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Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Published: June 9th, 2023



1Department of Biology, Duke University, 2Department of Biology, University of Massachusetts, 3Orlando Data Science LLC, 4Department of Computer Science, Duke University

One challenge of analyzing synchronized time-series experiments is that the experiments often differ in the length of recovery from synchrony and the cell-cycle period. Thus, the measurements from different experiments cannot be analyzed in aggregate or readily compared. Here, we describe a method for aligning experiments to allow for phase-specific comparisons.

Investigating the cell cycle often depends on synchronizing cell populations to measure various parameters in a time series as the cells traverse the cell cycle. However, even under similar conditions, replicate experiments display differences in the time required to recover from synchrony and to traverse the cell cycle, thus preventing direct comparisons at each time point. The problem of comparing dynamic measurements across experiments is exacerbated in mutant populations or in alternative growth conditions that affect the synchrony recovery time and/or the cell-cycle period.

We have previously published a parametric mathematical model named Characterizing Loss of Cell Cycle Synchrony (CLOCCS) that monitors how synchronous populations of cells release from synchrony and progress through the cell cycle. The learned parameters from the model can then be used to convert experimental time points from synchronized time-series experiments into a normalized time scale (lifeline points). Rather than representing the elapsed time in minutes from the start of the experiment, the lifeline scale represents the progression from synchrony to cell-cycle entry and then through the phases of the cell cycle. Since lifeline points correspond to the phase of the average cell within the synchronized population, this normalized time scale allows for direct comparisons between experiments, including those with varying periods and recovery times. Furthermore, the model has been used to align cell-cycle experiments between different species (e.g., Saccharomyces cerevisiae and Schizosaccharomyces pombe), thus enabling direct comparison of cell-cycle measurements, which may reveal evolutionary similarities and differences.

Time-series measurements made on synchronized populations of cells as they progress through the cell cycle is a standard method for investigating the mechanisms that control cell-cycle progression1,2,3,4,5,6,7,8. The ability to make comparisons across synchrony/release time-series experiments is vital to our understanding of these dynamic processes. The use of replicate experiments to corroborate finding....

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1. Collecting cell-cycle phase and experimental data

  1. Synchronize the cells with respect to the cell cycle using the desired synchronization method (e.g., centrifugal elutriation as described in Leman et al.18 or mating pheromone arrest as described in Rosebrock19; both Leman et al.18 and Rosebrock19 also include methods for the release from synchrony). Begin sampling throughout the time series, ens.......

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The steps described in the above protocol and in the workflow in Figure 1 were applied to five cell-cycle synchronized time-series experiments to demonstrate two representative comparisons: between replicates with different synchrony methods (mating pheromone and centrifugal elutriation18) and sequencing platforms (RNA-sequencing [RNA-seq] and microarray), as well as across experimental conditions. Multiple experiments were performed with S. cerevisiae, .......

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This paper presents a method for more accurately and quantitatively assessing data from time-series experiments on synchronized populations of cells. The method utilizes learned parameters from CLOCCS, a Bayesian inference model that uses input cell-cycle phase data, such as budding data and flow-cytometric DNA content data, to parameterize each experiment14,15. CLOCCS uses the input cell-cycle phase data to infer the parameters for each experiment, which are the.......

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S. Campione and S. Haase were supported by funding from the National Science Foundation (DMS-1839288) and the National Institutes of Health (5R01GM126555). Additionally, the authors would like to thank Huarui Zhou (Duke University) for comments on the manuscript and for beta testing the protocol. We also thank Francis Motta (Florida Atlantic University) and Joshua Robinson for their help with the Java code.


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NameCompanyCatalog NumberComments
2x PBSFor Fixative Solution. Described in Leman 2014.
4% formaldehydeFor Fixative Solution.
100% EthanolFor flow cytometry fixation. Described in Haase 2002.
Flow CytometerFor flow cytometry protocol.
Java 19
MicroscopeFor counting cells and buds.
Protease solutionFor flow cytometry protocol. Described in Haase 2002.
RNAse A solutionFor flow cytometry protocol. Described in Haase 2002.
SYTOX Green Nucleic Acid StainInvitrogenS7020For flow cytometry staining. Described in Haase 2002.
TrispH 7.5

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