TraCurate: Efficiently curating cell tracks

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

How to predict relapse in leukemia using time series data: A comparative in silico study

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

Study of pallial neurogenesis in shark embryos and the evolutionary origin of the subventricular zone

The dorsal part of the developing telencephalon is one of the brain areas that has suffered most drastic changes throughout vertebrate evolution. Its evolutionary increase in complexity was thought to be partly achieved by the appearance of a new neurogenic niche in the embryonic subventricular zone (SVZ). Here, a new kind of amplifying progenitors (basal progenitors) expressing Tbr2, undergo a second round of divisions, which is believed to have contributed to the expansion of the neocortex. Accordingly, the existence of a pallial SVZ has been classically considered exclusive of mammals. However, the lack of studies in ancient vertebrates precludes any clear conclusion about the evolutionary origin of the SVZ and the neurogenic mechanisms that rule pallial development. In this work, we explore pallial neurogenesis in a basal vertebrate, the shark Scyliorhinus canicula, through the study of the expression patterns of several neurogenic markers. We found that apical progenitors and radial migration are present in sharks, and therefore, their presence must be highly conserved throughout evolution. Surprisingly, we detected a subventricular band of ScTbr2-expressing cells, some of which also expressed mitotic markers, indicating that the existence of basal progenitors should be considered an ancestral condition rather than a novelty of mammals or amniotes. Finally, we report that the transcriptional program for the specification of glutamatergic pallial cells (Pax6, Tbr2, NeuroD, Tbr1) is also present in sharks. However, the segregation of these markers into different cell types is not clear yet, which may be linked to the lack of layering in anamniotesThis work was supported by the Spanish Ministerio de Economía y Competitividad-FEDER (BFU2014-5863-1P)S

Mast Cells Occupy Stable Clonal Territories in Adult Steady-State Skin.

Mast cells (MCs) are tissue-resident hematopoietic cells intensely studied for their role as effectors in allergic immune responses. Yolk sac-derived embryonic MCs first populate tissues and are later replaced by definitive MCs. We show that definitive MC progenitors expand locally in skin and form clonal colonies that cover stable territories. In MC-deficient skin, colonies grow by proliferation of MCs at the border of the clonal territory. Clonal growth ceases at common borders of neighboring colonies. In steady state, colony self-renewal is independent of bone marrow contribution, and the clonal architecture remains fixed if not disturbed by skin inflammation. Inflammatory cues increase MC density setpoint, stimulating the influx of new progenitors from the bone marrow as well as proliferation of skin-resident cells. The expanding new arrivals disrespect territories of preexisting MC clones. We conclude that during a limited window early in development, definitive MC precursors efficiently enter the skin, expand, and self-maintain, occupying stable territories. In adulthood, circulating progenitors, excluded from steady-state skin, are recruited only into inflamed skin where they clonally expand alongside proliferating skin-resident MCs, disorganizing the original architecture of clonal territories

TraCurate: Efficiently curating cell tracks

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

A ROS-dependent mechanism promotes CDK2 phosphorylation to drive progression through S phase

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

Elucidating functional heterogeneity in hematopoietic progenitor cells: A combined experimental and modeling approach

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

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

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

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

How to predict relapse in leukaemia using time series data: A comparative in silico study

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