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A generalized model for multi-marker analysis of cell cycle progression in synchrony experiments.

Mayhew MB, Robinson JW, Jung B, Haase SB, Hartemink AJ - Bioinformatics (2011)

Bottom Line: We use our model to estimate the duration of post-cytokinetic attachment between a S.cerevisiae mother and daughter cell.The Java implementation is fast and extensible, and includes a graphical user interface.Our model provides a powerful and flexible cell cycle analysis tool, suitable to any type or combination of binary markers.

View Article: PubMed Central - PubMed

Affiliation: Program in Computational Biology and Bioinformatics, Department of Computer Science, Center for Systems Biology, Institute for Genome Sciences and Policy, Duke University, Durham, NC 27708, USA. michael.mayhew@duke.edu

ABSTRACT

Motivation: To advance understanding of eukaryotic cell division, it is important to observe the process precisely. To this end, researchers monitor changes in dividing cells as they traverse the cell cycle, with the presence or absence of morphological or genetic markers indicating a cell's position in a particular interval of the cell cycle. A wide variety of marker data is available, including information-rich cellular imaging data. However, few formal statistical methods have been developed to use these valuable data sources in estimating how a population of cells progresses through the cell cycle. Furthermore, existing methods are designed to handle only a single binary marker of cell cycle progression at a time. Consequently, they cannot facilitate comparison of experiments involving different sets of markers.

Results: Here, we develop a new sampling model to accommodate an arbitrary number of different binary markers that characterize the progression of a population of dividing cells along a branching process. We engineer a strain of Saccharomyces cerevisiae with fluorescently labeled markers of cell cycle progression, and apply our new model to two image datasets we collected from the strain, as well as an independent dataset of different markers. We use our model to estimate the duration of post-cytokinetic attachment between a S.cerevisiae mother and daughter cell. The Java implementation is fast and extensible, and includes a graphical user interface. Our model provides a powerful and flexible cell cycle analysis tool, suitable to any type or combination of binary markers.

Availability: The software is available from: http://www.cs.duke.edu/~amink/software/cloccs/.

Contact: michael.mayhew@duke.edu; amink@cs.duke.edu.

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Related in: MedlinePlus

Model fits for α-factor-treated cells (A) and elutriated cells (B) using four different binary markers collected at regular intervals over two to three cell cycles. Shaded bands represent 95% credible intervals for posterior inferences. Consistent with previous analytical and experimental observations, the time required for a yeast population to recover from synchronization and enter the cell cycle is longer following elutriation than treatment with α-factor (Bellí et al., 2001; Orlando et al., 2007). This is reflected in the rightward shift of the posterior curves for the elutriated cells relative to the posterior curves for the α-factor-treated cells.
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Figure 4: Model fits for α-factor-treated cells (A) and elutriated cells (B) using four different binary markers collected at regular intervals over two to three cell cycles. Shaded bands represent 95% credible intervals for posterior inferences. Consistent with previous analytical and experimental observations, the time required for a yeast population to recover from synchronization and enter the cell cycle is longer following elutriation than treatment with α-factor (Bellí et al., 2001; Orlando et al., 2007). This is reflected in the rightward shift of the posterior curves for the elutriated cells relative to the posterior curves for the α-factor-treated cells.

Mentions: The CLOCCS model fits and inferred cell cycle subintervals of each marker for the two image datasets are shown in Figures 4 and 5. In both cases, the posterior fits track the data well. Furthermore, the order of cell cycle events reflected in the order of peaks of marker visibility (Fig. 4) and the positioning of the marker-specific subintervals (Fig. 5) matches current biological knowledge of the budding yeast cell cycle. With minimal prior constraints (ss−=ls+; Table 1) on the order of each pair of marker-specific parameters, CLOCCS infers the formation of the myosin ring followed by emergence of the bud and appearance of the short and then long mitotic spindle (Bi et al., 1998; Hartwell, 1974). Another CLOCCS-inferred parameter, the average time at which a cell recovers from synchronization and enters the cell cycle (μ0), recapitulates analytical and experimental observations that elutriated cells take longer to recover on average than cells treated with α-factor [Table 1; (Bellí et al., 2001; Orlando et al., 2007)]. Parameter estimates for both datasets were comparable, demonstrating the robustness of the model to different synchronization methods. In particular, estimates of cell cycle duration (λ) are consistent with empirical evidence [Table 1; 96.9 min from Lord and Wheals (1981)]. The proportion of dead cells estimated for both the α-factor and elutriation datasets was close to zero, while estimates for the proportion of early cells differed between the two datasets. More specifically, the α-factor-treated cells showed a significant proportion of early cells with myosin rings, but proportions with later markers were close to zero. In contrast, estimates from the elutriated cells revealed a significant proportion of early cells with myosin rings present, a slightly smaller proportion of budded early cells, and smaller proportions still with short and long mitotic spindles. These estimates reflect the timing of appearance of these markers as well as our expectation that early cells are not too far advanced in their cell cycle progression. The model was able to identify this relationship between the proportions of early elutriated cells with different markers without the specification of any constraints on inference of the parameters.Fig. 4.


A generalized model for multi-marker analysis of cell cycle progression in synchrony experiments.

Mayhew MB, Robinson JW, Jung B, Haase SB, Hartemink AJ - Bioinformatics (2011)

Model fits for α-factor-treated cells (A) and elutriated cells (B) using four different binary markers collected at regular intervals over two to three cell cycles. Shaded bands represent 95% credible intervals for posterior inferences. Consistent with previous analytical and experimental observations, the time required for a yeast population to recover from synchronization and enter the cell cycle is longer following elutriation than treatment with α-factor (Bellí et al., 2001; Orlando et al., 2007). This is reflected in the rightward shift of the posterior curves for the elutriated cells relative to the posterior curves for the α-factor-treated cells.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3117372&req=5

Figure 4: Model fits for α-factor-treated cells (A) and elutriated cells (B) using four different binary markers collected at regular intervals over two to three cell cycles. Shaded bands represent 95% credible intervals for posterior inferences. Consistent with previous analytical and experimental observations, the time required for a yeast population to recover from synchronization and enter the cell cycle is longer following elutriation than treatment with α-factor (Bellí et al., 2001; Orlando et al., 2007). This is reflected in the rightward shift of the posterior curves for the elutriated cells relative to the posterior curves for the α-factor-treated cells.
Mentions: The CLOCCS model fits and inferred cell cycle subintervals of each marker for the two image datasets are shown in Figures 4 and 5. In both cases, the posterior fits track the data well. Furthermore, the order of cell cycle events reflected in the order of peaks of marker visibility (Fig. 4) and the positioning of the marker-specific subintervals (Fig. 5) matches current biological knowledge of the budding yeast cell cycle. With minimal prior constraints (ss−=ls+; Table 1) on the order of each pair of marker-specific parameters, CLOCCS infers the formation of the myosin ring followed by emergence of the bud and appearance of the short and then long mitotic spindle (Bi et al., 1998; Hartwell, 1974). Another CLOCCS-inferred parameter, the average time at which a cell recovers from synchronization and enters the cell cycle (μ0), recapitulates analytical and experimental observations that elutriated cells take longer to recover on average than cells treated with α-factor [Table 1; (Bellí et al., 2001; Orlando et al., 2007)]. Parameter estimates for both datasets were comparable, demonstrating the robustness of the model to different synchronization methods. In particular, estimates of cell cycle duration (λ) are consistent with empirical evidence [Table 1; 96.9 min from Lord and Wheals (1981)]. The proportion of dead cells estimated for both the α-factor and elutriation datasets was close to zero, while estimates for the proportion of early cells differed between the two datasets. More specifically, the α-factor-treated cells showed a significant proportion of early cells with myosin rings, but proportions with later markers were close to zero. In contrast, estimates from the elutriated cells revealed a significant proportion of early cells with myosin rings present, a slightly smaller proportion of budded early cells, and smaller proportions still with short and long mitotic spindles. These estimates reflect the timing of appearance of these markers as well as our expectation that early cells are not too far advanced in their cell cycle progression. The model was able to identify this relationship between the proportions of early elutriated cells with different markers without the specification of any constraints on inference of the parameters.Fig. 4.

Bottom Line: We use our model to estimate the duration of post-cytokinetic attachment between a S.cerevisiae mother and daughter cell.The Java implementation is fast and extensible, and includes a graphical user interface.Our model provides a powerful and flexible cell cycle analysis tool, suitable to any type or combination of binary markers.

View Article: PubMed Central - PubMed

Affiliation: Program in Computational Biology and Bioinformatics, Department of Computer Science, Center for Systems Biology, Institute for Genome Sciences and Policy, Duke University, Durham, NC 27708, USA. michael.mayhew@duke.edu

ABSTRACT

Motivation: To advance understanding of eukaryotic cell division, it is important to observe the process precisely. To this end, researchers monitor changes in dividing cells as they traverse the cell cycle, with the presence or absence of morphological or genetic markers indicating a cell's position in a particular interval of the cell cycle. A wide variety of marker data is available, including information-rich cellular imaging data. However, few formal statistical methods have been developed to use these valuable data sources in estimating how a population of cells progresses through the cell cycle. Furthermore, existing methods are designed to handle only a single binary marker of cell cycle progression at a time. Consequently, they cannot facilitate comparison of experiments involving different sets of markers.

Results: Here, we develop a new sampling model to accommodate an arbitrary number of different binary markers that characterize the progression of a population of dividing cells along a branching process. We engineer a strain of Saccharomyces cerevisiae with fluorescently labeled markers of cell cycle progression, and apply our new model to two image datasets we collected from the strain, as well as an independent dataset of different markers. We use our model to estimate the duration of post-cytokinetic attachment between a S.cerevisiae mother and daughter cell. The Java implementation is fast and extensible, and includes a graphical user interface. Our model provides a powerful and flexible cell cycle analysis tool, suitable to any type or combination of binary markers.

Availability: The software is available from: http://www.cs.duke.edu/~amink/software/cloccs/.

Contact: michael.mayhew@duke.edu; amink@cs.duke.edu.

Show MeSH
Related in: MedlinePlus