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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

Samples from a dividing budding yeast culture were collected at fixed intervals (here, 8 min) for a predetermined duration. We acquired DIC and fluorescence microscopy images for one field of view of the sample. We then repositioned the microscope to acquire images for another field of view of the sample. We continued this procedure a variable number of times (F) for each sample, stopping when we acquired images of~100 cells per time point.
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Figure 2: Samples from a dividing budding yeast culture were collected at fixed intervals (here, 8 min) for a predetermined duration. We acquired DIC and fluorescence microscopy images for one field of view of the sample. We then repositioned the microscope to acquire images for another field of view of the sample. We continued this procedure a variable number of times (F) for each sample, stopping when we acquired images of~100 cells per time point.

Mentions: Cell characteristics were manually quantified on a population level. For each time point, the number of cells with a bud, with a myosin ring, with a short mitotic spindle and with a long mitotic spindle were recorded. Budding was quantified using a light microscope, while we quantified myosin ring and SPB status with a fluorescent microscope. For each feature, we counted all cells across the multiple fields of view of a given time point to get the number of cells in the sample with that feature (Figs 2 and 3). A cell was counted as having a short mitotic spindle if two SPBs were visible inside the mother cell; it was counted as having a long mitotic spindle if a myosin ring was between the two SPBs.


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)

Samples from a dividing budding yeast culture were collected at fixed intervals (here, 8 min) for a predetermined duration. We acquired DIC and fluorescence microscopy images for one field of view of the sample. We then repositioned the microscope to acquire images for another field of view of the sample. We continued this procedure a variable number of times (F) for each sample, stopping when we acquired images of~100 cells per time point.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

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

Figure 2: Samples from a dividing budding yeast culture were collected at fixed intervals (here, 8 min) for a predetermined duration. We acquired DIC and fluorescence microscopy images for one field of view of the sample. We then repositioned the microscope to acquire images for another field of view of the sample. We continued this procedure a variable number of times (F) for each sample, stopping when we acquired images of~100 cells per time point.
Mentions: Cell characteristics were manually quantified on a population level. For each time point, the number of cells with a bud, with a myosin ring, with a short mitotic spindle and with a long mitotic spindle were recorded. Budding was quantified using a light microscope, while we quantified myosin ring and SPB status with a fluorescent microscope. For each feature, we counted all cells across the multiple fields of view of a given time point to get the number of cells in the sample with that feature (Figs 2 and 3). A cell was counted as having a short mitotic spindle if two SPBs were visible inside the mother cell; it was counted as having a long mitotic spindle if a myosin ring was between the two SPBs.

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