Deriving Quantitative Cell Biological Information from Dye-Dilution Lymphocyte Proliferation Experiments

  • Koushik Roy
  • Maxim Nikolaievich Shokhirev
  • Simon Mitchell
  • Alexander HoffmannEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 1707)


The dye-dilution assay is a powerful tool to study lymphocyte expansion dynamics. By combining time course dye-dilution experiments with computational analysis, quantitative information about cell biological parameters, such as percentage of cells dividing, time of division, and time of death, can be produced. Here, we describe the method to generate quantitative cell biological insights from dye-dilution experiments. We describe experimental methods for generating dye-dilution data with murine lymphocytes and then describe the computational data analysis workflow using a recently developed software package called FlowMax. The aim is to interpret the dye-dilution data quantitatively and objectively, such that cell biological parameters can be reported with an appropriate measure of confidence, which in turn depends on the quality and quantity of available data.

Key words

Lymphocyte B cell Proliferation Lymphocyte dynamics CFSE assay Quantitative dye dilution Cell biological parameter FlowMax 



This work was supported by NIH grant R01 AI132731 (A.H.) and from NIH-NCI CCSG: P30 014195, and the Helmsley Trust (M.N.S.).


  1. 1.
    de Fries R, Mitsuhashi M (1995) Quantification of mitogen induced human lymphocyte proliferation: comparison of alamarBlue assay to 3H-thymidine incorporation assay. J Clin Lab Anal 9:89–95CrossRefPubMedGoogle Scholar
  2. 2.
    Denizot F, Lang R (1986) Rapid colorimetric assay for cell growth and survival. Modifications to the tetrazolium dye procedure giving improved sensitivity and reliability. J Immunol Methods 89:271–277CrossRefPubMedGoogle Scholar
  3. 3.
    Hawkins ED, Hommel M, Turner ML, Battye FL, Markham JF et al (2007) Measuring lymphocyte proliferation, survival and differentiation using CFSE time-series data. Nat Protoc 2:2057–2067CrossRefPubMedGoogle Scholar
  4. 4.
    Quah BJ, Warren HS, Parish CR (2007) Monitoring lymphocyte proliferation in vitro and in vivo with the intracellular fluorescent dye carboxyfluorescein diacetate succinimidyl ester. Nat Protoc 2:2049–2056CrossRefPubMedGoogle Scholar
  5. 5.
    Hasbold J, Gett AV, Rush JS, Deenick E, Avery D et al (1999) Quantitative analysis of lymphocyte differentiation and proliferation in vitro using carboxyfluorescein diacetate succinimidyl ester. Immunol Cell Biol 77:516–522CrossRefPubMedGoogle Scholar
  6. 6.
    Shokhirev MN, Hoffmann A (2013) FlowMax: a computational tool for maximum likelihood Deconvolution of CFSE time courses. PLoS One 8:e67620CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Teodorovic LS, Riccardi C, Torres RM, Pelanda R (2012) Murine B cell development and antibody responses to model antigens are not impaired in the absence of the TNF receptor GITR. PLoS One 7:e31632CrossRefPubMedGoogle Scholar
  8. 8.
    Klein AB, Witonsky SG, Ahmed SA, Holladay SD, Gogal RM Jr et al (2006) Impact of different cell isolation techniques on lymphocyte viability and function. J Immunoassay Immunochem 27:61–76CrossRefPubMedGoogle Scholar
  9. 9.
    Lyons AB, Hasbold J, Hodgkin PD (2001) Flow cytometric analysis of cell division history using dilution of carboxyfluorescein diacetate succinimidyl ester, a stably integrated fluorescent probe. Methods Cell Biol 63:375–398CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2018

Authors and Affiliations

  • Koushik Roy
    • 1
  • Maxim Nikolaievich Shokhirev
    • 2
  • Simon Mitchell
    • 1
  • Alexander Hoffmann
    • 1
    Email author
  1. 1.Department of Microbiology, Immunology, and Molecular Genetics, Institute for Quantitative and Computational BiosciencesUniversity of CaliforniaLos AngelesUSA
  2. 2.The Salk Institute for Biological StudiesLa JollaUSA

Personalised recommendations