study the spatiotemporal dynamics of these cells within their stem cell niche and learn more about aberrant, but also unperturbed, normal hematopoiesis. However, this requires careful quantitative statistical analysis of the spatial and temporal behavior of cells and the interaction with their microenvironment. Moreover, such quantification is a prerequisite for the construction of hypothesis-driven mathematical models that can provide mechanistic explanations by generating spatiotemporal dynamics that can be directly compared to experimental observations. Here, we provide a brief overview of statistical methods in analyzing spatial distribution of cells, cell motility, cell shapes, and cellular genealogies. We also describe cell-based modeling formalisms that allow researchers to simulate emergent behavior in a multicellular system based on a set of hypothesized mechanisms. Together, these methods provide a quantitative workflow for the analytic and synthetic study of the spatiotemporal behavior of hematopoietic stem and progenitor cells.

Statistical and mathematical modeling are crucial to describe, interpret, compare, and predict the behavior of complex biological systems including the organization of hematopoietic stem and progenitor cells in the bone marrow environment. The current prominence of high-resolution and live-cell imaging data provides an unprecedented opportunity to study the spatiotemporal dynamics of these cells within their stem cell niche and learn more about aberrant, but also unperturbed, normal hematopoiesis. However, this requires careful quantitative statistical analysis of the spatial and temporal behavior of cells and the interaction with their microenvironment. Moreover, such quantification is a prerequisite for the construction of hypothesis-driven mathematical models that can provide mechanistic explanations by generating spatiotemporal dynamics that can be directly compared to experimental observations. Here, we provide a brief overview of statistical methods in analyzing spatial distribution of cells, cell motility, cell shapes, and cellular genealogies. We also describe cell-based modeling formalisms that allow researchers to simulate emergent behavior in a multicellular system based on a set of hypothesized mechanisms. Together, these methods provide a quantitative workflow for the analytic and synthetic study of the spatiotemporal behavior of hematopoietic stem and progenitor cells.

Pluripotent embryonic stem cells (ESCs) have the potential to differentiate into cells of all three germ layers. This unique property has been extensively studied on the intracellular, transcriptional level. However, ESCs typically form clusters of cells with distinct size and shape, and establish spatial structures that are vital for the maintenance of pluripotency. Even though it is recognized that the cells' arrangement and local interactions play a role in fate decision processes, the relations between transcriptional and spatial patterns have not yet been studied. We present a systems biology approach which combines live-cell imaging, quantitative image analysis, and multiscale, mathematical modeling of ESC growth. In particular, we develop quantitative measures of the morphology and of the spatial clustering of ESCs with different expression levels and apply them to images of both in vitro and in silico cultures. Using the same measures, we are able to compare model scenarios with different assumptions on cell–cell adhesions and intercellular feedback mechanisms directly with experimental data. Applying our methodology to microscopy images of cultured ESCs, we demonstrate that the emerging colonies are highly variable regarding both morphological and spatial fluorescence patterns. Moreover, we can show that most ESC colonies contain only one cluster of cells with high self-renewing capacity. These cells are preferentially located in the interior of a colony structure. The integrated approach combining image analysis with mathematical modeling allows us to reveal potential transcription factor related cellular and intercellular mechanisms behind the emergence of observed patterns that cannot be derived from images directly. © 2015 International Society for Advancement of Cytometry

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 (

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

A detailed understanding of the mechanisms maintaining the hierarchical balance of cell types in hematopoiesis will be important for the therapeutic manipulation of normal and leukemic cells. Mathematical modeling is expected to make an important contribution to this area, but the iterative development of increasingly accurate models will rely on repeated validation using experimental data of sufficient resolution to distinguish between alternative model scenarios. The multipotent hematopoietic progenitor FDCP-Mix cells maintain a hierarchy from self-renewal to post-mitotic differentiation in vitro and are accessible to detailed analysis. Here, we report the development of a combined mathematical modeling and experimental approach to study the principles underlying heterogeneity in FDCP-Mix cultures. We adapt a single-cell based model of hematopoiesis to the conditions of cell culture and describe an association between proliferative history and phenotype of FDCP-Mix cells. While data derived from population studies are incapable of distinguishing between three mechanistically different model scenarios, statistical analysis of single cell tracking data provides a resolution sufficient to select one of them. This scenario favors differences between granulocytic and monocytic lineage with respect to their proliferative behavior and death rates as a mechanistic explanation for the observed heterogeneity. Our results demonstrate the power of a combined experimental/modeling approach in which single cell fate analysis is the key to revealing regulatory principles at the cellular level.

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.

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.

Pluripotent mouse embryonic stem cells (mESCs) show heterogeneous expression levels of transcription factors (TFs) involved in pluripotency regulation, among them Nanog and Rex1. The expression of both TFs can change dynamically between states of high and low activity, correlating with the cells' capacity for self-renewal. Stochastic fluctuations as well as sustained oscillations in gene expression are possible mechanisms to explain this behaviour, but the lack of suitable data hampered their clear distinction. Here, we present a systems biology approach in which novel experimental data on TF heterogeneity is complemented by an agent-based model of mESC self-renewal. Because the model accounts for intracellular interactions, cell divisions and heredity structures, it allows for evaluating the consistency of the proposed mechanisms with data on population growth and on TF dynamics after cell sorting. Our model-based analysis revealed that a bistable, noise-driven network model fulfils the minimal requirements to consistently explain Nanog and Rex1 expression dynamics in heterogeneous and sorted mESC populations. Moreover, we studied the impact of TF-related proliferation capacities on the frequency of state transitions and demonstrate that cellular genealogies can provide insights into the heredity structures of mESCs.