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Comparative Study
. 2014 Oct;9(10):1360-5.
doi: 10.4161/15592294.2014.967589.

A comparison of DNA methylation specific droplet digital PCR (ddPCR) and real time qPCR with flow cytometry in characterizing human T cells in peripheral blood

John K Wiencke  1 , Paige M Bracci, George Hsuang, Shichun Zheng, Helen Hansen, Margaret R Wrensch, Terri Rice, Melissa Eliot, Karl T Kelsey
Affiliations
Comparative Study

A comparison of DNA methylation specific droplet digital PCR (ddPCR) and real time qPCR with flow cytometry in characterizing human T cells in peripheral blood

John K Wiencke et al. Epigenetics. 2014 Oct.

Abstract

Quantitating the copy number of demethylated CpG promoter sites of the CD3Z gene can be used to estimate the numbers and proportions of T cells in human blood and tissue. Quantitative methylation specific PCR (qPCR) is useful for studying T cells but requires extensive calibration and is imprecise at low copy numbers. Here we compared the performance of a new digital PCR platform (droplet digital PCR or ddPCR) to qPCR using bisulfite converted DNA from 157 blood specimens obtained from ambulatory care controls and patients with primary glioma. We compared both ddPCR and qPCR with conventional flow cytometry (FACS) evaluation of CD3 positive T cells. Repeated measures on the same blood sample revealed ddPCR to be less variable than qPCR. Both qPCR and ddPCR correlated significantly with FACS evaluation of peripheral blood CD3 counts and CD3/total leukocyte values. However, statistical measures of agreement showed that linear concordance was stronger for ddPCR than for qPCR and the absolute values were closer to FACS for ddPCR. Both qPCR and ddPCR could distinguish clinically significant differences in T cell proportions and performed similarly to FACS. Given the higher precision, greater accuracy, and technical simplicity of ddPCR, this approach appears to be a superior DNA methylation based method than conventional qPCR for the assessment of T cells.

Keywords: CV, correlation coefficient; DMR, differentially methylated regions; DNA methylation; FACS; FACS fluorescence activated cell sorting; MS, methylation specific; T cells; ddPCR, droplet digital polymerase chain reaction; digital PCR; qPCR, quantitative polymerase chain reaction; real time PCR.

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Figures

Figure 1.
Figure 1.
Scatter plot with fitted linear regression line of T cell proportions measured by ddPCR (Y-axis) and qPCR (X-axis) in all samples.
Figure 2.
Figure 2.
Jittered box and whisker plots for the % CD3 to total leukocyte values for flow cytometry (FACS), ddPCR and qPCR. Individual values are depicted by open circles, the bottom of the box is the 25th percentile, the line within the box is the 50th percentile (median) and the top of the box is the 75th percentile of the data. The top and bottom whiskers represent the value at 1.5 times the interquartile range (the distance between the 25th and 75th percentile). Outliers appear outside of the whiskers.
Figure 3.
Figure 3.
Bland Altman plots for visual assessment of the concordance of 2 methods. Difference in values (y-axis) measured by different methods among controls, e.g., ddPCR value minus flow cytometry (FACS) value, and qPCR value minus FACS value, are plotted against the gold standard FACS values (x-axis) as plots (A) and (B), respectively. Concordance between the values measured by each method is reflected by the scatter around the horizontal value of zero such that perfect concordance is depicted when all points lie along the zero horizontal.
Figure 4.
Figure 4.
Plots of observed versus predicted values from linear regression of the ddPCR and qPCR measured ratio of T cells to Total leukocytes (x-axis) to the flow cytometry (FACS) measured ratio (y-axis) among controls. A tighter cluster of points around the line depicts good fit and linearity whereas systematic deviation of points suggests nonlinearity of the association e.g. as seen in (B) where data suggest larger errors are associated with larger predicted values. (A) Fitted regression equation for ddPCR (y) and FACS (x) for plot: Yhat = 5.26 + 0.97*X. (B) Fitted regression equation for qPCR (y) and FACS (x) for plot: Yhat = 5.53 + 1.62*X.
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