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

Summary

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.

Abstract

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.

Introduction

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 progression^{1,2,3,4,5,6,7,8}. 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....

Protocol

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.......

Discussion

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 experiment^14,15. CLOCCS uses the input cell-cycle phase data to infer the parameters for each experiment, which are the.......

Materials

Name	Company	Catalog Number	Comments
2x PBS			For Fixative Solution. Described in Leman 2014.
4% formaldehyde			For Fixative Solution.
100% Ethanol			For flow cytometry fixation. Described in Haase 2002.
CLOCCS			https://gitlab.com/haase-lab-group/cloccs_alignment.git
Flow Cytometer			For flow cytometry protocol.
Git			https://git-scm.com/
Java 19			https://www.oracle.com/java/technologies/downloads/#java19
Microscope			For counting cells and buds.
Miniconda			https://docs.conda.io/en/latest/
Protease solution			For flow cytometry protocol. Described in Haase 2002.
RNAse A solution			For flow cytometry protocol. Described in Haase 2002.
SYTOX Green Nucleic Acid Stain	Invitrogen	S7020	For flow cytometry staining. Described in Haase 2002.
Tris			pH 7.5