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

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

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

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

Funding: V.B. and J.K. were supported by a Wellcome Trust Senior Research Fellowship to N.P. (090868/Z/09/Z)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.Publisher PDFPeer reviewe

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

From Wiley via Jisc Publications RouterHistory: received 2020-08-03, rev-recd 2021-03-30, accepted 2021-04-06, pub-print 2021-05, pub-electronic 2021-05-25Article version: VoRPublication status: PublishedFunder: Wellcome; Id: http://dx.doi.org/10.13039/100004440; Grant(s): 106185/Z/14/Z, 103986/Z/14/Z, 215189/Z/19/Z, 220001/Z/19/Z, 204057/Z/16/Z, 220001/Z/19/ZAbstract: 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

An assessment of multimodal imaging of subsurface text in mummy cartonnage using surrogate papyrus phantoms

Ancient Egyptian mummies were often covered with an outer casing, panels and masks made from cartonnage: a lightweight material made from linen, plaster, and recycled papyrus held together with adhesive. Egyptologists, papyrologists, and historians aim to recover and read extant text on the papyrus contained within cartonnage layers, but some methods, such as dissolving mummy casings, are destructive. The use of an advanced range of different imaging modalities was investigated to test the feasibility of non-destructive approaches applied to multi-layered papyrus found in ancient Egyptian mummy cartonnage. Eight different techniques were compared by imaging four synthetic phantoms designed to provide robust, well-understood, yet relevant sample standards using modern papyrus and replica inks. The techniques include optical (multispectral imaging with reflection and transillumination, and optical coherence tomography), X-ray (X-ray fluorescence imaging, X-ray fluorescence spectroscopy, X-ray micro computed tomography and phase contrast X-ray) and terahertz-based approaches. Optical imaging techniques were able to detect inks on all four phantoms, but were unable to significantly penetrate papyrus. X-ray-based techniques were sensitive to iron-based inks with excellent penetration but were not able to detect carbon-based inks. However, using terahertz imaging, it was possible to detect carbon-based inks with good penetration but with less sensitivity to iron-based inks. The phantoms allowed reliable and repeatable tests to be made at multiple sites on three continents. The tests demonstrated that each imaging modality needs to be optimised for this particular application: it is, in general, not sufficient to repurpose an existing device without modification. Furthermore, it is likely that no single imaging technique will to be able to robustly detect and enable the reading of text within ancient Egyptian mummy cartonnage. However, by carefully selecting, optimising and combining techniques, text contained within these fragile and rare artefacts may eventually be open to non-destructive imaging, identification, and interpretation

Intravital imaging of SRF and Notch signalling identifies a key role for EZH2 in invasive melanoma cells.

The acquisition of cell motility is an early step in melanoma metastasis. Here we use intravital imaging of signalling reporter cell-lines combined with genome-wide transcriptional analysis to define signalling pathways and genes associated with melanoma metastasis. Intravital imaging revealed heterogeneous cell behaviour in vivo: less than 10% of cells were motile and both singly moving cells and streams of cells were observed. Motile melanoma cells had increased Notch- and SRF-dependent transcription. Subsequent genome-wide analysis identified an overlapping set of genes associated with high Notch and SRF activity. We identified EZH2, a histone methyltransferase in the Polycomb Repressor Complex 2, as a regulator of these genes. Heterogeneity of EZH2 levels is observed in melanoma models and co-ordinated up-regulation of genes positively regulated by EZH2 is associated with melanoma metastasis. EZH2 was also identified as regulating the amelanotic phenotype of motile cells in vivo by suppressing expression of the P-glycoprotein Oca2. Analysis of patient samples confirmed an inverse relationship between EZH2 levels and pigment. EZH2 targeting with siRNA and chemical inhibition reduced invasion in mouse and human melanoma cell lines. The EZH2 regulated SRF target genes KIF2C and KIF22 are required for melanoma cell invasion and important for lung colonisation. We propose that heterogeneity in EZH2 levels leads to heterogeneous expression of a cohort of genes associated with motile behaviour including KIF2C and KIF22. EZH2 dependent increased expression of these genes promotes melanoma cell motility and early steps in metastasis