Quantitating effector and regulatory T lymphocytes in immune responses by limiting dilution analysis modeling.

Although there is currently no doubt that regulatory lymphocytes represent a master player in the immune system, a major unresolved problem is the accurate quantitation of these cells among unfractionated cell populations. This difficulty mainly arises because there are no specific immunophenotypic markers that can reliably discriminate between effector and regulatory lymphocytes. To face this problem, we have developed computational models of limiting dilution analyses addressing the question of the accurate estimation of the frequencies of effector and regulatory cells functionally engaged in an immune response. A set of generic equations were provided to form a framework for modeling limiting dilution data, enabling discrimination between qualitatively different models of suppression. These models include either one or two subpopulations of regulatory cells, featured by either low or potent regulatory activity. The potential of this modeling approach was illustrated by the accurate determination of the frequencies of effector and regulatory T lymphocytes in one real limiting dilution experiment of CD4+ CD25+ T lymphocytes performed in the context of an allogeneic response in the human system. The crucial advantage of the limiting dilution method over the "static, phenotype-based" method is the dynamic evaluation of effector and regulatory T cell biology through their actual functional activity.

Detection of suppressor T lymphocytes and estimation of their frequency in limiting dilution assays by generalized linear regression modeling.

The estimate of the frequency of suppressor T lymphocytes in unfractionated cell populations remains challenging, mainly because these regulatory cells do not display specific immunophenotypic markers. In this paper, we describe a novel theoretical approach for quantifying the frequency of suppressor cells. This method is based on limiting dilution data modeling, and allows the simultaneous estimation of the frequencies of both proliferating and suppressor cells. We used previously published biological data, characterizing the inhibiting activity of suppressor T cell clones. Starting from these data, we propose a mathematical model describing the interaction between suppressor and proliferating T cells, and applied to a Poisson process. Limiting dilution data corresponding to this non-single-hit, suppressor two-target Poisson model were artificially generated, then modeled according to a generalized linear regression procedure. Deviation from the single-hit Poisson model was revealed by a statistical slope test, and a stepwise analysis of the regression appeared to be an efficient method that strongly argued in favor of the presence of suppressor cells. By using the frequency of proliferating T cells calculated in the first step of the regression, we demonstrated the possibility to provide a reasonable estimate of the frequency of suppressor T cells. Based on these findings, a practical decision-making procedure is given to perform standard analyses of limiting dilution data.