Quantifying CFSE label decay in flow cytometry data

Banks, Harvey Thomas; Choi, Amanda; Huffman, Tori; Nardini, John T.; Poag, Laura; Thompson, W. Clayton

doi:10.1016/j.aml.2012.12.010

Cited by 15 publications

(10 citation statements)

References 20 publications

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“…Gompertz decay). Such rates are consistent with the sequence of chemical reactions known to occur during the labelling process [18]. If data are not collected in the first day after labelling with CFSE (as in the data collected for this report) then, as we shall see below, exponential decay is sufficient.…”

Section: Mathematical Modelling Of Cfse Datasupporting

confidence: 69%

A novel statistical analysis and interpretation of flow cytometry data

Banks

Kapraun

Thompson

et al. 2013

Journal of Biological Dynamics

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A recently developed class of models incorporating the cyton model of population generation structure into a conservation-based model of intracellular label dynamics is reviewed. Statistical aspects of the data collection process are quantified and incorporated into a parameter estimation scheme. This scheme is then applied to experimental data for PHA-stimulated CD4+ T and CD8+ T cells collected from two healthy donors. This novel mathematical and statistical framework is shown to form the basis for accurate, meaningful analysis of cellular behaviour for a population of cells labelled with the dye carboxyfluorescein succinimidyl ester and stimulated to divide.

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Section: Mathematical Modelling Of Cfse Datasupporting

confidence: 69%

A novel statistical analysis and interpretation of flow cytometry data

Banks

Kapraun

Thompson

et al. 2013

Journal of Biological Dynamics

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“…This follows the convention of [4], in which the authors assume that an exponential decay model is su cient to describe decay of CFSE in experiments for which the rst data collection time occurs after approximately 24 hours. We note that the decay of intracellular CFSE has been observed to occur very rapidly during the rst 24 hours after initial labeling and much slower thereafter [6,22] and that this observation can be fully supported with molecular-level modeling [1]. Thus, when data are collected in the rst 24 hours, it is more accurate to describe the rate of loss of uorescence intensity with a time-varying rate, as in a Gompertz decay model.…”

Section: Parameterization For a Speci C Mathematical Modelmentioning

confidence: 91%

Analysis of variability in estimates of cell proliferation parameters for cyton-based models using CFSE-based flow cytometry data

Banks¹,

Kapraun²,

Link³

et al. 2014

Journal of Inverse and Ill-Posed Problems

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In this article we assess variability in cell proliferation dynamics observed for CD4+ and CD8+ T cells collected from two healthy donors. We review a recently developed class of models that incorporates the so-called "cyton model" for cell numbers into a conservation-based PDE model for cell population dynamics and describe a statistical model that relates CFSE-based ow cytometry data to such models. A parameter estimation scheme is summarized and then applied to a large body of data to assess experimental variability (variation in parameter estimates as identical experiments are replicated) and biological variability (di erences in parameter estimates obtained for di erent donors and cell types) in the context of these models. Variability in the data obtained from replicated experiments is also discussed. The results of this study indicate that many of the cyton model parameters for describing cell proliferation can be reliably estimated using our approach; however, they also show that substantial changes to our mathematical model and/or experimental procedures may be required to ensure identi ability of the remaining cell proliferation parameters.

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“…However, a random and uneven partition of mass between the sister cells is considered as an axiom in cell biology since many years (10). Although the detailed knowledge of the intracellular reactions which affect the turnover and intracellular heterogeneity of CFSE labeled proteins is currently limited (11), it is broadly accepted that CFSE binds indiscriminately to intracellular proteins and the fluorescence intensity of any single cell is roughly proportional to the total number of CFSE molecules bound to proteins within that cell (12). The latter study proposed a method for the analysis of CFSE-labeling experiments which also considered the possibility of an unequal division of CFSE molecules between the daughter cells.…”

Section: Introductionmentioning

confidence: 99%

Asymmetry of Cell Division in CFSE-Based Lymphocyte Proliferation Analysis

et al. 2013

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Flow cytometry-based analysis of lymphocyte division using carboxyfluorescein succinimidyl ester (CFSE) dye dilution permits acquisition of data describing cellular proliferation and differentiation. For example, CFSE histogram data enable quantitative insight into cellular turnover rates by applying mathematical models and parameter estimation techniques. Several mathematical models have been developed using different types of deterministic or stochastic approaches. However, analysis of CFSE proliferation assays is based on the premise that the label is halved in the two daughter cells. Importantly, asymmetry of protein distribution in lymphocyte division is a basic biological feature of cell division with the degree of the asymmetry depending on various factors. Here, we review the recent literature on asymmetric lymphocyte division and CFSE-based lymphocyte proliferation analysis. We suggest that division- and label-structured mathematical models describing CFSE-based cell proliferation should take into account asymmetry and time-lag in cell proliferation. Utilization of improved modeling algorithms will permit straightforward quantification of essential parameters describing the performance of activated lymphocytes.

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Quantifying CFSE label decay in flow cytometry data

Cited by 15 publications

References 20 publications

A novel statistical analysis and interpretation of flow cytometry data

A novel statistical analysis and interpretation of flow cytometry data

Analysis of variability in estimates of cell proliferation parameters for cyton-based models using CFSE-based flow cytometry data

Asymmetry of Cell Division in CFSE-Based Lymphocyte Proliferation Analysis

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