Modelling T cell proliferation: Dynamics heterogeneity depending on cell differentiation, age, and genetic background

Abstract

Cell proliferation is the common characteristic of all biological systems. The immune system insures the maintenance of body integrity on the basis of a continuous production of diversified T lymphocytes in the thymus. This involves processes of proliferation, differentiation, selection, death and migration of lymphocytes to peripheral tissues, where proliferation also occurs upon antigen recognition. Quantification of cell proliferation dynamics requires specific experimental methods and mathematical modelling. Here, we assess the impact of genetics and aging on the immune system by investigating the dynamics of proliferation of T lymphocytes across their differentiation through thymus and spleen in mice. Our investigation is based on single-cell multicolour flow cytometry analysis revealing the active incorporation of a thymidine analogue during S phase after pulse-chase-pulse experiments in vivo, versus cell DNA content. A generic mathematical model of state transition simulates through Ordinary Differential Equations (ODEs) the evolution of single cell behaviour during various durations of labelling. It allows us to fit our data, to deduce proliferation rates and estimate cell cycle durations in sub-populations. Our model is simple and flexible and is validated with other durations of pulse/chase experiments. Our results reveal that T cell proliferation is highly heterogeneous but with a specific “signature” that depends upon genetic origins, is specific to cell differentiation stages in thymus and spleen and is altered with age. In conclusion, our model allows us to infer proliferation rates and cell cycle phase durations from complex experimental 5-ethynyl-2'-deoxyuridine (EdU) data, revealing T cell proliferation heterogeneity and specific signatures.

Quantitative Analysis Reveals Reciprocal Regulations Underlying Recovery Dynamics of Thymocytes and Thymic Environment

“…1(b)). This slow recovery of the CD4 SP cells is consistent with the lack of a proliferation capacity of the CD4 SP cells [13,35], which leads to a prolonged recovery. The CD4 SP dynamics can be reproduced by no proliferation and mTEC-dependent death and outflux that represent the negative selection of the SP cells by the mTECs (Fig.…”

“…However, we have inconsistent evidences on the self-proliferation ability of the DP cells and its speed, which might depend on strains [35]; some of which showed that the DP cells proliferate little [22,36] whereas others suggest the DP cells can proliferate faster than the other types of the thymocytes [35,37]. Our model coordinates these properties by an auto-inhibitory regulation of the DP proliferation represented by the logistic term in Eq.1, which can realize a fast proliferation during the recovery period and its slowdown at the steady state.…”

The Rules of Human T Cell Fate in vivo

“…Additionally, the action of telomerase, which is up-regulated in dividing lymphocytes (47), may mean that most T cell death is independent of the Hayflick limit. Time since haematopoiesis will be related to the age of the individual subject, the association between age of the individual and T cell dynamics has recently been studied in mice (48).…”

Quantifying the Limits of Immunotherapies in Mice and Men

“…Values were calculated assuming that 10 8 T-cells are introduced; however, delivery rates due to any other desired number or blood concentration of cells could be calculated by multiplying results by the ratio of the desired number to 10 8 or multiplying blood concentration by the total blood volume in the target species. Although models to predict expansion of a T-cell population have been studied in the past [9,30], it is difficult to quantify cellular proliferation in or fractional recirculation from a given tissue. However, a maximum rate of delivery due to anatomy can be estimated with greater confidence, thus proliferation was not considered.…”