QuantStudio 12K Flex OpenArray® System as a Tool for High-Throughput Genotyping and Gene Expression Analysis

  • Chiara Broccanello
  • Letizia Gerace
  • Piergiorgio StevanatoEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 2065)


Real time technology provides great advancements over PCR-based methods for a broad range of applications. With the increased availability of sequencing information, there is a need for the development and application of high-throughput real time PCR genotyping and gene expression methods that significantly broaden the current screening capabilities. Thermo Fisher Scientific (USA) has released a platform (QuantStudio™ 12K Flex system coupled with OpenArray® technology) with key elements required for high-throughput SNP genotyping and gene expression analysis. This allows for a rapid screening of large numbers of TaqMan® assays (up to 256) in many samples (up to 480) per run. This advanced real-time method involves the use of an array composed of 3,000 through-holes running on the QuantStudio™ 12K with OpenArray® block. The aim of this chapter is to outline the OpenArray® approach while providing a comprehensive in-depth review of the scientific literature on this topic. In agreement with a large number of independent studies, we conclude that the use of OpenArray® technology is a rapid and accurate method for high-throughput and large-scale systems biology studies with high specificity and sensitivity.

Key words

TaqMan® assay OpenArray® plate SNP genotyping Association analysis Gene and miRNA expression profiling 


  1. 1.
    Lamas A, Franco CM, Regal P et al (2016) High-throughput platforms in real-time PCR and applications. In: Polymerase chain reaction for biomedical applications. InTech, pp 15–38Google Scholar
  2. 2.
    Stevens J, Heid C, Livak KJ et al (1996) Real time quantitative PCR. Genome Res 6:986–994CrossRefGoogle Scholar
  3. 3.
    Stevanato P, Broccanello C, Biscarini F et al (2013) High-throughput RAD-SNP genotyping for characterization of sugar beet genotypes. Plant Mol Biol Rep 32:691–696Google Scholar
  4. 4.
    Stevanato P, Trebbi D, Saccomani M et al (2017) Single nucleotide polymorphism markers linked to root elongation rate in sugar beet. Biol Plantarum 61:48–54CrossRefGoogle Scholar
  5. 5.
    Broccanello C, Stevanato P, Biscarini F et al (2015) A new polymorphism on chromosome 6 associated with bolting tendency in sugar beet. BMC Genet 16(1)Google Scholar
  6. 6.
    Biscarini F, Marini S, Stevanato P et al (2015) Developing a parsimonius predictor for binary traits in sugar beet (Beta vulgaris). Mol Breed 35(1)Google Scholar
  7. 7.
    Biscarini F, Nazzicari N, Broccanello C et al (2016) “Noisy beets”: impact of phenotyping errors on genomic predictions for binary traits in Beta vulgaris. Plant Methods 12:36CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Stevanato P, Broccanello C, Moliterni VMC et al (2018) Innovative approaches to evaluate sugar beet responses to changes in sulfate availability. Front Plant Sci 9:14CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Barone V, Baglieri A, Stevanato P et al (2017) Root morphological and molecular responses induced by microalgae extracts in sugar beet (Beta vulgaris L.). J Appl Phycol 30:1061–1071CrossRefGoogle Scholar
  10. 10.
    Muranty H, Troggio M, Sadok IB et al (2015) Accuracy and responses of genomic selection on key traits in apple breeding. Hortic Res 215:60Google Scholar
  11. 11.
    Trebbi D, Ravi S, Broccanello C et al (2019) Identification and validation of SNP markers linked to seed toxicity in Jatropha curcas L. Scientific reports 9:10220Google Scholar
  12. 12.
    Ross JP, Mohtashami S, Leveille E et al (2018) Association study of essential tremor genetic loci in Parkinson’s disease. Neurobiol Aging 66:178–e13CrossRefGoogle Scholar
  13. 13.
    Denomme GA, Schanen MJ (2015) Mass-scale donor red cell genotyping using real-time array technology. Immunohematology 31:69–74PubMedGoogle Scholar
  14. 14.
    Cook RW, Middlebrook B, Wilkinson J et al (2018) Analytic validity of decision dx-melanoma, a gene expression profile test for determining metastatic risk in melanoma patients. Diagn Pathol 13:13CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Cavalleri T, Angelici L, Favero C et al (2017) Plasmatic extracellular vesicle microRNAs in malignant pleural mesothelioma and asbestos-exposed subjects suggest a 2-miRNA signature as potential biomarker of disease. PLoS One 12(5)CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Gnani D, Romito I, Artuso S et al (2017) Focal adhesion kinase depletion reduces human hepatocellular carcinoma growth by repressing enhancer of zeste homolog 2. Cell Death Differ 24:889CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Almeida TA, Quispe-Ricalde A, de Oca FM et al (2014) A high-throughput open-array qPCR gene panel to identify housekeeping genes suitable for myometrium and leiomyoma expression analysis. Gynecol Oncol 134:138–143CrossRefGoogle Scholar
  18. 18.
    Hudson J, Duncavage E, Tamburrino A et al (2013) Overexpression of miR-10a and miR-375 and downregulation of YAP1 in medullary thyroid carcinoma. Exp Mol Pathol 95:62–67CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Farr RJ, Januszewski AS, Joglekar MV et al (2015) A comparative analysis of high-throughput platforms for validation of a circulating microRNA signature in diabetic retinopathy. Sci Rep 5:10375CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Noce A, Pazzola M, Dettori ML et al (2016) Variations at regulatory regions of the milk protein genes are associated with milk traits and coagulation properties in the Sarda sheep. Anim Genet 47:717–726CrossRefGoogle Scholar
  21. 21.
    Viale E, Zanetti E, Ozdemir D et al (2017) Development and validation of a novel SNP panel for the genetic characterization of Italian chicken breeds by next-generation sequencing discovery and array genotyping. Poultry Sci 96:3858–3866CrossRefGoogle Scholar
  22. 22.
    Pomeroy R, Duncan G, Sunar-Reeder B et al (2009) A low-cost, high-throughput, automated single nucleotide polymorphism assay for forensic human DNA applications. Anal Biochem 395:61–67CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2020

Authors and Affiliations

  • Chiara Broccanello
    • 1
  • Letizia Gerace
    • 2
  • Piergiorgio Stevanato
    • 1
    Email author
  1. 1.DAFNAEUniversità Degli Studi di PadovaLegnaro (PD)Italy
  2. 2.Thermo Fisher Scientific, Life Sciences SolutionsMonza (MB)Italy

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