scholarly journals Developmental switching inPhysarum polycephalum: Petri net analysis of single cell trajectories of gene expression indicates responsiveness and genetic plasticity of the Waddington quasipotential landscape

2017 ◽  
Author(s):  
Britta Werthmann ◽  
Wolfgang Marwan
Keyword(s):  
Gene Expression ◽  
Petri Nets ◽  
Single Cell ◽  
Cell Fate ◽  
Petri Net ◽  
Single Cells ◽  
Cell Fate Decision ◽  
Stimulus Pulse ◽  
Fate Decision ◽  
Cell Trajectories

AbstractThe developmental switch to sporulation inPhysarum polycephalumis a phytochrome-mediated far-red light-induced cell fate decision that synchronously encompasses the entire multinucleate plasmodial cell and is associated with extensive reprogramming of the transcriptome. By repeatedly taking samples of single cells after delivery of a light stimulus pulse, we analysed differential gene expression in two mutant strains and in a heterokaryon of the two strains all of which display a different propensity for making the cell fate decision. Multidimensional scaling of the gene expression data revealed individually different single cell trajectories eventually leading to sporulation. Characterization of the trajectories as walks through states of gene expression discretized by hierarchical clustering allowed the reconstruction of Petri nets that model and predict the observed behavior. Structural analyses of the Petri nets indicated stimulus- and genotype-dependence of both, single cell trajectories and of the quasipotential landscape through which these trajectories are taken. The Petri net-based approach to the analysis and decomposition of complex cellular responses and of complex mutant phenotypes may provide a scaffold for the data-driven reconstruction of causal molecular mechanisms that shape the topology of the quasipotential landscape.

scholarly journals Timing of gene expression in a cell-fate decision system

2017 ◽  
Author(s):  
Delphine Aymoz ◽  
Carme Solé ◽  
Jean-Jerrold Pierre ◽  
Marta Schmitt ◽  
Eulàlia de Nadal ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Fate ◽  
Mapk Pathway ◽  
Single Cells ◽  
Cell Fate Decision ◽  
Decision System ◽  
Mating Partner ◽  
Mating Pathway ◽  
Fate Decision ◽  
Kinetics Of

AbstractDuring development, morphogens provide extracellular cues allowing cells to select a specific fate by inducing complex transcriptional programs. The mating pathway in budding yeast offers simplified settings to understand this process. Pheromone secreted by the mating partner triggers the activity of a MAPK pathway, which results in the expression of hundreds of genes. Using a dynamic expression reporter, we quantified the kinetics of gene expression in single cells upon exogenous pheromone stimulation and in the physiological context of mating. In both conditions, we observed striking differences in the timing of induction of mating-responsive promoters. Biochemical analyses and generation of synthetic promoter variants demonstrated how the interplay between transcription factor binding and nucleosomes contribute to determine the kinetics of transcription in a simplified cell-fate decision system.One Sentence SummaryQuantitative and dynamic single cell measurements in the yeast mating pathway uncover a complex temporal orchestration of gene expression events.

scholarly journals Shaping development by stochasticity and dynamics in gene regulation

Open Biology ◽  
2017 ◽  
Vol 7 (5) ◽  
pp. 170030 ◽  
Author(s):  
Peng Dong ◽  
Zhe Liu
Keyword(s):  
Gene Expression ◽  
Gene Regulation ◽  
Single Cell ◽  
Cell Fate ◽  
Single Molecule ◽  
Large Scale ◽  
Single Cells ◽  
Individual Gene ◽  
Functional Specification ◽  
Spatio Temporal

Animal development is orchestrated by spatio-temporal gene expression programmes that drive precise lineage commitment, proliferation and migration events at the single-cell level, collectively leading to large-scale morphological change and functional specification in the whole organism. Efforts over decades have uncovered two ‘seemingly contradictory’ mechanisms in gene regulation governing these intricate processes: (i) stochasticity at individual gene regulatory steps in single cells and (ii) highly coordinated gene expression dynamics in the embryo. Here we discuss how these two layers of regulation arise from the molecular and the systems level, and how they might interplay to determine cell fate and to control the complex body plan. We also review recent technological advancements that enable quantitative analysis of gene regulation dynamics at single-cell, single-molecule resolution. These approaches outline next-generation experiments to decipher general principles bridging gaps between molecular dynamics in single cells and robust gene regulations in the embryo.

scholarly journals Integrated time-lapse and single-cell transcription studies highlight the variable and dynamic nature of human hematopoietic cell fate commitment

2017 ◽  
Author(s):  
Alice Moussy ◽  
Jérémie Cosette ◽  
Romuald Parmentier ◽  
Cindy da Silva ◽  
Guillaume Corre ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Single Cell ◽  
Cell Fate ◽  
Morphological Changes ◽  
Gene Expression Pattern ◽  
Time Lapse ◽  
Dynamic Nature ◽  
Transcriptional Changes ◽  
Fate Decision

AbstractIndividual cells take lineage commitment decisions in a way that is not necessarily uniform. We address this issue by characterizing transcriptional changes in cord blood derived CD34+ cells at the single-cell level and integrating data with cell division history and morphological changes determined by time-lapse microscopy. We show, that major transcriptional changes leading to a multilineage-primed gene expression state occur very rapidly during the first cell cycle. One of the two stable lineage-primed patterns emerges gradually in each cell with variable timing. Some cells reach a stable morphology and molecular phenotype by the end of the first cell cycle and transmit it clonally. Others fluctuate between the two phenotypes over several cell cycles. Our analysis highlights the dynamic nature and variable timing of cell fate commitment in hematopoietic cells, links the gene expression pattern to cell morphology and identifies a new category of cells with fluctuating phenotypic characteristics, demonstrating the complexity of the fate decision process, away from a simple binary switch between two options as it is usually envisioned.

Stepwise cell fate decision pathways during osteoclastogenesis at single-cell resolution

2020 ◽  
Vol 2 (12) ◽  
pp. 1382-1390
Author(s):  
Masayuki Tsukasaki ◽  
Nam Cong-Nhat Huynh ◽  
Kazuo Okamoto ◽  
Ryunosuke Muro ◽  
Asuka Terashima ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Fate ◽  
Cell Fate Decision ◽  
Fate Decision

scholarly journals Timing of gene expression in a cell‐fate decision system

2018 ◽  
Vol 14 (4) ◽  
Author(s):  
Delphine Aymoz ◽  
Carme Solé ◽  
Jean‐Jerrold Pierre ◽  
Marta Schmitt ◽  
Eulàlia de Nadal ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Fate ◽  
Cell Fate Decision ◽  
Decision System ◽  
A Cell ◽  
Fate Decision

scholarly journals Decision tree models and cell fate choice

2020 ◽  
Author(s):  
Ivan Croydon Veleslavov ◽  
Michael P.H. Stumpf
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Cell Fate ◽  
Cell Types ◽  
Lineage Tree ◽  
Tree Models ◽  
Fate Decision ◽  
Average Gene ◽  
Lineage Trees ◽  
Cell Data

AbstractSingle cell transcriptomics has laid bare the heterogeneity of apparently identical cells at the level of gene expression. For many cell-types we now know that there is variability in the abundance of many transcripts, and that average transcript abun-dance or average gene expression can be a unhelpful concept. A range of clustering and other classification methods have been proposed which use the signal in single cell data to classify, that is assign cell types, to cells based on their transcriptomic states. In many cases, however, we would like to have not just a classifier, but also a set of interpretable rules by which this classification occurs. Here we develop and demonstrate the interpretive power of one such approach, which sets out to establish a biologically interpretable classification scheme. In particular we are interested in capturing the chain of regulatory events that drive cell-fate decision making across a lineage tree or lineage sequence. We find that suitably defined decision trees can help to resolve gene regulatory programs involved in shaping lineage trees. Our approach combines predictive power with interpretabilty and can extract logical rules from single cell data.

scholarly journals Newly developed single-cell computational approach elucidates the stabilization of Oct4 expression in the E3.25 mouse preimplantation embryo

2018 ◽  
Author(s):  
Daniela Gerovska ◽  
Marcos J. Arauzo-Bravo
Keyword(s):  
Single Cell ◽  
Cell Fate ◽  
Clustering Algorithm ◽  
Preimplantation Embryo ◽  
Outer Cell ◽  
Cell Fate Decision ◽  
Oct4 Expression ◽  
Mouse Preimplantation Embryo ◽  
Fate Decision ◽  
Canonical Wnt

AbstractThe time of onset of the second cell fate decision in the mouse preimplantation embryo is still unknown. Ohnishi et al. (2014) looked for cell heterogeneity in the ICM at E3.25 that could indicate the time preceding the apparent segregation of PE and EPI at E3.5, but were not able to detect an early splitting transcriptomics event using state-of-the-art clustering techniques. We developed a new clustering algorithm, hierarchical optimal k-means (HOkM), and identified from single cell (sc) transcriptomics microarray data two groups of ICM cells during the 32 to 64 mouse embryo transition: from embryos with less than 34 cells, and more than 33 cells, corresponding to two developmental sub-stages. The genes defining these sub-stages indicate that the development of the embryo to 34 cells triggers a dramatic event as a result of which Bsg is high expressed, the canonical Wnt pathway is activated, Oct4 is stabilized to high expression and the chromatin remodeling program is initialized to establish a very early narve pluripotent state from the preceding totipotency. We characterized our HOkM partition comparing with independent scRNA-seq datasets. It was staggering to discover that from the 3.4360×1010 possible bi-partitions of the E3.25 data of Ohnishi et al. (2014), our HOkM discovered one partition that shares the biological features of the early and late 32 ICM cells of Posfai et al. (2017). We propose that the stabilization of Oct4 expression is a non-cell autonomous process that requires a minimal number of four inner cell contacts acquired during the transition from a homogeneous outer-cell environment to a heterogeneous inner/outer cell environment formed by the niche of a kernel of at least six inner cells covered by trophectoderm.

scholarly journals Disentangling a Complex Response in Cell Reprogramming and Probing the Waddington Landscape by Automatic Construction of Petri Nets

2019 ◽  
Author(s):  
Viktoria Rätzel ◽  
Britta Werthmann ◽  
Markus Haas ◽  
Jan Strube ◽  
Wolfgang Marwan
Keyword(s):  
Petri Nets ◽  
Cell Fate ◽  
Single Cells ◽  
Genetic Alterations ◽  
Basins Of Attraction ◽  
Cellular Reprogramming ◽  
Automatic Construction ◽  
Complex Response ◽  
Uncertain Structure ◽  
Fate Decision

We analyzed the developmental switch to sporulation of a multinucleate Physarum polycephalum plasmodial cell, a complex response to phytochrome photoreceptor activation. Automatic construction of Petri nets from trajectories of differential gene expression in single cells revealed alternative, genotype-dependent interconnected developmental routes and identified metastable states, commitment points, and subsequent irreversible steps together with molecular signatures associated with cell fate decision and differentiation. Formation of transition-invariants in mutants that are locked in a proliferative state is remarkable considering the view that oncogenic alterations may cause the formation of cancer attractors. We conclude that the Petri net approach is useful to probe the Waddington landscape of cellular reprogramming, to disentangle developmental routes for the reconstruction of the gene regulatory network, and to understand how genetic alterations or physiological conditions reshape the landscape eventually creating new basins of attraction. Unraveling the complexity of pathogenesis, disease progression, and drug response or the analysis of attractor landscapes in other complex systems of uncertain structure might be additional fields of application.

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