Stem Cell Fate Analysis Revisited: Interpretation of Individual Clone Dynamics in the Light of a New Paradigm of Stem Cell Organization

Many experimental findings on heterogeneity, flexibility, and plasticity of tissue stem cells are currently challenging stem cell concepts that assume a cell intrinsically predefined, unidirectional differentiation program. In contrast to these classical concepts, nonhierarchical self-organizing systems provide an elegant and comprehensive alternative to explain the experimental data. Here we present the application of such a self-organizing concept to quantitatively describe the hematopoietic stem cell system. Focusing on the analysis of individual-stem-cell fates and clonal dynamics, we particularly discuss implications of the theoretical results on the interpretation of experimental findings. We demonstrate that it is possible to understand hematopoietic stem cell organization without assumptions on unidirectional developmental hierarchies, preprogrammed asymmetric division events or other assumptions implying the existence of a predetermined stem cell entity. The proposed perspective, therefore, changes the general paradigm of thinking about stem cells

New experimental and theoretical investigations of hematopoietic stem cells and chronic myeloid leukemia

We report on a focused workshop of The Leukemia and Lymphoma Society that was held at Goldsmiths, University of London in 2008. During this workshop we discussed new clinical and experimental data in chronic myeloid leukemia (CML) research, particularly focusing on the validity (or otherwise) of corresponding mathematical models and simulations. We were specifically interested in whether the models could shed light on any of the fundamental mechanisms underlying this disease. Moreover, we were aiming to form a new community of clinicians and modelers looking at this disease and to define a common language and theoretical framework within which collaboration could flourish. The workshop showed the role that models can play, not just in trying to fit to existing data or predicting what individual mechanisms or system behaviors might occur, but also in challenging the orthodoxy of the concept of a stem cell and concepts such as “differentiation” and “determination”. For years the prevailing view of a stem cell has been an entity (object) with a fixed set of behaviors and with a pre-determined fate. New perspectives in modeling, coupled with the new data that are being accumulated in the genesis of CML and its treatment, questions these assumptions. We propose how we can reach a consensus about a functional view of stem cells in a more continuous and flexible way and how, within this context, we can investigate the significance of modeling results and how they might impact on our interpretation of experimental observations and the development of new clinical strategies. This paper reports on the workshop and the state-of-the-art models and data from experimental and clinical trials, and sets out a roadmap for more interdisciplinary collaboration between modelers, wet-lab experimentalists, and clinicians interested in CML. It is our strong belief that a more integrated and coherent interdisciplinary approach will further advance the treatment of CML in future years

Domain specific cues improve robustness of deep learning based segmentation of ct volumes

Machine Learning has considerably improved medical image analysis in the past years. Although data-driven approaches are intrinsically adaptive and thus, generic, they often do not perform the same way on data from different imaging modalities. In particular Computed tomography (CT) data poses many challenges to medical image segmentation based on convolutional neural networks (CNNs), mostly due to the broad dynamic range of intensities and the varying number of recorded slices of CT volumes. In this paper, we address these issues with a framework that combines domain-specific data preprocessing and augmentation with state-of-the-art CNN architectures. The focus is not limited to optimise the score, but also to stabilise the prediction performance since this is a mandatory requirement for use in automated and semi-automated workflows in the clinical environment. The framework is validated with an architecture comparison to show CNN architecture-independent effects of our framework functionality. We compare a modified U-Net and a modified Mixed-Scale Dense Network (MS-D Net) to compare dilated convolutions for parallel multi-scale processing to the U-Net approach based on traditional scaling operations. Finally, we propose an ensemble model combining the strengths of different individual methods. The framework performs well on a range of tasks such as liver and kidney segmentation, without significant differences in prediction performance on strongly differing volume sizes and varying slice thickness. Thus our framework is an essential step towards performing robust segmentation of unknown real-world samples

Factor graph analysis of live cell-imaging data reveals mechanisms of cell fate decisions

Motivation: Cell fate decisions have a strong stochastic component. The identification of the underlying mechanisms therefore requires a rigorous statistical analysis of large ensembles of single cells that were tracked and phenotyped over time. Results: We introduce a probabilistic framework for testing elementary hypotheses on dynamic cell behavior using time-lapse cell-imaging data. Factor graphs, probabilistic graphical models, are used to properly account for cell lineage and cell phenotype information. Our model is applied to time-lapse movies of murine granulocyte-macrophage progenitor (GMP) cells. It decides between competing hypotheses on the mechanisms of their differentiation. Our results theoretically substantiate previous experimental observations that lineage instruction, not selection is the cause for the differentiation of GMP cells into mature monocytes or neutrophil granulocytes. Availability and implementation: The Matlab source code is available at http://treschgroup.de/Genealogies.html Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics onlin

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 accounts 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. Index Terms — cell tracking, lineage trees, temporal networks, information theory, stem cells 1

Quantitative prediction of long-term molecular response in TKI-treated CML – Lessons from an imatinib versus dasatinib comparison

Longitudinal monitoring of BCR-ABL transcript levels in peripheral blood of CML patients treated with tyrosine kinase inhibitors (TKI) revealed a typical biphasic response. Although second generation TKIs like dasatinib proved more efficient in achieving molecular remission compared to first generation TKI imatinib, it is unclear how individual responses differ between the drugs and whether mechanisms of drug action can be deduced from the dynamic data. We use time courses from the DASISION trial to address statistical differences in the dynamic response between first line imatinib vs. dasatinib treatment cohorts and we analyze differences between the cohorts by fitting an established mathematical model of functional CML treatment to individual time courses. On average, dasatinib-treated patients show a steeper initial response, while the long-term response only marginally differed between the treatments. Supplementing each patient time course with a corresponding confidence region, we illustrate the consequences of the uncertainty estimate for the underlying mechanisms of CML remission. Our model suggests that the observed BCR-ABL dynamics may result from different, underlying stem cell dynamics. These results illustrate that the perception and description of CML treatment response as a dynamic process on the level of individual patients is a prerequisite for reliable patient-specific response predictions and treatment optimizations