期刊
FRONTIERS IN IMMUNOLOGY
卷 14, 期 -, 页码 -出版社
FRONTIERS MEDIA SA
DOI: 10.3389/fimmu.2023.1157705
关键词
naive T cells; mathematical model; immunology; mouse model; dynamics
类别
The dynamics of cell populations are often studied using pulse-chase DNA labeling techniques combined with mathematical models. In this study, an alternative method using a cell fate-mapping mouse model is explored, in which dividing cells constitutively express a fluorescent protein. The lifespans and division rates of naive CD4 and CD8 T cells are measured using a pulse-chase approach with this reporter mouse system, and the estimates are consistent with other published methods. It is proposed that modeling the timecourses of labeled cell frequencies in both the population of interest and its precursor simultaneously can provide unbiased parameter estimates and full measures of uncertainty. Ki67 reporter mice offer a promising system for modeling cellular dynamics.
The dynamics of cell populations are frequently studied in vivo using pulse-chase DNA labeling techniques. When combined with mathematical models, the kinetic of label uptake and loss within a population of interest then allows one to estimate rates of cell production and turnover through death or onward differentiation. Here we explore an alternative method of quantifying cellular dynamics, using a cell fate-mapping mouse model in which dividing cells can be induced to constitutively express a fluorescent protein, using a Ki67 reporter construct. We use a pulse-chase approach with this reporter mouse system to measure the lifespans and division rates of naive CD4 and CD8 T cells using a variety of modeling approaches, and show that they are all consistent with estimates derived from other published methods. However we propose that to obtain unbiased parameter estimates and full measures of their uncertainty one should simultaneously model the timecourses of the frequencies of labeled cells within both the population of interest and its precursor. We conclude that Ki67 reporter mice provide a promising system for modeling cellular dynamics.
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