extending mirqc’s dynamic range: amplifying the view of limiting rna samples - download the...

Scientific article

Abstract: The original microRNA quality control (miRQC) study provided an in-depth analysis of

commercially available microRNA (miRNA) quantification platforms. Specifically, twelve different

microarray, real-time PCR and small RNA sequencing platforms were assessed for reproducibility,

sensitivity, accuracy, specificity and concordance of differential expression using a variety of

sample types. Overall, each platform exhibited specific strengths and weaknesses, leading to the

final suggestion that a platform should be chosen on the basis of the experimental setting and the

specific research questions. With this suggestion in mind, and the fact that liquid miRNA biopsies

are an area of intense interest, we sought to expand the original miRQC study. For our “miRQC

extension,” we benchmarked the QIAGEN miScript® PCR System with and without preamplification,

and included a specific focus on routinely used biofluids. Concurrently, we benchmarked the

miScript PCR System against another SYBR® Green miRNA detection platform. Overall, QIAGEN

miScript demonstrated strong reproducibility and accuracy as well as superior detection rate

and sensitivity in biofluids. Collectively, QIAGEN miScript provides the leading solution for novel

miRNA discoveries.

Brian Dugan and Jonathan M. Shaffer

QIAGEN Sciences Inc., Frederick, MD, USA

Extending miRQC’s dynamic range: amplifying the view of limiting RNA samples

Contents

Introduction __________________________________________ 1

Materials and methods ________________________________ 2

Samples ________________________________________ 2

miRNA quantification _____________________________ 2

Data pre-processing ______________________________ 3

Results and discussion _________________________________ 3

miScript PCR System: reproducibility and accuracy ____ 3

Comparison of QIAGEN and Exiqon platforms _______ 4

Reproducibility, accuracy and titration response ______ 5

Detection rate and sensitivity _______________________ 6

miRNA detection in biofluids _______________________ 7

Detection of miRNA from equal sample inputs ________ 7

Conclusions __________________________________________ 9

References __________________________________________ 12

Introduction

miRNAs are approximately 21 nucleotide small noncoding

RNAs that play a prominent role in virtually all normal and

disease biological processes. Originally discovered only 20 years

ago, miRNA changes have been correlated with gene expression

changes in development, differentiation, signal transduction,

infection, aging and disease. Specifically in cancer, miRNA

have been associated with cell proliferation, resistance to

apoptosis, invasiveness and differentiation. Interestingly, miRNAs

exhibit stable expression in circulation and continually growing

evidence associates circulating miRNAs with normal and disease

biology. As a result, miRNAs are currently being explored

as minimally invasive biomarkers for disease detection and

monitoring using serum, plasma and cerebrospinal fluid (CSF),

as well as virtually all biofluids (1).

For circulating miRNA expression analysis, where miRNAs are

often expressed at very low levels and cDNAs are analyzed

www.qiagen.com2 QIAGEN

for the quantification of miRNA including reproducibility,

sensitivity, accuracy, specificity and concordance of differential

expression. In the study, 12 different microarray, real-time PCR

and small RNA sequencing platforms were benchmarked using

20 standardized positive and negative control samples,

including human universal reference RNA, human brain RNA and

titrations thereof, human serum samples and synthetic spikes from

miRNA family members with varying homology. Overall, each

platform exhibited various strengths. Specifically, QIAGEN’s

miScript PCR System showed high levels of reproducibility,

accuracy and sensitivity, which are key features important to

every miRNA quantification study. However, the conclusions

were drawn using only 372 of miScript’s industry leading 2400

primer assays, and miScript’s preamplification platform was not

benchmarked. As a result, Biogazelle and QIAGEN teamed

together to extend the miRQC study to the entire portfolio of

miScript human assays, benchmark the miScript preamplification

workflow and specifically focus on the quantification of miRNA

from biofluids. Concurrently, the QIAGEN miScript PCR System

was compared with the other main SYBR Green platform; the

Exiqon miRCURY LNA™ microRNA qPCR System.

Materials and methods

Samples

Four reference total RNA samples from the original miRQC

study were tested, including miRQC A (Agilent Technologies:

Universal Human miRNA Reference RNA), miRQC B (Agilent

Technologies: human brain RNA), miRQC C (75% miRQC A +

25% miRQC B) and miRQC D (25% miRQC A + 75% miRQC B).

Total RNA was isolated from serum (BioreclamationIVT) and CSF

(generously provided by Professor James Dear, The University

of Edinburgh) samples using the miRNeasy Serum/Plasma Kit

(QIAGEN).

miRNA quantification

All experiments were performed by Biogazelle (Ghent, Belgium).

Three platforms were used for miRNA quantification. First, the

QIAGEN miScript PCR System without preamplification was

used in conjunction with the miScript miRBase Profiler (miRNome)

Arrays (2402 total assays). Second, the miScript PCR System

with preamplification was used in conjunction with the miScript

miRBase Profiler miRNome Arrays. Third, the Exiqon miRCURY

across hundreds of qPCR wells, special considerations need to

be taken, paramount being that miRNA molecules must be at

a high enough copy per qPCR reaction to reduce the effect of

sampling ambiguity (i.e., technical variability). According to

Ståhlberg and Kubista, the minimal number of target molecules

per qPCR well must be 35 to reduce sampling ambiguity (2).

Consider the following two scenarios for quantification of

a miRNA present at 100 copies. In scenario 1, which does

not include preamplification, the 100-copy miRNA is reverse-

transcribed, and the cDNA is applied to a plate of 384 miRNA

assays. This results in the 100-copy miRNA being present

at 0.26 copies per qPCR well, which is markedly below the

recommended minimum copy number for efficient, reproducible

quantification. In scenario 2, which includes 12 cycles of

intermediate amplification after cDNA synthesis, the 100-copy

target would be present at more than 500 copies per qPCR

well. This is well within the recommended range for efficient,

reproducible quantification, and enables efficient, reproducible

quantification. If preamplification is not included in biofluid

analyses, particularly for CSF, expression will only be observed

for the most robustly expressed miRNAs, leaving you with a

truncated view of the full expression profile of your sample;

ultimately, you will be missing data.

The QIAGEN miScript PCR System provides various platforms

and optimized workflows to quantify any miRNA from any

sample type, whether it is derived from cells, fresh or frozen

tissue, FFPE tissue or biofluids. Specifically for low RNA content

samples, such as biofluids, preamplification options are

available that enable robust quantification from the lowest RNA

amounts, including cell-free nucleic acid, miRNA encapsulated

in exosomes and even single cells. Each of the miScript platforms

can be paired with bench-verified individual miScript Primer

Assays or pre-formatted miScript miRNA PCR Arrays to enable

quantification of over 2400 annotated human miRNAs. In

addition to human, bench-verified assays are available for

common model organisms, including but not limited to mouse,

rat, dog and cow. Additionally, custom assays can be derived

for any miRNA of interest, including novel miRNAs identified

during small RNA sequencing. The miScript System offers the

most comprehensive sample, species and assay solutions for

quantitation studies.

The original microRNA quality control (miRQC) study represented

the first comprehensive assessment of the factors most important

Scientific article www.qiagen.com 3

LNA microRNA qPCR System was used in conjunction with

the Exiqon microRNA Ready-to-Use PCR, Human panel I+II,

V4.M. The QIAGEN miScript PCR System includes up to 2402

miRNA assays across seven 384-well PCR arrays, while the

Exiqon system consists of up to 752 assay targets across two

384-well plates. Both platforms utilize SYBR Green as well as

a universal reverse-transcription reaction. All experiments were

done in technical duplicate and performed according to the

manufacturer’s specific instructions unless indicated otherwise.

Data pre-processing

Raw CT: values were processed as described in the miRQC

study. Briefly, a detection cutoff was defined based on the

fraction of single positives (i.e., miRNAs only expressed in

one of two replicates) of replicate expression profiles. The CT:

cutoff was chosen as such that it eliminates 95% of the single

positives. For the QIAGEN platforms, two different CT: cutoffs

were defined, one for the workflow without pre-amplification

(QNP) and one for the workflow with pre-amplification (QP).

The CT: cutoff for the workflow without pre-amplification was

based on replicate CT: values from all four miRQC samples. This

resulted in a CT: cutoff of 31.86 cycles. A similar CT: cutoff was

obtained when considering replicate serum samples profiled

without pre-amplification (CT = 31.35) suggesting that the

algorithm applied to determine the CT: cutoff is robust and

sample-independent. For the workflow with pre-amplification,

replicate serum samples were considered, resulting in a CT: cutoff

of 30.05. For the Exiqon platform, a CT: cutoff of 34.92 was

obtained when considering replicate miRQC samples. These

cutoffs were applied throughout the entire data analysis

workflow. Data were normalized using the global mean as

described in the original miRQC study. For details about the

performance parameters and how they were calculated, refer

to the original miRQC study (3).

Results and discussion

The original miRQC study tested multiple methodologies

for miRNA quantification, including qPCR and small RNA

sequencing. Each platform exhibited specific strengths and

weaknesses, leading to the final suggestion that a platform

should be chosen on the basis of the experimental setting and

the specific research questions. With this suggestion in mind,

and the fact that liquid miRNA biopsies are an area of intense

interest, we sought to expand the original miRQC study. For our

“miRQC extension,” we benchmarked the QIAGEN miScript

PCR System and the QIAGEN miScript PCR System with

preamplification. In addition, we incorporated routine biofluids

such as serum and CSF in the testing. Concurrently, we

benchmarked the miScript PCR System against the Exiqon

miRCURY LNA microRNA qPCR System. Overall, this miRQC

extension was intended to measure key metrics paramount for

successful miRNA quantification studies of the most relevant

sample types including reproducibility, accuracy, detection rate

and sensitivity in biofluids. The size of the QIAGEN miRNome

and the Exiqon miRNome vary substantially, with QIAGEN

offering over 2400 assays in preformatted arrays, while Exiqon

only offers 752. With over 1700 more miRNA targets, there is

greater opportunity for novel discovery outside the original 750

miRNA set. Due to Exiqon offering only 752 assays, a common

set of 746 miRNAs was selected for the direct comparison

experiments.

miScript PCR System: reproducibility and accuracy

A high-performance miRNA quantification platform should

exhibit strong reproducibility and accuracy. In practice, this

enables high confidence that “observed results” are in fact “real

results.” To determine reproducibility, miRQC sample C (75%

miRQC A + 25% miRQC B) and miRQC D (25% miRQC A + 75%

miRQC B) were quantified and analyzed for the replicate

expression difference (log2) using the miScript PCR System

without preamplification and the full 2402 assay miRNome.

Figure 1, panel A shows a replicate expression correlation plot.

Both replicate 1 and replicate 2 show high levels of concordance

and extremely low variability sample to sample. Panel B depicts

the cumulative distribution of replicate expression differences.

This is measuring the deviation in the observed replicate CT values

from replicate 1 to replicate 2. The goal is to limit expression

differences between replicates, and the lower the value the

better. In the plot, the area left of the curve is measured and

correlated to an average fold change. Here the observed ALC

between both replicates was 0.346, or an average of only

1.27-fold replicate expression difference. Collectively, this

figure demonstrates that the miScript PCR System, across the

entire portfolio of miScript miRNA assays, enables highly

reproducible results.


Accuracy of the miScript PCR System without preamplification

was evaluated using miRNAs from the total 2402 assay list that

were only expressed in either miRQC A or miRQC B (as these

should theoretically have a 3-fold difference in expression

between miRQC C and D). Figure 2 shows the fold change

Replicate 222

–6

–6 22Replicate 1 (log2)

Replicate expression difference (log2)

Cumulative fraction (%)1.0

0.8

0.6

0.4

0.2

0

0 0.80.60.40.2 1.0

ALC = 0.346

Figure 1. Assessment of miScript PCR System reproducibility. A Correlation of miRNA expression between miRQC samples C and D and their replicates. B ALC-value (Area Left of the Curve) of 0.346 (corresponding to an average 1.27-fold replicate expression difference) was obtained.

Figure 2. Assessment of miScript PCR System accuracy. Fold-change (miRQC C/D or miRQC D/C) are shown for miRNAs only expressed in miRQC A or miRQC B. The dashed line indicates the theoretical 3-fold fold change, and the black line indicates the median fold-change.

MAQC C/D (D/C)8

6

4

2

0quartile 1-4 quartile 2-4

1st quartileExpression

2nd quartile 3rd quartile 4th quartile

between C and D when considering all miRQC A/B-specific

miRNAs (quartile 1–4) or only those miRQC A/B-specific miRNAs

with expression values in the upper 3 quartiles (quartile 2–4).

The theoretical fold change (3-fold) is indicated by a dashed

line, and the median fold-change is indicated by the black

line. Whether quartile 1–4 or only quartile 2–4 was assessed,

the median fold difference was close to the theoretical 3-fold

change demonstrating high accuracy of the miScript PCR System.

Comparison of QIAGEN and Exiqon platforms

Accurately quantifying miRNA is a challenge, but there are a

number of key aspects that differentiate SYBR Green and probe-

based systems. First, both the QIAGEN and Exiqon systems

employ universal reverse-transcription. By using universal reverse-

transcription, all miRNA can be converted to cDNA. In addition,

as referenced in the miRQC study, probe-based qPCR, Taqman,

showed the lowest reproducibility when compared to SYBR Green

miRNA qPCR. In addition, SYBR green based technologies showed

superior reproducible detection of miRNA’s from miRQC samples.

To benchmark the QIAGEN miScript PCR System, another SYBR

Green platform, the Exiqon miRCURY LNA qPCR System was

chosen. Both the QIAGEN and Exiqon systems are universal

reverse-transcription platforms and use SYBR Green. The goal

was to determine specific differences in performance of the

technologies. QIAGEN currently offers more than 3x the number

of assays for miRNA profiling, so only the overlapping targets

were compared for this discussion.

In order to directly compare the platforms, a common set of

748 assays was selected, and reproducibility, accuracy and

titration response were directly compared. Further, the ability to

reproducibly detect miRNAs from serum and CSF were also

compared. Finally, since the standard miScript PCR System

(without preamplification) and the Exiqon platform suggest

different starting amounts of RNA, each platform was run with

each suggested starting amount, 20 ng per 384-well plate

(recommended by Exiqon for quantifiable RNA from cells and

tissues) and 500 ng per 384-well plate (recommended by

QIAGEN for quantifiable RNA from cells and tissues). With these

varying input recommendations, there is supposition that the

starting input directly affects the ability to detect miRNA in samples.

With this last test, we are testing sensitivity and debunking

the theory of whether or not one platform offers improved

sensitivity versus the other when preamplification is not included.


Reproducibility, accuracy and titration response

In Figure 3, the reproducibility of the QIAGEN platform without

preamplification is compared to the Exiqon platform using

miRQC samples A–B. Panels 3A and 3B show the replicate

correlation plots for miScript and Exiqon, respectively. For each

platform, replicates 1 and 2 show high levels of concordance

and extremely low variability sample to sample. Panels 3C and

3D depict the cumulative distribution of replicate expression

differences, and measures the deviation in the observed

replicate CT values from replicate 1 to replicate 2. In the plot,

the area left of the curve is measured and correlated to an

average fold change. Here the observed ALC between both

replicates was very good for both platforms (< 0.4) with

miScript exhibiting a slight edge in reproducibility.

Figure 3. Assessment of miScript PCR System reproducibility. Correlation of miRNA expression between miRQC samples A-D for the QIAGEN A and Exiqon B platforms. C and D ALC-values, which are measurements for reproducibility, were determined for both the QIAGEN and Exiqon platforms.

Replicate 2

QIAGEN

22

–6


Replicate 2

Exiqon

12

–8


Replicate expression difference (log2)

Cumulative fraction (%)1.0

0.8

0.6

0.4

0.2

0

0 0.80.60.40.2 1.0

ALC0 0.80.60.40.2 1.0

Figure 4. Assessment of QIAGEN and Exiqon platform accuracy using a common set of assays. Fold-change (miRQC C/D or miRQC D/C) are shown for miRNAs only expressed in miRQC A or miRQC B.The dashed line indicates the theoretical 3-fold fold change, and the black line indicates the median fold-change. In addition, the media deviation from the expected C/D or D/C ratio is shown.

MAQC C/D (D/C)8

6

4

2

0quartile 1-4

QIAGEN Exiqon

median deviation1.1 1.41.31.2 1.5

MAQC C/D (D/C)8

6

4

2

0quartile 2-4

QIAGEN Exiqon

median deviation1.1 1.41.31.2 1.5

1st quartileExpression

2nd quartile 3rd quartile 4th quartile


Accuracy of the QIAGEN and Exiqon platforms were evaluated

using miRNAs that were only expressed in either miRQC A or

miRQCB (as these should theoretically have a 3-fold difference

in expression between miRQC C and D). Figure 4 illustrates the

fold change between C and D when considering all miRQC

A/B-specific miRNAs (quartile 1–4) or only those miRQC A/B-

specific miRNAs with expression values in the upper 3 quartiles

(quartile 2–4). The theoretical fold change (3-fold) is indicated

by a dashed line, and the median fold-change is indicated by

the black line. Whether quartile 1–4 or only quartile 2–4 was

assessed, the median fold difference was close to the theoretical

3-fold change demonstrating comparable accuracy of both

QIAGEN and Exiqon platforms.

Titration response is the ability of a miRNA quantification

platform to call results “correctly.” Figure 5 summarizes the results

of titration response testing for both the QIAGEN and the Exiqon

platforms. Assessments of titration response rely on expression

patterns of miRNAs in all four miRQC samples A–D, and the

theory is tied to the ratio of the miRQC C (75% A + 25% B) and

miRQC D (25% A + 75% B) samples. For example, if “miRNA 1”

has greater expression in miRQC A than miRQC B, then

AmiRNA 1 > CmiRNA 1 > DmiRNA 1 > BmiRNA 1. Titration

response is the ability to detect the samples in the correct order,

and the fraction of “titrating miRNAs” can be determined.

Panels 5A and 5B summarize the fraction of titrating miRNAs

of the QIAGEN and Exiqon platforms, respectively, across bins

of increasing expression difference between samples miRQC A

and miRQC B (x-axis). The titration response was then plotted

as a function of the cumulative bin size (Panel 5C), and the area

under the curve (AUC: Panel 5D) was determined. An AUC = 1

would indicate that all miRNAs are titrating. Both QIAGEN

and Exiqon demonstrate high titration responses with both have

AUC values close to 0.8.

Detection rate and sensitivity

In order to discover novel miRNA signatures from low RNA

content samples, such as biofluids, a platform should have

high detection sensitivity. Detection rate and sensitivity can

be assessed by measuring the number of double positives as

well as single positives during expression profiling of replicate

samples. The higher the number of double positives and the

lower the number of single positives results in a higher detection

rate sensitivity. For this analysis, miRQC samples A–D were

included. In Figure 6, miScript displayed a substantially higher

number of double positives (50%: 600 to 400) and a lower

percentage of single positives (6% vs 10%) when compared to

Exiqon. Together, these results demonstrate a superior detection

rate and enhanced sensitivity for QIAGEN platform compared

to the Exiqon platform.

Figure 5. Assessment of QIAGEN and Exiqon titration response. For the A QIAGEN and B Exiqon platforms, the fraction of titrating miRNAs is plotted across bins of increasing expression difference between miRQCA and miRQCB. C The titration response for the miRNAs is plotted. D The AUC values, which reflect titration response, demonstrate the titration response is virtually identical for both platforms (QIAGENt: ~0.788 & Exiqon: ~0.792)

0.75 0.76 0.77 0.78 0.79 0.80AUC

QIAGENFraction titrading miRNAs 1

0

–3 30Expression difference (MAQCA-MAQCB)

Expression difference (MAQCA-MAQCB)

ExiqonFraction titrading miRNAs 1

0

–3 30

FC ranked miRNAs as fraction of total

Fraction titrading miRNAs 1

0

0 10

0.75 0.76 0.77 0.78 0.79 0.80AUC

QIAGENFraction titrading miRNAs 1

0

–3 30Expression difference (MAQCA-MAQCB)

Expression difference (MAQCA-MAQCB)

ExiqonFraction titrading miRNAs 1

0

–3 30

FC ranked miRNAs as fraction of total

Fraction titrading miRNAs 1

0

0 10


miRNA detection in biofluids

Thus far, the head-to-head analyses suggest a competitive

advantage for QIAGEN with respect to detection rate and

sensitivity, both of which are critical for biofluid detection.

However, these tests were performed on non-limiting amounts

of RNA, and may not reflect the realities of detection from low

RNA content samples, such as serum and CSF. This is becoming

increasingly important, as many applications are currently

focused on the ability to detect biomarkers, including miRNA,

through the use of low sample input or less invasive techniques,

which is driving an evolution in research testing. One of these

approaches is liquid biopsy, or the ability to detect analytes

through blood-based testing. To test these real world type

samples, both platforms were compared in their ability to detect

the conserved set of miRNA targets across platforms.

For this experiment, the ability to reproducibly detect miRNA

from two CSF samples as well as two serum samples was

compared (Figure 7). In a comparison of two CSF samples, CSF

A and CSF B there was a significant difference in the number

of miRNA reproducibly detected when comparing platforms. In

CSF A, for example, Exiqon detected 36 miRNA. From the

same sample, which was isolated using the same technique, the

QIAGEN miScript system was able to detect 150 miRNA –

almost 5x greater miRNA than Exiqon. The same was true for

CSF B sample as Exiqon detected 24 miRNA while QIAGEN

again detected 5x the amount of miRNA at 127. Serum samples

were tested under similar conditions, an A and B replicate were

tested across both platforms. Here again, the detection sensitivity

was measured using the number of like miRNA that were able

to be detected from the same samples. QIAGEN miScript

detected miRNA at 2x greater sensitivity, in Serum A sample

247 miRNA were detected for the miScript system while 90

were detected using Exiqon miRCURY. Serum B sample yielded

a similar result, as QIAGEN miScript detected 287 compared

to MiRCURY’s 98.

While many aspects of both the QIAGEN and Exiqon per-

formance were comparable for reference samples, such as

reproducibility, accuracy and titration response, there was a

clear difference in limited sample performance. Detection of

nucleic acids in biofluids can be highly variable. As a result,

sensitivity in these fluids is a key to detecting and quantifying

these miRNA.

Detection of miRNA from equal sample inputs

Sample input amount/concentration may be a key factor in

detection sensitivity. Theoretically, the lower the minimum input

level, the assay may be assumed to exhibit greater sensitivity.

As sample input concentrations decrease, the number of available

copies decreases. This holds true in comparing the difference

between a 500 ng input and a 20 ng input amount.

This is an important feature in the comparison of miRNA

quantification systems, as lower sample input levels can be

Figure 7. Assessment of QIAGEN and Exiqon detection from common biofluids. For CSF samples A and Serum samples B, the number of reproducibly detected miRNAs were determined for replicate samples.

Figure 6. Assessment of QIAGEN and Exiqon detection rate and sensitivity. Across the replicates of all four miRQC samples (A-D), the number of double positives A and percentage of single positives B was determined.

Reproducibly detected miRNAs

CSF

Serum

150125100

755025

0CSF A CSF B CSF A CSF B

QIAGEN Exiqon

Reproducibly detected miRNAs 300250200150100

500

Serum A Serum B Serum A Serum B

QIAGEN Exiqon

0 200 400 600 800

Single positives (%)

Double positives (%)

0 2 4 6 8 10 12 14 16


perceived to offer greater sensitivity when compared to kits with

higher recommended levels. To test this theory, QIAGEN again

employed Biogazelle to compare the detection of a like set of

miRNA, 750 targets, with two distinct sample input levels. Both

platforms were tested across the miRQC A reference samples.

This time each platform was tested at the QIAGEN protocol

input, 500 ng, as well as the Exiqon protocol input, 20 ng.

Both arrays were then performed with qPCR and analysis being

conducted by Biogazelle. By looking at both sets of array

performance, QIAGEN shows greater detection sensitivity for

the reference sample at both input levels (Figure 8). For 500 ng

of input, QIAGEN detects roughly 10% more miRNA 555 to

517. In demonstration of the flexibility of the miScript platform,

at the Exiqon recommended levels of 20 ng, the miScript system

demonstrated a 25% greater detection, 424 to 344 observed

miRNA.

While the performance of both systems shows that increased

sample input will yield increased miRNA detection, the question

remains – does the lower input amount lead to greater sensitivity?

By comparing the recommended input levels of both platforms

against each other, again miScript considerably out performs

Exiqon MiRCURY. When looking at how many miRNA are

detected from the reference sample, miScript shows an almost

2 to 1 advantage.

Number of miRNA 600500400300200100

0QIAGEN InputLevel (500 ng)

Exiqon InputLevel (20 ng)

QIAGEN Exiqon

Number of miRNA 600500400300200100

0miRNA detected by both recommended protocols

QIAGEN Exiqon

Figure 8. Assessment of miRNA detection from equal sample input. A The number of miRNA detected for both the recommended QIAGEN RNA Input (500 ng per 384-well plate) and Exiqon RNA Input (20 ng per 384-well plate) were determined. B Number of miRNAs detected for the QIAGEN and Exiqon platforms when their respective RNA amount input requirement is followed.


Conclusion

The purpose of this study was to extend the miRQC study of

the QIAGEN miScript PCR System, including a comparison to

a similar SYBR Green platform. In doing this, both platforms’

ability to reproducibly detect and quantify miRNA was

benchmarked on multiple sample types, at multiple input levels

and compared across a number of key performance metrics.

The data demonstrates that the QIAGEN platform has superior

performance, particular when compared to the Exiqon platform

during detection rate, sensitivity and biofluid detection, with

respect to reproducibility, accuracy, detection of miRNA in

body fluids and detection sensitivity at miRNA detected per

nanogram of input.

Limiting samples including the liquid biopsy samples are the

focus of many studies looking to identify potential biomarkers

or utilize miRNA as possible diagnostic or prognostic markers.

By looking at serum and CSF, these limiting inputs were tested to

demonstrate real world application performance. Here again,

QIAGEN miScript showed superior detection sensitivity in CSF

where 5x miRNA were detected on average versus MiRCURY

as well in serum, where miScript detected 3x the number of

miRNA targets.

Finally, discussions of sensitivity may often include the input

levels into quantification experiments. By comparing two

different sample input levels, 500 ng and 20 ng, both platforms’

performance were assessed. Here again, miScript showed

greater sensitivity at both the 500 ng and 20 ng levels.

QIAGEN miScript miRNA PCR System offers superior sensitivity,

reproducibility and accuracy in miRNA quantification from

samples as low as a single cell to any tissue type. In addition,

miScript offers the most comprehensive target list with the

largest miRNome, most species (animals and plants) and the

largest collection of cataloged pathway- and disease-focused

PCR arrays available. QIAGEN miScript is the most flexible,

most accurate and most sensitive miRNA quantification platform

available today.

For up-to-date licensing information and product-specific disclaimers, see the respective QIAGEN kit handbook or user

manual. QIAGEN kit handbooks and user manuals are available at www.qiagen.com or can be requested from QIAGEN

Technical Services or your local distributor.

For more information on QIAGEN’s miRNA portfolio, visit www.qiagen.com/miScript.

Trademarks: QIAGEN®, Sample to Insight®, miScript® (QIAGEN Group), SYBR® (Life Technologies Corporation), Exiqon®, miRCURY®, LNA™ (Exiqon).

© 2016 QIAGEN, all rights reserved. PROM-9221-001

Ordering www.qiagen.com/contact Technical Support support.qiagen.com Website www.qiagen.com

Ordering Information

Product Contents Cat. no.

miRNeasy Serum Plasma Isolate total RNA from serum, plasma and Biofluids 217184

miRNeasy Mini Kit Isolate high quality total RNA from animal tissues and cells 217004

miScript miRNome PCR Array Profile all available miRNA from human, mouse or rat – V16 331222

miScript miRNA miRBase Profiler Profile all the most up to date human miRNome, 2402 Targets Available in 384-well format

331223

miScript PreAMP Primer Mix Reagents and specific assays for amplification of miRNA targets for low sample input

331241

miScript PreAMP PCR Kit 331452

miScript Single Cell qPCR Kit Universal preamplification of all miRNA from as low as a single cell. Can also be used for ultra-low sample amounts greater than an individual cell

331053

1099072 01/2016

References

1. Weber, J.A. et al. (2010) The microRNA spectrum in 12 body fluids. Clinical Chemistry 56,1733.

2. Ståhlberg, A. and Kubista, M. (2014) The workflow of single-cell expression profiling using quantitative real-time PCR. Expert. Rev. Mol Diagn. 14, 323.

3. Mestdagh, P. et al. (2014) Evaluation of quantitative miRNA expression platforms in the microRNA quality control (miRQC) study. Nature Methods 11, 971.

extending mirqc’s dynamic range: amplifying the view of limiting rna samples - download the...

Health & Medicine