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Noller K, Cahan P. Cell cycle expression heterogeneity predicts degree of differentiation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.19.604184. [PMID: 39091773 PMCID: PMC11291076 DOI: 10.1101/2024.07.19.604184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/04/2024]
Abstract
Methods that predict fate potential or degree of differentiation from transcriptomic data have identified rare progenitor populations and uncovered developmental regulatory mechanisms. However, some state-of-the-art methods are too computationally burdensome for emerging large-scale data and all methods make inaccurate predictions in certain biological systems. We developed a method in R (stemFinder) that predicts single cell differentiation time based on heterogeneity in cell cycle gene expression. Our method is computationally tractable and is as good as or superior to competitors. As part of our benchmarking, we implemented four different performance metrics to assist potential users in selecting the tool that is most apt for their application. Finally, we explore the relationship between differentiation time and cell fate potential by analyzing a lineage tracing dataset with clonally labelled hematopoietic cells, revealing that metrics of differentiation time are correlated with the number of downstream lineages.
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Affiliation(s)
- Kathleen Noller
- Institute for Cell Engineering, Johns Hopkins University, Baltimore MD USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore MD USA
| | - Patrick Cahan
- Institute for Cell Engineering, Johns Hopkins University, Baltimore MD USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore MD USA
- Department of Molecular Biology and Genetics, Johns Hopkins University, Baltimore MD USA
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2
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Liu Y, Zhang SY, Kleijn IT, Stumpf MPH. Approximate Bayesian computation for inferring Waddington landscapes from single-cell data. ROYAL SOCIETY OPEN SCIENCE 2024; 11:231697. [PMID: 39076359 PMCID: PMC11285904 DOI: 10.1098/rsos.231697] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/26/2023] [Accepted: 05/01/2024] [Indexed: 07/31/2024]
Abstract
Single-cell technologies allow us to gain insights into cellular processes at unprecedented resolution. In stem cell and developmental biology snapshot data allow us to characterize how the transcriptional states of cells change between successive cell types. Here, we show how approximate Bayesian computation (ABC) can be employed to calibrate mathematical models against single-cell data. In our simulation study, we demonstrate the pivotal role of the adequate choice of distance measures appropriate for single-cell data. We show that for good distance measures, notably optimal transport with the Sinkhorn divergence, we can infer parameters for mathematical models from simulated single-cell data. We show that the ABC posteriors can be used (i) to characterize parameter sensitivity and identify dependencies between different parameters and (ii) to construct representations of the Waddington or epigenetic landscape, which forms a popular and interpretable representation of the developmental dynamics. In summary, these results pave the way for fitting mechanistic models of stem cell differentiation to single-cell data.
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Affiliation(s)
- Yujing Liu
- School of Mathematics and Statistics, University of Melbourne, Melbourne, Australia
| | - Stephen Y. Zhang
- School of Mathematics and Statistics, University of Melbourne, Melbourne, Australia
| | | | - Michael P. H. Stumpf
- School of Mathematics and Statistics, University of Melbourne, Melbourne, Australia
- School of BioScience, University of Melbourne, Melbourne, Australia
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3
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Camacho-Aguilar E, Yoon ST, Ortiz-Salazar MA, Du S, Guerra MC, Warmflash A. Combinatorial interpretation of BMP and WNT controls the decision between primitive streak and extraembryonic fates. Cell Syst 2024; 15:445-461.e4. [PMID: 38692274 PMCID: PMC11231731 DOI: 10.1016/j.cels.2024.04.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 10/10/2023] [Accepted: 04/10/2024] [Indexed: 05/03/2024]
Abstract
BMP signaling is essential for mammalian gastrulation, as it initiates a cascade of signals that control self-organized patterning. As development is highly dynamic, it is crucial to understand how time-dependent combinatorial signaling affects cellular differentiation. Here, we show that BMP signaling duration is a crucial control parameter that determines cell fates upon the exit from pluripotency through its interplay with the induced secondary signal WNT. BMP signaling directly converts cells from pluripotent to extraembryonic fates while simultaneously upregulating Wnt signaling, which promotes primitive streak and mesodermal specification. Using live-cell imaging of signaling and cell fate reporters together with a simple mathematical model, we show that this circuit produces a temporal morphogen effect where, once BMP signal duration is above a threshold for differentiation, intermediate and long pulses of BMP signaling produce specification of mesoderm and extraembryonic fates, respectively. Our results provide a systems-level picture of how these signaling pathways control the landscape of early human development.
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Affiliation(s)
| | - Sumin T Yoon
- Department of Biosciences, Rice University, Houston, TX 77005, USA
| | | | - Siqi Du
- Department of Biosciences, Rice University, Houston, TX 77005, USA
| | - M Cecilia Guerra
- Department of Biosciences, Rice University, Houston, TX 77005, USA
| | - Aryeh Warmflash
- Department of Biosciences, Rice University, Houston, TX 77005, USA; Department of Bioengineering, Rice University, Houston, TX 77005, USA.
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4
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Andersson E, Sjö M, Kaji K, Olariu V. CELLoGeNe - An energy landscape framework for logical networks controlling cell decisions. iScience 2022; 25:104743. [PMID: 35942105 PMCID: PMC9356104 DOI: 10.1016/j.isci.2022.104743] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 06/01/2022] [Accepted: 07/05/2022] [Indexed: 11/29/2022] Open
Abstract
Experimental and computational efforts are constantly made to elucidate mechanisms controlling cell fate decisions during development and reprogramming. One powerful computational method is to consider cell commitment and reprogramming as movements in an energy landscape. Here, we develop Computation of Energy Landscapes of Logical Gene Networks (CELLoGeNe), which maps Boolean implementation of gene regulatory networks (GRNs) into energy landscapes. CELLoGeNe removes inadvertent symmetries in the energy landscapes normally arising from standard Boolean operators. Furthermore, CELLoGeNe provides tools to visualize and stochastically analyze the shapes of multi-dimensional energy landscapes corresponding to epigenetic landscapes for development and reprogramming. We demonstrate CELLoGeNe on two GRNs governing different aspects of induced pluripotent stem cells, identifying experimentally validated attractors and revealing potential reprogramming roadblocks. CELLoGeNe is a general framework that can be applied to various biological systems offering a broad picture of intracellular dynamics otherwise inaccessible with existing methods. CELLoGeNe – Computation of Energy Landscapes of Logical Gene Networks Cell states as landscape attractors Maintenance and acquisition of cell pluripotency applications Single cell stochastic landscape navigation and visualization tool
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5
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Tyson JJ, Csikasz-Nagy A, Gonze D, Kim JK, Santos S, Wolf J. Time-keeping and decision-making in living cells: Part II. Interface Focus 2022. [PMCID: PMC9184961 DOI: 10.1098/rsfs.2022.0024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Affiliation(s)
- John J. Tyson
- Department of Biological Sciences, Virginia Polytechnic Institute & State University, Blacksburg, VA 24061, USA
| | - Attila Csikasz-Nagy
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, 1088 Budapest, Hungary
| | - Didier Gonze
- Unit of Theoretical Chronobiology, Université Libre de Bruxelles, 1050 Brussels, Belgium
| | - Jae Kyoung Kim
- Department of Mathematical Sciences, KAIST, Daejeon 34141, South Korea
- Biomedical Mathematics Group, Institute for Basic Science, Daejeon 34126, South Korea
| | - Silvia Santos
- Quantitative Stem Cell Biology Laboratory, The Francis Crick Institute, London NW1 1AT, UK
| | - Jana Wolf
- Mathematical Modeling of Cellular Processes, Max Delbrück Center for Molecular Medicine, 13125 Berlin, Germany
- Department of Mathematics and Computer Science, Free University, 14195 Berlin, Germany
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6
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Li C, Dong J, Li J, Zhu W, Wang P, Yao Y, Wei C, Han H. Deciphering landscape dynamics of cell fate decision via a Lyapunov method. Comput Biol Chem 2022; 98:107689. [DOI: 10.1016/j.compbiolchem.2022.107689] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 04/15/2022] [Accepted: 04/25/2022] [Indexed: 11/29/2022]
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7
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Abstract
Network inference is a notoriously challenging problem. Inferred networks are associated with high uncertainty and likely riddled with false positive and false negative interactions. Especially for biological networks we do not have good ways of judging the performance of inference methods against real networks, and instead we often rely solely on the performance against simulated data. Gaining confidence in networks inferred from real data nevertheless thus requires establishing reliable validation methods. Here, we argue that the expectation of mixing patterns in biological networks such as gene regulatory networks offers a reasonable starting point: interactions are more likely to occur between nodes with similar biological functions. We can quantify this behaviour using the assortativity coefficient, and here we show that the resulting heuristic, functional assortativity, offers a reliable and informative route for comparing different inference algorithms.
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8
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Hettich J, Gebhardt JCM. Periodic synchronization of isolated network elements facilitates simulating and inferring gene regulatory networks including stochastic molecular kinetics. BMC Bioinformatics 2022; 23:13. [PMID: 34986805 PMCID: PMC8729106 DOI: 10.1186/s12859-021-04541-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Accepted: 12/16/2021] [Indexed: 11/10/2022] Open
Abstract
Background The temporal progression of many fundamental processes in cells and organisms, including homeostasis, differentiation and development, are governed by gene regulatory networks (GRNs). GRNs balance fluctuations in the output of their genes, which trace back to the stochasticity of molecular interactions. Although highly desirable to understand life processes, predicting the temporal progression of gene products within a GRN is challenging when considering stochastic events such as transcription factor–DNA interactions or protein production and degradation.
Results We report a method to simulate and infer GRNs including genes and biochemical reactions at molecular detail. In our approach, we consider each network element to be isolated from other elements during small time intervals, after which we synchronize molecule numbers across all network elements. Thereby, the temporal behaviour of network elements is decoupled and can be treated by local stochastic or deterministic solutions. We demonstrate the working principle of this modular approach with a repressive gene cascade comprising four genes. By considering a deterministic time evolution within each time interval for all elements, our method approaches the solution of the system of deterministic differential equations associated with the GRN. By allowing genes to stochastically switch between on and off states or by considering stochastic production of gene outputs, we are able to include increasing levels of stochastic detail and approximate the solution of a Gillespie simulation. Thereby, CaiNet is able to reproduce noise-induced bi-stability and oscillations in dynamically complex GRNs. Notably, our modular approach further allows for a simple consideration of deterministic delays. We further infer relevant regulatory connections and steady-state parameters of a GRN of up to ten genes from steady-state measurements by identifying each gene of the network with a single perceptron in an artificial neuronal network and using a gradient decent method originally designed to train recurrent neural networks. To facilitate setting up GRNs and using our simulation and inference method, we provide a fast computer-aided interactive network simulation environment, CaiNet. Conclusion We developed a method to simulate GRNs at molecular detail and to infer the topology and steady-state parameters of GRNs. Our method and associated user-friendly framework CaiNet should prove helpful to analyze or predict the temporal progression of reaction networks or GRNs in cellular and organismic biology. CaiNet is freely available at https://gitlab.com/GebhardtLab/CaiNet. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-021-04541-6.
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Affiliation(s)
- Johannes Hettich
- Institute of Biophysics, Ulm University, Albert-Einstein-Allee 11, 89081, Ulm, Germany
| | - J Christof M Gebhardt
- Institute of Biophysics, Ulm University, Albert-Einstein-Allee 11, 89081, Ulm, Germany.
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9
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Samanta T, Kar S. Unraveling the origin of glucose mediated disparate proliferation dynamics of cancer stem cells. J Theor Biol 2021; 526:110774. [PMID: 34044006 DOI: 10.1016/j.jtbi.2021.110774] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Revised: 05/19/2021] [Accepted: 05/20/2021] [Indexed: 02/07/2023]
Abstract
Cancer stem cells (CSCs) often switch on their self-renewal programming aggressively to cause a relapse of cancer. Intriguingly, glucose triggers the proliferation propensities in CSCs by controlling the expression of the key transcription factor-like Nanog. However, the factors that critically govern this glucose-stimulated proliferation dynamics of CSCs remain elusive. Herein, by proposing a mathematical model of glucose-mediated Nanog regulation, we showed that the differential proliferation behavior of CSCs and cell-type similar to CSCs can be explained by considering the experimentally observed varied expression levels of key positive (STAT3) and negative (p53) regulators of Nanog. Our model reconciles various experimental observations and predicts ways to fine-tune the proliferation dynamics of these cell types in a context-dependent manner. In future, these modeling insights will be useful in developing improved therapeutic strategies to get rid of harmful CSCs.
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Affiliation(s)
- Tagari Samanta
- Department of Chemistry, IIT Bombay, Powai, Mumbai 400076, India.
| | - Sandip Kar
- Department of Chemistry, IIT Bombay, Powai, Mumbai 400076, India.
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10
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Wu Y, Zhang W. The Role of E3s in Regulating Pluripotency of Embryonic Stem Cells and Induced Pluripotent Stem Cells. Int J Mol Sci 2021; 22:1168. [PMID: 33503896 PMCID: PMC7865285 DOI: 10.3390/ijms22031168] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2020] [Revised: 01/15/2021] [Accepted: 01/20/2021] [Indexed: 12/14/2022] Open
Abstract
Pluripotent embryonic stem cells (ESCs) are derived from early embryos and can differentiate into any type of cells in living organisms. Induced pluripotent stem cells (iPSCs) resemble ESCs, both of which serve as excellent sources to study early embryonic development and realize cell replacement therapies for age-related degenerative diseases and other cell dysfunction-related illnesses. To achieve these valuable applications, comprehensively understanding of the mechanisms underlying pluripotency maintenance and acquisition is critical. Ubiquitination modifies proteins with Ubiquitin (Ub) at the post-translational level to monitor protein stability and activity. It is extensively involved in pluripotency-specific regulatory networks in ESCs and iPSCs. Ubiquitination is achieved by sequential actions of the Ub-activating enzyme E1, Ub-conjugating enzyme E2, and Ub ligase E3. Compared with E1s and E2s, E3s are most abundant, responsible for substrate selectivity and functional diversity. In this review, we focus on E3 ligases to discuss recent progresses in understanding how they regulate pluripotency and somatic cell reprogramming through ubiquitinating core ESC regulators.
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Affiliation(s)
| | - Weiwei Zhang
- College of Life Sciences, Capital Normal University, Beijing 100048, China;
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11
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Plant AL, Halter M, Stinson J. Probing pluripotency gene regulatory networks with quantitative live cell imaging. Comput Struct Biotechnol J 2020; 18:2733-2743. [PMID: 33101611 PMCID: PMC7560648 DOI: 10.1016/j.csbj.2020.09.025] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 09/14/2020] [Accepted: 09/15/2020] [Indexed: 11/12/2022] Open
Abstract
Live cell imaging uniquely enables the measurement of dynamic events in single cells, but it has not been used often in the study of gene regulatory networks. Network components can be examined in relation to one another by quantitative live cell imaging of fluorescent protein reporter cell lines that simultaneously report on more than one network component. A series of dual-reporter cell lines would allow different combinations of network components to be examined in individual cells. Dynamical information about interacting network components in individual cells is critical to predictive modeling of gene regulatory networks, and such information is not accessible through omics and other end point techniques. Achieving this requires that gene-edited cell lines are appropriately designed and adequately characterized to assure the validity of the biological conclusions derived from the expression of the reporters. In this brief review we discuss what is known about the importance of dynamics to network modeling and review some recent advances in optical microscopy methods and image analysis approaches that are making the use of quantitative live cell imaging for network analysis possible. We also discuss how strategies for genetic engineering of reporter cell lines can influence the biological relevance of the data.
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Affiliation(s)
- Anne L Plant
- Biosystems and Biomaterials Division, National Institute of Standards and Technology, United States
| | - Michael Halter
- Biosystems and Biomaterials Division, National Institute of Standards and Technology, United States
| | - Jeffrey Stinson
- Biosystems and Biomaterials Division, National Institute of Standards and Technology, United States
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12
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Samanta T, Kar S. Fine-tuning Nanog expression heterogeneity in embryonic stem cells by regulating a Nanog transcript-specific microRNA. FEBS Lett 2020; 594:4292-4306. [PMID: 32969052 DOI: 10.1002/1873-3468.13936] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2020] [Revised: 09/02/2020] [Accepted: 09/02/2020] [Indexed: 12/24/2022]
Abstract
In embryonic stem cells (ESCs), the transcription factor Nanog maintains the stemness of ESCs despite exhibiting heterogeneous expression patterns under varied culture conditions. Efficient fine-tuning of Nanog expression heterogeneity could enable ESC proliferation and differentiation along specific lineages to be regulated. Herein, by employing a stochastic modeling approach, we show that Nanog expression heterogeneity can be controlled by modulating the regulatory features of a Nanog transcript-specific microRNA, mir-296. We demonstrate how and why the extent of origin-dependent fluctuations in Nanog expression level can be altered by varying either the binding efficiency of the microRNA-mRNA complex or the expression level of mir-296. Moreover, our model makes experimentally feasible and insightful predictions to maneuver Nanog expression heterogeneity explicitly to achieve cell-type-specific differentiation of ESCs.
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Affiliation(s)
| | - Sandip Kar
- Department of Chemistry, IIT Bombay, Mumbai, India
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13
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Brackston RD, Lakatos E, Stumpf MPH. Transition state characteristics during cell differentiation. PLoS Comput Biol 2018; 14:e1006405. [PMID: 30235202 PMCID: PMC6168170 DOI: 10.1371/journal.pcbi.1006405] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2018] [Revised: 10/02/2018] [Accepted: 07/27/2018] [Indexed: 12/11/2022] Open
Abstract
Models describing the process of stem-cell differentiation are plentiful, and may offer insights into the underlying mechanisms and experimentally observed behaviour. Waddington's epigenetic landscape has been providing a conceptual framework for differentiation processes since its inception. It also allows, however, for detailed mathematical and quantitative analyses, as the landscape can, at least in principle, be related to mathematical models of dynamical systems. Here we focus on a set of dynamical systems features that are intimately linked to cell differentiation, by considering exemplar dynamical models that capture important aspects of stem cell differentiation dynamics. These models allow us to map the paths that cells take through gene expression space as they move from one fate to another, e.g. from a stem-cell to a more specialized cell type. Our analysis highlights the role of the transition state (TS) that separates distinct cell fates, and how the nature of the TS changes as the underlying landscape changes-change that can be induced by e.g. cellular signaling. We demonstrate that models for stem cell differentiation may be interpreted in terms of either a static or transitory landscape. For the static case the TS represents a particular transcriptional profile that all cells approach during differentiation. Alternatively, the TS may refer to the commonly observed period of heterogeneity as cells undergo stochastic transitions.
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Affiliation(s)
- Rowan D. Brackston
- Centre for Integrative Systems Biology and Bioinformatics, Department of Life Sciences, Imperial College London, London, United Kingdom
| | - Eszter Lakatos
- Centre for Integrative Systems Biology and Bioinformatics, Department of Life Sciences, Imperial College London, London, United Kingdom
| | - Michael P. H. Stumpf
- Centre for Integrative Systems Biology and Bioinformatics, Department of Life Sciences, Imperial College London, London, United Kingdom
- School of BioScience and School of Mathematics and Statistics, University of Melbourne, Melbourne, Australia
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14
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Olariu V, Peterson C. Kinetic models of hematopoietic differentiation. WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE 2018; 11:e1424. [PMID: 29660842 PMCID: PMC6191385 DOI: 10.1002/wsbm.1424] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/27/2017] [Revised: 02/13/2018] [Accepted: 03/16/2018] [Indexed: 01/02/2023]
Abstract
As cell and molecular biology is becoming increasingly quantitative, there is an upsurge of interest in mechanistic modeling at different levels of resolution. Such models mostly concern kinetics and include gene and protein interactions as well as cell population dynamics. The final goal of these models is to provide experimental predictions, which is now taking on. However, even without matured predictions, kinetic models serve the purpose of compressing a plurality of experimental results into something that can empower the data interpretation, and importantly, suggesting new experiments by turning "knobs" in silico. Once formulated, kinetic models can be executed in terms of molecular rate equations for concentrations or by stochastic simulations when only a limited number of copies are involved. Developmental processes, in particular those of stem and progenitor cell commitments, are not only topical but also particularly suitable for kinetic modeling due to the finite number of key genes involved in cellular decisions. Stem and progenitor cell commitment processes have been subject to intense experimental studies over the last decade with some emphasis on embryonic and hematopoietic stem cells. Gene and protein interactions governing these processes can be modeled by binary Boolean rules or by continuous-valued models with interactions set by binding strengths. Conceptual insights along with tested predictions have emerged from such kinetic models. Here we review kinetic modeling efforts applied to stem cell developmental systems with focus on hematopoiesis. We highlight the future challenges including multi-scale models integrating cell dynamical and transcriptional models. This article is categorized under: Models of Systems Properties and Processes > Mechanistic Models Developmental Biology > Stem Cell Biology and Regeneration.
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Affiliation(s)
- Victor Olariu
- Department of Computational Biology, Lund University, Lund, Sweden
| | - Carsten Peterson
- Department of Computational Biology, Lund University, Lund, Sweden
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15
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Akberdin IR, Omelyanchuk NA, Fadeev SI, Leskova NE, Oschepkova EA, Kazantsev FV, Matushkin YG, Afonnikov DA, Kolchanov NA. Pluripotency gene network dynamics: System views from parametric analysis. PLoS One 2018; 13:e0194464. [PMID: 29596533 PMCID: PMC5875786 DOI: 10.1371/journal.pone.0194464] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2017] [Accepted: 03/02/2018] [Indexed: 01/06/2023] Open
Abstract
Multiple experimental data demonstrated that the core gene network orchestrating self-renewal and differentiation of mouse embryonic stem cells involves activity of Oct4, Sox2 and Nanog genes by means of a number of positive feedback loops among them. However, recent studies indicated that the architecture of the core gene network should also incorporate negative Nanog autoregulation and might not include positive feedbacks from Nanog to Oct4 and Sox2. Thorough parametric analysis of the mathematical model based on this revisited core regulatory circuit identified that there are substantial changes in model dynamics occurred depending on the strength of Oct4 and Sox2 activation and molecular complexity of Nanog autorepression. The analysis showed the existence of four dynamical domains with different numbers of stable and unstable steady states. We hypothesize that these domains can constitute the checkpoints in a developmental progression from naïve to primed pluripotency and vice versa. During this transition, parametric conditions exist, which generate an oscillatory behavior of the system explaining heterogeneity in expression of pluripotent and differentiation factors in serum ESC cultures. Eventually, simulations showed that addition of positive feedbacks from Nanog to Oct4 and Sox2 leads mainly to increase of the parametric space for the naïve ESC state, in which pluripotency factors are strongly expressed while differentiation ones are repressed.
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Affiliation(s)
- Ilya R. Akberdin
- Federal Research Center Institute of Cytology and Genetics SB RAS, Novosibirsk, Russia
- Novosibirsk State University, Novosibirsk, Russia
- San Diego State University, San Diego, CA, United States of America
| | - Nadezda A. Omelyanchuk
- Federal Research Center Institute of Cytology and Genetics SB RAS, Novosibirsk, Russia
- Novosibirsk State University, Novosibirsk, Russia
| | - Stanislav I. Fadeev
- Novosibirsk State University, Novosibirsk, Russia
- Sobolev Institute of Mathematics SB RAS, Novosibirsk, Russia
| | - Natalya E. Leskova
- Federal Research Center Institute of Cytology and Genetics SB RAS, Novosibirsk, Russia
- Novosibirsk State University, Novosibirsk, Russia
| | - Evgeniya A. Oschepkova
- Federal Research Center Institute of Cytology and Genetics SB RAS, Novosibirsk, Russia
- Novosibirsk State University, Novosibirsk, Russia
| | - Fedor V. Kazantsev
- Federal Research Center Institute of Cytology and Genetics SB RAS, Novosibirsk, Russia
- Novosibirsk State University, Novosibirsk, Russia
| | - Yury G. Matushkin
- Federal Research Center Institute of Cytology and Genetics SB RAS, Novosibirsk, Russia
- Novosibirsk State University, Novosibirsk, Russia
| | - Dmitry A. Afonnikov
- Federal Research Center Institute of Cytology and Genetics SB RAS, Novosibirsk, Russia
- Novosibirsk State University, Novosibirsk, Russia
| | - Nikolay A. Kolchanov
- Federal Research Center Institute of Cytology and Genetics SB RAS, Novosibirsk, Russia
- Novosibirsk State University, Novosibirsk, Russia
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16
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Lin YT, Hufton PG, Lee EJ, Potoyan DA. A stochastic and dynamical view of pluripotency in mouse embryonic stem cells. PLoS Comput Biol 2018; 14:e1006000. [PMID: 29451874 PMCID: PMC5833290 DOI: 10.1371/journal.pcbi.1006000] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2017] [Revised: 03/01/2018] [Accepted: 01/19/2018] [Indexed: 12/26/2022] Open
Abstract
Pluripotent embryonic stem cells are of paramount importance for biomedical sciences because of their innate ability for self-renewal and differentiation into all major cell lines. The fateful decision to exit or remain in the pluripotent state is regulated by complex genetic regulatory networks. The rapid growth of single-cell sequencing data has greatly stimulated applications of statistical and machine learning methods for inferring topologies of pluripotency regulating genetic networks. The inferred network topologies, however, often only encode Boolean information while remaining silent about the roles of dynamics and molecular stochasticity inherent in gene expression. Herein we develop a framework for systematically extending Boolean-level network topologies into higher resolution models of networks which explicitly account for the promoter architectures and gene state switching dynamics. We show the framework to be useful for disentangling the various contributions that gene switching, external signaling, and network topology make to the global heterogeneity and dynamics of transcription factor populations. We find the pluripotent state of the network to be a steady state which is robust to global variations of gene switching rates which we argue are a good proxy for epigenetic states of individual promoters. The temporal dynamics of exiting the pluripotent state, on the other hand, is significantly influenced by the rates of genetic switching which makes cells more responsive to changes in extracellular signals.
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Affiliation(s)
- Yen Ting Lin
- Theoretical Division and Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
- School of Physics and Astronomy, The University of Manchester, Manchester, United Kingdom
| | - Peter G. Hufton
- School of Physics and Astronomy, The University of Manchester, Manchester, United Kingdom
| | - Esther J. Lee
- Department of Bioengineering, Rice University, Houston, Texas, United States of America
| | - Davit A. Potoyan
- Department of Chemistry, Iowa State University, Ames, Iowa, United States of America
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17
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Pfeuty B, Kress C, Pain B. Network Features and Dynamical Landscape of Naive and Primed Pluripotency. Biophys J 2018; 114:237-248. [PMID: 29320691 PMCID: PMC5773751 DOI: 10.1016/j.bpj.2017.10.033] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2017] [Revised: 10/02/2017] [Accepted: 10/16/2017] [Indexed: 12/31/2022] Open
Abstract
Although the broad and unique differentiation potential of pluripotent stem cells relies on a complex transcriptional network centered around Oct4, Sox2, and Nanog, two well-distinct pluripotent states, called "naive" and "primed", have been described in vitro and markedly differ in their developmental potential, their expression profiles, their signaling requirements, and their reciprocal conversion. Aiming to determine the key features that segregate and coordinate these two states, data-driven optimization of network models is performed to identify relevant parameter regimes and reduce network complexity to its core structure. Decision dynamics of optimized networks is characterized by signal-dependent multistability and strongly asymmetric transitions among naive, primed, and nonpluripotent states. Further model perturbation and reduction approaches reveal that such a dynamical landscape of pluripotency involves a functional partitioning of the regulatory network. Specifically, two overlapping positive feedback modules, Klf4/Esrrb/Nanog and Oct4/Nanog, stabilize the naive or the primed state, respectively. In turn, their incoherent feedforward and negative feedback coupling mediated by the Erk/Gsk3 module is critical for robust segregation and sequential progression between naive and primed states before irreversible exit from pluripotency.
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Affiliation(s)
- Benjamin Pfeuty
- Laboratoire de Physique des Lasers, Atomes et Molécules, Université de Lille, CNRS, Villeneuve d'Ascq, France.
| | - Clémence Kress
- Stem Cell and Brain Research Institute, Univ. Lyon, Université Claude Bernard Lyon 1, INSERM, INRA, U1208, USC1361, Bron, France
| | - Bertrand Pain
- Stem Cell and Brain Research Institute, Univ. Lyon, Université Claude Bernard Lyon 1, INSERM, INRA, U1208, USC1361, Bron, France
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18
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Jolly MK, Tripathi SC, Jia D, Mooney SM, Celiktas M, Hanash SM, Mani SA, Pienta KJ, Ben-Jacob E, Levine H. Stability of the hybrid epithelial/mesenchymal phenotype. Oncotarget 2017; 7:27067-84. [PMID: 27008704 PMCID: PMC5053633 DOI: 10.18632/oncotarget.8166] [Citation(s) in RCA: 287] [Impact Index Per Article: 41.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2016] [Accepted: 03/07/2016] [Indexed: 12/16/2022] Open
Abstract
Epithelial-to-Mesenchymal Transition (EMT) and its reverse – Mesenchymal to Epithelial Transition (MET) – are hallmarks of cellular plasticity during embryonic development and cancer metastasis. During EMT, epithelial cells lose cell-cell adhesion and gain migratory and invasive traits either partially or completely, leading to a hybrid epithelial/mesenchymal (hybrid E/M) or a mesenchymal phenotype respectively. Mesenchymal cells move individually, but hybrid E/M cells migrate collectively as observed during gastrulation, wound healing, and the formation of tumor clusters detected as Circulating Tumor Cells (CTCs). Typically, the hybrid E/M phenotype has largely been tacitly assumed to be transient and ‘metastable’. Here, we identify certain ‘phenotypic stability factors’ (PSFs) such as GRHL2 that couple to the core EMT decision-making circuit (miR-200/ZEB) and stabilize hybrid E/M phenotype. Further, we show that H1975 lung cancer cells can display a stable hybrid E/M phenotype and migrate collectively, a behavior that is impaired by knockdown of GRHL2 and another previously identified PSF - OVOL. In addition, our computational model predicts that GRHL2 can also associate hybrid E/M phenotype with high tumor-initiating potential, a prediction strengthened by the observation that the higher levels of these PSFs may be predictive of poor patient outcome. Finally, based on these specific examples, we deduce certain network motifs that can stabilize the hybrid E/M phenotype. Our results suggest that partial EMT, i.e. a hybrid E/M phenotype, need not be ‘metastable’, and strengthen the emerging notion that partial EMT, but not necessarily a complete EMT, is associated with aggressive tumor progression.
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Affiliation(s)
- Mohit Kumar Jolly
- Center for Theoretical Biological Physics, Rice University, Houston, TX, USA.,Department of Bioengineering, Rice University, Houston, TX, USA
| | - Satyendra C Tripathi
- Department of Clinical Cancer Prevention, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Dongya Jia
- Center for Theoretical Biological Physics, Rice University, Houston, TX, USA.,Graduate Program in Systems, Synthetic and Physical Biology, Rice University, Houston, TX, USA
| | - Steven M Mooney
- Department of Biology, University of Waterloo, Waterloo, ON, Canada
| | - Muge Celiktas
- Department of Clinical Cancer Prevention, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Samir M Hanash
- Department of Clinical Cancer Prevention, University of Texas MD Anderson Cancer Center, Houston, TX, USA.,Red and Charline McCombs Institute for The Early Detection and Treatment of Cancer, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Sendurai A Mani
- Department of Translational Molecular Pathology, University of Texas MD Anderson Cancer Center, Houston, TX, USA.,Metastasis Research Center, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Kenneth J Pienta
- The James Brady Urological Institute, and Departments of Urology, Oncology, Pharmacology and Molecular Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Eshel Ben-Jacob
- Center for Theoretical Biological Physics, Rice University, Houston, TX, USA.,Graduate Program in Systems, Synthetic and Physical Biology, Rice University, Houston, TX, USA.,School of Physics and Astronomy and The Sagol School of Neuroscience, Tel-Aviv University, Tel-Aviv, Israel
| | - Herbert Levine
- Center for Theoretical Biological Physics, Rice University, Houston, TX, USA.,Department of Bioengineering, Rice University, Houston, TX, USA.,Department of Physics and Astronomy, Rice University, Houston, TX, USA.,Department of Biosciences, Rice University, Houston, TX, USA
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19
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Godwin S, Ward D, Pedone E, Homer M, Fletcher AG, Marucci L. An extended model for culture-dependent heterogenous gene expression and proliferation dynamics in mouse embryonic stem cells. NPJ Syst Biol Appl 2017; 3:19. [PMID: 28794899 PMCID: PMC5543144 DOI: 10.1038/s41540-017-0020-5] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2016] [Revised: 05/31/2017] [Accepted: 06/20/2017] [Indexed: 12/18/2022] Open
Abstract
During development, pluripotency is a transient state describing a cell's ability to give rise to all three germ layers and germline. Recent studies have shown that, in vitro, pluripotency is highly dynamic: exogenous stimuli provided to cultures of mouse embryonic stem cells, isolated from pre-implantation blastocysts, significantly affect the spectrum of pluripotency. 2i/LIF, a recently defined serum-free medium, forces mouse embryonic stem cells into a ground-state of pluripotency, while serum/LIF cultures promote the co-existence of ground-like and primed-like mouse embryonic stem cell subpopulations. The latter heterogeneity correlates with temporal fluctuations of pluripotency markers, including the master regulator Nanog, in single cells. We propose a mathematical model of Nanog dynamics in both media, accounting for recent experimental data showing the persistence of a small Nanog Low subpopulation in ground-state pluripotency mouse embryonic stem cell cultures. The model integrates into the core pluripotency Gene Regulatory Network both inhibitors present in 2i/LIF (PD and Chiron), and feedback interactions with genes found to be differentially expressed in the two media. Our simulations and bifurcation analysis show that, in ground-state cultures, Nanog dynamics result from the combination of reduced noise in gene expression and the shift of the system towards a monostable, but still excitable, regulation. Experimental data and agent-based modelling simulations indicate that mouse embryonic stem cell proliferation dynamics vary in the two media, and cannot be reproduced by accounting only for Nanog-dependent cell-cycle regulation. We further demonstrate that both PD and Chiron play a key role in regulating heterogeneity in transcription factor expression and, ultimately, mouse embryonic stem cell fate decision.
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Affiliation(s)
- Simon Godwin
- Department of Engineering Mathematics, University of Bristol, Bristol, BS8 1UB UK
| | - Daniel Ward
- Department of Engineering Mathematics, University of Bristol, Bristol, BS8 1UB UK
| | - Elisa Pedone
- Department of Engineering Mathematics, University of Bristol, Bristol, BS8 1UB UK.,School of Cellular & Molecular Medicine, University of Bristol, Bristol, BS8 1TD UK
| | - Martin Homer
- Department of Engineering Mathematics, University of Bristol, Bristol, BS8 1UB UK
| | - Alexander G Fletcher
- School of Mathematics and Statistics, University of Sheffield, Sheffield, S3 7RH UK.,Bateson Centre, University of Sheffield, Sheffield, S10 2TN UK
| | - Lucia Marucci
- Department of Engineering Mathematics, University of Bristol, Bristol, BS8 1UB UK.,School of Cellular & Molecular Medicine, University of Bristol, Bristol, BS8 1TD UK.,BrisSynBio, University of Bristol, Bristol, BS8 1TQ UK
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20
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Nanog Dynamics in Mouse Embryonic Stem Cells: Results from Systems Biology Approaches. Stem Cells Int 2017; 2017:7160419. [PMID: 28684962 PMCID: PMC5480057 DOI: 10.1155/2017/7160419] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2016] [Accepted: 04/20/2017] [Indexed: 12/12/2022] Open
Abstract
Mouse embryonic stem cells (mESCs), derived from the inner cell mass of the blastocyst, are pluripotent stem cells having self-renewal capability and the potential of differentiating into every cell type under the appropriate culture conditions. An increasing number of reports have been published to uncover the molecular mechanisms that orchestrate pluripotency and cell fate specification using combined computational and experimental methodologies. Here, we review recent systems biology approaches to describe the causes and functions of gene expression heterogeneity and complex temporal dynamics of pluripotency markers in mESCs under uniform culture conditions. In particular, we focus on the dynamics of Nanog, a key regulator of the core pluripotency network and of mESC fate. We summarize the strengths and limitations of different experimental and modeling approaches and discuss how various strategies could be used.
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21
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Olariu V, Manesso E, Peterson C. A deterministic method for estimating free energy genetic network landscapes with applications to cell commitment and reprogramming paths. ROYAL SOCIETY OPEN SCIENCE 2017; 4:160765. [PMID: 28680655 PMCID: PMC5493897 DOI: 10.1098/rsos.160765] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/30/2016] [Accepted: 05/12/2017] [Indexed: 06/07/2023]
Abstract
Depicting developmental processes as movements in free energy genetic landscapes is an illustrative tool. However, exploring such landscapes to obtain quantitative or even qualitative predictions is hampered by the lack of free energy functions corresponding to the biochemical Michaelis-Menten or Hill rate equations for the dynamics. Being armed with energy landscapes defined by a network and its interactions would open up the possibility of swiftly identifying cell states and computing optimal paths, including those of cell reprogramming, thereby avoiding exhaustive trial-and-error simulations with rate equations for different parameter sets. It turns out that sigmoidal rate equations do have approximate free energy associations. With this replacement of rate equations, we develop a deterministic method for estimating the free energy surfaces of systems of interacting genes at different noise levels or temperatures. Once such free energy landscape estimates have been established, we adapt a shortest path algorithm to determine optimal routes in the landscapes. We explore the method on three circuits for haematopoiesis and embryonic stem cell development for commitment and reprogramming scenarios and illustrate how the method can be used to determine sequential steps for onsets of external factors, essential for efficient reprogramming.
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Affiliation(s)
- Victor Olariu
- Computational Biology and Biological Physics, Department of Astronomy and Theoretical Physics, Lund University, Lund 22362, Sweden
- Center for Models of Life, Niels Bohr Institute, University of Copenhagen, Copenhagen 2100, Denmark
| | - Erica Manesso
- Computational Biology and Biological Physics, Department of Astronomy and Theoretical Physics, Lund University, Lund 22362, Sweden
| | - Carsten Peterson
- Computational Biology and Biological Physics, Department of Astronomy and Theoretical Physics, Lund University, Lund 22362, Sweden
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22
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Olariu V, Nilsson J, Jönsson H, Peterson C. Different reprogramming propensities in plants and mammals: Are small variations in the core network wirings responsible? PLoS One 2017; 12:e0175251. [PMID: 28384293 PMCID: PMC5383272 DOI: 10.1371/journal.pone.0175251] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2016] [Accepted: 03/22/2017] [Indexed: 01/07/2023] Open
Abstract
Although the plant and animal kingdoms were separated more than 1,6 billion years ago, multicellular development is for both guided by similar transcriptional, epigenetic and posttranscriptional machinery. One may ask to what extent there are similarities and differences in the gene regulation circuits and their dynamics when it comes to important processes like stem cell regulation. The key players in mouse embryonic stem cells governing pluripotency versus differentiation are Oct4, Sox2 and Nanog. Correspondingly, the WUSCHEL and CLAVATA3 genes represent a core in the Shoot Apical Meristem regulation for plants. In addition, both systems have designated genes that turn on differentiation. There is very little molecular homology between mammals and plants for these core regulators. Here, we focus on functional homologies by performing a comparison between the circuitry connecting these players in plants and animals and find striking similarities, suggesting that comparable regulatory logics have been evolved for stem cell regulation in both kingdoms. From in silico simulations we find similar differentiation dynamics. Further when in the differentiated state, the cells are capable of regaining the stem cell state. We find that the propensity for this is higher for plants as compared to mammalians. Our investigation suggests that, despite similarity in core regulatory networks, the dynamics of these can contribute to plant cells being more plastic than mammalian cells, i.e. capable to reorganize from single differentiated cells to whole plants—reprogramming. The presence of an incoherent feed-forward loop in the mammalian core circuitry could be the origin of the different reprogramming behaviour.
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Affiliation(s)
- Victor Olariu
- Computational Biology and Biological Physics, Lund University, Lund, Sweden
- Center for Models of Life, Niels Bohr Institute, University of Copenhagen, Copenhagen, Denmark
| | - Julia Nilsson
- Computational Biology and Biological Physics, Lund University, Lund, Sweden
| | - Henrik Jönsson
- Computational Biology and Biological Physics, Lund University, Lund, Sweden
- Sainsbury Laboratory, University of Cambridge, Cambridge, United Kingdom
- Department of Applied Mathematics and Theoretical Physics (DAMTP), University of Cambridge, Cambridge, United Kingdom
- * E-mail: (HJ); (CP)
| | - Carsten Peterson
- Computational Biology and Biological Physics, Lund University, Lund, Sweden
- * E-mail: (HJ); (CP)
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23
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Cell Fate Specification Based on Tristability in the Inner Cell Mass of Mouse Blastocysts. Biophys J 2017; 110:710-722. [PMID: 26840735 DOI: 10.1016/j.bpj.2015.12.020] [Citation(s) in RCA: 49] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2015] [Revised: 12/08/2015] [Accepted: 12/09/2015] [Indexed: 01/18/2023] Open
Abstract
During development, interactions between transcription factors control the specification of different cell fates. The regulatory networks of genetic interactions often exhibit multiple stable steady states; such multistability provides a common dynamical basis for differentiation. During early murine embryogenesis, cells from the inner cell mass (ICM) can be specified in epiblast (Epi) or primitive endoderm (PrE). Besides the intracellular gene regulatory network, specification is also controlled by intercellular interactions involving Erk signaling through extracellular Fgf4. We previously proposed a model that describes the gene regulatory network and its interaction with Erk signaling in ICM cells. The model displays tristability in a range of Fgf4 concentrations and accounts for the self-organized specification process observed in vivo. Here, we further investigate the origin of tristability in the model and analyze in more detail the specification process by resorting to a simplified two-cell model. We also carry out simulations of a population of 25 cells under various experimental conditions to compare their outcome with that of mutant embryos or of embryos submitted to exogenous treatments that interfere with Fgf signaling. The results are analyzed by means of bifurcation diagrams. Finally, the model predicts that heterogeneities in extracellular Fgf4 concentration play a primary role in the spatial arrangement of the Epi/PrE cells in a salt-and-pepper pattern. If, instead of heterogeneities in extracellular Fgf4 concentration, internal fluctuations in the levels of expression of the transcription factors are considered as a source of randomness, simulations predict the occurrence of unrealistic switches between the Epi and the PrE cell fates, as well as the evolution of some cells toward one of these states without passing through the previous ICM state, in contrast to what is observed in vivo.
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24
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Richard A, Boullu L, Herbach U, Bonnafoux A, Morin V, Vallin E, Guillemin A, Papili Gao N, Gunawan R, Cosette J, Arnaud O, Kupiec JJ, Espinasse T, Gonin-Giraud S, Gandrillon O. Single-Cell-Based Analysis Highlights a Surge in Cell-to-Cell Molecular Variability Preceding Irreversible Commitment in a Differentiation Process. PLoS Biol 2016; 14:e1002585. [PMID: 28027290 PMCID: PMC5191835 DOI: 10.1371/journal.pbio.1002585] [Citation(s) in RCA: 128] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2016] [Accepted: 09/22/2016] [Indexed: 12/31/2022] Open
Abstract
In some recent studies, a view emerged that stochastic dynamics governing the switching of cells from one differentiation state to another could be characterized by a peak in gene expression variability at the point of fate commitment. We have tested this hypothesis at the single-cell level by analyzing primary chicken erythroid progenitors through their differentiation process and measuring the expression of selected genes at six sequential time-points after induction of differentiation. In contrast to population-based expression data, single-cell gene expression data revealed a high cell-to-cell variability, which was masked by averaging. We were able to show that the correlation network was a very dynamical entity and that a subgroup of genes tend to follow the predictions from the dynamical network biomarker (DNB) theory. In addition, we also identified a small group of functionally related genes encoding proteins involved in sterol synthesis that could act as the initial drivers of the differentiation. In order to assess quantitatively the cell-to-cell variability in gene expression and its evolution in time, we used Shannon entropy as a measure of the heterogeneity. Entropy values showed a significant increase in the first 8 h of the differentiation process, reaching a peak between 8 and 24 h, before decreasing to significantly lower values. Moreover, we observed that the previous point of maximum entropy precedes two paramount key points: an irreversible commitment to differentiation between 24 and 48 h followed by a significant increase in cell size variability at 48 h. In conclusion, when analyzed at the single cell level, the differentiation process looks very different from its classical population average view. New observables (like entropy) can be computed, the behavior of which is fully compatible with the idea that differentiation is not a “simple” program that all cells execute identically but results from the dynamical behavior of the underlying molecular network. A single-cell transcriptomics analysis offers a new dynamical view of the differentiation process, involving an increase in between-cell variability prior to commitment. The differentiation process has classically been seen as a stereotyped program leading from one progenitor toward a functional cell. This vision was based upon cell population-based analyses averaged over millions of cells. However, new methods have recently emerged that allow interrogation of the molecular content at the single-cell level, challenging this view with a new model suggesting that cell-to-cell gene expression stochasticity could play a key role in differentiation. We took advantage of a physiologically relevant avian cellular model to analyze the expression level of 92 genes in individual cells collected at several time-points during differentiation. We first observed that the process analyzed at the single-cell level is very different and much less well ordered than the population-based average view. Furthermore, we showed that cell-to-cell variability in gene expression peaks transiently before strongly decreasing. This rise in variability precedes two key events: an irreversible commitment to differentiation, followed by a significant increase in cell size variability. Altogether, our results support the idea that differentiation is not a “simple” series of well-ordered molecular events executed identically by all cells in a population but likely results from dynamical behavior of the underlying molecular network.
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Affiliation(s)
- Angélique Richard
- Univ Lyon, ENS de Lyon, Univ Claude Bernard, CNRS UMR 5239, INSERM U1210, Laboratory of Biology and Modelling of the Cell, 46 allée d’Italie Site Jacques Monod, F-69007, Lyon, France
| | - Loïs Boullu
- Inria Team Dracula, Inria Center Grenoble Rhône-Alpes, France
- Université de Lyon, Université Lyon 1, CNRS UMR 5208, Institut Camille Jordan 43 blvd du 11 novembre 1918, F-69622 Villeurbanne-Cedex, France
- Département de Mathématiques et de statistiques de l’Université de Montréal, Pavillon André-Aisenstadt, 2920, chemin de la Tour, Montréal (Québec) H3T 1J4 Canada
| | - Ulysse Herbach
- Univ Lyon, ENS de Lyon, Univ Claude Bernard, CNRS UMR 5239, INSERM U1210, Laboratory of Biology and Modelling of the Cell, 46 allée d’Italie Site Jacques Monod, F-69007, Lyon, France
- Inria Team Dracula, Inria Center Grenoble Rhône-Alpes, France
- Université de Lyon, Université Lyon 1, CNRS UMR 5208, Institut Camille Jordan 43 blvd du 11 novembre 1918, F-69622 Villeurbanne-Cedex, France
| | - Arnaud Bonnafoux
- Univ Lyon, ENS de Lyon, Univ Claude Bernard, CNRS UMR 5239, INSERM U1210, Laboratory of Biology and Modelling of the Cell, 46 allée d’Italie Site Jacques Monod, F-69007, Lyon, France
- Inria Team Dracula, Inria Center Grenoble Rhône-Alpes, France
- The CoSMo company. 5 passage du Vercors – 69007 LYON – France
| | - Valérie Morin
- Univ Lyon, Univ Claude Bernard, CNRS UMR 5310 - INSERM U1217, Institut NeuroMyoGène, F-69622 Villeurbanne-Cedex, France
| | - Elodie Vallin
- Univ Lyon, ENS de Lyon, Univ Claude Bernard, CNRS UMR 5239, INSERM U1210, Laboratory of Biology and Modelling of the Cell, 46 allée d’Italie Site Jacques Monod, F-69007, Lyon, France
| | - Anissa Guillemin
- Univ Lyon, ENS de Lyon, Univ Claude Bernard, CNRS UMR 5239, INSERM U1210, Laboratory of Biology and Modelling of the Cell, 46 allée d’Italie Site Jacques Monod, F-69007, Lyon, France
| | - Nan Papili Gao
- Institute for Chemical and Bioengineering, ETH Zurich, Zurich, Switzerland
- Swiss Institute of Bioinformatics, Quartier Sorge - Batiment Genopode, 1015 Lausanne Switzerland
| | - Rudiyanto Gunawan
- Institute for Chemical and Bioengineering, ETH Zurich, Zurich, Switzerland
- Swiss Institute of Bioinformatics, Quartier Sorge - Batiment Genopode, 1015 Lausanne Switzerland
| | - Jérémie Cosette
- Genethon – Institut National de la Santé et de la Recherche Médicale – INSERM, Université d’Evry-Val-d’Essone – 1 rue de l’internationale 91000 Evry, France
| | - Ophélie Arnaud
- RIKEN - Center for Life Science Technologies (Division of Genomic Technologies)—CLST (DGT), 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
| | | | - Thibault Espinasse
- Université de Lyon, Université Lyon 1, CNRS UMR 5208, Institut Camille Jordan 43 blvd du 11 novembre 1918, F-69622 Villeurbanne-Cedex, France
| | - Sandrine Gonin-Giraud
- Univ Lyon, ENS de Lyon, Univ Claude Bernard, CNRS UMR 5239, INSERM U1210, Laboratory of Biology and Modelling of the Cell, 46 allée d’Italie Site Jacques Monod, F-69007, Lyon, France
| | - Olivier Gandrillon
- Univ Lyon, ENS de Lyon, Univ Claude Bernard, CNRS UMR 5239, INSERM U1210, Laboratory of Biology and Modelling of the Cell, 46 allée d’Italie Site Jacques Monod, F-69007, Lyon, France
- Inria Team Dracula, Inria Center Grenoble Rhône-Alpes, France
- * E-mail:
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25
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Pir P, Le Novère N. Mathematical Models of Pluripotent Stem Cells: At the Dawn of Predictive Regenerative Medicine. Methods Mol Biol 2016; 1386:331-50. [PMID: 26677190 DOI: 10.1007/978-1-4939-3283-2_15] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Regenerative medicine, ranging from stem cell therapy to organ regeneration, is promising to revolutionize treatments of diseases and aging. These approaches require a perfect understanding of cell reprogramming and differentiation. Predictive modeling of cellular systems has the potential to provide insights about the dynamics of cellular processes, and guide their control. Moreover in many cases, it provides alternative to experimental tests, difficult to perform for practical or ethical reasons. The variety and accuracy of biological processes represented in mathematical models grew in-line with the discovery of underlying molecular mechanisms. High-throughput data generation led to the development of models based on data analysis, as an alternative to more established modeling based on prior mechanistic knowledge. In this chapter, we give an overview of existing mathematical models of pluripotency and cell fate, to illustrate the variety of methods and questions. We conclude that current approaches are yet to overcome a number of limitations: Most of the computational models have so far focused solely on understanding the regulation of pluripotency, and the differentiation of selected cell lineages. In addition, models generally interrogate only a few biological processes. However, a better understanding of the reprogramming process leading to ESCs and iPSCs is required to improve stem-cell therapies. One also needs to understand the links between signaling, metabolism, regulation of gene expression, and the epigenetics machinery.
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Affiliation(s)
- Pınar Pir
- Babraham Institute, Babraham Research Campus, Cambridge, CB22 3AT, UK.
| | - Nicolas Le Novère
- Babraham Institute, Babraham Research Campus, Cambridge, CB22 3AT, UK.
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26
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Olariu V, Lövkvist C, Sneppen K. Nanog, Oct4 and Tet1 interplay in establishing pluripotency. Sci Rep 2016; 6:25438. [PMID: 27146218 PMCID: PMC4857071 DOI: 10.1038/srep25438] [Citation(s) in RCA: 44] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2015] [Accepted: 04/18/2016] [Indexed: 01/12/2023] Open
Abstract
A few central transcription factors inside mouse embryonic stem (ES) cells and induced pluripotent stem (iPS) cells are believed to control the cells’ pluripotency. Characterizations of pluripotent state were put forward on both transcription factor and epigenetic levels. Whereas core players have been identified, it is desirable to map out gene regulatory networks which govern the reprogramming of somatic cells as well as the early developmental decisions. Here we propose a multiple level model where the regulatory network of Oct4, Nanog and Tet1 includes positive feedback loops involving DNA-demethylation around the promoters of Oct4 and Tet1. We put forward a mechanistic understanding of the regulatory dynamics which account for i) Oct4 overexpression is sufficient to induce pluripotency in somatic cell types expressing the other Yamanaka reprogramming factors endogenously; ii) Tet1 can replace Oct4 in reprogramming cocktail; iii) Nanog is not necessary for reprogramming however its over-expression leads to enhanced self-renewal; iv) DNA methylation is the key to the regulation of pluripotency genes; v) Lif withdrawal leads to loss of pluripotency. Overall, our paper proposes a novel framework combining transcription regulation with DNA methylation modifications which, takes into account the multi-layer nature of regulatory mechanisms governing pluripotency acquisition through reprogramming.
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Affiliation(s)
- Victor Olariu
- Centre for Models of Life, Niels Bohr Institute, University of Copenhagen, Copenhagen, Denmark.,Computational Biology and Biological Physics, Department of Astronomy and Theoretical Physics, Lund University, Lund, Sweden
| | - Cecilia Lövkvist
- Centre for Models of Life, Niels Bohr Institute, University of Copenhagen, Copenhagen, Denmark
| | - Kim Sneppen
- Centre for Models of Life, Niels Bohr Institute, University of Copenhagen, Copenhagen, Denmark.,Centre for Models of Life, Niels Bohr Institute, University of Copenhagen, Copenhagen, Denmark
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27
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Rangarajan N, Fox Z, Singh A, Kulkarni P, Rangarajan G. Disorder, oscillatory dynamics and state switching: the role of c-Myc. J Theor Biol 2015; 386:105-14. [PMID: 26408335 DOI: 10.1016/j.jtbi.2015.09.013] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2015] [Revised: 09/12/2015] [Accepted: 09/15/2015] [Indexed: 12/15/2022]
Abstract
In this paper, using the intrinsically disordered oncoprotein Myc as an example, we present a mathematical model to help explain how protein oscillatory dynamics can influence state switching. Earlier studies have demonstrated that, while Myc overexpression can facilitate state switching and transform a normal cell into a cancer phenotype, its downregulation can reverse state-switching. A fundamental aspect of the model is that a Myc threshold determines cell fate in cells expressing p53. We demonstrate that a non-cooperative positive feedback loop coupled with Myc sequestration at multiple binding sites can generate bistable Myc levels. Normal quiescent cells with Myc levels below the threshold can respond to mitogenic signals to activate the cyclin/cdk oscillator for limited cell divisions but the p53/Mdm2 oscillator remains nonfunctional. In response to stress, the p53/Mdm2 oscillator is activated in pulses that are critical to DNA repair. But if stress causes Myc levels to cross the threshold, Myc inactivates the p53/Mdm2 oscillator, abrogates p53 pulses, and pushes the cyclin/cdk oscillator into overdrive sustaining unchecked proliferation seen in cancer. However, if Myc is downregulated, the cyclin/cdk oscillator is inactivated and the p53/Mdm2 oscillator is reset and the cancer phenotype is reversed.
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Affiliation(s)
| | - Zach Fox
- Biomedical Engineering, University of Delaware, Newark, DE, USA
| | - Abhyudai Singh
- Biomedical Engineering, University of Delaware, Newark, DE, USA; Electrical and Computer Engineering, University of Delaware, Newark, DE, USA
| | - Prakash Kulkarni
- Department of Urology and Oncology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.
| | - Govindan Rangarajan
- Department of Mathematics, Indian Institute of Science, Bangalore, India; Centre for Neuroscience, Indian Institute of Science, Bangalore, India.
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Herberg M, Roeder I. Computational modelling of embryonic stem-cell fate control. Development 2015; 142:2250-60. [DOI: 10.1242/dev.116343] [Citation(s) in RCA: 46] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
The maintenance of pluripotency in embryonic stem cells (ESCs), its loss during lineage specification or its re-induction to generate induced pluripotent stem cells are central topics in stem cell biology. To uncover the molecular basis and the design principles of pluripotency control, a multitude of experimental, but also an increasing number of computational, studies have been published. Here, we consider recent reports that apply computational or mathematical modelling approaches to describe the regulatory processes that underlie cell fate decisions in mouse ESCs. We summarise the principles, the strengths and potentials but also the limitations of different computational strategies.
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Affiliation(s)
- Maria Herberg
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden D-01307, Germany
| | - Ingo Roeder
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden D-01307, Germany
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29
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Herberg M, Zerjatke T, de Back W, Glauche I, Roeder I. Image-based quantification and mathematical modeling of spatial heterogeneity in ESC colonies. Cytometry A 2015; 87:481-90. [DOI: 10.1002/cyto.a.22598] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2014] [Revised: 09/11/2014] [Accepted: 11/06/2014] [Indexed: 01/29/2023]
Affiliation(s)
- Maria Herberg
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden; Dresden Germany
| | - Thomas Zerjatke
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden; Dresden Germany
| | - Walter de Back
- Center for Information Services and High Performance Computing, Technische Universität Dresden; Dresden Germany
| | - Ingmar Glauche
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden; Dresden Germany
| | - Ingo Roeder
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden; Dresden Germany
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30
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Bessonnard S, De Mot L, Gonze D, Barriol M, Dennis C, Goldbeter A, Dupont G, Chazaud C. Gata6, Nanog and Erk signaling control cell fate in the inner cell mass through a tristable regulatory network. Development 2014; 141:3637-48. [DOI: 10.1242/dev.109678] [Citation(s) in RCA: 142] [Impact Index Per Article: 14.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
During blastocyst formation, inner cell mass (ICM) cells differentiate into either epiblast (Epi) or primitive endoderm (PrE) cells, labeled by Nanog and Gata6, respectively, and organized in a salt-and-pepper pattern. Previous work in the mouse has shown that, in absence of Nanog, all ICM cells adopt a PrE identity. Moreover, the activation or the blockade of the Fgf/RTK pathway biases cell fate specification towards either PrE or Epi, respectively. We show that, in absence of Gata6, all ICM cells adopt an Epi identity. Furthermore, the analysis of Gata6+/− embryos reveals a dose-sensitive phenotype, with fewer PrE-specified cells. These results and previous findings have enabled the development of a mathematical model for the dynamics of the regulatory network that controls ICM differentiation into Epi or PrE cells. The model describes the temporal dynamics of Erk signaling and of the concentrations of Nanog, Gata6, secreted Fgf4 and Fgf receptor 2. The model is able to recapitulate most of the cell behaviors observed in different experimental conditions and provides a unifying mechanism for the dynamics of these developmental transitions. The mechanism relies on the co-existence between three stable steady states (tristability), which correspond to ICM, Epi and PrE cells, respectively. Altogether, modeling and experimental results uncover novel features of ICM cell fate specification such as the role of the initial induction of a subset of cells into Epi in the initiation of the salt-and-pepper pattern, or the precocious Epi specification in Gata6+/− embryos.
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Affiliation(s)
- Sylvain Bessonnard
- Clermont Université, Université d'Auvergne, Laboratoire GReD, Clermont-Ferrand F-63000, France
- Inserm, UMR1103, Clermont-Ferrand F-63001, France
- CNRS, UMR6293, Clermont-Ferrand F-63001, France
| | - Laurane De Mot
- Unité de Chronobiologie théorique, Faculté des Sciences, Université Libre de Bruxelles (ULB), Campus Plaine, CP 231, Brussels B-1050, Belgium
| | - Didier Gonze
- Unité de Chronobiologie théorique, Faculté des Sciences, Université Libre de Bruxelles (ULB), Campus Plaine, CP 231, Brussels B-1050, Belgium
| | - Manon Barriol
- Clermont Université, Université d'Auvergne, Laboratoire GReD, Clermont-Ferrand F-63000, France
- Inserm, UMR1103, Clermont-Ferrand F-63001, France
- CNRS, UMR6293, Clermont-Ferrand F-63001, France
| | - Cynthia Dennis
- Clermont Université, Université d'Auvergne, Laboratoire GReD, Clermont-Ferrand F-63000, France
- Inserm, UMR1103, Clermont-Ferrand F-63001, France
- CNRS, UMR6293, Clermont-Ferrand F-63001, France
| | - Albert Goldbeter
- Unité de Chronobiologie théorique, Faculté des Sciences, Université Libre de Bruxelles (ULB), Campus Plaine, CP 231, Brussels B-1050, Belgium
- Stellenbosch Institute for Advanced Study (STIAS), Wallenberg Research Center at Stellenbosch University, Stellenbosch 7600, South Africa
| | - Geneviève Dupont
- Unité de Chronobiologie théorique, Faculté des Sciences, Université Libre de Bruxelles (ULB), Campus Plaine, CP 231, Brussels B-1050, Belgium
| | - Claire Chazaud
- Clermont Université, Université d'Auvergne, Laboratoire GReD, Clermont-Ferrand F-63000, France
- Inserm, UMR1103, Clermont-Ferrand F-63001, France
- CNRS, UMR6293, Clermont-Ferrand F-63001, France
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31
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Lakatos D, Travis ED, Pierson KE, Vivian JL, Czirok A. Autocrine FGF feedback can establish distinct states of Nanog expression in pluripotent stem cells: a computational analysis. BMC SYSTEMS BIOLOGY 2014; 8:112. [PMID: 25267505 PMCID: PMC4189679 DOI: 10.1186/s12918-014-0112-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/12/2014] [Accepted: 08/08/2014] [Indexed: 11/24/2022]
Abstract
Background The maintenance of stem cell pluripotency is controlled by a core cluster of transcription factors, NANOG, OCT4 and SOX2 – genes that jointly regulate each other’s expression. The expression of some of these genes, especially of Nanog, is heterogeneous in a population of undifferentiated stem cells in culture. Transient changes in expression levels, as well as heterogeneity of the population is not restricted to this core regulator, but involve a large number of other genes that include growth factors, transcription factors or signal transduction proteins. Results As the molecular mechanisms behind NANOG expression heterogeneity is not yet understood, we explore by computational modeling the core transcriptional regulatory circuit and its input from autocrine FGF signals that act through the MAP kinase cascade. We argue that instead of negative feedbacks within the core NANOG-OCT4-SOX2 transcriptional regulatory circuit, autocrine signaling loops such as the Esrrb - FGF - ERK feedback considered here are likely to generate distinct sub-states within the “ON” state of the core Nanog switch. Thus, the experimentally observed fluctuations in Nanog transcription levels are best explained as noise-induced transitions between negative feedback-generated sub-states. We also demonstrate that ERK phosphorilation is altered and being anti-correlated with fluctuating Nanog expression – in accord with model simulations. Our modeling approach assigns an empirically testable function to the transcriptional regulators Klf4 and Esrrb, and predict differential regulation of FGF family members. Conclusions We argue that slow fluctuations in Nanog expression likely reflect individual cell-specific changes in parameters of an autocrine feedback loop, such as changes in ligand capture efficiency, receptor numbers or the presence of crosstalks within the MAPK signal transduction pathway. We proposed a model that operates with binding affinities of multiple transcriptional regulators of pluripotency, and the activity of an autocrine signaling pathway. The resulting model produces varied expression levels of several components of pluripotency regulation, largely consistent with empirical observations reported previously and in this present work. Electronic supplementary material The online version of this article (doi:10.1186/s12918-014-0112-4) contains supplementary material, which is available to authorized users.
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Herberg M, Kalkan T, Glauche I, Smith A, Roeder I. A model-based analysis of culture-dependent phenotypes of mESCs. PLoS One 2014; 9:e92496. [PMID: 24643025 PMCID: PMC3958526 DOI: 10.1371/journal.pone.0092496] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2013] [Accepted: 02/21/2014] [Indexed: 02/02/2023] Open
Abstract
Mouse embryonic stem cells (mESCs) can be maintained in a proliferative and undifferentiated state over many passages (self-renewal) while retaining the potential to give rise to every cell type of the organism (pluripotency). Autocrine FGF4/Erk signalling has been identified as a major stimulus for fate decisions and lineage commitment in these cells. Recent findings on serum-free culture conditions with specific inhibitors (known as 2i) demonstrate that the inhibition of this pathway reduces transcription factor heterogeneity and is vital to maintain ground state pluripotency of mESCs. We suggest a novel mathematical model to explicitly integrate FGF4/Erk signalling into an interaction network of key pluripotency factors (namely Oct4, Sox2, Nanog and Rex1). The envisaged model allows to explore whether and how proposed mechanisms and feedback regulations can account for different expression patterns in mESC cultures. We demonstrate that an FGF4/Erk-mediated negative feedback is sufficient to induce molecular heterogeneity with respect to Nanog and Rex1 expression and thus critically regulates the propensity for differentiation and the loss of pluripotency. Furthermore, we compare simulation results on the transcription factor dynamics in different self-renewing states and during differentiation with experimental data on a Rex1GFPd2 reporter cell line using flow cytometry and qRT-PCR measurements. Concluding from our results we argue that interaction between FGF4/Erk signalling and Nanog expression qualifies as a key mechanism to manipulate mESC pluripotency. In particular, we infer that ground state pluripotency under 2i is achieved by shifting stable expression pattern of Nanog from a bistable into a monostable regulation impeding stochastic state transitions. Furthermore, we derive testable predictions on altering the degree of Nanog heterogeneity and on the frequency of state transitions in LIF/serum conditions to challenge our model assumptions.
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Affiliation(s)
- Maria Herberg
- Institute for Medical Informatics and Biometry, Medical Faculty Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
- * E-mail:
| | - Tüzer Kalkan
- Wellcome Trust-Medical Research Council Cambridge Stem Cell Institute, University of Cambridge, Cambridge, United Kingdom
| | - Ingmar Glauche
- Institute for Medical Informatics and Biometry, Medical Faculty Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Austin Smith
- Wellcome Trust-Medical Research Council Cambridge Stem Cell Institute, University of Cambridge, Cambridge, United Kingdom
| | - Ingo Roeder
- Institute for Medical Informatics and Biometry, Medical Faculty Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
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