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

Alineación de datos de series temporales sincronizadas para comparaciones entre experimentos

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.

Autor destacado: Alineación de datos de series temporales sincronizadas utilizando el modelo de caracterización de la pérdida de sincronía del ciclo celular para comparaciones entre experimentos  

Alineación de datos sincronizados de series temporales utilizando el modelo de caracterización de la pérdida de sincronía del ciclo celular para comparaciones entre experimentos

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  

Conversión de puntos de tiempo a puntos de línea de vida utilizando las funciones de conversión de Python y los parámetros CLOCCS

Para comenzar, active el entorno Conda ingresando el comando en el terminal. Utilice el comando para abrir un bloc de notas interactivo de Python. Cree un nuevo bloc de notas de Python en la carpeta deseada, proporcionando un nombre apropiado.A continuación, importe el archivo de Python que contiene las funciones de alineación ejecutando el comando en la primera celda. Si utiliza datos de gemación como datos de fase del ciclo celular. Importe un marco de datos que contenga el porcentaje de bytes en cada punto de tiempo ejecutando el comando en una celda nueva.A continuación, alinee los datos en ciernes con una escala de tiempo de punto de línea de vida introduciendo la función en una nueva celda. A continuación, importe el marco de datos experimental en el bloc de notas ejecutando el comando en una nueva celda. Alinee los datos experimentales con una escala de punto de tiempo de línea de vida introduciendo la función en una nueva celda.A continuación, introduzca el comando en una nueva celda para descargar el conjunto de datos alineados con la línea de vida. Los datos de gemación recopilados del RNAseq de la Condición 2 mostraron el porcentaje de tope a lo largo del tiempo tanto para la escala de tiempo no alineada en minutos como para la escala de tiempo alineada en los puntos de la línea de vida. Los datos de citometría de flujo recopilados para el conjunto de datos de la Condición 2 trazados para un punto de tiempo seleccionado mostraron que los datos coincidían con la fase determinada por la alineación.

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

148 vistas. Para comenzar, active el entorno Conda ingresando el comando en el terminal. Utilice el comando para abrir un bloc de notas interactivo de Python. Cree un nuevo bloc de notas de Python en la carpeta deseada, proporcionando un nombre apropiado.A continuación, importe el archivo de Python que contiene las funciones de alineación ejecutando el comando en la primera celda. Si utiliza datos de gemación como datos de fase del ciclo celular. Importe un marco de datos que contenga el porcen...

Video: 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 ...