705680

research-article2017

JLAXXX10.1177/2472630317705680SLAS Technology: Translating Life Sciences InnovationBasu

Review

Digital Assays Part I: Partitioning
Statistics and Digital PCR

SLAS Technology
﻿1­–18© 2017 Society for Laboratory
Automation and Screening
https://doi.org/10.1177/2472630317705680
DOI: 10.1177/2472630317705680
journals.sagepub.com/home/jla

Amar S. Basu1

Abstract
A digital assay is one in which the sample is partitioned into many small containers such that each partition contains
a discrete number of biological entities (0, 1, 2, 3, …). A powerful technique in the biologist’s toolkit, digital assays
bring a new level of precision in quantifying nucleic acids, measuring proteins and their enzymatic activity, and probing
single-cell genotypes and phenotypes. Part I of this review begins with the benefits and Poisson statistics of partitioning,
including sources of error. The remainder focuses on digital PCR (dPCR) for quantification of nucleic acids. We discuss fivecommercial instruments that partition samples into physically isolated chambers (cdPCR) or droplet emulsions (ddPCR).
We compare the strengths of dPCR (absolute quantitation, precision, and ability to detect rare or mutant targets) withthose of its predecessor, quantitative real-time PCR (dynamic range, larger sample volumes, and throughput). Lastly, we
describe several promising applications of dPCR, including copy number variation, quantitation of circulating tumor DNA
and viral load, RNA/miRNA quantitation with reverse transcription dPCR, and library preparation for next-generation
sequencing. This review is intended to give a broad perspective to scientists interested in adopting digital assays into theirworkflows. Part II focuses on digital protein and cell assays.
Keywords
lab-on-a-chip, microtechnology, microfluidics, microtechnology, molecular biology, automated biology, robotics andinstrumentation, engineering, nanobiotech, nanotechnology

Introduction
For the purposes of this article, we define a biological assay
as the quantification of the concentration or activity of abiological entity in a sample container. A digital assay is
one in which the sample is first partitioned into many small
containers such that each partition contains a discrete number of biological entities (0, 1, 2, 3, …). The term digitalassay should not be confused with digital microfluidics1 or
single-molecule imaging techniques,2–4 which are reviewed
elsewhere.

Digital versus Analog Assays
Figure 1 compares a conventional analog assay with a digital assay. Analog assays, typically performed in a vial or a
microplate, provide a readout signal A proportional to thebulk sample concentration C. Readout is commonly performed using spectrophotometry, fluorimetry, or nonopticalmethods. For example, fluorescence intensity emitted froma probe may be used to quantify the concentration of DNA,or the enzymatic activity of a protein kinase. By contrast, ina digital assay, the sample is first diluted and partitioned
into small containers such that each partition contains a discrete number of biological entities. Each partition is

individually assayed, giving a 0 or 1 result if any molecules
are present. One common misconception is that digitalassays require a limiting dilution where each container has
either zero or one molecules.5 While this can be done, it isgenerally more efficient to have higher loading factors.
Exploiting Poisson statistics, digital assays can perform
precise counting with as many as six entities per partition,although the noise increases at high loading factors. The
sample concentration can be estimated by counting the fraction of empty partitions E (see below).
The term digital assay originates from the field of digitalcomputers, where computations performed in logic circuits
give one of two possible binary outputs. Digital signals areused extensively throughout engineering, particularly incomputation and wireless communications. In each case,1

Department of Electrical and Computer Engineering, andDepartment of Biomedical Engineering, Wayne State University,
Detroit, MI, USA
Received Jan 20, 2017.
Corresponding Author:
Amar S. Basu, ECE Department, Wayne State University, 5050 Anthony
Wayne Dr., Detroit, MI 48202, USA.
Email: amar.basu@wayne.edu

2SLAS Technology ﻿single-cell secretions to the extracellular environment.9–11
The second of these, the enrichment effect, improves theanalysis of complex mixtures by purifying the target ofinterest from interfering compounds. More specifically, it
increases the ratio of the target of interest versus the background. In an illustrative example (Fig. 2B), partitioning
results in 8× purification in one replicate, and complete
purification in another replicate. In digital PCR (dPCR), theenrichment effect improves amplification efficiency of lowabundance mutant nucleotides against a wild-type background, which is useful for applications like cancer
diagnostics.12 The concentration effect scales inversely withsample volume, while the enrichment effect scales with thenumber of partitions. A third benefit of partitioning is that adigital assay provides superior precision and linearity whencounting copies of targets, simply because it measures individual molecules rather than an ensemble concentration.
Precision improves with the number of partitions, sincemore single entities can be resolved within a large population. Finally, increasing the number of partitions extends the
dynamic range, that is, the range of sample concentrationthat can be measured.

Partitioning Methods: Chambers and Droplets

Figure 1. Comparison of digital assays vs. conventional analog
biological assays.

the information is encoded in a series of ones and zeros. The
advantage of digital signals is the reduced burden on detection elements; each needs only to distinguish between two
signals: high and low. When this concept is carried over into
biological sensors, it simplifies instrumentation—the sensor needs only to tell the difference between a positive andnegative partition. The ratio of positive to negative partitions can then be related to the number of molecules in thesample, using Poisson statistics.

Benefits of Partitioning
Partitioning provides several benefits to digital assays overtheir conventional counterparts, particularly as the numberof partitions increases and the partition volumes become
small. Two notable effects of partitioning are illustrated in
Figure 2. The first of these is the concentration effect: thelimit of detection (LOD) is improved because a small reaction volume increases the effective concentration of the target molecule.5 This makes it possible, for example, toamplify and detect single molecules of DNA6–8 andPartitioning is accomplished using two general approaches:(1) arrays of physically isolated chambers or wells or (2)droplet emulsions where the reaction is conducted in water
droplets separated by a continuous oil phase. Either
approach can provide partition volumes ranging from femtoliters to almost microliter volumes.
In the case of chamber arrays, digital assays have been conducted in commercially available 96-, 384-, or 1536-wellplates. However, as described above, scaling to more wells
improves the dynamic range and the ability to enrich mutant
samples. Scaling can be achieved by miniaturizing the wells
using photolithographic and microfabrication processes. For
example, photolithography and silicon etching have been used
to fabricate 50,000 wells for digital enzyme-linked immunosorbent assay (ELISA), each with a 50 fL volume.13 Similar
platforms for performing digital enzyme assays, with wells
designed to accommodate 1.4 fL volumes, have also been fabricated by micromolding a flexible elastomer (PDMS) against
a 1 µm silicon structure.14 Chamber arrays can also be made inplastics using micromolding15 or imprint lithography,16,17 andin other materials using variants of soft lithography.18 The resolution of the manufacturing process determines volume variability. Using photolithography, tolerances can be as small as
hundreds of nanometers, and with nanoscale imprint lithography, they can be <25 nm. Volume variability contributes to
uncertainty in digital counting assays, as described in the next
section.
The second approach for partitioning utilizes droplet
emulsions in oil. Monodisperse droplets can be made using

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Figure 2. Benefits of partitioning
in digital assays. Shown are two
illustrative examples: (A) increase ineffective concentration and (B) theenrichment effect, i.e., the reductionof interfering compounds.5

microfluidic t-junctions where an aqueous stream is combined with a stream of oil,19 or by flow-focusing crossgeometries where the aqueous stream is combined with two
orthogonal streams of oil.20 The oil should be nonreactive
and have low solubility to prevent the diffusion of reactants
between reactors; often, fluorocarbon oils are chosen forthis purpose. Stable droplet generation requires the additionof surfactants to stabilize the water oil interface and prevent
droplet coalescence.21–24 The surfactant must be optimized
so that it does not interfere with the assay, and keeps small
biomolecules from diffusing into the oil. For more details
on droplet microfluidics, the reader is referred to several
excellent reviews on the subject, including a comprehensive 2012 review by Guo et al.25 and a 2010 review by
Theberge et al.26 Tran et al.’s 201327 and Teh et al.’s 200828
reviews cover technical developments in droplet microfluidics technology, including droplet processing components
and their integration in laboratory workflows. Lagu et al.’s201329 and Zagnoni and Cooper’s 201130 reviews focus on
the application of droplet microfluidics for single-cell biology, highlighting the physics of droplet formation andmanipulation, cell encapsulation methods, a variety of sensing and sorting methods, and the use of confinement insingle-cell analysis.
Each of the two approaches (chambers or droplets) has
its respective benefits. In general, the advantage of droplet
systems is that they can generate as many partitions asneeded. Droplet dPCR (ddPCR) systems, for example, canprovide as many as 10 million partitions. However, droplets
are generated serially, which requires minutes to hours togenerate sufficient partitions. Step emulsification systems

address this issue by massive parallelization, allowing themto generate tens of thousands of droplets in minutes while
maintaining relatively uniform droplet size.31,32 The volume
uncertainty in droplet systems is due to the polydispersity ofdroplet generators, usually <5%, while volume uncertainty
for lithographically defined chambers is determined by the
manufacturing tolerances, as described above. For example,
commercial dPCR systems using chamber and droplet
implementations typically have <3% uncertainty.33 In either
implementation, care must be taken to avoid adsorption of
biomolecules to the interfaces, which can lead to sample
loss or enzyme deactivation. Physical chambers can becoated with blocking agents (e.g., bovine serum albumin) toprevent the adsorption and deactivation of biomolecules.14
Similarly, droplet systems can utilize amphiphilic surfactants, which present a blocking group such as polyethylene
glycol (PEG) facing the aqueous side, and a hydrophobic
tail facing the oil side.22,24,34

Organization and Scope of This Review
This two-part review article will cover the most popular
types of digital assays, including nucleic acids (dPCR), proteins (digital ELISA and enzymatic activity), and cells(single-cell gene expression, proteomics, and transcriptomics).
The large variety of assays is accompanied by an equallywide range of applications, each of which will be covered in
the respective sections. Part I covers partitioning statistics,
and then moves on to dPCR concepts, systems, and applications. Part II35 discusses digital protein assays, digital cellassays, and their respective applications. Given the

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SLAS Technology ﻿readership of SLAS journals, the intended audience is thescientific community using and developing laboratory automation. Accordingly, the article covers fundamental concepts, the methods and types of digital assays reported in
the literature, commercial products, and future trends.

Partitioning Statistics
In digital assays, partitioning statistics are used to determine
sample concentrations and confidence intervals. Partitioning
the sample into small containers results in a statistical distribution of targets (DNA molecules, proteins, or cells) among thepartitions (Fig. 1). Partitioning is similar to aliquotting, a common step in a laboratory workflow; however, the latter typically involves such a large number of targets per aliquot thatthe statistical variation between samples is ignored. By contrast, in digital assays, the number of targets in the sample (m)is typically less than or on the order of the number of partitions(n). As a result, there is significant statistical variation in thenumber of molecules per partition. The average number of targets per partition (l) depends on the sample concentration (C)and the number of partitions (n).
mλ = = C ⋅ VdnHere, m is the number of targets in the sample and Vd is thepartition volume.
The probability that a partition will contain k copies of
the target is governed by the binomial and Poisson distributions.36 It is worthwhile to understand the intuition behindthese distributions, as they are central to digital assays. The
binomial distribution is a discrete probability distributionthat gives the likelihood of k successes in m trials, each witha probability p.37 For example, when rolling a die, the binomial distribution gives the probability of rolling a 4 inexactly 7 out of 10 trials. In the case of digital assays,
throwing the die is analogous to “throwing” one target
entity randomly into an array of partitions, and a success iswhen a target falls in the selected partition. If there are npartitions, and the partitioning process is perfectly random,
the probability that the target appears in the selected partition is 1/n. This trial is repeated m times, once for each molecule in the sample. The binomial distribution, shown
below, gives the probability of k successes, that is, the probability that the selected partition contains exactly kmolecules.
k m 1   1 
p ( k ) =     1 −  k  n   n m−kk m λ   λ =     1 −  k  m   m 

m−kDigital assays typically employ a large number of partitions. When n is large, and the probability of a successful
trial (1 / n) is small, the binomial distribution can be approximated by the Poisson distribution.

p (k ) =λ k e−λk!

The mean and variance of the Poisson distribution are both
equal to µ = σ 2 = λ .36,38 Note that the probability of anempty partition is E = p ( 0 ) = e − λ . Therefore, the numberof targets (m) and the sample concentration C can be easily
calculated from the percentage of empty partitions (E).38,39

λ = − ln ( E )C=ln ( E )
λ
=−
Vd
Vd

m = nλ = −n ln ( E )Figure 3 shows calculated Poisson distributions for various
values of sample concentration l. As l increases, the proportion of empty partitions falls to zero, making quantitation impossible. To statistically have at least one empty
partition (nE > 1), the number of targets must be less thann In (n).5 This is the ideal limit on the upper dynamic range.
In practice, both the upper and lower end of the dynamic
range are limited by sources of error.

Sources of Error
In conventional analog assays, measurement uncertainty is
typically limited by the resolution of the detection instrument(Fig. 1), for example, the ability of a fluorimeter to resolve
small differences in emitted fluorescence. In digital assays
like dPCR, the detection instrument needs only to identify
whether a partition has 0 or >1 targets, which makes data
analysis less dependent on the properties of the detector or
assay chemistry. Digital assays have two sources of error:
subsampling error and partitioning error.5 Subsampling error
sets the lower detection limit at low concentrations and isindependent of the instrument, while partitioning error dominates at high concentrations and may depend on the sampling
and partitioning instrument.
Subsampling errors arise in any biological assay (digitalor analog), which does not analyze the full volume of thesample, but rather a subsample of it, resulting in statisticalvariation between replicate tests. For example, a diagnostic
sample like blood serum could be several milliliters, whilea typical dPCR system can handle only 20 µL samples. The
subsampling process introduces an unavoidable source oferror, particularly when there are few targets within theoriginal sample to detect. When subsampling a fraction of alarger sample, the standard deviation of the targets per subsample is m , where m is the expected number of targets
in the subsample.40 The normalized measurement uncertainty

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Figure 3. Poisson encapsulation statistics. (A) Probability that a partition contains k entities, for various sample concentrations l.
(B) Probability of zero, one, two, and three entities per partition for various sample concentrations l. Both A and B are calculatedusing the Poisson distribution.

due to subsampling is given below. For a 95% confidence
interval (CI), zc should be 1.96.
u S = zc

σm
m
= zc
m
m

Partitioning errors, which are specific to digital assays,
occur because the distribution of targets among partitions
may differ from one experiment to the next. In a set of replicate experiments, there is a variance in the number of
empty partitions E that propagates to a corresponding variance in the calculated concentration l.5 An estimate of partitioning error can be found based on the analysis of Dubeet al.,39 which models the partitioning as a binomial process.
The standard deviation in the proportion of negative partitions E is σ E = E (1 − E ) / n , which propagates to errors
in l and m. The normalized uncertainty due to partitioning
is given byuP =∆m n ( λmax − λmin ) 1  E + zcσ E == ln λnλ  E − z cσ E mThis variation becomes significant at high concentrations(large l), when very few partitions are empty ( E → 0) , andat small l, when nearly all are empty ( E → 1) (Fig. 3). At
low concentrations, the partitioning error incorporates subsampling error, as both errors are based on the same concepts of binomial and Poisson random processes. In
practice, partitioning errors are often determined empirically by conducting multiple replicates of an experimentwith the same copy number.
Figure 4 compares the relative contributions of subsampling and partitioning errors versus sample concentrations
at 20,000 and 1 million partitions. At large l, partitioning
error limits the maximum concentration that can be measured, while both errors increase at l < 1. In practice, thesample concentration is chosen to minimize error. For
example, in dPCR, the sample concentration is typicallyFigure 4. Subsampling errors and partitioning errors indigital assays assuming (A) 20,000 partitions and (B) 1 million
partitions.

adjusted such that l ranges from 0.001 to 65,38 (and in somecases, up to 840). At n = 1,000,000, the partitioning error
drops significantly to <3%, illustrating the benefits of morepartitions.
The Poisson model does not take into account variation inpartition volume, which can skew the distribution of targets,particularly at high concentrations.12,41 As described by
Pinheiro et al.,42 the volume uncertainty uVd can be combined

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SLAS Technology ﻿with partitioning uncertainties (up to form an expanded
uncertainty in copy number, expressed as a percentage.42

u=C(u p )2u +  Vd 
V 
 d 

2

Here, Vd is the mean partition volume and C is a coveragefactor between 2.05 to 2.18, which provides a 95% confidence level that the measurement falls within the calculated
uncertainty. As the number of partitions is increased from
102 to 105, up becomes insignificant, and the expanded
uncertainty drops from >10% to about 1%. Beyond this,error bars are limited by uncertainty in the partition volume.
Thus, it has been suggested that dPCR instrument manufacturers publish volume uncertainty so that it can be included
in confidence interval calculations.12,41

Digital PCR
The most mature digital assay, digital PCR (dPCR) is amethod for absolute quantification of a nucleotide
sequence.5 Although the first paper dates back to 1999,43
dPCR did not gain widespread use until the first commercial instruments became available in 2007.44 dPCR is basedon the same biochemical principles as quantitative real-time(qPCR), but differs in how quantitation is performed. In
qPCR, a sample containing target DNA, PCR reagents, andfluorescent probes is thermally cycled, while monitoring
fluorescence at each cycle. During each replication cycle,hydrolysis probes (e.g., Taqman) or intercalating DNA
binding dyes (Evagreen or SYBRgreen) exponentially
increase fluorescence emission at each cycle, with a ratedependent on amplification efficiency (Fig. 5). Hydrolysis
probes release fluorescence when cleaved during each replication cycle, while DNA intercalating dyes increase fluorescence emission when bound to double-stranded DNA.
Hydrolysis probes typically offer higher specificity, sensitivity, and quantitation. In all cases, the number of cycles
required to achieve a detectable fluorescence, Ct, is related
to the number of targets in the original sample.
In dPCR, the detection chemistry is essentially identical;
however, the sample, reagents, and probes are first partitioned into many replicate reactions (Fig. 1), with commercial systems generating between 1000 and 10 million
partitions.43 The partitions are then subjected to excess roundsof thermal cycling (>30), so that any partition that contains
one or more DNA targets becomes fluorescent, regardless of
amplification efficiency. Then, the proportion of nonfluorescent partitions, through Poisson statistics described above, isused to calculate the target concentration within a welldefined confidence interval.5,39,45 To have a sufficient number

Figure 5. Amplification and detection chemistry in dPCR (samefor qPCR). (A) Hydrolysis probes (e.g., Taqman). (B) DNA
binding probes (e.g., SYBRGreen or Evagreen).

of empty partitions, high-concentration samples must first be
diluted so that l is within a range suitable for Poisson quantitation (0.001–6) with tight confidence intervals.5,38

Digital PCR Systems
Figure 6 and Table 1 compare five commercially available
dPCR systems, which fall into two categories, dependingon the way they partition and detect samples.46 The first category, chamber-based digital PCR (cdPCR) systems, utilize
physical structures as partitions. Vogelstein, who coined theterm digital PCR in 1999, performed sample partitioning by
manually pipetting samples and a PCR mix into a commercial 96-well plate.43 However, he recognized the benefit ofincreasing the number of partitions, noting that in 1536well plates, the theoretical sensitivity in mutation detection
would be ~0.1%, and that very high partition systems could
be limited only by polymerase errors. True to this assertion,
modern cdPCR systems have scaled to more partitions withsmaller volumes. For example, Thermo Fisher’s Quantstudio
3D (Fig. 6B) is an “open-array plate” that consists of 20,000
thru-holes, each with a diameter of 60 µm, etched through a
microscope slide. The inner surfaces are coated with ahydrophilic surface, enabling samples to be loaded via capillary action to form 0.72 nL partitions. The plate is thenimmersed in an immiscible fluid (oil) to seal the sample on
both sides, preventing evaporation or cross-contamination
between reactions. Fluidigm’s BioMark system (Fig. 6A)
consists of microfluidic channels and chambers with flexible valves that route the sample to the chambers and seal

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Figure 6. Commercial dPCR systems. (A,B) cdPCR systems. (A) Fluidigm BioMark system, which includes the 48.770 chip, (top)
BioMark HD cycler, (bottom) EP1 reader, and (not shown) IFC controller. Middle: Fluorescence image after cycling from a gene
expression study. From Warren et al.112 Copyright 2006 National Academy of Sciences. (B) Thermo Fisher Quantstudio 3D,
including (left) an open-array chip with 20,000 thru-hole chambers, (right) a reader, and (not shown) a thermal cycler. (C–E) ddPCR
systems. (C) Bio-Rad QX100/200, which includes (left) a droplet generation chip, (middle) a drop reader chip, and the respective
instruments. Right: Two-channel scatter plots from copy number variation study. Reproduced from Hindson et al.6 with permission
from the American Chemical Society. (D) Raindance Raindrop system, consisting of source and sense chips, along with theirrespective instruments. Lower: Two color fluorescent drops after amplification in mutation detection. Reproduced from Pekin etal.73 with permission from the Royal Society of Chemistry. Right: Typical two-channel scatter plots from mutation detection.51 (E)Stilla Naica System, including (left) Sapphire chip and (middle) Naica Geode drop generator/thermal cycler, and (right) Naica Prism3
reader. Lower inset: Three-color fluorescent droplets after amplification. Where not otherwise indicated, images are reprinted withpermission from the respective system manufacturers.

them during reactions. The 48.770 chip contains 48 panelsof 770 chambers (36,960 total), each with a 0.85 nL volume. Carl Hansen’s group47 expanded Fluidigm’s concept
to 1 million chambers, utilizing an immiscible fluid to help
with chamber isolation. The SlipChip dPCR system48 (notshown) partitions samples by “slipping” two plates withsurface-etched microchannel and well structures, providing
1280 compartments with 2.6 nL volumes.
The second category, ddPCR systems, performs PCRs inwater-in-oil droplet emulsions, as described in the introduction

section. The PCR mix in each droplet must also include PCRcompatible surfactants, often proprietary, to stabilize the emulsion and ensure chemical isolation during amplification.22 The
first report of ddPCR in 200749 demonstrated single-copy
detection limits using Taqman hydrolysis probes in 10 pL
droplets within 18 cycles. This technology was spun out from
Lawrence Livermore Labs into QuantaLife, a start-up thatdeveloped the first commercial ddPCR system and eventuallysold it to Bio-Rad. Bio-Rad’s current offering (Fig. 6C) is the
QX200 ddPCR system, which generates up to 20,000 droplets

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Table 1. Comparison of Commercial dPCR Systems.
MethodqPCRInstrument—

Partitions per
sample; samples
per chipPartition volume
Volumeuncertainty
Dynamic range
Sample size
Copy number
precision
Precision (95% CI)
Multiplexing
Rare mutation
detection
Rare sequence
detection
Throughput—

Instrument +sample cost

cdPCR

ddPCR

Thermo Fisher
Quantstudio 3D
20,000 ×
1 sample

Fluidigm BioMark Bio-Rad QX200
qdPCR 37K
770 × 48 samples 15,000–20,000
× 8 samples

Stilla Naica

0.72 nL
N/A

0.85 nL
0.70%

0.837 nL
0.8%

0.43 nL8%

4.39 pL
2.90%>9 log0.03–200 µL
1–2 copies,
1.5- to 2-fold
Varies≤5 colors
Varies5 log
14.5 µL
7–8 copies,
1.2-fold±10%
2 colors
1/10002 log
4 µL
4–5 copies,
1.2-foldN/A≤5 colors
N/A

5 log
20 µL
5–6 copies,
1.2-fold±10%
2 colors
1/200,000

6 log
50 µL
>6, possibly
1.1-fold±10%
2 colors
1/200,000

Varies

N/A

N/A

1/1,000,000

48 samples
in ~4 h$200K–$250K +$400/chip

96 samples
in 5 h$80K–$100K +$5/sample

5 log
20 µL
5–6 copies,
1.2-fold±10%
3 colors
Est. similar
to QX200
Est. similar to
QX200
12 samples
in 2 h$80K–$100K +$7/sample——

1000s of samples 24 samples
per dayin ~4 hTypically$45K + $10/
$25K–$50K + sample$2/sample

30,000 ×
4 samples

Raindance
Raindrop Plus
Up to 10 million

1/1,000,000
8 samples in 4 h~$120K + $10/
sampledPCR systems are compared to each another and qPCR. Data compiled from references 33, 46, and 60 and system datasheets,50–52,61–67 andcommunications with manufacturers. Specifications not available are marked with N/A.

with volumes of ~1 nL.40,50 Raindance Technology’s Raindrop
system (Fig. 6D) 51 generates up to 10 million droplets withvolumes of 5 pL, utilizing an array of eight parallel drop generators to increase throughput. Raindance was acquired by
Bio-Rad in January 2017. The Stilla Naica System for Crystal
Digital PCR52 (Fig. 6E) utilizes step emulsion droplet generators,32,53,54 which can be massively parallelized to hundreds of
nozzles; however, Stilla’s current product focuses on a moderate number of droplets (30,000) with short workflow times (2h) and increased readout capabilities (three fluorescence channels). A unique feature of step emulsion generators is that they
do not require the flow of oil; hence, in the Stilla system, the oil
is preloaded into the Sapphire chip, simplifying operation andreducing potential contamination.
It is evident that compared with cdPCR systems, ddPCR
can readily scale to more partitions in a cost-effective manner. As mentioned earlier, increasing the number of partitions provides several benefits: First, adding partitions
proportionally increases dynamic range, so a larger range ofsamples can be accommodated without dilution. Second, it
improves the ability to detect rare targets in the presence ofsimilar nucleotide sequences or inhibitors, due to the enrichment effect (Fig. 2B). This is helpful in detecting singlenucleotide polymorphisms (SNPs) and other rare alleles ina largely wild-type population, such as circulating tumor
DNA (ctDNA) (to be discussed later). Third, it is better atresolving copy number variations (CNVs), particularly
when the target concentration is low.55
The commercial systems also differ in how they monitor
fluorescence during PCR, either by fluorescence imaging or
laser-induced fluorescence (LIF) cytometry. The former
approach is used in the Fluidigm BioMark system, which is
unique in that it can measure fluorescence at each partition
after each cycle. The BioMark system can therefore operate
in qPCR or dPCR mode. Thermo Fisher’s Quantstudio 12K,
a predecessor to the Quantstudio 3D, also provides both
capabilities. Real-time, intracycle monitoring on all partitions may help to eliminate false positives in dPCR, but limits the maximum number of supported partitions.
Quantstudio 3D and Naica52 also use fluorescent imaging,but perform only a single-endpoint fluorescence measurement upon the conclusion of all cycles. Fluorescence imaging, when extended to a wide field of view, can analyze as
many as 1 million droplets simultaneously.56 The second
approach, LIF droplet flow cytometry,57 is used by theddPCR systems from Bio-Rad and Raindance, which serially analyze droplet fluorescence using flow cytometers
operating at hundreds or thousands of drops per second. LIF
can achieve higher sensitivity and dynamic range thanimaging, but its serial nature reading requires more time.
Multiplexing capabilities, offered on all systems to varying degrees, are useful for oncology and food analysis,

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where multiple targets are quantified simultaneously. The
QX200, Raindrop, and Quantstudio 3D support two fluorescence channels at nonoverlapping spectral wavelengths,
and Naica provides three. One way to increase the numberof fluorophores is amplitude multiplexing, where probes ofsubstantially different intensities can be simultaneously
used on the same fluorescence channel.58 In traditional flow
cytometry, amplitude multiplexing comes at the expense of
dynamic range, but in digital assays the trade-off is lesssevere because the fluorescence is used only for counting
and not for direct analog quantification. The Raindance system claims to multiplex up to 10 different probes by using 5
amplitude multiplexed probes on two fluorescence channels. For example, a 7-plex assay was used to quantify common mutations in oncogenes.59 Although not officiallyoffered, other systems in principle should also be able toachieve amplitude multiplexing to various degrees.

Comparison of dPCR with qPCR

3.

Since the transformational invention of PCR in 1983,
nucleic acid quantitation has evolved from a qualitativetechnique (PCR + electrophoresis), to semiquantitative(qPCR), and now, with the advent of dPCR, to absolute
quantitation. Compared with its most recent predecessor,
qPCR, dPCR provides benefits in three areas.461.

2.

Absolute quantitation. The qPCR workflow requires
calibration with standard curves because the relationship between Ct and a target concentration canvary by a factor of 1000, depending on many factors: the target sequence, background, PCR chemistry, primer efficiency, probe intensity, and instrument
itself.12 Generating a standard curve requires sample
dilutions over a 5 log range, with multiple replicatesat each concentration. Aside from being timeconsuming, it requires additional sample, and calibration results may vary from lab to lab. Based on
statistical analysis, the digital nature of dPCR provides an absolute quantification of target number,independent of the above factors. As a result, dPCR
does not require calibration and is more consistentacross laboratories and varying assay conditions.68
While it is calibration-free, precise quantitation withtight confidence intervals requires that the sample
concentration fall within the dynamic range to minimize partitioning error; thus, dPCR may still require
a titration step when dealing with unknown concentrations. Absolute quantitation makes dPCR well
suited for precision diagnostics and for quantifying
DNA libraries for next-generation sequencing(NGS).
Precision. Precision is the ability to reproducibly
resolve small differences in copy number, and isnecessary for studies in CNV and gene expression.
In qPCR, since the fluorescence doubles at eachcycle, a single assay can typically resolve only a
twofold difference in copy number. Better precision,that is, tighter confidence intervals, can be improvedby increasing replicates. One study achieved 1.5fold resolution with 7 replicates and 1.25-fold resolution with 18 replicates.45 However, increasing
replicates requires additional sample with fixed concentration. In dPCR, the desired precision can beachieved by increasing the number of partitions,which reduces partitioning error. ddPCR routinely
achieves 1.25-fold resolution and can resolve 1.16fold resolution. In Hindson’s study of CNV,7 ddPCR
provides much greater precision (coefficient of variation [CV] decreased 37%–86%) than qPCR, andreproducibility is improved by a factor of 7 whileproviding single-molecule sensitivity.
Detection of rare targets. A hallmark of dPCR is its
ability to detect rare mutants by partitioning, which
enriches the mutant relative to the wild type. BothqPCR and dPCR can detect single molecules in “clean”
single-plex amplifications. However, in qPCR, inhibitors or excess background DNA can impact amplification efficiency and alter the relationship between Ct
and target number.69 This is especially problematic inrare mutant detection (RMD), wherein the DNA withina sample may contain mutant and wild-type alleles
that are both amplified during thermal cycling. If in
100-fold excess or more, the wild-type molecule
often overwhelms the lower-concentration target byconsuming polymerase, nucleotides, and fluorescentprobes to the detriment of the lower-concentration target.5,12 In dPCR, partitioning enriches the target
from the background (Fig. 2B), which improves
amplification efficiency and tolerance to inhibitors.
Simultaneously, more partitions increase dynamic
range, allowing a high-concentration wild type and a
low-concentration mutant to be detected in the samerun. The same principles apply to rare sequence detection (RSD).70,71 ddPCR systems can resolve raremutants (RMD) with <1 in 200,000, and RSD at <1 in
1 million.72,73 RMD and RSD are useful in noninvasive
diagnostics for detecting circulating DNA fromtumors, fetal cells, or viruses.

On the other hand, qPCR still wins in other areas.
1.

Dynamic range. Dynamic range, the range of sample concentration within which a PCR system canperform quantitation, is a significant strength ofqPCR; it achieves a range of >9 log versus up to 6
log for dPCR (although 4–5 log is more typical ofdPCR).12 On the low end of the range, qPCR and10

SLAS Technology ﻿

2.

3.

dPCR can both detect single molecules, as describedabove; however, on the upper end of the range, thetwo methods differ. In qPCR, the inverse exponential relationship between Ct and target concentration
results in a dynamic range that can be as much as 10log. In dPCR, the maximum number of quantifiabletargets is limited by the number of partitions to ~6n,but <1.5n is typically used to minimize partitioning
error.38 Bio-Rad’s 20,000-droplet system achieves
about a 4 log range, but can be extended to 5 log bysplitting the sample between eight sample wells run
simultaneously on the same chip. Raindance’s system, with 5 million to 10 million droplets, can reach
6 log.51 If the sample concentration is above thedynamic range, it must be diluted, introducing an
additional step into the workflow. As mentioned
above, a large dynamic range is useful in rare mutation detection, and for measuring large differencesin expression between two genes.46
Large volume samples. In clinical diagnostics,
patient samples, such as serum, can be in the milliliter range. If the instrument’s sample volume is muchsmaller, subsampling errors may occur, especially at
low concentration. It may fail to collect a minimum
number of rare nucleic acid targets necessary forquantitation.12 As a rule of thumb, in order to have
95% confidence, the lower LOD is three moleculesin the sample volume. In this regard, qPCR has anadvantage because it can typically accommodate
large-volume samples (up 200 µL), while dPCR
systems have small sample size (20 µL), necessitated by design. In cdPCR, small chambers reducechip fabrication and reagent costs, and permit amanageable field of view for fluorescent imaging.
In ddPCR, rapid drop generation works best at
submillimeter-length scales, resulting in drop volumes of nanoliters down to femtoliters.74 By comparison, qPCR has no such limit, making it better
suited for detecting low concentrations in larger
sample volumes.
Throughput. Sample throughput is the number of
unique samples that can be processed per day. qPCR
wins in this regard because samples are not partitioned, and are read during thermal cycling.
Commercial qPCR systems can process thousandsof samples per workday.46 By contrast, the dPCR
workflow requires both pre-PCR partitioning andpost-PCR reads of each well, limiting throughput.
For example, when Raindance’s system is set to 10
million partitions, it can process 16 samples per 8 h
workday. The Bio-Rad QX200 system, with only
20,000 partitions, can perform ~96 samples in 5 h.
The Stilla Naica System processes 12 samples in <2
h: 15 min to generate drops, 60 min to amplify, and15 min to read. While dPCR may have higher precision than a single qPCR assay, Weaver et al. argue
that qPCR can obtain similar precision by running
replicates, and still achieve higher throughput.45 In
laboratory environments where many similar samples are processed with a fixed method and a single
calibration, qPCR is more suitable.
Other aspects of qPCR and dPCR systems to consider
include the following:
1.

2.

3.

Sample recovery. The recovery of PCR amplified
samples is of interest when preparing DNA for
NGS. Global recovery of sample is provided byboth qPCR and dPCR systems, by either pipetting or
breaking the droplet emulsion.40 However, therecovery of specific partitions in dPCR is not yet
available in commercial systems. In principle, it canbe done in cdPCR systems by pipetting sample frompositive chambers, or in ddPCR systems by sorting
droplets of interest using fluorescence-activateddroplet sorting.57
Ease of use. Arguments can be made for both methods in terms of ease of use. Laboratories are morefamiliar with qPCR, but dPCR does not require calibration, making it more amenable to automation or
lay operation. Absolute quantitation also makes it
easier for laboratories to reproduce results. However,the lower dynamic range of dPCR means that samples may require predilution. The high throughput
of qPCR is more appropriate in laboratory applications where many assays are conducted with a single
calibration step, while dPCR may be more simple touse when absolute quantitation is needed on dissimilar samples.
Cost. Considering both capital and per sample
reagent costs, dPCR is currently more expensive
than qPCR. For example, the Bio-Rad ddPCR system (drop generator, PCR cycler, and reader) costs$90K–$100K, and the reagent cost per sample is~$5.60 By comparison, qPCR instruments are in the$25K–$50K range, and the reagent cost is $2.
However, the per sample cost in qPCR may be offset by the added costs of calibration standards,
whereas dPCR is calibration-free. Given that commercial dPCR instruments have only been availablefor <10 years, it is expected that their cost will comedown over time.

Applications of Digital PCR
Based on the comparisons above, dPCR is best suited inapplications where the key requirements are precise andaccurate quantification and the ability to detect rare mutants

11

Basu

Figure 7. Applications of dPCR. (A) dPCR applications generally exploit one or more of three key capabilities of dPCR. (B) Basic
types of genomic CNV. (C) CNV of three loci from HapMap samples. Reproduced from Hindson et al.6 with permission from theAmerican Chemical Society. (D) Origins of ctDNA. Reprinted from Wan et al.92 by permission from Macmillan Publishers Ltd.,Nature Reviews Cancer, copyright 2017. (E) Scatter plot of KRAS-mutated DNA (green channel) vs. wild type (multiplexed redchannel). Reproduced from Pekin et al.73 with permission from the Royal Society of Chemistry. (F) Viral load monitoring of humancytomegalovirus in four patients.103 Amended with permission from the American Society for Microbiology. (G) Quantitation oflibraries for NGS, comparing ddPCR with capillary electrophoretic quantitation.108 Images reprinted with permission.

or sequences. On the other hand, qPCR is preferable when
dynamic range or throughput is the driver. Examples of dPCR
applications (Fig. 7) include CNV for genotyping genetic
diversity and disease processes, detection of ctDNA, quantification of viral load and low-level pathogens, and quantification of NGS libraries. Each is discussed in turn below.
Copy Number Variation Genotyping. CNVs (Fig. 7B) aresequences of >1 kb pairs that are present in variable copies

in as much as 6%–19% of the human genome,75–77 as well
as plants.78 CNVs are considered structural alterations of
the genome, and may include the deletion or addition ofentire genes, along with their regulatory regions. A richsource of genetic diversity among individuals, CNVs canalter gene dosage, modify protein coding regions, unmask
recessive alleles, and influence a variety of complex diseasephenotypes,75 including susceptibility to HIV, autism, andschizophrenia, as well as the process of cancer metastasis.79

12
For example, fewer copies of the CCL3L1 chemokine genecan make an individual more susceptible to HIV,80 but extra
copies of the EGFR gene can lead to increased risk forbreast cancer.81 CNV analysis requires quantifying the ratioof a target nucleotide sequence compared with a reference.
The first reports of human CNV analysis in 2004 utilizedcomparative genomic hybridization and complementary
microarrays,82,83 and later, sequencing and qPCR provided
more quantitative capabilities. Like qPCR, dPCR can quantify CNV in hours rather than days (vs. sequencing), but its
advantage over qPCR is its ability to resolve small differences in copy number. Where qPCR can statistically resolve
twofold differences (zero to two copies), and with replicates
1.25-fold (up to four copies),45 a single dPCR run canresolve CNVs up to eight copies. Increasing partition numbers to 1 million or more improves resolution to 1.1-folddifferences even in low concentrations ( λ < 0.1).84 The
improved resolution enables the researcher to identify notjust single deletions and duplications, but more complex
alterations that play a role in disease phenotypes. However,one potential caveat specific to dPCR is the potential fortandem copies, that is, two attached copies that share thesame partition, and underestimate copy number. In most
systems, tandem copies can be alleviated by using restriction enzymes that preferably cut outside the amplicon, andhave less sensitivity to methylation.85 In both qPCR anddPCR, CNV requires the coamplification of a target with afixed reference locus, and then is calculated by the ratio of
the positive events for each. In heterogenous samples where
the reference gene itself could have CNV, two reference lociare recommended.
Fluidigm’s BioMark system (N = 768) was the first commercial dPCR system to report CNV studies in 2008, demonstrating 15% resolution of RPP30 spiked in two haploid
genomes.86 To avoid tandem copies, the authors opted forspecific target amplification (STA) instead of restriction
enzymes. Prior to dPCR, the target was preamplified
together with a reference gene (RNase P) for five cycles,
producing separated amplicons at the same ratio as the original sample. STA avoids the stochastic nature of restriction
enzymes, but has the potential to introduce bias during thepreamplification. In 2011, the Bio-Rad system (N = 20,000)
demonstrated precise CNV analysis of up to six copies of
MRGPRX1 in HapMap samples6 (Fig. 7C). The same system has been used to measure copy number amplification of
HER2 in breast cancer samples.87,88 The more recent
Quantstudio 3D system demonstrated a resolution of up to
eight copies with <2.6% CV in samples spiked with aknown copy number of CCL3L1, and 7% CV for the HER2
gene in breast cancer clinical samples.89 Using a combination of sequencing and dPCR, Ni et al. provided early evidence that CNVs in circulating tumor cells are specific tocancer types and reproducible among patients, and arethought to play a role in metastasis.79 Copy number assays

SLAS Technology ﻿
also have applications in prenatal screens for fetal aneupoloidy.90 Trisomy is typically diagnosed by cytogenetic
karyotyping of chromosomes obtained from amniocentesisor chorionic villus samples; however, the test can take asmuch as 1–2 weeks. Using the Fluidigm digital array chip,
Fan et al. accurately identified all cases of trisomy in samples within hours. The high-copy-number precision ofdPCR is well suited to detecting a 1.5-fold change associated with trisomy.90
Liquid Biopsy: Detecting Circulating Tumor DNA. dPCR can beused for liquid biopsy,91 a cancer diagnostic that measures
ctDNA released into a patient’s bloodstream by tumor apoptosis, necrosis, or secretion92 (Fig. 7D). Less invasive thandirect biopsy, liquid biopsy is particularly attractive when
the tumor is not accessible. Several somatic mutations in
ctDNA (KRAS, BRAF, NRAS, TP53, PIK3CA, etc.) represent a new generation of cancer biomarkers that can predict,for example, the progression of metastatic tumors 93, a melanoma’s response to therapies,94 or a colorectal tumor’s
resistance to chemotherapy drugs.93,95 For example, a wideranging study by Bettegowda et al.93 demonstrated the clinical utility of ctDNA in monitoring the progress and evenpotential morbidity of a variety of cancers. They found thatthe fraction of patients that had detectable levels of ctDNA
increased depending on the stage of cancer: 47% of patientswith stage I cancer, and 55%, 69%, and 82% of patientswith stage II, III, and IV cancers, respectively. The concentration of ctDNA also increased with stage, and those withlower blood levels of ctDNA lived significantly longer thanthose with higher levels. The authors concluded that theclinical utility of ctDNA could only be determined by longitudinal studies, necessitating a convenient and sensitive
method for liquid biopsy.
A challenge in liquid biopsy is that ctDNA is typically<1% of fragmented DNA in the bloodstream.71 Moreover,
only a fraction of the ctDNA (<10%) is mutated, depending
on how advanced the cancer may be.71 A hallmark of dPCR
is its ability to detect rare mutants by partitioning, which
enriches the mutant relative to the wild type. ddPCR systems from Raindance and Bio-Rad can resolve rare mutants(RMD) at frequencies as low as 0.0005%,72,73 comparedwith 1% for qPCR and 2% for NGS.91 Furthermore, while
NGS can only detect if a mutation is present, dPCR canquantify it as well, enabling one to monitor the progressionof the disease. When measuring ctDNA, the fact that tumor
DNA is as little as 5% makes it proportionally more challenging to resolve subtle changes. The high-copy-number
resolution of dPCR (as little as 1.1) is an added benefit indiagnostics that require longitudinal monitoring, such as
viral load.
Several studies have shown promising results, beginningwith a clinical breast cancer study that detected ctDNA
using the Fluidigm BioMark system.96 The prevalence ofBasu
the PIK3CA and TP53 loci was shown to be a better predictor of tumor burden than counting circulating tumor cells or
conducting an immunoassay for cancer antigen CA15-3.
Using a variant of the Raindance system, Taly et al.59,73
devised a multiplex ddPCR method to detect mutations in
the KRAS oncogene in patients with colorectal cancer (Fig.
7E). Using amplitude multiplexing, they were able to detect
up to five mutations in a single experiment,97 and achieved
an RMD sensitivity of 0.0005% (1 in 200,000). Endoplasmic
growth factor (EGFR)–activating mutations make the tumor
more susceptible to therapies based on tyrosine kinase
inhibitors (TKIs).64,65 Stilla’s Crystal Digital PCR64,65 system was shown to detect 3 EGFR mutations involved in
TKI susceptibility, all at a frequency of 0.05% comparedwith the wild-type population.64,65 Oxnard’s study of lungcancer patients98 used the Bio-Rad QX100 system to regularly monitor EGFR mutations during targeted tumor therapy. Quantitation of EGFR T790M, with a sensitivity of
0.005%, predicted whether the tumor would be susceptible
to the targeted drug (erlotinib) or develop resistance to it.
Moreover, the mutations were detected up to 16 weeks
before tumor growth could be observed via radiometry.
These studies show the promise of dPCR in early diagnostics and in guiding therapeutic treatments.
Infectious Disease: Rare Sequence Quantification of Viral or Bacterial Load. Precise quantification of circulating viral orbacterial DNA is critical not only for early diagnosis, butalso to monitor the progression of infection and response totreatment.99 HIV patients undergoing retroviral therapy are
regularly monitored for residual infection, and immunosuppressed organ transplant patients are proactively screened
for cytomegalovirus (CMV), Epstein–Barr virus, adenovirus, and others. Early diagnostic approaches for identifying
pathogens relied on light microscopy; however, thisapproach was laborious and could not identify small cells
like viruses. Since the 1990s, the gold standard has become
qPCR, which quantifies viral or bacterial load by measuring
circulating DNA. However, in qPCR, the construction ofcalibration curves and amplification efficiency can vary
greatly from lab to lab, resulting in as much as 20%–30%
CV at low template concentrations.99 dPCR provides (1)absolute quantification of target sequences, (2) in somecases, lower CVs at low concentration, and (3) tolerance to
PCR inhibitors like sodium dodecyl sulfate (SDS) and heparin.69,100 Sedlak and Gerome’s 2013 and 2014 reviews99,100
outline the use of dPCR in viral and microbial diagnostics,
including the measurement of occult RNA, HIV-1, and adenovirus using commercial dPCR products.
In 2008, an early variant of the Raindance platform wasused to amplify and detect spiked-in adenovirus solutions
with a LOD as low as 1 template per 167 droplets, or 92 per
microliter.101 This chip used continuous-flow ddPCR
where droplets shuttle between hot and cold zones during

13
amplification, and cross “neck-down” regions where theirfluorescence is detected. In 2013, Strain et al.102 used the
Bio-Rad QX100 system to quantify HIV pol and 2-LTR
circles in more than 150 clinical blood samples. For HIV
pol, they determined that ddPCR was 5× more precise thanqPCR for template frequencies less than 300 copies per million cells. The ddPCR LOD, determined by subsampling
error, was 14 copies per million cells (95% CI). However,the dynamic range of 105 required that the sample be diluted
to accommodate the high amount of normalization gene.
Two studies compared the QX100 ddPCR system with
qPCR in measuring CMV spiked in clinical samples.103,104
While Hayden et al.’s initial study suggested that dPCR had
less sensitivity and precision than qPCR in clinical samples,104 Sedlak’s subsequent report showed that with equal
sample volumes, ddPCR achieved the same sensitivity andbetter precision at loads of >104 IU/mL.103 The authors also
demonstrated longitudinal CMV monitoring in a patient
population (Fig. 7F). On a broader note, although dPCR did
improve precision, it did not ultimately improve clinicaloutcomes, and therefore, with its lower throughput andhigher costs, replacing qPCR may not yet be justified.103
Looking ahead, one of the challenges in virology is quantitation at near- or subtherapeutic thresholds. Currently,
qPCR can detect 40–60 copies/mL, but cannot quantitate
it.103 For dPCR to provide precision at such low concentrations would require that the sample be preconcentrated toreduce subsampling error. A second opportunity is quantitation of viral standards. With its ability to perform absolute
quantitation, ddPCR systems (Bio-Rad and Raindance) are
more suitable than qPCR when validating CMV standards
sold by commercial sources.105 Lastly, as noted by Sedlak,
the role of dPCR in viral diagnostics may include the detection of not only viral sequences, but also their mutations,which lead to antiviral drug resistance.99 Such a capability
would require rare sequence, as well as rare mutant, detection (Fig. 7A).
Quantitation and Preamplification of Next-Generation Sequencing Libraries. NGS begins with a high-quality DNA library
in precise quantities. Constructing an NGS library (e.g.,
Illumina) includes several steps: fragmentation of the
genomic sample, size selection, end repair, adapter ligation,
PCR enrichment, and quantitation. Of these, dPCR has
proved to be useful in evaluating the integrity of size selection, enrichment, and quantitation. Using the Raindancesystem, Didelot et al.106 used ddPCR to quantify the size
distribution and integrity of DNA fragments. They performed multiplex ddPCR with four primer probes, each targeted to different lengths of DNA. In doing so, theapproximate distribution of nucleotide sequence lengths
could be digitally quantified by counting the number ofamplified species resulting from each probe. When thesame distribution is analyzed using qPCR, it has CVs on the

14

SLAS Technology ﻿order of 10%–50%. After library construction, a portion of
the genomic sample is selected for sequencing. Targeted
sequencing often requires unbiased amplification of predesignated loci, usually by PCR with a selected primer library.
The common issue of biased amplification can be addressedby dPCR. Partitioning fragmented DNA into small containers effectively enriches the target sequences against background, thus improving the amplification of rare sequencesor those difficult to amplify. Using a variant of the Raindance system, Tewhey et al.107 amplified nearly 4000 targets simultaneously using a highly multiplexed primer
library. First, multiple droplet libraries containing each of
the primers were generated and pooled. Then, the primer
library drops were serially merged with droplets containing
the genomic sample. After PCR, the droplet emulsion was
broken, and the amplified DNA was enriched and sequenced
with high specificity and sensitivity. The last but importantuse of dPCR is quantitation. To achieve high-yield andhigh-quality sequencing, NGS sequencers require a relatively narrow range of DNA loading capacity (e.g., Illumina
suggests 60 gigabases per lane108). Too little DNA will
result in poor coverage, low read depth, and failure to detect
SNPs or rare sequences. Too much will result in overclustering, where all sequences may not be resolved. DNA
quantitation with spectrophotometry or fluorescence isqualitative and has limited sensitivity, requiring more than
1000× more sample than the sequencer itself, and thus
wasting precious sample.109 Using the Fluidigm dPCR chipwith universal adapter primers, White et al. demonstrated
absolute quantitation of as few as 100 template molecules,
and was able to sequence with nanogram-scale libraries
rather than microgram ones.109 In another example, quantitation by the Raindance ddPCR system correlated moreclosely with sequencing yield compared than the Agilent
Bioanalyzer, which uses capillary electrophoresis and fluorescence108 (Fig. 7G). In this study, 11 samples weresequenced using an Illumina HiSeq 2500. Prior to loading,the DNA was quantified using both methods, and 60 GB
was loaded onto each lane based on the ddPCR quantitation. The ddPCR provides more accurate and precise quantitation, which correlates more closely to yield than the
Bioanalyzer; however, it should be noted that the Bioanalyzer also provides sizing data. Postsequencing, ddPCR canbe used to validate any discovered target sequences. Due tothe excellent quantitation and amplification capabilities ofdPCR, it is rapidly becoming a standard tool in NGS.

dPCR by performing a reverse transcription step manuallyprior to dPCR in the Bio-Rad QX100 system.7 RT-dPCR
had 37%–86% lower variability and 7× better day-to-day
reproducibility than its qPCR counterpart, and with a similar detection limit. In serum samples, RT-dPCR could better
distinguish between samples that were positive or negative
for miR-141, a prostate cancer marker. Similar studies havevalidated plasma miRNA biomarkers for lung110 and breast
cancer.111 Intracellular RNA profiling of single cells wasdemonstrated by Warren et al., using the Fluidigm digitalarray chip (N = 1200).112 Harvested cells were stained andsorted by fluorescence-activated cell sorting (FACS) before
being individually lysed and transferred to the chip. Theirstudy investigated gene expression in five types of hematopoietic precursors, revealing heterogeneous expression of
PU.1 and GADPH, housekeeping genes previously thought
to remain constant. Gene expression studies with RT-dPCR
offer absolute quantitation of mRNA even at the single-celllevel. Higher-throughput alternatives to RT-dPCR now
include recent methods for single-cell transcriptome
sequencing,113 to be covered in Part II of this review. Lastly,
RT-dPCR has also been applied to viral monitoring of GBVC, an occult RNA virus that can slow the progression of
HIV.99,114 After total cell RNA isolation and reverse transcription, White et al. used the Fluidigm 48.770 chip (N =770) for dPCR, demonstrating 11% variability comparedwith 23% for qPCR, and a LOD of 3–10 copies/µL, lower
than that for qPCR.

Quantifying mRNA through Reverse Transcriptase dPCR. By
adding a reverse transcription step, dPCR can quantify
RNA levels with virtually all the benefits of digital partitioning. Of particular interest is the detection of circulating
microRNAs (miRNAs), which are known to be biomarkers
for cancer, endocrine dysfunction, and other diseases.7
Hindson et al. demonstrated reverse transcriptase (RT)

Digital assays partition samples into thousands of wells
or droplets, and utilize Poisson statistics to calculate thesample concentration with absolute precision. The mostmature digital assay is dPCR, evidenced by several commercial instruments currently used by researchers for awide variety of applications. With the advent of dPCR,
nucleic acid amplification has evolved from qualitative

Food and Agricultural Testing. dPCR has also found use in thetesting of environmental samples (seeds, plant material, soil,
and wastewater), where inhibitors from the DNA isolation process can decrease the efficiency and reproducibility of qPCR.115
Morisset et al.116 demonstrated the utility of ddPCR for quantifying genetically modified organisms (GMOs) in maize feed
samples.117 A duplex ddPCR assay compared levels of theMON810 transgene with those of the HMG reference gene.
The assay achieved a sensitivity of five target DNA copies anda wide dynamic range, similar to that of qPCR, and reported
better repeatability and better tolerance to seed powder inhibitors than qPCR. dPCR has been used to quantify pathogenic
bacteria in plants,118 and Rački et al. used RT-dPCR to detect
pepper mild mottle virus and showed it was more tolerant tocomplex matrices and inhibitors than qPCR.115

Summary and Conclusions: Part IBasu(PCR + electrophoresis) to semiquantitative (qPCR), and now
to absolute quantitation. dPCR substantially improves precision in counting single molecules and resolving a small number of copies in the presence of inhibitors or wild-type
populations. However, it does so at the expense of throughput.
The applications of dPCR center primarily around three key
capabilities: rare mutation detection (ctDNA), RSD (viral load,bacterial load, and circulating miRNA), and precise copy number quantitation (CNV and NGS libraries). Looking to the
future, the use of dPCR in quantifying circulating nucleic acids
is gaining widespread acceptance and holds considerable
promise in improving detection limits and lab-to-lab reproducibility. To further standardize methodologies and results,
Huggett et al. published the dMIQE (Minimum Information
for Publication of Quantitative Digital PCR Experiments).41
The use of dPCR is also quickly becoming a standard technique in NGS library prep.109 Having covered digital assays
with nucleic acids on Part I of this review, Part II will focus on
digital approaches in proteins and cells.
Acknowledgments
The author thanks Dr. Rémi Dangla for insightful comments anddiscussions on the manuscript, and Priyan Weerappuli for a
detailed manuscript review.

Declaration of Conflicting Interests
The author declared no potential conflicts of interest with respect
to the research, authorship, and/or publication of this article.

Funding
The author disclosed receipt of the following financial support for
the research, authorship, and/or publication of this article: The
author gratefully acknowledges support from the National Science
Foundation Division of Chemical, Bioengineering, Environmental,
and Transport Systems (CBET), award numbers 1236764 and
1512544, and from the Division of Electrical, Communications
and Cyber Systems (ECCS), award number 1232226.

References
1. Choi, K.; Ng, A. H. C.; Fobel, R.; et al. Digital Microfluidics.
Annu. Rev. Anal. Chem. 2012, 5 (1), 413–440.
2. Sengupta, P.; Van Engelenburg, S.; Lippincott-Schwartz,
J. Visualizing Cell Structure and Function with PointLocalization Superresolution Imaging. Dev. Cell 2012, 23 (6),1092–1102.
3. Rust, M. J.; Bates, M.; Zhuang, X. Sub-Diffraction-Limit
Imaging by Stochastic Optical Reconstruction Microscopy(STORM). Nat. Methods 2006, 3 (10), 793–796.
4. Betzig, E.; Patterson, G. H.; Sougrat, R.; et al. Imaging
Intracellular Fluorescent Proteins at Nanometer Resolution.
Science 2006, 313 (5793), 1642–1645.
5. Bizouarn, F. In Quantitative Real-Time PCR; Biassoni, R.,
Raso, A., Eds.; Methods in Molecular Biology; Springer:
New York, 2014; Vol. 1160, pp 27–41.

15
6. Hindson, B. J.; Ness, K. D.; Masquelier, D. A.; et al. HighThroughput Droplet Digital PCR System for Absolute
Quantitation of DNA Copy Number. Anal. Chem. 2011, 83(22), 8604–8610.
7. Hindson, C. M.; Chevillet, J. R.; Briggs, H. A.; et al. Absolute
Quantification by Droplet Digital PCR versus Analog RealTime PCR. Nat. Methods 2013, 10 (10), 1003–1005.
8. Henrich, T. J.; Gallien, S.; Li, J. Z.; et al. Low-Level Detection
and Quantitation of Cellular HIV-1 DNA and 2-LTR Circles
Using Droplet Digital PCR. J. Virol. Methods 2012, 186 (1–2), 68–72.
9. Agresti, J. J.; Antipov, E.; Abate, A. R.; et al. UltrahighThroughput Screening in Drop-Based Microfluidics for
Directed Evolution. Proc. Natl. Acad. Sci. U.S.A. 2010, 107(9), 4004–4009.
10. Wang, B. L.; Ghaderi, A.; Zhou, H.; et al. Microfluidic HighThroughput Culturing of Single Cells for Selection Based on
Extracellular Metabolite Production or Consumption. Nat.
Biotechnol. 2014, 32 (5), 473–478.
11. Ostafe, R.; Prodanovic, R.; Lloyd Ung, W.; et al. A highThroughput Cellulase Screening System Based on Droplet
Microfluidics. Biomicrofluidics 2014, 8 (4), 041102.
12. Huggett, J. F.; Cowen, S.; Foy, C. A. Considerations for
Digital PCR as an Accurate Molecular Diagnostic Tool. Clin.
Chem. 2015, 61 (1), 79–88.
13. Rissin, D. M.; Kan, C. W.; Campbell, T. G.; et al. SingleMolecule Enzyme-Linked Immunosorbent Assay Detects
Serum Proteins at Subfemtomolar Concentrations. Nat.
Biotechnol. 2010, 28 (6), 595–599.
14. Rondelez, Y.; Tresset, G.; Tabata, K. V.; et al. MicrofabricatedArrays of Femtoliter Chambers Allow Single Molecule
Enzymology. Nat. Biotechnol. 2005, 23 (3), 361–365.
15. Heckele, M.; Schomburg, W. K. Review on Micro Molding ofThermoplastic Polymers. J. Micromech. Microeng. 2004, 14(3), R1–R14.
16. Ahn, S. H.; Guo, L. J. Large-Area Roll-to-Roll and Rollto-Plate Nanoimprint Lithography: A Step toward HighThroughput Application of Continuous Nanoimprinting. ACS
Nano 2009, 3 (8), 2304–2310.
17. Chou, S. Y.; Krauss, P. R.; Renstrom, P. J. Imprint Lithography
with 25-Nanometer Resolution. Science 1996, 272 (5258),
85–87.
18. Xia, Y.; Whitesides, G. M. Soft Lithography. Annu. Rev.
Mater. Sci. 1998, 28 (1), 153–184.
19. Thorsen, T.; Roberts, R. W.; Arnold, F. H.; et al. Dynamic
Pattern Formation in a Vesicle-Generating Microfluidic
Device. Phys. Rev. Lett. 2001, 86 (18), 4163–4166.
20. Anna, S. L.; Bontoux, N.; Stone, H. A. Formation of
Dispersions Using “Flow Focusing” in Microchannels. Appl.
Phys. Lett. 2003, 82 (3), 364–366.
21. Joensson, H. N.; Andersson Svahn, H. Droplet Microfluidics—ATool for Single-Cell Analysis. Angew. Chem. Int. Ed. 2012, 51(49), 12176–12192.
22. Baret, J.-C. Surfactants in Droplet-Based Microfluidics. Lab.
Chip 2012, 12 (3), 422.
23. Holt, D. J.; Payne, R. J.; Chow, W. Y.; et al. Fluorosurfactantsfor Microdroplets: Interfacial Tension Analysis. J. Colloid
Interface Sci. 2010, 350(1), 205–211.

16
24. Holtze, C.; Rowat, A. C.; Agresti, J. J.; et al. Biocompatible
Surfactants for Water-in-Fluorocarbon Emulsions. Lab. Chip
2008, 8 (10), 1632–1639.
25. Guo, M. T.; Rotem, A.; Heyman, J. A.; et al. Droplet
Microfluidics for High-Throughput Biological Assays. Lab.
Chip 2012, 12 (12), 2146.
26. Theberge, A. B.; Courtois, F.; Schaerli, Y.; et al. Microdropletsin Microfluidics: An Evolving Platform for Discoveries in
Chemistry and Biology. Angew. Chem. Int. Ed. 2010, 5846–
5868.
27. Tran, T. M.; Lan, F.; Thompson, C. S.; et al. From Tubes toDrops: Droplet-Based Microfluidics for Ultrahigh-Throughput
Biology. J. Phys. Appl. Phys. 2013, 46 (11), 114004.
28. Teh, S.-Y.; Lin, R.; Hung, L.-H.; et al. Droplet Microfluidics.
Lab. Chip 2008, 8 (2), 198–220.
29. Lagus, T. P.; Edd, J. F. A Review of the Theory, Methods andRecent Applications of High-Throughput Single-Cell Droplet
Microfluidics. J. Phys. Appl. Phys. 2013, 46 (11), 114005.
30. Zagnoni, M.; Cooper, J. M. In Methods in Cell Biology;
Elsevier: Amsterdam, 2011; Vol. 102, pp 23–48.
31. Amstad, E.; Chemama, M.; Eggersdorfer, M.; et al. Robust
Scalable High Throughput Production of Monodisperse
Drops. Lab Chip 2016, 16 (21), 4163–4172.
32. Dangla, R.; Fradet, E.; Lopez, Y.; et al. The PhysicalMechanisms of Step Emulsification. J. Phys. Appl. Phys.
2013, 46 (11), 114003.
33. Dong, L.; Meng, Y.; Sui, Z.; et al. Comparison of Four Digital
PCR Platforms for Accurate Quantification of DNA Copy
Number of a Certified Plasmid DNA Reference Material. Sci.
Rep. 2015, 5, 13174.
34. Song, H.; Chen, D. L.; Ismagilov, R. F. Reactions in Dropletsin Microfluidic Channels. Angew. Chem. Int. Ed. 2006, 45(44), 7336.
35. Basu, A. S. Digital Assays, Part II: Digital Protein and Cell
Assays. SLAS Technol. J. In press.
36. Moon, S.; Ceyhan, E.; Gurkan, U. A.; et al. Statistical
Modeling of Single Target Cell Encapsulation. PLoS One
2011, 6 (7), e21580.
37. Spiegel, M. R.; Stephens, L. J. Schaum’s Outline of Theory
and Problems of Statistics, 3rd ed.; Schaum’s Outline Series;
McGraw-Hill: New York, 1999.
38. Bio-Rad Corporation. Measurement and Statistics of ddPCR
Experiments; Version 1.0; Bio-Rad Corporation: Hercules,CA, 2012.
39. Dube, S.; Qin, J.; Ramakrishnan, R. Mathematical Analysis of
Copy Number Variation in a DNA Sample Using Digital PCR
on a Nanofluidic Device. PLoS One 2008, 3 (8), e2876.
40. Bio-Rad Corporation. Droplet Digital PCR Applications
Guide; Bio-Rad Corporation: Hercules, CA, 2014.
41. Huggett, J. F.; Foy, C. A.; Benes, V.; et al. The Digital
MIQE Guidelines: Minimum Information for Publication of
Quantitative Digital PCR Experiments. Clin. Chem. 2013, 59(6), 892–902.
42. Pinheiro, L. B.; Coleman, V. A.; Hindson, C. M.; et al.
Evaluation of a Droplet Digital Polymerase Chain Reaction
Format for DNA Copy Number Quantification. Anal. Chem.
2012, 84 (2), 1003–1011.
43. Vogelstein, B.; Kinzler, K. W. Digital PCR. Proc. Natl. Acad.
Sci. U.S.A. 1999, 96 (16), 9236–9241.

SLAS Technology ﻿
44. Morley, A. A. Digital PCR: A brief history. Biomol. Detect.
Quantif. 2014, 1 (1), 1–2.
45. Weaver, S.; Dube, S.; Mir, A.; et al. Taking qPCR to a
Higher Level: Analysis of CNV Reveals the Power of HighThroughput qPCR to Enhance Quantitative Resolution.
Methods 2010, 50 (4), 271–276.
46. Baker, M. Digital PCR Hits Its Stride. Nat. Methods 2012, 9(6), 541–544.
47. Heyries, K. A.; Tropini, C.; VanInsberghe, M.; et al.
Megapixel Digital PCR. Nat. Methods 2011, 8 (8), 649–651.
48. Shen, F.; Du, W.; Kreutz, J. E.; et al. Digital PCR on a
SlipChip. Lab. Chip 2010, 10 (20), 2666.
49. Beer, N. R.; Hindson, B. J.; Wheeler, E. K.; et al. On-Chip,
Real-Time, Single-Copy Polymerase Chain Reaction in
Picoliter Droplets. Anal. Chem. 2007, 79 (22), 8471–8475.
50. Bio-Rad Corporation. QX200 Droplet Digital PCR System
Brochure; Bio-Rad Corporation: Hercules, CA 2015.
51. Raindance Technologies. RainDrop Plus Digital PCR System—
Product Brief; Raindance Technologies: Billerica, MA, 2016.
52. Stilla Techologies. Multiplex Crystal Digital PCR:
Quantifying EGFR Mutations; Stilla Techologies: Villejuif,
France, 2015.
53. Dangla, R.; Kayi, S. C.; Baroud, C. N. Droplet Microfluidics
Driven by Gradients of Confinement. Proc. Natl. Acad. Sci.
U.S.A. 2013, 110 (3), 853–858.
54. Sim, S. P. C.; Kang, T. G.; Yobas, L.; et al. The Shape of a Step
Structure as a Design Aspect to Control Droplet Generation in
Microfluidics. J. Micromech. Microeng. 2010, 20 (3).
55. Whale, A. S.; Huggett, J. F.; Cowen, S.; et al. Comparisonof Microfluidic Digital PCR and Conventional Quantitative
PCR for Measuring Copy Number Variation. Nucleic Acids
Res. 2012, 40 (11), e82.
56. Hatch, A. C.; Fisher, J. S.; Tovar, A. R.; et al. 1-Million
Droplet Array with Wide-Field Fluorescence Imaging for
Digital PCR. Lab. Chip 2011, 11 (22), 3838.
57. Baret, J. C.; Miller, O. J.; Taly, V.; et al. FluorescenceActivated Droplet Sorting (FADS): Efficient Microfluidic
Cell Sorting Based on Enzymatic Activity. Lab. Chip 2009, 9(13), 1850–1858.
58. Miotke, L.; Lau, B. T.; Rumma, R. T.; et al. High Sensitivity
Detection and Quantitation of DNA Copy Number and Single
Nucleotide Variants with Single Color Droplet Digital PCR.
Anal. Chem. 2014, 86 (5), 2618–2624.
59. Taly, V.; Pekin, D.; Benhaim, L.; et al. Multiplex Picodroplet
Digital PCR to Detect KRAS Mutations in Circulating DNA
from the Plasma of Colorectal Cancer Patients. Clin. Chem.
2013, 59 (12), 1722–1731.
60. Koepfli, C.; Nguitragool, W.; Hofmann, N. E.; et al. Sensitive
and Accurate Quantification of Human Malaria Parasites
Using Droplet Digital PCR (ddPCR). Sci. Rep. 2016, 6,39183.
61. Applied Biosystems. QuantStudio 12K Flex Real-Time PCR
System; Life Technologies: Waltham, MA, 2011.
62. Thermo Fisher Scientific. QuantStudio 3D Digital PCR
System Product Bulletin; Thermo Fisher Scientific: Waltham,MA, 2015.
63. Fluidigm Corporation. The Study of Copy Number Variations(CNVs) Using the 12.765 Digital Array™ IFC; Fluidigm
Corporation: South San Francisco, CA, 2010.

Basu
64. Stilla Techologies. Naica System for Crystal Digital PCR;
Stilla Techologies: Villejuif, France, 2015.
65. Madic, J.; Zocevic, A.; Senlis, V.; et al. Three-Color Crystal
Digital PCR. Biomol. Detect. Quantif. 2016, 10, 34–46.
66. Raindance Technologies. ThunderBolts System/RainDance
Source Operator’s Manual, Rev. B; Raindance Technologies:
Billerica, MA, 2016.
67. Raindance Technologies. Raindrop Sense Operators Manual,
Rev. B; Raindance Technologies: Billerica, MA, 2016.
68. Bizouarn, F. Digital PCR: Improving Nucleic Acid
Quantification. Genet. Eng. Biotechnol. News 2012, 32 (9),32–33.
69. Dingle, T. C.; Sedlak, R. H.; Cook, L.; et al. Tolerance ofDroplet-Digital PCR vs Real-Time Quantitative PCR to
Inhibitory Substances. Clin. Chem. 2013, 59 (11), 1670–1672.
70. Taly, V.; Pekin, D.; Abed, A. E.; et al. Detecting Biomarkers
with Microdroplet Technology. Trends Mol. Med. 2012, 18(7), 405–416.
71. Diehl, F.; Li, M.; Dressman, D.; et al. Detection and
Quantification of Mutations in the Plasma of Patients with
Colorectal Tumors. Proc. Natl. Acad. Sci. U.S.A. 2005, 102(45), 16368–16373.
72. Bio-Rad Corporation. Droplet Digital PCR: Ultra-Sensitive
Rare Event Detection Using Bio-Rad’s QX100 Droplet Digital
PCR System Application Note, Rev. B; Bio-Rad Corporation:
Hercules, CA, 2013.
73. Pekin, D.; Skhiri, Y.; Baret, J.-C.; et al. Quantitative andSensitive Detection of Rare Mutations Using Droplet-Based
Microfluidics. Lab. Chip 2011, 11 (13), 2156.
74. Trivedi, V.; Doshi, A.; Kurup, G. K.; et al. A Modular
Approach for the Generation, Storage, Mixing, and Detectionof Droplet Libraries for High Throughput Screening. Lab.
Chip 2010, 10, 2433–2442.
75. Feuk, L.; Carson, A. R.; Scherer, S. W. Structural Variation in
the Human Genome. Nat. Rev. Genet. 2006, 7 (2), 85–97.
76. Lobo, I. Copy Number Variation and Genetic Disease. Nat.
Educ. 2008, 1 (1), 65.
77. Redon, R.; Ishikawa, S.; Fitch, K. R.; et al. Global Variationin Copy Number in the Human Genome. Nature 2006, 444(7118), 444–454.
78. Żmieńko, A.; Samelak, A.; Kozłowski, P.; et al. Copy Number
Polymorphism in Plant Genomes. TAG Theor. Appl. Genet.
Theor. Angew. Genet. 2014, 127 (1), 1–18.
79. Ni, X.; Zhuo, M.; Su, Z.; et al. Reproducible Copy Number
Variation Patterns among Single Circulating Tumor Cells ofLung Cancer Patients. Proc. Natl. Acad. Sci. U.S.A. 2013, 110(52), 21083–21088.
80. Gonzalez, E. The Influence of CCL3L1 Gene-Containing
Segmental Duplications on HIV-1/AIDS Susceptibility.
Science 2005, 307 (5714), 1434–1440.
81. Peiró, G.; Mayr, D.; Hillemanns, P.; et al. Analysis of HER-2/
neu Amplification in Endometrial Carcinoma by Chromogenic
In Situ Hybridization. Correlation with Fluorescence In Situ
Hybridization, HER-2/neu, p53 and Ki-67 Protein Expression,and Outcome. Mod. Pathol. 2004, 17 (3), 227–287.
82. Iafrate, A. J.; Feuk, L.; Rivera, M. N.; et al. Detection of
Large-Scale Variation in the Human Genome. Nat. Genet.
2004, 36 (9), 949–951.

17
83. Sebat, J.; Lakshmi, B.; Troge, J.; et al. Large-Scale Copy
Number Polymorphism in the Human Genome. Science 2004,
305 (5683), 525–528.
84. Whale, A. S.; Huggett, J. F.; Cowen, S.; et al. Comparisonof Microfluidic Digital PCR and Conventional Quantitative
PCR for Measuring Copy Number Variation. Nucleic Acids
Res. 2012, 40 (11), e82.
85. Karlin-Neumann, G.; Montesclaros, L.; Heredia, N.; et al.
Probing Copy Number Variations Using Bio-Rad’s QX100™
Droplet Digital™ PCR System; Bulletin 6277; Bio-Rad
Corporation: Hercules, CA, 2012.
86. Qin, J.; Jones, R. C.; Ramakrishnan, R. Studying Copy
Number Variations Using a Nanofluidic Platform. Nucleic
Acids Res. 2008, 36 (18), e116.
87. Gevensleben, H.; Garcia-Murillas, I.; Graeser, M. K.; et al.
Noninvasive Detection of HER2 Amplification with Plasma
DNA Digital PCR. Clin. Cancer Res. 2013, 19 (12), 3276–3284.
88. Heredia, N. J.; Belgrader, P.; Wang, S.; et al. Droplet Digital™
PCR Quantitation of HER2 Expression in FFPE Breast Cancer
Samples. Methods San Diego Calif 2013, 59 (1), S20–S23.
89. Thermo Fisher Scientific. Copy Number Variation Analysis
Using the QuantStudio 3D Digital PCR System; Thermo
Fisher Scientific; Waltham, MA: 2016.
90. Fan, H. C.; Blumenfeld, Y. J.; El-Sayed, Y. Y.; et al.
Microfluidic Digital PCR Enables Rapid Prenatal Diagnosis
of Fetal Aneuploidy. Am. J. Obstet. Gynecol. 2009, 200 (5),543.e1–543.e7.
91. Diaz, L. A.; Bardelli, A. Liquid Biopsies: Genotyping Circulating
Tumor DNA. J. Clin. Oncol. 2014, 32 (6), 579–586.
92. Wan, J. C. M.; Massie, C.; Garcia-Corbacho, J.; et al.
Liquid Biopsies Come of Age: Towards Implementation of
Circulating Tumour DNA. Nat. Rev. Cancer 2017, 17, 223–
238.
93. Bettegowda, C.; Sausen, M.; Leary, R. J.; et al. Detection of
Circulating Tumor DNA in Early- and Late-Stage Human
Malignancies. Sci. Transl. Med. 2014, 6 (224), 224ra24.
94. Chang-Hao Tsao, S.; Weiss, J.; Hudson, C.; et al. Monitoring
Response to Therapy in Melanoma by Quantifying Circulating
Tumour DNA with Droplet Digital PCR for BRAF and NRAS
Mutations. Sci. Rep. 2015, 5, 11198.
95. Lievre, A.; Bachet, J.-B.; Boige, V.; et al. KRAS Mutations as
an Independent Prognostic Factor in Patients with Advanced
Colorectal Cancer Treated with Cetuximab. J. Clin. Oncol.
2008, 26 (3), 374–379.
96. Dawson, S.-J.; Tsui, D. W. Y.; Murtaza, M.; et al. Analysis
of Circulating Tumor DNA to Monitor Metastatic Breast
Cancer. N. Engl. J. Med. 2013, 368 (13), 1199–1209.
97. Zhong, Q.; Bhattacharya, S.; Kotsopoulos, S.; et al. Multiplex
Digital PCR: Breaking the One Target per Color Barrier of
Quantitative PCR. Lab. Chip 2011, 11 (13), 2167.
98. Oxnard, G. R.; Paweletz, C. P.; Kuang, Y.; et al. Noninvasive
Detection of Response and Resistance in EGFR-Mutant Lung
Cancer Using Quantitative Next-Generation Genotypingof Cell-Free Plasma DNA. Clin. Cancer Res. 2014, 20 (6),1698–1705.
99. Sedlak, R. H.; Jerome, K. R. Viral Diagnostics in the Era ofDigital Polymerase Chain Reaction. Diagn. Microbiol. Infect.
Dis. 2013, 75 (1), 1–4.

18100. Hall Sedlak, R.; Jerome, K. R. The potential Advantages ofDigital PCR for Clinical Virology Diagnostics. Expert Rev.
Mol. Diagn. 2014, 14 (4), 501–507.
101. Kiss, M. M.; Ortoleva-Donnelly, L.; Beer, N. R.; et al.
High-Throughput Quantitative Polymerase Chain Reactionin Picoliter Droplets. Anal. Chem. 2008, 80 (23), 8975–
8981.
102. Strain, M. C.; Lada, S. M.; Luong, T.; et al. Highly Precise
Measurement of HIV DNA by Droplet Digital PCR. PLoS
One 2013, 8 (4), e55943.
103. Sedlak, R. H.; Cook, L.; Cheng, A.; et al. Clinical Utility ofDroplet Digital PCR for Human Cytomegalovirus. J. Clin.
Microbiol. 2014, 52 (8), 2844–2848.
104. Hayden, R. T.; Gu, Z.; Ingersoll, J.; et al. Comparison ofDroplet Digital PCR to Real-Time PCR for Quantitative
Detection of Cytomegalovirus. J. Clin. Microbiol. 2013, 51(2), 540–546.
105. Hayden, R. T.; Gu, Z.; Sam, S. S.; et al. Comparative Evaluationof Three Commercial Quantitative Cytomegalovirus Standards
by Use of Digital and Real-Time PCR. J. Clin. Microbiol.
2015, 53 (5), 1500–1505.
106. Didelot, A.; Kotsopoulos, S. K.; Lupo, A.; et al. Multiplex
Picoliter-Droplet Digital PCR for Quantitative Assessmentof DNA Integrity in Clinical Samples. Clin. Chem. 2013, 59(5), 815–823.
107. Tewhey, R.; Warner, J. B.; Nakano, M.; et al.
Microdroplet-Based PCR Enrichment for Large-Scale
Targeted Sequencing. Nat. Biotechnol. 2009, 27 (11),1025–1031.
108. Raindance Technologies. Using EvaGreen Dye on the
RainDrop Digital PCR System to Quantify NGS Libraries;
Raindance Technologies: Billerica, MA, 2015.

SLAS Technology ﻿
109. White, R. A.; Blainey, P. C.; Fan, H. C.; et al. Digital PCR
Provides Sensitive and Absolute Calibration for HighThroughput Sequencing. BMC Genomics 2009, 10, 116.
110. Ma, J.; Li, N.; Guarnera, M.; et al. Quantification of Plasma
miRNAs by Digital PCR for Cancer Diagnosis. Biomark.
Insights 2013, 8, 127–136.
111. Ferracin, M.; Lupini, L.; Salamon, I.; et al. Absolute
Quantification of Cell-Free MicroRNAs in Cancer Patients.
Oncotarget 2015, 6 (16), 14545–14555.
112. Warren, L.; Bryder, D.; Weissman, I. L.; et al. Transcription
Factor Profiling in Individual Hematopoietic Progenitors
by Digital RT-PCR. Proc. Natl. Acad. Sci. U.S.A. 2006, 103(47), 17807–17812.
113. Macosko, E. Z.; Basu, A.; Satija, R.; et al. Highly Parallel
Genome-Wide Expression Profiling of Individual Cells
Using Nanoliter Droplets. Cell 2015, 161 (5), 1202–1214.
114. White, R. A.; Quake, S. R.; Curr, K. Digital PCR Provides
Absolute Quantitation of Viral Load for an Occult RNA
Virus. J. Virol. Methods 2012, 179 (1), 45–50.
115. Rački, N.; Dreo, T.; Gutierrez-Aguirre, I.; et al. Reverse
Transcriptase Droplet Digital PCR Shows High Resilience
to PCR Inhibitors from Plant, Soil and Water Samples.
Plant Methods 2014, 10 (1).
116. Morisset, D.; Štebih, D.; Milavec, M.; et al. Quantitative
Analysis of Food and Feed Samples with Droplet Digital
PCR. PLoS One 2013, 8 (5), e62583.
117. Milavec, M.; Dobnik, D.; Yang, L.; et al. GMO
Quantification: Valuable Experience and Insights for the
Future. Anal. Bioanal. Chem. 2014, 406 (26), 6485–6497.
118. Gutiérrez-Aguirre, I.; Rački, N.; Dreo, T.; et al. In Plant
Pathology; Lacomme, C., Ed.; Springer: New York, 2015;
Vol. 1302, pp 331–347.