Literature DB >> 15847808

Quantifying cell turnover using CFSE data.

Vitaly V Ganusov1, Sergei S Pilyugin, Rob J de Boer, Kaja Murali-Krishna, Rafi Ahmed, Rustom Antia.   

Abstract

The CFSE dye dilution assay is widely used to determine the number of divisions a given CFSE labelled cell has undergone in vitro and in vivo. In this paper, we consider how the data obtained with the use of CFSE (CFSE data) can be used to estimate the parameters determining cell division and death. For a homogeneous cell population (i.e., a population with the parameters for cell division and death being independent of time and the number of divisions cells have undergone), we consider a specific biologically based "Smith-Martin" model of cell turnover and analyze three different techniques for estimation of its parameters: direct fitting, indirect fitting and rescaling method. We find that using only CFSE data, the duration of the division phase (i.e., approximately the S+G2+M phase of the cell cycle) can be estimated with the use of either technique. In some cases, the average division or cell cycle time can be estimated using the direct fitting of the model solution to the data or by using the Gett-Hodgkin method [Gett A. and Hodgkin, P. 2000. A cellular calculus for signal integration by T cells. Nat. Immunol. 1:239-244]. Estimation of the death rates during commitment to division (i.e., approximately the G1 phase of the cell cycle) and during the division phase may not be feasible with the use of only CFSE data. We propose that measuring an additional parameter, the fraction of cells in division, may allow estimation of all model parameters including the death rates during different stages of the cell cycle.

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Year:  2005        PMID: 15847808     DOI: 10.1016/j.jim.2005.01.011

Source DB:  PubMed          Journal:  J Immunol Methods        ISSN: 0022-1759            Impact factor:   2.303


  35 in total

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2.  Quantifying lymphocyte kinetics in vivo using carboxyfluorescein diacetate succinimidyl ester (CFSE).

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3.  Computational analysis of CFSE proliferation assay.

Authors:  Tatyana Luzyanina; Sonja Mrusek; John T Edwards; Dirk Roose; Stephan Ehl; Gennady Bocharov
Journal:  J Math Biol       Date:  2006-11-09       Impact factor: 2.259

4.  Determining the expected variability of immune responses using the cyton model.

Authors:  Vijay G Subramanian; Ken R Duffy; Marian L Turner; Philip D Hodgkin
Journal:  J Math Biol       Date:  2007-11-03       Impact factor: 2.259

5.  A model of immune regulation as a consequence of randomized lymphocyte division and death times.

Authors:  E D Hawkins; M L Turner; M R Dowling; C van Gend; P D Hodgkin
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6.  Quantitative analysis of T cell homeostatic proliferation.

Authors:  Cheng-Rui Li; Sharon Santoso; David D Lo
Journal:  Cell Immunol       Date:  2008-03-03       Impact factor: 4.868

7.  Distributed parameter identification for a label-structured cell population dynamics model using CFSE histogram time-series data.

Authors:  Tatyana Luzyanina; Dirk Roose; Gennady Bocharov
Journal:  J Math Biol       Date:  2008-12-19       Impact factor: 2.259

8.  Mathematical models for CFSE labelled lymphocyte dynamics: asymmetry and time-lag in division.

Authors:  Tatyana Luzyanina; Jovana Cupovic; Burkhard Ludewig; Gennady Bocharov
Journal:  J Math Biol       Date:  2013-12-13       Impact factor: 2.259

9.  A single-cell pedigree analysis of alternative stochastic lymphocyte fates.

Authors:  E D Hawkins; J F Markham; L P McGuinness; P D Hodgkin
Journal:  Proc Natl Acad Sci U S A       Date:  2009-07-24       Impact factor: 11.205

10.  A mechanistic model for bromodeoxyuridine dilution naturally explains labelling data of self-renewing T cell populations.

Authors:  Vitaly V Ganusov; Rob J De Boer
Journal:  J R Soc Interface       Date:  2012-11-08       Impact factor: 4.118

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