背景:血液转录组学分析被广泛用于提供生理状态的详细图片,以及用于诊断和监测对疫苗的免疫反应的潜在结果。然而,从技术角度来看,多物种转录组分析仍然是一个挑战,迫切需要标准化的工作流程来进行种间比较。
结果:这里,我们提出了一个单一和完整的总RNA-Seq工作流程,以从人类和临床前模型中通常使用的动物的血液样本中生成可靠的转录组数据。最多六个个体和四个不同物种的血液样本(兔子,非人灵长类动物,小鼠和人)被提取并测序,一式三份。使用不同的湿实验室和干实验室标准评估工作流程,包括RNA的质量和数量,图书馆的摩尔浓度,原始测序读数的数量,Phred-score质量,GC含量,核糖体RNA和珠蛋白耗竭的表现,残留DNA的存在,杂乱,编码基因的百分比,表达的基因数量,稀疏曲线中存在饱和高原。我们确定了验证转录组学工作流程的关键标准及其相关阈值。在这项研究中,我们还对转录组数据进行了自动分析,简化了对生成数据集的验证.
结论:我们的研究开发了一种端到端的工作流程,应改善血液转录组学研究中的标准化和种间比较。在疫苗和药物开发的背景下,来自临床前模型的RNA测序数据可以直接与临床数据进行比较,并用于识别有价值的潜在生物标志物,以监测安全性和有效性。
BACKGROUND: Blood transcriptomic analysis is widely used to provide a detailed picture of a physiological state with potential outcomes for applications in diagnostics and monitoring of the immune response to vaccines. However, multi-species transcriptomic analysis is still a challenge from a technological point of view and a standardized workflow is urgently needed to allow interspecies comparisons.
RESULTS: Here, we propose a single and complete total RNA-Seq workflow to generate reliable transcriptomic data from blood samples from humans and from animals typically used in preclinical models. Blood samples from a maximum of six individuals and four different species (rabbit, non-human primate, mouse and human) were extracted and sequenced in triplicates. The workflow was evaluated using different wet-lab and dry-lab criteria, including RNA quality and quantity, the library molarity, the number of raw sequencing reads, the Phred-score quality, the GC content, the performance of ribosomal-RNA and globin depletion, the presence of residual DNA, the strandness, the percentage of coding genes, the number of genes expressed, and the presence of saturation plateau in rarefaction curves. We identified key criteria and their associated thresholds to be achieved for validating the transcriptomic workflow. In this study, we also generated an automated analysis of the transcriptomic data that streamlines the validation of the dataset generated.
CONCLUSIONS: Our study has developed an end-to-end workflow that should improve the standardization and the inter-species comparison in blood transcriptomics studies. In the context of vaccines and drug development, RNA sequencing data from preclinical models can be directly compared with clinical data and used to identify potential biomarkers of value to monitor safety and efficacy.