Stochasticity in the miR-9/Hes1 oscillatory network can account for clonal heterogeneity in the timing of differentiation

Relaxation and Noise-Driven Oscillations in a Model of Mitotic Spindle Dynamics

During cell division, the mitotic spindle moves dynamically through the cell to position the chromosomes and determine the ultimate spatial position of the two daughter cells. These movements have been attributed to the action of cortical force generators which pull on the astral microtubules to position the spindle, as well as pushing events by these same microtubules against the cell cortex and plasma membrane. Attachment and detachment of cortical force generators working antagonistically against centring forces of microtubules have been modelled previously (Grill et al. in Phys Rev Lett 94:108104, 2005) via stochastic simulations and mean-field Fokker-Planck equations (describing random motion of force generators) to predict oscillations of a spindle pole in one spatial dimension. Using systematic asymptotic methods, we reduce the Fokker-Planck system to a set of ordinary differential equations (ODEs), consistent with a set proposed by Grill et al., which can provide accurate predictions of the conditions for the Fokker-Planck system to exhibit oscillations. In the limit of small restoring forces, we derive an algebraic prediction of the amplitude of spindle-pole oscillations and demonstrate the relaxation structure of nonlinear oscillations. We also show how noise-induced oscillations can arise in stochastic simulations for conditions in which the mean-field Fokker-Planck system predicts stability, but for which the period can be estimated directly by the ODE model and the amplitude by a related stochastic differential equation that incorporates random binding kinetics.

Timing mechanisms: insights from comparative neural differentiation systems

Temporal control is central to deploy and coordinate genetic programs during development. At present, there is limited understanding of the molecular mechanisms that govern the duration and speed of developmental processes. Timing mechanisms may run in parallel and/or interact with each other to integrate temporal signals throughout the organism. In this piece, we consider findings on the extrinsic control of developmental tempo and discuss the intrinsic roles of cell cycle, metabolic rates, protein turnover, and post-transcriptional mechanisms in the regulation of tempo during neural development.

Sequential and additive expression of miR-9 precursors control timing of neurogenesis

MicroRNAs (miRs) have an important role in tuning dynamic gene expression. However, the mechanism by which they are quantitatively controlled is unknown. We show that the amount of mature miR-9, a key regulator of neuronal development, increases during zebrafish neurogenesis in a sharp stepwise manner. We characterize the spatiotemporal profile of seven distinct microRNA primary transcripts (pri-mir)-9s that produce the same mature miR-9 and show that they are sequentially expressed during hindbrain neurogenesis. Expression of late-onset pri-mir-9-1 is added on to, rather than replacing, the expression of early onset pri-mir-9-4 and -9-5 in single cells. CRISPR/Cas9 mutation of the late-onset pri-mir-9-1 prevents the developmental increase of mature miR-9, reduces late neuronal differentiation and fails to downregulate Her6 at late stages. Mathematical modelling shows that an adaptive network containing Her6 is insensitive to linear increases in miR-9 but responds to stepwise increases of miR-9. We suggest that a sharp stepwise increase of mature miR-9 is created by sequential and additive temporal activation of distinct loci. This may be a strategy to overcome adaptation and facilitate a transition of Her6 to a new dynamic regime or steady state.

Bilateral Feedback in Oscillator Model Is Required to Explain the Coupling Dynamics of Hes1 with the Cell Cycle

Biological processes are governed by the expression of proteins, and for some proteins, their level of expression can fluctuate periodically over time (i.e., they oscillate). Many oscillatory proteins (e.g., cell cycle proteins and those from the HES family of transcription factors) are connected in complex ways, often within large networks. This complexity can be elucidated by developing intuitive mathematical models that describe the underlying critical aspects of the relationships between these processes. Here, we provide a mathematical explanation of a recently discovered biological phenomenon: the phasic position of the gene Hes1’s oscillatory expression at the beginning of the cell cycle of an individual human breast cancer stem cell can have a predictive value on how long that cell will take to complete a cell cycle. We use a two-component model of coupled oscillators to represent Hes1 and the cell cycle in the same cell with minimal assumptions. Inputting only the initial phase angles, we show that this model is capable of predicting the dynamic mitosis to mitosis behaviour of Hes1 and predicting cell cycle length patterns as found in real-world experimental data. Moreover, we discover that bidirectional coupling between Hes1 and the cell cycle is critical within the system for the data to be reproduced and that nonfixed asymmetry in the interactions between the oscillators is required. The phase dynamics we present here capture the complex interplay between Hes1 and the cell cycle, helping to explain nongenetic cell cycle variability, which has critical implications in cancer treatment contexts.

Nonmodular oscillator and switch based on RNA decay drive regeneration of multimodal gene expression

Periodic gene expression dynamics are key to cell and organism physiology. Studies of oscillatory expression have focused on networks with intuitive regulatory negative feedback loops, leaving unknown whether other common biochemical reactions can produce oscillations. Oscillation and noise have been proposed to support mammalian progenitor cells’ capacity to restore heterogenous, multimodal expression from extreme subpopulations, but underlying networks and specific roles of noise remained elusive. We use mass-action-based models to show that regulated RNA degradation involving as few as two RNA species—applicable to nearly half of human protein-coding genes—can generate sustained oscillations without explicit feedback. Diverging oscillation periods synergize with noise to robustly restore cell populations’ bimodal expression on timescales of days. The global bifurcation organizing this divergence relies on an oscillator and bistable switch which cannot be decomposed into two structural modules. Our work reveals surprisingly rich dynamics of post-transcriptional reactions and a potentially widespread mechanism underlying development, tissue regeneration, and cancer cell heterogeneity.

Differential phase register of Hes1 oscillations with mitoses underlies cell-cycle heterogeneity in ER+ breast cancer cells

Tumors exhibit heterogeneities that are not due to mutations, including cancer stem cells with different potencies. We show that the cancer stem-cell state predisposed to dormancy in vivo has a highly variable and long cell cycle. Using single-cell live imaging for the transcriptional repressor Hes1 (a key molecule in cancer), we show a type of circadian-like oscillatory expression of Hes1 in all cells in the population. The most potent cancer stem cells tend to divide around the trough of the Hes1 oscillatory wave, a feature predictive of a long cell cycle. A concept proposed here is that the position of cell division with respect to the Hes1 wave is predictive of its prospective cell-cycle length and cancer cellular substate.

Here, we study the dynamical expression of endogenously labeled Hes1, a transcriptional repressor implicated in controlling cell proliferation, to understand how cell-cycle length heterogeneity is generated in estrogen receptor (ER)⁺ breast cancer cells. We find that Hes1 shows oscillatory expression with ∼25 h periodicity and during each cell cycle has a variable peak in G1, a trough around G1–S transition, and a less variable second peak in G2/M. Compared to other subpopulations, the cell cycle in CD44^HighCD24^Low cancer stem cells is longest and most variable. Most cells divide around the peak of the Hes1 expression wave, but preceding mitoses in slow dividing CD44^HighCD24^Low cells appear phase-shifted, resulting in a late-onset Hes1 peak in G1. The position, duration, and shape of this peak, rather than the Hes1 expression levels, are good predictors of cell-cycle length. Diminishing Hes1 oscillations by enforcing sustained expression slows down the cell cycle, impairs proliferation, abolishes the dynamic expression of p21, and increases the percentage of CD44^HighCD24^Low cells. Reciprocally, blocking the cell cycle causes an elongation of Hes1 periodicity, suggesting a bidirectional interaction of the Hes1 oscillator and the cell cycle. We propose that Hes1 oscillations are functionally important for the efficient progression of the cell cycle and that the position of mitosis in relation to the Hes1 wave underlies cell-cycle length heterogeneity in cancer cell subpopulations.

HES1 protein oscillations are necessary for neural stem cells to exit from quiescence

Quiescence is a dynamic process of reversible cell cycle arrest. High-level persistent expression of the HES1 transcriptional repressor, which oscillates with an ultradian periodicity in proliferative neural stem cells (NSCs), is thought to mediate quiescence. However, it is not known whether this is due to a change in levels or dynamics. Here, we induce quiescence in embryonic NSCs with BMP4, which does not increase HES1 level, and we find that HES1 continues to oscillate. To assess the role of HES1 dynamics, we express persistent HES1 under a moderate strength promoter, which overrides the endogenous oscillations while maintaining the total HES1 level within physiological range. We find that persistent HES1 does not affect proliferation or entry into quiescence; however, exit from quiescence is impeded. Thus, oscillatory expression of HES1 is specifically required for NSCs to exit quiescence, a finding of potential importance for controlling reactivation of stem cells in tissue regeneration and cancer.

Inferring kinetic parameters of oscillatory gene regulation from single cell time-series data

Gene expression dynamics, such as stochastic oscillations and aperiodic fluctuations, have been associated with cell fate changes in multiple contexts, including development and cancer. Single cell live imaging of protein expression with endogenous reporters is widely used to observe such gene expression dynamics. However, the experimental investigation of regulatory mechanisms underlying the observed dynamics is challenging, since these mechanisms include complex interactions of multiple processes, including transcription, translation and protein degradation. Here, we present a Bayesian method to infer kinetic parameters of oscillatory gene expression regulation using an auto-negative feedback motif with delay. Specifically, we use a delay-adapted nonlinear Kalman filter within a Metropolis-adjusted Langevin algorithm to identify posterior probability distributions. Our method can be applied to time-series data on gene expression from single cells and is able to infer multiple parameters simultaneously. We apply it to published data on murine neural progenitor cells and show that it outperforms alternative methods. We further analyse how parameter uncertainty depends on the duration and time resolution of an imaging experiment, to make experimental design recommendations. This work demonstrates the utility of parameter inference on time course data from single cells and enables new studies on cell fate changes and population heterogeneity.

Deciphering the Role of Fluctuation Dependent Intercellular Communication in Neural Stem Cell Development

Neural stem cells (NPCs) efficiently communicate in an intercellular manner to govern specific cell fate decisions during the developmental process despite withstanding the fluctuating cellular environment. How these fluctuations from diverse origins functionally affect the precise cell fate decision making remains elusive. By taking a stochastic mathematical modeling approach, we unravel that the transcriptional variability arising within an NPC population due to intermittent cell cycle events significantly influences the neuron to NPC ratio during development. Our model proficiently quantifies the impact of different sources of heterogeneities in maintaining an exact neuron to NPC ratio and predicts plausible experimental ways to fine-tune the development of NPCs. In the future, these modeling insights may lead to better therapeutic avenues to regenerate neurons from NPCs.

Time-delayed and stochastic effects in a predator-prey model with ratio dependence and Holling type III functional response

In this article, we derive and analyze a novel predator-prey model with account for maturation delay in predators, ratio dependence, and Holling type III functional response. The analysis of the system's steady states reveals conditions on predation rate, predator growth rate, and maturation time that can result in a prey-only equilibrium or facilitate simultaneous survival of prey and predators in the form of a stable coexistence steady state, or sustain periodic oscillations around this state. Demographic stochasticity in the model is explored by means of deriving a delayed chemical master equation. Using system size expansion, we study the structure of stochastic oscillations around the deterministically stable coexistence state by analyzing the dependence of variance and coherence of stochastic oscillations on system parameters. Numerical simulations of the stochastic model are performed to illustrate stochastic amplification, where individual stochastic realizations can exhibit sustained oscillations in the case, where deterministically the system approaches a stable steady state. These results provide a framework for studying realistic predator-prey systems with Holling type III functional response in the presence of stochasticity, where an important role is played by non-negligible predator maturation delay.

A dynamic, spatially periodic, micro-pattern of HES5 underlies neurogenesis in the mouse spinal cord

Ultradian oscillations of HES Transcription Factors (TFs) at the single-cell level enable cell state transitions. However, the tissue-level organisation of HES5 dynamics in neurogenesis is unknown. Here, we analyse the expression of HES5 ex vivo in the developing mouse ventral spinal cord and identify microclusters of 4-6 cells with positively correlated HES5 level and ultradian dynamics. These microclusters are spatially periodic along the dorsoventral axis and temporally dynamic, alternating between high and low expression with a supra-ultradian persistence time. We show that Notch signalling is required for temporal dynamics but not the spatial periodicity of HES5. Few Neurogenin 2 cells are observed per cluster, irrespective of high or low state, suggesting that the microcluster organisation of HES5 enables the stable selection of differentiating cells. Computational modelling predicts that different cell coupling strengths underlie the HES5 spatial patterns and rate of differentiation, which is consistent with comparison between the motoneuron and interneuron progenitor domains. Our work shows a previously unrecognised spatiotemporal organisation of neurogenesis, emergent at the tissue level from the synthesis of single-cell dynamics.

smiFISH and embryo segmentation for single-cell multi-gene RNA quantification in arthropods

Recently, advances in fluorescent in-situ hybridization techniques and in imaging technology have enabled visualization and counting of individual RNA molecules in single cells. This has greatly enhanced the resolution in our understanding of transcriptional processes. Here, we adapt a recently published smiFISH protocol (single-molecule inexpensive fluorescent in-situ hybridization) to whole embryos across a range of arthropod model species, and also to non-embryonic tissues. Using multiple fluorophores with distinct spectra and white light laser confocal imaging, we simultaneously detect and separate single RNAs from up to eight different genes in a whole embryo. We also combine smiFISH with cell membrane immunofluorescence, and present an imaging and analysis pipeline for 3D cell segmentation and single-cell RNA counting in whole blastoderm embryos. Finally, using whole embryo single-cell RNA count data, we propose two alternative single-cell variability measures to the commonly used Fano factor, and compare the capacity of these three measures to address different aspects of single-cell expression variability.

Here, the authors combine single-molecule inexpensive FISH (smiFISH) with cell membrane immunofluorescence and mathematical methods to enable whole embryo segmentation in 3D image stacks and mRNA quantification of multiple genes in each cell of the embryo.

The regulation of cortical neurogenesis

The mammalian cerebral cortex is the pinnacle of brain evolution, reaching its maximum complexity in terms of neuron number, diversity and functional circuitry. The emergence of this outstanding complexity begins during embryonic development, when a limited number of neural stem and progenitor cells manage to generate myriads of neurons in the appropriate numbers, types and proportions, in a process called neurogenesis. Here we review the current knowledge on the regulation of cortical neurogenesis, beginning with a description of the types of progenitor cells and their lineage relationships. This is followed by a review of the determinants of neuron fate, the molecular and genetic regulatory mechanisms, and considerations on the evolution of cortical neurogenesis in vertebrates leading to humans. We finish with an overview on how dysregulation of neurogenesis is a leading cause of human brain malformations and functional disabilities.

Molecular crosstalk: Notch can manipulate Hes1 and miR-9 behavior

We propose a Hes1-Notch-miR-9 regulatory network and studied the regulating mechanism of miR-9 and Hes1 dynamics driven by Notch. Change in Notch concentration, which serves as a stress signal, can trigger the dynamics of Hes1 and miR-9 at five different states, namely, sTable (2), sustain (1) and mixed (2) states those may correspond to different cellular states. Further, this Notch stress signal introduce time reversal oscillation, which behaves as backward wave, after a certain threshold value of the stress signal and defends the system from moving to apoptosis. We also observe heterogeneous patterns of Hes1, miR-9 and other molecular species in various two dimensional parameter spaces and found that the variability in the patterns is triggered by Hill coefficient and Hes1 stress signal. The phase or bifurcation diagram in time period of oscillation (T_N) driven by Notch signal provides all five states, predicts minimum threshold value T_N^c beyond which tendency to build up backward wave starts and T_N^c serves as bifurcation point of the system.

Dynamic properties of noise and Her6 levels are optimized by miR-9, allowing the decoding of the Her6 oscillator

Noise is prevalent in biology and has been widely quantified using snapshot measurements. This static view obscures our understanding of dynamic noise properties and how these affect gene expression and cell state transitions. Using a CRISPR/Cas9 Zebrafish her6::Venus reporter combined with mathematical and in vivo experimentation, we explore how noise affects the protein dynamics of Her6, a basic helix-loop-helix transcriptional repressor. During neurogenesis, Her6 expression transitions from fluctuating to oscillatory at single-cell level. We identify that absence of miR-9 input generates high-frequency noise in Her6 traces, inhibits the transition to oscillatory protein expression and prevents the downregulation of Her6. Together, these impair the upregulation of downstream targets and cells accumulate in a normally transitory state where progenitor and early differentiation markers are co-expressed. Computational modelling and double smFISH of her6 and the early neurogenesis marker, elavl3, suggest that the change in Her6 dynamics precedes the downregulation in Her6 levels. This sheds light onto the order of events at the moment of cell state transition and how this is influenced by the dynamic properties of noise. Our results suggest that Her/Hes oscillations, facilitated by dynamic noise optimization by miR-9, endow progenitor cells with the ability to make a cell state transition.

Order Through Disorder: The Characteristic Variability of Systems

Randomness characterizes many processes in nature, and therefore its importance cannot be overstated. In the present study, we investigate examples of randomness found in various fields, to underlie its fundamental processes. The fields we address include physics, chemistry, biology (biological systems from genes to whole organs), medicine, and environmental science. Through the chosen examples, we explore the seemingly paradoxical nature of life and demonstrate that randomness is preferred under specific conditions. Furthermore, under certain conditions, promoting or making use of variability-associated parameters may be necessary for improving the function of processes and systems.

Stochastic dynamics in a time-delayed model for autoimmunity

In this paper we study interactions between stochasticity and time delays in the dynamics of immune response to viral infections, with particular interest in the onset and development of autoimmune response. Starting with a deterministic time-delayed model of immune response to infection, which includes cytokines and T cells with different activation thresholds, we derive an exact delayed chemical master equation for the probability density. We use system size expansion and linear noise approximation to explore how variance and coherence of stochastic oscillations depend on parameters, and to show that stochastic oscillations become more regular when regulatory T cells become more effective at clearing autoreactive T cells. Reformulating the model as an Itô stochastic delay differential equation, we perform numerical simulations to illustrate the dynamics of the model and associated probability distributions in different parameter regimes. The results suggest that even in cases where the deterministic model has stable steady states, in individual stochastic realisations, the model can exhibit sustained stochastic oscillations, whose variance increases as one gets closer to the deterministic stability boundary. Furthermore, in the regime of bi-stability, whereas deterministically the system would approach one of the steady states (or periodic solutions) depending on the initial conditions, due to the presence of stochasticity, it is now possible for the system to reach both of those dynamical states with certain probability. Biological significance of this result lies in highlighting the fact that since normally in a laboratory or clinical setting one would observe a single individual realisation of the course of the disease, even for all parameters characterising the immune system and the strength of infection being the same, there is a proportion of cases where a spontaneous recovery can be observed, and similarly, where a disease can develop in a situation that otherwise would result in a normal disease clearance.

Patterning via local cell-cell interactions in developing systems

Spatial patterning during embryonic development emerges from the differentiation of progenitor cells that share the same genetic program. One of the main challenges in systems biology is to understand the relationship between gene network and patterning, especially how the cells communicate to coordinate their differentiation. This review aims to describe the principles of pattern formation from local cell-cell interactions mediated by the Notch signalling pathway. Notch mediates signalling via direct cell-cell contact and regulates cell fate decisions in many tissues during embryonic development. Here, I will describe the patterning mechanisms via different Notch ligands and the critical role of Notch oscillations during the segmentation of the vertebrate body, brain development, and blood vessel formation.

Noise in the Vertebrate Segmentation Clock Is Boosted by Time Delays but Tamed by Notch Signaling

Taming cell-to-cell variability in gene expression is critical for precise pattern formation during embryonic development. To investigate the source and buffering mechanism of expression variability, we studied a biological clock, the vertebrate segmentation clock, controlling the precise spatiotemporal patterning of the vertebral column. By counting single transcripts of segmentation clock genes in zebrafish, we show that clock genes have low RNA amplitudes and expression variability is primarily driven by gene extrinsic sources, which is suppressed by Notch signaling. We further show that expression noise surprisingly increases from the posterior progenitor zone to the anterior segmentation and differentiation zone. Our computational model reproduces the spatial noise profile by incorporating spatially increasing time delays in gene expression. Our results, suggesting that expression variability is controlled by the balance of time delays and cell signaling in a vertebrate tissue, will shed light on the accuracy of natural clocks in multi-cellular systems and inspire engineering of robust synthetic oscillators.

Intrinsic noise, Delta-Notch signalling and delayed reactions promote sustained, coherent, synchronized oscillations in the presomitic mesoderm

Using a stochastic individual-based modelling approach, we examine the role that Delta-Notch signalling plays in the regulation of a robust and reliable somite segmentation clock. We find that not only can Delta-Notch signalling synchronize noisy cycles of gene expression in adjacent cells in the presomitic mesoderm (as is known), but it can also amplify and increase the coherence of these cycles. We examine some of the shortcomings of deterministic approaches to modelling these cycles and demonstrate how intrinsic noise can play an active role in promoting sustained oscillations, giving rise to noise-induced quasi-cycles. Finally, we explore how translational/transcriptional delays can result in the cycles in neighbouring cells oscillating in anti-phase and we study how this effect relates to the propagation of noise-induced stochastic waves.

Endogenous miRNA sponges mediate the generation of oscillatory dynamics for a non-coding RNA network

Oscillations are crucial to the normal function of living organisms, across a wide variety of biological processes. In eukaryotes, oscillatory dynamics are thought to arise from interactions at the protein and RNA levels; however, the role of non-coding RNA in regulating these dynamics remains understudied. In this work, we show how non-coding RNA acting as microRNA (miRNA) sponges in a conserved miRNA - transcription factor feedback motif, can give rise to oscillatory behaviour, and how to test for this experimentally. Control of these non-coding RNA can dynamically create oscillations or stability, and we show how this behaviour predisposes to oscillations in the stochastic limit. These results, supported by emerging evidence for the role of miRNA sponges in development, point towards key roles of different species of miRNA sponges, such as circular RNA, potentially in the maintenance of yet unexplained oscillatory behaviour. These results help to provide a paradigm for understanding functional differences between the many redundant, but distinct RNA species thought to act as miRNA sponges in nature, such as long non-coding RNA, pseudogenes, competing mRNA, circular RNA, and3' UTRs.

Quantitative single-cell live imaging links HES5 dynamics with cell-state and fate in murine neurogenesis

During embryogenesis cells make fate decisions within complex tissue environments. The levels and dynamics of transcription factor expression regulate these decisions. Here, we use single cell live imaging of an endogenous HES5 reporter and absolute protein quantification to gain a dynamic view of neurogenesis in the embryonic mammalian spinal cord. We report that dividing neural progenitors show both aperiodic and periodic HES5 protein fluctuations. Mathematical modelling suggests that in progenitor cells the HES5 oscillator operates close to its bifurcation boundary where stochastic conversions between dynamics are possible. HES5 expression becomes more frequently periodic as cells transition to differentiation which, coupled with an overall decline in HES5 expression, creates a transient period of oscillations with higher fold expression change. This increases the decoding capacity of HES5 oscillations and correlates with interneuron versus motor neuron cell fate. Thus, HES5 undergoes complex changes in gene expression dynamics as cells differentiate.

The cloning and activity of human Hes1 gene promoter

The aim of the current study was to obtain and analyze the activity of the human Hes1 gene promoter. The genomic DNA of human HeLa cell was used as template, polymerase chain reaction (PCR) was used to amplify the 5' end sequence of Hes1 gene and then the amplified segment was connected to pMD18‑T vector. Subsequently, double enzyme digestion was used for identification and the sequence was detected; the promoter with the correct sequence was inserted into pGL3‑Basic, and the sequence was identified by double enzyme digestion. The recombinant DNA with correct sequence was transiently transfected into cervical cancer cells, and the dual luciferase reporter gene assay system was used to detect the activity of the promoter. The results demonstrated that the human Hes1 gene promoter amplified by PCR was the same as that of the sequence in the gene bank, and the dual luciferase reporter gene assay system demonstrated that there was promoter activity in cervical cancer cells. In conclusion, the Hes1 luciferase reporter recombinant vector was successfully established and transfected into HeLa cells to verify that it has promoter activity, and the core area of the promoter has several tumor‑promoting and tumor suppressor genes. This provides a basis for understanding the regulatory mechanism of Hes1 transcription and translation.

Stochastic loss and gain of symmetric divisions in the C. elegans epidermis perturbs robustness of stem cell number

Biological systems are subject to inherent stochasticity. Nevertheless, development is remarkably robust, ensuring the consistency of key phenotypic traits such as correct cell numbers in a certain tissue. It is currently unclear which genes modulate phenotypic variability, what their relationship is to core components of developmental gene networks, and what is the developmental basis of variable phenotypes. Here, we start addressing these questions using the robust number of Caenorhabditis elegans epidermal stem cells, known as seam cells, as a readout. We employ genetics, cell lineage tracing, and single molecule imaging to show that mutations in lin-22, a Hes-related basic helix-loop-helix (bHLH) transcription factor, increase seam cell number variability. We show that the increase in phenotypic variability is due to stochastic conversion of normally symmetric cell divisions to asymmetric and vice versa during development, which affect the terminal seam cell number in opposing directions. We demonstrate that LIN-22 acts within the epidermal gene network to antagonise the Wnt signalling pathway. However, lin-22 mutants exhibit cell-to-cell variability in Wnt pathway activation, which correlates with and may drive phenotypic variability. Our study demonstrates the feasibility to study phenotypic trait variance in tractable model organisms using unbiased mutagenesis screens.

Gene expression analysis of bovine embryonic disc, trophoblast and parietal hypoblast at the start of gastrulation

In cattle early gastrulation-stage embryos (Stage 5), four tissues can be discerned: (i) the top layer of the embryonic disc consisting of embryonic ectoderm (EmE); (ii) the bottom layer of the disc consisting of mesoderm, endoderm and visceral hypoblast (MEH); (iii) the trophoblast (TB); and (iv) the parietal hypoblast. We performed microsurgery followed by RNA-seq to analyse the transcriptome of these four tissues as well as a developmentally earlier pre-gastrulation embryonic disc. The cattle EmE transcriptome was similar at Stages 4 and 5, characterised by the OCT4/SOX2/NANOG pluripotency network. Expression of genes associated with primordial germ cells suggest their presence in the EmE tissue at these stages. Anterior visceral hypoblast genes were transcribed in the Stage 4 disc, but no longer by Stage 5. The Stage 5 MEH layer was equally similar to mouse embryonic and extraembryonic visceral endoderm. Our data suggest that the first mesoderm to invaginate in cattle embryos is fated to become extraembryonic. TGFβ, FGF, VEGF, PDGFA, IGF2, IHH and WNT signals and receptors were expressed, however the representative members of the FGF families differed from that seen in equivalent tissues of mouse embryos. The TB transcriptome was unique and differed significantly from that of mice. FGF signalling in the TB may be autocrine with both FGFR2 and FGF2 expressed. Our data revealed a range of potential inter-tissue interactions, highlighted significant differences in early development between mice and cattle and yielded insight into the developmental events occurring at the start of gastrulation.

Notch/Hes signaling and miR-9 engage in complex feedback interactions controlling neural progenitor cell proliferation and differentiation

Canonical Notch signaling has diverse functions during nervous system development and is critical for neural progenitor self-renewal, timing of differentiation and specification of various cell fates. A key feature of Notch-mediated self-renewal is its fluctuating activity within the neural progenitor cell population and the oscillatory expression pattern of the Notch effector Hes1 and its target genes. A negative feedback loop between Hes1 and neurogenic microRNA miR-9 was found to be part of this oscillatory clock. In a recent study we discovered that miR-9 expression is further modulated by direct binding of the Notch intracellular domain/RBPj transcriptional complex to the miR-9_2 promoter. In turn, miR-9 not only targets Hes1 but also Notch2 to attenuate Notch signaling and promote neuronal differentiation. Here, we discuss how the two interwoven feedback loops may provide an additional fail-save mechanism to control proliferation and differentiation within the neural progenitor cell population. Furthermore, we explore potential implications of miR-9-mediated regulation of Notch/Hes1 signaling with regard to neural progenitor homeostasis, patterning, timing of differentiation and tumor formation.

Identifying stochastic oscillations in single-cell live imaging time series using Gaussian processes

Multiple biological processes are driven by oscillatory gene expression at different time scales. Pulsatile dynamics are thought to be widespread, and single-cell live imaging of gene expression has lead to a surge of dynamic, possibly oscillatory, data for different gene networks. However, the regulation of gene expression at the level of an individual cell involves reactions between finite numbers of molecules, and this can result in inherent randomness in expression dynamics, which blurs the boundaries between aperiodic fluctuations and noisy oscillators. This underlies a new challenge to the experimentalist because neither intuition nor pre-existing methods work well for identifying oscillatory activity in noisy biological time series. Thus, there is an acute need for an objective statistical method for classifying whether an experimentally derived noisy time series is periodic. Here, we present a new data analysis method that combines mechanistic stochastic modelling with the powerful methods of non-parametric regression with Gaussian processes. Our method can distinguish oscillatory gene expression from random fluctuations of non-oscillatory expression in single-cell time series, despite peak-to-peak variability in period and amplitude of single-cell oscillations. We show that our method outperforms the Lomb-Scargle periodogram in successfully classifying cells as oscillatory or non-oscillatory in data simulated from a simple genetic oscillator model and in experimental data. Analysis of bioluminescent live-cell imaging shows a significantly greater number of oscillatory cells when luciferase is driven by a Hes1 promoter (10/19), which has previously been reported to oscillate, than the constitutive MoMuLV 5’ LTR (MMLV) promoter (0/25). The method can be applied to data from any gene network to both quantify the proportion of oscillating cells within a population and to measure the period and quality of oscillations. It is publicly available as a MATLAB package.

Technological advances now allow us to observe gene expression in real-time at a single-cell level. In a wide variety of biological contexts this new data has revealed that gene expression is highly dynamic and possibly oscillatory. It is thought that periodic gene expression may be useful for keeping track of time and space, as well as transmitting information about signalling cues. Classifying a time series as periodic from single cell data is difficult because it is necessary to distinguish whether peaks and troughs are generated from an underlying oscillator or whether they are aperiodic fluctuations. To this end, we present a novel tool to classify live-cell data as oscillatory or non-oscillatory that accounts for inherent biological noise. We first demonstrate that the method outperforms a competing scheme in classifying computationally simulated single-cell data, and we subsequently analyse live-cell imaging time series. Our method is able to successfully detect oscillations in a known genetic oscillator, but it classifies data from a constitutively expressed gene as aperiodic. The method forms a basis for discovering new gene expression oscillators and quantifying how oscillatory activity alters in response to changes in cell fate and environmental or genetic perturbations.