Steps to achieve quantitative measurements of microRNA using two step droplet digital PCR
Steps to achieve quantitative measurements of microRNA using two step droplet digital PCR
Erica V. Stein 0 1
David L. Duewer 1
Natalia Farkas 1
Erica L. Romsos 1
Lili Wang 0 1
Kenneth D. Cole 0 1
0 Biosystems and Biomaterials Division, Materials Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, Maryland, United States of America, 2 Chemical Sciences Division, Materials Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, Maryland, United States of America, 3 Engineering Physics Division, Physical Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, Maryland, United States of America, 4 Biomolecular Measurement Division, Materials Measurement Laboratory, National Institute of Standards and Technology , Gaithersburg, Maryland , United States of America
1 Editor: Soheil S. Dadras, University of Connecticut Health Center , UNITED STATES
Data Availability Statement: All relevant data are
within the paper and its Supporting Information
Funding: This research was supported by the
National Institute of Standards and Technology
under the Scientific and Technical Research and
Services Program. Certain commercial equipment,
instruments or materials are identified in this
manuscript to adequately specify the experimental
procedure. Such identification does not imply
recommendation or endorsement by the National
Droplet digital PCR (ddPCR) is being advocated as a reference method to measure rare
genomic targets. It has consistently been proven to be more sensitive and direct at
discerning copy numbers of DNA than other quantitative methods. However, one of the largest
obstacles to measuring microRNA (miRNA) using ddPCR is that reverse transcription
efficiency depends upon the target, meaning small RNA nucleotide composition directly effects
primer specificity in a manner that prevents traditional quantitation optimization strategies.
Additionally, the use of reagents that are optimized for miRNA measurements using quanti
tative real-time PCR (qRT-PCR) appear to either cause false positive or false negative
detection of certain targets when used with traditional ddPCR quantification methods. False
readings are often related to using inadequate enzymes, primers and probes. Given that
two-step miRNA quantification using ddPCR relies solely on reverse transcription and uses
proprietary reagents previously optimized only for qRT-PCR, these barriers are substantial.
Therefore, here we outline essential controls, optimization techniques, and an efficacy
model to improve the quality of ddPCR miRNA measurements. We have applied two-step
principles used for miRNA qRT-PCR measurements and leveraged the use of synthetic
miRNA targets to evaluate ddPCR following cDNA synthesis with four different commercial
kits. We have identified inefficiencies and limitations as well as proposed ways to circumvent
identified obstacles. Lastly, we show that we can apply these criteria to a model system to
confidently quantify miRNA copy number. Our measurement technique is a novel way to
quantify specific miRNA copy number in a single sample, without using standard curves for
individual experiments. Our methodology can be used for validation and control
measurements, as well as a diagnostic technique that allows scientists, technicians, clinicians, and
regulators to base miRNA measures on a single unit of measurement rather than a ratio of
Institute of Standards and Technology, nor does it
imply that the materials or equipment identified are
necessarily the best available for the purpose.
MicroRNAs (miRNA) are short noncoding RNA oligonucleotides that were discovered in
Caenorhabditis elegans over two-decades ago. Upon their discovery, miRNAs were thought
of as mundane epigenetic regulators of gene expression. Since that time, researchers have
uncovered notable roles for miRNA in almost every area of biology including cell-to-cell
communication, gene regulation, metabolism, and host-pathogen response [
ubiquitous functions have made them direct targets for diagnostic, prognostic, and therapeutic
discovery, however their approval for clinical use has encountered many regulatory and practical
miRNAs are notoriously difficult to measure using conventional clinical techniques such as
standard or quantitative real-time polymerase chain reaction (qRT-PCR) or microarray [
Principles leveraged for years to optimize DNA- or mRNA-based qRT-PCR assays and
microarrays often cannot be used similarly for miRNA measurements. For example, endogenous
controls are necessary in addition to standard curves to calculate exact copy number using
qRT-PCR. However, there are no stable, ubiquitous endogenous controls that can be used
for normalization when quantifying miRNA [
]. miRNA levels can be below detection limits
of conventional qRT-PCR or fold change can be too discrete for microarray detection [
Whereas some investigators have relied on pre-amplification to circumvent this challenge,
reviews are mixed on whether this skews the data disproportionally, especially among certain
]. And while microarray and qRT-PCR measurement technologies have been
shown to be a valid method for determining fold differences between different samples , a
widely recognized challenge is how to measure individual miRNA copy or concentration at
limiting dilutions accurately and without bias [
]. Digital PCR (dPCR) has shown significant
potential as a new measurement capability to solve many of these specific issues.
The measurement community has often tried to overcome metrology challenges by
expanding technological capability and breath. Digital PCR uses real-time or end-point data
collection to separate targets into partitions. These partitions are either made by separating copies
into physical chambers via microfluidics or by creating water-in-oil immersion droplets that
hydrostatically separate targets. Respective technologies are named chamber dPCR (cdPCR)
and droplet dPCR (ddPCR). Given a few basic quantitative assumptions, such as (1) targets
independently segregate, (2) targets are fully accessible to probes and primers, (3) droplet
volume is known, and (4) each partition contains a limited number of targets, then absolute copy
number of genomic material can be calculated by applying Poisson correction [
One caveat of using dPCR for miRNA measurements is that some of the original limitations
still exist, which creates the same measurement uncertainty seen with qRT-PCR or microarray.
For example, miRNA has such short target sequences that primer and probe optimization
strategies, such as melting temperature, length, and guanine-cytosine content, are inherently more
difficult and off-target hybridization with heterogeneous samples or low yield via molecular
dropout can occur [
]. Additionally, dPCR does not seem to rectify problems associated
with performing either one-step or two-step PCR reactions. One-step means that reagents for
both cDNA synthesis and PCR are combined in one mix or container and two-step means that
cDNA synthesis is independent of PCR . In both one-step and two-step reactions, miRNA
must be reverse transcribed to cDNA before PCR can be performed. For our studies we focus
on two-step ddPCR, although one-step dPCR, two-step dPCR, one-step qRT-PCR and two-step
qRT-PCR are all associated with inherent inefficiencies introduced within the reverse
transcription step [
]. Technology to directly and accurately count miRNA is still limited.
Nevertheless, one of the clear benefits to using ddPCR to quantify miRNA is that scientists
can avoid some of the perennially unanswered questions like: how do we translate measurement
2 / 26
of controlled mixtures to absolute quantification in a single sample [
]? Instead we now enter
a new type of debate for this field, one that generally occurs as new and promising technology
comes to market. This is the debate over how to use preexisting companion products on new
technology. One clear way industry has capitalized on this opportunity is to optimize new
proprietary reagents specifically for their instruments. For example, specific ddPCR technologies
require certain buffers and enzymes due to oil-droplet compatibility issues and use of
homemade or competitor products results in measurement errors [
]. Here our measurement
strategies account for all compatibility issues.
Lastly, significant obstacles also exist in the cDNA synthesis step required for two-step
ddPCR measurements. Currently, all the miRNA cDNA synthesis kits on the market were
created and optimized specifically for qRT-PCR. These products can introduce bias when used
with ddPCR reagents [
]. For example, no template controls (NTC) will show amplification
of products regardless of the sterility of the experiment [
]. Non-specific positives in template
controls, while consistently seen in quantitative ddPCR measurements, have been essentially
undetectable using qRT-PCR because of technological limitations, which perhaps lead to the
lack of recognition it deserved. However, now that we have accepted these inadequacies we
can determine ways to limit and quantify control issues for more exact measurements.
First, we have designed a few ways to optimize a two-step miRNA dPCR reaction using
available cDNA synthesis kits and droplet digital PCR System. Secondly, we evaluated different
cDNA Synthesis kits commonly used with these workflows to establish repeatability precision
], predicted loss, limit of detection, and relative measurement models for targets of interest.
We developed protocol recommendations that are agnostic of a specific product so they can be
applied evenly to any manufacturer's product. Lastly, we applied these principles to measure
cell-associated miRNA from an established cell line to demonstrate the possibility of using
spike-in controls and endogenous markers for more accurate quantification (Fig 1). The use of
spike-in controls here is a way to normalize measurements and compute an accurate miRNA
copy number. Therefore, using our guidance and recommended work-flow will improve the
quality of the miRNA measurements.
Materials and methods
THP-1 cell line (TIB-202; ATCC) was purchased directly from American Type Culture
Collection for this project (Manassas, VA). In accordance with policy, the research was reviewed and
approved by the human subjects review process before research involving human or animal
subjects was conducted. THP-1 cells were cultured in RPMI 1640 Medium (Thermo Fisher;
Grand Island, NY) containing L-glutamine, penicillin-streptomycin, sodium pyruvate,
betamercaptoethanol (Sigma; Saint Louis, MO), and endotoxin-free fetal bovine serum (Gemi
BioProducts; Sacramento, CA). THP-1 cells were differentiated by adding 10 ng/mL of phorbol
12- myristate 13- acetate (P8139; Sigma; Saint Louis, MO) to cell culture media without
betamercaptoethanol (M3148; Sigma; Saint Louis, MO) for 48 h and stimulated with 20 ng/mL
ultra-pure Lipopolysaccharide from E. coli O111:B4 (tlrl-eblps; Invivogen; San Diego, CA) for
at least 4 h.
All cells were lifted using Versene/EDTA (Thermo Fisher; Grand Island, NY), flash frozen
in liquid nitrogen and stored at -80ÊC until processing.
miRNA and total RNA
Mature miRNA sequences were identified using miR-Base (www.mirbase.org, Table 1) [
The sequences were used to order synthetic oligonucleotide miRNAs from Integrated DNA
3 / 26
Fig 1. Procedure for analyzing microRNA (miRNA) in a clinical sample. (A) Prepare samples either by
isolating total RNA from cells or acquiring synthetic miRNA oligonucleotides. miRNA spike-in controls are
added at different points within the RNA extraction process to control for loss of material associated with
different steps. (B) cDNA is synthesized using miRNA-specific cDNA synthesis kits. The kits contain reagents
that will add adaptor sequences onto the target miRNA so that primers and probes can respectively bind
either during cDNA synthesis or during PCR steps. All cDNA synthesis kits show signs of non-specific cDNA
products so it is recommended to create one reaction without RNA template (NTC) and one reaction without
enzymes (NEC) for each round of cDNA synthesis. (C) Droplet digital PCR (ddPCR) is done by taking the
cDNA synthesized from miRNA template and creating water-in-oil droplets that each contain zero to a few
target sequences. The cDNA template might need to be diluted to obtain the optimal number of targets per
droplet. Results are reported as florescence intensity. Either the user or the instrument software can define a
threshold. Droplets that fluoresce at an amplitude higher than the defined threshold will be positive for target
sequence and those below threshold are negative for target sequence [
]. (D) Average fraction positive
4 / 26
droplet in both the NTC and NEC are subtracted from sample of interest and Poisson distribution can be
applied to the remaining fraction negative total droplets. The resultant is the targets per droplet (λ') and can
then be manipulated to give targets per microliter, copies of cDNA per microliter, copies of miRNA per
microliter, and concentration. By performing a titration of synthetic miRNA oligonucleotide with a known
concentration, one can create a power model that defines how much miRNA input corresponds to a miRNA
value following ddPCR. This model will account for any loss assumed in both cDNA synthesis and ddPCR
steps. (E) A clinical sample is processed using spike-in controls and validated endogenous miRNA
measurements that have been titrated previously. A 96-well plate for a sample contains primers and probes to
measure sample-specific values for spike-in controls and validated endogenous. These experimental values
are inserted into the power model developed during miRNA titration and converted to predicted values.
Graphing experimental targets per droplet (λ') versus predicted miRNA copies/μL of validated spike-in or
endogenous miRNA generates a new power curve that can be applied to all remaining sample targets of
interest. These resultants are finite miRNA copy number. Values can be extrapolated to concentration or
measurement per cell number using the Avogadro constant or previously measured cell number. Results can
be used for clinical diagnosis, prognosis, or treatment purposes.
Technologies (Coralville, Iowa). Lyophilized synthetic oligonucleotide miRNA was solubilized
at the manufacturer's concentration (Table 1) using RNAase-free 10 mmol/L Tris-HCl 1mM
ethylenediaminetetraacetic acid, pH 7.5 buffer solution.
Total RNA was extracted from cells using a NuceloSpin miRNA kit (Machney-Nagel;
DuÈren, Germany) using manufacturer's suggested protocol (Fig 1A). Synthetic and
cell-associated RNA was stored in individual aliquots at -80ÊC.
Synthetic miRNA oligonucleotides were serially diluted (schematic of dilution shown in S1
Fig) and absorbance was measured (Fig 1A) using a Take3 micro-volume plate on a Synergy
MX plate reader (BioTek; Winooski, VT). Data was initially collected using Gen5 software
(BioTek; Winooski, VT) and exported into Excel (Microsoft; Redmond, WA). Take3
microvolume plate and Synergy MX plate reader were both calibrated for absorbance based on
manufacturer's instructions. Estimated extinction coefficients and molecular weight were
calculated using Integrated DNA Technologies' OligoAnalyzer 3.1 (Coralville, IA; www.idtdna.
com/calc/analyzer, Table 1)[
]. A new synthetic oligonucleotide miRNA concentration in
ng/μL was calculated based on these UV absorption measurements and extinction coefficients
(Table 2). Purity of synthetic oligonucleotide miRNA was measured using the RNA 6000
Nano kit and analyzed on a 2100 Bioanalyzer Instrument using their 2100 Expert Software
(Agilent Technologies; Santa Clara, CA). UV measurements were normalized to microRNA
Peak (% of Total) and number of copies (x 1013) was calculated using the molecular weight
and Avogadro's number.
Total RNA extract was measured using RNA 6000 Nano kit and analyzed on a 2100
Bioanalyzer Instrument using their 2100 Expert Software (Agilent Technologies; Santa Clara, CA).
Cell-associated total RNA concentration was calculated based on extinction coefficient and
other proprietary formulations built into the Bioanalyzer 2100 and 2100 Expert Software
(Agilent Technologies; Santa Clara, CA).
TaqMan Small RNA Assays Kits, containing both cDNA synthesis enzymes, buffers and
genespecific primers (Table 3) were purchased from Thermo Fisher Scientific (Grand Island, NY).
cDNA was synthesized based on the manufacturer's instructions with the following
amendments: total reaction volume was halved by proportionally dividing all components and
reverse transcriptase was added individually to reaction tubes. Primers were used at suggested
concentrations for reverse transcription.
QScript miRNA cDNA Synthesis Kit was purchased from Quanta Biosciences (Beverly,
MA) and cDNA synthesis was (Fig 1B) based on manufacturer's instructions with the
following amendments: total reaction volume was halved by proportionally dividing all components
and reverse transcription temperature was decreased to 37ÊC.
Universal cDNA Synthesis Kit II, containing miRCURY LNA miRNA PCR,
Polyadenylation and cDNA synthesis kit II, was purchased from Exiqon (Vedbaek, Denmark), miScript II
PCR Primer Dilution (Factor of X)
Annealing Temperature (oC)
61 to 62
6 / 26
Reverse Transcription Kit was purchased from Qiagen (Hilden, Germany) and cDNA was
synthesized (Fig 1B). Both kits were only modified from manufacturer's instructions by dividing
reaction volume and respective components in half.
cDNA controls were made for all kits by omitting the components as described in results
section. Volumes and components were proportionally decreased in all instances to reduce the
amount of template sample needed.
PCR annealing temperature validation
cDNA was amplified using AmpliTaq Gold, PCR Buffer, Magnesium Chloride Solution,
dNTPs, (Thermo Fisher Scientific; Grand Island, NY) and primers for each respective kit
(Table 3). Primers were purchased from all respective companies, except in the case of primers
associated with Quanta Bioscience. Quanta Bioscience primers were developed based on
manufacture's guidance and purchased from Integrated DNA Technologies (Coralville, IA).
Appropriate engineering and manual controls were used to prevent contamination
including: master mix made using a clean hood prior to adding any template, clean gloves, and
PCRclean reagents and consumables. Concentrations of components, cycle conditions, including
initial annealing temperature ranges, were chosen based on manufacturer's recommendations.
Amplicons were analyzed using the FlashGel System (Lonza; Basel, Switzerland) per
manufacturer's instructions for small amplicons.
Droplet digital PCR
For all experiments indicated below, a master mix was initially made containing all respective
components, except template, in a clean hood within a PCR-sterile room (Fig 1C). Reaction
master mixes were aliquoted in a separate room and template was added using sterile techniques.
Corresponding primer dilutions are indicated (Table 3). Primers for Thermo Fisher
Scientific, Qiagen, and Exiqon are proprietary and therefore dilution factors are relative to
manufacturer's stock. Initial concentrations for Quanta Bioscience Primers were 10 μmol/L.
For Thermo Fisher Scientific reactions, Supermix for Probes containing dUTP (Bio-Rad;
Hercules, CA) was used in the ddPCR master mix. cDNA from the TaqMan Small RNA Assay
Kit was serially diluted in water and added as technical replicates. Droplets were generated
using Bio-Rad's manual droplet generator and droplet generating oil for probes (Bio-Rad;
For Exiqon, Qiagen, and Quanta Bioscience experiments, a ddPCR master mix containing
respective primers (Table 3) and Bio-Rad EvaGreen Supermix (Bio-Rad; Hercules, CA) was
made. cDNA from Exiqon cDNA Synthesis Kit II, Qiagen's miScript Kit, or Quanta
Bioscience's qScript kit, were individually diluted serially into water and added to respective
reactions as technical replicates. Droplets were generated using Bio-Rad's manual droplet
generator and EvaGreen droplet generating oil (Bio-Rad; Hercules, CA).
Droplets were hardened using cycling conditions shown in Table 4. The annealing
temperatures are based on the results from annealing temperature optimization studies.
All droplets were analyzed using the QX200 Droplet Digital System (Bio Rad; Hercules,
CA). Data was acquired using 1-dimensional or 2-dimensional based plotting systems as
recommended by the manufacturer. Thresholds were set by excluding only the true negative
Data was exported from each relative manufacturer's software into a spreadsheet-based
analysis system where appropriate. Data was analyzed, graphed and correlated using either
7 / 26
# of Cyclesa Cycling Step
1 Enzyme Activation
Supermix for Probes containing dUTP
Temperature (ÊC) Time (min)
See Table 1 1
5 to 1
# of Cyclesa
GraphPad Prism, R Statistical Programming using RStudio [
], or Microsoft Excel.
Specific graphical or statistical analysis software is indicated in the figure legends.
Measurement of synthetic oligonucleotides
Synthetic oligonucleotides are a convenient and easy way to optimize reaction conditions
while simultaneously controlling multiple variables [
]. We began by choosing two
nonhomologous and two homologous human miRNAs to evaluate. For our non-human miRNA
we choose two Caenorhabditis elegans (cel) miRNA homologs, cel-miR-238 and cel-miR-39
(Table 3) [
]. For miRNAs homologous to Homo sapiens (hsa), we choose two miRNAs
implicated in different disease etiologies that could be traced to specific cell subsets.
Hsa-miR155 and hsa-miR-223 have been shown to be differentiated in multiple types of diseases [22±
24]. It has been shown that hsa-miR-155 plays a role in macrophage polarization [
] and that
hsa-miR-223 has a role in neutrophil function [
In later experiments, the non-homologous miRNAs would be used as spike-in controls and
the miRNAs homologous to Homo sapiens would be measured endogenously in samples of
interest. It is important to computationally check non-homologous spike-in sequence
homology to human miRNA and identify any places where the spike-in controls might hybridize to
human mRNA, as well as run a wet-lab validation study with a non-precious sample to identify
limit of detection (LOD). We used miR-Base [
] to identify the sequence of each miRNA
(Table 1) and confirm that they did not have sequence homology to other miRNA in humans
or other species. Additionally, a validation study entails quantifying off-target effects in total
RNA purified from a non-precious sample that does not have any exogenous synthetic miRNA
spiked into it. Although these off-target effects can be hypothesized using bioinformatics it is
important to determine an experimental value upfront to assess if maybe a different spike-in
target could be used (S2 Fig). Lastly, it was important for economic and practical reasons to use
miRNAs that were compatible with readily available and cost-effective primers and probes. For
initial experiments (Figs 2±6), synthetic oligonucleotides were created, purchased, tested, and
utilized for all targets of interest.
Upon receiving the synthetic oligonucleotides, we measured the absorbance using a
microvolume compatible plate reader. UV spectrophotometer measurements of nucleic material has
recognized inherent issues. However, we often use it because it serves as a good benchmark for
concentration measurement [
]. The purpose for its use in this study is as a relative method
for quantitative analysis of miRNA.
Absorbance values were measured at four different dilutions for each oligonucleotide.
Values were converted to concentrations of nanograms per microliter for reference and plotted
8 / 26
Fig 2. Analytical data for synthetic oligonucleotides. Manufacturer reported that synthetic oligonucleotides
had a specific molecular weight and diluting them as instructed would yield a concentration of 100 μmol/L. A
microvolume UV spectrophotometer was used to acquire absorbance data for four synthetic oligonucleotides
and (A) graphed as mean measured concentration versus manufacturer intended concentration. Data from the
chip-based automated electrophoresis system is shown as (B) electronic gel or (C) individual spectral data.
Yellow box on electropherograms represents observed individual peaks corresponding to synthetic miRNA
oligonucleotides. Graph (A) was developed using ggplot2 in RStudio. Imagines (B) and (C) are directly from
2100 Expert Software (Agilent Technologies; Santa Clara, CA).
9 / 26
Fig 3. Annealing temperature evaluation using PCR. (A) Traditional gel electrophoresis pictures after polymerase chain reaction
(PCR) with increasing annealing temperatures done after cDNA synthesis with cDNA KitsºAº, ªBº, ªCº, or ªDº. (B) Chip-based
automated electrophoresis system gel image of cDNA amplified using PCR. Annealing temperature ranges as indicated per kit in
degree Celsius: cDNA Kit ªAº = 52, 56, 58, 60, 62, 65; cDNA Kit ªBº = 50, 52, 55, 58, 60, 62; cDNA Kit ªCº = 50, 53, 56, 58, 60, 62;
cDNA Kit ªDº = 52, 56, 58, 60, 62, 65. ªLº indicates a ladder lane. Synthetic oligonucleotide targets are listed on left by homologous
against their dilution values (Fig 2A). Using R statistical programming [
], we calculated the
mean concentration, uncertainty, and regression coefficient of each synthetic oligonucleotide
dilution. All values had small uncertainties and the only deviation from expected
concentration, based on manufacturer's estimated concentration, was hsa-miR-155 (Table 2). To
confirm that our synthetic oligonucleotides were free of partial products or solubilized impurities
we evaluated each oligomer on a chip-based automated electrophoresis system.
Both gel (Fig 2B) and spectral data (Fig 2C) demonstrated clean bands and sharp peaks,
respectively, for all oligonucleotides apart from hsa-miR-155. Hsa-miR-155 showed multiple
bands and a wide distribution of apparent molecular weights. The chip-based automated
10 / 26
Fig 4. Quantification of synthetic miRNA using four different cDNA synthesis kits. (A) cel-miR-238, (B)
cel-miR-39, and (C) hsa-miR-223 were measured using ddPCR following cDNA synthesis with cDNA
synthesis kit ªAº, ªBº, ªCº, or ªDº. Multiple technical replicates were performed over a series of months and
data was normalized for no template control and no enzyme control. Final data was graphed as miRNA
copies/μL master mix. Predicted values were estimated based on UV absorbance values and chip-based
automated electrophoresis system analysis. Dotted line represents predicted value. Each individual point
represents a mean for a specific experimental date. The measurement uncertainty is graphed on each data
set. Graphs were developed and analyzed using GraphPad Prism. (*) implies that there is 95% confidence
that the measured value for an individual dataset is significantly different then the hypothetical predicted value
as indicated by the dotted line.
electrophoresis system software uses their ladder as a standard for concentration
determination by comparing areas under the peaks of the ladder to those of the sample. Furthermore, the
RNA integrity number (RIN) is calculated using ribosome RNA, which is not present in our
synthetic miRNA oligonucleotides. miRNA peak as percentage of total peak was calculated by
drawing a nominal region around the single peak within the electropherogram (Fig 2C, yellow
box). We attempted to normalize concentrations obtained using absorbance measurements
for all oligonucleotides by multiplying the concentration determined by UV absorbance and
specific miRNA percentage of total area of spectral data representing pure miRNA (Table 2).
Since there is no single hsa-miR-155 peak, we could not definitively identify the percentage of
pure full-length miRNA product in the stock.
We believe that the impurities associated with our synthetic hsa-miR-155 oligonucleotide
may not be characteristic of endogenously expressed hsa-miR-155. However, the heterogeneity
of the hsa-miR-155 synthetic oligonucleotide makes accurate quantification difficult, so we
excluded exogenous measurements for hsa-miR-155 in all remaining technical exogenous
validation experiments (Figs 3±6). Instead, we focused on evaluating only cel-miR-238,
cel-miR39, and hsa-miR-223 as assay controls. We use these normalized UV concentration values
(Table 2) for cel-miR-238, cel-miR-39, and hsa-miR-223 throughout the paper.
Optimizing polymerase chain reaction conditions
Primer designing is a skill that uses fine-tuned principles to predict the best oligonucleotide to
specifically hybridize to the target of interest [
]. Unfortunately, most reagents used in
twostep ddPCR for miRNA quantification are proprietary, which complicates conventional
primer design techniques. For example, adaptor sequences, which are the oligonucleotide
extensions added to miRNA cDNA, are used so that targets are long enough for primer
hybridization. Depending on the miRNA cDNA Synthesis kit, a general polyA tail, hairpin
loop, a universal tag sequence, or a combination of technologies are added to the end of the
cDNA either before or during reverse transcription. These technologies and their proprietary
nature make identifying the true length of the intended products nearly impossible and create
ambiguity when trying to optimize hybridization conditions. An additional complication of
using conventional primer design is that the native mature miRNA targets are only 21 to 24
nucleotides in length [
] so there is a limited choice of hybridization sites that optimize
specificity. These two substantial obstacles leave an investigator with limited ability to apply
traditional primer and probe optimization strategies.
We began by performing a standard annealing temperature experiment to determine if
annealing temperature had any effect on non-specific products and amplification efficacy (Fig
]. In addition to full length targets, non-specific products could also be observed at
other molecular lengths at all annealing temperatures for all kits (Table 5). The source of these
varying molecular weight products was investigated using different negative controls. We
recognized early in our experiments that the traditional no template control, which simply omits
12 / 26
Fig 5. miRNA titration curves. All synthetic oligonucleotides were combined into a target master mix and subsequently diluted prior to cDNA synthesis.
cDNA master mix was minimally diluted prior to ddPCR quantification. Values were normalized by subtracting average NTC and NEC from total positive
droplets per kit. All three cDNA synthesis kits, ªAº, ªBº, and ªCº graphs were organized by three targets: (A, D) cel-miR-238, (B, E) cel-miR-39, or (C, F)
hsa-miR-223. cDNA synthesis kits were used to synthesize cDNA and data were plotted as (A to C) UV measured as determined by dilution factor times
UV measured versus normalized experimental targets per well. (D to F) Normalized targets per well were extrapolated to miRNA copies/μL by multiplying
by droplet volume, cDNA dilution factor, miRNA dilution factor, dilution into master mix, and molecular weight. The predicted copies of miRNA (Table 2) is
shown as a black line. Gray shading indicates the 95% coverage interval for predicted miRNA copy number. miRNA dilutions are reported in arbitrary
units and correspond to 1×, 0.2×, 0.04×, 0.008×, and 0.0016×. The standard uncertainty is shown for each point, a result of three technical replicates for
one experiment. Graphs are shown as log-log scale for visualization; they were developed using GraphPad Prism. Statics were estimated using RStudio.
RNA template prior to cDNA synthesis would not be sufficient [
]. We therefore postulated
that there were a variety of different negative controls available for these kits, including (1)
13 / 26
Fig 6. Model for miRNA quantification using synthetic oligonucleotides. miRNA titrations were
performed with (A, D, G) cDNA Kit ªAº, (B, E, H) cDNA Kit ªBº, and (C, F, I) cDNA Kit ªCº. The cDNA synthesis
kits and targets were measured with droplet digital PCR system. Average no template control (NTC) and no
enzyme control (NEC) were similarly measured for each experiment. Fraction positive targets per droplet
were calculated either without subtracting average NEC and NTC positives (λ; open circles, purple line) or
with subtracting NEC and NTC positive (λ'; closed red circles, red line) droplets. For each kit and each target
(A to C) cel-miR-238, (D to F) cel-miR-39, and (G to I) hsa-miR-223, both λ (dashed purple line) or λ' (solid red
line) were graphed on a single x-axis versus estimated 104 miRNA copies/μL master mix. A simple power
curve, y = axb, was calculated for normalized data, λ'. Limit of quantification (LOQ) was predicted based on
the lowest possible value that satisfied the power curve. Individual data points are shown with corresponding
standard uncertainties. Microsoft Excel was used to calculate and graph data.
omitting RNA prior to cDNA synthesis, (2) omitting enzymes involved in cDNA synthesis, (3)
omitting cDNA in ddPCR reaction, (4) omitting Supermix for the ddPCR reaction, and (5)
omitting primers or probes at any level. We found that omitting Supermix or primers very
rarely gave any positive droplets and attribute the few positive droplet occurrences to
technician contamination. When we omitted cDNA in the annealing temperature PCR experiment,
the abundance of non-specific byproducts tended to correlate with the amount and size of
primers (Table 5). For example, cDNA Kit ªDº, which alone uses primers in both the cDNA
synthesis step and ddPCR step (Fig 1B) has double the number of non-specific bands for target
cel-miR-238 compared to any other kit. Consequently, we decided that these three controls are
14 / 26
Annealing Temperatures (ÊC)
+, single band or smear present.
++, double band present.
+++ triple band present.
valuable to run occasionally, but are not essential for every reaction given limited sample and
reagent availability. Therefore, it is our recommendation to use the following controls: (1)
omitting RNA prior to cDNA synthesis, which we can call a true no template control (NTC)
15 / 26
and (2) omitting enzymes involved in cDNA Synthesis, which can be called no enzyme control
In NTC and NEC reactions, non-specific products were observed in all cDNA Synthesis
kits depending on the temperature and target. Table 5 lists instances where non-specific bands
were observed for NTC and NEC controls. Each positive sign indicates one various size
nonspecific band. Some bands were hypothesized to be primers, but others corresponded to full
length targets. For example, a band of approximate target cDNA size was seen in each of the
following reactions: with cDNA Kit ªAº, at cel-miR-39, in the NTC; with cDNA Kit ªBº at
celmiR-238, in the NEC; with cDNA Kit ªCº at hsa-miR-223, in the Poly(A)Polymerase NEC;
and with cDNA Kit ªDº, at cel-miR-238, in the NTC. Using these conventional PCR annealing
temperature analyses we chose an optimal annealing temperature that would minimize as
many non-specific products as possible (Table 3).
We also explored the effect of temperature on the reverse transcription step for the cDNA Kit
ªCº. In this kit, reactants are added in two different steps: first poly(A) polymerase and reaction
buffers are added for tail extension to the template and then reverse transcriptase is added for
cDNA synthesis (Fig 1B). All other kits contain enzymatic mixtures with all components and
therefore restrict ability to test non-specific product formation resulting from a singular enzyme
and a single cycle reaction. The manufacturer of cDNA Kit ªCº recommends performing the
polyA extension at 37ÊC and reverse transcription at 42ÊC. However, given that all other kits used
similar conditions for both adaptor extension and reverse transcription, we tested different reverse
transcriptase temperatures to see if we could standardize temperature for both enzymatic reactions.
We found no specific differences when using either 37ÊC or 42ÊC for reverse transcription when
examined using gel electrophoresis so we choose to perform both enzymatic reactions at 37ÊC.
In a further effort to check specificity and purity, we also evaluated PCR-amplified products
using a chip-based automated electrophoresis system (Fig 3B). We used the optimized
annealing temperatures for all reactions. Non-specific products were seen under most conditions,
often with varying sizes. All kits showed at least one non-specific product in NTC or NEC.
These non-specific products have the potential to skew the precision of the quantitative
measurements. Therefore, we decided to do repeated tests evaluating technical variation in
experimental miRNA copy number data when quantifying synthetic oligonucleotides using ddPCR.
Relative concentration and copy numbers for kits and targets
We performed multiple experiments using a mixture of miRNA synthetic oligonucleotides,
generally at equal ratios to each other. In each case, the miRNA synthetic oligonucleotides
were diluted approximately 105 to 109 fold and synthesized into single stranded cDNA (Fig
1B) as shown in our schematic in S1 Fig. Following reverse transcription, the cDNA was
diluted to a nominal concentration and copy number measured using a ddPCR system (Fig
1C). For these experiments, cDNA was stored at 4ÊC or -20ÊC based on manufacturer's
instructions, cDNA mixtures were evaluated using ddPCR anytime between zero and five days
following cDNA synthesis. However we suggest storing cDNA at 4ÊC. Storage at 4ÊC,
especially in instances where multiple freeze-thaws might occur, has been shown to reduce DNA
accumulation on the sides of storage container and improve recovery [
Fig 4 shows experimentally-derived miRNA copy number for each cDNA synthesis Kit.
The dashed line represents predicted miRNA copy number as derived in previous experiments
(Fig 2 and Table 2). All samples were normalized to controls by subtracting the average
fraction positive NTC and NEC from sample fraction positive and then by applying Poisson's
distribution directly to the fraction negative as calculated by one minus fraction positive (Fig 1D).
This value is the normalized copies per droplet (λ').
16 / 26
These preliminary data demonstrate that the mean measured miRNA copy number in
cDNA Kits ªAº, ªBº, and ªDº is significantly different (p < 0.05) from the predicted miRNA
copy number for all three targets (Fig 4). Conversely, although certain measurements for
cDNA Kit ªCº were higher than the predicted copy number, only the mean measured
celmiR-238 copy number was significantly different from predicted copy number (Fig 4A). Each
test was done by using a one-sample t test comparing each actual mean to theoretical mean
after having passed the Shapiro-Wilk normality test [
We compared both the repeatability (the spread of technical replications over different
days) and the average copy number detected in all trials. cDNA Kit ªCº has the lowest
coefficient of variation for all measurements of target concentration with 10.3%, 11.5%, and 23.9%
for cel-miR-238 (Fig 4A), cel-miR-39 (Fig 4B), and hsa-miR-223 (Fig 4C), respectively.
However, cDNA Kit ªBº has the lowest standard uncertainty for both cel-miR-238 (Fig 4A) and
hsa-miR-223 (Fig 4C), at 2.58 × 1012 miRNA copies per microliter (copies/μL) and 4.34 × 1012
copies/μL, respectively. Whereas the uncertainty for cDNA Kit ªDº is 1.80 × 1012 copies/μL for
cel-miR-39 (Fig 4B). Therefore, we conclude that all kits show some marginal target-specific
Our results are in accordance with other metrology institutes that reported similar findings
depending on the target and kit [
]. We further extrapolated these data to create an accurate
measurement model that can be used to quantitatively measure any miRNA copy number,
agnostic of cDNA synthesis kit preference. We began by controlling our experiments for
variables to allow for a fair comparison. For example, because cDNA Kit ªDº uses gene specific
primers in the cDNA synthesis kit, is only compatible with a different ddPCR supermix from
the other three kits, did not produce significantly better results, and is cost prohibitive to use
for high throughput experiments, we concentrated all future experiments and results on
cDNA Kits ªAº to ªCº. Next, to quantitatively measure miRNA copy number, a titration curve
needs to be run using validated (Figs 2 and 3) synthetic oligonucleotides homologous to the
control miRNAs (Fig 1C). These data then can be applied to any clinical sample to accurately
measure copy number for any miRNA of interest (Fig 1D).
Titration of miRNA for ddPCR quantification
Reverse transcription is not a concerted reaction [
]. Therefore, to quantify the limit of
quantification, estimate non-specific positive droplets, and create a formula to convert
experimentally measured miRNA to predicted values, we performed a miRNA titration assay. This
assay assumes there will be a tangible loss within the steps to measure miRNA (Fig 1, panels A
to C), but the goal is to quantify units of miRNA with the least uncertainty.
We choose to measure the miRNA as copies/μL. The droplet digital PCR computes droplets
with florescence as positive and droplet without florescence as negative. A fraction negative is
calculated and Poisson's correction is applied to compute targets per ddPCR droplet volume.
We measured the microliter volume and its approximate 95% coverage interval of Evagreen
Supermix droplets as (0.783 ± 0.018) nL [
]. Dividing the number of targets per droplet by
the microliters per droplet and adjusting for dilution factor yields copies miRNA per
microliter (Fig 1D).
Experimental versus expected copy number are linearly related for all targets (Fig 5A±5C).
Experimental targets per well versus UV measured targets per well are strongly correlated (R2
> 0.99) when represented by the simple power curve y = axb, where ªaº is the scale constant,
ªbº is the scaling exponent, ªxº is a UV measurement, and ªyº is the ddPCR result. We
evaluated each calculated miRNA copies/μL for all dilutions in a series. The predicted miRNA copy
number is shown with a 95% coverage interval. Cel-miR-39 has multiple data points that fall
17 / 26
Limit of Quantification
(104 microRNA copies/μL)
Limit of Quantification (targets/droplet)
outside of this confidence interval, partially for cDNA kit ªAº and ªBº (Fig 5D±5F).
Nevertheless, the targets appear to segregate independently with random dispersion patterns [
which satisfies key assumptions necessary for using Poisson's distribution to model ddPCR
and further extrapolate data to report copy number. Therefore, we can use this titration data to
propose a model that allows us to calculate the limit of quantification (LOQ), correlate loss,
and accurately calculate miRNA copy number.
Each target, for every kit, has their own specific NTC and NEC values. We plot the raw
values (λ) and the normalized values (λ') on the same graph (Fig 6). Normalized values were
calculated by subtracting NTC and NEC positives before converting data to targets per droplet
(Fig 1D). Hence, by optimizing for lowest uncertainty and percent residual difference between
predicted absolute and normalized copies per droplet we derive a LOQ for each target and
each kit (Table 6). Additionally, we use the LOQ value and the corresponding scaling factor
ªaº and exponent ªbº to derive the predicted limiting miRNA copy number per microliter
master mix that might be detected reliably.
Application of principles for clinical quantification of miRNA
To test the quantitative miRNA measurement capabilities for clinical purposes, we took the
monocytic leukemia cell line THP-1 and differentiated them into M1-like macrophages using
phorbol-12-myristate-13-acetate and ultra-pure Lipopolysaccharide from E. coli. Cells were
flash frozen in liquid nitrogen and stored at -80ÊC prior to total RNA isolation. We added two
exogenous spike-in controls, cel-miR-238 and cel-miR-39. One spike-in control was added at
the beginning of total RNA isolation at a maximum concentration and one was added at the
end of the total RNA isolation at a minimum concentration (Fig 1A). Specifically, the synthetic
cel-miR-238 oligonucleotide was spiked-in at approximately 50 nmol/L concentration and
added after cell lysis and major debris clearing. The synthetic cel-miR-39 was spiked-in at
approximately 50 pmol/L concentration prior to freezing total isolated RNA in individual
aliquots. All spike-in concentrations were substantially above the LOD and LOQ, which
minimizes uncertainty (S2 Fig). Data using the chip-based automated electrophoresis system
showed a wide array of differentially-sized RNA components within our total RNA isolate (Fig
7A), with a RIN of 8.60 and total concentration of 522 ng/μL.
Using our normalization method and plotting for mean targets per droplet for each kit and
target, cel-miR-238 consistently has the highest droplet counts and cel-miR-39 consistently
has the lowest (Fig 7B). Endogenously expressed hsa-miR-238 and hsa-miR-155 both have
counts in between these spike-in values.
18 / 26
Fig 7. Quantitative miRNA measurements in THP-1 cells. Total RNA was extracted from macrophage
derived THP-1 cells and was (A) analyzed for integrity, purity, and concentration using the chip-based
automated electrophoresis system. (B) Total RNA was reverse transcribed by cDNA synthesis kit ªAº, ªBº, or
ªCº and specific miRNA targets were measured using ddPCR. (C) Each kit, ªAº (×), ªBº (+), andºCº (ӿ), was
used to make cDNA and total number of targets were counted via ddPCR. Average NTC and NEC were
similarly measured for each experiment. For all targets, cel-miR-238 (gray), cel-miR-39 (orange),
hsa-miR223 (brown), and hsa-miR-155 (red), fraction positive targets per droplet were calculated by subtracting NEC
and NTC positive and applying Poisson distribution. Predicted 104 miRNA copies/μL for cel-miR-238 (gray
square), cel-miR-39 (orange square), and hsa-miR-223 (brown square) was calculated using pre-existing
power curve values (Table 6). For each respective kit, a new power curve was developed using experimental
versus predicted cel-mir-238, cel-miR-39, and hsa-miR-223 concentration (Table 7). This new power curve
was applied to respective, endogenously measured, experimental hsa-miR-155 λ' to generate predicted
hsamiR-155 (red circle) copies/μL. The standard uncertainty is shown on the graph for hsa-miR-155 for each
respective point (dark red line). For visualization purposes, data is either graphed on one chart or separated
out based on cDNA synthesis kit. New power models are calculated only for individual cDNA synthesis kits
19 / 26
and both equation and correlation coefficient are interlayered. Graphs are single representations of repeated
biological trials. Microsoft Excel was used to calculate and graph data.
Spiking-in synthetic miRNA oligonucleotides at highest and lowest ranges and testing an
endogenous miRNA within that range is optimal for statistical modeling purposes.
Furthermore, a minimum of three measurements are necessary to create a curve; we choose to use two
spike-in controls and one endogenous marker [
]. We used the power function as depicted in
Fig 1D and developed in Fig 6 to calculate predicted miRNA copy number of cel-miR-238,
celmiR-39, and hsa-miR-223, given the measured mean targets per droplet from our
macrophage-derived THP-1 monocytes. We graphed the measured experimental targets per droplet
versus the predicted miRNA copy number for cel-miR-238, cel-miR-39, and hsa-miR-223 (Fig
7C) and calculated a new specific power curve (Table 7; Fig 1E). This new power curve can
now be applied to all other targets of interest, such as endogenously expressed hsa-miR-155.
Using the optimized annealing temperature, we measured endogenously expressed
hsamiR-155 in the THP-1 cells. Previously, we discovered that our synthetic hsa-miR-155 was
impure, containing non-specific contaminates that lead to ambiguity when measuring
concentration using a UV spectrophotometer (Fig 2 and Table 2). Therefore, we did not have a proper
synthetic miRNA oligonucleotide to titrate hsa-miR-155. However, applying our new specific
power curve for each kit (Fig 7C) we normalize experimental hsa-miR-155 concentrations to
obtain an accurate estimate of miRNA copy number. The experimentally derived targets per
droplet (λ') versus miRNA copies/μL master mix are plotted on each respected graph for
visualization purposes. Specific values and their relative uncertainties are shown in Table 7. The
resultant is an accurate hsa-miR-155 copy number that can be used to evaluate a patient's
normal physiological state and used to prescribe or modify a drug treatment plan.
Notably, the three kits used to analyze endogenous hsa-miR-155 have different values
(Table 7). Scientists at National Institute of Standards and Technology (NIST) have developed
the ªNIST Consensus Builderº (NICOB) [
] for summarizing replicate measurements from
different laboratories, measurement methods, or experimental methods. The analysis methods
included in NICOB use the uncertainty associated with these different results. We used the
data from Table 7 to estimate the predicted hsa-miR-155 copy numbers. We determined that
while the consensus estimate for hsa-miR-155 is 2.8 × 105 copies/μL, the associated standard
uncertainty is 6.5 × 104 copies/μL, with a 95% coverage interval of (1.8 to 4.3) × 105 copies/μL.
Given this large uncertainty, more measurements are needed to determine a useful consensus
value for the has-miR-155 copies/μL of master mix.
We can use these same principles to analyze cell-associated miRNA from patient peripheral
blood mononuclear cells (PBMCs). We separated out CD14+ monocytes and CD66b+CD16
+ neutrophils from whole blood using negative selection magnetic beads (S1 Methods). Purity
of CD14+ monocyte and CD66bCD16+ neutrophil populations were confirmed using flow
cytometry and hsa-miR-155 was quantified in each cell subset group. Total RNA was purified
from a population of each cell subset and reverse transcribed into cDNA using cDNA
Synthesis Kit ªB.º Four targets, cel-miR-238, cel-miR-39, hsa-miR-155, and hsa-miR-223 were
measured using ddPCR. Copy number of cel-miR-238, cel-miR-39, and hsa-miR-223 were
calculated using our power curve (Fig 1D) and the experimentally derived targets per droplet (λ')
versus miRNA copies/μL master mix are plotted on each respected graph for visualization
purposes. A sample-specific power curve was generated and the hsa-miR-155 experimentally
derived targets per droplet (λ') copy number was applied to calculate miRNA copies (S3 Fig).
We can normalize our predicted hsa-miR-155 copy number to the total number of cells
(cell counts per extraction can be found S1 Methods), keep as per microliter, or extrapolate to
mass concentration using the Avogadro constant ( 6.022 × 1023 copies/mol). Alternatively,
within the clinic, one could convert the measured miRNA copy numbers per unit of blood.
Using this method (Fig 1), an accurate miRNA copy number can be determined and
extrapolated to a unit of measure that is most aptly suited for diagnosticians. Similarly, any other
miRNAs can be accurately quantified using this new specific power curve and be applied in the
clinical for prognostic, diagnostic, or therapeutic purposes.
The use of miRNA for disease detection, prognosis, outcome, and therapy is a growing market
predicted to get much larger in the upcoming years [
]. However, use of miRNA in precision
medicine poses quite a few challenges. Some of these challenges include: (1) disease-associated
miRNA regulation has not been clearly demonstrated, meaning studies often require
genomewide screens prior to specific biomarker identification, (2) conventional measurement
techniques require normalization to endogenous controls characterized by stable targets found in
all tissue types at equal levels, (3) probe and primer optimization is inadequate for targets of 22
to 24 nucleotides, which is the size of mature miRNA, and (4) measurement techniques are
costly and low throughput, meaning rapid clinical testing still requires significant buy-in from
payers, doctors, and patients [
]. Some of these issues can be mitigated by better and more
reliable measurement techniques.
Here we show that there is a cost-, time- and reagent-efficient way to quantitatively measure
cell-associated miRNA with ddPCR using spike-in controls. Additionally, a proof of concept
was demonstrated to measure additional endogenous miRNA resulting in increased confidence
in the measurements of miRNA within a single sample. Most notably, these measurement
recommendations will for the first time allow for finite miRNA copy number or concentration
read-outs for a sample, independent of a disease-free or acute time point or condition.
We began by building on previous measurement concepts and studies to clarify and
improve miRNA measurement techniques in individual cells. Based on manufacturer
feedback, primers and probes are optimally designed by applying the Digital Minimum
Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) Guidelines [
however technologies used for miRNA quantification, including primer and probes necessary
for digital measurements and cDNA synthesis reagents, are proprietary and therefore severe
obstacles exist for using conventional design methods. Instead, when first measuring miRNA,
one must account for variation associated with reverse transcription, in either one-step or
two-step, and unusually small target sequences. Here we used synthetic oligonucleotides
homologous to known miRNA, either in C. elegans or H. sapiens. We discovered that one issue
with synthetic oligonucleotides is variation in production efficiency and purity. The
quantitative UV absorbance measurement of our hsa-miR-155 synthetic oligonucleotide was artificially
high, digital gel electrophoresis showed multiple bands, and digital capillary electrophoresis
demonstrated a broad band with no clear majority peak. When using the synthetic
hsa-miR155 oligonucleotide for preliminary titration experiments, the experimental values were
significantly diminished from the predicted values, especially in comparison to all other synthetic
21 / 26
oligonucleotides. Upon further research we noted that these skewed measurements could be
related to miRNA hsa-miR-155 containing a guanine quadruplex that makes it difficult to
synthesize artificially [
]. A second batch of the synthetic hsa-miR-155 was produced using RNase
free HPLC purification; initial evaluation of this new synthetic hsa-miR-155 oligonucleotide
using chip-based automated electrophoresis system shows a tight peak and single band, both
indicating a much cleaner product. We conclude that: (1) not all synthetic miRNA
oligonucleotides are suitable for measurement standards and (2) there is a need to check purity of synthetic
oligonucleotides using multiple modalities prior to proceeding with development of any assays.
Earlier publications focused on evaluating one-step versus two-step RNA quantification,
different reagents used within the ddPCR or qRT-PCR quantification, and the use of
endogenous controls versus spike-in controls [
4, 6, 13, 14, 36
]. Here, our sole interest is to determine
how to leverage the current products on the market to make them more accurate, unbiased,
and metrologically traceable [
] when quantitatively measuring biomarkers. We therefore
employed very basic optimization techniques such as evaluating PCR annealing temperature.
Table 5 shows the drastic number of non-specific amplification products that are observed
with each of the kits. Since each kit uses different technologies for miRNA reverse
transcription, one can hypothesize the possibility of different optimized annealing temperatures for
each associated reverse transcription kit. It is therefore very important to validate annealing
temperature with a few different template types prior to solidifying the experimental design.
It is very difficult to accurately measure the exact conversation rate associated with the
reverse transcription in any cDNA synthesis reaction. All four kits demonstrated less than
ideal conversion rate between miRNA and their end-step as measured via ddPCR. We argue
that one should be able to formulate a model that allows accurate quantification of any
miRNA in a sample via a series of normalization steps by simultaneously measuring at least
three known and validated miRNAs in the same sample for the same reaction. Here we used
two spike-in controls homologous to C. elegans. And while we tested some other C. elegans
miRNA homologs, we found them to have more NTC and NEC positive droplets, significantly
different annealing temperatures, or other sources of measurement difficulties. The use of
non-cognate spike-in controls was also investigated by NIST scientists and we believe this
option shows significant potential given the propensity for spike-in controls homologous to
other organism to still hybridize to regions of human mRNA (S2 Fig). And while we accounted
for this measurement bias when calculating uncertainty, these off-target effects still limit our
assay range, specificity, and sensitivity. The only major obstacle with using non-cognate
spikein controls is the lack of readily available reagents for all the cDNA synthesis kits we tested.
This option seems viable and favorable for future applications if it can be made cost-effective.
Spike-in controls also suffer from high between-sample variability [
], we therefore
recommend each sample be measured in tandem with the spike-in controls and vetted
endogenous markers (Fig 6C). Furthermore, the strength of the model is improved by increasing the
number of spike-in controls to create ªpoolsº of targets at a larger dynamic range [
are limitations to increasing the diversity and dynamic range of spike-in controls, including
economic constraints and amount of sample material. As we have shown here, by performing
validated miRNA titration with just three targets, one can apply the power curve to create a
new model that is used to compute exact miRNA measurements for subsequent measurements
in that sample. It is important to note however, that with multiple freeze-thaws or dilutions,
miRNA measurements can change. Thus, it is our additional recommendation to run a vetted
endogenous marker and spike-in control for every sample and every experiment.
We originally choose to test two endogenous miRNAs to standardize and measure along
with our spike-in controls. There is a potential for spike-in controls to suffer from different
measurement challenges then endogenous measurements and therefore to have confidence in
22 / 26
your model, we suggest that at least one endogenous marker be used. However, the same
endogenous miRNAs should not be applied as standardized controls for all sample types and
measurement since there is no proven consistently expressed miRNA for all samples [
therefore recommend selecting endogenous miRNAs that are ubiquitously expressed at higher
levels in the sample of interest to titrate and validate as ªendogenous markersº for one's
specific assay. These markers should be calibrated similarly to the spike-in controls by creating
titration curves that evaluate their random and independent dispersion among droplets,
measurement linearity, and limit of quantification. In our initial screening and validation, we
discovered that the synthetic oligonucleotide to hsa-miR-155 was inadequate for synthetic
miRNA titration relative endogenous measurements. We therefore proceeded with
hsa-miR223 as our endogenous marker.
Synthetic miRNA oligonucleotide titration quantification and model fitting prior to
running an assay on patients is important to understand limitations and normalizations for an
assay. For instance, all the cDNA kits tested have characteristic limits of quantification (LOQ)
Ðthe minimum number of targets per droplet that can be confidently measured. Knowing the
LOQ can help explain variability in experimental measurements if one dilutes the spike-in
control or test sample too much (Fig 1A). Additionally, miRNA titration curves are needed to
predict values in the test sample (Fig 7C, ªPredictedº). This predicted value will ultimately lead
to the model that applies to all unknown targets (Fig 1D). After this initial assay validation,
one can rely on limited sample wells to test NTC, NEC, spike-in values, and endogenous
markers to confirm the accuracy of an individual assay. If the individual sample assay is working
properly, then plotting experimental versus predicted copy number with the endogenous
marker will result in a highly correlated power model that can confidently be applied to all
unknown markers on that plate (Fig 1E).
In addition to increasing the number of spike-in controls or endogenous markers, the
predicted value of unknowns will be strengthened by running more biological and technical
replicates. The next step for this project is to apply these quantitative measurement techniques to
many normal human samples to determine normal physiological levels of miRNA of interest.
Consensus and feasibility need to be addressed for each miRNA value to reduce uncertainty in
the measurements and determine a clinically acceptable value.
In the current market of uncertainty around validity, safety, and effectiveness of bioassays
used to benchmark disease diagnosis, prognosis, and treatment options, it is important to
consider necessary improvements in robustness and accuracy of the measurement techniques.
miRNA has a promising future in all areas of the biotechnology field, but it is important to
first outline opportunities to standardize the tests. Here we propose a procedure that can be
used to screen a single patient for abnormal miRNA levels. Leveraging digital PCR and
whatever cDNA synthesis technology is preferred by the individual laboratory, these techniques
can be applied rapidly to come up with accurate concentrations or copy numbers of miRNA in
a high throughput manner.
S1 Fig. Titration steps for calibrating synthetic miRNA oligonucleotides. Example of
dilutions performed to titrate synthetic miRNA oligonucleotides using a stock concentration of
100 μmol/L. Diagram shows microliter volume transferred between microcentrifuge tubes,
dilutions, and corresponding concentration following dilution. Numbers (1)±(5) indicate
concentrations that are small enough to detect on our droplet digital PCR instrument. Dilutions
and concentrations are nominally defined.
23 / 26
S2 Fig. Baseline cell-associated miRNA expression compared to spike-in values. Total RNA
from additional THP-1 cells was isolated without the addition of synthetic miRNA spike-in, as
described in the general methods section. Mean experimental lambda for cel-miR-238 (brown),
cel-miR-39 (gray), hsa-miR-155 (white), and hsa-miR-223 (blue) is plotted for THP-1 cells
without spiked-in synthetic miRNA (solid bars) and with spiked-in synthetic miRNA (checkered
bars). Data from THP-1 cells with spiked-in synthetic miRNA is the same as plotted in Fig 7.
S3 Fig. Quantification of cell-associated hsa-miR-155 in peripheral blood cell subsets.
Total RNA was extracted from primary CD14+ monocytes and CD66b+CD16+ neutrophils.
RNA was quantified and tested for integrity and then used to make cDNA using kit ªB.º
Targets of cel-miR-238, cel-miR-39, hsa-miR-223, and hsa-miR-155 per droplet were measured
using droplet digital PCR. Cel-miR-238, cel-miR-39, and hsa-miR-223 targets per droplet were
converted to copies per microliter using our titration curve described in Fig 6 and a power
curve was generated for this sample. The sample-specific power curve was used to convert
hsamiR-155 targets per droplet into copies per microliter. Predicted has-miR-155 copy number
with associated uncertainty is shown for each cell subset group.
S1 Methods. Supplemental methods for supplemental figures.
S1 Appendix. Complete raw data files from this study. The complete raw data set can be
accessed by clicking https://doi.org/10.18434/M32Q1V. A file titled ªddPCR Raw Data_Stein
et al PLOSOne 2017.xlsxº will download.
The authors would like to thank Jamie Almeida, Hua-Jun He, Peter Vallone, Jennifer
McDaniel, Sarah Munro, and Scott Pine at NIST for valuable discussions and suggestions; special
thanks to Margaret C. Kline and Kevin Kiesler at NIST for helpful discussions and aid in
interpreting data. The authors also extend their gratitude towards Muneesh Tewari at University
of Michigan and John Chevillet at RainDance Technologies for insight into their work on
miRNA quantification. The authors want to thank Heba Degheidy at Food and Drug
Administration for her partnership in our quantitative flow cytometry projects. Lastly, authors are
grateful for the advice received from an anonymous reviewer that helped us further improve
our experimental design.
Data curation: Erica V. Stein.
Formal analysis: Erica V. Stein, David L. Duewer, Natalia Farkas.
Investigation: Erica V. Stein, Natalia Farkas, Erica L. Romsos.
Methodology: Erica V. Stein, David L. Duewer, Natalia Farkas, Erica L. Romsos, Lili Wang,
Kenneth D. Cole.
Project administration: Erica V. Stein.
Resources: Lili Wang, Kenneth D. Cole.
24 / 26
Supervision: Lili Wang, Kenneth D. Cole.
Validation: Erica V. Stein.
Visualization: David L. Duewer.
Writing ± original draft: Erica V. Stein.
Writing ± review & editing: Erica V. Stein, David L. Duewer, Natalia Farkas, Erica L. Romsos,
Lili Wang, Kenneth D. Cole.
25 / 26
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