title: Normalization matters: tracking the best strategy for sperm miRNA quantification.
abstract: What is the most reliable normalization strategy for sperm microRNA (miRNA) quantitative Reverse Transcription Polymerase Chain Reactions (qRT-PCR) using singleplex assays?
  
  
  
  The use of the average expression of hsa-miR-100-5p and hsa-miR-30a-5p as sperm miRNA qRT-PCR data normalizer is suggested as an optimal strategy.
  
  
  
  Mean-centering methods are the most reliable normalization strategies for miRNA high-throughput expression analyses. Nevertheless, specific trustworthy reference controls must be established in singleplex sperm miRNA qRT-PCRs.
  
  
  
  Cycle threshold (Ct) values from previously published sperm miRNA expression profiles were normalized using four approaches: (i) Mean-Centering Restricted (MCR) method (taken as the reference strategy); (ii) expression of the small nuclear RNA RNU6B; (iii) expression of four miRNAs selected by the Concordance Correlation Restricted (CCR) algorithm: hsa-miR-100-5p, hsa-miR-146b-5p, hsa-miR-92a-3p and hsa-miR-30a-5p; (iv) the combination of two of these miRNAs that achieved the highest proximity to MCR.
  
  
  
  Expression profile data from 736 sperm miRNAs were taken from previously published studies performed in fertile donors (n = 10) and infertile patients (n = 38). For each tested normalizer molecule, expression ubiquity and uniformity across the different samples and populations were assessed as indispensable requirements for being considered as valid candidates. The reliability of the different normalizing strategies was compared to MCR based on the set of differentially expressed miRNAs (DE-miRNAs) detected between populations, the corresponding predicted targets and the associated enriched biological processes.
  
  
  
  All tested normalizers were found to be ubiquitous and non-differentially expressed between populations. RNU6B was the least uniformly expressed candidate across samples. Data normalization through RNU6B led to dramatically misguided results when compared to MCR outputs, with a null prediction of target genes and enriched biological processes. Hsa-miR-146b-5p and hsa-miR-92a-3p were more uniformly expressed than RNU6B, but their results still showed scant proximity to the reference method. The highest resemblance to MCR was achieved by hsa-miR-100-5p and hsa-miR-30a-5p. Normalization against the combination of both miRNAs reached the best proximity rank regarding the detected DE-miRNAs (Area Under the Curve = 0.8). This combination also exhibited the best performance in terms of the target genes predicted (72.3% of True Positives) and their corresponding enriched biological processes (70.4% of True Positives).
  
  
  
  Not applicable.
  
  
  
  This study is focused on sperm miRNA qRT-PCR analysis. The use of the selected normalizers in other cell types or tissues would still require confirmation.
  
  
  
  The search for new fertility biomarkers based on sperm miRNA expression using high-throughput assays is one of the upcoming challenges in the field of reproductive genetics. In this context, validation of the results using singleplex assays would be mandatory. The normalizer strategy suggested in this study would provide a universal option in this area, allowing for normalization of the validated data without causing meaningful variations of the results. Instead, qRT-PCR data normalization by RNU6B should be discarded in sperm-miRNA expression studies.
  
  
  
  This work was supported by the 2014/SGR00524 project (Agència de Gestió d'Ajuts Universitaris i de Recerca, Generalitat de Catalunya, Spain) and UAB CF-180034 grant (Universitat Autònoma de Barcelona). Celia Corral-Vazquez is a recipient of a Personal Investigador en Formació grant UAB/PIF2015 (Universitat Autònoma de Barcelona). The authors report no conflict of interest.
authors.affiliation: Genetics of Male Fertility Group, Unitat de Biologia Cel·lular (Facultat de Biociències), Universitat Autònoma de Barcelona, Cerdanyola del Vallès 08193, Spain.
authors.forename: Celia
authors.initials: C
authors.lastname: Corral-Vazquez
authors.affiliation: Genetics of Male Fertility Group, Unitat de Biologia Cel·lular (Facultat de Biociències), Universitat Autònoma de Barcelona, Cerdanyola del Vallès 08193, Spain.
authors.forename: Joan
authors.initials: J
authors.lastname: Blanco
authors.affiliation: Genetics of Male Fertility Group, Unitat de Biologia Cel·lular (Facultat de Biociències), Universitat Autònoma de Barcelona, Cerdanyola del Vallès 08193, Spain.
authors.forename: Albert
authors.initials: A
authors.lastname: Salas-Huetos
authors.affiliation: Genetics of Male Fertility Group, Unitat de Biologia Cel·lular (Facultat de Biociències), Universitat Autònoma de Barcelona, Cerdanyola del Vallès 08193, Spain.
authors.forename: Francesca
authors.initials: F
authors.lastname: Vidal
authors.affiliation: Genetics of Male Fertility Group, Unitat de Biologia Cel·lular (Facultat de Biociències), Universitat Autònoma de Barcelona, Cerdanyola del Vallès 08193, Spain ester.anton@uab.cat.
authors.forename: Ester
authors.initials: E
authors.lastname: Anton
mesh_terms: D000328:Adult; D016022:Case-Control Studies; D006801:Humans; D007248:Infertility, Male / Q000235:genetics* / Q000473:pathology; D008297:Male; D035683:MicroRNAs / Q000235:genetics*; D012342:RNA, Small Nuclear / Q000235:genetics; D012015:Reference Standards; D020133:Reverse Transcriptase Polymerase Chain Reaction / Q000592:standards*; D013094:Spermatozoa / Q000378:metabolism* / Q000473:pathology
chemical_list: C544266:MIRN100 microRNA, human; C542542:MIRN146 microRNA, human; C546086:MIRN30b microRNA, human; C541874:MIRN92 microRNA, human; D035683:MicroRNAs; D012342:RNA, Small Nuclear; C405486:U6 small nuclear RNA
keywords: infertility; miRNA; normalization; qRT-PCR; sperm