Reconstruction of cell population dynamics using CFSE

<p>Abstract</p> <p>Background</p> <p>Quantifying cell division and death is central to many studies in the biological sciences. The fluorescent dye CFSE allows the tracking of cell division <it>in vitro </it>and <it>in vivo </it>and provides a ri...

Full description

Bibliographic Details
Main Authors: Callard Robin, Moon Simon, Strid Jessica, Chan Cliburn, Yates Andrew, George Andrew JT, Stark Jaroslav
Format: Article
Language:English
Published: BMC 2007-06-01
Series:BMC Bioinformatics
Online Access:http://www.biomedcentral.com/1471-2105/8/196
id doaj-ffbc8f858f1e446eb84d5661d58dc06a
record_format Article
spelling doaj-ffbc8f858f1e446eb84d5661d58dc06a2020-11-24T21:04:43ZengBMCBMC Bioinformatics1471-21052007-06-018119610.1186/1471-2105-8-196Reconstruction of cell population dynamics using CFSECallard RobinMoon SimonStrid JessicaChan CliburnYates AndrewGeorge Andrew JTStark Jaroslav<p>Abstract</p> <p>Background</p> <p>Quantifying cell division and death is central to many studies in the biological sciences. The fluorescent dye CFSE allows the tracking of cell division <it>in vitro </it>and <it>in vivo </it>and provides a rich source of information with which to test models of cell kinetics. Cell division and death have a stochastic component at the single-cell level, and the probabilities of these occurring in any given time interval may also undergo systematic variation at a population level. This gives rise to heterogeneity in proliferating cell populations. Branching processes provide a natural means of describing this behaviour.</p> <p>Results</p> <p>We present a likelihood-based method for estimating the parameters of branching process models of cell kinetics using CFSE-labeling experiments, and demonstrate its validity using synthetic and experimental datasets. Performing inference and model comparison with real CFSE data presents some statistical problems and we suggest methods of dealing with them.</p> <p>Conclusion</p> <p>The approach we describe here can be used to recover the (potentially variable) division and death rates of any cell population for which division tracking information is available.</p> http://www.biomedcentral.com/1471-2105/8/196
collection DOAJ
language English
format Article
sources DOAJ
author Callard Robin
Moon Simon
Strid Jessica
Chan Cliburn
Yates Andrew
George Andrew JT
Stark Jaroslav
spellingShingle Callard Robin
Moon Simon
Strid Jessica
Chan Cliburn
Yates Andrew
George Andrew JT
Stark Jaroslav
Reconstruction of cell population dynamics using CFSE
BMC Bioinformatics
author_facet Callard Robin
Moon Simon
Strid Jessica
Chan Cliburn
Yates Andrew
George Andrew JT
Stark Jaroslav
author_sort Callard Robin
title Reconstruction of cell population dynamics using CFSE
title_short Reconstruction of cell population dynamics using CFSE
title_full Reconstruction of cell population dynamics using CFSE
title_fullStr Reconstruction of cell population dynamics using CFSE
title_full_unstemmed Reconstruction of cell population dynamics using CFSE
title_sort reconstruction of cell population dynamics using cfse
publisher BMC
series BMC Bioinformatics
issn 1471-2105
publishDate 2007-06-01
description <p>Abstract</p> <p>Background</p> <p>Quantifying cell division and death is central to many studies in the biological sciences. The fluorescent dye CFSE allows the tracking of cell division <it>in vitro </it>and <it>in vivo </it>and provides a rich source of information with which to test models of cell kinetics. Cell division and death have a stochastic component at the single-cell level, and the probabilities of these occurring in any given time interval may also undergo systematic variation at a population level. This gives rise to heterogeneity in proliferating cell populations. Branching processes provide a natural means of describing this behaviour.</p> <p>Results</p> <p>We present a likelihood-based method for estimating the parameters of branching process models of cell kinetics using CFSE-labeling experiments, and demonstrate its validity using synthetic and experimental datasets. Performing inference and model comparison with real CFSE data presents some statistical problems and we suggest methods of dealing with them.</p> <p>Conclusion</p> <p>The approach we describe here can be used to recover the (potentially variable) division and death rates of any cell population for which division tracking information is available.</p>
url http://www.biomedcentral.com/1471-2105/8/196
work_keys_str_mv AT callardrobin reconstructionofcellpopulationdynamicsusingcfse
AT moonsimon reconstructionofcellpopulationdynamicsusingcfse
AT stridjessica reconstructionofcellpopulationdynamicsusingcfse
AT chancliburn reconstructionofcellpopulationdynamicsusingcfse
AT yatesandrew reconstructionofcellpopulationdynamicsusingcfse
AT georgeandrewjt reconstructionofcellpopulationdynamicsusingcfse
AT starkjaroslav reconstructionofcellpopulationdynamicsusingcfse
_version_ 1716770049285423104