microRNA  (miRNA) normalisation  (7)
microRNA  (miRNA)   &   quantitative real-time RT-PCR (1)
microRNA  (miRNA)   &   quantitative real-time RT-PCR (2)
microRNA  (miRNA)   &   quantitative real-time RT-PCR (3)
microRNA  (miRNA)   &   quantitative real-time RT-PCR (4)
microRNA  (miRNA)   &   quantitative real-time RT-PCR (5)
microRNA  REVIEWS (6)
mirtrons  (8)
latest microRNA papers (9)  ... NEW

RNA interference (RNAi)        small inhibiting RNA  (siRNA)       small activating RNA  (saRNA)

microRNA normalisation in array experiments:

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.
=> Which are the best endogen microRNA normalizers ?
=> Can we apply a comparable normalising strategy as done for mRNAs ?

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:

  • check for good total RNA integrity
  • select one or more stable internal reference microRNA or suitable smallRNAs (via Genorm or Normfinder)
  • calculate reference-gene-index of selected normalizers (geometric average of reference gene copy numbers   or   arithmetic average of reference gene Cq values)
  • apply relative quantification strategy (comparable to mRNA relative quantification)
  • apply PCR efficiency correction (if wanted)
  • for microRNA normalistion strategies see papers below
  • or find some more ideas in the Relative Quantification Section

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 files

MicroRNA 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.

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.

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.

A sensitive array for microRNA expression profiling (miChip) based on locked nucleic acids (LNA).
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.

A personalized microRNA microarray normalization method using a logistic regression model.
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

MOTIVATION: MicroRNA (miRNA) is a set of newly discovered non-coding small RNA molecules. Its significant effects have contributed to a number of critical biological events including cell proliferation, apoptosis development, as well as tumorigenesis. High-dimensional genomic discovery platforms (e.g. microarray) have been employed to evaluate the important roles of miRNAs by analyzing their expression profiling. However, because of the small total number of miRNAs and the absence of well-known endogenous controls, the traditional normalization methods for messenger RNA (mRNA) profiling analysis could not offer a suitable solution for miRNA analysis. The need for the establishment of new adaptive methods has come to the forefront. RESULTS: Locked nucleic acid (LNA)-based miRNA array was employed to profile miRNAs using colorectal cancer cell lines under different treatments. The expression pattern of overall miRNA profiling was pre-evaluated by a panel of miRNAs using Taqman-based quantitative real-time polymerase chain reaction (qRT-PCR) miRNA assays. A logistic regression model was built based on qRT-PCR results and then applied to the normalization of miRNA array data. The expression levels of 20 additional miRNAs selected from the normalized list were post-validated. Compared with other popularly used normalization methods, the logistic regression model efficiently calibrates the variance across arrays and improves miRNA microarray discovery accuracy.
AVAILABILITY:  Datasets and R package are available at  http://gauss.usouthal.edu/publ/logit/

Intra-platform repeatability and inter-platform comparability of microRNA microarray technology.
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.

Impact of normalization on miRNA microarray expression profiling.
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

Profiling miRNA levels in cells with miRNA microarrays is becoming a widely used technique. Although normalization methods for mRNA gene expression arrays are well established, miRNA array normalization has so far not been investigated in detail. In this study we investigate the impact of normalization on data generated with the Agilent miRNA array platform. We have developed a method to select nonchanging miRNAs (invariants) and use them to compute linear regression normalization coefficients or variance stabilizing normalization (VSN) parameters. We compared the invariants normalization to normalization by scaling, quantile, and VSN with default parameters as well as to no normalization using samples with strong differential expression of miRNAs (heart-brain comparison) and samples where only a few miRNAs are affected (by p53 overexpression in squamous carcinoma cells versus control). All normalization methods performed better than no normalization. Normalization procedures based on the set of invariants and quantile were the most robust over all experimental conditions tested. Our method of invariant selection and normalization is not limited to Agilent miRNA arrays and can be applied to other data sets including those from one color miRNA microarray platforms, focused gene expression arrays, and gene expression analysis using quantitative PCR.

Comparison of normalization methods with microRNA microarray
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.

©  editor@gene-quantification.info