A robust internal control for high-precision DNA methylation analyses by droplet digital PCR
Pharo et al. Clinical Epigenetics
A robust internal control for high-precision DNA methylation analyses by droplet digital PCR
Heidi D. Pharo 0 1 2 3
Kim Andresen 0 1 2
Kaja C. G. Berg 0 1 2
Ragnhild A. Lothe 0 1 2 3
Marine Jeanmougin 0 1 2
Guro E. Lin 0 1 2 3
0 KG Jebsen Colorectal Cancer Research Centre, Oslo University Hospital , Oslo , Norway
1 Department of Molecular Oncology, Institute for Cancer Research, Oslo University Hospital, the Norwegian Radium Hospital , PO Box 4950, Nydalen, NO-0424 Oslo , Norway
2 Centre for Cancer Biomedicine, Faculty of Medicine, University of Oslo , Oslo , Norway
3 Department of Biosciences, The Faculty of Mathematics and Natural Sciences, University of Oslo , Oslo , Norway
Background: Droplet digital PCR (ddPCR) allows absolute quantification of nucleic acids and has potential for improved non-invasive detection of DNA methylation. For increased precision of the methylation analysis, we aimed to develop a robust internal control for use in methylation-specific ddPCR. Methods: Two control design approaches were tested: (a) targeting a genomic region shared across members of a gene family and (b) combining multiple assays targeting different pericentromeric loci on different chromosomes. Through analyses of 34 colorectal cancer cell lines, the performance of the control assay candidates was optimized and evaluated, both individually and in various combinations, using the QX200™ droplet digital PCR platform (Bio-Rad). The best-performing control was tested in combination with assays targeting methylated CDO1, SEPT9, and VIM. Results: A 4Plex panel consisting of EPHA3, KBTBD4, PLEKHF1, and SYT10 was identified as the best-performing control. The use of the 4Plex for normalization reduced the variability in methylation values, corrected for differences in template amount, and diminished the effect of chromosomal aberrations. Positive Droplet Calling (PoDCall), an R-based algorithm for standardized threshold determination, was developed, ensuring consistency of the ddPCR results. Conclusion: Implementation of a robust internal control, i.e., the 4Plex, and an algorithm for automated threshold determination, PoDCall, in methylation-specific ddPCR increase the precision of DNA methylation analysis.
Digital PCR; Internal control; Methylation; Normalization; PoDCall; 4Plex
Digital PCR (dPCR) enables absolute quantification of
nucleic acids. The principle behind the method was
described already in 1992 [
], but its use was for many
years hampered by lack of suitable protocols and
instrumentation. Technology development during the last
decade has led to several commercial systems for dPCR,
resulting in a rapid increase in the publication rate of
dPCR studies [
]. With the concomitant increase in
liquid biopsy analyses for cancer screening, for detection of
minimal residual disease after surgery, and for monitoring
cancer patients, the need for high-precision analyses of
circulating tumor-derived nucleic acid molecules is
obvious, but not necessarily implemented.
With the dPCR technology, a PCR mixture can be
randomly divided into a large number of partitions.
Individual PCRs are performed inside each partition, and
based on the fraction of fluorescence-positive partitions,
the absolute quantity of the target can be calculated [
One of the most commonly used platforms is the droplet
digital PCR (ddPCR), where the partitions are
represented by thousands of nanoliter-scale droplets, formed
by water-in-oil emulsion [
The sample partitioning inherent for ddPCR
considerably reduces the competition from any background
DNA, allowing detection of minimal amounts of a target
of interest. The sensitivity is in principle only limited by
the number of droplets analyzed [
], and the method
has been demonstrated to trace one mutated gene copy
in the background of 200,000 wild-type molecules [
This makes ddPCR particularly valuable for analyses of
various types of non-invasive biomarkers, such as
detection of KRAS mutations in the blood of colorectal cancer
patients, predicting lack of response to targeted
], screening for metastatic breast cancer by small
increases in HER2 copy number in plasma samples [
gene expression analyses to detect hepatocellular
carcinoma from circulating tumor cells [
], and detection of
bladder cancer among hematuria patients [
Although the ddPCR technology has great potential
also for DNA methylation analyses, only few studies
have been published so far [
]. The lack of
consensus regarding how to perform standardized experiments
might be a contributing factor. Generation of consistent
methylation data is dependent on the use of a suitable
control for normalization, as previously demonstrated
for other PCR-based DNA methylation analyses [
The aims of the present study were to develop a robust
internal control for ddPCR DNA methylation analyses
and demonstrate its value in terms of increased
precision of the normalized methylation data.
DNA from cancer cell lines
DNA from 34 colorectal cancer cell lines (Caco2, CL-11,
CL-34, CL-40, Co115, Colo205, Colo320, Colo678, DLD-1,
EB, FRI, HCC2998, HCT116, HCT15, HT29, IS1, IS3,
KM12, LoVo, LS1034, LS174T, NCI-H508, RKO, SW1116,
SW1463, SW403, SW48, SW480, SW620, SW837, SW948,
TC71, V9P, and WiDr) was isolated using either a standard
phenol-chloroform protocol or a magnetic bead approach
(Maxwell® 16 System; Promega). Cell lines were either
purchased from cell line repositories or kindly provided by
collaborators, as previously described [
of the cell lines was performed by short tandem repeat
testing, as reported by Ahmed et al. [
]. DNA concentrations
were measured using a NanoDrop 1000 Spectrophotometer
(Thermo Fisher Scientific). DNA copy number data
(Affymetrix Genome-Wide Human SNP 6.0 microarrays)
were available for all cell lines [
The EpiTect Bisulfite Kit (Qiagen) was used for bisulfite
conversion of 1.3 μg DNA according to the
manufacturers’ standard protocol. After conversion in the MJ
Mini Personal Thermal Cycler (Bio-Rad Laboratories),
the samples were automatically purified and eluted in
40 μl elution buffer by the QIAcube System (Qiagen).
Design and development of candidate internal controls
With the aim of developing a control for
methylationspecific ddPCR that targeted multiple non-CpG containing
loci located on different chromosomes, two approaches
were tested. In the first approach, “A,” a common sequence
shared by several members of a gene family (the Aldolase A
family; ALDOA, and the Cytochrom C family; CYCS) was
targeted. This approach implied introduction of only one
control assay into the target gene reaction, with the
rationale of reducing the chances of interference with target
amplification. In the second approach, “B,” multiple assays,
targeting different loci in the exonic part of various genes
located close to the centromeres (n = 13; ALDH1B1,
ANKRD30A, EPHA3, HAO2, IGFBPL1, ITGAD, KBTBD4,
MRPS5, NIPA2, PLEKHF1, SAMSN1, SYT10, and TTC5),
were designed and tested in different combinations. These
control gene candidates were chosen from a list of
pericentromeric reference genes, previously suggested by Bio-Rad
to be stable in regard to copy number variations [
approach implied introduction of several control assays into
the target gene reaction (see Additional file 1: Table S1 for
assay sequences and their chromosomal locations). The
best-performing control (VIC-labeled) was tested in
combination with assays targeting methylated CDO1,
SEPT9, and VIM (FAM-labeled), through ddPCR analyses
of 34 colorectal cancer cell lines. Finally, the performance
of the control was compared to two previously published
single locus controls, ACTB [
] and C-LESS [
Digital droplet PCR
The QX200™ Droplet Digital™ PCR System (Bio-Rad) was
used for analyses. The ddPCR reaction mixture consisted
of 1x ddPCR Supermix for Probes (Bio-Rad), 900 nM of
each primer, 250 nM of the probe, and approximately
30 ng bisulfite-converted DNA template, in a final volume
of 22 μl. Droplets were generated by the QX200 Droplet
Generator (Bio-Rad), using 20 μl of the ddPCR mixture
and 70 μl droplet generation oil (Bio-Rad). Samples were
transferred to a 96-well PCR plate (Bio-Rad) and sealed in
the PX1 PCR Plate Sealer (Bio-Rad). The PCR was
performed in a T100 Thermal Cycler (Bio-Rad; see
Additional file 1: Table S2 for PCR cycling conditions).
The fluorescence signals were measured by the QX200
Droplet Reader (Bio-Rad). For each experiment, the
following control samples were included: two
methylationpositive controls (commercially available in vitro
methylated DNA (IVD); Zymo Research), one
methylationnegative control (bisulfite-treated DNA from normal blood
of healthy donors), one non-bisulfite-converted IVD
sample, and a non-template control (NTC; water). All analyses
were performed according to the digital MIQE guidelines
(Additional file 2) [
Data from the QX200 Droplet Reader was analyzed in
QuantaSoft version 1.7.4.0917 (Bio-Rad). The Positive
Droplet Calling (PoDCall) algorithm was developed
inhouse to determine well-specific thresholds that
discriminated positive droplets, i.e., containing the target, from
negative droplets, i.e., did not contain the target. The
PoDCall workflow is illustrated in Fig. 1 and
summarized in the following. Amplitude values were extracted
from QuantaSoft and used as input data for PoDCall.
First, the multimodality of the distribution of the
amplitude values was assessed using Hartigan’s dip test [
Two strategies were applied depending on the
significance of the test. If non-significant, i.e., p value > 0.05,
the distribution was assumed to be unimodal and the
threshold was set as the maximum amplitude value, after
testing for potential outliers. Alternatively, if the test was
significant, a Gaussian mixture model was fitted to the
distribution of droplets, using the R package “mclust,”
version 5.2.3 [
]. Next, the number of mixture
components in the distribution was assessed by a likelihood
ratio test, whose significance was approximated by using
a bootstrap approach with 700 replications. Finally, the
threshold was defined as the average value between the
modes of the first and second components. The
resulting thresholds, representing the output data from
PoDCall, were manually entered in QuantaSoft. Based on the
fraction of positive droplets, concentrations of
methylated copies/μl were calculated by the software.
Normalized concentrations were generated by dividing
the concentration of the target gene on the
concentration of the control. These normalized values were then
multiplied by a constant, i.e., the mean concentration of
the control among all analyzed cell lines, in order to
have them in the same range as the non-normalized
The statistical analyses were performed using R version
3.2.2. In order to investigate how normalized
concentrations were affected by chromosomal aberrations, cell
lines were stratified according to the presence of
deletions, gains, deletions and gains (both), or no aberration.
Differences in mean among the groups were investigated
The 4Plex panel is the best-performing control
The gene family approach for designing an internal
control (approach A) provided poor results (Additional file 1:
Figure S1) and was discarded from further analyses. The
ALDOA assay resulted in an IVD concentration of ~ 400
copies/microliter (Additional file 1: Figure S1A), which
was lower than expected given the number of loci
targeted by this assay (n = 3; Additional file 1: Table S1).
For CYCS, no positive droplet band was detected
(Additional file 1: Figure S1B).
For the approach that combined single assays targeting
different loci in the exonic part of various pericentromeric
genes (approach B), nine (ALDH1B1, EPHA3, IGFBPL1,
KBTBD4, MRPS5, PLEKHF1, SAMSN1, SYT10, and
TTC5) of the 13 designed assays showed a clear
separation between positive and negative droplets (Fig. 2a and
Additional file 1: Figure S2). EPHA3, KBTBD4, PLEKHF1,
and SYT10 had similar amplitude value of the negative
droplet cluster (around 2000; Fig. 2a), and merging these
assays into a control panel resulted in clear separation
between positive and negative droplets (Fig. 2b).
Combinations with other assays, e.g., ALDH1B1 and SAMSN1,
which had a higher amplitude value of the negative droplet
cluster (~ 2500–2800; Additional file 1: Figure S2), resulted
in reduced separation (Fig. 2c). Thus, the four-assay panel
consisting of EPHA3, KBTBD4, PLEKHF1, and SYT10,
termed the 4Plex, was identified as the best-performing
control. Across all samples analyzed, the 4Plex provided a
consistent amplification pattern, with V9P as an exception.
This cell line displayed a shift in the droplet pattern,
comprising a significant reduction of the negative droplet
peak, and simultaneous increase of the positive droplet
peak (Additional file 1: Figure S3).
The 4Plex has minor impact on amplification of the target gene
The assays comprised in the 4Plex are labeled with VIC
and run in the same reaction as the FAM-labeled assays
that measure the methylation of CDO1, SEPT9, and
VIM. To evaluate whether the presence of the 4Plex had
an impact on the amplification of the target gene,
non-normalized target gene concentrations (methylated
copies/μl) from experiments with and without the 4Plex
control were compared. The resulting non-normalized
concentrations were highly consistent for both CDO1
and SEPT9 (Fig. 3). For VIM, discrepancies between the
concentrations resulting from the experiments with and
without the 4Plex were observed (median absolute
difference of 21%). However, this was comparable with resulting
discrepancies from using the alternative controls ACTB [
(median absolute difference of 23%) and C-LESS [
(median absolute difference of 38%; Additional file 1: Figure S4).
The 4Plex corrects for differences in template amounts and can act as a template-loading control
Since the target assays in the present study (CDO1,
SEPT9, and VIM) are designed to amplify methylated
sequences only, the 4Plex represents an internal control
for normalization for these analyses. Despite the use of the
same theoretical input amount for all samples in this work
(based on the input in the bisulfite conversion), the 4Plex
revealed concentration differences across the cell line
panel (Fig. 4a). Moreover, comparing non-normalized and
4Plex-normalized concentrations of the target genes
across the cell line panel, large differences were observed
for the samples with the highest and lowest 4Plex
concentrations (Fig. 4b). Finally, inclusion of the 4Plex
discriminates true methylation-negative samples (e.g., KM12;
Fig. 5) from potential false methylation-negative samples
lacking template (NTC; Fig. 5).
4Plex-normalized concentrations show less variance than non-normalized target gene concentrations
Non-normalized and 4Plex-normalized concentrations of
the target genes were compared among replicates of two
different samples (SW48 and SW480). For both samples,
normalized concentrations of CDO1 showed lower
variance than the non-normalized concentrations (Fig. 6; 28.5
vs. 183 for SW48 and 20.3 vs. 356 for SW480). The same
tendency of reduced variability after normalization was
seen for SEPT9 and VIM (Additional file 1: Figure S5).
Normalization by the 4Plex diminishes the effect of chromosomal aberrations
To evaluate the potential impact of chromosomal
aberrations on the 4Plex compared to the previously suggested
single locus controls ACTB [
] and C-LESS [
normalized concentrations of the target genes were
compared in groups of colorectal cancer cell lines
harboring no aberrations, gain, loss, or both gain and loss in the
control loci (Additional file 1: Table S3). As shown in
Fig. 7, chromosomal aberrations significantly affected the
ACTB-normalized concentrations (blue boxes) of CDO1,
SEPT9, and VIM (P < 0.001, P < 0.001, and P = 0.016,
respectively) as well as the C-LESS-normalized
concentrations of the same target genes (pink boxes; P < 0.001, P <
0.001, and P = 0.012, respectively). In contrast, the 4Plex
(green boxes) was found to diminish the effect of
chromosomal aberrations when analyzing these three target genes
(P = 0.131, P = 0.109, and P = 0.011).
The ddPCR technology allows highly sensitive
quantification of nucleic acids and has great potential for
analyses of DNA methylation. In the present work, we have
developed a robust internal control for
methylationspecific ddPCR, the 4Plex. This control consists of four
individual pericentromeric assays and is analyzed in the
same reaction as the target of interest. We demonstrate
that normalization using the 4Plex standardizes the
results by increasing the precision of the target
quantification. Such precision is especially important for the
rapidly evolving field of liquid biopsies, which holds
great promise for disease detection, monitoring, and
emergence of drug resistance [
Two different strategies are typically used for robust
quantification of methylated targets in ddPCR analyses.
In line with standard mutation/SNP assays, primers
binding to bisulfite converted DNA, independent of the
methylation status, can be paired with probes with
different fluorescent marks, one binding to the methylated
version and the other to the unmethylated version of the
target sequence. With such a design, the ratio between
methylated and unmethylated DNA can be determined,
and the use of an internal control would be limited to
normalizing for minor technical variations, such as
pipetting inaccuracies. This represents a convenient design
for absolute quantification, but can be challenging for
DNA methylation analyses where the number of CpGs
in the target region of interest, e.g., gene promoters, is
often high, and the presence of such CpG sites in the
primer binding sites may bias the amplification. A
commonly used alternative, often seen in qMSP/MethyLight
analyses, is designing an assay where both primers and
probe bind exclusively to the methylated version of the
target. This type of analysis requires an internal control
for normalization, preferably reflecting the total amount
of amplifiable template in the reaction.
In the present study, two approaches for developing
such an internal control for normalization in ddPCR
analyses were evaluated. The gene family approach (A)
provided poor results for both alternatives tested.
Amplification was either failing or resulting in a lower than
expected concentration (Additional file 1: Figure S1).
The latter could potentially be explained by a
degenerated base in the sense primer, causing less efficient
binding to one or more of the targeted loci. In contrast,
approach (B), consisting of combining several individual
assays into a combined control panel, was successful and
resulted in the 4Plex.
Using the 4Plex as an internal control in
methylationspecific ddPCR has several advantages. In addition to
reducing the overall variability in methylation values and
increasing the reproducibility, the 4Plex can adjust for
unforeseen variations in the experimental pipeline.
Although equal amounts of DNA, as measured by the
NanoDrop, were loaded into the bisulfite treatment and
subsequent ddPCR reaction in the present study, the
4Plex revealed significant DNA concentration
differences across samples (Fig. 4a). Normalization by the
4Plex thereby prevented over- and underestimation of
methylation levels (Fig. 4b). This is highly relevant for
analyses of clinical material, where the DNA quality and
integrity is typically varying [
As expected, and in line with single locus references, the
4Plex served as a template-loading control that allowed
distinguishing between true methylation-negative samples
and template-negative samples (Fig. 5). Moreover, as the
4Plex consists of four assays and has a considerably higher
concentration than the target gene, it could also be used
to establish a lower threshold for allowing scoring of
samples. With a very low signal from the control, it is
unlikely that the reaction contains enough template to
detect potential methylation. Such a lower loading
threshold, revealing samples that cannot be robustly determined,
will reduce the number of false negatives.
Chromosomal aberrations are common in various
diseases, and cancer in particular [
], and will affect
the normalization if present in the control locus [
The importance of using an internal control that targets
multiple loci is known from qMSP/MethyLight analyses
] and was recently also emphasized by Uehiro and
colleagues for ddPCR DNA methylation analyses . In
MethyLight, the transposable ALU element, containing
more than one million copies spread out in the human
genome, represents a robust internal control. However,
for ddPCR methylation analyses, the ALU element is too
abundant and saturates the reaction (data not shown).
The 4Plex on the other hand amplifies four loci in the
genome, located on different chromosomes, without
reaching saturation. When used as an internal control in
the present study, the 4Plex reduced the effect of
chromosomal aberrations on normalized methylation values of
the target gene. In contrast, the use of the single locus
controls ACTB and C-LESS caused significant deviations
in methylation values (Fig. 7). An additional advantage
with the 4Plex is that it only quantifies template that can
be amplified by the targeted assays, i.e., bisulfite-converted
DNA, in contrast to the C-LESS control that amplifies its
target independent of bisulfite conversion status [
To evaluate whether the presence of the 4Plex had an
impact on the amplification of the target gene, the target
was run alone and in combination with the 4Plex. The
resulting methylation concentrations were highly
consistent for two of three genes tested (CDO1 and SEPT9;
Fig. 3). Interestingly, for VIM, the discrepancies from
using the 4Plex were smaller than the observed
discrepancies from using the single locus controls ACTB and
C-LESS (Additional file 1: Figure S4).
In ddPCR methylation analyses, rain, i.e., droplets that
fall between the positive and negative clusters, is a known
12, 13, 31
] (visible in Figs. 2 and 5), making
methylation concentrations sensitive to inconsistent
threshold determination. To standardize the analyses,
PoDCall, an algorithm for automated threshold
calculations, was developed (Fig. 1). PoDCall contributed to
standardization through well-specific scoring of positive
and negative droplets and thus increased the consistency
of the methylation data. However, it is likely that PoDCall
will be applicable also for other types of ddPCR analyses
where rain is observed. Finally, PoDCall was also useful in
order to correct for unexpected technical artifacts in
the ddPCR analyses, such as shifts in baseline
fluorescence between samples (Additional file 1: Figure S6).
Improved accuracy of ddPCR analyses by using
automated threshold determination has also been
underscored by others [
] but has to our knowledge
received limited recognition.
The 4Plex performed well across all samples analyzed,
with V9P as an exception (Additional file 1: Figure S3).
This is most likely explained by a significant
chromosomal amplification observed for the PLEKHF1 locus in
this cell line. In contrast, a “normal” droplet distribution
pattern was seen across a series of more than 100
colorectal cancer tissues (data not shown), indicating that
such pattern aberrations are rare. Furthermore, the
4Plex was successfully used in recent analyses of non- to
minimally invasive material from bladder cancer- and
cholangiocarcinoma patients, respectively (Pharo et al.,
unpublished, Vedeld et al., unpublished), underscoring
that this control can be applied across cancer types.
In conclusion, the 4Plex internal control increases the
precision of methylation-specific ddPCR analyses by
reducing the variability in methylation concentrations,
correcting for variable input amount, and by reducing the effect of
chromosomal aberrations. PoDCall, the algorithm for
automated threshold determination, contributes to additional
consistency of the ddPCR results. We advocate for
implementation of the 4Plex and PoDCall as a standard in
methylation-specific ddPCR analyses.
Additional file 1: Table S1. Sequence information for the ddPCR assays
used in the present study. Table S2. The PCR thermal cycling conditions
(T100 Thermal Cycler, Bio-Rad). Table S3. Gene copy number states of
ACTB, C-LESS, and the 4Plex in the 34 colorectal cancer cell lines. Figure S1.
Droplet dPCR amplification of a non-CpG containing sequence shared by
members of a gene family (approach A) provides poor results. Figure S2.
Individual control assay candidates from approach B. Figure S3. The 4Plex
shows a consistent amplification pattern across the cell line panel with V9P
as an exception. Figure S4. Non-normalized VIM concentrations are lower
with a control assay included in the reaction. Figure S5. A tendency of
lower variation in 4Plex-normalized target gene concentrations is seen in
replicates of the same sample. Figure S6. PoDCall, the algorithm for
automated threshold determination, corrects for shifts in baseline
fluorescence between samples and performs better than the
QuantaSoft software. (DOCX 791 kb)
Additional file 2: dMIQE checklist for authors, reviewers, and editors.
(DOCX 262 kb)
ddPCR: Droplet digital PCR; DNA: Deoxyribonucleic acid; dPCR: Digital PCR;
IVD: In vitro methylated DNA; NTC: Non-template control; PCR: Polymerase
chain reaction; qMSP: Quantitative methylation-specific PCR
We are grateful to Mette Eknaes for the cell culturing.
The work was supported by grants from the South-Eastern Norway Regional
Health Authority (G. E. Lind, funding H. D. Pharo as a PhD student), the Research
Council of Norway through project number 239961 (G. E. Lind), Stiftelsen
Kristian Gerhard Jebsen (R. A. Lothe), and the Norwegian Cancer Society
(72190-PR2006-0442; R. A. Lothe, funding K. C. G. Berg as a PhD student).
Availability of data and materials
The datasets used and analyzed during the current study are available from
the corresponding author on reasonable request.
HDP, KA, and KGB contributed to the acquisition of data. HDP, MJ, KA, KGB, RAL,
and GEL analyzed and interpreted the data. HDP drafted the manuscript. HDP,
MJ, and GEL prepared the manuscript. GEL conceived and supervised the study.
All authors were involved in revising the manuscript and have approved the
Ethics approval and consent to participate
Consent for publication
The authors declare that they have no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.
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