An age-dependent branching process model for the analysis of CFSE-labeling experiments.

Ollivier Hyrien, Rui Chen, Martin S Zand
Author Information
  1. Ollivier Hyrien: Department of Biostatistics & Computational Biology, University of Rochester Medical Center, Rochester, NY 14642, USA. Ollivier_Hyrien@urmc.rochester.edu

Abstract

BACKGROUND: Over the past decade, flow cytometric CFSE-labeling experiments have gained considerable popularity among experimentalists, especially immunologists and hematologists, for studying the processes of cell proliferation and cell death. Several mathematical models have been presented in the literature to describe cell kinetics during these experiments.
RESULTS: We propose a multi-type age-dependent branching process to model the temporal development of populations of cells subject to division and death during CFSE-labeling experiments. We discuss practical implementation of the proposed model; we investigate a competing risk version of the process; and we identify the classes of cellular dependencies that may influence the expectation of the process and those that do not. An application is presented where we study the proliferation of human CD8+ T lymphocytes using our model and a competing risk branching process.
CONCLUSIONS: The proposed model offers a widely applicable approach to the analysis of CFSE-labeling experiments. The model fitted very well our experimental data. It provided reasonable estimates of cell kinetics parameters as well as meaningful insights into the processes of cell division and cell death. In contrast, the competing risk branching process could not describe the kinetics of CD8+ T cells. This suggested that the decision of cell division or cell death may be made early in the cell cycle if not in preceding generations. Also, we show that analyses based on the proposed model are robust with respect to cross-sectional dependencies and to dependencies between fates of linearly filiated cells.
REVIEWERS: This article was reviewed by Marek Kimmel, Wai-Yuan Tan and Peter Olofsson.

References

  1. Math Biosci. 1999 Jun;159(1):47-78 [PMID: 10361805]
  2. J Immunol Methods. 2000 Sep 21;243(1-2):147-54 [PMID: 10986412]
  3. J Immunol. 2004 Sep 15;173(6):3763-72 [PMID: 15356123]
  4. Proc Natl Acad Sci U S A. 2009 Aug 11;106(32):13457-62 [PMID: 19633185]
  5. Bull Math Biol. 2008 Jan;70(1):21-44 [PMID: 17701260]
  6. Math Biosci. 1996 Oct 1;137(1):25-50 [PMID: 8854661]
  7. Proc Natl Acad Sci U S A. 2007 Mar 20;104(12):5032-7 [PMID: 17360353]
  8. Theor Biol Med Model. 2006 May 17;3:21 [PMID: 16707014]
  9. J Immunol Methods. 1994 May 2;171(1):131-7 [PMID: 8176234]
  10. BMC Bioinformatics. 2007 Jun 12;8:196 [PMID: 17565685]
  11. Proc Natl Acad Sci U S A. 1998 Aug 4;95(16):9488-93 [PMID: 9689107]
  12. Biophys J. 2003 May;84(5):3414-24 [PMID: 12719268]
  13. Biometrics. 2005 Mar;61(1):199-207 [PMID: 15737094]
  14. Bull Math Biol. 2006 Jul;68(5):1011-31 [PMID: 16832737]
  15. J Immunol Methods. 2005 Mar;298(1-2):183-200 [PMID: 15847808]
  16. J Immunol. 2007 Oct 15;179(8):5006-13 [PMID: 17911585]
  17. Exp Cell Res. 1977 May;106(2):405-7 [PMID: 558891]
  18. Nat Immunol. 2000 Sep;1(3):239-44 [PMID: 10973282]
  19. Biostatistics. 2011 Jan;12(1):173-91 [PMID: 20732974]
  20. J Immunol. 2006 Feb 15;176(4):2173-82 [PMID: 16455973]
  21. Blood. 2007 Feb 15;109(4):1611-9 [PMID: 17032927]
  22. J Stat Plan Inference. 2011 Jul 1;141(7):2209-2227 [PMID: 21552356]
  23. J Theor Biol. 2010 May 21;264(2):443-9 [PMID: 20171973]
  24. J Immunol. 2001 Jan 15;166(2):795-9 [PMID: 11145652]
  25. Math Biosci. 2005 Feb;193(2):255-74 [PMID: 15748733]
  26. J Theor Biol. 2003 Nov 21;225(2):275-83 [PMID: 14575660]
  27. Biometrics. 2010 Jun;66(2):567-77 [PMID: 19508238]
  28. Mol Cell Biol. 2010 Feb;30(3):640-56 [PMID: 19917720]

Grants

  1. R01 NS039511-10/NINDS NIH HHS
  2. R01 NS039511-07/NINDS NIH HHS
  3. R01 NS039511/NINDS NIH HHS
  4. R01 CA134839-04/NCI NIH HHS
  5. R01 CA134839-03/NCI NIH HHS
  6. R01 CA134839-02/NCI NIH HHS
  7. R01 CA134839/NCI NIH HHS
  8. R01 CA134839-01/NCI NIH HHS
  9. 2R01 NS039511/NINDS NIH HHS
  10. R01 NS039511-08/NINDS NIH HHS
  11. 1R01 AI069351/NIAID NIH HHS
  12. N01-AI-050020/NIAID NIH HHS
  13. R01 NS039511-09/NINDS NIH HHS

MeSH Term

CD8-Positive T-Lymphocytes
Cell Proliferation
Flow Cytometry
Fluoresceins
Humans
Models, Theoretical
Succinimides

Chemicals

5-(6)-carboxyfluorescein diacetate succinimidyl ester
Fluoresceins
Succinimides

Word Cloud

Created with Highcharts 10.0.0cellmodelprocessexperimentsCFSE-labelingdeathbranchingkineticscellsdivisionproposedcompetingriskdependenciesprocessesproliferationpresenteddescribeage-dependentmayCD8+TanalysiswellBACKGROUND:pastdecadeflowcytometricgainedconsiderablepopularityamongexperimentalistsespeciallyimmunologistshematologistsstudyingSeveralmathematicalmodelsliteratureRESULTS:proposemulti-typetemporaldevelopmentpopulationssubjectdiscusspracticalimplementationinvestigateversionidentifyclassescellularinfluenceexpectationapplicationstudyhumanlymphocytesusingCONCLUSIONS:offerswidelyapplicableapproachfittedexperimentaldataprovidedreasonableestimatesparametersmeaningfulinsightscontrastsuggesteddecisionmadeearlycycleprecedinggenerationsAlsoshowanalysesbasedrobustrespectcross-sectionalfateslinearlyfiliatedREVIEWERS:articlereviewedMarekKimmelWai-YuanTanPeterOlofsson

Similar Articles

Cited By