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. 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 {\it 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.Comment: 36 pages, 17 figure

The embryonic epidermis of Xenopus tropialis: developing a model system for the study of mucociliary epithelia

Mucociliary epithelia are found in the human airways and act as the first line of defence against inhaled foreign agents. Mucus traps potentially damaging particles and the cilia transport the mucus away from the airways to remove the threat. Modelling mucociliary epithelia for research purposes is challenging. This is because the airways are enclosed and are thus difficult to study directly. Instead, tissue is extracted or in vitro techniques are employed. Whilst these systems are useful, there is a need for accessible in vivo models to complement them. In this thesis I assess a new model system for studying mucociliary epithelia. This system is the larval epidermis of the amphibian, Xenopus tropicalis. Its epidermis comprises multi-ciliated cells that beat in a polarised direction reminiscent of those found in the human airways. It is also proposed to have a number of other cell types including mucus-secreting cells, but very little is known about them. The epidermis is open and accessible to manipulation meaning that it has great potential to be used in the study of mucociliary epithelia in live, native conditions. Such a system would be a valuable addition to the current models employed. However, the epidermis has not been thoroughly characterized before so its utility as a model system remains speculative.To develop and evaluate this new model, I fully characterize the epidermis, showing that it has five distinguishable cell types. This includes a population of cells called ionocytes that are shown to be essential for the health and function of the epidermis. I also test for the presence of mucins, the structural component of mucus, secreted from the epidermis in order to evaluate the proposal that mucus-secreting cells are present in the epidermis. A mucin-like protein called otogelin is identified. After characterizing the epidermal cell types, I compare them to the human mucociliary epithelium and consider potential applications and future perspectives for this model.EThOS - Electronic Theses Online ServiceWellcome TrustGBUnited Kingdo

Rab32 Regulates Melanosome Transport in Xenopus Melanophores by Protein Kinase A Recruitment

SummaryIntracellular transport is essential for cytoplasm organization, but mechanisms regulating transport are mostly unknown. In Xenopus melanophores, melanosome transport is regulated by cAMP-dependent protein kinase A (PKA) [1]. Melanosome aggregation is triggered by melatonin, whereas dispersion is induced by melanocyte-stimulating hormone (MSH) [2]. The action of hormones is mediated by cAMP: High cAMP in MSH-treated cells stimulates PKA, whereas low cAMP in melatonin-treated cells inhibits it. PKA activity is typically restricted to specific cell compartments by A-kinase anchoring proteins (AKAPs) [3]. Recently, Rab32 has been implicated in protein trafficking to melanosomes [4] and shown to function as an AKAP on mitochondria [5]. Here, we tested the hypothesis that Rab32 is involved in regulation of melanosome transport by PKA. We demonstrated that Rab32 is localized to the surface of melanosomes in a GTP-dependent manner and binds to the regulatory subunit RIIα of PKA. Both RIIα and Cβ subunits of PKA are required for transport regulation and are recruited to melanosomes by Rab32. Overexpression of wild-type Rab32, but not mutants unable to bind PKA or melanosomes, inhibits melanosome aggregation by melatonin. Therefore, in melanophores, Rab32 is a melanosome-specific AKAP that is essential for regulation of melanosome transport

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

This work was supported by a Wellcome Trust Four-Year PhD Studentship in Basic Science to J.B. (219992/Z/19/Z) and a Wellcome Trust Senior Research Fellowship to N.P. (090868/Z/09/Z). C.M. was supported by a Sir Henry Wellcome Fellowship (103986/Z/14/Z) and University of Manchester Presidential Fellowship. M.R.’s work was supported by a Wellcome Trust Investigator Award (204832/B/16/Z).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.Publisher PDFPeer reviewe

Dynamical gene regulatory networks are tuned by transcriptional autoregulation with microRNA feedback

From Springer Nature via Jisc Publications RouterHistory: received 2020-03-05, accepted 2020-07-06, registration 2020-07-21, pub-electronic 2020-07-31, online 2020-07-31, collection 2020-12Publication status: PublishedFunder: Wellcome Trust; doi: http://dx.doi.org/10.13039/100004440; Grant(s): 110566/Z/15/Z, 090868/Z/09/ZFunder: Biotechnology and Biological Sciences Research Council; doi: http://dx.doi.org/10.13039/501100000268; Grant(s): BB/M011275/1Abstract: Concepts from dynamical systems theory, including multi-stability, oscillations, robustness and stochasticity, are critical for understanding gene regulation during cell fate decisions, inflammation and stem cell heterogeneity. However, the prevalence of the structures within gene networks that drive these dynamical behaviours, such as autoregulation or feedback by microRNAs, is unknown. We integrate transcription factor binding site (TFBS) and microRNA target data to generate a gene interaction network across 28 human tissues. This network was analysed for motifs capable of driving dynamical gene expression, including oscillations. Identified autoregulatory motifs involve 56% of transcription factors (TFs) studied. TFs that autoregulate have more interactions with microRNAs than non-autoregulatory genes and 89% of autoregulatory TFs were found in dual feedback motifs with a microRNA. Both autoregulatory and dual feedback motifs were enriched in the network. TFs that autoregulate were highly conserved between tissues. Dual feedback motifs with microRNAs were also conserved between tissues, but less so, and TFs regulate different combinations of microRNAs in a tissue-dependent manner. The study of these motifs highlights ever more genes that have complex regulatory dynamics. These data provide a resource for the identification of TFs which regulate the dynamical properties of human gene expression

Spatiotemporal lipid profiling during early embryo development of Xenopus laevis using dynamic Time-of-Flight Secondary Ion Mass Spectrometry (ToF-SIMS) Imaging

Time-of-Flight secondary ion mass spectrometry (ToF-SIMS) imaging has been used for the direct analysis of single intact Xenopus laevis (X. laevis) embryo surfaces, locating multiple lipids during fertilisation and the early embryo development stages with sub-cellular lateral resolution (~4 Microns). The method avoids the complicated sample preparation for lipid analysis of the embryos, which requires selective chemical extraction of a pool of samples and chromatographic separation, while preserving the spatial distribution of biological species. The results show ToF-SIMS is capable of profiling multiple components (e.g., glycerophosphocholine, sphingomyelin, cholesterol, vitamin E, diacylglycerol, triacylglycerol) in a single X. laevis embryo. We observe lipid remodelling during fertilisation and early embryo development via time course sampling. The study also reveals the lipid distribution on the gametes fusion site. The methodology used in the study opens the possibility of studying developmental biology using high resolution imaging MS and of understanding the functional role of the biological molecules

Evading the annotation bottleneck: using sequence similarity to search non-sequence gene data.

RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are.BACKGROUND: Non-sequence gene data (images, literature, etc.) can be found in many different public databases. Access to these data is mostly by text based methods using gene names; however, gene annotation is neither complete, nor fully systematic between organisms, and is also not generally stable over time. This provides some challenges for text based access, especially for cross-species searches. We propose a method for non-sequence data retrieval based on sequence similarity, which removes dependence on annotation and text searches. This work was motivated by the need to provide better access to large numbers of in situ images, and the observation that such image data were usually associated with a specific gene sequence. Sequence similarity searches are found in existing gene oriented databases, but mostly give indirect access to non-sequence data via navigational links. RESULTS: Three applications were built to explore the proposed method: accessing image data, literature and gene names. Searches are initiated with the sequence of the user's gene of interest, which is searched against a database of sequences associated with the target data. The matching (non-sequence) target data are returned directly to the user's browser, organised by sequence similarity. The method worked well for the intended application in image data management. Comparison with text based searches of the image data set showed the accuracy of the method. Applied to literature searches it facilitated retrieval of mostly high relevance references. Applied to gene name data it provided a useful analysis of name variation of related genes within and between species. CONCLUSION: This method makes a powerful and useful addition to existing methods for searching gene data based on text retrieval or curated gene lists. In particular the method facilitates cross-species comparisons, and enables the handling of novel or otherwise un-annotated genes. Applications using the method are quick and easy to build, and the data require little maintenance. This approach largely circumvents the need for annotation, which can be a major obstacle to the development of genomic scale data resources

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

From The Royal Society via Jisc Publications RouterHistory: received 2021-05-12, accepted 2021-08-26, collection 2021-09, pub-electronic 2021-09-29Article version: VoRPublication status: PublishedFunder: Wellcome Trust; Id: http://dx.doi.org/10.13039/100004440; Grant(s): 090868/Z/09/Z, 103986/Z/14/Z, 204832/B/16/Z, 219992/Z/19/ZGene 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

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

This work was supported by a Wellcome Trust Senior Research Fellowship (090868/Z/09/Z) and a Wellcome Trust Investigator Award (224394/Z/21/Z) to N.P. and a Medical Research Council Career Development Award to C.S.M. (MR/V032534/1). J.B. was supported by a Wellcome Trust Four-Year PhD Studentship in Basic Science (219992/Z/19/Z). Open Access funding provided by The University of Manchester.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.Publisher PDFPeer reviewe