Literature DB >> 23034350

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

Vitaly V Ganusov1, Rob J De Boer.   

Abstract

Bromodeoxyuridine (BrdU) is widely used in immunology to detect cell division, and several mathematical models have been proposed to estimate proliferation and death rates of lymphocytes from BrdU labelling and de-labelling curves. One problem in interpreting BrdU data is explaining the de-labelling curves. Because shortly after label withdrawal, BrdU+ cells are expected to divide into BrdU+ daughter cells, one would expect a flat down-slope. As for many cell types, the fraction of BrdU+ cells decreases during de-labelling, previous mathematical models had to make debatable assumptions to be able to account for the data. We develop a mechanistic model tracking the number of divisions that each cell has undergone in the presence and absence of BrdU, and allow cells to accumulate and dilute their BrdU content. From the same mechanistic model, one can naturally derive expressions for the mean BrdU content (MBC) of all cells, or the MBC of the BrdU+ subset, which is related to the mean fluorescence intensity of BrdU that can be measured in experiments. The model is extended to include subpopulations with different rates of division and death (i.e. kinetic heterogeneity). We fit the extended model to previously published BrdU data from memory T lymphocytes in simian immunodeficiency virus-infected and uninfected macaques, and find that the model describes the data with at least the same quality as previous models. Because the same model predicts a modest decline in the MBC of BrdU+ cells, which is consistent with experimental observations, BrdU dilution seems a natural explanation for the observed down-slopes in self-renewing populations.

Entities:  

Mesh:

Substances:

Year:  2012        PMID: 23034350      PMCID: PMC3565791          DOI: 10.1098/rsif.2012.0617

Source DB:  PubMed          Journal:  J R Soc Interface        ISSN: 1742-5662            Impact factor:   4.118


  36 in total

1.  Increased turnover of T lymphocytes in HIV-1 infection and its reduction by antiretroviral therapy.

Authors:  H Mohri; A S Perelson; K Tung; R M Ribeiro; B Ramratnam; M Markowitz; R Kost; A Hurley; L Weinberger; D Cesar; M K Hellerstein; D D Ho
Journal:  J Exp Med       Date:  2001-11-05       Impact factor: 14.307

2.  Division-linked generation of death-intermediates regulates the numerical stability of memory CD8 T cells.

Authors:  Jeffrey C Nolz; Deepa Rai; Vladimir P Badovinac; John T Harty
Journal:  Proc Natl Acad Sci U S A       Date:  2012-04-02       Impact factor: 11.205

3.  Quantifying cell turnover using CFSE data.

Authors:  Vitaly V Ganusov; Sergei S Pilyugin; Rob J de Boer; Kaja Murali-Krishna; Rafi Ahmed; Rustom Antia
Journal:  J Immunol Methods       Date:  2005-03       Impact factor: 2.303

4.  Estimating lymphocyte division and death rates from CFSE data.

Authors:  Rob J De Boer; Vitaly V Ganusov; Dejan Milutinović; Philip D Hodgkin; Alan S Perelson
Journal:  Bull Math Biol       Date:  2006-05-16       Impact factor: 1.758

5.  Measuring lymphocyte proliferation, survival and differentiation using CFSE time-series data.

Authors:  Edwin D Hawkins; Mirja Hommel; Marian L Turner; Francis L Battye; John F Markham; Philip D Hodgkin
Journal:  Nat Protoc       Date:  2007       Impact factor: 13.491

6.  Hematopoietic stem cells reversibly switch from dormancy to self-renewal during homeostasis and repair.

Authors:  Anne Wilson; Elisa Laurenti; Gabriela Oser; Richard C van der Wath; William Blanco-Bose; Maike Jaworski; Sandra Offner; Cyrille F Dunant; Leonid Eshkind; Ernesto Bockamp; Pietro Lió; H Robson Macdonald; Andreas Trumpp
Journal:  Cell       Date:  2008-12-12       Impact factor: 41.582

7.  Kinetics of in vivo proliferation and death of memory and naive CD8 T cells: parameter estimation based on 5-bromo-2'-deoxyuridine incorporation in spleen, lymph nodes, and bone marrow.

Authors:  Elisabetta Parretta; Giuliana Cassese; Angela Santoni; John Guardiola; Antonia Vecchio; Francesca Di Rosa
Journal:  J Immunol       Date:  2008-06-01       Impact factor: 5.422

8.  Differential effects of HIV viral load and CD4 count on proliferation of naive and memory CD4 and CD8 T lymphocytes.

Authors:  Sharat Srinivasula; Richard A Lempicki; Joseph W Adelsberger; Chiung-Yu Huang; Joshua Roark; Philip I Lee; Adam Rupert; Randy Stevens; Irini Sereti; H Clifford Lane; Michele Di Mascio; Joseph A Kovacs
Journal:  Blood       Date:  2011-05-11       Impact factor: 22.113

9.  Turnover rates of B cells, T cells, and NK cells in simian immunodeficiency virus-infected and uninfected rhesus macaques.

Authors:  Rob J De Boer; Hiroshi Mohri; David D Ho; Alan S Perelson
Journal:  J Immunol       Date:  2003-03-01       Impact factor: 5.422

10.  The rescaling method for quantifying the turnover of cell populations.

Authors:  Sergei S Pilyugin; Vitaly V Ganusov; Kaja Murali-Krishna; Rafi Ahmed; Rustom Antia
Journal:  J Theor Biol       Date:  2003-11-21       Impact factor: 2.691

View more
  13 in total

1.  Primary cilia are required in a unique subpopulation of neural progenitors.

Authors:  Cheuk Ka Tong; Young-Goo Han; Jugal K Shah; Kirsten Obernier; Cristina D Guinto; Arturo Alvarez-Buylla
Journal:  Proc Natl Acad Sci U S A       Date:  2014-08-11       Impact factor: 11.205

2.  Bone marrow is the preferred site of memory CD4+ T cell proliferation during recovery from sepsis.

Authors:  Tomasz Skirecki; Patrycja Swacha; Grażyna Hoser; Jakub Golab; Dominika Nowis; Ewa Kozłowska
Journal:  JCI Insight       Date:  2020-05-21

3.  Merkel cells are long-lived cells whose production is stimulated by skin injury.

Authors:  Margaret C Wright; Gregory J Logan; Alexa M Bolock; Adam C Kubicki; Julie A Hemphill; Timothy A Sanders; Stephen M Maricich
Journal:  Dev Biol       Date:  2016-12-18       Impact factor: 3.582

4.  Plasmodium suppresses expansion of T cell responses to heterologous infections.

Authors:  Chelsi E White; Nicolas F Villarino; Sarah S Sloan; Vitaly V Ganusov; Nathan W Schmidt
Journal:  J Immunol       Date:  2014-12-10       Impact factor: 5.422

5.  Three-Dimensional Environment Sustains Morphological Heterogeneity and Promotes Phenotypic Progression During Astrocyte Development.

Authors:  Swarnalatha Balasubramanian; John A Packard; Jennie B Leach; Elizabeth M Powell
Journal:  Tissue Eng Part A       Date:  2016-06       Impact factor: 3.845

6.  Dissociation of doublecortin expression and neurogenesis in unipolar brush cells in the vestibulocerebellum and dorsal cochlear nucleus of the adult rat.

Authors:  N Paolone; S Manohar; S H Hayes; K M Wong; R J Salvi; J S Baizer
Journal:  Neuroscience       Date:  2014-01-23       Impact factor: 3.590

7.  Quantifying T lymphocyte turnover.

Authors:  Rob J De Boer; Alan S Perelson
Journal:  J Theor Biol       Date:  2013-01-09       Impact factor: 2.691

Review 8.  Mathematics in modern immunology.

Authors:  Mario Castro; Grant Lythe; Carmen Molina-París; Ruy M Ribeiro
Journal:  Interface Focus       Date:  2016-04-06       Impact factor: 3.906

9.  Temporal fate mapping reveals age-linked heterogeneity in naive T lymphocytes in mice.

Authors:  Thea Hogan; Graeme Gossel; Andrew J Yates; Benedict Seddon
Journal:  Proc Natl Acad Sci U S A       Date:  2015-11-25       Impact factor: 11.205

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

Authors:  Daniella Schittler; Frank Allgöwer; Rob J De Boer
Journal:  BMC Syst Biol       Date:  2013-08-12
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.