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

Autor: Nick E Phillips, Cerys Manning, Nancy Papalopulu, Magnus Rattray
Jazyk: angličtina
Rok vydání: 2016
Předmět:
Luminescence
QH301-705.5
Molecular Networks (q-bio.MN)
Normal Distribution
Gene Expression
Biochemistry
Quantitative Biology - Quantitative Methods
Cell Line
Mice
Biological Clocks
Image Processing
Computer-Assisted
Genetics
Animals
Quantitative Biology - Molecular Networks
Biology (General)
Quantitative Methods (q-bio.QM)
Statistical Data
Stochastic Processes
Covariance
Physics
Electromagnetic Radiation
Biology and Life Sciences
Random Variables
Probability Theory
Synthetic Genetic Systems
Circadian Oscillators
White Noise
FOS: Biological sciences
Luminescent Measurements
Physical Sciences
Signal Processing
Engineering and Technology
Genetic Oscillators
Synthetic Biology
Single-Cell Analysis
Bioluminescence
Chronobiology
Synthetic Gene Oscillators
Mathematics
Statistics (Mathematics)
Research Article
Zdroj: PLoS Computational Biology
PLoS Computational Biology, Vol 13, Iss 5, p e1005479 (2017)
Popis: 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.
Author summary 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.
Databáze: OpenAIRE
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