Baseline

If there are some reasonable amplification curves, the next parameter that should be examined is the baseline setting. Each real-time PCR instrument

Figure 2.6

Effect of baseline settings. A. Amplification view showing an incorrect baseline setting (3-15 cycles) and the resulting artifacts: (a) half rainbows and (b) a full rainbow, plotted using a logarithmic scale for the y axis (ARn). B. The same curves in the amplification view plotted using a linear scale for the y axis. C. The same curves corrected to a baseline of 3-7 cycles in the amplification view plotted using a linear scale for the y axis. D. The same curves corrected to a baseline of 3-7 cycles in the amplification view plotted using a logarithmic scale for the y axis.

has baseline-setting algorithms that determine the background noise of the detector and reagents using the fluorescence generated in a designated number of cycles at the beginning of the run. This may be a fixed number of cycles for all samples or adaptive for each sample, depending on the type of instrument that is being used. Baseline anomalies may be slight or severe. Look at the left hand corner of the Amplification plot. Are there 'half-

Figure 2.7

Effect of baseline settings on a standard curve. A. Standard curve of the data shown in Figure 2.4 before adjusting the baseline. B. Standard curve of the data shown in Figure 2.4 after adjusting the baseline.

rainbows' or what looks like half an arc or hump between cycles 1 and 10? These arcs frequently start above the threshold, as shown in Figure 2.6A.a. In extreme cases, complete 'rainbows' in which the total curve is affected, may be seen. The 'rainbows' may appear under the threshold and the Ct may register as 40 (Figure 2.6A.b) or the curve may cross the threshold in two places (not shown). Examination of the Raw Spectra view or the Multicomponent view, which allows viewing of the raw fluorescence in an individual well, will clarify if there is real amplification or not. At what cycle does the lowest Ct appear? If the lowest Ct is less than the upper limit of the baseline setting (the upper limit of the baseline setting is greater than the lowest Ct), then the baseline should be adjusted. A general rule of thumb is to set the upper limit of the baseline 2-3 cycles less than the lowest Ct. A good way to determine exactly where to set the baseline on many machines, is to examine the amplification curves with the y axis (ARn) set to a linear scale rather than a logarithmic scale. If the baseline is set with the upper limit of the baseline too high, then the curves exhibiting a very low Ct, will appear below the baseline, as in Figure 2.6B. Adjust the baseline until the linear part of the curves follow the baseline, as in Figure 2.6C. Correct adjustment of the baseline should result in proper looking curves in the logarithmic view with disappearance of the rainbows, as in Figure 2.6D. In this example, the effect of adjusting the baseline results in seven usable points in the standard curve (Figure 2.7A and 2.7B), instead of six and the efficiency of the standard curve improves from 114% to 104%. It is also possible to have the upper limit of the baseline set too low, so be sure to check it in the linear view, even if the curves look acceptable in the logarithmic view. The most notable effect from adjusting the baseline will usually be on samples with the lowest Ct and the highest amount of template. However, limiting the number of cycles over which the baseline is calculated can interfere with the detection of less abundant targets, in some rare cases. In cases of severe overloading of the template, it may be possible to eliminate the offending samples from the analysis as in the case of a standard curve where there are multiple doses from which values can be calculated. However, this may affect the efficiency as demonstrated in the example above. If the problematic wells are critical to the experiment, for example the reference gene, then it may be necessary to repeat the experiment using a lesser amount of template, remembering that a two-fold dilution of template will only change the Ct by a factor of 1. Gene expression being too high is common to reference or standardization genes because they are so abundant.

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