Technical variables in high-throughput miRNA expression profiling: Much work remains to be done

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Abstract

MicroRNA (miRNA) gene expression profiling has provided important insights into plant and animal biology. However, there has not been ample published work about pitfalls associated with technical parameters in miRNA gene expression profiling. One source of pertinent information about technical variables in gene expression profiling is the separate and more well-established literature regarding mRNA expression profiling. However, many aspects of miRNA biochemistry are unique. For example, the cellular processing and compartmentation of miRNAs, the differential stability of specific miRNAs, and aspects of global miRNA expression regulation require specific consideration. Additional possible sources of systematic bias in miRNA expression studies include the differential impact of pre-analytical variables, substrate specificity of nucleic acid processing enzymes used in labeling and amplification, and issues regarding new miRNA discovery and annotation. We conclude that greater focus on technical parameters is required to bolster the validity, reliability, and cultural credibility of miRNA gene expression profiling studies.

Introduction

Over the past 5 years, literally dozens of distinct miRNA gene expression profiling platforms (miGEPs) have been introduced. Studies using miGEPs have helped to establish that miRNA biology is fundamentally important in plants and animals with clinical implications for human diseases. Excellent prior reviews have described some of the important aspects of miRNA profiling (see for example [1], [2], [3]). However, research literature to improve quality control for miGEPs has not developed in parallel. Nor has there been commensurate published work about the effects of pre-analytical and other technical variables in miRNA gene expression profiling.

The purpose of this review is to describe some technical parameters that may be relevant to miRNA expression profiling. Unfortunately, there are many stages of a gene expression study where systematic bias can be introduced. The expression ‘garbage in, garbage out’ can be applied to gene expression profiling; however, in the context of high-throughput techniques, subtle bias can be more problematic than manifestly flawed data. This review is not oriented toward ‘solving’ technical problems. Instead, we wish to begin bringing important technical parameters to light because no variable will have the same significance for each miGEP. In order to not repeat the ‘learning curve’ of the mRNA profiling field, it seems advisable to focus on technical parameters for the sake of improving the validity and reliability of miRNA gene expression profiling studies.

Section snippets

General considerations

Relative to miRNA studies, mRNA profiling parameters have been assessed over a longer time, and with greater attention to technical details. Recent reviews of mRNA-related expression profiling have identified potential sources for systematic biases in expression profiling, along with strategies to overcome those potential pitfalls [4], [5], [6] (see Table 1). The initial studies using high-throughput mRNA profiling microarrays were performed before the technical parameters for those studies

MiRNA research is a fast-moving field and produces an evolving set of technical challenges for miRNA profiling

The study of miRNAs is still in its infancy. The field has grown explosively, with many new biological paradigms discovered. Researchers interested in miRNA expression profiling cannot ignore the technical implications of these novel data.

At the most basic level, the full complement of miRNAs expressed in most animals including humans has not been determined. As of December 2007, the Rfam registry (v. 10.1) has annotated 563 human miRNA genes, an increase from 475 miRNAs in v. 9.2. However,

Conclusion

Papers about miGEPS are accumulating quickly (Fig. 2). In high-throughput miGEP studies, the saying applies: “the devil is in the details”. Unfortunately, the details for every system (every profiling platform, cell type, disease, and experimental design) will be distinct. The impact of each technical parameter will hence be different–however subtly–for each of the profiling platforms. This argues for the need of more work on the topic of technical parameters in miGEP studies. Yet such papers

Acknowledgements

We thank Ms. Willa Huang for technical help. Funding was provided through NIH K08 NS050110 andNIH P30 49008400 (PTN), and the Kentucky Tobacco Research and Development Center (KTRDC), the USDA-NRI grants 2006-35301-17115 and 2006-35100-17433, the NSF MCB-0718029 (Subaward No. S-00000260), and the NIH 5 R03 AI 068934-02 (GT).

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