Normalization strategies for microRNA profiling experiments: a ‘normal’ way to a hidden layer of complexity?
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.
Basic summary of miRNA profiling by RT-qPCR, microarray and small RNA-seq, including corresponding normalization methods
|MicroRNA RT-qPCR||MicroRNA microarray||Small RNA-seq|
|Throughput||Medium to high||High||Ultra high|
|Required amount of RNA||10 ng–700 ng||100 ng–10,000 ng||250 ng–10,000 ng|
|Data generation||1 day||Up to more than 2 days||Up to more than 1 week|
|Limit of detection||10−22 mol||10−15 – 10−18 mol||10−15 mol|
|Data information||Assumption based; dependent on the number and nature of targeted transcripts||Assumption based; dependent on the number and nature of targeted transcripts||Assumption free, de novo identification of transcripts within the small RNA transcriptome|
|Data analysis||Low expenditure of time||Moderate expenditure of time||Considerable expenditure of time|
|Memory capacity requirements||Low||Low||High|
|Preferential field of application||Relative and absolute quantification; validation of other miRNA profiling approaches||Relative and absolute quantification of miRNA regulation, miRNA biomarker identification, routine application and higher throughput with respect to sample number compared to small RNA-seq||De novo identification of small RNAs, simultaneous relative quantification of different small RNA species, holistic picture of the small RNA transcriptome|
|Common normalization strategies||Invariant-based (e.g., stable reference small non-coding RNAs)||Quantile||Scaling to library or sub-library (e.g., miRNA) size|
|Plate normalizing factor||Invariant-based||Trimmed mean of M values|
|Global mean expression||Scaling (e.g., Z-score, mean, median, 75th percentile)|
|Personalized logistic regression model|
Each profiling approach has specific advantages and disadvantages which should be weighted depending on the research context. Aspects of consideration range from economic and time capacity issues to sample availability, required sensitivity and dynamic range, absolute or relative quantification, as well as the biological hypothesis to be tested. RT-qPCR has superior sensitivity (Chen et al. 2005) and requires low time expenditure. Microarray-based techniques have the advantage of being relatively cost-effective, relatively quick from RNA labeling to data generation and simple to use (Pradervand et al. 2010). Ultra high throughput DNA sequencing allows for the de novo detection and relative quantification of miRNAs, but requires a considerable amount of time for data generation and data analysis. The dynamic range of sequencing depends on the sequencing depth.
Meyer SU, Pfaffl MW, Ulbrich SE. (2010) Normalization strategies for microRNA profiling experiments: a ‘normal’ way to a hidden layer of complexity? Biotechnol Lett 32(12):1777-88. [article]