Lymphocyte kinetics: the interpretation of labelling data

Asquith, Becca; Debacq, Christophe; Macallan, Derek C.; Willems, Luc; Bangham, Charles R. M.

doi:10.1016/s1471-4906(02)02337-2

Cited by 106 publications

(196 citation statements)

References 15 publications

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“…In the memory T cell population, we found that the decay rate of labeled cells is higher than the average production rate, indicating that the turnover of cells that picked up label was higher than the turnover of the average cell in the memory population. This finding is in line with previous labeling studies, which all showed that the loss of labeled cells exceeded their production (28,30,33). Unexpectedly, we found that this was not the case for the naïve T cell population, because no significant loss of labeled cells was observed in any of the five individuals during the 16 weeks after label cessation.…”

Section: Discussionsupporting

confidence: 93%

“…It is important to realize that the accrual of label during label administration is truly representative of the T cell population as a whole, whereas the loss of label after label cessation is only based on those cells that have picked up label by cell division. Therefore, we based our analyses on a so-called kinetic heterogeneity model in which the average turnover rate of the T cell population is not necessarily equal to the loss rate of labeled cells (28). The median turnover rates of naïve CD4 and CD8 T cells were found to be as low as p ϭ 0.0005 and 0.0003 per day, corresponding to median half-lives of 1,517 and 2,374 days for naïve CD4 and CD8 T cells, respectively ( Fig.…”

Section: Resultsmentioning

confidence: 99%

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Sparse production but preferential incorporation of recently produced naïve T cells in the human peripheral pool

Vrisekoop

Braber

Boer

et al. 2008

Proc. Natl. Acad. Sci. U.S.A.

198

327

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In mice, recent thymic emigrants (RTEs) make up a large part of the naïve T cell pool and have been suggested to be a distinct short-lived pool. In humans, however, the life span and number of RTEs are unknown. Although 2 H2O labeling in young mice showed high thymic-dependent daily naïve T cell production, long term upand down-labeling with 2 H2O in human adults revealed a low daily production of naïve T cells. Using mathematical modeling, we estimated human naïve CD4 and CD8 T cell half-lives of 4.2 and 6.5 years, respectively, whereas memory CD4 and CD8 T cells had half-lives of 0.4 and 0.7 year. The estimated half-life of recently produced naïve T cells was much longer than these average half-lives. Thus, our data are incompatible with a substantial short-lived RTE population in human adults and suggest that the few naïve T cells that are newly produced are preferentially incorporated in the peripheral pool. recent thymic emigrants ͉ T cell half-lives ͉ T cell production T he role of the thymus in HIV infection is still poorly understood (1, 2). On the one hand, thymic failure has been suggested to play a crucial role in CD4 T cell loss during HIV infection (3), and rapid thymic rebound has been proposed to be responsible for T cell reconstitution during anti-viral treatment (4). However, it has been argued that thymic output in adults might be too low to have a large impact on CD4 T cell depletion (5). In general, these issues are addressed with estimates of thymic output, naïve and memory T cell production rates, and life spans that are simply extrapolated from observations in mice, monkeys, and lymphopenic or irradiated humans (6-11).Naïve T cells are generally thought to turnover relatively slowly, but it has been suggested that, in mice, a considerable part of the naïve T cell pool consists of RTEs with relatively rapid turnover (9, 10, 12). In humans, naïve T cell numbers, T cell receptor excision circles (TRECs), and expression of CD31 have been used to measure thymic output (7,13,14). Dion et al. (4) observed rapid changes in the Sj/V␤ TREC ratio within 3 months after infection with HIV, which suggested the presence of a rapidly turning over RTE pool in human adults containing most of the TRECs in the periphery, similar to young rodents and chickens (15, 16). However, because TRECs are long-lived, none of these approaches is specific for T cells that have recently emigrated from the thymus (1, 2, 5), and, therefore, they fail to quantify thymic output in humans.Peripheral T cell proliferation might also contribute to the maintenance of the naïve T cell pool in human adults; however, it is unclear which fraction of these cells remains in the naïve T cell pool (17). The contribution of RTEs and peripheral T cell proliferation to the maintenance of the naïve T cell pool can only be determined by studying the fate of newly produced T cells. In vivo labeling with stable isotopes in combination with appropriate mathematical analysis of these data provides a way to obtain T cell decay and production rates ...

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Section: Discussionsupporting

confidence: 93%

Section: Resultsmentioning

confidence: 99%

Sparse production but preferential incorporation of recently produced naïve T cells in the human peripheral pool

Vrisekoop

Braber

Boer

et al. 2008

Proc. Natl. Acad. Sci. U.S.A.

198

327

View full text Add to dashboard Cite

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“…Since each model yields its own particular parameter estimates, it seems difficult, if not impossible, to determine which estimates reflect the true kinetic parameters of CD4 1 T cells. Similar concerns about how the choice of the model affects data interpretation have been raised in a recent paper (Asquith et al 2002) that appeared after the submission of this paper. We have demonstrated that some of these problems can be solved by computing an 'average turnover rate' by averaging the death rates of all subpopulations in the model.…”

Section: Discussionmentioning

confidence: 55%

Estimating average cellular turnover from 5–bromo–2'–deoxyuridine (BrdU) measurements

Boer

Mohri

et al. 2003

Proc. R. Soc. Lond. B

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Cellular turnover rates in the immune system can be determined by labelling dividing cells with 5-bromo-29-deoxyuridine (BrdU) or deuterated glucose ( 2 H-glucose). To estimate the turnover rate from such measurements one has to fit a particular mathematical model to the data. The biological assumptions underlying various models developed for this purpose are controversial. Here, we fit a series of different models to BrdU data on CD4 1 T cells from SIV 2 and SIV 1 rhesus macaques. We first show that the parameter estimates obtained using these models depend strongly on the details of the model. To resolve this lack of generality we introduce a new parameter for each model, the 'average turnover rate', defined as the cellular death rate averaged over all subpopulations in the model. We show that very different models yield similar estimates of the average turnover rate, i.e. ca. 1% day 2 1 in uninfected monkeys and ca. 2% day 2 1 in SIV-infected monkeys. Thus, we show that one can use BrdU data from a possibly heterogeneous population of cells to estimate the average turnover rate of that population in a robust manner.

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“…Several authors [13,[25][26][27] allowed for an external source of cells, for example coming from the thymus or from a compartment of quiescent cells, and by allowing the generation of unlabelled cells during the de-labelling phase they were able to explain the observed down-slopes. Others [28][29][30] argued that labelled cells have recently divided, and that recently divided cells should have a faster death rate than non-divided unlabelled cells, which also allows for a decline of the fraction of BrdU þ cells. Several authors in the field of immunology [23,28,31] and in the field of haematopoietic stem cells [21,24,32] have argued that the loss of BrdU þ cells can be explained by BrdU dilution during the de-labelling phase.…”

Section: Introductionmentioning

confidence: 99%

A mechanistic model for bromodeoxyuridine dilution naturally explains labelling data of self-renewing T cell populations

Ganusov

Boer²

2013

J. R. Soc. Interface.

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Bromodeoxyuridine (BrdU) is widely used in immunology to detect cell division, and several mathematical models have been proposed to estimate proliferation and death rates of lymphocytes from BrdU labelling and delabelling curves. One problem in interpreting BrdU data is explaining the de-labelling curves. Because shortly after label withdrawal, BrdU þ cells are expected to divide into BrdU þ daughter cells, one would expect a flat down-slope. As for many cell types, the fraction of BrdU þ cells decreases during de-labelling, previous mathematical models had to make debatable assumptions to be able to account for the data. We develop a mechanistic model tracking the number of divisions that each cell has undergone in the presence and absence of BrdU, and allow cells to accumulate and dilute their BrdU content. From the same mechanistic model, one can naturally derive expressions for the mean BrdU content (MBC) of all cells, or the MBC of the BrdU þ subset, which is related to the mean fluorescence intensity of BrdU that can be measured in experiments. The model is extended to include subpopulations with different rates of division and death (i.e. kinetic heterogeneity). We fit the extended model to previously published BrdU data from memory T lymphocytes in simian immunodeficiency virusinfected and uninfected macaques, and find that the model describes the data with at least the same quality as previous models. Because the same model predicts a modest decline in the MBC of BrdU þ cells, which is consistent with experimental observations, BrdU dilution seems a natural explanation for the observed down-slopes in self-renewing populations.

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Lymphocyte kinetics: the interpretation of labelling data

Cited by 106 publications

References 15 publications

Sparse production but preferential incorporation of recently produced naïve T cells in the human peripheral pool

Sparse production but preferential incorporation of recently produced naïve T cells in the human peripheral pool

Estimating average cellular turnover from 5–bromo–2'–deoxyuridine (BrdU) measurements

A mechanistic model for bromodeoxyuridine dilution naturally explains labelling data of self-renewing T cell populations

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