Human Milk MicroRNA and Total RNA Differ Depending on Milk Fractionation

MicroRNA have been recently discovered in human milk signifying potentially important functions for both the lactating breast and the infant. Whilst human milk microRNA have started to be explored, little data exist on the evaluation of sample processing, and analysis to ensure that a full spectrum of microRNA can be obtained. Human milk comprises three main fractions: cells, skim milk, and lipids. Typically, the skim milk fraction has been measured in isolation despite evidence that the lipid fraction may contain more microRNA. This study aimed to standardize isolation of microRNA and total RNA from all three fractions of human milk to determine the most appropriate sampling and analysis procedure for future studies. Three different methods from eight commercially available kits were tested for their efficacy in extracting total RNA and microRNA from the lipid, skim, and cell fractions of human milk. Each fraction yielded different concentrations of RNA and microRNA, with the highest quantities found in the cell and lipid fractions, and the lowest in skim milk. The column‐based phenol‐free method was the most efficient extraction method for all three milk fractions. Two microRNAs were expressed and validated in the three milk fractions by qPCR using the three recommended extraction kits for each fraction. High expression levels were identified in the skim and lipid milk factions for these microRNAs. These results suggest that careful consideration of both the human milk sample preparation and extraction protocols should be made prior to embarking upon research in this area. J. Cell. Biochem. 116: 2397–2407, 2015. © 2015 The Authors. Journal of Cellular Biochemistry Published by Wiley Periodicals, Inc.     

Quality Assessment and Data Analysis for microRNA Expression Arrays

MicroRNAs are small (~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.     

用聚乙二醇分离富集microRNA的方法探讨

目的探讨用聚乙二醇（polyethylene glycol,PEG）溶液分离富集miRNA的操作方法和分离富集效果,并与Ambion公司的miRNA分离试剂盒分离效果进行比较。方法用PEG溶液和Ambion公司的miRNA分离试剂盒从肝脏组织总RNA中分离富集miRNA,用变性琼脂糖和变性聚丙烯酰胺凝胶电泳鉴定分离效果,并在富集的miRNA中用RT—PCR扩增miR-122以鉴定是否有效地回收了miRNA。结果PEG和Ambion公司的miRNA分离试剂盒都能有效地富集miRNA,PEG富集的RNA片段比Ambion公司的试剂盒的大。结论PEG溶液能有效地分离富集miRNA,和Ambion公司的miRNA分离试剂盒分离效果相当,并具有操作简便、快捷及成本低廉的优点。     

Extraction of high-quality RNA from pancreatic tissues for gene expression studies

Extracting RNA from pancreatic tissue is notoriously challenging because of the organ's high RNase content. Standard methods using TriPure or TRIzol classically yield RNA of sufficient quality for routine gene expression analysis but not for microarray or deep sequencing analysis. Here we developed a simple method to extract high-quality RNA from mouse pancreas. Our method uses an RNase inhibitor and combines different protocols using guanidium thiocyanate–phenol extraction. It enables reproducible isolation of RNA with an RNA integrity number around 9.     

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.     

Impact of RNA extraction from limited samples on microarray results

To move microarray technology into the diagnostic realm, the impact of technical parameters, such as sample preparation and RNA extraction, needs to be understood and minimized. We evaluated the impact of two RNA extraction methods, DNase treatment and the amount of hybridized cDNA probe, on the outcome of microarray results. The results for both RNA extraction methods were comparable, although one method resulted in residual DNA that slightly affected the microarray results. As little as one microgram of total RNA could be used to synthesize a cDNA probe and resulted in a gene expression profile that was similar to one produced using 5 micrograms total RNA, even though the overall signal intensity was lower. These experiments illustrate that microarray technology holds great promise for the use of limited clinical samples in the diagnostic setting.     

Stripping custom microRNA microarrays and the lessons learned about probe-slide interactions

Microarrays have been used extensively in gene expression profiling and genotyping studies. To reduce the high cost and enhance the consistency of microarray experiments, it is often desirable to strip and reuse microarray slides. Our genome-wide analysis of microRNA expression involves the hybridization of fluorescently labeled nucleic acids to custom-made, spotted DNA microarrays based on GAPSII-coated slides. We describe here a simple and effective method to regenerate such custom microarrays that uses a very low-salt buffer to remove labeled nucleic acids from microarrays. Slides can be stripped and reused multiple times without significantly compromising data quality. Moreover, our analyses of the performance of regenerated slides identifies parameters that influence the attachment of oligonucleotide probes to GAPSII slides, shedding light on the interactions between DNA and the microarray surface and suggesting ways in which to improve the design of oligonucleotide probes.     

Elimination of Ligation Dependent Artifacts in T4 RNA Ligase to Achieve High Efficiency and Low Bias MicroRNA Capture

Adapter ligation is a critical first step in many microRNA analysis methods including microarray, qPCR, and sequencing. Previous studies have shown that ligation bias can have dramatic effects on both the fidelity of expression profiles and reproducibility across samples. We have developed a method for high efficiency and low bias microRNA capture by 3′ adapter ligation using T4 RNA ligase that does not require pooled adapters. Using a panel of 20 microRNA, we investigated the effects of ligase type, PEG concentration, ligase amount, adapter concentration, incubation time, incubation temperature, and adapter design on capture efficiency and bias. Of these factors, high PEG% was found to be critical in suppressing ligation bias. We obtained high average capture efficiency and low CV across the 20 microRNA panel, both in idealized buffer conditions (86%±10%) and total RNA spiking conditions (64%±17%). We demonstrate that this method is reliable across microRNA species that previous studies have had difficulty capturing and that our adapter design performs significantly better than the common adapter designs. Further, we demonstrate that the optimization methodology must be specifically designed for minimizing bias in order to obtain the ideal reaction parameters.     

Microarray analysis of cytoplasmic versus whole cell RNA reveals a considerable number of missed and false positive mRNAs

With no known exceptions, every published microarray study to determine differential mRNA levels in eukaryotes used RNA extracted from whole cells. It is assumed that the use of whole cell RNA in microarray gene expression analysis provides a legitimate profile of steady-state mRNA. Standard labeling methods and the prevailing dogma that mRNA resides almost exclusively in the cytoplasm has led to the long-standing belief that the nuclear RNA contribution is negligible. We report that unadulterated cytoplasmic RNA uncovers differentially expressed mRNAs that otherwise would not have been detected when using whole cell RNA and that the inclusion of nuclear RNA has a large impact on whole cell gene expression microarray results by distorting the mRNA profile to the extent that a substantial number of false positives are generated. We conclude that to produce a valid profile of the steady-state mRNA population, the nuclear component must be excluded, and to arrive at a more realistic view of a cell''s gene expression profile, the nuclear and cytoplasmic RNA fractions should be analyzed separately.