conversion of time points to lifeline points using the python conversion functions and the cloccs parameters

Alignement des données de séries chronologiques synchronisées pour les comparaisons entre expériences

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.

Pleins feux sur l’auteur : Alignement de données de séries chronologiques synchronisées à l’aide du modèle de caractérisation de la perte de synchronisation du cycle cellulaire pour les comparaisons i

Alignement de données de séries chronologiques synchronisées à l’aide du modèle de synchronisation de perte de cycle cellulaire caractérisant pour les comparaisons entre expériences

A major challenge of analyzing time-series experiments is that they often differ in the length of recovery from synchrony and the cell cycle period. So measurements from different experiments cannot readily be compared or analyzed in aggregate without alignment. Clocks lifeline alignment is a method for phase specific and biologically relevant comparisons between experiments and for aggregating multiple replicate experiments both of which were previously difficult or impossible.Previously phase specific and biologically relevant comparisons were made by tracking landmark events. However, using these ad hoc methods, subtle but important differences remain undetected. Comparison between time series experiments is particularly difficult between experiments with significant differences in timing such as in mutant populations or growth conditions that affect the synchrony recovery time or the cell cycle period.Clocks lifeline alignment allows for phase specific comparison in these cases. We have used clocks to align time series transcriptomic and proteomic data, which allowed for the direct comparison between the dynamics of the mRNA and the corresponding protein. Furthermore, we align time series experiments across different species, providing critical insights into cell cycle evolutionary changes.

Watch this Scientific Journal Video about Conversion of Time Points to Lifeline Points Using the Python Conversion Functions and the CLOCCS Parameters at JoVE.com

Conversion of Time Points to Lifeline Points Using the Python Conversion Functions and the CLOCCS Parameters  

Conversion de points temporels en points de ligne de vie à l’aide des fonctions de conversion Python et des paramètres CLOCCS

Pour commencer, activez l’environnement Conda en entrant la commande dans le terminal. Utilisez la commande pour ouvrir un bloc-notes Python interactif. Créez un nouveau bloc-notes Python dans le dossier souhaité, en fournissant un nom approprié.Importez ensuite le fichier Python contenant les fonctions d’alignement en exécutant la commande dans la première cellule. Si vous utilisez des données bourgeonnantes comme données de phase du cycle cellulaire. Importez une trame de données contenant le pourcentage de bourgeons à chaque point temporel en exécutant la commande dans une nouvelle cellule.Alignez ensuite les données naissantes sur une échelle de temps de point de vie en entrant la fonction dans une nouvelle cellule. Ensuite, importez la trame de données expérimentale dans le bloc-notes en exécutant la commande dans une nouvelle cellule. Alignez les données expérimentales sur une échelle de points temporels de ligne de vie en entrant la fonction dans une nouvelle cellule.Entrez ensuite la commande dans une nouvelle cellule pour télécharger l’ensemble de données alignées sur la ligne de vie. Les données naissantes recueillies à partir de la condition 2 RNAseq ont montré le pourcentage d’anomalie au fil du temps pour l’échelle de temps non alignée en minutes et l’échelle de temps alignée en points de ligne de vie. Les données de cytométrie en flux recueillies pour l’ensemble de données de la condition 2 tracé pour un point temporel sélectionné ont montré que les données correspondaient à la phase déterminée par l’alignement.

conversion-time-points-to-lifeline-points-using-python-conversion

Research

JoVE Journal

Biology

Conversion of Time Points to Lifeline Points Using the Python Conversion Functions and the CLOCCS Parameters

145 vues. Pour commencer, activez l’environnement Conda en entrant la commande dans le terminal. Utilisez la commande pour ouvrir un bloc-notes Python interactif. Créez un nouveau bloc-notes Python dans le dossier souhaité, en fournissant un nom approprié.Importez ensuite le fichier Python contenant les fonctions d’alignement en exécutant la commande dans la première cellule. Si vous utilisez des données bourgeonnantes comme données de phase du cycle cellulaire. Importez une trame de ...

Vidéo: Conversion of Time Points to Lifeline Points Using the Python Conversion Functions and the CLOCCS Parameters  

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.

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 ...

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Biologie

Installing CLOCCS and Using it to Parameterize the Experiments

To begin, download the CLOCCS alignment repo by entering the command into the terminal. Create a conda environment using the conda.rec. yml file by entering the command into the terminal in the folder where the CLOCCS alignment repo was cloned.Double-click the cloccs_v2023. jar file located in the CLOCCS folder in the CLOCCS alignment repo and wait for a graphical user interface to open. The screen allows input options for the CLOCCS run and displays the results once run.Input the experimental conditions by specifying the temperature in Celsius using the text box labeled Temperature and specify the synchronization method using the dropdown menu Synchro.method. To input the settings for the budding data, choose the option Bud for budding yeast from the model type dropdown menu. Import the data by uploading a file using the Select File button, or by typing it into the text input boxes of the Data Import panel.The first column specifies the time points, and the other two columns specify the budding data. To input the settings for flow cytometric data, select the Flow section from the Model type dropdown menu. Import the data using the Data Import panel and click Select File.Then select the time points in the Times for fitting box for which a flow cytometric CLOCCS fit should be plotted. Once all the inputs have been selected for either budding or flow cytometry, click the Apply button and then the Sample button at the top of the screen. View the budding curve or flow cytometry plots in the Predicted Fits tab.Obtain the CLOCCS parameters from the fit by selecting the Posterior Parameters tab. The resulting table has each row consisting of the parameter, with the final row being the posterior. The columns consist of the predicted parameter for the mean, 2.5%lower confidence interval, 97.5%upper confidence interval, and acceptance rate.The CLOCCS budding curves were properly fit as demonstrated by the data points overlaying the corresponding fit curve with a small 95%confidence band. With flow cytometry cell cycle phase data, CLOCCS produces a CLOCCS fit for each selected time point.

Installation de CLOCCS et utilisation de celui-ci pour paramétrer les expériences

To begin, activate the Conda environment by entering the command into the terminal. Use the command to open an interactive Python notebook. Create a new Python notebook in the desired folder, providing an appropriate name.Then import the Python file containing the alignment functions by running the command in the first cell. If using budding data as the cell cycle phase data. Import a data frame containing the percent budded at each time point by running the command in a new cell.Then align the budding data to a lifeline point timescale by entering the function into a new cell. Next, import the experimental data frame into the notebook by running the command in a new cell. Align the experimental data to a lifeline time point scale by entering the function into a new cell.Then enter the command into a new cell to download the lifeline aligned data set. The budding data collected from the Condition 2 RNAseq showed the percent butted over time for both the unaligned timescale in minutes, and the aligned timescale in lifeline points. The flow cytometry data collected for the Condition 2 dataset plotted for a selected time points showed that the data matched the phase determined by the alignment.

Comparing Budding Curves, Flow Cytometry Data, and Experimental Data

Plot the budding curves before alignment using the Python utility function by entering the command into a new cell. Next, plot the budding curves after alignment. Using the Python utility function.Use the provided plot line graph comparison in the Python utilities vial to perform line graph comparisons on the original, aligned or aligned and interpolated data frame. By typing the command into a new cell, import a CSV or TSV gene list file into the notebook using the command in a new cell. Next, use the provided function plot heat map comparison in the Python utilities file to perform a heat map comparison on the aligned interpolated and phase aligned data frame by typing the command into a new cell.A comparison of the aligned and unaligned transcriptomatic data showed that before alignment the first peak expression of the microarray experiments appeared aligned with the second peak of the RNA-seq Experiment. However, after alignment, the first cell cycle peaks of each dataset are appropriately aligned. Comparison of the cell cycle phase data across experiments with varying periods exhibited visible period differences in the unaligned budding curves.Whereas clocks alignment made the three curves look remarkably similar, making comparisons of experimental data possible, the cell cycle phase data for each comparable lifeline point was not identical between the two conditions. Comparison of the transcriptomic data across experiments with varying periods showed that before alignment the transcript dynamics of CDC20 were non-overlapping. But after alignment, the peaks occurred on the same cell cycle phase but the shapes of the curves were different.The genes were plotted as heat maps in the same order for all three conditions for both unaligned and aligned.