A single-cell pedigree analysis of alternative stochastic lymphocyte fates

In contrast to most stimulated lymphocytes, B cells exposed to Toll-like receptor 9 ligands are nonself-adherent, allowing individual cells and families to be followed in vitro for up to 5 days. These B cells undergo phases typical of an adaptive response, dividing up to 6 times before losing the impetus for further growth and division and eventually dying by apoptosis. Using long-term microscopic imaging, accurate histories of individual lymphocyte fates were collected. Quantitative analysis of family relationships revealed that times to divide of siblings were strongly related but these correlations were progressively lost through consecutive divisions. A weaker, but significant, correlation was also found for death times among siblings. Division cessation is characterized by a loss of cell growth and the division in which this occurs is strongly inherited from the original founder cell and is related to the size this cell reaches before its first division. Thus, simple division-based dilution of factors synthesized during the first division may control the maximum division reached by stimulated cells. The stochastic distributions of times to divide, times to die, and divisions reached are also measured. Together, these results highlight the internal cellular mechanisms that control immune responses and provide a foundation for the development of new mathematical models that are correct at both single-cell and population levels.

[1]  J. Tyson,et al.  Sloppy size control of the cell division cycle. , 1986, Journal of theoretical biology.

[2]  J. Smith,et al.  Do cells cycle? , 1973, Proceedings of the National Academy of Sciences of the United States of America.

[3]  Ertugrul M. Ozbudak,et al.  Regulation of noise in the expression of a single gene , 2002, Nature Genetics.

[4]  Alan S. Perelson,et al.  Different Dynamics of CD4+ and CD8+ T Cell Responses During and After Acute Lymphocytic Choriomeningitis Virus Infection 1 , 2003, The Journal of Immunology.

[5]  G FROESE,et al.  THE DISTRIBUTION AND INTERDEPENDENCE OF GENERATION TIMES OF HELA CELLS. , 1964, Experimental cell research.

[6]  P. Hodgkin,et al.  A model of immune regulation as a consequence of randomized lymphocyte division and death times , 2007, Proceedings of the National Academy of Sciences.

[7]  V. Cristofalo,et al.  Analysis of cell division by time‐lapse cinematographic studies of hydrocortisone‐treated embryonic lung fibroblasts , 1984, Journal of cellular physiology.

[8]  Philip D. Hodgkin,et al.  Quantitative Regulation of B Cell Division Destiny by Signal Strength , 2008, The Journal of Immunology.

[9]  H. Madoc‐Jones,et al.  VARIATIONS IN THE GENERATION TIMES OF A STRAIN OF RAT SARCOMA CELLS IN CULTURE. , 1965, Experimental cell research.

[10]  R. Staudte,et al.  The bifurcating autoregression model in cell lineage studies. , 1986, Biometrics.

[11]  S. Ely,et al.  Homeostatic cell-cycle control by BLyS: Induction of cell-cycle entry but not G1/S transition in opposition to p18INK4c and p27Kip1. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[12]  Cliburn Chan,et al.  Reconstruction of cell population dynamics using CFSE , 2007, BMC Bioinformatics.

[13]  N. Friedman,et al.  Stochastic protein expression in individual cells at the single molecule level , 2006, Nature.

[14]  M. Bender,et al.  Radiation-induced mammalian cell death: lapse-time cinemicrographic observations. , 1966, Experimental cell research.

[15]  LaRoy N. Castor,et al.  A G1 rate model accounts for cell-cycle kinetics attributed to ‘transition probability’ , 1980, Nature.

[16]  A. Valleron,et al.  Time-lapse cinematography studies of cell cycle and mitosis duration. , 1977, Experimental cell research.

[17]  S. Cooper,et al.  The continuum model: statistical implications. , 1982, Journal of theoretical biology.

[18]  Philip D Hodgkin,et al.  Determining the expected variability of immune responses using the cyton model , 2008, Journal of mathematical biology.

[19]  Rustom Antia,et al.  The role of models in understanding CD8+ T-cell memory , 2005, Nature Reviews Immunology.

[20]  Elissa K. Deenick,et al.  Stochastic Model of T Cell Proliferation: A Calculus Revealing IL-2 Regulation of Precursor Frequencies, Cell Cycle Time, and Survival1 , 2003, The Journal of Immunology.

[21]  Rustom Antia,et al.  Quantifying cell turnover using CFSE data. , 2005, Journal of immunological methods.

[22]  P. D. MINOR,et al.  Explanation of Degree of Correlation of Sibling Generation Times in Animal Cells , 1974, Nature.

[23]  S. Tangye,et al.  Evidence from the generation of immunoglobulin G–secreting cells that stochastic mechanisms regulate lymphocyte differentiation , 2004, Nature Immunology.

[24]  A. L. Koch.  Does the variability of the cell cycle result from one or many chance events? , 1980, Nature.

[25]  R. Ahmed,et al.  The rescaling method for quantifying the turnover of cell populations. , 2003, Journal of theoretical biology.

[26]  Alan S. Perelson,et al.  Estimating Lymphocyte Division and Death Rates from CFSE Data , 2006, Bulletin of mathematical biology.

[27]  John T. Chang,et al.  Asymmetric T Lymphocyte Division in the Initiation of Adaptive Immune Responses , 2007, Science.

[28]  J. Borghans,et al.  Quantification of T‐cell dynamics: from telomeres to DNA labeling , 2007, Immunological reviews.