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A new model to simulate and analyze proliferating cell populations in BrdU labeling experiments.

Schittler D, Allgöwer F, De Boer RJ - BMC Syst Biol (2013)

Bottom Line: It thereby allows to account for noise as well as possibly space-dependent heterogeneity in the effective label uptake of the individual cells in a population.Furthermore, it enables more informative comparison with experimental data: The population-level label distribution is provided as a model output, thereby increasing the information content compared to existing models that give the fraction of labeled cells or the mean label intensity.The presented model is to our knowledge the first one that predicts the full label distribution for BrdU labeling experiments.

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ABSTRACT

Background: This paper presents a novel model for proliferating cell populations in labeling experiments. It is especially tailored to the technique of Bromodeoxyuridine (BrdU), which is taken up by dividing cells and thus accumulates with increasing division number during uplabeling. The study of the evolving label intensities of BrdU labeled cell populations is aimed at quantifying proliferation properties such as division and death rates.

Results: In contrast to existing models, our model considers a labeling efficacy that follows a distribution, rather than a uniform value. It thereby allows to account for noise as well as possibly space-dependent heterogeneity in the effective label uptake of the individual cells in a population. Furthermore, it enables more informative comparison with experimental data: The population-level label distribution is provided as a model output, thereby increasing the information content compared to existing models that give the fraction of labeled cells or the mean label intensity.

Conclusion: The presented model is to our knowledge the first one that predicts the full label distribution for BrdU labeling experiments. Thus, it can exploit more information, namely the full intensity distribution, from labeling measurements, and thereby opens up new quantitative insights into cell proliferation.

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Noisy label uptake. Simulations of a proliferating cell population, assuming noise in labeling efficacy. Predicted overall label distribution m(x/t) during uplabeling (left), and during delabeling (right). A noise level of σ = 0.2 is assumed.
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Figure 1: Noisy label uptake. Simulations of a proliferating cell population, assuming noise in labeling efficacy. Predicted overall label distribution m(x/t) during uplabeling (left), and during delabeling (right). A noise level of σ = 0.2 is assumed.

Mentions: The obtained label distribution is shown in Figure 1 for σ = 0.2. The left panel in Figure 1 depicts the simulated overall label distribution in the cell population during uplabeling. Although the population and thus its label distribution is composed of cells with (more than two) different division numbers, the distribution exhibits only one peak (day 2, 4) or two peaks with a strong overlap (day 6, 8, 10). This highlights that under such labeling conditions, it is hardly possible to determine the number of cells that have divided once apart from cells that have divided more than once. The right panel in Figure 2 shows the overall label distribution during delabeling. In this phase, separated peaks occur, but only at very low label intensities. These may, in real experiments, well lay below the detection threshold and thus not appear in the data [4].


A new model to simulate and analyze proliferating cell populations in BrdU labeling experiments.

Schittler D, Allgöwer F, De Boer RJ - BMC Syst Biol (2013)

Noisy label uptake. Simulations of a proliferating cell population, assuming noise in labeling efficacy. Predicted overall label distribution m(x/t) during uplabeling (left), and during delabeling (right). A noise level of σ = 0.2 is assumed.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3750481&req=5

Figure 1: Noisy label uptake. Simulations of a proliferating cell population, assuming noise in labeling efficacy. Predicted overall label distribution m(x/t) during uplabeling (left), and during delabeling (right). A noise level of σ = 0.2 is assumed.
Mentions: The obtained label distribution is shown in Figure 1 for σ = 0.2. The left panel in Figure 1 depicts the simulated overall label distribution in the cell population during uplabeling. Although the population and thus its label distribution is composed of cells with (more than two) different division numbers, the distribution exhibits only one peak (day 2, 4) or two peaks with a strong overlap (day 6, 8, 10). This highlights that under such labeling conditions, it is hardly possible to determine the number of cells that have divided once apart from cells that have divided more than once. The right panel in Figure 2 shows the overall label distribution during delabeling. In this phase, separated peaks occur, but only at very low label intensities. These may, in real experiments, well lay below the detection threshold and thus not appear in the data [4].

Bottom Line: It thereby allows to account for noise as well as possibly space-dependent heterogeneity in the effective label uptake of the individual cells in a population.Furthermore, it enables more informative comparison with experimental data: The population-level label distribution is provided as a model output, thereby increasing the information content compared to existing models that give the fraction of labeled cells or the mean label intensity.The presented model is to our knowledge the first one that predicts the full label distribution for BrdU labeling experiments.

View Article: PubMed Central - HTML - PubMed

ABSTRACT

Background: This paper presents a novel model for proliferating cell populations in labeling experiments. It is especially tailored to the technique of Bromodeoxyuridine (BrdU), which is taken up by dividing cells and thus accumulates with increasing division number during uplabeling. The study of the evolving label intensities of BrdU labeled cell populations is aimed at quantifying proliferation properties such as division and death rates.

Results: In contrast to existing models, our model considers a labeling efficacy that follows a distribution, rather than a uniform value. It thereby allows to account for noise as well as possibly space-dependent heterogeneity in the effective label uptake of the individual cells in a population. Furthermore, it enables more informative comparison with experimental data: The population-level label distribution is provided as a model output, thereby increasing the information content compared to existing models that give the fraction of labeled cells or the mean label intensity.

Conclusion: The presented model is to our knowledge the first one that predicts the full label distribution for BrdU labeling experiments. Thus, it can exploit more information, namely the full intensity distribution, from labeling measurements, and thereby opens up new quantitative insights into cell proliferation.

Show MeSH
Related in: MedlinePlus