比较 NGS 和 NanoString 平台在外周血单核细胞转录组分析中的应用,以开发高级心衰生物标记物。

Journal of biological methods Pub Date : 2020-01-03 eCollection Date: 2020-01-01 DOI:10.14440/jbm.2020.300
Galyna Bondar, Wenjie Xu, David Elashoff, Xinmin Li, Emmanuelle Faure-Kumar, Tra-Mi Bao, Tristan Grogan, Jim Moose, Mario C Deng
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引用次数: 0

摘要

为了准备创建一种在手术/介入疗法前预测晚期心力衰竭(AdHF)患者1年生存状况的临床检测方法,并为未来的检测方法选择合适的临床检测平台,我们比较了基因发现阶段使用的新一代测序(NGS)与临床检测开发阶段使用的NanoString平台的特性。2015年至2016年,在一家三级学术医疗中心的25名AdHF患者中,我们采集了PBMC样本并等分,进行了NGS RNA全转录组测序,并与NanoString的PanCancer IO 360基因表达研究面板上的770个基因进行了比较。在进行统计分析之前,对 NanoString 和 NGS 的表达值进行了对数转换。我们计算了每个样本的皮尔逊相关系数,比较了 NanoString 和 NGS 在一组匹配基因和一组样本中每个匹配基因的基因表达值。基因按 NGS 平均表达量分组,并计算每组的 NanoString-NGS 相关性。在 NanoString 面板的 770 个基因中,有 734 个基因在两个平台之间重叠,并显示出较高的样本内相关性。在单个样本中,两个平台的相关性取决于表达水平。低表达组与中/高表达组的 NGS 平均相关性分别为 0.21 与 0.58-0.68,NanoString 平均相关性分别为 0.07-0.34 与 0.59-0.70。NanoString 表现出较高的可重复性(100 毫微克输入的 R 2 > 0.99)、灵敏度(检测和量化的探针数在 100 到 500 之间)和稳健性(不同 RNA 输入浓度、试剂盒和结果下的基因特征得分相似)。NGS 和 NanoString 的数据高度相关。这些平台在生物标记物开发过程中发挥着重要的互补作用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Comparing NGS and NanoString platforms in peripheral blood mononuclear cell transcriptome profiling for advanced heart failure biomarker development.

In preparation to create a clinical assay that predicts 1-year survival status of advanced heart failure (AdHF) patients before surgical/interventional therapies and to select the appropriate clinical assay platform for the future assay, we compared the properties of next generation sequencing (NGS) used in the gene discovery phase to the NanoString platform used in the clinical assay development phase. In 25 AdHF patients in a tertiary academic medical center from 2015 to 2016, PBMC samples were collected and aliquoted for NGS RNA whole transcriptome sequencing and compared to 770 genes represented on NanoString's PanCancer IO 360 Gene Expression research panel. Prior to statistical analysis, NanoString and NGS expression values were log transformed. We computed Pearson correlation coefficients for each sample, comparing gene expression values between NanoString and NGS across the set of matched genes and for each of the matched genes across the set of samples. Genes were grouped by average NGS expression, and the NanoString-NGS correlation for each group was computed. Out of 770 genes from the NanoString panel, 734 overlapped between both platforms and showed high intrasample correlation. Within an individual sample, there was an expression-level dependent correlation between both platforms. The low- vs. intermediate/high-expression groups showed NGS average correlation 0.21 vs. 0.58-0.68, respectively, and NanoString average correlation 0.07-0.34 vs. 0.59-0.70, respectively. NanoString demonstrated high reproducibility (R 2 > 0.99 for 100 ng input), sensitivity (probe counts between 100 and 500 detected and quantified), and robustness (similar gene signature scores across different RNA input concentrations, cartridges, and outcomes). Data from NGS and NanoString were highly correlated. These platforms play a meaningful, complementary role in the biomarker development process.

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