Evaluating robustness and sensitivity of the NanoString technologies nCounter platform to enable multiplexed gene expression analysis of clinical samples

MH Veldman-Jones, R Brant, C Rooney, C Geh… - Cancer research, 2015 - AACR
MH Veldman-Jones, R Brant, C Rooney, C Geh, H Emery, CG Harbron, M Wappett…
Cancer research, 2015AACR
Abstract Analysis of clinical trial specimens such as formalin-fixed paraffin-embedded
(FFPE) tissue for molecular mechanisms of disease progression or drug response is often
challenging and limited to a few markers at a time. This has led to the increasing importance
of highly multiplexed assays that enable profiling of many biomarkers within a single assay.
Methods for gene expression analysis have undergone major advances in biomedical
research, but obtaining a robust dataset from low-quality RNA samples, such as those …
Abstract
Analysis of clinical trial specimens such as formalin-fixed paraffin-embedded (FFPE) tissue for molecular mechanisms of disease progression or drug response is often challenging and limited to a few markers at a time. This has led to the increasing importance of highly multiplexed assays that enable profiling of many biomarkers within a single assay. Methods for gene expression analysis have undergone major advances in biomedical research, but obtaining a robust dataset from low-quality RNA samples, such as those isolated from FFPE tissue, remains a challenge. Here, we provide a detailed evaluation of the NanoString Technologies nCounter platform, which provides a direct digital readout of up to 800 mRNA targets simultaneously. We tested this system by examining a broad set of human clinical tissues for a range of technical variables, including sensitivity and limit of detection to varying RNA quantity and quality, reagent performance over time, variability between instruments, the impact of the number of fields of view sampled, and differences between probe sequence locations and overlapping genes across CodeSets. This study demonstrates that Nanostring offers several key advantages, including sensitivity, reproducibility, technical robustness, and utility for clinical application. Cancer Res; 75(13); 2587–93. ©2015 AACR.
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