Anal. Chem. 2005, 77, 2243-2246
Technical Notes
Data Analysis for a Dual-Channel Virus Counter Carrie L. Stoffel and Kathy L. Rowlen*
Department of Chemistry and Biochemistry, University of Colorado, Boulder, Colorado 80309
A simple algorithm is presented for quantitative analysis of simultaneous events on a dual-channel flow cytometer designed specifically for virus counting. The algorithm, which is based on matrix analysis of burst lag times, was evaluated using baculovirus samples that had previously been quantified by the plaque titer method. The results indicated statistical reliability for the algorithm, with three of six samples yielding the same value, within error, for viruses per unit volume as the plaque titer. The other three samples yielded values within a factor ∼2, which was deemed acceptable given the limitations of the plaque titer method. Viruses are becoming increasingly important in the field of biotechnology.1-10 For many applications, it is important to know the virus count per unit volume. A variety of techniques have been developed to quantify viruses; some of these techniques are able to distinguish between active and inactive virus particles, such as the plaque assay,11-13 while others are unable to distinguish between active and inactive virus particles in a sample, such as flow cytometry and epifluorescence microscopy.14-22 * Corresponding author. E-mail:
[email protected]. (1) Cory, S. J.; Hails, R. S.; Sait, S. M. In The Baculoviruses; Miller, L. K., Ed.; Plenum Press: New York, 1997. (2) Carbonell, L. F.; Hodge, M. R.; Tomalski, M. D.; Miller, L. K. Gene 1988, 73, 409-418. (3) Maeda, S.; Kawai, T.; Obinata, M.; Fujiwara, H.; Horiuchi, T.; Saeki, Y.; Sato, Y.; Furusawa, M. Nature 1985, 315, 592-594. (4) Smith, G. E.; Summers, M. D.; Fraser, M. J. Mol. Cell. Biol. 1983, 3, 21562165. (5) Luque, T.; O’Reilly, D. R. Mol. Biotechnol. 1999, 13, 153-163. (6) Grabherr, R.; Ernst, W.; Doblhoff-Dier, O.; Sara, M.; Katinger, H. Biotechniques 1997, 22, 730-735. (7) Boyce, F. M.; Bucher, N. L. R. Proc. Natl. Acad. Sci. U.S.A. 1996, 93, 2358-2352. (8) Sandig, V.; Hoffman, C.; Steinert, S.; Jennings, G.; Schlag, P.; Strauss, M. Hum. Gene Ther. 1996, 7, 1937-1945. (9) Crook, N. E.; Clem, R. J.; Miller, L. K. J. Virol. 1993, 67, 2168-2174. (10) Chejanovsky, N.; Gershburg, E. Virology 1995, 209, 519-525. (11) Dimmock, N. J.; Easton, A. J.; Leppard, K. N. Introduction to Modern Virology, 5th ed.; Oxford, Blackwell Science: Malden, MA, 2001. (12) Hink, W. F.; Strauss, M. J. Invertebr. Pathol. 1977, 29, 390-391. (13) Hink, W. F.; Vail, P. V. J. Invertebr. Pathol. 1973, 22, 168-174. (14) Hennes, K. P.; Suttle, C. A. Limnol. Oceanogr. 1995, 40, 1050-1055. (15) Ferris, M. M.; Stoffel, C. L.; Maurer, T. T.; Rowlen, K. L. Anal. Biochem. 2002, 304, 249-256. (16) Ferris, M. M.; McCabe, M. O.; Doan, L. G.; Rowlen, K. L. Anal. Chem. 2002, 74, 1849-1856. (17) McSharry, J. J. Clin. Microbiol. Rev. 1994, 7, 576-604. (18) Shapiro, H. M. Practical Flow Cytometry, 3rd ed.; Wiley-Liss Inc.: New York, 1995. 10.1021/ac048626l CCC: $30.25 Published on Web 02/15/2005
© 2005 American Chemical Society
Recently, a dual-channel virus counter (DCVC) was developed in our laboratory to quantify the population of active virus particles in a sample. The instrument was based on flow cytometric principles. In this case, a dual-channel light collection system was used to evaluate whether the viruses were “intact”. The genome and the protein capsid of baculoviruses were stained with two distinct fluorescent dyes. Both dyes were excited by 532-nm light but the emission maximums were separated by over 50 nm. A fluorescence burst event that occurred simultaneously on the DNA and protein emission collection channels represented a virus particle containing both DNA and protein. The hypothesis was a count of virus particles that contained both DNA and protein would yield a better estimate of “active” virus particles in the sample. A key component in analysis of simultaneous events is the algorithm used to process raw data. While a variety of approaches have been described for analysis of two-channel sytems,23-27 the algorithm detailed herein is simple and easily implemented. Instrument design, characterization, and proof-of-principle for counting baculovirus (BV) will be published elsewhere.28 EXPERIMENTAL SECTION Two-Channel Data Processing. Data were acquired on two channels with a 12-bit data acquisition board controlled by LabVIEW 7.0. The data processing discussed here required multiple steps, all of which could be automated. The raw data from BVs was collected from two channels and written to a text file in a 4 × 160 000 array, unless otherwise noted. This array contained two sets of xy data. These xy data files contained fluorescence intensity (y) as a function of time (x) for both the DNA and protein channels. A MATLAB program was written to separate the array into two separate data files, one for each detector. When the raw data had been separated into two data files, GRAMS AI was used (19) Brussaard, C. P. D.; Marie, D.; Bratbak, G. J. Virol. Methods 2000, 85, 175-182. (20) Marie, D.; Brussaard, C. P. D.; Thyrhaug, R.; Bratbak, G.; Vaulot, D. Appl. Environ. Microbiol. 1999, 65, 45-52. (21) Chen, F.; Lu, J. R.; Binder, B. J.; Liu, Y. C.; Hodson, R. E. Appl. Environ. Microbiol. 2001, 67, 539-545. (22) Brussaard, C. P. D. Appl. Environ. Microbiol. 2004, 70, 1506-1513. (23) Castro, A.; Williams, J. G. K. Anal. Chem. 1997, 69, 3915-3920. (24) Castro, A.; Okinaka, R. T. Analyst 2000, 125, 9-11. (25) Agronskaia, A.; Schins, J. M.; de Grooth, B. G.; Greve, J. Anal. Chem. 1999, 71, 4684-4689. (26) Gruber, F.; Falkner, F. G.; Dorner, F.; Hammerle, T. Appl. Environ. Microbiol. 2001, 67, 2837-2839. (27) Lehman, J. M.; Laffin, J.; Jacobberger, J. W.; Fogleman, D. Cytometry 1988, 9, 52-59. (28) Stoffel, C. L.; Rowlen, K. L. Cytometry, Submitted.
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Figure 1. (A) Schematic representation of the two-channel detection of simultaneous events. Asterisk indicates concurrent events. (B) DCVC data from a sample of BV at a final concentration of 5.4 × 107 particles/mL stained with POPO-3 and Sypro Red in the presence of SDS. Data shown are (a) the protein channel and (b) the DNA channel. The data were collected at 80 kHz at a flow rate of 32 ( 1 µL/h.
to correct the baseline of each data set to zero. A schematic of simultaneous events and a representative data set are shown in Figure 1. A threshold was selected for each channel, 0.02 for the DNA and protein channels (threshold selections discussed in detail later in the text). Peaks above the threshold were selected and appearance times for the DNA channel (td) and for the protein channel (tp) were recorded. Burst Identification Analysis. Once the fluorescence burst events above the threshold had been recorded, the data were subjected to burst identification analysis similar to that performed by Castro and Williams.23 To determine which peaks on the DNA and protein channels were occurring simultaneously, the lag time between peaks had to be calculated. The appearance times of peaks that occurred on the DNA channel and the protein channel were compiled into two separate lists. MATLAB was used to calculate the lag time between peaks using eq 1.
Lj,k ) tdj - tpk
(1)
Here tdj is the appearance time of peak j on the DNA channel, where j ) 1, 2, 3, ...n, and n is the total number of peaks on the DNA channel, tpk is the appearance time of peak k of the protein channel, where k ) 1, 2, 3, ... m, and m is the total number of peaks on the protein channel. Lj,k is defined as the lag time between DNA channel peak j and protein channel peak k. The lag time between every peak on the DNA channel and every peak on the protein channel was calculated to generate a complete list of lag times between all of the peaks in the data set. A schematic of the lag time calculations is provided in Figure 2. The resulting list of lag times was compiled into a histogram (see Figure 3.) The number of peaks found with a “near zero” lag time corresponded to the number of simultaneous DNA and protein events in the data set. The number of simultaneous events 2244
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Figure 2. Schematic of the calculations performed by the MATLAB burst identification analysis program. Every protein channel peak appearance time (tpk) is subtracted from every DNA channel peak appearance time (tdj) to produce a list of lag times between all peaks in the data set.
reported by the lag time histogram depended on the bin size chosen for the histogram. To determine the bin size, or allowed lag time between peaks, that yielded the most accurate count of concurrent peaks a manual evaluation was performed for comparison. If the bin size is too big, peaks that are not actually simultaneous are counted. If the bin size is too small not all simultaneous peaks are counted. RESULTS AND DISCUSSION Data were collected for samples of BV stained with POPO-3 and Sypro Red in the presence of SDS. The final concentration of
1, the bin sizes of 37.5 and 50 µs yielded nearly identical results. Nb was within 3.6 ( 1.3 and 3.5 ( 1.3% of the number of peaks found by inspection for 37.5- and 50-µs bin sizes, respectively. The results obtained by all bin sizes were found to be independent of the flow rate. A bin size of 50 µs was used for all data analysis discussed here. Once the histogram bin size was optimized, the number of concurrent peaks determined by the burst identification analysis was used to calculate the sample concentration (C) using the following equation:
C ) Nb(3.6 × 106)/tF(DE%)
Figure 3. Histogram of lag times between peaks from both channels for a BV sample containing 9.76 × 107 particles/mL at a flow rate of 26.4 ( 0.1 µL/h. The histogram bin size was 50 µs. Table 1. Optimization of Near Zero Lag Time Bin Size for Burst Identification bin size (µs)
|Ni - Nb| × 100a/Ni
standard error (%)
12.5 37.5 50 125
15 3.6 3.5 22
2 1.3 1.3 6
a N is the number of concurrent peaks found using bins, N is the b i number of peaks found by inspection.
Here Nb is the number of concurrent peaks found in the data set with a bin size of 50 µs, t is the duration of the data set (in s), F is the flow rate (in µL/h), and DE% is the detection efficiency (0.17). The factor 3.6 × 106 is a result of converting from seconds to hours and microliters to milliliters. To check for systematic error, the BV data had to be evaluated to ensure there was no trend between the number of concurrent peaks found and the total number of peaks found on either the DNA or protein channel. Within a data set, the number of concurrent peaks found by burst identification analysis (Nb) was compared to the total number of peaks found on either the DNA (ND) or protein channel (NP). The following equations were used to calculate the percentage of peaks (Pc) on the DNA or protein channel that were concurrent.
DNA channel:
Pc ) Nb/ND × 100 Protein channel:
the BV samples was 4.2 × 107 particles/mL. Data were collected at three different flow rates: 20, 44, and 56 µL/h. Data sets were manually inspected for the number of concurrent peaks above the threshold. The number of concurrent peaks found by inspection (Ni) was compared to the number of concurrent peaks found using a variety of histogram bin sizes. Bin sizes of 12.5, 37.5, 50, and 125 µs were evaluated. Each bin size was used to construct a histogram like the one in Figure 3. Each histogram had one bin centered at zero, designating the near zero lag time bin. For example, the near zero lag time for a bin size of 50 µs contained all peaks found to have a lag time between -25 and +25 µs. The optimal bin size was defined as the one that produced the number of concurrent peaks in the near zero lag time bin (Nb) that was closest to the number of concurrent peaks found by inspection (Ni). As reported in Table
(2)
Pc ) Nb/NP × 100 (3)
The percent of concurrent peaks found was calculated for data from six different BV samples. As anticipated, no fixed relationship was found between the number of concurrent peaks counted and the total number of peaks detected on either channel, which means that neither the DNA nor protein channel alone would yield consistent virus concentration results. The percentage of peaks on either channel that were concurrent ranged from 38 to 100% for the DNA channel and from 1 to 48% for the protein channel. The measured baculovirus concentrations were compared to results obtained by a standard plaque assay; the data are summarized in Table 2. The DCVC yielded a linear response to serial dilutions of BV and a blind study of six unpurified BV samples. The DCVC results were compared to the results obtained
Table 2. BV Sample Concentrations Reported by Various Techniques plaque assay (pfu/mL)
error
2.14 × 108 1.86 × 108 1.35 × 108 2.44 × 108 2.18 × 108 1.37 × 108 1.35 × 108 7.82 × 107 1.61 × 108
5.35 × 107 4.65 × 107 3.37 × 107 6.10 × 107 5.45 × 107 3.42 × 107 3.37 × 107 1.95 × 107 4.02 × 107
one-channel flow cytometer (particles/mL)
error
6.11 × 108 4.39 × 108 1.39 × 109
3.44 × 107 4.39 × 107 1.02 × 108
DCVC (particles/mL)
error
2.35 × 108 9.57 × 107 3.70 × 107 1.66 × 108 8.16 × 107 4.42 × 107
7.70 × 106 6.03 × 106 2.74 × 106 5.23 × 106 5.46 × 106 5.49 × 106
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by plaque assay. For three BV samples the concentrations obtained by the DCVC were the same, within error, as those reported by plaque assay. For the other three BV samples the concentrations obtained by the DCVC were smaller than the reported plaque assay concentrations by factors ranging from 2.3 to 3.7. Unpurified BV samples were also analyzed with a singlechannel flow cytometer. The instrument design was similar to that reported here, with only one channel for detecting fluorescently labeled DNA events. The results obtained by the one-channel flow cytometer were consistently higher than the reported plaque assay results, Table 2, by a factor ranging from 2.4 to 10. This observation was attributed to the fact that the single-channel instrument enumerated only DNA events. Since an unpurified BV sample also contains cell debris from the BV host cells, it is likely that there was non-BV DNA in the sample that was fluorescently labeled and subsequently enumerated. The DCVC offered a better count of active BV particles in an unpurified sample than the single-channel flow cytometer. However, the BV sample concentrations reported by the DCVC were
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lower than the plaque assay results for three of the samples analyzed. While additional protocols are being explored for sample preparation prior to analysis with the DCVC, it is entirely possible, given the limitations of the plaque titer method, that the results presented herein for the DCVC are more accurate than the plaque titer values. Further work is required to address this issue. In summary, the algorithm presented here for use with a dualchannel virus counter is simple, flexible in application, and provides reliable count values. ACKNOWLEDGMENT The authors gratefully acknowledge indirect support from the National Science Foundation and Dr. Kurt Christensen and Prof. Dean Edwards of the University of Colorado Cancer Center and Health Sciences Center (supported by Cancer Center Grant NIH-NCI P30CA4934) for providing baculovirus samples. Received for review September 16, 2004. Accepted January 6, 2005. AC048626L
