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microRNA normalisation in real-time RT-qPCR Data normalisation in microRNA experiments using qRT-PCR is a new challenge in gene quantification analysis. The reliability of any relative RT-PCR experiment can be improved by including an invariant endogenous control (reference gene) in the assay to correct for sample to sample variations in the qRT-PCR efficiency and errors in sample quantification. A biologically meaningful reporting of target mRNA copy numbers requires accurate and relevant normalisation to some standard and is strongly recommended in microRNA qRT-PCR. => But the
quality of normalized quantitative expression data cannot be better
than the quality of the normalizer itself. Any variation in the normalizer will obscure real changes and produce artifactual changes. Real-time RT-PCR-specific errors in the quantification of microRNA transcripts are easily compounded with any variation in the amount of starting material between the samples, e.g. caused by sample-to-sample variation and cDNA sample loading variation. This is especially relevant when the samples have been obtained from different individuals, different tissues and different time courses, and will result in the misinterpretation of the derived expression profile of the target genes. => Therefore, normalisation of target gene expression levels must be performed to compensate intra- and inter-kinetic RT-PCR variations (sample-to-sample & run-to-run variations). Data normalisation can be carried out against one or more endogenous unregulated reference gene transcript or against total cellular DNA or RNA content (molecules/g total DNA/RNA and concentrations/g total DNA/RNA). Normalisation according the total cellular RNA content is increasingly used, but little is known about the total RNA content of cells or even about the microRNA or mRNA concentrations. The content per cell or per gram tissue may vary in different tissues in vivo, in cell culture (in vitro), between individuals and under different experimental conditions. Nevertheless, it has been shown that normalisation to total cellular RNA is the least unreliable method. It requires an accurate quantification of the isolated total RNA or mRNA or microRNA fraction by optical density at 260 nm, Lab-on-Chip capillary electrophoresis instruments, or Ribogreen RNA Quantification Kit. To normalize the absolute amount according to a single reference gene (or better a set of multiple stable reference genes), further sets of kinetic PCR reactions has to be performed for the invariant endogenous control(s) on all experimental samples and the relative abundance values are calculated for internal control as well as for the target gene. For each target gene sample, the relative abundance value obtained is divided by the value derived from the control sequence in the corresponding target gene. The normalized values for different biological samples can then directly be compared. The workflow:
TALK - Better appreciation of true biological miRNA expression differences using an improved version of the global mean normalization strategy by Jo Vandesompele, RNAi and miRNA world congress, Boston 2011 Whole-Genome RT-qPCR MicroRNA Expression Profiling Pieter Mestdagh, Stefaan Derveaux, and Jo Vandesompele Chapter 10 in Michael Kaufmann and Claudia Klinger (eds.) Functional Genomics: Methods and Protocols, Methods in Molecular Biology, vol. 815, Springer Science+Business Media, LLC 2012 MicroRNAs (miRNAs) are
small noncoding RNA molecules that function as negative regulators of
gene expression. They are essential components of virtually every
biological process and deregulated miRNA expression has been reported
in a multitude of human diseases including cancer. Owing to their small
size (20–22 nucleotides), accurate quantification of miRNA expression
is particularly challenging. In this chapter, we present different
RT-qPCR technologies that enable whole genome miRNA expression
quantification.
Profound Effect of Profiling Platform and Normalization Strategy on Detection of Differentially Expressed MicroRNAs -- A Comparative Study. Meyer SU, Kaiser S, Wagner C, Thirion C, Pfaffl MW. Physiology Weihenstephan, ZIEL Research Center for Nutrition and Food Sciences Technische Universität München, Freising, Germany. PLoS One. 2012;7(6):e38946 BACKGROUND: Adequate
normalization minimizes the effects of systematic technical variations
and is a prerequisite for getting meaningful biological changes.
However, there is inconsistency about miRNA normalization performances
and recommendations. Thus, we investigated the impact of seven
different normalization methods (reference gene index, global geometric
mean, quantile, invariant selection, loess, loessM, and generalized
procrustes analysis) on intra- and inter-platform performance of two
distinct and commonly used miRNA profiling platforms.
METHODOLOGY/PRINCIPAL
FINDINGS: We included data from miRNA profiling analyses derived from a
hybridization-based platform (Agilent Technologies) and an RT-qPCR
platform (Applied Biosystems). Furthermore, we validated a subset of
miRNAs by individual RT-qPCR assays. Our analyses incorporated data
from the effect of differentiation and tumor necrosis factor alpha
treatment on primary human skeletal muscle cells and a murine skeletal
muscle cell line. Distinct normalization methods differed in their
impact on (i) standard deviations, (ii) the area under the receiver
operating characteristic (ROC) curve, (iii) the similarity of
differential expression. Loess, loessM, and quantile analysis were most
effective in minimizing standard deviations on the Agilent and TLDA
platform. Moreover, loess, loessM, invariant selection and generalized
procrustes analysis increased the area under the ROC curve, a measure
for the statistical performance of a test. The Jaccard index revealed
that inter-platform concordance of differential expression tended to be
increased by loess, loessM, quantile, and GPA normalization of AGL and
TLDA data as well as RGI normalization of TLDA data.
CONCLUSIONS/SIGNIFICANCE:
We recommend the application of loess, or loessM, and GPA normalization
for miRNA Agilent arrays and qPCR cards as these normalization
approaches showed to (i) effectively reduce standard deviations, (ii)
increase sensitivity and accuracy of differential miRNA expression
detection as well as (iii) increase inter-platform concordance. Results
showed the successful adoption of loessM and generalized procrustes
analysis to one-color miRNA profiling experiments.
Data Normalization Strategies for MicroRNA Quantification. Schwarzenbach H, da Silva AM, Calin G, Pantel K Clin Chem. 2015 61(11): 1333-13342 BACKGROUND: Different
technologies, such as quantitative real-time PCR or microarrays, have
been developed to measure microRNA (miRNA) expression levels.
Quantification of miRNA transcripts implicates data normalization using
endogenous and exogenous reference genes for data correction. However,
there is no consensus about an optimal normalization strategy. The
choice of a reference gene remains problematic and can have a serious
impact on the actual available transcript levels and, consequently, on
the biological interpretation of data.
CONTENT: In this review
article we discuss the reliability of the use of small RNAs, commonly
reported in the literature as miRNA expression normalizers, and compare
different strategies used for data normalization.
SUMMARY: A workflow
strategy
is proposed for normalization of miRNA expression data in an attempt to
provide a basis for the establishment of a global standard procedure
that will allow comparison across studies.
Identification of reference microRNAs and suitability of archived hemopoietic samples for robust microRNA expression profiling. Viprey VF, Corrias MV, Burchill SA. Children's Cancer Research Group, Leeds Institute of Molecular Medicine, Section of Experimental Oncology, Leeds LS9 7TF, UK Anal Biochem. 2012 Feb 15;421(2): 566-572 In many cancers,
including neuroblastoma, microRNA (miRNA) expression profiling of
peripheral blood (PB) and bone marrow (BM) may increase understanding
of the metastatic process and lead to the identification of clinically
informative biomarkers. The quality of miRNAs in PB and BM samples
archived in PAXgene™ blood RNA tubes from large-scale clinical studies
and the identity of reference miRNAs for standard reporting of data are
to date unknown. In this study, we evaluated the reliability of
expression profiling of 377 miRNAs using quantitative polymerase chain
reaction (qPCR) in PB and BM samples (n=90) stored at -80 °C for up
to 5 years in PAXgene™ blood RNA tubes. There was no correlation with
storage time and variation of expression for any single miRNA (r <
0.50). The profile of miRNAs isolated as small RNAs or co-isolated with
small/large RNAs was highly correlated (r=0.96). The mean expression of
all miRNAs and the geNorm program identified miR-26a, miR-28-5p, and
miR-24 as the most stable reference miRNAs. This study describes
detailed methodologies for reliable miRNA isolation and profiling of PB
and BM, including reference miRNAs for qPCR normalization, and
demonstrates the suitability of clinical samples archived at -80 °C
into PAXgene™ blood RNA tubes for miRNA expression studies.
Identification of suitable reference genes for qPCR analysis of serum microRNA in gastric cancer patients. Song J, Bai Z, Han W, Zhang J, Meng H, Bi J, Ma X, Han S, Zhang Z. Department of General Surgery, Beijing Friendship Hospital, Capital Medical University, 95 Yongan Road, Xuanwu District, Beijing 100050, People's Republic of China. Dig Dis Sci. 2012 Apr;57(4): 897-904 BACKGROUND: Circulating
microRNA expression profiles may be promising biomarkers for diagnosis
and assessment of the prognosis of cancer patients. Quantitative
polymerase chain reaction (qPCR) is a sensitive technique for
estimating expression levels of circulating microRNAs. However, there
is no current consensus on the reference genes for qPCR analysis of
circulating microRNAs.
AIMS: In this study we
tried to identify suitable reference genes for qPCR analysis of serum
microRNA in gastric cancer patients and healthy individuals.
METHODS: Six microRNAs
(let-7a, miR-16, miR-93, miR-103, miR-192, and miR-451) and RNU6B were
chosen as candidate reference genes on the basis of the literature.
Expression levels of these candidates were analyzed by qPCR in serum
samples from 40 gastric cancer patients and 20 healthy volunteers. The
geNorm, Normfinder, bestkeeper, and comparative delta-Ct method
algorithms were used to select the most suitable reference gene from
the seven candidates. This was then validated by normalizing the
expression levels of serum miR-21 across all gastric cancer patients
and healthy volunteers.
RESULTS: The algorithms
revealed miR-16 and miR-93 were the most stably expressed reference
genes, with stability values of 1.778 and 2.213, respectively, for
serum microRNA analysis across all the patients and healthy controls.
The effect of different normalization strategies was compared; when
normalized to the serum volume there were no significant differences
between patients and controls. However, when the data were normalized
to miR-93, miR-16, or miR-93 and miR-16 combined, significant
differences were detected.
CONCLUSIONS: Our results
demonstrated that reference gene choice for qPCR data analysis has a
great effect on the study outcome, and that it is necessary to choose a
suitable reference for reliable expression data. We recommend miR-16
and miR-93 as suitable reference genes for serum miRNA analysis for
gastric cancer patients and healthy controls.
Suitable reference genes for relative quantification of miRNA expression in prostate cancer. Schaefer A, Jung M, Miller K, Lein M, Kristiansen G, Erbersdobler A, Jung K. Department of Urology, University Hospital Charité, Berlin, Germany. Exp Mol Med. 2010 Nov 30;42(11): 749-758. Real time quantitative
PCR (qPCR) is the method of choice for miRNA expression studies. For
relative quantification of miRNAs, normalization to proper reference
genes is mandatory. Currently, no validated reference genes for miRNA
qPCR in prostate cancer are available. In this study, the expression of
four putative reference genes (hsa-miR-16, hsa-miR-130b, RNU6-2,
SNORD7) was examined with regard to their use as normalizer. After
SNORD7 was already shown an inappropriate reference gene in preliminary
experiments using total RNA pools, we studied the expression of the
putative reference genes in tissue and normal adjacent tissue sample
pairs from 76 men with untreated prostate carcinoma collected after
radical prostatectomy. hsa-miR-130b and RNU6-2 showed no significantly
different expression between the matched malignant and non-malignant
tissue samples, whereas hsa-miR-16 was significantly underexpressed in
malignant tissue. Softwares geNorm and Normfinder predicted hsa-
miR-130b and the geometric mean of hsa-miR-130b and RNU6-2 as the most
stable reference genes. Normalization of the four miRNAs hsa-miR-96,
hsa- miR-125b, hsa-miR-205, and hsa-miR-375, which were previously
shown to be regulated, shows that normalization to hsa-mir-16 can lead
to biased results. We recommend using hsa-miR-130b or the geometric
mean of hsa-miR-130b and small RNA RNU6-2 for normalization in miRNA
expression studies of prostate cancer.
Normalization strategies for microRNA profiling experiments: a 'normal' way to a hidden layer of complexity? Meyer SU, Pfaffl MW,
Ulbrich SE
Biotechnol Lett. 2010 Aug 12 Physiology Weihenstephan, ZIEL Research Center for Nutrition and Food Sciences, Technische Universität München, Weihenstephaner Berg 3, 85354, Freising, Germany MicroRNA (miRNA)
profiling is a first important step in
elucidating miRNA functions. Real time quantitative PCR (RT-qPCR) and
microarray hybridization approaches as well as ultra high throughput
sequencing of miRNAs (small RNA-seq) are popular and widely used
profiling methods. All of these profiling approaches face significant
introduction of bias. Normalization, often an underestimated aspect of
data processing, can minimize systematic technical or experimental
variation and thus has significant impact on the detection of
differentially expressed miRNAs. At present, there is no consensus
normalization method for any of the three miRNA profiling approach.
Several normalization techniques are currently in use, of which some
are similar to mRNA profiling normalization methods, while others are
specifically modified or developed for miRNA data. The characteristic
nature of miRNA molecules, their composition and the resulting data
distribution of profiling experiments challenges the selection of
adequate normalization techniques. Based on miRNA profiling studies and
comparative studies on normalization methods and their performances,
this review provides a critical overview of commonly used and newly
developed normalization methods for miRNA RT-qPCR, miRNA hybridization
microarray, and small RNA-seq datasets. Emphasis is laid on the
complexity, the importance and the potential for further optimization
of normalization techniques for miRNA profiling datasets.
Comprehensive human adipose tissue mRNA and microRNA endogenous control selection for quantitative real-time-PCR normalization. Neville MJ, Collins JM, Gloyn AL, McCarthy MI, Karpe F. Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Oxford, UK Obesity (Silver Spring). 2011 Apr;19(4): 888-892 The accurate
quantification of cellular and tissue mRNA and microRNA content is
reliant upon the selection of stable endogenous control transcripts for
normalizing quantitative real-time-PCR (qRT-PCR) data. Using the
combination of unbiased and informed approaches and a wide range of
human adipose tissues and cells, we sought to identify invariant
control transcripts for mRNA and microRNA. A total of 26 mRNA
transcript candidates were selected from the literature. MicroRNA
candidates were selected from a microRNA-microarray (Agilent, n = 22
tissues), and together with candidates from the literature resulted in
14 different microRNAs. The variability of these mRNA and microRNA
transcripts were then tested in a large (n = 180) collection of a
variety of human adipose tissues and cell samples. Phosphoglycerate
kinase-1 (PGK1) and peptidylprolyl isomerase A (PPIA) were identified
as the most stable mRNAs across all tissues and panels. MiR-103 was
overall the most stable microRNA transcript across all biological
backgrounds. Several proposed and commonly used normalization
transcripts were found to be highly variable. We then tested the effect
on expression of two established adipocyte-related transcripts (fatty
acid binding protein 4 (FABP4) and microRNA-145 (miR-145)), either
normalized to the optimal or a commonly used controls transcript. This
test clearly indicated that spurious results could arise from using
less stable control transcripts for mRNA and microRNA qRT-PCR.
supplement filesMicroRNA expression profiling to identify and validate reference genes for relative quantification in colorectal cancer. Chang KH, Mestdagh P, Vandesompele J, Kerin MJ, Miller N. Department of Surgery, National University of Ireland, Galway, Republic of Ireland. BMC Cancer. 2010 Apr 29;10:173 BACKGROUND: Advances in
high-throughput technologies and bioinformatics have transformed gene
expression profiling methodologies. The results of microarray
experiments are often validated using reverse transcription
quantitative PCR (RT-qPCR), which is the most sensitive and
reproducible method to quantify gene expression. Appropriate
normalisation of RT-qPCR data using stably expressed reference genes is
critical to ensure accurate and reliable results. Mi(cro)RNA expression
profiles have been shown to be more accurate in disease classification
than mRNA expression profiles. However, few reports detailed a robust
identification and validation strategy for suitable reference genes for
normalisation in miRNA RT-qPCR studies.
METHODS: We adopt and
report a systematic approach to identify the most stable reference
genes for miRNA expression studies by RT-qPCR in colorectal cancer
(CRC). High-throughput miRNA profiling was performed on ten pairs of
CRC and normal tissues. By using the mean expression value of all
expressed miRNAs, we identified the most stable candidate reference
genes for subsequent validation. As such the stability of a panel of
miRNAs was examined on 35 tumour and 39 normal tissues. The effects of
normalisers on the relative quantity of established oncogenic (miR-21
and miR-31) and tumour suppressor (miR-143 and miR-145) target miRNAs
were assessed.
RESULTS: In the array
experiment, miR-26a, miR-345, miR-425 and miR-454 were identified as
having expression profiles closest to the global mean. From a panel of
six miRNAs (let-7a, miR-16, miR-26a, miR-345, miR-425 and miR-454) and
two small nucleolar RNA genes (RNU48 and Z30), miR-16 and miR-345 were
identified as the most stably expressed reference genes. The combined
use of miR-16 and miR-345 to normalise expression data enabled
detection of a significant dysregulation of all four target miRNAs
between tumour and normal colorectal tissue.
CONCLUSIONS: Our study
demonstrates that the top six most stably expressed miRNAs (let-7a,
miR-16, miR-26a, miR-345, miR-425 and miR-454) described herein should
be validated as suitable reference genes in both high-throughput and
lower throughput RT-qPCR colorectal miRNA studies.
miRNA expression profiling - from reference genes to global mean normalization. Barbara D’haene1, Pieter Mestdagh2, Jan Hellemans1, Jo Vandesompele1,2 1 Biogazelle, Zwijnaarde, Belgium; 2 Center for Medical Genetics, Ghent University, Ghent, Belgium MicroRNAs (miRNAs) are an important class of gene regulators, acting on several aspects of cellular function such as differentiation, cell cycle control and stemness. These master regulators constitute an invaluable source of biomarkers, and several miRNA signatures correlating with patient diagnosis, prognosis and response to treatment have been identified. Within this exciting field of research, whole-genome RT-qPCR based miRNA profiling in combination with a global mean normalization strategy has proven to be the most sensitive and accurate approach for high-throughput miRNA profiling (Mestdagh et al., Genome Biology, 2009). In this chapter, we summarize the power of the previously described global mean normalization method in comparison to the multiple reference gene normalization method using the most stably expressed small RNA controls. In addition, we compare the original global mean method to a modified global mean normalization strategy based on the attribution of equal weight to each individual miRNA during normalization. This modified algorithm is implemented in Biogazelle’s qbasePLUS software and is presented here for the first time. Identification by Real-time PCR of 13 mature microRNAs differentially expressed in colorectal cancer and non-tumoral tissues. Molecular Cancer 2006, 5:29 E Bandrés*1, E Cubedo1, X Agirre2, R Malumbres1, R Zárate1, N Ramirez1, A Abajo1, A Navarro3, I Moreno4, M Monzó3 and J García-Foncillas1 MicroRNAs (miRNAs)
are short non-coding RNA molecules playing regulatory roles by
repressing translation or
cleaving RNA transcripts. Although the number of verified human miRNA
is still expanding, only few
have been functionally described. However, emerging evidences suggest
the potential
involvement of altered regulation of miRNA in pathogenesis of cancers
and these genes are thought to
function as both tumours suppressor and oncogenes. In our study, we
examined by Real-Time PCR the expression of 156 mature miRNA in
colorectal cancer. The analysis
by several bioinformatics algorithms of colorectal tumours and adjacent
nonneoplastic tissues from patients
and colorectal cancer cell lines allowed identifying a group of 13
miRNA whose
expression is significantly altered in this tumor. The most
significantly deregulated miRNA being miR-31,
miR-96, miR-133b, miR-135b, miR-145, and miR-183. In addition, the
expression level of
miR-31 was correlated with the stage of CRC tumor. Our results suggest
that miRNA expression profile could have relevance to the biological
and clinical
behavior of colorectal neoplasia.
Expression
profiling of microRNA using real-time quantitative PCR, how to use it
and what is available.
Benes V, Castoldi M. European Molecular Biology Laboratory, Heidelberg D 69117, Germany. Methods. 2010 Apr;50(4): 244-249 We review different
methodologies to estimate the
expression levels of microRNAs (miRNAs) using real-time quantitative
PCR (qPCR). As miRNA analysis is a fast changing research field, we
have introduced novel technological approaches and compared them to
existing qPCR profiling methodologies. qPCR also remains the method of
choice for validating results obtained from whole-genome screening
(e.g. with microarray). In contrast to presenting a stepwise
description of different platforms, we discuss expression profiling of
mature miRNAs by qPCR in four sequential sections: (1) cDNA synthesis;
(2) primer design; (3) detection of amplified products; and (4) data
normalization. We address technical challenges associated with each of
these and outline possible solutions.
A novel and
universal method for microRNA
RT-qPCR data normalization.
Mestdagh P, Van Vlierberghe P, De Weer A, Muth D, Westermann F, Speleman F, Vandesompele J. Center for Medical Genetics, Ghent University Hospital, De Pintelaan 185, Ghent, Belgium Genome Biol. 2009;10(6): R64 Gene expression
analysis of microRNA molecules is
becoming
increasingly important. In this study we assess the use of the mean
expression value of all expressed microRNAs in a given sample as a
normalization factor for microRNA real-time quantitative PCR data and
compare its performance to the currently adopted approach. We
demonstrate that the mean expression value outperforms the current
normalization strategy in terms of better reduction of technical
variation and more accurate appreciation of biological changes.
Systematic
comparison of microarray profiling, real-time PCR, and next-generation
sequencing technologies for
measuring differential microRNA expression.
Git A, Dvinge H, Salmon-Divon M, Osborne M, Kutter C, Hadfield J, Bertone P, Caldas C. Cancer Research UK, Cambridge Research Institute, Li Ka Shing Centre, Cambridge CB2 0RE, United Kingdom RNA. 2010 May;16(5): 991-1006 RNA abundance and DNA
copy number are routinely measured
in high-throughput using microarray and next-generation sequencing
(NGS) technologies, and the attributes of different platforms have been
extensively analyzed. Recently, the application of both microarrays and
NGS has expanded to include microRNAs (miRNAs), but the relative
performance of these methods has not been rigorously characterized. We
analyzed three biological samples across six miRNA microarray platforms
and compared their hybridization performance. We examined the utility
of these platforms, as well as NGS, for the detection of differentially
expressed miRNAs. We then validated the results for 89 miRNAs by
real-time RT-PCR and challenged the use of this assay as a "gold
standard." Finally, we implemented a novel method to evaluate
false-positive and false-negative rates for all methods in the absence
of a reference method.
A
modified LOESS normalization applied to microRNA arrays: a comparative
evaluation.
Risso D, Massa MS, Chiogna M, Romualdi C. Department of Statistical Sciences, University of Padova, via C. Battisti 241 and Department of Biology, University of Padova, via U. Bassi 58/B, 35121 Padova, Italy. Bioinformatics. 2009 25(20): 2685-2691 MOTIVATION: Microarray
normalization is a fundamental
step
in removing systematic bias and noise variability caused by technical
and experimental artefacts. Several approaches, suitable for
large-scale genome arrays, have been proposed and shown to be effective
in the reduction of systematic errors. Most of these methodologies are
based on specific assumptions that are reasonable for whole-genome
arrays, but possibly unsuitable for small microRNA (miRNA) platforms.
In this work, we propose a novel normalization (loessM), and we
investigate, through simulated and real datasets, the influence that
normalizations for two-colour miRNA arrays have on the identification
of differentially expressed genes. RESULTS: We show that normalizations
usually applied to large-scale arrays, in several cases, modify the
actual structure of miRNA data, leading to large portions of false
positives and false negatives. Nevertheless, loessM is able to
outperform other techniques in most experimental scenarios. Moreover,
when usual assumptions on differential expression distribution are
missed, channel effect has a strikingly negative influence on small
arrays, bias that cannot be removed by normalizations but rather by an
appropriate experimental design. We find that the combination of loessM
with eCADS, an experimental design based on biological replicates
dye-swap recently proposed for channel-effect reduction, gives better
results in most of the experimental conditions in terms of
specificity/sensitivity both on simulated and real data.
AVAILABILITY: LoessM R
function is freely available at http://gefu.cribi.unipd.it/papers/miRNA-simulation/
Improved
microRNA quantification in total RNA from clinical samples.
Andreasen D, Fog JU, Biggs W, Salomon J, Dahslveen IK, Baker A, Mouritzen P. Exiqon A/S, Skelstedet 16, DK-2950 Vedbaek, Denmark Methods. 2010 Apr;50(4): S6-9. microRNAs are small
regulatory RNAs that are currently
emerging as new biomarkers for cancer and other diseases. In order for
biomarkers to be useful in clinical settings, they should be accurately
and reliably detected in clinical samples such as formalin fixed
paraffin embedded (FFPE) sections and blood serum or plasma. These
types of samples represent a challenge in terms of microRNA
quantification. A newly developed method for microRNA qPCR using Locked
Nucleic Acid (LNA)-enhanced primers enables accurate and reproducible
quantification of microRNAs in scarce clinical samples. Here we show
that LNA-based microRNA qPCR enables biomarker screening using very low
amounts of total RNA from FFPE samples and the results are compared to
microarray analysis data. We also present evidence that the addition of
a small carrier RNA prior to total RNA extraction, improves microRNA
quantification in blood plasma and laser capture microdissected (LCM)
sections of FFPE samples.
Measuring
microRNAs: comparisons of microarray and quantitative PCR measurements,
and of
different total RNA prep methods.
Ach RA, Wang H, Curry B. Agilent Laboratories, Agilent Technologies, Santa Clara, CA 95051, USA. robert_ach@agilent.com BMC Biotechnol. 2008 8: 69. BACKGROUND: Determining
the expression levels of
microRNAs
(miRNAs) is of great interest to researchers in many areas of biology,
given the significant roles these molecules play in cellular
regulation. Two common methods for measuring miRNAs in a total RNA
sample are microarrays and quantitative RT-PCR (qPCR). To understand
the results of studies that use these two different techniques to
measure miRNAs, it is important to understand how well the results of
these two analysis methods correlate. Since both methods use total RNA
as a starting material, it is also critical to understand how
measurement of miRNAs might be affected by the particular method of
total RNA preparation used. RESULTS: We measured the expression of 470
human miRNAs in nine human tissues using Agilent microarrays, and
compared these results to qPCR profiles of 61 miRNAs in the same
tissues. Most expressed miRNAs (53/60) correlated well (R > 0.9)
between the two methods. Using spiked-in synthetic miRNAs, we further
examined the two miRNAs with the lowest correlations, and found the
differences cannot be attributed to differential sensitivity of the two
methods. We also tested three widely-used total RNA sample prep methods
using miRNA microarrays. We found that while almost all miRNA levels
correspond between the three methods, there were a few miRNAs whose
levels consistently differed between the different prep techniques when
measured by microarray analysis. These differences were corroborated by
qPCR measurements. CONCLUSION: The correlations between Agilent miRNA
microarray results and qPCR results are generally excellent, as are the
correlations between different total RNA prep methods. However, there
are a few miRNAs whose levels do not correlate between the microarray
and qPCR measurements, or between different sample prep methods.
Researchers should therefore take care when comparing results obtained
using different analysis or sample preparation methods.
Normalization
of microRNA expression levels in quantitative RT-PCR assays:
identification of suitable reference RNA targets in normal and cancerous human solid tissues. Peltier HJ, Latham GJ. Asuragen, Inc., Austin, Texas 78744, USA. RNA. 2008 14(5): 844-852 Proper normalization is
a critical but often an
underappreciated aspect of quantitative gene expression
analysis. This study describes the identification and
characterization of appropriate reference RNA targets for the
normalization of microRNA (miRNA) quantitative RT-PCR
data. miRNA microarray data from dozens of normal and
disease human tissues revealed ubiquitous and stably expressed
normalization
candidates for evaluation by qRT-PCR. miR-191 and miR-103, among
others,
were found to be highly consistent in their expression across 13 normal
tissues and five pair of distinct tumor/normal
adjacent tissues. These miRNAs were statistically
superior to the most commonly used reference RNAs used in miRNA
qRT-PCR experiments, such as 5S rRNA, U6 snRNA, or total RNA. The most
stable
normalizers were also highly conserved across flash-frozen and
formalin-fixed
paraffin-embedded lung cancer tumor/NAT sample sets, resulting in the
confirmation of one well-documented oncomir (let-7a), as well as the
identification
of novel oncomirs. These findings constitute the first report
describing
the rigorous normalization of miRNA qRT-PCR data and have important
implications
for proper experimental design and accurate data interpretation.
Identification
of suitable endogenous control genes for microRNA gene expression analysis
in human breast cancer.
Davoren PA, McNeill RE, Lowery AJ, Kerin MJ, Miller N. Department of Surgery, National University of Ireland, Galway, Ireland. BMC Mol Biol. 2008 9: 76. The discovery of
microRNAs (miRNAs) added an extra level of intricacy to the already
complex system regulating gene expression. These single-stranded RNA
molecules,
18-25 nucleotides in length, negatively regulate gene expression
through
translational inhibition or mRNA cleavage. The discovery that aberrant
expression
of specific miRNAs contributes to human disease has fueled much
interest
in profiling the expression of these molecules. Real-time quantitative
PCR
(RQ-PCR) is a sensitive and reproducible gene expression quantitation
technique
which is now being used to profile miRNA expression in cells and
tissues.
To correct for systematic variables such as amount of starting
template, RNA quality and enzymatic efficiencies,
RQ-PCR data is commonly normalised to an endogenous
control (EC) gene, which ideally, is stably-expressed across the test
sample
set. A universal endogenous control suitable for every tissue type,
treatment
and disease stage has not been identified and is unlikely to exist, so,
to avoid introducing further error in the
quantification of expression data it is necessary that
candidate ECs be validated in the samples of interest. While ECs have
been validated for quantification of mRNA expression in various
experimental settings, to date there is no report of
the validation of miRNA ECs for expression profiling in
breast tissue. In this study, the expression of five miRNA
genes (let-7a, miR-10b, miR-16, miR-21 and miR-26b) and three small
nucleolar
RNA genes (RNU19, RNU48 and Z30) was examined across malignant, benign
and
normal breast tissues to determine the most appropriate normalisation
strategy.
This is the first study to identify reliable ECs for analysis of miRNA
by
RQ-PCR in human breast tissue.
High-throughput
stem-loop RT-qPCR miRNA expression profiling using minute amounts of
input RNA.
Mestdagh P, Feys T, Bernard N, Guenther S, Chen C, Speleman F, Vandesompele J. Center for Medical Genetics, Ghent University Hospital, Ghent, Belgium. Nucleic Acids Res. 2008 36(21): e143 MicroRNAs (miRNAs) are
an emerging class of small non-coding RNAs implicated in a wide
variety of cellular processes. Research in this field is accelerating,
and the growing number of miRNAs emphasizes the need
for high-throughput and sensitive detection methods.
Here we present the successful evaluation of the Megaplex
reverse transcription format of the stem-loop primer-based real-time
quantitative
polymerase chain reaction (RT-qPCR) approach to quantify miRNA
expression.
The Megaplex reaction provides simultaneous reverse transcription of
450
mature miRNAs, ensuring high-throughput detection. Further, the
introduction of a complementary DNA pre-amplification
step significantly reduces the amount of input RNA
needed, even down to single-cell level. To evaluate possible
pre-amplification
bias, we compared the expression of 384 miRNAs in three different
cancer cell lines with Megaplex RT, with or without an additional
pre-amplification
step. The normalized Cq values of all three sample pairs showed a
good correlation with maintenance of differential miRNA expression
between the cell lines. Moreover, pre-amplification
using 10 ng of input RNA enabled the detection of
miRNAs that were undetectable when using Megaplex alone with 400 ng of
input RNA. The high specificity of RT-qPCR together with a superior
sensitivity
makes this approach the method of choice for high-throughput miRNA
expression
profiling.
Facile
means for quantifying microRNA expression by real-time PCR.
Shi R, Chiang VL. North Carolina State University, Raleigh, NC 27695-7247, USA Biotechniques. 2005 39(4): 519-525 MicroRNAs (miRNAs) are
20-24 nucleotide RNAs that are predicted to play
regulatory roles in animals and plants. Here we report a simple and
sensitive real-time PCR method for quantifying the expression of plant
miRNAs. Total RNA, including miRNAs, was polyadenylated and
reverse-transcribed with a poly(T) adapter into cDNAs for real-time PCR
using the miRNA-specific forward primer and the sequence complementary
to the poly(T) adapter as the reverse primer. Several Arabidopsis miRNA
sequences were tested using SYBR Green reagent, demonstrating that this
method, using as little as 100 pg total RNA, could readily discriminate
the expression of miRNAs having asfew as one nucleotide sequence
difference. This method also revealed miRNA tissue-specific expression
patterns that cannot be resolved by Northern blot analysis and may
therefore be widely useful for characterizing miRNA expression in
plants as well as in animals.
A
single-molecule method for the quantitation of microRNA gene expression.
Neely LA, Patel S, Garver J, Gallo M, Hackett M, McLaughlin S, Nadel M, Harris J, Gullans S, Rooke J. US Genomics, 12 Gill Street, Suite 4700, Woburn, Massachusetts 01801, USA Nat Methods. 2006 (1): 41-46 MicroRNAs (miRNA) are
short endogenous noncoding RNA molecules that
regulate fundamental cellular processes such as cell differentiation,
cell proliferation and apoptosis through modulation of gene expression.
Critical to understanding the role of miRNAs in this regulation is a
method to rapidly and accurately quantitate miRNA gene expression.
Existing methods lack sensitivity, specificity and typically require
upfront enrichment, ligation and/or amplification steps. The Direct
miRNA assay hybridizes two spectrally distinguishable fluorescent
locked nucleic acid (LNA)-DNA oligonucleotide probes to the miRNA of
interest, and then tagged molecules are directly counted on a
single-molecule detection instrument. In this study, we show the assay
is sensitive to femtomolar concentrations of miRNA (500 fM), has a
three-log linear dynamic range and is capable of distinguishing among
miRNA family members. Using this technology, we quantified expression
of 45 human miRNAs within 16 different tissues, yielding a quantitative
differential expression profile that correlates and expands upon
published results.
Endogenous
Controls
for Real-Time Quantitation of miRNA Using TaqMan MicroRNA Assays.
Applied Biosystems - Application Note MicroRNAs
(miRNAs) are small noncoding RNAs whose function has been implicated in
a wide range of fundamental cellular processes including cell
proliferation, cell differentiation, and cell death. Quantitation of
miRNA gene expression levels has become an essential step in
understanding these mechanisms, and has shown great promise in
identifying effective biomarkers correlative with human disease1,2.
Applied Biosystems has developed an extensive set of TaqMan®
MicroRNA Assays, novel stem-loop RT and real-time PCR assays, for the
quantitation of mature miRNA expression3. TaqMan® Assays are the
ideal choice for these applications because of their unsurpassed
sensitivity, specificity, and wide dynamic range. Additionally, far
less input material is required compared to microarrays and other
alternative technologies. When performing these experiments,variation
in the amount of starting material, sample collection, RNA preparation
and quality, and reverse transcription (RT) efficiency can contribute
to quantification errors. Normalization to endogenous control genes is
currently the most accurate method to correct for potential RNA input
or RT efficiency biases. Careful selection of an appropriate control or
set of controls is extremely important as significant variation has
been observed between samples, even for the most commonly used
housekeeping genes, including ACTB (ß-Actin) and GAPDH4. An ideal
endogenous control generally demonstrates gene expression that is
relatively constant and highly abundant across tissues and cell types.
However, one must still validate the chosen endogenous control or set
of controls for the target cell, tissue, or treatment5, as no single
control can serveas a universal endogenous control for
all experimental conditions. When considering endogenous controls
suitable for use with TaqMan MicroRNA Assays, it is important that they
share similar properties, such as RNA stability and size, and are
amenable to the miRNA assay design. A number of reports indicate that
other classes of small non-coding RNAs (ncRNAs) are expressed both
abundantly and stably, making them good endogenous control candidates.
We have performed a systematic study of a set of human ncRNA species
ranging in size from 45 to 200 nucleotides, including transfer RNA
(tRNA), small nuclear RNA (snRNA) and small nucleolar RNA (snoRNA) 6
across a relatively wide variety of tissues and cell lines to determine
their suitability as endogenous controls when quantitating miRNA
expression levels using real-time PCR methods.
Normalization strategy is critical for the outcome of miRNA expression analyses in the rat heart. Brattelid T, Aarnes EK, Helgeland E, Guvaåg S, Eichele H, Jonassen AK. Department of Biomedicine, Faculty of Medicine and Dentistry, University of Bergen, Norway. Physiol Genomics. 2011 43(10): 604-610 Since normalization strategies plays a pivotal role for obtaining reliable results when performing quantitative PCR (qPCR) analyses, this study investigated several miRNA normalization candidates in regards to their efficiency as normalization standards in the ischemic reperfused ex vivo rat heart, with special reference to regulation of the miRNAs miR-1 and miR-101b. The possibility of including primers for several miRNAs in one reverse transcription (RT) reaction was also investigated. Langendorff perfused rat hearts were subjected to 30 min regional ischemia and 0, 1, 5, 15, or 120 min reperfusion. Total RNA was isolated and reverse transcribed for miRNA qPCR analysis. Normalization candidates were evaluated by the NormFinder and geNorm algorithms and the following stability expression rank order was obtained: sno202 < U6B < U87 < snoRNA < 4.5S RNA A < Y1 < 4.5S RNA B < GAPDH. Applying U6B as a normalizer it was found that miR-1 and miR-101b was downregulated in the ischemic reperfused myocardium. Furthermore, up to three primers could be included in one RT reaction by replacing RNase-free water with two supplemental sets of primers in the TaqMan MicroRNA assay protocol. This study demonstrates the importance of validating normalization standards when performing miRNA expression analyses by qPCR, and that miR-1 and miR-101b may play an important role during early reperfusion of the ischemic rat heart. The use of microRNAs as reference genes for quantitative polymerase chain reaction in soybean. Kulcheski FR, Marcelino-Guimaraes FC, Nepomuceno AL, Abdelnoor RV, Margis R. Centre of Biotechnology, Laboratory of Genomes and Plant Population, Federal University of Rio Grande do Sul-UFRGS, CEP 91501-970, Porto Alegre, RS, Brazil. Anal Biochem. 2010 Nov 15;406(2): 185-192 Reverse transcription quantitative real-time polymerase chain reaction (RT-qPCR) is a robust and widely applied technique used to investigate gene expression. However, for correct analysis and interpretation of results, the choice of a suitable gene to use as an internal control is a crucial factor. These genes, such as housekeeping genes, should have a constant expression level in different tissues and across different conditions. The advances in genome sequencing have provided high-throughput gene expression analysis and have contributed to the identification of new genes, including microRNAs (miRNAs). The miRNAs are fundamental regulatory genes of eukaryotic genomes, acting on several biological functions. In this study, miRNA expression stability was investigated in different soybean tissues and genotypes as well as after abiotic or biotic stress treatments. The present study represents the first investigation into the suitability of miRNAs as housekeeping genes in plants. The transcript stability of 10 miRNAs was compared to those of six previously reported housekeeping genes for the soybean. In this study, we provide evidence that the expression stabilities of miR156b and miR1520d were the highest across the soybean experiments. Furthermore, these miRNAs genes were more stable than the most commonly protein-coding genes used in soybean gene expression studies involving RT-qPCR. microRNA
normalisation of microRNA arrays
How to choose a normalization strategy for miRNA quantitative real-time (qPCR) arrays Deo A, Carlsson J, Lindlöf A. Systems Biology Research Centre, University of Skövde, Box 408 Skövde, 541 28, Sweden J Bioinform Comput Biol. 2011 9(6):795-812. Low-density arrays for
quantitative real-time PCR (qPCR) are
increasingly being used as an experimental technique for miRNA
expression profiling. As with gene expression profiling using
microarrays, data from such experiments needs effective analysis
methods to produce reliable and high-quality results. In the
pre-processing of the data, one crucial analysis step is normalization,
which aims to reduce measurement errors and technical variability among
arrays that might have arisen during the execution of the experiments.
However, there are currently a number of different approaches to choose
among and an unsuitable applied method may induce misleading effects,
which could affect the subsequent analysis steps and thereby any
conclusions drawn from the results. The choice of normalization method
is hence an important issue to consider. In this study we present the
comparison of a number of data-driven normalization methods for TaqMan
low-density arrays for qPCR and different descriptive statistical
techniques that can facilitate the choice of normalization method. The
performance of the normalization methods was assessed and compared
against each other as well as against standard normalization using
endogenous controls. The results clearly show that the data-driven
methods reduce variation and represent robust alternatives to using
endogenous controls.
Quality assessment and data analysis for microRNA expression arrays. Sarkar D, Parkin R, Wyman S, Bendoraite A, Sather C, Delrow J, Godwin AK, Drescher C, Huber W, Gentleman R, Tewari M. Fred Hutchinson Cancer Research Center, 1100 Fairview Ave N, Seattle, WA 98109, USA. Nucleic Acids Res. 2009 Feb;37(2):e17 MicroRNAs are small
(approximately 22 nt) RNAs that
regulate gene expression and play important roles in both normal and
disease physiology. The use of microarrays for global characterization
of microRNA expression is becoming increasingly popular and has the
potential to be a widely used and valuable research tool. However,
microarray profiling of microRNA expression raises a number of data
analytic challenges that must be addressed in order to obtain reliable
results. We introduce here a universal reference microRNA reagent set
as well as a series of nonhuman spiked-in synthetic microRNA controls,
and demonstrate their use for quality control and between-array
normalization of microRNA expression data. We also introduce diagnostic
plots designed to assess and compare various normalization methods. We
anticipate that the reagents and analytic approach presented here will
be useful for improving the reliability of microRNA microarray
experiments.
Evaluation of normalization methods for two-channel microRNA microarrays. Zhao Y, Wang E, Liu H, Rotunno M, Koshiol J, Marincola FM, Landi MT, McShane LM. Division of Cancer Treatment and Diagnosis, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA. J Transl Med. 2010 Jul 21;8:69. BACKGROUND: MiR arrays distinguish themselves from gene expression arrays by their more limited number of probes, and the shorter and less flexible sequence in probe design. Robust data processing and analysis methods tailored to the unique characteristics of miR arrays are greatly needed. Assumptions underlying commonly used normalization methods for gene expression microarrays containing tens of thousands or more probes may not hold for miR microarrays. Findings from previous studies have sometimes been inconclusive or contradictory. Further studies to determine optimal normalization methods for miR microarrays are needed. METHODS: We evaluated many different normalization methods for data generated with a custom-made two channel miR microarray using two data sets that have technical replicates from several different cell lines. The impact of each normalization method was examined on both within miR error variance (between replicate arrays) and between miR variance to determine which normalization methods minimized differences between replicate samples while preserving differences between biologically distinct miRs. RESULTS: Lowess normalization generally did not perform as well as the other methods, and quantile normalization based on an invariant set showed the best performance in many cases unless restricted to a very small invariant set. Global median and global mean methods performed reasonably well in both data sets and have the advantage of computational simplicity. CONCLUSIONS: Researchers need to consider carefully which assumptions underlying the different normalization methods appear most reasonable for their experimental setting and possibly consider more than one normalization approach to determine the sensitivity of their results to normalization method used. A
comparison of normalization techniques for microRNA microarray data.
Rao Y, Lee Y, Jarjoura D, Ruppert AS, Liu CG, Hsu JC, Hagan JP. The Ohio State University, USA. Stat Appl Genet Mol Biol. 2008;7(1): Article 22 Normalization of
expression levels applied to microarray data can help in reducing
measurement error. Different methods, including cyclic loess, quantile
normalization and median or mean normalization, have been utilized to
normalize microarray data. Although there is considerable literature
regarding normalization techniques for mRNA microarray data, there are
no publications comparing normalization techniques for microRNA (miRNA)
microarray data, which are subject to similar sources of measurement
error. In this paper, we compare the performance of cyclic loess,
quantile normalization, median normalization and no
normalization for a single-color microRNA microarray dataset. We show
that the quantile normalization method works best in reducing
differences in miRNA expression values for replicate tissue samples. By
showing that the total mean squared error are lowest across almost all
36 investigated tissue samples, we are assured that the bias correction
provided by quantile normalization is not outweighed by additional
error variance that can arise from a more complex normalization method.
Furthermore, we show that quantile normalization does not achieve these
results by compression of scale.
Castoldi M, Schmidt S, Benes V, Noerholm M, Kulozik AE, Hentze MW, Muckenthaler MU. Department of Pediatric Oncology, Hematology and Immunology, University of Heidelberg, Germany. RNA. 2006 12(5): 913-920 MicroRNAs represent a
class of short (approximately 22
nt), noncoding regulatory RNAs involved in development,
differentiation, and metabolism. We describe a novel
microarray platform for genome-wide profiling of mature miRNAs (miChip)
using locked nucleic acid (LNA)-modified capture
probes. The biophysical properties of LNA were
exploited to design probe sets for uniform, high-affinity
hybridizations
yielding highly accurate signals able to discriminate between single
nucleotide differences and, hence, between closely related miRNA family
members. The superior detection sensitivity
eliminates the need for RNA size selection and/or
amplification. MiChip will greatly simplify miRNA expression profiling
of biological and clinical samples.
miChip:
an array-based method for microRNA expression profiling using locked
nucleic acid capture probes.
Mirco Castoldi, Sabine Schmidt, Vladimir Benes, Matthias W Hentze & Martina U Muckenthaler Nature Protocols 3, - 321 - 329 (2008) MicroRNAs (miRNAs)
represent a class of short (22 nt) noncoding RNAs that control gene
expression post-transcriptionally. Microarray technology is frequently
applied to monitor miRNA expression levels but is challenged by (i) the
short length of miRNAs that offers little sequence for appending
detection molecules; (ii) low copy number of some miRNA; and (iii) a
wide range of predicted melting temperatures (Tm) versus their DNA
complementary sequences. We recently developed a microarray platform
for genome-wide profiling of miRNAs (miChip) by applying locked nucleic
acid (LNA)-modified capture probes. Here, we provide detailed protocols
for the generation of the miChip microarray platform, the preparation
and fluorescent labeling of small RNA containing total RNA, its
hybridization to the immobilized LNA-modified capture probes and the
post-hybridization handling of the microarray. Starting from the intact
tissue sample, the entire protocol takes approx3 d to yield highly
accurate and sensitive data about miRNA expression levels.
Wang B, Wang XF, Howell P, Qian X, Huang K, Riker AI, Ju J, Xi Y. Department of Mathematics and Statistics, University of South Alabama, Mobile, AL 36688, USA. Bioinformatics. 2010 Jan 15;26(2):228-34 AVAILABILITY:
Datasets and R package are available at http://gauss.usouthal.edu/publ/logit/
Sato F, Tsuchiya S, Terasawa K, Tsujimoto G. Department of Nanobio Drug Discovery, Graduate School of Pharmaceutical Sciences, Kyoto University, Kyoto, Kyoto, Japan. PLoS One. 2009;4(5):e5540. Epub 2009 May 14. Over the last decade,
DNA microarray technology has
provided a great contribution to the life sciences. The MicroArray
Quality Control (MAQC) project demonstrated the way to analyze the
expression microarray. Recently, microarray technology has been
utilized to analyze a comprehensive microRNA expression profiling.
Currently, several platforms of microRNA microarray chips are
commercially available. Thus, we compared repeatability and
comparability of five different microRNA microarray platforms (Agilent,
Ambion, Exiqon, Invitrogen and Toray) using 309 microRNAs probes, and
the Taqman microRNA system using 142 microRNA probes. This study
demonstrated that microRNA microarray has high intra-platform
repeatability and comparability to quantitative RT-PCR of microRNA.
Among the five platforms, Agilent and Toray array showed relatively
better performances than the others. However, the current lineup of
commercially available microRNA microarray systems fails to show good
inter-platform concordance, probably because of lack of an adequate
normalization method and severe divergence in stringency of detection
call criteria between different platforms. This study provided the
basic information about the performance and the problems specific to
the current microRNA microarray systems.
Pradervand S, Weber J, Thomas J, Bueno M, Wirapati P, Lefort K, Dotto GP, Harshman K. Lausanne DNA Array Facility, Center for Integrative Genomics, University of Lausanne, CH-1015 Lausanne, Switzerland. RNA. 2009 Mar;15(3):493-501 Hua YJ, Tu K, Tang ZY, Li YX, Xiao HS. Bioinformatics Center, The Center of Functional Genomics, Key Lab of System Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, People's Republic of China. Genomics. 2008 Aug;92(2):122-8. Epub 2008 Jun 2. MicroRNAs (miRNAs) are a group of RNAs that play important roles in regulating gene expression and protein translation. In a previous study, we established an oligonucleotide microarray platform to detect miRNA expression. Because it contained only hundreds of probes, data normalization was difficult. In this study, the microarray data for eight miRNAs extracted from inflamed rat dorsal root ganglion (DRG) tissue were normalized using 15 methods and compared with the results of real-time polymerase chain reaction. It was found that the miRNA microarray data normalized by the print-tip loess method were the most consistent with results from real-time polymerase chain reaction. Moreover, the same pattern was also observed in 14 different types of rat tissue. This study compares a variety of normalization methods and will be helpful in the preprocessing of miRNA microarray data. |