Cutting Edge: Naive T Cells Masquerading as Memory Cells

Murali‐Krishna, Kaja; Ahmed, Rafi

doi:10.4049/jimmunol.165.4.1733

Cited by 428 publications

(451 citation statements)

References 25 publications

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“…The model predicts that the turnover rates are division number dependent. Similar analysis of in vivo data on the dynamics of P-14 tg CTL after adoptive transfer into irradiated mice [13] confirmed our findings. Therefore, the use of homogenous models for the analysis of cell kinetics deserves a caution.…”

Section: Resultssupporting

confidence: 89%

“…In [9], the model variables A j (t) + B j (t) were fitted to in vivo data on proliferation of P-14 Tg naive CD8 T cells after adoptive transfer into an irradiated host, taken from [13] and summarized in Table 3 in [9]. A partly heterogenous version of the above model, in which the division and death rates of naive cells are different from those of the divided cells, was studied in [6] on in vitro data from [10].…”

Section: A Version Of Smith-martin Modelmentioning

confidence: 99%

“…The cell growth kinetics in vivo is characterized by data from [13] summarized in Table 3 in [9]. This data set was used to estimate the parameters of the heterogenous compartmental model.…”

Section: In Vivo Cell Growthmentioning

confidence: 99%

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Computational analysis of CFSE proliferation assay

et al. 2006

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CFSE based tracking of the lymphocyte proliferation using flow cytometry is a powerful experimental technique in immunology allowing for the tracing of labelled cell populations over time in terms of the number of divisions cells undergone. Interpretation and understanding of such population data can be greatly improved through the use of mathematical modelling. We apply a heterogeneous linear compartmental model, described by a system of ordinary differential equations similar to those proposed by Kendall. This model allows division number-dependent rates of cell proliferation and death and describes the rate of changes in the numbers of cells having undergone j divisions. The experimental data set that we specifically analyze specifies the following characteristics of the kinetics of PHA-induced human T lymphocyte proliferation assay in vitro: (i) the total number of live cells, (ii) the total number of dead but not disintegrated cells and (iii) the number of cells divided j times. Following the maximum likelihood approach for data fitting, we estimate the model parameters which, in particular, present the CTL birth-and death rate "functions". It is the first study of CFSE labelling data which convincingly shows that the lymphocyte proliferation and death both in vitro and in vivo are division number dependent. For the first time, the confidence in the estimated parameter values is analyzed by comparing three major methods: the technique based on the variance-covariance matrix, the profile-likelihood-based approach and the bootstrap technique. We compare results and performance of these methods with respect to their robustness and computational cost. We show that for evaluating mathematical models of differing complexity the information-theoretic approach, based upon indicators measuring the information loss for a particular model (Kullback-Leibler information), provides a consistent basis. We specifically discuss methodological and computational difficulties in parameter identification with CFSE data, e.g., the loss of confidence in the parameter estimates starting around the 6-th division. Overall, our study suggests that the heterogeneity inherent in cell kinetics should be explicitly incorporated into the structure of mathematical models.

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Section: Resultssupporting

confidence: 89%

Section: A Version Of Smith-martin Modelmentioning

confidence: 99%

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Computational analysis of CFSE proliferation assay

et al. 2006

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“…It has long been observed that transfer of small numbers of T cells into lymphopenic hosts results in T-cell expansion, a process described as homeostatic proliferation [9][10][11][12][13][14][15][16]. As the T cells proliferate, they assume an Ag-experienced or memory phenotype, which is indicated by upregulation of CD44, Ly6C and CD122 (IL-2-IL-15Rβ) [12,15].…”

Section: A Link Between Lymphodepletion and Augmented Immune Functionmentioning

confidence: 99%

“…As the T cells proliferate, they assume an Ag-experienced or memory phenotype, which is indicated by upregulation of CD44, Ly6C and CD122 (IL-2-IL-15Rβ) [12,15]. This T-cell expansion and acquisition of a memory phenotype is also associated with enhanced effector functions, determined by ex vivo analysis of interferon-γ (IFN-γ) release and cytolysis [12,14,15].…”

Section: A Link Between Lymphodepletion and Augmented Immune Functionmentioning

confidence: 99%

Sinks, suppressors and antigen presenters: how lymphodepletion enhances T cell-mediated tumor immunotherapy

Klebanoff

Khong

Antony

et al. 2005

Trends in Immunology

404

288

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Lymphodepletion followed by adoptive cell transfer (ACT) of autologous, tumor-reactive T cells boosts antitumor immunotherapeutic activity in mouse and in humans. In the most recent clinical trials, lymphodepletion together with ACT has an objective response rate of 50% in patients with solid metastatic tumors. The mechanisms underlying this recent advance in cancer immunotherapy are beginning to be elucidated and include: the elimination of cellular cytokine 'sinks' for homeostatic γ C -cytokines, such as interleukin-7 (IL-7), IL-15 and possibly IL-21, which activate and expand tumor-reactive T cells; the impairment of CD4 + CD25 + regulatory T (Treg) cells that suppress tumorreactive T cells; and the induction of tumor apoptosis and necrosis in conjunction with antigenpresenting cell activation. Knowledge of these factors could be exploited therapeutically to improve the in vivo function of adoptively transferred, tumor-reactive T cells for the treatment of cancer.

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Reevaluation of the origin of CD44high "memory phenotype" CD8 T cells: comparison between memory CD8 T cells and thymus-independent CD8 T cells

et al. 2001

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CD44highCD8 T cells in naive mice, which increase with age, and are often referred to as memory CD8 T cells. However, since thymus‐independent CD8 T cells have also been shown to be CD44high, the origin of the CD44highCD8 T cells in naive mice remains unclear. In this study, we compared the characteristics of memory CD8 T cells and thymus‐independent CD8 T cells in TCR transgenic mice to clarify the origin of the CD44highCD8 T cells in naive normal mice. The memory and thymus‐independent CD8 T cells showed differences in surface molecules, spontaneous cell death, cytokine production, and response to IL‐2R binding of cytokines. Importantly, the "memory phenotype" CD8 T cells in naive normal mice showed similar characteristics to the thymus‐independent CD8 T cells, but differed greatly from "true" memory CD8 T cells in the TCR transgenic mice. Therefore, we conclude that a significant part of the CD44high memory phenotype CD8 Tcells in naive normal mice represents thymus‐independent CD8 T cells, which may participate in age‐related changes in immune responses.

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Cutting Edge: Naive T Cells Masquerading as Memory Cells

Cited by 428 publications

References 25 publications

Computational analysis of CFSE proliferation assay

Computational analysis of CFSE proliferation assay

Sinks, suppressors and antigen presenters: how lymphodepletion enhances T cell-mediated tumor immunotherapy

Reevaluation of the origin of CD44high "memory phenotype" CD8 T cells: comparison between memory CD8 T cells and thymus-independent CD8 T cells

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