Abstract
The outcome of Ag exposure is dictated by complex regulation of T cell proliferation. The rates of proliferation and survival are altered by numerous signals that the cell receives and integrates to achieve a net response. We have illustrated previously how small changes in kinetic parameters can lead to large differences, even under conditions of saturating IL2. In this study, we examine the effect of varying IL2 concentration on T cell response and develop a model incorporating additional parameters of proliferation and survival. Strikingly, the proportion of cells that enter the first division, but not the time at which they enter, is dramatically altered by IL2. Furthermore, the survival and average division time of cells in later divisions are also altered by IL2 concentration. Together, the small simultaneous effects on these parameters result in large differences in total cell number. These results reveal how in vitro systems may exaggerate the contribution of IL2, and thus how costimuli or additional helper cells that alter IL2 concentration, even by relatively small amounts, will generate large in vitro differences in cell number and therefore appear obligatory. Furthermore, they illustrate how a quantitative model of T cell activation can clarify how complex signal integration is handled by T cells in situ, and therefore more appropriately aid development of a theory of behavior.
The analysis of T cells in vitro has driven much of our development of theories of activation, including the concept of helper cells and APCsupplied costimulation (1, 2, 3). However, this approach has yielded consistent conceptual difficulties when explored in detail. For over 20 years, a central concept in our understanding of T cell activation has been that an Ag signal alone is insufficient, while an Ag signal plus another costimulatory signal leads to activation (4, 5, 6, 7, 8). The search for the molecular identity of the costimulatory signal has uncovered many candidates, both soluble and APC surface bound (3, 9, 10, 11, 12, 13, 14). However, in most cases, the deletion of the costimulatory gene does not eliminate T cell responses in vivo (14, 15, 16, 17, 18, 19, 20, 21, 22). As a result, the meaning of the term costimulator has altered and is no longer used to imply an essential second signal. Typically, the term is now taken to mean any molecule that can increase activation of T cells under suboptimal stimulation conditions in vitro. This change in understanding of costimulation has created a gulf between our experiential understanding of T cell activation and one of our cherished theoretical paradigms, the twosignal theory (4, 5, 6, 7, 8). Adding to this confusing picture is the role of the T cell growth factor IL2. In vitro experiments have long identified this autocrine cytokine as a critical, possibly essential, element in T cell proliferation (23, 24, 25, 26). Furthermore, potent costimuli, such as agonists of CD28, act to a large extent by promoting production of this factor (9, 27, 28, 29). Nevertheless, mice deficient in IL2 are able to mount diminished, but effective, immune responses (30, 31, 32). At best, it seems, qualitative models based on in vitro analyses have been misleading for the development of a theoretical understanding of the correct role for costimuli and growth factors in T cell activation.
To resolve these difficulties, we have explored the possibility of a quantitative understanding of T cell behavior that would enable us to assess the contribution of TCR engagement, costimulation, and IL2 to the individual kinetic features of T cell proliferation. This more quantitative framework, we believe, will allow us to reconcile, and explore experimentally, questions of signal integration, and therefore achieve better insight into the regulation of T cells in vivo. Previously, we used a fourparameter model for describing T cell proliferation (33). Our approach assumed independent operation of the survival and proliferation components to behavior, and that variation in the population could be described by appropriate probability distributions. Thus, one parameter described the initial exponential rate of cell death exhibited by the population when cells were placed in culture, two parameters gave the Gaussian probability distribution for the time that the cells took to enter the first division, and the final (deterministic) parameter described the subsequent division rate (33). This model was developed to describe T cell proliferation in a system in which IL2 was saturating; however, a complete model of T cell proliferation must also be able to describe the T cell behavior under conditions of changing IL2 concentration, as occurs over time and with alterations to the range of costimulatory signals. When the fourparameter model was applied to data from T cells stimulated at different IL2 concentrations, there were inconsistencies that could only be solved by incorporation of new parameters. In this study, we present a refined model that better accommodates the observed features of T cell proliferation when IL2 levels are suboptimal. This amended model incorporates two new parameters: a precursor frequency for entry into the first division and a death rate for cells in division 1 or greater (D1^{+} cells).^{4}
Materials and Methods
Mice
Female B10.BR mice were obtained from the Animal Resource Center (Canning Vale, Australia). Mice were maintained under specific pathogenfree conditions and were used between 8 and 18 wk of age.
Reagents and Abs
CFSE was obtained from Molecular Probes (Eugene, OR). Human rIL2 (rhIL2) was purchased from Endogen (Woburn, MA). Murine IL2 (mIL2) was a gift of G. Zurawski (DNAX Research Institute of Molecular and Cellular Biology, Palo Alto, CA). AntimIL2 was purified from the hybridoma S4B6 (34, 35). Demecolcine was purchased from SigmaAldrich (St. Louis, MO). PEconjugated anti5bromo2′deoxyuridine (BrdU)^{5} and isotype control were purchased from BD PharMingen (San Diego, CA).
Cell preparation and culture
Cell suspensions were prepared from lymph nodes (axillary, brachial, cervical, inguinal, and paraaortic). Cells were enriched for CD4^{+} T cells by complement lysis using a mixture of B220specific (RA3.3A1), CD8specific (31 M), and heat stable Agspecific (J11d) mAbs. In some experiments, a CD25specific (7D4) mAb was also included. The remaining cells were labeled with a mixture of Abs against B220 (RA3.6B2), IA (b, d, q haplotypes) and IE^{dk} (M5/114.15.2), CD25 (PC61), and CD8 (YTS169). Labeled cells were then depleted using antirat IgG DYNAbeads (Dynal Biotech, Oslo, Norway). The resulting purified CD4^{+} T cells were further fractionated on the basis of CD62L expression by labeling with CD62Lbiotin (MEL14), followed by streptavidin MACS beads (Miltenyi Biotec, Bergisch Gladbach, Germany). The cells were then passed through a MACS MS column held in a magnetic field by a MiniMACS magnet (Miltenyi Biotec). The resulting population was typically >93% CD4^{+}CD62L^{+}.
Cells were labeled with CFSE, as described previously (36). Culture medium was RPMI 1640 medium with lglutamine (Life Technologies, Grand Island, NY) supplemented with 10% heatinactivated FCS (Life Technologies), 5 × 10^{−5} M 2ME, 100 μg/ml streptomycin, and 100 U/ml penicillin, all from SigmaAldrich. T cells were stimulated at 3 × 10^{4}/200 μl with platebound antiCD3 (1452C11) in flatbottom 96well plates (BD Labware, Franklin Lakes, NJ).
For costimulation experiments, an antiCD28 mAb (37.51) was added in soluble form.
Determining cell number
The absolute number of cells in culture at each time was determined by adding a known number of CaliBRITE beads (BD Biosciences, San Jose, CA) to each well before harvest. CaliBRITE beads and cells can be distinguished by flow cytometry on the basis of their forward and side scatter properties. The ratio of live cells to beads was then used to calculate the total number of cells in each culture (37). Flow cytometry was performed on a FACScan or FACSCalibur (BD Biosciences), and analysis was done using CellQuest (BD Biosciences) or FlowJo (Tree Star, San Carlos, CA).
The number of dead blasts found in each culture was an important additional data set to help constrain the fitting of the model to time course data. Previous studies have shown that CFSElabeled dead cells retain the dye and can be identified by flow cytometry (38, 39). To determine the number of dead cells at each time of harvest, gates were set based on their distinctive forward and side scatter characteristics. Cells falling in the dead cell gate could clearly be categorized as having died as either an undivided cell, or while proliferating as a blast. Using these gates, the absolute number of dead undivided cells and blasts in each culture was determined with reference to CaliBRITE beads as for live cells (37). Using these methods, the fate of most of the input cells (>85%) could be accounted for over 4 days (data not shown).
Determining cells in each division
CFSE Modeler (ScienceSpeak, Canberra, Australia) was used to determine the number of cells in each division from CFSE profiles (33). Precursor cohort analysis was performed, as described previously (33). Briefly, precursor numbers were calculated by dividing the number of cells in division i by 2^{i}. A Gaussian distribution was fitted to the plots of precursor number against division number using nonlinear regression analysis in GraphPad Prism (GraphPad Software, San Diego, CA). The means of the fitted Gaussian distributions were plotted against time, and straight lines were fitted using linear regression analysis in GraphPad Prism.
Modeling
The number of cells expected in each division was calculated in a modified manner to that described previously (33). The new model incorporated the additional parameters p, the proportion of the cell population that will participate in division (the precursors), and d, the proportion of a cohort of cells that die while traversing a complete cell division (for division numbers ≥1). As previously described (33), the variation in the time of entry of cells into first division within the population is assumed to follow a Gaussian (or, in some instances, a logGaussian) distribution given as φ with mean and SD μ and ς, respectively. Once the cells have entered their first division, it is assumed they divide with equal subsequent division time (b), although this is a simplification. As for the previous model, it is assumed that the rate of death of undivided cells proceeds according to an exponential decay rate constant (k) independently of activation up until the first division (33).
When a number of starting cells (N_{0}) are placed in culture, the number of live and dead cells that are to be found in each division i (in which i is an integer) when the culture is harvested at time T_{f} (given in hours) can be calculated if all parameters are given.
Live undivided cells (i = 0) will comprise nonprecursor cells and precursor cells for whom their designated time to division is greater than T_{f}. It is assumed both are subject to the same rate of exponential death. Therefore, at T_{f}, the number of live nonprecursors (NP_{0}) is given by:
And the number of undivided precursor cells (P_{0}) is essentially:
To calculate the above term and subsequent values of cells in each division, the appropriate integrals were solved numerically using the trapezoidal rule. To do this, the distribution was split into 15min intervals (up to 240 h), with time of entry into first division given by t (in which t is in hours). Therefore, we calculated the total number of precursors in division 0 (P_{0}), as follows:
And the total number of live cells in division 0 (L_{0}) is:
All precursor cell cohorts with a time to divide less than T_{f} will have divided at least once. For these cells, we need to calculate how many have entered division, which division they are now in, how many remain alive, and how many dead cells have been left behind in each division.
Thus, the total number of precursors in division i (for i >0) is given by:
The e^{−kt} term is used instead of e^{−kTf}, as these cells have entered division and are therefore no longer subject to the exponential decay of undivided cells.
For i >0, the number of live cells in division i (L_{i}) is based on this precursor number, but also takes into account the doubling of cell numberwith division and the proportion of cells that die each division. Cells are assumed to die evenly across the division.
Where the term accounts for the cell death and doubling in previous divisions, the term gives the fraction of how far the cells are through the current division, and thus gives the proportion of cells that have died in the current division.
The number of dead blasts that are found in division i is given by:
Thus, the total number of dead blasts is given by:
Where gives the least integer greater than or equal to
A Microsoft Excel (Microsoft, Redmond, WA) spreadsheet (xcelmodel) was used to give numerical solutions to the above equations. The model predicted values for total number of live cells per division and total number of dead blasts, and was then compared with experimental data, and the parameters were fitted to obtain the least sum of squares between predicted and experimental data. The values for N_{0} and k were predetermined by the number of cells known to be added to culture and the exponential decay seen in nonstimulated cultures
BrdU analysis
Cultured T cells were pulsed at day 4 with 100 μg/ml BrdU (SigmaAldrich) for 4 h before harvesting. Cells were fixed in 2% formaldehyde for 10 min. Tween 20 (ICN, Irvine, CA) was then added to give a final concentration of 0.5% formaldehyde and 0.1% Tween 20 and incubated overnight. DNase I (Boehringer Mannheim, Mannheim, Germany) was added at 100 μg/ml (in 50 mM TrisHCl, pH 7.4, 10 mM MgCl_{2}, 100 μg/ml BSA) for 30 min at 37°C before BrdU incorporation was detected using an antiBrdU Ab.
Demecolcine analysis
For direct analysis of time of entry into first division, cells were stimulated in the presence of 5 ng/ml demecolcine (40). Flow cytometric analysis of CFSElabeled cells confirmed that there was no division in the presence of demecolcine (data not shown). After various times, cultures were pulsed with [^{3}H]TdR (ICN; 1 μCi/well) for several hours before harvesting. Incorporation of radioactivity was measured using a Betaplate counter (PharmaciaLKB, Uppsala, Sweden). LogGaussian distributions were fitted to the data using nonlinear regression analysis in GraphPad Prism.
Results
Quantitative effects of IL2 on T cell proliferation kinetics
To examine the quantitative effects of IL2 concentration on T cell proliferation, CFSElabeled cells were stimulated with antiCD3 in the presence of an antimouse IL2 mAb (S4B6) and various concentrations of hIL2. hIL2 is active on mouse T cells while being resistant to the inhibitory effects of the S4B6 Ab. As noted previously (42), cells divided asynchronously so that at any time point cells can be found spread across a range of divisions (Fig. 1⇓A). At higher concentrations of IL2, the cells progressed through a greater number of divisions so that after 99 h, most cells in cultures activated in the presence of 50 U/ml of IL2 were found in division 4 or 5. In contrast, in cultures exposed to only 1.25 U/ml, very few cells had reached division 5 by this time, with most cells being found in divisions 2–4. This increased progression through division may result from a decrease in the time to first division, from a decrease in subsequent division time, or from a combination of the two.
As a first attempt to determine the average time to first division (μ_{tdl}) and the subsequent division time (b) of T cells in the above cultures, precursor cohort plots were constructed (Fig. 1⇑B) and Gaussian curves were fitted, as described previously (33). The mean division numbers were then plotted against time, and linear regression analysis was performed (Fig. 1⇑C). The reciprocal of the slope of the resulting line gives an indication of the average subsequent division times (b) and the intercept with division 1 the mean time to first division (μ_{tdl}) (33). By this method, the intercept with division 1 was relatively constant at ∼50 h for 50, 5, and 2.5 U/ml of hIL2. In contrast, the slope of the line decreased as IL2 concentration was lowered consistent with progressively longer division times. When plotted in this way, data for 1.25 U/ml of IL2 were difficult to fit with Gaussian curves, as the cells had not moved far enough into division and the fitting was therefore subject to unacceptably large errors. Nevertheless, this analysis suggests that IL2 concentration was not altering the average time to first division, while exerting a profound effect on the subsequent division rate.
A sixparameter model: a variable precursor frequency and variable rate of death in dividing cells
It was our intention to develop a useful quantitative model that could predict the number of stimulated T cells found in culture at various IL2 concentrations. Previous analysis of cell numbers after stimulation using the fourparameter model of proliferation was done in the presence of saturating IL2, and did not take account of death of cells in divisions after 0 (D1^{+} cells). In the experiments described in this work, cultures containing low IL2 concentrations displayed a proportionally large number of dead cells (data not shown). Thus, the earlier described fourparameter model required modification to resolve the proliferation behavior of cells in the presence of suboptimal IL2. For this reason, a more complete model of proliferation was developed that incorporated a parameter describing the proportion of D1^{+} cells that die in each division (d). This was assumed to be constant for all divisions after the first. The death rate per division of D1^{+} cells was subtracted from each division and assumed to operate in a linear manner such that cells harvested halfway through a division will have lost onehalf of the designated proportion of cells.
In addition to the above modification, a precursor frequency parameter (p) for cells entering the first division was introduced, as preliminary investigations revealed that, under suboptimal stimulation conditions, not all cells will be stimulated to divide (data not shown). The precursor frequency was introduced as a proportional multiplier of the total input cell number and essentially altered the area under the time to first division probability distribution. The method for calculating the predicted number of cells found in each division at a nominated culture harvest time based on the use of six parameters is described in detail in Materials and Methods.
IL2 concentration does not alter the rate of initial cell death (k)
The new sixparameter model described above was to be used to analyze experimental data to extract values for each parameter, if possible. When fitting a complex model in this way, it is useful to constrain as many parameters as possible. One possible parameter that we reasoned might be fixed was the rate of cell death when cells were first placed in culture. Previously, we found that cell survival followed an exponential curve and was not affected for the first 30 h in culture by the presence or absence of antiCD3 (33). To examine the effect of different concentrations of hIL2 on the rate of cell death, CFSElabeled naive CD4^{+} T cells were placed in culture in the presence of varying concentrations of hIL2. The cultures were harvested at various time points, and the absolute number of live cells was determined. As shown in Fig. 2⇓A, a significant proportion of cells dies within the first 24–48 h of culture; however, this rate of death did not appear to be altered by the concentration of IL2 present. Exponential decay curves were fitted to the data using GraphPad Prism (Fig. 2⇓A), and the decay constants obtained were plotted against IL2 concentration (Fig. 2⇓B). The results of this fitting confirm that IL2 does not significantly alter the initial rate of death when cells are placed in culture. Thus, the value of the exponential decay constant could be determined for unstimulated cultures and assumed to be identical for cultures containing various concentrations of IL2.
Fitting of data to sixparameter model
The new sixparameter model was then used to more closely examine the effect of IL2 concentration on all aspects of T cell proliferation. CFSElabeled naive CD4^{+} T cells were stimulated with antiCD3 in the presence of the mIL2neutralizing Ab S4B6 and various concentrations of hIL2. The cells were harvested at various times, and the total number of live and dead cells (both blast and undivided) was determined, as described in Materials and Methods. As observed previously, there was a steep decrease in cell number at the beginning of culture (Fig. 3⇓A). Consistent with the above data and previous results, this cell death was similar for all cultures until ∼40 h. At high concentrations of IL2, however, the cell number began to increase after 48 h as the cells began to divide. This increase in live cell number was accompanied by the appearance of dead blasts. In contrast, at low concentrations of IL2, the cell number did not increase again, but remained in decline. As observed earlier, IL2 concentration also affected the progression of cells through division (Fig. 3⇓B).
The sixparameter model was fitted to the data for total cells found in each division by reducing the sum of squares for live cells per division and total dead blasts. Values for each parameter could be found such that fitted lines closely followed the experimental data both in terms of total cell numbers (Fig. 3⇑A) and cells per division (Fig. 3⇑B). The optimum values for each parameter determined for each IL2 concentration are plotted in Fig. 3⇑, C–G. The fitting again suggested that IL2 did not alter the mean time taken to enter the first division (Fig. 3⇑C). In contrast, IL2 concentration dramatically affected the precursor frequency, division time, proportion dying per division, and SD of time to first division (Fig. 3⇑, D–G).
Independent testing of parameters confirms predictions of model fitting
Although the sixparameter model fitted well to the experimental data, independent tests of the predictions and assumptions of the model were conducted to verify the accuracy of the model and the conclusions reached.
IL2 affects the division rate of T cells
Initially, the prediction of the model that increasing IL2 concentration would decrease the average division time consistently across all divisions was tested by exposing cells to a brief pulse (4 h) with BrdU. As the cells are unsynchronized with respect to position in cell cycle, only those that enter S phase within the pulse time are labeled. The faster the cell population is dividing, the more cells that will enter S phase in the pulse period, and thus the higher the percentage of BrdU^{+} cells will be. Cells were labeled with CFSE so that BrdU incorporation and division number could be assessed simultaneously (Fig. 4⇓A). The proportion of BrdU^{+} cells was constant across divisions greater than 1 (Fig. 4⇓B), as previously reported for saturating concentrations of IL2 (42), reflecting the relatively constant rate of proliferation once the cells have begun proliferating. At lower concentrations of IL2, the percentage of BrdU^{+} cells was decreased, revealing a reduced proportion of cells in S phase consistent with a decreased division rate in these cultures. This change in BrdU incorporation with IL2 concentration (Fig. 4⇓C) mirrors the effect seen on division time in our model fitting (Fig. 3⇑F).
IL2 regulates the proportion of cells entering first division
Next, the entry into the first division was investigated directly. To measure the time the cells took to enter the first division, a previously reported system that uses the cell cycle inhibitor demecolcine (40) was used. Demecolcine inhibits cells in metaphase of the cell cycle; thus, the cells are able to replicate their DNA, but do not undergo cell division. As a consequence, DNA replication is only possible from cells entering their first division. Cultures containing demecolcine were pulsed briefly with [^{3}H]TdR at varying times to detect DNA replication. Fig. 5⇓A shows that cells cultured in the presence of higher concentrations of IL2 incorporated greater levels of TdR; however, the time at which the cells began to incorporate TdR was not noticeably altered. Furthermore, the increased accuracy of the demecolcine method of measuring time to first division allowed a finer resolution of the distribution of cells entering division. Fitting analysis with GraphPad Prism revealed that this distribution more accurately approximated a logGaussian rather than the Gaussian curve we had used previously. When logGaussian curves were fitted to the data (Fig. 5⇓A), the mean, width, and area under the curve for each IL2 concentration could be extracted. The mean and width of the time to first division distribution decreased slightly with decreasing IL2 concentration (Fig. 5⇓, B and D). In contrast, the area under the curve was dramatically reduced at lower concentrations of IL2 (Fig. 5⇓C). The area under the curve reflects the precursor frequency, so this result confirms the prediction from fitting the sixparameter model that IL2 would act to increase the number of cells that are able to enter the first division.
AntiCD3 concentration affects time to first division and precursor frequency
The result that IL2 did not greatly affect time to first division was surprising, so the ability of the demecolcine system to detect changes in time to first division was tested. Cells were stimulated with suboptimal concentrations of antiCD3, and time to first division was measured using the demecolcine system. Reducing the concentration of antiCD3 decreased the level of TdR incorporated (Fig. 5⇑E) and seemed to delay the onset of proliferation. Fitting of logGaussian curves to the data revealed that, in contrast to the effect of IL2, decreasing the antiCD3 concentration increased the mean time the cells took to enter the first division (Fig. 5⇑F). However, in common with IL2, the strength of stimulation through CD3 also altered the proportion of cells able to enter the first division (Fig. 5⇑G).
A restricted window for entry into division: delayed addition of IL2 cannot rescue cells
Given that the IL2 concentration determined the proportion of cells that could enter division at a time determined by the antiCD3 concentration, we examined the outcome on entry of cells into division when IL2 addition was delayed by up to 48 h. We reasoned that if sufficient IL2 was not available at the time cells were due to enter division, later addition of IL2 would rescue these cells, recruiting them into division. As a result, we would expect to see the same area under the TdR incorporation curve, regardless of when the IL2 was added. Cells were stimulated with antiCD3, and IL2 was added at the start of culture or after 24, 34, or 48 h, and the entry of cells into division was measured using the demecolcine system. Cultures that received delayed addition of IL2 displayed reduced incorporation of TdR for at least 20 h after the IL2 was added before reaching similar levels as the control cultures (Fig. 5⇑H). This result indicates that 20 h was required for IL2 to achieve its full effect on promoting the entry of cells into division. Furthermore, when IL2 addition was delayed, the area under the curve was much lower than that of cultures that were continuously exposed to IL2, implying that cells were unable to be rescued. That is, if a cell has not been exposed to sufficient IL2 when its time to divide occurs (as determined by factors such as the level of antiCD3 stimulation), the opportunity to divide is lost.
Discussion
A combination of modeling methods and additional confirmatory experiments was used in this study to dissect the manner in which IL2 promotes T lymphocyte growth. A summary of the conclusions is depicted in Fig. 6⇓. T cells when placed in culture begin to die in a manner that can be approximated to an exponential decay curve. The presence of IL2 does not alter this rate of death over the first 24 h. Strikingly, the average time that cells took to enter the first division was also not greatly altered by the concentration of IL2. In contrast, the apparent precursor frequency was dramatically altered, such that far fewer cells within the population entered division in the presence of low levels of IL2. Furthermore, cells cultured at low concentrations of IL2 were found to take longer to complete subsequent divisions, and a larger fraction of them died before the next division was undertaken. The quantitative outcome of these changes could be accommodated within a sixparameter model that accurately described total cell numbers and number of live cells in each division at various times after initiation of culture (Fig. 3⇑).
When the precise value of each parameter is determined for each IL2 concentration, as shown in Fig. 3⇑, the manner in which subtle simultaneous effects on entry into division, division rate, and survival converge to induce large changes in cell number is revealed. For example, 30,000 T cells placed in culture with antiCD3 and an IL2 concentration in culture of 1 U/ml will yield only 1,700 live cells after 5 days. However, raising the IL2 concentration to 3 U/ml will increase the number of live cells after 5 days to over 18,000. This 10fold increase in cell number results from only a 30, 40, and 40% change in the parameters death per division, precursor frequency, and division time, respectively. This example helps illustrate how in vitro systems are highly sensitive to manipulations that alter, even a small amount, the total IL2 concentration. The amount of IL2 produced by TCRstimulated T cells alone is weak. Nevertheless, it is not zero, and high levels of stimulation and/or high cell culture densities are able to generate sufficient IL2 to initiate proliferation (Fig. 5⇑H). Costimulation assays favor the use of low density and suboptimal stimulation, such that the endogenous IL2 concentration will not sustain significant proliferation. Under these conditions, even small increases in IL2 production induced by a costimulator, such as CD28 ligation, can yield significant increases in cell number. As a result, data that show marked differences in cell number or TdR incorporation in cultures with and without the costimulus are possible, leading to the somewhat misleading qualitative interpretation that an added stimulus is essential for T cell activation and proliferation.
The quantitative view of multiple signal integration can be extended to accommodate accessory cell licensing by CD4 cells recently reported to serve as a replacement, or adjunct to Th cell action for promoting CTL activation (43, 44, 45). Thus, we propose increased expression of costimulatory molecules, such as CD40, on the APC induced by CD4 helpers can enhance T cell proliferation in ways additional to cytokines (such as altering the time to division and/or the survival through divisions). The quantitative sum of all of these effects (costimulators and soluble molecules) dictates the subsequent rate of T cell proliferation. The relative contribution of costimulatory molecules and soluble growth factors will depend on the stimulation conditions, and neither should be considered as obligatory pathways, despite the practical reality that an experimental system can be manipulated to yield convincing data consistent with such a view.
These studies may help explain the paradox concerning the difference in requirement for IL2 in vitro compared with in vivo (23, 24, 25, 26, 30, 31, 32, 46, 47, 48). Potentially multiple stimuli in vivo, including other common γchain ligands, may be summed by the T cell to alter the parameters of proliferation and thereby lessen the single contribution of IL2induced changes to these parameters. Furthermore, there is accumulating evidence that TCR engagement with costimulation is sufficient to induce some rounds of T cell proliferation in the absence of IL2, or other γchain ligands (49, 50). Thus, the in vitro/in vivo paradox can be resolved by noting three features of IL2 that lead to its markedly exaggerated importance in vitro. First, IL2 is the major potential growthpromoting factor produced in vitro and may not be so in vivo; second, rapid accumulation of the cytokine within the confines of the in vitro culture allows high, stable concentrations to develop that probably never occur in vivo; and third, as we have seen, relatively small changes in IL2 can have large effects after a few days in culture. Thus, while in vitro analyses are useful and even essential to demonstrate the quantitative operation of IL2, studies of mice deficient in IL2, or components of the IL2R must complement this work to more appropriately apportion the contribution of this cytokine to in vivo responses.
Our experiments determined that IL2 did not alter the time at which entry into the first division occurred, although it did alter the time to traverse subsequent divisions. The former observation was surprising, and appeared to be inconsistent with the important early studies of Cantrell and Smith (26, 51). These authors have previously shown that IL2 concentration, IL2R levels, and the duration of the IL2/IL2R interaction determine the time that activated T cells take to enter S phase (26). These earlier experiments, however, were conducted on T cells that had already been activated with mitogens and then rested without IL2. Their reported regulation of entry into divisions, therefore, probably more closely reflects what is occurring in divisions after 1 than it does the entry of naive cells into the first division. Thus, these reports are consistent with our finding that IL2 does not alter the time taken to enter the first division, but does alter the time taken to enter subsequent divisions.
Wells, Gudmundsdottir, and Turka (39) observed previously that the proportion of cells entering division following antiCD3 stimulation is regulated by both the strength of TCR engagement and the provision of costimulatory signals. They also noted that only ∼60% of T cells were able to enter division under maximum stimulatory conditions (39). Our analysis extends this work to reveal that IL2 concentration is a further variable in determining the frequency of cells entering cycle even when all cells show signs of activation. Additionally, our experiments reveal that if IL2 is not present at the time that cells are designated to divide by the strength of CD3 stimulation, they cannot be rescued by later addition of IL2 (Fig. 5⇑H). This finding raises the question of the fate of these cells. Perhaps they become anergic or undergo apoptosis after a further culture period. Our attempts to test the latter possibility by detecting increased death in the undivided population in the absence of IL2 were hampered by the high intrinsic rate of death (data not shown). Thus, no conclusion could be reached, and further investigation will be required to determine the fate of these belowprecursor threshold cells.
The addition of two new parameters was not the only modification we propose for the original Gett/Hodgkin model. We suggest that the original Gaussian distribution describing entry into the first division should also be replaced with a logGaussian function. This adjustment is a result of the greater accuracy in measuring time to first division with the demecolcine method. Such a pattern of entry into division is still consistent with a stochastic model of T cell proliferation and is reminiscent of the logGaussian distribution of many receptors, including CD3 and IL2R, on the surface of T cell populations (26, 52). Thus, a simple explanation is that there may be a linear relation between the logGaussian growth factor receptor number and lognormal time to division consistent with the original experiments of Cantrell and Smith (26, 51). Whether the variation in receptor number and/or time to division results from differences in additional factors, such as the age of the cell, its activation history, or level of self reactivity, or is inherent to the construction of the cell, is currently unknown. Nevertheless, it is noteworthy that this variability within the population ensures a full range of quantitative outcomes following stimulation, such that the summation of a large series of all or none decisions for division or death for each cell in the population results in a smooth transition from low cell to high cell numbers generated as IL2 is increased. Thus, by this example, variability should not be viewed as an imperfection in the population, but potentially as an essential element in the smooth quantitative operation of the T cell response.
The sixparameter model presented in this work has three probabilistic parameters that are used to describe the variability within the cell population: two describing time to first division and one for the rate of initial cell death. Clearly, the pattern of death (d) and the time to divide in subsequent divisions (b) would also be better described by probability functions once the shape of these distributions can be experimentally determined. An example in which this may be relevant is demonstrated by the results of our fitting of the sixparameter model, which implied that decreasing IL2 should increase the variation in the time to first division distribution. Our demecolcine data, however, revealed that, if anything, there was a slight decrease in the spread of entry into first division as IL2 concentration was reduced. The solution to this inconsistency is presumably that a degree of variability also exists in the time that cells take to reach subsequent divisions. As a result, over time the spread of cells through division is the net result of compounding the variation in time to first and subsequent divisions. The above discrepancy in the effect of IL2 on time to first division variability can thus be explained by the reasonable assumption that decreasing IL2 concentration increases the variability in time taken to enter subsequent divisions. The variable b, therefore, gives the average time to next division, and thus its value may be skewed by the accumulation of very slowly dividing (or divisionarrested) cells. Methods for investigating variation in time to enter subsequent divisions and the presence of nondividing cells are currently being developed.
A further potential variable not addressed as yet in our model is the possibility that the value of the death rate d may change with consecutive divisions. Renno et al. (53) found that superantigenstimulated T cells in vivo exhibit an increase in death rate at later divisions. Furthermore, although our model incorporated a linear rate of cell death across division, it is possible that this death may be better described by other functions, such as the exponential decay or a lognormal distribution. We have not found it necessary to include this additional level of complexity to fit T cell proliferation data in vitro; however, given the results of Renno et al., attempts to model other T cell activation systems may require this additional amendment to our model.
In conclusion, these studies help to resolve some of the discrepancies observed between in vitro and in vivo results. Quantitative modeling of in vitro systems allows exploration of the complex signal integration while keeping a more appropriate perspective on the importance of each stimuli. Thus, in vitro analysis continues to be of use in exploring the response to multiple stimuli.
Acknowledgments
We thank John Murray for critical review of the manuscript; Tony Basten for enthusiastic support of this work; and Alan Baxter, Barbara Fazekas de St Groth, Stuart Tangye, and Miles Davenport for helpful comments and advice.
Footnotes

↵1 This work was supported by the National Health and Medical Research Council of Australia. E.K.D. was supported by an Australian postgraduate award. P.D.H. is a Senior Research Fellow of the National Health and Medical Research Council of Australia.

↵2 Current address: Institute for Research in Biomedicine, Via Vincenzo Vela 6, 6500 Bellinzona, Switzerland.

↵3 Address correspondence and reprint requests to Dr. Philip D. Hodgkin at the current address: The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, Victoria, 3050, Australia. Email address: Hodgkin{at}wehi.edu.au

↵4 Abbreviations used in this paper: BrdU, 5bromo2′deoxyuridine; hIL, human IL; mIL, murine IL.
 Received December 17, 2002.
 Accepted March 6, 2003.
 Copyright © 2003 by The American Association of Immunologists