PDF(Pubmed)
Abstract：
Over the last decade, the field of systems vaccinology has emerged, in which high throughput transcriptomics and other omics assays are used to probe changes of the innate and adaptive immune system in response to vaccination. The goal of this study was to benchmark key technical and analytical parameters of RNA sequencing (RNA-seq) in the context of a multi-site, double-blind randomized vaccine clinical trial.
We collected longitudinal peripheral blood mononuclear cell (PBMC) samples from 10 subjects before and after vaccination with a live attenuated Francisella tularensis vaccine and performed RNA-Seq at two different sites using aliquots from the same sample to generate two replicate datasets (5 time points for 50 samples each). We evaluated the impact of (i) filtering lowly-expressed genes, (ii) using external RNA controls, (iii) fold change and false discovery rate (FDR) filtering, (iv) read length, and (v) sequencing depth on differential expressed genes (DEGs) concordance between replicate datasets. Using synthetic mRNA spike-ins, we developed a method for empirically establishing minimal read-count thresholds for maintaining fold change accuracy on a per-experiment basis. We defined a reference PBMC transcriptome by pooling sequence data and established the impact of sequencing depth and gene filtering on transcriptome representation. Lastly, we modeled statistical power to detect DEGs for a range of sample sizes, effect sizes, and sequencing depths.
Our results showed that (i) filtering lowly-expressed genes is recommended to improve fold-change accuracy and inter-site agreement, if possible guided by mRNA spike-ins (ii) read length did not have a major impact on DEG detection, (iii) applying fold-change cutoffs for DEG detection reduced inter-set agreement and should be used with caution, if at all, (iv) reduction in sequencing depth had a minimal impact on statistical power but reduced the identifiable fraction of the PBMC transcriptome, (v) after sample size, effect size (i.e. the magnitude of fold change) was the most important driver of statistical power to detect DEG. The results from this study provide RNA sequencing benchmarks and guidelines for planning future similar vaccine studies.
We collected longitudinal peripheral blood mononuclear cell (PBMC) samples from 10 subjects before and after vaccination with a live attenuated Francisella tularensis vaccine and performed RNA-Seq at two different sites using aliquots from the same sample to generate two replicate datasets (5 time points for 50 samples each). We evaluated the impact of (i) filtering lowly-expressed genes, (ii) using external RNA controls, (iii) fold change and false discovery rate (FDR) filtering, (iv) read length, and (v) sequencing depth on differential expressed genes (DEGs) concordance between replicate datasets. Using synthetic mRNA spike-ins, we developed a method for empirically establishing minimal read-count thresholds for maintaining fold change accuracy on a per-experiment basis. We defined a reference PBMC transcriptome by pooling sequence data and established the impact of sequencing depth and gene filtering on transcriptome representation. Lastly, we modeled statistical power to detect DEGs for a range of sample sizes, effect sizes, and sequencing depths.
Our results showed that (i) filtering lowly-expressed genes is recommended to improve fold-change accuracy and inter-site agreement, if possible guided by mRNA spike-ins (ii) read length did not have a major impact on DEG detection, (iii) applying fold-change cutoffs for DEG detection reduced inter-set agreement and should be used with caution, if at all, (iv) reduction in sequencing depth had a minimal impact on statistical power but reduced the identifiable fraction of the PBMC transcriptome, (v) after sample size, effect size (i.e. the magnitude of fold change) was the most important driver of statistical power to detect DEG. The results from this study provide RNA sequencing benchmarks and guidelines for planning future similar vaccine studies.
摘要：
未经批准：在过去的十年中，系统疫苗学领域已经出现，其中高通量转录组学和其他组学测定用于探测对疫苗接种反应的先天和适应性免疫系统的变化。这项研究的目的是在多位点的背景下对RNA测序（RNA-seq）的关键技术和分析参数进行基准测试。双盲随机疫苗临床试验。
UNASSIGNED：我们收集了10名受试者在用减毒活的土力弗朗西斯菌疫苗接种前后的纵向外周血单核细胞（PBMC）样品，并使用来自同一样品的等分试样在两个不同位点进行RNA-Seq，以生成两个重复数据集（每个50个样品的5个时间点）。我们评估了(i)过滤低表达基因的影响，（ii）使用外部RNA对照，(iii)倍数变化和错误发现率(FDR)过滤，(iv)读取长度，和(v)重复数据集之间的差异表达基因(DEGs)一致性的测序深度。使用合成的mRNA刺突蛋白，我们开发了一种根据经验建立最小读取计数阈值的方法,以在每个实验的基础上保持倍数变化的准确性.我们通过汇集序列数据定义了参考PBMC转录组，并建立了测序深度和基因过滤对转录组表示的影响。最后,我们对一系列样本大小的DEG检测统计能力进行了建模，效果大小,和排序深度。
UNASSIGNED：我们的结果表明，（i）建议过滤低表达的基因以提高倍数变化的准确性和位点间的一致性，如果可能，通过mRNA尖峰蛋白（ii）阅读长度对DEG检测没有重大影响，(iii)对DEG检测应用倍数变化截止值减少了内部一致性，应谨慎使用，如果有的话，（iv）测序深度的减少对统计能力的影响最小，但减少了PBMC转录组的可识别部分，(V)样本量后，效应大小(即倍数变化的大小)是检测DEG的统计能力的最重要驱动因素。这项研究的结果为规划未来的类似疫苗研究提供了RNA测序基准和指南。
UNASSIGNED：我们收集了10名受试者在用减毒活的土力弗朗西斯菌疫苗接种前后的纵向外周血单核细胞（PBMC）样品，并使用来自同一样品的等分试样在两个不同位点进行RNA-Seq，以生成两个重复数据集（每个50个样品的5个时间点）。我们评估了(i)过滤低表达基因的影响，（ii）使用外部RNA对照，(iii)倍数变化和错误发现率(FDR)过滤，(iv)读取长度，和(v)重复数据集之间的差异表达基因(DEGs)一致性的测序深度。使用合成的mRNA刺突蛋白，我们开发了一种根据经验建立最小读取计数阈值的方法,以在每个实验的基础上保持倍数变化的准确性.我们通过汇集序列数据定义了参考PBMC转录组，并建立了测序深度和基因过滤对转录组表示的影响。最后,我们对一系列样本大小的DEG检测统计能力进行了建模，效果大小,和排序深度。
UNASSIGNED：我们的结果表明，（i）建议过滤低表达的基因以提高倍数变化的准确性和位点间的一致性，如果可能，通过mRNA尖峰蛋白（ii）阅读长度对DEG检测没有重大影响，(iii)对DEG检测应用倍数变化截止值减少了内部一致性，应谨慎使用，如果有的话，（iv）测序深度的减少对统计能力的影响最小，但减少了PBMC转录组的可识别部分，(V)样本量后，效应大小(即倍数变化的大小)是检测DEG的统计能力的最重要驱动因素。这项研究的结果为规划未来的类似疫苗研究提供了RNA测序基准和指南。
