Direct and sensitive miRNA profiling from low-input total RNA

We have developed a sensitive, accurate, and multiplexed microRNA (miRNA) profiling assay that is based on a highly efficient labeling method and novel microarray probe design. The probes provide both sequence and size discrimination, yielding in most cases highly specific detection of closely related mature miRNAs. Using a simple, single-vial experimental protocol, 120 ng of total RNA is directly labeled using Cy3 or Cy5, without fractionation or amplification, to produce precise and accurate measurements that span a linear dynamic range from 0.2 amol to 2 fmol of input miRNA. The results can provide quantitative estimates of the miRNA content for the tissues studied. The assay is also suitable for use with formalin-fixed paraffin-embedded clinical samples. Our method allows rapid design and validation of probes for simultaneous quantitative measurements of all human miRNA sequences in the public databases and to new miRNA sequences as they are reported.     

Missing value imputation for microRNA expression data by using a GO-based similarity measure

Missing values are commonly present in microarray data profiles. Instead of discarding genes or samples with incomplete expression level, missing values need to be properly imputed for accurate data analysis. The imputation methods can be roughly categorized as expression level-based and domain knowledge-based. The first type of methods only rely on expression data without the help of external data sources, while the second type incorporates available domain knowledge into expression data to improve imputation accuracy. In recent years, microRNA (miRNA) microarray has been largely developed and used for identifying miRNA biomarkers in complex human disease studies. Similar to mRNA profiles, miRNA expression profiles with missing values can be treated with the existing imputation methods. However, the domain knowledge-based methods are hard to be applied due to the lack of direct functional annotation for miRNAs. With the rapid accumulation of miRNA microarray data, it is increasingly needed to develop domain knowledge-based imputation algorithms specific to miRNA expression profiles to improve the quality of miRNA data analysis. We connect miRNAs with domain knowledge of Gene Ontology (GO) via their target genes, and define miRNA functional similarity based on the semantic similarity of GO terms in GO graphs. A new measure combining miRNA functional similarity and expression similarity is used in the imputation of missing values. The new measure is tested on two miRNA microarray datasets from breast cancer research and achieves improved performance compared with the expression-based method on both datasets. The experimental results demonstrate that the biological domain knowledge can benefit the estimation of missing values in miRNA profiles as well as mRNA profiles. Especially, functional similarity defined by GO terms annotated for the target genes of miRNAs can be useful complementary information for the expression-based method to improve the imputation accuracy of miRNA array data. Our method and data are available to the public upon request.     

An optimized isolation and labeling platform for accurate microRNA expression profiling

MicroRNAs (miRNAs) are small, noncoding RNAs that regulate gene expression in both plants and animals. miRNA genes have been implicated in a variety of important biological processes, including development, differentiation, apoptosis, fat metabolism, viral infection, and cancer. Similar to protein-coding messenger RNAs, miRNA expression varies between tissues and developmental states. To acquire a better understanding of global miRNA expression in tissues and cells, we have developed isolation, labeling, and array procedures to measure the relative abundance of all of the known human mature miRNAs. The method relies on rapid isolation of RNA species smaller than ~40 nucleotides (nt), direct and homogenous enzymatic labeling of the mature miRNAs with amine modified ribonucleotides, and hybridization to antisense DNA oligonucleotide probes. A thorough performance study showed that this miRNA microarray system can detect subfemtomole amounts of individual miRNAs from <1 mug of total RNA, with 98% correlation between independent replicates. The system has been applied to compare the global miRNA expression profiles in 26 different normal human tissues. This comprehensive analysis identified miRNAs that are preferentially expressed in one or a few related tissues and revealed that human adult tissues have unique miRNA profiles. This implicates miRNAs as important components of tissue development and differentiation. Taken together, these results emphasize the immense potential of microarrays for sensitive and high-throughput analysis of miRNA expression in normal and disease states.     

Highly sensitive and specific microRNA expression profiling using BeadArray technology

We have developed a highly sensitive, specific and reproducible method for microRNA (miRNA) expression profiling, using the BeadArray™ technology. This method incorporates an enzyme-assisted specificity step, a solid-phase primer extension to distinguish between members of miRNA families. In addition, a universal PCR is used to amplify all targets prior to array hybridization. Currently, assay probes are designed to simultaneously analyse 735 well-annotated human miRNAs. Using this method, highly reproducible miRNA expression profiles were generated with 100–200 ng total RNA input. Furthermore, very similar expression profiles were obtained with total RNA and enriched small RNA species (R² ≥ 0.97). The method has a 3.5–4 log (10⁵–10⁹ molecules) dynamic range and is able to detect 1.2- to 1.3-fold-differences between samples. Expression profiles generated by this method are highly comparable to those obtained with RT–PCR (R² = 0.85–0.90) and direct sequencing (R = 0.87–0.89). This method, in conjunction with the 96-sample array matrix should prove useful for high-throughput expression profiling of miRNAs in large numbers of tissue samples.     

Quantitation of microRNAs using a modified Invader assay

The short lengths of microRNAs (miRNAs) present a significant challenge for detection and quantitation using conventional methods for RNA analysis. To address this problem, we developed a quantitative, sensitive, and rapid miRNA assay based on our previously described messenger RNA Invader assay. This assay was used successfully in the analysis of several miRNAs, using as little as 50-100 ng of total cellular RNA or as few as 1,000 lysed cells. Its specificity allowed for discrimination between miRNAs differing by a single nucleotide, and between precursor and mature miRNAs. The Invader miRNA assay, which can be performed in unfractionated detergent lysates, uses fluorescence detection in microtiter plates and requires only 2-3 h incubation time, allowing for parallel analysis of multiple samples in high-throughput screening analyses.     

Label-free high-throughput microRNA expression profiling from total RNA

MicroRNAs (miRNAs) are key biological regulators and promising disease markers whose detection technologies hold great potentials in advancing fundamental research and medical diagnostics. Currently, miRNAs in biological samples have to be labeled before being applied to most high-throughput assays. Although effective, these labeling-based approaches are usually labor-intensive, time-consuming and liable to bias. Besides, the cross-hybridization of co-existing miRNA precursors (pre-miRNAs) is not adequately addressed in most assays that use total RNA as input. Here, we present a hybridization-triggered fluorescence strategy for label-free, microarray-based high-throughput miRNA expression profiling. The total RNA is directly applied to the microarray with a short fluorophore-linked oligonucleotide Universal Tag which can be selectively captured by the target-bound probes via base-stacking effects. This Stacking-Hybridized Universal Tag (SHUT) assay has been successfully used to analyze as little as 100 ng total RNA from human tissues, and found to be highly specific to homogenous miRNAs. Superb discrimination toward single-base mismatch at the 5' or 3' end has been demonstrated. Importantly, the pre-miRNAs generated negligible signals, validating the direct use of total RNA.     

Detection of Cancer with Serum miRNAs on an Oligonucleotide Microarray

Micro RNAs (miRNAs) are a class of small, non-coding RNA species that play critical roles throughout cellular development and regulation. miRNA expression patterns taken from various tissue types often point to the cellular lineage of an individual tissue type, thereby being a more invariant hallmark of tissue type. Recent work has shown that these miRNA expression patterns can be used to classify tumor cells, and that this classification can be more accurate than the classification achieved by using messenger RNA gene expression patterns. One aspect of miRNA biogenesis that makes them particularly attractive as a biomarker is the fact that they are maintained in a protected state in serum and plasma, thus allowing the detection of miRNA expression patterns directly from serum. This study is focused on the evaluation of miRNA expression patterns in human serum for five types of human cancer, prostate, colon, ovarian, breast and lung, using a pan-human microRNA, high density microarray. This microarray platform enables the simultaneous analysis of all human microRNAs by either fluorescent or electrochemical signals, and can be easily redesigned to include newly identified miRNAs. We show that sufficient miRNAs are present in one milliliter of serum to detect miRNA expression patterns, without the need for amplification techniques. In addition, we are able to use these expression patterns to correctly discriminate between normal and cancer patient samples.     

mRNA and microRNA quality control for RT-qPCR analysis

The importance of high quality sample material, i.e. non-degraded or fragmented RNA, for classical gene expression profiling is well documented. Hence, the analysis of RNA quality is a valuable tool in the preparation of methods like RT-qPCR and microarray analysis. For verification of RNA integrity, today the use of automated capillary electrophoresis is state of the art. Following the recently published MIQE guidelines, these pre-PCR evaluations have to be clearly documented in scientific publication to increase experimental transparency.RNA quality control may also be integrated in the routine analysis of new applications like the investigation of microRNA (miRNA) expression, as there is little known yet about factors compromising the miRNA analysis. Agilent Technologies is offering a new lab-on-chip application for the 2100 Bioanalyzer making it possible to quantify miRNA in absolute amounts [pg] and as a percentage of small RNA [%]. Recent results showed that this analysis method is strongly influenced by total RNA integrity. Ongoing RNA degradation is accompanied by the formation of small RNA fragments leading to an overestimation of miRNA amount on the chip. Total RNA integrity is known to affect the performance of RT-qPCR as well as the quantitative results in mRNA expression profiling. The actual study identified a comparable effect for miRNA gene expression profiling. Using a suitable normalization method could partly reduce the impairing effect of total RNA integrity.     

Comparison of Hepatocellular Carcinoma miRNA Expression Profiling as Evaluated by Next Generation Sequencing and Microarray

MicroRNA (miRNA) expression profiling has proven useful in diagnosing and understanding the development and progression of several diseases. Microarray is the standard method for analyzing miRNA expression profiles; however, it has several disadvantages, including its limited detection of miRNAs. In recent years, advances in genome sequencing have led to the development of next-generation sequencing (NGS) technologies, which significantly advance genome sequencing speed and discovery. In this study, we compared the expression profiles obtained by next generation sequencing (NGS) with the profiles created using microarray to assess if NGS could produce a more accurate and complete miRNA profile. Total RNA from 14 hepatocellular carcinoma tumors (HCC) and 6 matched non-tumor control tissues were sequenced with Illumina MiSeq 50-bp single-end reads. Micro RNA expression profiles were estimated using miRDeep2 software. As a comparison, miRNA expression profiles for 11 out of 14 HCCs were also established by microarray (Agilent human microRNA microarray). The average total sequencing exceeded 2.2 million reads per sample and of those reads, approximately 57% mapped to the human genome. The average correlation for miRNA expression between microarray and NGS and subtraction were 0.613 and 0.587, respectively, while miRNA expression between technical replicates was 0.976. The diagnostic accuracy of HCC, p-value, and AUC were 90.0%, 7.22×10⁻⁴, and 0.92, respectively. In summary, NGS created an miRNA expression profile that was reproducible and comparable to that produced by microarray. Moreover, NGS discovered novel miRNAs that were otherwise undetectable by microarray. We believe that miRNA expression profiling by NGS can be a useful diagnostic tool applicable to multiple fields of medicine.     

Focus on RNA isolation: obtaining RNA for microRNA (miRNA) expression profiling analyses of neural tissue

MicroRNAs (miRNAs) are present in all known plant and animal tissues and appear to be somewhat concentrated in the mammalian nervous system. Many different miRNA expression profiling platforms have been described. However, relatively little research has been published to establish the importance of 'upstream' variables in RNA isolation for neural miRNA expression profiling. We tested whether apparent changes in miRNA expression profiles may be associated with tissue processing, RNA isolation techniques, or different cell types in the sample. RNA isolation was performed on a single brain sample using eight different RNA isolation methods, and results were correlated using a conventional miRNA microarray and then cross-referenced to Northern blots. Differing results were seen between samples obtained using different RNA isolation techniques and between microarray and Northern blot results. Another complication of miRNA microarrays is tissue-level heterogeneity of cellular composition. To investigate this phenomenon, miRNA expression profiles were determined and compared between highly-purified primary cerebral cortical cell preparations of rat primary E15-E18 neurons versus rat primary E15-E18 astrocytes. Finally, to assess the importance of dissecting human brain gray matter from subjacent white matter in cerebral cortical studies, miRNA expression profiles were compared between gray matter and immediately contiguous white matter. The results suggest that for microarray studies, cellular composition is important, and dissecting white matter from gray matter improves the specificity of the results. Based on these data, recommendations for miRNA expression profiling in neural tissues, and considerations worthy of further study, are discussed.     

Correlation analysis of external RNA controls reveals its utility for assessment of microarray assay

Quality control of a microarray experiment has become an important issue for both research and regulation. External RNA controls (ERCs), which can be either added to the total RNA level (tERCs) or introduced right before hybridization (cERCs), are designed and recommended by commercial microarray platforms for assessment of performance of a microarray experiment. However, the utility of ERCs has not been fully realized mainly due to the lack of sufficient data resources. The US Food and Drug Administration (FDA)-led community-wide Microarray Quality Control (MAQC) study generates a large amount of microarray data with implementation of ERCs across several commercial microarray platforms. The utility of ERCs in quality control by assessing the ERCs’ concentration-response behavior was investigated in the MAQC study. In this work, an ERC-based correlation analysis was conducted to assess the quality of a microarray experiment. We found that the pairwise correlations of tERCs are sample independent, indicating that the array data obtained from different biological samples can be treated as technical replicates in analysis of tERCs. Consequently, the commonly used quality control method of applying correlation analysis on technical replicates can be adopted for assessing array performance based on different biological samples using tERCs. The proposed approach is sensitive to identifying outlying assays and is not dependent on the choice of normalization method.