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ARTICLES Measurement of Unequal DNA Partitioning in Tetrahymena pyriformis Using Slit-Scanning Flow Cytometry Pamela J. Sweeney, Friedrich Srienc, and A. G. Fredrickson’ Institute for Advanced Studies in Biological Process Technology, University of Minnesota, St. Paul, Minnesota 55108,and Department of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, Minnesota 55455
Slit-scanning flow cytometry allows analysis of the locations of components along the long axes of cells. This is useful in determining how the components of a dividing cell are partitioned into the two daughter cells. Ciliated protozoa, unlike most eukaryotic cells, do not partition their DNA equally between daughter cells. We have used slitscanning flow cytometry to measure the distribution of this unequal DNA partitioning in the ciliate Tetrahymena pyriformis. Our results show that the difference in the amounts of DNA alloted t o sister nuclei varies from cell t o cell but, on the average, increases with the DNA content of the mother cell. However, the average difference in the fraction of the mother cell DNA partitioned to each daughter cell is more or less independent of the DNA content of the mother cell and is about 8.5% of that DNA content. Slit-scanning flowcytometry also allows determination of the DNA distributions of dividing and newborn cells, determinations that are difficult to make with good statistical precision by other means. The measured newborn and dividing cell DNA distributions are broad, and this must be the result of repeated rounds of unequal partitioning of DNA.
Introduction Most traditional cell growth models consider biomass to be uniform. These models are often sufficient for describing cell mass in steady-state cultures, but they may not be appropriate for describing the physiological properties of cells in other situations, such as transient growth (Fredrickson et al., 1967,1971;Williams, 1980;Fredrickson, 1991). Such models do not allow for cell cycle dispersion within the population and thus cannot reflect single-cell behavior. Accurate models of cell growth must take into account the variation in cell properties within the population. One important source of variability is the unequal allotment of cell components to daughter cells at cell division. Thus, it is important to measure this uneven partitioning in dividing cells in order to describepopulation heterogeneities. Ciliated protozoa are of interest in engineering applications because of their role of consuming bacteria in wastewater treatment processes. Growth and reproduction of ciliates must be described using a segregated, structured modeling approach because it has proven difficult to model these processes using traditional microbial growth models (Ramkrishna,1979;Williams, 1980; Sambanis et al., 1987; Srienc et al., 1987;Fredrickson, 1991)and because ciliates exhibit a wide variation in DNA content within the population. These cells have a large amount of DNA in a macronucleus which does not necessarily divide evenly at cell division. The high ploidy level, about 50-ploid for Tetrahymena (Corliss, 1973),
* Author to whom correspondence should be addressed at the Department of Chemical Engineering and Materials Science. 8756-7938/94/3010-0019$04.50/0
allows the cells to divide their DNA unequally without danger of losing genetic information. The resulting heterogeneity of DNA content may lead to a variation in the amounts of RNA and, therefore, protein synthesis and growth, which is unaccounted for by traditional models. The goal of this work is to quantify the heterogeneity of the DNA content and partitioning in the ciliate Tetrahymena. Slit-scanning flow cytometry previously has been used for observation of human cells (Wheeless and Patten, 1973a,b;Cambier and Wheeless, 19751,determination of chromosome shape (Gray et al., 1977;Cram et al., 1977), and detection of buds on yeast cells (Block et al., 1990). In flow cytometry,a narrow sample stream is injected into a stream of “sheath” fluid, so that cells are lined up one after the other. Near the entrance the flow is not fully developed yet, meaning that oblong particles will line up with their long axis in the direction of the accelerating flow (Fulwyler, 1977;Kay and Wheeless, 1977;Lucas and Pinkel, 1986). The stained cells then flow through a laser beam, which excites fluorescent dyes. As the cell flows through the laser beam, the intensity of the fluorescence emitted changes, depending on which part of the cell is being excited by the laser. This is useful for the analysis of DNA partitioning in ciliates because it allows localization of the macronuclear DNA along the long axis of the dividing cell. Although other workers (McDonald, 1958;Cleffmann, 1968;Doerder and DeBault, 1975;and many others, noted in Discussion) have performed this analysis using observations of about 100 individual cells with methods such as microspectrophotometry,slit-scanningflow cytometry allows the rapid measurement of larger samples required
0 1994 American Chemical Society and American Instkute of Chemical Engineers
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to obtain statistically representative distributions of the variation in partitioning.
Materials and Methods Culture Growth Conditions. Cultures of Tetrahymena pyriformis GL (originally obtained from Dr. M. A. Gorovsky,Department of Biology, University of Rochester, Rochester,NY) were maintained in stock cultures of PPYE (2% proteose peptone and 0.1% yeast extract) at 23 "C with weekly reculturing. PPYE was prepared in a lox concentrated solution and centrifuged at 7000g for 30 min to remove particles in the manner of Gorovsky et al. (1975). This stock solution was stored at -20 "C. Before use, it was thawed, diluted 10-fold, and autoclaved. Experimental cultures were grown in PPYEG medium: PPYE medium supplemented with 0.5 % glucose added, after autoclaving, from a 20% stock solution that had been autoclaved separately. To prepare cultures that were in balanced growth, cultures were grown in 50 mL of medium in 250-mL Erlenmeyer flasks at 25 "C aerated by shaking at 200 rpm in an orbital shaker (Lab-Line, Chicago, IL). Cells were recultured daily until the average volume, as measured by an electronic particle counter (Electrozone Celloscope, Particle Data Inc., Elmhurst, IL), in exponential growth remained constant at about 4100 pm3, according to a calibration performed with 20.15 pm diameter microspheres (Coulter, Hialeah, FL). The generation time under these conditions was 5.8 h. The feeding assay (see below) was performed, and cells were harvested and fixed for DNA staining when the cell concentration was about 100 000 cells/mL, which is in midexponential growth phase. Cells were fixed by the addition of formaldehyde to a final concentration of 1% ! , Labeling of Feeding Cells. A particle uptake assay similar to that of Lavin et al. (1990) was performed to distinguish doublets from dividing cells. One culture was diluted with fresh medium to about 80 000 cellslmL. This cell concentration is too high for quantitative evaluation of the feeding rate, but is suitable for qualitative detection of the feeding activity, as desired here. Green fluorescent microspheres(diameter 1.89 pm, Polysciences, Warrington, PA) were added to the medium to a concentration of lo6/ mL. The culture was returned to the shaker and allowed to feed on the beads for 15 min. Because of the high cell concentration,the microspheres were replenished at 5-min intervals by the addition of one-half of the original number of microspheres in order to assure that there was still a large number of microspheres in solution available for uptake. After 15 min, 5-mL samples were fixed on ice in 1%formaldehyde and allowed to settle at 4 "C for 5-6 h. DNA Staining. After the settling step, the supernatant was aspirated, and the cells were resuspended in phosphate-buffered saline (PBS: 8 g/L NaCl, 0.2 g/L KC1, 2 g/L NaZHP04, and 0.4 g/L KH2P04, pH 7.3) with 0.5% Tween 20 (Sigma, St. Louis, MO). Settling rather than centrifugation served to wash out any uningested microspheres that might remain in the microsphere-fed culture. Cells were centrifuged out of the Tween solution (750g, 4 "C, 2 min) and resuspended in 1mL of 1 mg/mL RNase in PBS at 37 "C for 2 h. Cells were washed in PBS and resuspended in 5 pg/mL propidium iodide (PI, Sigma) in PBS. Cells were stained on ice for at least 30 min and analyzed on the flow cytometer in PI solution. Flow Cytometry. Cells were analyzed on a Cytofluorograf 11s flow cytometer (Ortho Diagnostic Systems, Westwood, MA) at a rate of 100cells/s. PI and microsphere fluorescence were excited by a Coherent Innova 90-5argon ion laser at a wavelength of 488 nm. Four signals were
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64 96 128 DNA (Area) Figure 1. Red fluorescence peak versus area frequency plot. Each dot represents a cell with a given combination of peak and areaof its DNA (red fluorescence)signal. Darker areas represent more cells with a given combination of properties. Binucleate (dividing) cells have high-DNA content (= signal area), but a signal peak height more similar to the low-DNA content (Gl) cells, since their DNA is in two smaller nuclei. Therefore, the dividing cells show up on this plot as a separate subpopulation below the rest of the cells. 0
32
acquired forward angle light scatter (FALS),an indicator of cell size; green fluorescence from ingested microspheres in fed cells; and red fluorescence from the DNA stain, in both the peak and area modes. The FALS signal was measured after passage through a 0.1 96 transmittance neutral density filter (Ortho Diagnostic Systems). Red fluorescence and green fluorescence were measured using a 590-nm long-pass filter (OG-590, Rolyn, Covina, CA) and a 515-530-nm band-pass filter (Ortho Diagnostic Systems), respectively. Slit Scanning. Waveforms were selected for storage by using the flow cytometer's electronics for sorting. The flow cytometer was programmed to send a sorting signal on the basis of a combination of several properties (discussed below). When a cell with the desired properties was encountered,the sort signal sent by the flow cytometer was used to trigger the oscilloscope to store the waveform. DNA (red fluorescence) waveforms were acquired using a Rapid Systems R2000 oscilloscope (Seattle, WA) interfaced with a Gateway 2000 microcomputer. For each cell, 1000 data points were acquired at a sampling frequency of 10 MHz, triggered by the sort signal of the flow cytometer. Signals were stored in files of 200 waveforms each. Ten files were stored per sample, for a total of 2000 waveforms per sample. Selection of Cells for Slit-Scanning Analysis. Debris was eliminated from analysis using the FALS vs red fluorescence (DNA) cytogram to distinguish it from the cells. Cells were selected as candidatesfor slit-scanning analysis on the basis of comparison of the peaks and areas of their DNA signals. Dividing cells have bimodal DNA signals which have relatively low peak heights compared to those of a unimodal signal of the same area. Thus, binucleate cells show up as a separate population on a red fluorescence peak vs area cytogram (Figure 1). Cells in this population composed 3-4 96 of the total population. Cell doublets also fall into this subpopulation of candidate cells. However, another property of Tetrahymena p y riformis cells can be used to distinguish between dividing cells and cell doublets. The oral apparatus of a T e t rahymena pyriformis cell is inoperative for some time before and for a short time after cell division, so that dividing cells cannot take up particles from their envi-
Machnol. Rug., 1994, Vol. I O . No. I
ronment. The culture was therefore fed on green fluorescent microspheres for 15 min, which was long enough for all of those cells that were capable of taking up microspheres to take up at least one. Thus, dividing cells were not labeled with green fluorescence, but since the nonfeeding cells composed only about 10% of the population, nearly all cell doublets were so labeled. On this basis, only those cells in the candidate subpopulation not showinggreen fluorescence were actually selected for slitscanning analysis. Cells selected amounted to about 20% of the subpopulation. This procedure was followed in the experiments which produced Figures 4, 5, 6, and 8. In Figure 7, these results are compared to experiments in which green fluorescence screening was not applied. The results of these two sets of experiments are substantially different, showing that enough cell doublets are present to make their exclusion from slit-scanning analysis necessary. Additional exclusions were made from the signals actually selected for slit-scanning analysis. Any cell whose signal had only one peak was excluded. Also excluded were signals with more than two peaks, as these probably represented clumps of cells. In addition, any signals in which the two nuclei were too far apart P62.5 pm) were also eliminated, as the nuclei giving such signals were probably not in the same cell since the cells were about 50 pm long. In total, the waveforms from 3 (for the fed cells) to 25% of the signals subjected to slit-scanning analysis were excluded on these bases. Data Analysis. The analysis of the waveforms, performed on a Gateway 2000 microcomputer (486CPU) using the ASYST programming language (Macmillan, New York), involves smoothing the data to remove noise, deconvolving to account for distortion due to laser beam shape (although this was omitted in the final procedure, as noted below), and then analyzing this data to choose where to draw the division point between cells. The signal is then integrated on both sides of this point to determine how much of the DNA is being allotted to each sister cell. To determine the division point, waveforms were analyzed by the reflection method previously described by Block et al. (1990). First, the wave was reflected about its higher peak. This curve was subtracted from the raw waveform. The resulting curve was then reflected about its peak. The division point is defined by the intersection of the two reflected curves.
Results Algorithm Testing. The accuracy of the DNA partitioning function determined depends on how accurately the analysis program assigns the DNA signal to one or the other of the nuclei. In order to determine the accuracy of the determination of the DNA ratio, ideal waveforms were simulated. I t was assumed that the nuclei of a dividing cell could be modeled as spheres whose centers were separated by a given distance (Figure 2a). For an ideal, infinitesimally thin laser beam hitting one of the nuclei at any point, the fluorescence signal would be proportional to the cross-sectional area a t that point (Figure 2b). In reality, however, the laser beam has a finite thickness and nonuniform intensity. The measured signals are generated through interaction of the fluorescent object with the intensity distribution of the laser beam (Figure 2c). The signals thus represent a convolution (Figure 2d) of the shape of the object with the intensity distribution of the focused light (Sharpless and Melamed, 1976; Norgren et al., 1982). The laser beam intensity distribution used in the waveform simulations was determined by measuring the
21 a) Nuclal
b) Ideal Slgual
e)
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h Time
Figure 2. Simulation of waveforms. (a) The daughter nuclei are assumed to be spheres of radii rl and r2 with their centers separated by distance dl. (b)The ideal signal: the cross-sectional area of the nuclei at each point along the long axis. (c)The laser beam shape function. (d) The simulated waveform obtained by convolution. signals for particles of known size and shape (5.75-pm latex microspheres,Polysciences). Sincethe cross-sectionalarea profile of these spheres was known, the laser intensity distribution was calculated by deconvolvingthe measured signal (Norgren et al., 1982). To evaluate different methods of analyzing the waveforms, the laser intensity distribution calculated above was convolvedwith cross-sectional area profiles calculated using various combinations of sizes of nuclei and distances separating the nuclei. This predicted the waveforms that would be expected for each combination of these physical parameters. The waveforms then took the place of the measured signals as input to the analysis program. The fraction of the signal allotted to each daughter nucleus was then compared with the ratio of the volume of the nuclei used to simulate the waveform. The simulated waveforms were used to investigate the accuracy of two different algorithms for determining the division point between the two nuclei. The reflection algorithm had previously been found to be superior to many other methods (Block et al., 1990). In the present work, it was compared with the simpler approach of drawing the division point at the minimum between the two peaks. Simulated waveforms with a wide range of parameters were used as input to analysis programs using each algorithm. While both methods gave very accurate estimates of the ratio of nuclei sizes for the realistic range of parameters (two separated nuclei, radii >3 pm), the reflection method was slightly more accurate, with an average error of 0.4% over the useful range of parameters compared to 0.6 % for the minimum method. Therefore, the reflection method was used for all waveform analyses. Misalignment of the cells as they pass through the laser beam could affect the accuracy of the analysis; if the long axis of the cell is not perpendicular to the laser beam, the fluorescence signals from the two nuclei will run closer
22
Biorechnol. frog., 1994, VOI.
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Figure 3. SampleDNA waveforms fromslit scanning. The upper waveform shows nearly equal DNA partitioning. The lower waveform shows more unequal division. together, and it will be difficult to resolve the separate parts of the fluorescence signal. Simulations similar to those described here was performed by Block et al. (1990) to determine how sensitive the analysis was to such misalignment. The results showed that the analysis gave accurate ratios for cells passing through the laser beam at angles up to 30" from the direction of flow. Greater misalignment than this should be extremely rare because of the flow properties in the flow chamber. Raw waveforms can be deconvolved before analysis to calculate the cross-sectional area profile, given the laser beam intensity distribution (calculated above) and the measured signal (Norgrenet ai.,1982). Theoretically,this should improve the analysis, since we could then use the cross-sectionalarea profile for determination of the division point and the amount of signal due to each daughter nucleus. However, when simulated waveforms were analyzed by the program, the average error in the fraction of the DNAallotted to each daughter for the realistic range of parameters (see above) was 1.3% when the waveforms were deconvolved versus 0.4 % when they were not. This procedure should have yielded perfect results (0 error), but apparently the numerical error in the deconvolution procedure is greater than the extremely small error in the estimation of the DNA ratio. Therefore, all subsequent analyses were performed on the raw waveforms. Experimental Results. Because the plane of division in ciliates is perpendicular to their long axis (and, therefore, the direction of flow), the two daughter nuclei of a dividing cell pass through the laser beam in sequence. Maxima in the red fluorescence are reached when the centers of the two daughter nuclei pass through the laser beam. Figure 3 shows two sample waves obtained from slit scanning that illustrate the variation in DNA partitioning in the
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population. One wave shows very similar amounts of DNA allotted to each daughter nucleus, while the other one shows quite different amounts: 44% to one daughter and 56% to the other. Waves such as those shown in Figure 3 were analyzed to determine the amount of DNA partitioned to each daughter cell. Figure 4a shows that the DNA content of daughter cells, as calculated from waveform analysis, is strongly correlated with the mother cell DNA content. For equal division all points on this plot would fall on a straight line of slope 0.5. Inequality of division and conservation of DNA cause points representing two sister cells to be spread at equal distances on either side of that line. The magnitude of this distance seems to increase with mother cell DNA content, meaning that larger mother nuclei divide less equally than smaller ones. DNA partitioning may also be visualized as the fraction of the mother's DNA each daughter nucleus receives. For equal partitioning this fraction would always be 0.5. Figure 4b allows us to see more clearly the symmetry about this value imposed by the conservation of DNA. While the difference in the amount of DNAreceivedby each daughter cell increases with increasing mother cell DNA content, the difference in the fraction, which averages8.596,changes very little with the mother's DNA content. The partitioning function required for segregated modeling is the frequency with which the daughter cells receive
Biotechnol. hog., 1994, Vol. 10, No. 1
a certain fraction of their mother’s DNA. This is shown in Figure 5. The partitioning function has a standard deviation of 0.051 (CV = 10%). The measured function is somewhat flatter than a gaussian and does not have long tails. The partitioning function is, however, unimodal, which indicates that equal division is the most probable event. While the partitioning function must be symmetric about the value 0.5 due to the conservationof DNAcontent, it could be that cells are most likely to give some unequal fraction of their DNA to their daughter cells. Were this the case, however, the partitioning function would be bimodal because there would have to be two maxima in the partitioning functions: at x and 1 - x . Another important feature of DNA partitioning is its relationship to the “size” (DNA content) of the macronucleus. To examine this, the partitioning function was calculated for mother cells in three different ranges of DNA content (see Figure 6). The average difference between daughter cells ranged from 8.1 to 8.9% of the mother cell DNA in the three different ranges, with the value increasing with DNA content. This small difference in the equality of partitioning occurs over a difference in DNA content of more than 50%, suggesting that any positive correlation that might exist is not very strong, as was expected from the scatter plot of observed values of the DNA fraction versus mother cell DNA content. There is little difference between the DNA distribution of the dividing cell population selected on the basis of the binucleate DNA signal and that of the population selected by eliminating feeding cells (data not shown). This seems to suggest that these signals represent dividing cells and not cell aggregates. However, as seen in Figure 7, the partitioning function calculated using all binucleate waveforms is much broader than that from only the nonfeeding dividing cells; these waves average an 11.5 % difference in daughter DNA instead of 8.5%. The broadening is caused by the random pairing of aggregated cells that were counted as dividing cells. Also shown in the figure is the partitioning function that would be calculated from a completely random pairing of cells in the population. The assumption that the measured distribution is a combination of dividing cells and aggregates gives an estimate of 34% aggregatesin the binucleate population (data not shown). This emphasizes the importance of detecting cell aggregates that must be eliminated from analysis by employing the feeding procedure. Slit scanning also allows examination of the DNA distribution of the dividing cells and the newborn cells. The daughter cell DNA distribution that would be predicted from equal division is not vastly different from the one calculated from waveform analysis, as seen in Figure 8a. The observed daughter cell distribution has a coefficient of variation (CV) of 23.8%. The daughter cell distribution predicted from equal division has the same shape as the mother cell DNA distribution (Figure 8b), so that both have CV values of 21.4 5%. The mother cell DNA distribution and the distribution predicted from equal division, then, are slightly narrower than the measured daughter cell DNA distribution.
Discussion The variation in DNA partitioning is often characterized in the literature (see below) in terms of the mean difference of the DNA content of the two daughter cells as a fraction
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Figure 5. Measured partitioning function (-) compared to a gaussian (- -) of the same mean and standard deviation.
of their average, or
where D1 is the DNA content of the larger daughter nucleus, DZthat of the smaller nucleus, and M = DI + DZ is the DNA content of the mother cell. We have calculated an average difference of 8.5 % of the maternal amount of DNA, (Dl - Dz)/M, as this is more meaningful for finding a partitioning function. We can see from the formula above that this corresponds to a difference of 17 % of the average daughter DNA content. The variation found in this study is larger than those others have found for strains of Tetrahymena;McDonald (1958) found 4.7% for Tetrahymena H, and for Tetrahymena thermophila, values of 8% (Cleffmann, 1968),6.2%,and 10.5% (Doerder and DeBault, 1975) have been found. The extent of the unevenness of division has been found to vary greatly in other ciliates. Euplotes eurystomus was foundto haveameandifferenceof 11.6%(Witt, 19771, while values of 33.8% and 37.3% have been found for Bursaria truncatella (Ruthman, 1964; Zech, 1966). In studies of Paramecium tetraurelia, mean difference values from 9 to 20.4% were found in one study (Berger and Morton, 1980) and values of 5.8 and 10 9% in another, where values of up to 63 % were found for a mutant strain (Berger and Schmidt, 1978). Values in other Paramecia were also within this range (Kimball, 1967; Kudrjavtsev, 1966). It has also been found that the DNA partitioning in Paramecium caudatum becomes more uneven with culture age, the mean difference being 10.4%for a young culture and 55.2% for an old one (Takagi and Kanazawa, 1982). The wide variation in mean difference values found for other ciliates suggests that the difference between our value of 17% and those reported by other researchers may be due to a number of things. These include differences in experimental methods, strain, and growth conditions. One possible difference in the methods is the amount of statistical variation, which is greatly reduced in this work because of our much larger sample size: over 1900 dividing cells in this study versus about 100 in others. Another difference in the methods is the algorithm used to determine the partitioning of DNA. Measurement of the accuracy of the algorithm using fluorescent microspheres is not feasible for a variety of reasons; however, as mentioned above, this algorithm was found to be very accurate when tested using simulated waveforms. The most important differences between the studies are most likely physiological. The strain of Tetrahymena ( T e t rahymena pyriformis GL) studied here is different from
Biotechnol. Prog., 1994, Vol. 10, No. 1
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a) 0.12
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Figure 6. Partitioning function calculated for three different ranges of mother cell DNA content. Signals were broken down into three ranges of total (mother cell) DNA content, and the partitioning function was calculated using only those signals. The lowest DNA range (-) may have a slightly higher frequency of more equal division, but the partitioning functions for the middle-DNArange(- -) and high-DNArange(--) are very similar.
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Figure 7. Partitioning function (-) comparedto that calculated for all binucleate cells (- -). Also included is the "partitioning function" that would be calculated from aggregation of random pairs of cells
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any of those used by the other researchers; the Paramecium studies described above show that there could be a 2-4fold difference in partitioning due to strain variations. The work of Takagi and Kanazawa (1982) shows that there could also be significant differences due to culture age. In addition, previous studies were done with cells grown at different temperatures in slightly different media; these differences in culture conditions may affect DNA partitioning. Because of the wide cell-to-cell variation in DNA content, the DNA distributions of Tetrahymena cells in the different phases of the cell cycle cannot be determined merely from the total DNA distribution (Figure 8c), as for most eukaryotic cells with similar fractions of the population in each cell cycle phase (at least 20% of the cells in each phase, from unpublished bromodeoxyuridine studies). Flow cytometric identification of the dividing cells and their DNA partitioning functions allows calculation of these distributions for large numbers of cells. The values of the coefficient of variation (CV) of the newborn (Gl)and dividing (G2)DNA distributions, 23.8 % and 21.4%,respectively, agree very well with those found for much smaller samples of Tetrahymena thermophila by Doerder and DeBault (1975). In five different experiments, they found CV values that ranged from 17.1 to 24.1% for G1 and from 17.5 to 23.3% for G2. In one case, the CV of the G1 cells was 0.4% lower than that of the G2 cells, but in the other four it was higher by 0.6, 0.7, 0.8, and 1.4%. The variation brought about by a single unequal
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division would cause the G1 CV to be larger than the G2 CV, but the measured difference is small relative to the overall variation in DNA content. The broadness of this distribution must, then, be due to the cumulative effects of many such unequal divisions. This suggests that any regulatory events to stop indefinite spreading of the DNA distribution are only rarely necessary. In Tetrahymena, these regulatory events include skipping DNA synthesis (for high-DNA cells), extruding chromatin, or undergoing an additional round of DNA synthesis (for low-DNA cells), but these events occur in only 2-3 % of divisions, according to Cleffmann (1968).
Acknowledgment This research was supported by the National Science Foundation (Grants No. NSF/BCS-8619399-02and NSF/ BCS-9001095) and by the Graduate School of the University of Minnesota. P.J.S. was the recipient of a NIH training grant fellowship in biotechnology.
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Literature Cited Berger, J. D.; Schmidt, H. J. Regulation of Macronuclear DNA Content in Paramecium tetraurelia. J. Cell Biol. 1978, 76, 116-126.
Berger,J. D.; Morton,G. T. Studieson the Macronuclei of Doublet Paramecium tetraurelia: Distribution of Macronuclei and DNA at Fission. J. Protozool. 1980,27,443-450. Block, D. E.; Eitzman, P. E.; Wangensteen, J. D.; Srienc, F. Slit Scanning of Saccharomyces cerevisiae Cells: Quantification of Asymmetric Cell Division and Cell Cycle Progression in Asynchronous Culture. Biotechnol. h o g . 1990,6,504-512. Cambier,J. L.; Wheeless,L. L. The Binucleate Celk Implications for Automated Analysis. Acta Cytol. 1975,19,281-285. Cleffmann, G. Regulierung der DNS-Menge im Makronucleus von Tetrahymena. Exp. Cell Res. 1968,50,193-207. Corliss, J. 0. History, Taxonomy, Ecology, and Evolution of Speciesof Tetrahymena. In Biology of Tetrahymena;Elliott, A. M., Ed.; Dowden, Hutchinson & Ross, Inc.: Stroudsburg, PA, 1973;pp 1-55. Cram, L. S.; Amdt-Jovin, D. J.; Grimwade, B.; Jovin, T. FluorescencePolarization and Pulse-width Analysis of Chromosomes by a Flow System. J.Histochem. Cytochem. 1979, 27,445-453.
Doerder, F. P.; DeBault, L. E. Cytofluorimetric Analysis of NuclearDNA During Meiosis,Fertilization and Macronuclear Development in the Ciliate Tetrahymena pyriformis, Syngen 1. J. Cell Sci. 1975, 17,472-493. Fredrickson, A. G. Segregated, Structured, Distributed Models and Their Role in MicrobialEcology: A Case Study Based on Work Done on the Filter-Feeding Ciliate Tetrahymena pyriformis. Microb. Ecol. 1991,22,139-159. Fredrickson, A. G.; Ramkrishna, D.; Tsuchiya, H. M. Statistics and Dynamics of Procaryotic Cell Populations. Math Biosci. 1967,1,327-374. Fredrickson, A. G.; Ramkrishna, D.; Tsuchiya, H. M. The
Kudrjavtsev, B. N. Changesof the DNA Content in Macro- and micronucleus of Paramecium putrinum in the interdivision phase. Acta Protozool. 1966,4,51-57. Lavin,D. P.; Hatzis, C.; Srienc, F.; Fredrickson, A. G. SizeEffects on the Uptake of Particles by Populations of Tetrahymena pyriformis. J. Protozool. 1990,37, 157-163. Lucas,J. N.; Pinkel, D. Orientation Measurementsof Microsphere Doublets and Metaphase Chromosomesin Flow. Cytometry 1986,7,575-581.
McDonald, B. B. Quantitative Aspects of Deoxyribose Nucleic Acid (DNA) Metabolism in an Amicronucleate Strain of Tetrahymena. Biol. Bull. 1958,114,71-94.
Norgren, R. M.; Gray, J. W.; Young, I. T. Restoration of Profiles from Slit-Scan Flow Cytometry. IEEE Trans. Biomed. Eng. 1982,29,101-106.
Ramkrishna, D. Statistical Models of Cell Populations. Adu. Biochem. Eng. 1979,11,1-47.
Ruthman, A. Autoradiographische und mikrophotometrische Untersuchungen zur DNS-Synthese im Makronucleus von Bursaria truncatella. Arch. Protistenk. 1964,107,117-130.
Sambanis, A.; Pavlou, S.; Fredrickson, A. G. Coexistence of Bacteria and Feeding Ciliates: Growth of Bacteria on AutochthonousSubstrates as a StabilizingFactor for Coexistence. Biotechnol. Bioeng. 1987,29,714-728.
Sharpless, T. K.; Melamed, M. R. Estimation of Cell Size from Pulse Shape in Flow Cytofluorometry. J. Histochem. Cytochem. 1976,24,257-264.
Srienc, F.; Fredrickson, A. G.; Lavin, D. P. Feeding Growth and Reproductionof Ciliate Microorganisms:an EngineeringView. Ann. N.Y. Acad. Sci. 1987,506,357-570.
Takagi, Y.; Kanazawa, N. Age-AssociatedChange in Macronuclear DNA Content in Paramecium Caudatum. J. Cell Sci. 1982,54,137-147.
Wheeless, L. L.; Patten, S. F. Slit-scan cytofluorometry. Acta Cytol. 19738,333-339.
Necessity of Including Structure in Mathematical Models of Unbalanced MicrobialGrowth. Chem.Eng. Prog. Symp. Ser.
Wheeless, L. L.; Patten, S. F. Slit-scan cytofluorometry: Basis for an Automated CytopathologyPrescreening System. Acta
1971,108,53-59.
Williams,F. M. On Understanding Predator-Prey Interactions. In Contemporary Microbial Ecology; Ellwood,D. C., Hedger, J. N., Latham, M. J., Lynch,J. M., Slater,J. H., Eds.; Academic Press: London, 1980; pp 349-375. Witt, P. L. Unequal Distribution of DNA in the Macronuclear Division of the Ciliate Euplotes eurystomus. Chromosoma 1977,60, 59-67. Zech, L. Dry Weight and DNA Content in Sisters of Bursaria truncatella During the Interdivision Interval. Exp. Cell Res.
Fulwyler, M. J. Hydrodynamic Orientation of Cells. J. Histochem. Cytochem. 1977,25,781-783.
Gorovsky, M. A.; Yao, M-C.; Keevert, J. B.; Pleger, G. L. Isolation of Micro- and Macronuclei of Tetrahvmena Dvriformis. In Methods in Cell Physiology; Prescott; D. M.,-Ed.i Academic Press: New York, 1975; Vol. IX, pp 311-327. Gray, J. W.; Peters, D.; Merrill, J. T.; Martin, R. Slit-scan Flow Cytometry of Mammalian Chromosomes. J. Histochem. Cytochem. 1979,27,441-444.
Kay,D. B.; Wheeless,L. L. ExperimentalFindingson Gynecologic Cell Orientation and Dynamics for Three Flow Nozzle Geometries. J. Histochem. Cytochem. 1977,25,870-874. Kimball,R. F. Persistent Intraclonal Variations in CellDry Mass and DNA Content in Paramecium aurelia. Exp. Cell. Res. 1967,48,378-394.
Cytol. 197313,391-394.
1966,44,599-605.
Accepted October 12,1993.' Abstract published in Advance ACS Abstracts, December 15, 1993.
