在这项研究中，我们深入研究了 RNA-Seq 和 NanoString 平台上基因表达数据的比较分析。虽然 RNA-Seq 覆盖了 19,671 个基因，NanoString 靶向了与病毒免疫反应相关的 773 个基因，但我们的主要关注点是在两个平台中发现的 754 个基因。我们的实验涉及 16 种不同的感染条件，来自 3D 气道器官组织等效物的样本受到三种病毒类型的影响：甲型流感病毒 (IAV)、人类偏肺病毒 (MPV) 和副流感病毒 3 (PIV3)。经过 UV（非活性病毒）和非 UV（活性病毒）处理后，每隔 24 小时和 72 小时记录一次感染后测量结果。将未经处理和模拟感染的 OTE 作为对照组，能够将病毒引起的变化与程序因素引起的变化区分开来。通过一系列方法论（包括 Spearman 相关性、距离相关性、Bland-Altman 分析、广义线性模型 Huber 回归、幅度-高度评分 (MAS) 算法和基因本体分析），该研究仔细对比了 RNA-Seq 和 NanoString 数据集。幅度-高度评分算法整合了基因表达变化的幅度（幅度）及其统计相关性（高度），提供了一种综合工具，用于根据特定病毒感染条件下的差异表达谱对基因进行优先级排序。我们观察到平台之间存在很强的一致性，特别是在识别关键的抗病毒防御基因方面。两个平台一致强调基因，包括 ISG15、MX1、RSAD2 和 OAS 家族成员（OAS1、OAS2、OAS3）。 IFIT 蛋白（IFIT1、IFIT2、IFIT3）因其在两个平台对抗病毒复制中的关键作用而受到强调。此外，CXCL10 和 CXCL11 被精确定位，揭示了器官组织等效物对病毒感染的先天免疫反应。虽然这两个平台都为病毒感染下类器官的遗传景观提供了宝贵的见解，但 NanoString 平台通常在 RNA-Seq 信号更微妙的情况下提供更详细的图片。来自两个平台的综合数据强调了它们在增进我们对病毒对肺类器官影响的理解方面的共同价值。版权所有 © 2024 Rezapour、Walker、Ornelles、Niazi、McNutt、Atala 和 Gurcan。

In this study, we delved into the comparative analysis of gene expression data across RNA-Seq and NanoString platforms. While RNA-Seq covered 19,671 genes and NanoString targeted 773 genes associated with immune responses to viruses, our primary focus was on the 754 genes found in both platforms. Our experiment involved 16 different infection conditions, with samples derived from 3D airway organ-tissue equivalents subjected to three virus types, influenza A virus (IAV), human metapneumovirus (MPV), and parainfluenza virus 3 (PIV3). Post-infection measurements, after UV (inactive virus) and Non-UV (active virus) treatments, were recorded at 24-h and 72-h intervals. Including untreated and Mock-infected OTEs as control groups enabled differentiating changes induced by the virus from those arising due to procedural elements. Through a series of methodological approaches (including Spearman correlation, Distance correlation, Bland-Altman analysis, Generalized Linear Models Huber regression, the Magnitude-Altitude Score (MAS) algorithm and Gene Ontology analysis) the study meticulously contrasted RNA-Seq and NanoString datasets. The Magnitude-Altitude Score algorithm, which integrates both the amplitude of gene expression changes (magnitude) and their statistical relevance (altitude), offers a comprehensive tool for prioritizing genes based on their differential expression profiles in specific viral infection conditions. We observed a strong congruence between the platforms, especially in identifying key antiviral defense genes. Both platforms consistently highlighted genes including ISG15, MX1, RSAD2, and members of the OAS family (OAS1, OAS2, OAS3). The IFIT proteins (IFIT1, IFIT2, IFIT3) were emphasized for their crucial role in counteracting viral replication by both platforms. Additionally, CXCL10 and CXCL11 were pinpointed, shedding light on the organ tissue equivalent's innate immune response to viral infections. While both platforms provided invaluable insights into the genetic landscape of organoids under viral infection, the NanoString platform often presented a more detailed picture in situations where RNA-Seq signals were more subtle. The combined data from both platforms emphasize their joint value in advancing our understanding of viral impacts on lung organoids.Copyright © 2024 Rezapour, Walker, Ornelles, Niazi, McNutt, Atala and Gurcan.