Title
Multi-gene technical assessment of qPCR and NanoString n-Counter analysis platforms in cynomolgus monkey cardiac allograft recipients
Document Type
Article
Publication Title
Cellular Immunology
Abstract
Quantitative gene expression profiling of cardiac allografts characterizes the phenotype of the alloimmune response, yields information regarding differential effects that may be associated with various anti-rejection drug regimens, and generates testable hypotheses regarding the pathogenesis of the chronic rejection lesions typically observed in non-human primate heart transplant models. The goal of this study was to assess interplatform performance and variability between the relatively novel NanoString nCounter Analysis System, ΔΔCT (relative) RT-qPCR, and standard curve (absolute) RT-qPCR utilizing cynomolgus monkey cardiac allografts. Methods for RNA isolation and preamplification were also systematically evaluated and effective methods are proposed. In this study, we demonstrate strong correlation between the two RT-qPCR methods, but variable and, at times, weak correlation between RT-qPCR and NanoString. NanoString fold change results demonstrate less sensitivity to small changes in gene expression than RT-qPCR. These findings appear to be driven by technical aspects of each platform that influence the conditions under which each technique is ideal. Collectively, our data contribute to the general effort to optimally utilize gene expression profiling techniques, not only for transplanted tissues, but for many other applications where accurate rank-order of gene expression versus precise quantification of absolute gene transcript number may be relatively valuable.
DOI
https://doi.org/10.1016/j.cellimm.2019.104019
Publication Date
1-2020
Recommended Citation
Bergbower EAS, Pierson RN 3rd, Azimzadeh AM. Multi-gene technical assessment of qPCR and NanoString n-Counter analysis platforms in cynomolgus monkey cardiac allograft recipients. Cell Immunol. 2020 Jan;347:104019. doi: 10.1016/j.cellimm.2019.104019. Epub 2019 Nov 8. PMID: 31744596; PMCID: PMC7060784.