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.     

Developmental microRNA expression profiling of murine embryonic orofacial tissue

BACKGROUND: Orofacial development is a multifaceted process involving precise, spatio‐temporal expression of a panoply of genes. MicroRNAs (miRNAs), the largest family of noncoding RNAs involved in gene silencing, represent critical regulators of cell and tissue differentiation. MicroRNA gene expression profiling is an effective means of acquiring novel and valuable information regarding the expression and regulation of genes, under the control of miRNA, involved in mammalian orofacial development. METHODS: To identify differentially expressed miRNAs during mammalian orofacial ontogenesis, miRNA expression profiles from gestation day (GD) ‐12, ‐13 and ‐14 murine orofacial tissue were compared utilizing miRXplore microarrays from Miltenyi Biotech. Quantitative real‐time PCR was utilized for validation of gene expression changes. Cluster analysis of the microarray data was conducted with the clValid R package and the UPGMA clustering method. Functional relationships between selected miRNAs were investigated using Ingenuity Pathway Analysis. RESULTS: Expression of over 26% of the 588 murine miRNA genes examined was detected in murine orofacial tissues from GD‐12–GD‐14. Among these expressed genes, several clusters were seen to be developmentally regulated. Differential expression of miRNAs within such clusters wereshown to target genes encoding proteins involved in cell proliferation, cell adhesion, differentiation, apoptosis and epithelial‐mesenchymal transformation, all processes critical for normal orofacial development. CONCLUSIONS: Using miRNA microarray technology, unique gene expression signatures of hundreds of miRNAs in embryonic orofacial tissue were defined. Gene targeting and functional analysis revealed that the expression of numerous protein‐encoding genes, crucial to normal orofacial ontogeny, may be regulated by specific miRNAs. Birth Defects Research (Part A), 2010. © 2010 Wiley‐Liss, Inc.     

Structural features of microRNA (miRNA) precursors and their relevance to miRNA biogenesis and small interfering RNA/short hairpin RNA design

We have established the structures of 10 human microRNA (miRNA) precursors using biochemical methods. Eight of these structures turned out to be different from those that were computer-predicted. The differences localized in the terminal loop region and at the opposite side of the precursor hairpin stem. We have analyzed the features of these structures from the perspectives of miRNA biogenesis and active strand selection. We demonstrated the different thermodynamic stability profiles for pre-miRNA hairpins harboring miRNAs at their 5'- and 3'-sides and discussed their functional implications. Our results showed that miRNA prediction based on predicted precursor structures may give ambiguous results, and the success rate is significantly higher for the experimentally determined structures. On the other hand, the differences between the predicted and experimentally determined structures did not affect the stability of termini produced through "conceptual dicing." This result confirms the value of thermodynamic analysis based on mfold as a predictor of strand section by RNAi-induced silencing complex (RISC).     

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

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.     

miRExpress: Analyzing high-throughput sequencing data for profiling microRNA expression

Background  

MicroRNAs (miRNAs), small non-coding RNAs of 19 to 25 nt, play important roles in gene regulation in both animals and plants. In the last few years, the oligonucleotide microarray is one high-throughput and robust method for detecting miRNA expression. However, the approach is restricted to detecting the expression of known miRNAs. Second-generation sequencing is an inexpensive and high-throughput sequencing method. This new method is a promising tool with high sensitivity and specificity and can be used to measure the abundance of small-RNA sequences in a sample. Hence, the expression profiling of miRNAs can involve use of sequencing rather than an oligonucleotide array. Additionally, this method can be adopted to discover novel miRNAs.     

miRAS: a data processing system for miRNA expression profiling study

Background  

The study of microRNAs (miRNAs) is attracting great considerations. Recent studies revealed that miRNAs play as important regulators of gene expression and some even as cancer players or inhibitors. Many studies try to discover new miRNAs and reveal the miRNA expression profile in cancer using a SAGE-based total RNA clone method. However, the data processing of this method is labor-intensive with several different biological databases and more than ten data processing steps involved.     

miRNA expression profiling for identification of potential breast cancer biomarkers

To identify micro RNA (miRNA) biomarker candidates for early detection of breast cancer and detection of minimal residual breast cancer, we performed miRNA expression profiling in pooled RNA samples from breast tumors, and from bone marrow mononuclear cells, peripheral blood mononuclear cells and plasma from healthy controls. We found substantially higher levels of five miRNAs in the breast tumors compared to the normal samples. However, validation of these miRNA levels, and seven other candidates selected from the literature, in individual samples from healthy controls and patients with non-metastatic breast cancer did not suggest further examination of their biomarker potential.     

miRNA expression profiling for identification of potential breast cancer biomarkers

To identify micro RNA (miRNA) biomarker candidates for early detection of breast cancer and detection of minimal residual breast cancer, we performed miRNA expression profiling in pooled RNA samples from breast tumors, and from bone marrow mononuclear cells, peripheral blood mononuclear cells and plasma from healthy controls. We found substantially higher levels of five miRNAs in the breast tumors compared to the normal samples. However, validation of these miRNA levels, and seven other candidates selected from the literature, in individual samples from healthy controls and patients with non-metastatic breast cancer did not suggest further examination of their biomarker potential.     

Validation of extraction methods for total RNA and miRNA from bovine blood prior to quantitative gene expression analyses

The benefit and precision of blood diagnosis by quantitative real-time PCR (qPCR) is limited by sampling procedures and RNA extraction methods. We have compared five different RNA extraction protocols from bovine blood regarding RNA and miRNA yield, quality, and most reproducible data in the qRT-PCR with the lowest point of quantification. Convincing results in terms of highest quantity, quality, and best performance for mRNA qPCR were obtained by leukocyte extraction following blood lysis as well as extraction of PAXgene stabilized blood. The best microRNA qPCR results were obtained for samples extracted by the leukocyte extraction method.     

Identification of methods for use of formalin-fixed, paraffin-embedded tissue samples in RNA expression profiling

Formalin-fixed paraffin-embedded (FFPE) tissue samples are a potentially valuable resource of expression information for medical research, but are under-utilized due to degradation and modification of the RNA. Using a random primer-based RNA amplification strategy, we have evaluated multiple protocols for the extraction and isolation of RNA from FFPE samples. We found that the RecoverAll RNA isolation procedure with three or four slices (ten-microns in thickness), supplemented with additional DNAse, gave optimal results. RNA integrity as assessed by Agilent Bioanalyzer, and amplification of the 28S ribosomal RNA, were predictive for the number of genes detected on Affymetrix arrays. We obtained expression data for colon and lung tumor and normal FFPE samples and matched frozen samples and found a high correlation between frozen and matched FFPE samples (R² between 0.82 and 0.89), while the signature sets in tumor versus normal comparisons were also quite similar. QPCR confirmed all 16 of the differential expression results from the microarrays that we tested. Differentially expressed signature genes from tumor versus matched normal FFPE tissue from colon and lung were identified as cancer-related, with 95 colon tumor and 67 lung tumor genes identified, respectively.     

Impact of RNA degradation on gene expression profiling

Background

Cancer is a heterogeneous disease caused by genomic aberrations and characterized by significant variability in clinical outcomes and response to therapies. Several subtypes of common cancers have been identified based on alterations of individual cancer genes, such as HER2, EGFR, and others. However, cancer is a complex disease driven by the interaction of multiple genes, so the copy number status of individual genes is not sufficient to define cancer subtypes and predict responses to treatments. A classification based on genome-wide copy number patterns would be better suited for this purpose.

Method

To develop a more comprehensive cancer taxonomy based on genome-wide patterns of copy number abnormalities, we designed an unsupervised classification algorithm that identifies genomic subgroups of tumors. This algorithm is based on a modified genomic Non-negative Matrix Factorization (gNMF) algorithm and includes several additional components, namely a pilot hierarchical clustering procedure to determine the number of clusters, a multiple random initiation scheme, a new stop criterion for the core gNMF, as well as a 10-fold cross-validation stability test for quality assessment.

Result

We applied our algorithm to identify genomic subgroups of three major cancer types: non-small cell lung carcinoma (NSCLC), colorectal cancer (CRC), and malignant melanoma. High-density SNP array datasets for patient tumors and established cell lines were used to define genomic subclasses of the diseases and identify cell lines representative of each genomic subtype. The algorithm was compared with several traditional clustering methods and showed improved performance. To validate our genomic taxonomy of NSCLC, we correlated the genomic classification with disease outcomes. Overall survival time and time to recurrence were shown to differ significantly between the genomic subtypes.

Conclusions

We developed an algorithm for cancer classification based on genome-wide patterns of copy number aberrations and demonstrated its superiority to existing clustering methods. The algorithm was applied to define genomic subgroups of three cancer types and identify cell lines representative of these subgroups. Our data enabled the assembly of representative cell line panels for testing drug candidates.