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Emmanuel S. Tzanakakis - One of the best experts on this subject based on the ideXlab platform.
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Distinct allelic patterns of nanog expression impart embryonic stem cell Population Heterogeneity.
PLoS computational biology, 2013Co-Authors: Emmanuel S. TzanakakisAbstract:Nanog is a principal pluripotency regulator exhibiting a disperse distribution within stem cell Populations in vivo and in vitro. Increasing evidence points to a functional role of Nanog Heterogeneity on stem cell fate decisions. Allelic control of Nanog gene expression was reported recently in mouse embryonic stem cells. To better understand how this mode of regulation influences the observed Heterogeneity of NANOG in stem cell Populations, we assembled a multiscale stochastic Population balance equation framework. In addition to allelic control, gene expression noise and random partitioning at cell division were considered. As a result of allelic Nanog expression, the distribution of Nanog exhibited three distinct states but when combined with transcriptional noise the profile became bimodal. Regardless of their allelic expression pattern, initially uniform Populations of stem cells gave rise to the same Nanog Heterogeneity within ten cell cycles. Depletion of NANOG content in cells switching off both gene alleles was slower than the accumulation of intracellular NANOG after cells turned on at least one of their Nanog gene copies pointing to Nanog state-dependent dynamics. Allelic transcription of Nanog also raises issues regarding the use of stem cell lines with reporter genes knocked in a single allelic locus. Indeed, significant divergence was observed in the reporter and native protein profiles depending on the difference in their half-lives and insertion of the reporter gene in one or both alleles. In stem cell Populations with restricted Nanog expression, allelic regulation facilitates the maintenance of fractions of self-renewing cells with sufficient Nanog content to prevent aberrant loss of pluripotency. Our findings underline the role of allelic control of Nanog expression as a prime determinant of stem cell Population Heterogeneity and warrant further investigation in the contexts of stem cell specification and cell reprogramming.
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Deconstructing stem cell Population Heterogeneity: single-cell analysis and modeling approaches.
Biotechnology Advances, 2013Co-Authors: Emmanuel S. TzanakakisAbstract:Isogenic stem cell Populations display cell-to-cell variations in a multitude of attributes including gene or protein expression, epigenetic state, morphology, proliferation and proclivity for differentiation. The origins of the observed Heterogeneity and its roles in the maintenance of pluripotency and the lineage specification of stem cells remain unclear. Addressing pertinent questions will require the employment of single-cell analysis methods as traditional cell biochemical and biomolecular assays yield mostly Population-average data. In addition to time-lapse microscopy and flow cytometry, recent advances in single-cell genomic, transcriptomic and proteomic profiling are reviewed. The application of multiple displacement amplification, next generation sequencing, mass cytometry and spectrometry to stem cell systems is expected to provide a wealth of information affording unprecedented levels of multiparametric characterization of cell ensembles under defined conditions promoting pluripotency or commitment. Establishing connections between single-cell analysis information and the observed phenotypes will also require suitable mathematical models. Stem cell self-renewal and differentiation are orchestrated by the coordinated regulation of subcellular, intercellular and niche-wide processes spanning multiple time scales. Here, we discuss different modeling approaches and challenges arising from their application to stem cell Populations. Integrating single-cell analysis with computational methods will fill gaps in our knowledge about the functions of Heterogeneity in stem cell physiology. This combination will also aid the rational design of efficient differentiation and reprogramming strategies as well as bioprocesses for the production of clinically valuable stem cell derivatives.
Nikos V. Mantzaris - One of the best experts on this subject based on the ideXlab platform.
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from single cell genetic architecture to cell Population dynamics quantitatively decomposing the effects of different Population Heterogeneity sources for a genetic network with positive feedback architecture
Biophysical Journal, 2007Co-Authors: Nikos V. MantzarisAbstract:Phenotypic cell-to-cell variability or cell Population Heterogeneity originates from two fundamentally different sources: unequal partitioning of cellular material at cell division and stochastic fluctuations associated with intracellular reactions. We developed a mathematical and computational framework that can quantitatively isolate both Heterogeneity sources and applied it to a genetic network with positive feedback architecture. The framework consists of three vastly different mathematical formulations: a), a continuum model, which completely neglects Population Heterogeneity; b), a deterministic cell Population balance model, which accounts for Population Heterogeneity originating only from unequal partitioning at cell division; and c), a fully stochastic model accommodating both sources of Population Heterogeneity. The framework enables the quantitative decomposition of the effects of the different Population Heterogeneity sources on system behavior. Our results indicate the importance of cell Population Heterogeneity in accurately predicting even average Population properties. Moreover, we find that unequal partitioning at cell division and sharp division rates shrink the region of the parameter space where the Population exhibits bistable behavior, a characteristic feature of networks with positive feedback architecture. In addition, intrinsic noise at the single-cell level due to slow operator fluctuations and small numbers of molecules further contributes toward the shrinkage of the bistability regime at the cell Population level. Finally, the effect of intrinsic noise at the cell Population level was found to be markedly different than at the single-cell level, emphasizing the importance of simulating entire cell Populations and not just individual cells to understand the complex interplay between single-cell genetic architecture and behavior at the cell Population level.
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Cell Population Heterogeneity in expression of a gene-switching network with fluorescent markers of different half-lives.
Journal of biotechnology, 2006Co-Authors: Stephanie Portle, Thomas B. Causey, Kim Wolf, George N. Bennett, Ka-yiu San, Nikos V. MantzarisAbstract:Abstract We studied the distribution of expression levels amongst the cells of an Escherichia coli Population carrying a gene-switching network, known as the genetic toggle. We employed two green fluorescent protein (GFP) reporter proteins with different half-lives and characterized the effect of isopropyl-β- d -thiogalactopyranoside (IPTG) inducer concentration on fluorescence distribution characteristics. Our flow cytometric measurements indicated that there is a spread of fluorescence phenotypes of one to three orders of magnitude, due to the highly heterogeneous nature of the cell Populations under investigation. Moreover, the shape of the distribution at a specific quasi-time-invariant reference state, defined for comparison purposes, strongly depended on inducer concentration. For very low and very high inducer concentrations, the distributions at the reference state are unimodal. On the contrary, for intermediate IPTG concentrations, two distinct subPopulations were formed below and above a single-cell threshold, resulting in distributions with a bimodal shape. The region of inducer concentrations where bimodality is observed is the same and independent of GFP half-life. Bimodal number density functions are not only obtained at the reference state. Transient studies revealed that even in cases where the distribution at the reference state is unimodal, the distribution becomes bimodal for a period of time required for the Population to pass through the single-cell induction threshold. However, this feature was only captured by the system with the reduced half-life GFP. A simple single-cell model was used to shed light into the effect of inducer concentration and GFP half-life on the shape of the experimentally measured number density functions. The wide range of fluorescent phenotypes and the inability of the average Population properties to fully characterize network behavior, indicate the importance of taking into account cell Population Heterogeneity when designing such a gene-switching network for biotechnological and biomedical applications.
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Single-cell gene-switching networks and heterogeneous cell Population phenotypes
Computers & Chemical Engineering, 2005Co-Authors: Nikos V. MantzarisAbstract:Abstract Cell Populations are heterogeneous systems in the sense that cellular properties are unevenly distributed amongst the cells of the Population. In this work, we present a novel methodological and computational framework for the quantitative assessment of the effects of cell Population Heterogeneity on the dynamics of cell Populations. It combines the cell Population balance modeling approach which accounts for cell Population Heterogeneity with the continuum modeling approach, which does not. We focus on Populations of cells carrying an artificial genetic network, known as the genetic toggle, consisting of a system of two promoter–repressor pairs. Detailed numerical simulations indicate that taking into account cell Population Heterogeneity leads to agreement with experimental data, while neglecting it results in significant qualitative and quantitative differences both transiently and at balanced growth. Furthermore, the simulations revealed the effect of systemic parameters on both the transient and balanced growth behavior, leading us to the formulation of specific hypotheses to interpret experimental results. Thus, cell Population balance modeling when used in conjunction with mechanistic single-cell models constitutes a powerful predictive tool which can provide invaluable quantitative insights into the biological system under consideration.
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Effects of Cell Population Heterogeneity on the Dynamics of Cell Populations
IFAC Proceedings Volumes, 2004Co-Authors: Nikos V. MantzarisAbstract:Abstract A novel methodological and computational framework for the quantitative assessment of the effects of cell Population Heterogeneity on the dynamics of cell Populations is presented. We focus on Populations of cells carrying an artificial genetic network, consisting of a system of two promoterrepressor pairs. Detailed numerical simulations indicate that taking into account cell Population Heterogeneity leads to agreement with experimental data, while neglecting it results in significant qualitative and quantitative differences both transiently and at balanced growth.
Niranjan Nagarajan - One of the best experts on this subject based on the ideXlab platform.
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lofreq a sequence quality aware ultra sensitive variant caller for uncovering cell Population Heterogeneity from high throughput sequencing datasets
Nucleic Acids Research, 2012Co-Authors: Andreas Wilm, Denis Bertrand, Grace Hui Ting Yeo, Swee Hoe Ong, Chang Hua Wong, Chiea Chuen Khor, Rosemary Petric, Martin L. Hibberd, Niranjan NagarajanAbstract:The study of cell-Population Heterogeneity in a range of biological systems, from viruses to bacterial isolates to tumor samples, has been transformed by recent advances in sequencing throughput. While the high-coverage afforded can be used, in principle, to identify very rare variants in a Population, existing ad hoc approaches frequently fail to distinguish true variants from sequencing errors. We report a method (LoFreq) that models sequencing run-specific error rates to accurately call variants occurring in <0.05% of a Population. Using simulated and real datasets (viral, bacterial and human), we show that LoFreq has near-perfect specificity, with significantly improved sensitivity compared with existing methods and can efficiently analyze deep Illumina sequencing datasets without resorting to approximations or heuristics. We also present experimental validation for LoFreq on two different platforms (Fluidigm and Sequenom) and its application to call rare somatic variants from exome sequencing datasets for gastric cancer. Source code and executables for LoFreq are freely available at http://sourceforge.net/projects/lofreq/.
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LoFreq: a sequence-quality aware, ultra-sensitive variant caller for uncovering cell-Population Heterogeneity from high-throughput sequencing datasets
Nucleic acids research, 2012Co-Authors: Andreas Wilm, Denis Bertrand, Grace Hui Ting Yeo, Swee Hoe Ong, Chang Hua Wong, Chiea Chuen Khor, Rosemary Petric, Martin L. Hibberd, Niranjan NagarajanAbstract:The study of cell-Population Heterogeneity in a range of biological systems, from viruses to bacterial isolates to tumor samples, has been transformed by recent advances in sequencing throughput. While the high-coverage afforded can be used, in principle, to identify very rare variants in a Population, existing ad hoc approaches frequently fail to distinguish true variants from sequencing errors. We report a method (LoFreq) that models sequencing run-specific error rates to accurately call variants occurring in
Anna Eliasson Lantz - One of the best experts on this subject based on the ideXlab platform.
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Quantitative Flow Cytometry to Understand Population Heterogeneity in Response to Changes in Substrate Availability in Escherichia coli and Saccharomyces cerevisiae Chemostats.
Frontiers in bioengineering and biotechnology, 2019Co-Authors: Anna-lena Heins, Krist V. Gernaey, Ted Johanson, Shanshan Han, Luisa Lundin, Magnus Carlquist, Søren J. Sørensen, Anna Eliasson LantzAbstract:Microbial cells in bioprocesses are usually described with averaged parameters. But in fact, single cells within Populations vary greatly in characteristics such as stress resistance, especially in response to carbon source gradients. Our aim was to introduce tools to quantify Population Heterogeneity in bioprocesses using a combination of reporter strains, flow cytometry, and easily comprehensible parameters. We calculated mean, mode, peak width, and coefficient of variance to describe distribution characteristics and temporal shifts in fluorescence intensity. The skewness and the slope of cumulative distribution function plots illustrated differences in distribution shape. These parameters are person-independent and precise. We demonstrated this by quantifying growth-related Population Heterogeneity of Saccharomyces cerevisiae and Escherichia coli reporter strains in steady-state of aerobic glucose-limited chemostat cultures at different dilution rates and in response to glucose pulses. Generally, slow-growing cells showed stronger responses to glucose excess than fast-growing cells. Cell robustness, measured as membrane integrity after exposure to freeze-thaw treatment, of fast-growing cells was strongly affected in subPopulations of low membrane robustness. Glucose pulses protected subPopulations of fast-growing but not slower-growing yeast cells against membrane damage. Our parameters could successfully describe Population Heterogeneity, thereby revealing physiological characteristics that might have been overlooked during traditional averaged analysis.
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The effect of acetate on Population Heterogeneity in different cellular characteristics of Escherichia coli in aerobic batch cultures
Biotechnology progress, 2019Co-Authors: Anna-lena Heins, Krist V. Gernaey, Luisa Lundin, Søren J. Sørensen, Inês Nunes, Anna Eliasson LantzAbstract:Acetate as the major by-product in industrial-scale bioprocesses with Escherichia coli is found to decrease process efficiency as well as to be toxic to cells, which has several effects like a significant induction of cellular stress responses. However, the underlying phenomena are poorly explored. Therefore, we studied time-resolved Population Heterogeneity of the E. coli growth reporter strain MG1655/pGS20PrrnBGFPAAV expressing destabilized green fluorescent protein during batch growth on acetate and glucose as sole carbon sources. Additionally, we applied five fluorescent stains targeting different cellular properties (viability as well as metabolic and respiratory activity). Quantitative analysis of flow cytometry data verified that bacterial Populations in the bioreactor are more heterogeneous in growth as well as stronger metabolically challenged during growth on acetate as sole carbon source, compared to growth on glucose or acetate after diauxic shift. Interestingly, with acetate as sole carbon source, significant subPopulations were found with some cells that seem to be more robust than the rest of the Population. In conclusion, following batch cultures Population Heterogeneity was evident in all measured parameters. Our approach enabled a deeper study of Heterogeneity during growth on the favored substrate glucose as well as on the toxic by-product acetate. Using a combination of activity fluorescent dyes proved to be an accurate and fast alternative as well as a supplement to the use of a reporter strain. However, the choice of combination of stains should be well considered depending on which Population traits to aim for.
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Dynamic single-cell analysis of Saccharomyces cerevisiae under process perturbation: comparison of different methods for monitoring the intensity of Population Heterogeneity
Journal of Chemical Technology & Biotechnology, 2014Co-Authors: Frank Delvigne, Ted Johanson, Jonathan Baert, Sébastien Gofflot, Annick Lejeune, Samuel Telek, Anna Eliasson LantzAbstract:BACKGROUND Single cell biology has attracted a lot of attention in recent years and has led to numerous fundamental results pointing out the Heterogeneity of clonal cell Populations. In this context, microbial phenotypic Heterogeneity under bioprocessing conditions needs to be further investigated. In this study, yeast based processes have been investigated by using on-line flow cytometry (FC) in combination with a fluorescent transcriptional reporter (GFP) and viability fluorescence tags (propidium iodide, PI). Methods aiming at expressing the dispersion of these fluorescence tags among the yeast Populations have been investigated for different bioreactor operating conditions. RESULTS Yeast viability was determined on the basis of PI uptake. Segregation between PI negative and positive subPopulations could be efficiently quantified on the basis of the mean-to-median ratio or the amplitude of the interquartile range. On the other hand, the same quantification could not be made for the segregation occurring at the level of GFP synthesis. Indeed, when cells were exposed to sub-lethal or mild stresses (such as in scale-down reactors) two GFP subPopulations could be visualized by real-time FC, but quantification by one of the above-mentioned methods was not possible. CONCLUSIONS Yeast Population Heterogeneity was observed in representative bioreactor operating conditions. Difficulties for the determination of segregation at the level of GFP synthesis point out the fact that one needs to understand the segregation mechanisms for the applied fluorescent reporters, to judge whether simple mathematical tools may be applied or if more sophisticated computational tools are needed for quantification of the microbial Population segregation. © 2014 Society of Chemical Industry
Andreas Wilm - One of the best experts on this subject based on the ideXlab platform.
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lofreq a sequence quality aware ultra sensitive variant caller for uncovering cell Population Heterogeneity from high throughput sequencing datasets
Nucleic Acids Research, 2012Co-Authors: Andreas Wilm, Denis Bertrand, Grace Hui Ting Yeo, Swee Hoe Ong, Chang Hua Wong, Chiea Chuen Khor, Rosemary Petric, Martin L. Hibberd, Niranjan NagarajanAbstract:The study of cell-Population Heterogeneity in a range of biological systems, from viruses to bacterial isolates to tumor samples, has been transformed by recent advances in sequencing throughput. While the high-coverage afforded can be used, in principle, to identify very rare variants in a Population, existing ad hoc approaches frequently fail to distinguish true variants from sequencing errors. We report a method (LoFreq) that models sequencing run-specific error rates to accurately call variants occurring in <0.05% of a Population. Using simulated and real datasets (viral, bacterial and human), we show that LoFreq has near-perfect specificity, with significantly improved sensitivity compared with existing methods and can efficiently analyze deep Illumina sequencing datasets without resorting to approximations or heuristics. We also present experimental validation for LoFreq on two different platforms (Fluidigm and Sequenom) and its application to call rare somatic variants from exome sequencing datasets for gastric cancer. Source code and executables for LoFreq are freely available at http://sourceforge.net/projects/lofreq/.
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LoFreq: a sequence-quality aware, ultra-sensitive variant caller for uncovering cell-Population Heterogeneity from high-throughput sequencing datasets
Nucleic acids research, 2012Co-Authors: Andreas Wilm, Denis Bertrand, Grace Hui Ting Yeo, Swee Hoe Ong, Chang Hua Wong, Chiea Chuen Khor, Rosemary Petric, Martin L. Hibberd, Niranjan NagarajanAbstract:The study of cell-Population Heterogeneity in a range of biological systems, from viruses to bacterial isolates to tumor samples, has been transformed by recent advances in sequencing throughput. While the high-coverage afforded can be used, in principle, to identify very rare variants in a Population, existing ad hoc approaches frequently fail to distinguish true variants from sequencing errors. We report a method (LoFreq) that models sequencing run-specific error rates to accurately call variants occurring in