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.

Alignment of Synchronized Time-Series Data for Cross-Experiment Comparisons

<ol>
	<li>Tyers, M., Tokiwa, G., Futcher, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Comparison+of+the+Saccharomyces+cerevisiae+G1+cyclins:+Cln3+may+be+an+upstream+activator+of+Cln1,+Cln2+and+other+cyclins.">Comparison of the Saccharomyces cerevisiae G1 cyclins: Cln3 may be an upstream activator of Cln1, Cln2 and other cyclins.</a> EMBO Journal. 12 (5), 1955-1968 (1993).</li><li>Schwob, E., Nasmyth, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=CLB5+and+CLB6,+a+new+pair+of+B+cyclins+involved+in+DNA+replication+in+Saccharomyces+cerevisiae.">CLB5 and CLB6, a new pair of B cyclins involved in DNA replication in Saccharomyces cerevisiae.</a> Genes and Development. 7, 1160-1175 (1993).</li><li>Polymenis, M., Schmidt, E. V. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Coupling+of+cell+division+to+cell+growth+by+translational+control+of+the+G1+cyclin+CLN3+in+yeast.">Coupling of cell division to cell growth by translational control of the G1 cyclin CLN3 in yeast.</a> Genes and Development. 11 (19), 2522-2531 (1997).</li><li>Spellman, P. T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Comprehensive+identification+of+cell+cycle-regulated+genes+of+the+yeast+Saccharomyces+cerevisiae+by+microarray+hybridization.">Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization.</a> Molecular Biology of the Cell. 9 (12), 3273-3297 (1998).</li><li>Cho, R. J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+genome-wide+transcriptional+analysis+of+the+mitotic+cell+cycle.">A genome-wide transcriptional analysis of the mitotic cell cycle.</a> Molecular Cell. 2 (1), 65-73 (1998).</li><li>Bar-Joseph, Z. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Analyzing+time+series+gene+expression+data.">Analyzing time series gene expression data.</a> Bioinformatics. 20 (16), 2493-2503 (2004).</li><li>Pramila, T., Wu, W., Miles, S., Noble, W. S., Breeden, L. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+Forkhead+transcription+factor+Hcm1+regulates+chromosome+segregation+genes+and+fills+the+S-phase+gap+in+the+transcriptional+circuitry+of+the+cell+cycle.">The Forkhead transcription factor Hcm1 regulates chromosome segregation genes and fills the S-phase gap in the transcriptional circuitry of the cell cycle.</a> Genes and Development. 20 (16), 2266-2278 (2006).</li><li>Orlando, D. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Global+control+of+cell-cycle+transcription+by+coupled+CDK+and+network+oscillators.">Global control of cell-cycle transcription by coupled CDK and network oscillators.</a> Nature. 453 (7197), 944-947 (2008).</li><li>Nash, R., Tokiwa, G., Anand, S., Erickson, K., Futcher, A. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+WHI1++gene+of+Saccharomyces+cerevisiae+tethers+cell+division+to+cell+size+and+is+a+cyclin+homolog.">The WHI1+ gene of Saccharomyces cerevisiae tethers cell division to cell size and is a cyclin homolog.</a> EMBO Journal. 7 (13), 4335-4346 (1988).</li><li>Basco, R. D., Segal, M. D., Reed, S. I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Negative+regulation+of+G1+and+G2+by+S-phase+cyclins+of+Saccharomyces+cerevisiae.">Negative regulation of G1 and G2 by S-phase cyclins of Saccharomyces cerevisiae.</a> Molecular and Cellular Biology. 15 (9), 5030-5042 (1995).</li><li>Mayhew, M. B., Robinson, J. W., Jung, B., Haase, S. B., Hartemink, A. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+generalized+model+for+multi-marker+analysis+of+cell+cycle+progression+in+synchrony+experiments.">A generalized model for multi-marker analysis of cell cycle progression in synchrony experiments.</a> Bioinformatics. 27 (13), 295-303 (2011).</li><li>Qu, Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cell+cycle+inhibitor+Whi5+records+environmental+information+to+coordinate+growth+and+division+in+yeast.">Cell cycle inhibitor Whi5 records environmental information to coordinate growth and division in yeast.</a> Cell Reports. 29 (4), 987-994 (2019).</li><li>Di Talia, S., Skotheim, J. M., Bean, J. M., Siggia, E. D., Cross, F. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+effects+of+molecular+noise+and+size+control+on+variability+in+the+budding+yeast+cell+cycle.">The effects of molecular noise and size control on variability in the budding yeast cell cycle.</a> Nature. 448 (7156), 947-951 (2007).</li><li>Orlando, D. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+probabilistic+model+for+cell+cycle+distributions+in+synchrony+experiments.">A probabilistic model for cell cycle distributions in synchrony experiments.</a> Cell Cycle. 6 (4), 478-488 (2007).</li><li>Orlando, D. A., Iversen, E. S., Hartemink, A. J., Haase, S. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+branching+process+model+for+flow+cytometry+and+budding+index+measurements+in+cell+synchrony+experiments.">A branching process model for flow cytometry and budding index measurements in cell synchrony experiments.</a> Annals of Applied Statistics. 3 (4), 1521-1541 (2009).</li><li>Duan, F., Zhang, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Correcting+the+loss+of+cell-cycle+synchrony+in+clustering+analysis+of+microarray+data+using+weights.">Correcting the loss of cell-cycle synchrony in clustering analysis of microarray data using weights.</a> Bioinformatics. 20 (11), 1766-1771 (2004).</li><li>Darzynkiewicz, Z., Halicka, H. D., Zhao, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cell+synchronization+by+inhibitors+of+DNA+replication+induces+replication+stress+and+DNA+damage+response:+analysis+by+flow+cytometry.">Cell synchronization by inhibitors of DNA replication induces replication stress and DNA damage response: analysis by flow cytometry.</a> Methods in Molecular Biology. 761, 85-96 (2011).</li><li>Leman, A. R., Bristow, S. L., Haase, S. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Analyzing+transcription+dynamics+during+the+budding+yeast+cell+cycle.">Analyzing transcription dynamics during the budding yeast cell cycle.</a> Methods in Molecular Biology. 1170, 295-312 (2014).</li><li>Rosebrock, A. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Synchronization+and+arrest+of+the+budding+yeast+cell+cycle+using+chemical+and+genetic+methods.">Synchronization and arrest of the budding yeast cell cycle using chemical and genetic methods.</a> Cold Spring Harbor Protocols. 2017 (1), (2017).</li><li>Haase, S. B., Reed, S. I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Improved+flow+cytometric+analysis+of+the+budding+yeast+cell+cycle.">Improved flow cytometric analysis of the budding yeast cell cycle.</a> Cell Cycle. 1 (2), 132-136 (2002).</li><li>Kelliher, C. M., Leman, A. R., Sierra, C. S., Haase, S. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Investigating+conservation+of+the+cell-cycle-regulated+transcriptional+program+in+the+fungal+pathogen,+Cryptococcus+neoformans.">Investigating conservation of the cell-cycle-regulated transcriptional program in the fungal pathogen, Cryptococcus neoformans.</a> PLoS Genetics. 12 (12), e1006453 (2016).</li><li>Kelliher, C. M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Layers+of+regulation+of+cell-cycle+gene+expression+in+the+budding+yeast+Saccharomyces+cerevisiae.">Layers of regulation of cell-cycle gene expression in the budding yeast Saccharomyces cerevisiae.</a> Molecular Biology of the Cell. 29 (22), 2644-2655 (2018).</li><li>Hughes, M. E., Hogenesch, J. B., Kornacker, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=JTK_CYCLE:+An+efficient+nonparametric+algorithm+for+detecting+rhythmic+components+in+genome-scale+data+sets.">JTK_CYCLE: An efficient nonparametric algorithm for detecting rhythmic components in genome-scale data sets.</a> Journal of Biological Rhythms. 25 (5), 372-380 (2010).</li><li>Deckard, A., Anafi, R. C., Hogenesch, J. B., Haase, S. B., Harer, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Design+and+analysis+of+large-scale+biological+rhythm+studies:+A+comparison+of+algorithms+for+detecting+periodic+signals+in+biological+data.">Design and analysis of large-scale biological rhythm studies: A comparison of algorithms for detecting periodic signals in biological data.</a> Bioinformatics. 29 (24), 3174-3180 (2013).</li><li>Smith, L. M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=An+intrinsic+oscillator+drives+the+blood+stage+cycle+of+the+malaria+parasite+Plasmodium+falciparum.">An intrinsic oscillator drives the blood stage cycle of the malaria parasite Plasmodium falciparum.</a> Science. 368 (6492), 754-759 (2020).</li></ol>

The authors have no conflicts of interest to disclose.

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...

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 findings can increase the confidence in the reproducibility of the conclusions. Furthermore, comparisons between environmental conditions, across mutants, and even between species can uncover many new insights into cell-cycle regulation. However, interexperimental variability in the recovery from synchrony and in the speed of cell-cycle progression impairs the ability to make time-point-to-time-point comparisons across replicates or between experiments with altered cell-cycle timing. Due to these challenges, replicates are often not included for the full time series (e.g., Spellman et al.4). When replicates for the entire time series are gathered, the data cannot be analyzed in aggregate, but rather a single replicate is used for analysis, and other replicates are often relegated to supplemental figures (e.g., Orlando et al.8). Furthermore, comparisons between experiments with different recovery or cell-cycle progression characteristics are difficult. The measurements of smaller intervals between an event of interest and a cell-cycle landmark (e.g., bud emergence, S-phase entry, or anaphase onset) can help reduce errors if these landmark events are tracked1,2,3,9,10,11,12. However, subtle but important differences may remain undetected or obscured using these ad hoc methods. Finally, single-cell analyses allow for analyzing cell-cycle progression without relying on synchronization or alignment13, though large-scale measurements in single-cell studies can be challenging and costly.
To overcome these difficulties, we developed the Characterizing Loss of Cell Cycle Synchrony (CLOCCS) model to aid the analysis of time-series measurements made on synchronized populations14,15. CLOCCS is a flexible mathematical model that describes the distribution of synchronized cells across cell-cycle phases as they are released from synchrony and progress through the cell cycle. The branching process framework enables the model to account for the asymmetric qualities of mother and daughter cells after division, as observed in S. cerevisiae, while still being useful for organisms that divide by fission, such as S. pombe. The model can take inputs from a diverse set of measurement types to specify the cell-cycle phase. It can ingest budding cell-cycle phase data, which includes measurements of the percent budded cells over time, allowing for the estimation of the number of cells outside of the unbudded G1 phase14,15. The model can also ingest flow cytometric data that measures the DNA content, thus enabling the assessment of landmark transitions from G1 to S, S to G2, and M to G115. Fluorescent morphological markers can also be used to identify the cell-cycle phase. The fluorescent labeling of myosin rings, nuclei, and spindle pole bodies (SPBs) can be used to determine the cell-cycle phase, and these were incorporated into the CLOCCS model11; however, these measurements will not be described in this protocol. Additionally, the septation index was used as an input for modeling data from S. pombe14. Thus, the model can be used for cell-cycle analyses in a variety of organisms and can be further expanded.
CLOCCS is a parametric model that allows for the full Bayesian inference of multiple parameters from the input data (e.g., budding percentage, DNA content). These parameters include the recovery time from synchrony, the length of the cell-cycle period (estimated separately for mother and daughter cells), and the average cell-cycle position of the cells at each time point. These parameters represent the behavior of the average cell in the population, enabling the researcher to map each time point to a cell-cycle position expressed as a lifeline point. The conversion to lifeline points depends on the CLOCCS parameters lambda (&#955;) and mu0 (&#181;0)14,15. The parameter &#955; corresponds to the average cell-cycle period of the mother cells. However, due to the mother-daughter delay14,15, this is not the average cell-cycle period of the full population that includes both the mother and daughter cells. CLOCCS additionally infers the parameter delta (&#948;), which corresponds to the mother-daughter delay and, thus, allows for the calculation of the average cell-cycle period of the full population. Finally, because each experiment begins after release from cell-cycle synchronization, the time required to recover from the synchronization method is represented by the CLOCCS parameter &#181;0. CLOCCS fits a model to the input cell-cycle phase data and then infers these parameters using a random walk Markov chain Monte Carlo algorithm14,15. By mapping multiple experiments to a common cell-cycle lifeline time scale, direct phase-specific comparisons can be made between replicates or experiments where the recovery time or cell-cycle periods are not identical8,14,15.
As synchronized populations lose synchrony at some rate over the course of the time series14,15,16,17, variability in the rate of synchrony loss can also impede quantitative comparisons across experiments. By identifying the location of populations and the variance in their distributions, CLOCCS accounts for differences in rates of synchrony loss. This powerful tool allows for specific and detailed comparisons across experiments, thus providing the ability to directly make relevant comparisons not only between replicates but also between environmental conditions, mutants, and even species that have dramatically different cell-cycle timing14,15.
This paper describes a method using CLOCCS to estimate parameters by fitting data from synchrony/release time-series experiments, map the data to a common lifeline scale, and then make relevant comparisons between replicates or experiments. Lifeline alignment allows for direct phase-specific comparisons across these experiments, which allows for the aggregation and comparison of replicates and for making more relevant comparisons across experiments with different recovery timings and cell-cycle periods.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>2x PBS</td>
			<td></td>
			<td></td>
			<td>For Fixative Solution. Described in Leman 2014.</td>
		</tr>
		<tr>
			<td>4% formaldehyde</td>
			<td></td>
			<td></td>
			<td>For Fixative Solution.</td>
		</tr>
		<tr>
			<td>100% Ethanol</td>
			<td></td>
			<td></td>
			<td>For flow cytometry fixation. Described in Haase 2002.</td>
		</tr>
		<tr>
			<td>CLOCCS</td>
			<td></td>
			<td></td>
			<td>https://gitlab.com/haase-lab-group/cloccs_alignment.git</td>
		</tr>
		<tr>
			<td>Flow Cytometer</td>
			<td></td>
			<td></td>
			<td>For flow cytometry protocol.</td>
		</tr>
		<tr>
			<td>Git</td>
			<td></td>
			<td></td>
			<td>https://git-scm.com/</td>
		</tr>
		<tr>
			<td>Java 19</td>
			<td></td>
			<td></td>
			<td>https://www.oracle.com/java/technologies/downloads/#java19</td>
		</tr>
		<tr>
			<td>Microscope</td>
			<td></td>
			<td></td>
			<td>For counting cells and buds.</td>
		</tr>
		<tr>
			<td>Miniconda</td>
			<td></td>
			<td></td>
			<td>https://docs.conda.io/en/latest/</td>
		</tr>
		<tr>
			<td>Protease solution</td>
			<td></td>
			<td></td>
			<td>For flow cytometry protocol. Described in Haase 2002.</td>
		</tr>
		<tr>
			<td>RNAse A solution</td>
			<td></td>
			<td></td>
			<td>For flow cytometry protocol. Described in Haase 2002.</td>
		</tr>
		<tr>
			<td>SYTOX Green Nucleic Acid Stain</td>
			<td>Invitrogen</td>
			<td>S7020</td>
			<td>For flow cytometry staining. Described in Haase 2002.</td>
		</tr>
		<tr>
			<td>Tris</td>
			<td></td>
			<td></td>
			<td>pH 7.5</td>
		</tr>
	</tbody>
</table>

alignment of synchronized time-series data using the characterizing loss of cell cycle synchrony model for cross-experiment 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.

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,&#160;...

Watch this Scientific Journal Video about Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons at JoVE.com

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

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.

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

alignment-synchronized-time-series-data-using-characterizing-loss

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1.2K Views.  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.

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

Assembly, Loading, and Alignment of an Analytical Ultracentrifuge Sample Cell

The analytical ultracentrifuge (AUC) is a powerful biophysical tool that allows us to record macromolecular sedimentation profiles during high speed centrifugation. When properly planned and executed, an AUC sedimentation velocity or sedimentation equilibrium experiment can reveal a great deal about a protein in regards to size and shape, sample purity, sedimentation coefficient, oligomerization states and protein-protein interactions.
This technique, however, requires a rigorous level of technical attention. Sample cells hold a sectored center piece sandwiched between two window assemblies. They are sealed with a torque pressure of around 120-140 in/lbs. Reference buffer and sample are loaded into the centerpiece sectors and then after sealing, the cells are precisely aligned into a titanium rotor so that the optical detection systems scan both sample and reference buffer in the same radial path midline through each centerpiece sector while rotating at speeds of up to 60, 000 rpm and under very high vacuum
Not only is proper sample cell assembly critical, sample cell components are very expensive and must be properly cared for to ensure they are in optimum working condition in order to avoid leaks and breakage during experiments. Handle windows carefully, for even the slightest crack or scratch can lead to breakage in the centrifuge. The contact between centerpiece and windows must be as tight as possible; i.e. no Newton s rings should be visible after torque pressure is applied. Dust, lint, scratches and oils on either the windows or the centerpiece all compromise this contact and can very easily lead to leaking of solutions from one sector to another or leaking out of the centerpiece all together. Not only are precious samples lost, leaking of solutions during an experiment will cause an imbalance of pressure in the cell that often leads to broken windows and centerpieces. In addition, plug gaskets and housing plugs must be securely in place to avoid solutions being pulled out of the centerpiece sector through the loading holes by the high vacuum in the centrifuge chamber. Window liners and gaskets must be free of breaks and cracks that could cause movement resulting in broken windows.
This video will demonstrate our procedures of sample cell assembly, torque, loading and rotor alignment to help minimize component damage, solution leaking and breakage during the perfect AUC experiment.

The analytical ultracentrifuge (AUC) sample cell holds sample and reference buffer and during experiments and is exposed to high vacuum and rotor speeds up to 60,000 rpm. This video will demonstrate the rigorous attention to detail necessary for assembly, loading and alignment of this very important component of an AUC experiment.

The analytical ultracentrifuge (AUC) is a powerful biophysical tool that allows us to record macromolecular sedimentation profiles during high speed ...

Preparation of Quality Inositol Pyrophosphates

Myo-inositol is present in nature either unmodified or in more complex phosphorylated derivates. Of the latest, the two most abundant in eukaryotic cells are inositol pentakisphosphate (IP5) and inositol hexakisphosphate (phytic acid or IP6). IP5 and IP6 are the precursors of inositol pyrophosphate molecules that contain one or more pyrophosphate bonds1. Phosphorylation of IP6 generates diphoshoinositolpentakisphosphate (IP7 or PP-IP5) and bisdiphoshoinositoltetrakisphosphate (IP8 or (PP)2-IP4). Inositol pyrophosphates have been isolated from all eukaryotic organisms so far studied. In addition, the two distinct classes of enzymes responsible for inositol pyrophosphate synthesis are highly conserved throughout evolution2-4.
The IP6 kinases (IP6Ks) posses an enormous catalytic flexibility, converting IP5 and IP6 to PP-IP4 and IP7 respectively and subsequently, by using these products as substrates, promote the generation of more complex molecules5,6. Recently, a second class of pyrophosphate generating enzymes was identified in the form of the yeast protein VIP1 (also referred as PP-IP5K), which is able to convert IP6 to IP7 and IP87,8.
Inositol pyrophosphates regulate many disparate cellular processes such as insulin secretion9, telomere length10,11, chemotaxis12, vesicular trafficking13, phosphate homeostasis14 and HIV-1 gag release15. Two mechanisms of actions have been proposed for this class of molecules. They can affect cellular function by allosterically interacting with specific proteins like AKT16. Alternatively, the pyrophosphate group can donate a phosphate to pre-phosphorylated proteins17. The enormous potential of this research field is hampered by the absence of a commercial source of inositol pyrophosphates, which is preventing many scientists from studying these molecules and this new post-translational modification. The methods currently available to isolate inositol pyrophosphates require sophisticated chromatographic apparatus18,19. These procedures use acidic conditions that might lead to inositol pyrophosphate degradation20 and thus to poor recovery. Furthermore, the cumbersome post-column desalting procedures restrict their use to specialized laboratories.
In this study we describe an undemanding method for the generation, isolation and purification of the products of the IP6-kinase and PP-IP5-kinases reactions. This method was possible by the ability of polyacrylamide gel electrophoresis (PAGE) to resolve highly phosphorylated inositol polyphosphates20. Following IP6K1 and PP-IP5K enzymatic reactions using IP6 as the substrate, PAGE was used to separate the generated inositol pyrophosphates that were subsequently eluted in water.

Inositol pyrophosphates play an important role in human pathologies such cancer, diabetes and obesity; however, the exact mechanism of action is a matter of dispute. The lack of commercially available inositol pyrophosphates renders detailed studies problematic. Here we describe a simple protocol to produce and isolate milligrams of inositol pyrophosphates.

Myo-inositol is present in nature either unmodified or in more complex phosphorylated derivates. Of the latest, the two most abundant in eukaryotic cells ...

Monitoring Kinase and Phosphatase Activities Through the Cell Cycle by Ratiometric FRET

F&ouml;rster resonance energy transfer (FRET)-based reporters1 allow the assessment of endogenous kinase and phosphatase activities in living cells. Such probes typically consist of variants of CFP and YFP, intervened by a phosphorylatable sequence and a phospho-binding domain. Upon phosphorylation, the probe changes conformation, which results in a change of the distance or orientation between CFP and YFP, leading to a change in FRET efficiency (Fig 1). Several probes have been published during the last decade, monitoring the activity balance of multiple kinases and phosphatases, including reporters of PKA2, PKB3, PKC4, PKD5, ERK6, JNK7, Cdk18, Aurora B9 and Plk19. Given the modular design, additional probes are likely to emerge in the near future10. 
Progression through the cell cycle is affected by stress signaling pathways 11. Notably, the cell cycle is regulated differently during unperturbed growth compared to when cells are recovering from stress12.Time-lapse imaging of cells through the cell cycle therefore requires particular caution. This becomes a problem particularly when employing ratiometric imaging, since two images with a high signal to noise ratio are required to correctly interpret the results. Ratiometric FRET imaging of cell cycle dependent changes in kinase and phosphatase activities has predominately been restricted to sub-sections of the cell cycle8,9,13,14.
Here, we discuss a method to monitor FRET-based probes using ratiometric imaging throughout the human cell cycle. The method relies on equipment that is available to many researchers in life sciences and does not require expert knowledge of microscopy or image processing.

FRET-based reporters are increasingly used to monitor kinase and phosphatase activities in live cells. Here we describe a method on how to use FRET-based reporters to assess cell cycle-dependent changes in target phosphorylation.

F&ouml;rster resonance energy transfer (FRET)-based reporters1 allow the assessment of endogenous kinase and phosphatase activities in living cells. Such ...

Analysis of Cell Cycle Position in Mammalian Cells

The regulation of cell proliferation is central to tissue morphogenesis during the development of multicellular organisms. Furthermore, loss of control of cell proliferation underlies the pathology of diseases like cancer. As such there is great need to be able to investigate cell proliferation and quantitate the proportion of cells in each phase of the cell cycle. It is also of vital importance to indistinguishably identify cells that are replicating their DNA within a larger population. Since a cell&prime;s decision to proliferate is made in the G1 phase immediately before initiating DNA synthesis and progressing through the rest of the cell cycle, detection of DNA synthesis at this stage allows for an unambiguous determination of the status of growth regulation in cell culture experiments.
DNA content in cells can be readily quantitated by flow cytometry of cells stained with propidium iodide, a fluorescent DNA intercalating dye. Similarly, active DNA synthesis can be quantitated by culturing cells in the presence of radioactive thymidine, harvesting the cells, and measuring the incorporation of radioactivity into an acid insoluble fraction. We have considerable expertise with cell cycle analysis and recommend a different approach. We Investigate cell proliferation using bromodeoxyuridine/fluorodeoxyuridine (abbreviated simply as BrdU) staining that detects the incorporation of these thymine analogs into recently synthesized DNA. Labeling and staining cells with BrdU, combined with total DNA staining by propidium iodide and analysis by flow cytometry1 offers the most accurate measure of cells in the various stages of the cell cycle. It is our preferred method because it combines the detection of active DNA synthesis, through antibody based staining of BrdU, with total DNA content from propidium iodide. This allows for the clear separation of cells in G1 from early S phase, or late S phase from G2/M. Furthermore, this approach can be utilized to investigate the effects of many different cell stimuli and pharmacologic agents on the regulation of progression through these different cell cycle phases.
In this report we describe methods for labeling and staining cultured cells, as well as their analysis by flow cytometry. We also include experimental examples of how this method can be used to measure the effects of growth inhibiting signals from cytokines such as TGF-&beta;1, and proliferative inhibitors such as the cyclin dependent kinase inhibitor, p27KIP1. We also include an alternate protocol that allows for the analysis of cell cycle position in a sub-population of cells within a larger culture5. In this case, we demonstrate how to detect a cell cycle arrest in cells transfected with the retinoblastoma gene even when greatly outnumbered by untransfected cells in the same culture. These examples illustrate the many ways that DNA staining and flow cytometry can be utilized and adapted to investigate fundamental questions of mammalian cell cycle control.

Determining the cell cycle position of a population of cells, or understanding how signals affect proliferation, can be readily measured by flow cytometry using this protocol. We report a simple experimental approach to staining cells and quantifying their position in the cell cycle.

The regulation of cell proliferation is central to tissue morphogenesis during the development of multicellular organisms. Furthermore, loss of control of ...

Synchronization of Caulobacter Crescentus for Investigation of the Bacterial Cell Cycle

The cell cycle is important for growth, genome replication, and development in all cells. In bacteria, studies of the cell cycle have focused largely on unsynchronized cells making it difficult to order the temporal events required for cell cycle progression, genome replication, and division. Caulobacter crescentus provides an excellent model system for the bacterial cell cycle whereby cells can be rapidly synchronized in a G0 state by density centrifugation. Cell cycle synchronization experiments have been used to establish the molecular events governing chromosome replication and segregation, to map a genetic regulatory network controlling cell cycle progression, and to identify the establishment of polar signaling complexes required for asymmetric cell division. Here we provide a detailed protocol for the rapid synchronization of Caulobacter NA1000 cells. Synchronization can be performed in a large-scale format for gene expression profiling and western blot assays, as well as a small-scale format for microscopy or FACS assays. The rapid synchronizability and high cell yields of Caulobacter make this organism a powerful model system for studies of the bacterial cell cycle.

Synchronization of Caulobacter Crescentus for Investigation of the Bacterial Cell Cycle

Synchronization of bacterial cells is essential for studies of the bacterial cell cycle and development. Caulobacter crescentus is synchronizable through density centrifugation allowing a rapid and powerful tool for studies of the bacterial cell cycle. Here we provide a detailed protocol for the synchronization of Caulobacter cells.

The cell cycle is important for growth, genome replication, and development in all cells.  In bacteria, studies of the cell cycle have focused largely on ...

Temporal Tracking of Cell Cycle Progression Using Flow Cytometry without the Need for Synchronization

This protocol describes a method to permit the tracking of cells through the cell cycle without requiring the cells to be synchronized. Achieving cell synchronization can be difficult for many cell systems. Standard practice is to block cell cycle progression at a specific stage and then release the accumulated cells producing a wave of cells progressing through the cycle in unison. However, some cell types find this block toxic resulting in abnormal cell cycling, or even mass death. Bromodeoxyuridine (BrdU) uptake can be used to track the cell cycle stage of individual cells. Cells incorporate this synthetic thymidine analog, while synthesizing new DNA during S phase. By providing BrdU for a brief period it is possible to mark a pool of cells that were in S phase while the BrdU was present. These cells can then be tracked through the remainder of the cell cycle and into the next round of replication, permitting the duration of the cell cycle phases to be determined without the need to induce a potentially toxic cell cycle block. It is also possible to determine and correlate the expression of both internal and external proteins during subsequent stages of the cell cycle. These can be used to further refine the assignment of cell cycle stage or assess effects on other cellular functions such as checkpoint activation or cell death.

This protocol describes the use of bromodeoxyuridine (BrdU) uptake to permit the temporal tracking of cells that were in S phase at a specific point in time. Addition of DNA dyes and antibody labeling facilitates detailed analysis of the fate of the S phase cells at later times.

This protocol describes a method to permit the tracking of cells through the cell cycle without requiring the cells to be synchronized. Achieving cell ...

Analysis of Beta-cell Function Using Single-cell Resolution Calcium Imaging in Zebrafish Islets

Pancreatic beta-cells respond to increasing blood glucose concentrations by secreting the hormone insulin. The dysfunction of beta-cells leads to hyperglycemia and severe, life-threatening consequences. Understanding how the beta-cells operate under physiological conditions and what genetic and environmental factors might cause their dysfunction could lead to better treatment options for diabetic patients. The ability to measure calcium levels in beta-cells serves as an important indicator of beta-cell function, as the influx of calcium ions triggers insulin release. Here we describe a protocol for monitoring the glucose-stimulated calcium influx in zebrafish beta-cells by using GCaMP6s, a genetically encoded sensor of calcium. The method allows monitoring the intracellular calcium dynamics with single-cell resolution in ex vivo mounted islets. The glucose-responsiveness of beta-cells within the same islet can be captured simultaneously under different glucose concentrations, which suggests the presence of functional heterogeneity among zebrafish beta-cells. Furthermore, the technique provides high temporal and spatial resolution, which reveals the oscillatory nature of the calcium influx upon glucose stimulation. Our approach opens the doors to use the zebrafish as a model to investigate the contribution of genetic and environmental factors to beta-cell function and dysfunction.

Beta-cell functionality is important for blood-glucose homeostasis, which is evaluated at single-cell resolution using a genetically encoded reporter for calcium influx.

Pancreatic beta-cells respond to increasing blood glucose concentrations by secreting the hormone insulin. The dysfunction of beta-cells leads to ...

Ex Vivo Method for Assessing the Mouse Reproductive Tract Spontaneous Motility and a MATLAB-based Uterus Motion Tracking Algorithm for Data Analysis

Dysmenorrhea, or painful cramping, is the most common symptom associated with menses in females and its severity can hinder women&#39;s everyday lives. Here, we present an easy and inexpensive method that would be instrumental for testing new drugs decreasing uterine contractility. This method utilizes the unique ability of the entire mouse reproductive tract to exhibit spontaneous motility when maintained ex vivo in a Petri dish containing oxygenated Krebs buffer. This spontaneous motility resembles the wave-like myometrial activity of the human uterus, referred to as endometrial waves. To demonstrate the effectiveness of the method, we employed a well-known uterine relaxant drug, epinephrine. We demonstrate that the spontaneous motility of the entire mouse reproductive tract can be quickly and reversibly inhibited by 1 &#181;M epinephrine in this Petri dish model. Documenting the changes of uterine motility can be easily done using an ordinary smart phone or a sophisticated digital camera. We developed a MATLAB-based algorithm allowing motion tracking to quantify spontaneous uterine motility changes by measuring the rate of uterine horn movements. A major advantage of this ex vivo approach is that the reproductive tract remains intact throughout the entire experiment, preserving all intrinsic intrauterine cellular interactions. The major limitation of this approach is that up to 10-20% of uteri may exhibit no spontaneous motility. Thus far, this is the first quantitative ex vivo method for assessing spontaneous uterine motility in a Petri dish model.

Uterine contractions are important for the well-being of females. However, pathologically increased contractility may result in dysmenorrhea, especially in younger females. Here, we describe a simple ex vivo preparation allowing quick assessment of the efficacy of smooth muscle relaxants that may be used for treating dysmenorrhea.

Dysmenorrhea, or painful cramping, is the most common symptom associated with menses in females and its severity can hinder women&#39;s everyday lives. ...

Live-Cell Imaging of the Life Cycle of Bacterial Predator Bdellovibrio bacteriovorus using Time-Lapse Fluorescence Microscopy

Bdellovibrio bacteriovorus is a small gram-negative, obligate predatory bacterium that kills other gram-negative bacteria, including harmful pathogens. Therefore, it is considered a living antibiotic. To apply B. bacteriovorus as a living antibiotic, it is first necessary to understand the major stages of its complex life cycle, particularly its proliferation inside prey. So far, it has been challenging to monitor successive stages of the predatory life cycle in real-time. Presented here is a comprehensive protocol for real-time imaging of the complete life cycle of B. bacteriovorus, especially during its growth inside the host. For this purpose, a system consisting of an agarose pad is used in combination with cell-imaging dishes, in which the predatory cells can move freely beneath the agarose pad while immobilized prey cells are able to form bdelloplasts. The application of a strain producing a fluorescently tagged &#946;-subunit of DNA polymerase III further allows chromosome replication to be monitored during the reproduction phase of the B. bacteriovorus life cycle.

Live-Cell Imaging of the Life Cycle of Bacterial Predator Bdellovibrio bacteriovorus using Time-Lapse Fluorescence Microscopy

Presented here is a protocol that describes monitoring of the complete life cycle of predatory bacterium Bdellovibrio bacteriovorus using time-lapse fluorescence microscopy in combination with an agarose pad and cell-imaging dishes.

Bdellovibrio bacteriovorus is a small gram-negative, obligate predatory bacterium that kills other gram-negative bacteria, including harmful pathogens. ...

Mass Spectrometry Analysis to Identify Ubiquitylation of EYFP-tagged CENP-A (EYFP-CENP-A)

Studying the structure and the dynamics of kinetochores and centromeres is important in understanding chromosomal instability (CIN) and cancer progression. How the chromosomal location and function of a centromere (i.e., centromere identity) are determined and participate in accurate chromosome segregation is a fundamental question. CENP-A is proposed to be the non-DNA indicator (epigenetic mark) of centromere identity, and CENP-A ubiquitylation is required for CENP-A deposition at the centromere, inherited through dimerization between cell division, and indispensable to cell viability.
Here we describe mass spectrometry analysis to identify ubiquitylation of EYFP-CENP-A K124R mutant suggesting that ubiquitylation at a different lysine is induced because of the EYFP tagging in the CENP-A K124R mutant protein. Lysine 306 (K306) ubiquitylation in EYFP-CENP-A K124R was successfully identified, which corresponds to lysine 56 (K56) in CENP-A through mass spectrometry analysis. A caveat is discussed in the use of GFP/EYFP or the tagging of high molecular weight protein as a tool to analyze the function of a protein. Current technical limit is also discussed for the detection of ubiquitylated bands, identification of site-specific ubiquitylation(s), and visualization of ubiquitylation in living cells or a specific single cell during the whole cell cycle.
The method of mass spectrometry analysis presented here can be applied to human CENP-A protein with different tags and other centromere-kinetochore proteins. These combinatory methods consisting of several assays/analyses could be recommended for researchers who are interested in identifying functional roles of ubiquitylation.

Mass Spectrometry Analysis to Identify Ubiquitylation of EYFP-tagged CENP-A EYFP-CENP-A

CENP-A ubiquitylation is an important requirement for CENP-A deposition at the centromere, inherited through dimerization between cell division, and indispensable to cell viability. Here we describe mass spectrometry analysis to identify ubiquitylation of EYFP-tagged CENP-A (EYFP-CENP-A) protein.