BackgroundThis 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.ResultsIn 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.We employ our model to study some naturally arising examples of heterogeneity in label uptake, which are not covered by existing models. With simulations of noisy and spacially heterogeneous label uptake, we demonstrate that our model contributes a more realistic quantitative description of labeling experiments.ConclusionThe 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.
[1]
Sebastian Bonhoeffer,et al.
Dynamic variation in cycling of hematopoietic stem cells in steady state and inflammation
,
2011,
The Journal of experimental medicine.
[2]
F. Allgöwer,et al.
Analysis and Simulation of Division- and Label-Structured Population Models
,
2012,
Bulletin of mathematical biology.
[3]
Vitaly V. Ganusov,et al.
A mechanistic model for bromodeoxyuridine dilution naturally explains labelling data of self-renewing T cell populations
,
2013,
Journal of The Royal Society Interface.
[4]
Boris Barbour,et al.
Functional antigen-independent synapses formed between T cells and dendritic cells
,
2001,
Nature Immunology.
[5]
Alan S. Perelson,et al.
Quantification of Cell Turnover Kinetics Using 5-Bromo-2′-deoxyuridine1
,
2000,
The Journal of Immunology.
[6]
A S Perelson,et al.
Rapid turnover of T lymphocytes in SIV-infected rhesus macaques.
,
1998,
Science.
[7]
C. McKean.
Figures
,
1970,
Five Long Winters.
[8]
Alan S. Perelson,et al.
Estimating Lymphocyte Division and Death Rates from CFSE Data
,
2006,
Bulletin of mathematical biology.