Extracellular vesicles (EVs) have great potential as a source for clinically relevant biomarkers since they can be readily isolated from biofluids and carry microRNA (miRNA), mRNA, and proteins that can reflect disease status. However, the biological and technical variability of EV content is unknown making comparisons between healthy subjects and patients difficult to interpret. In this study, we sought to establish a laboratory and bioinformatics analysis pipeline to analyse the small RNA content within EVs from patient serum that could serve as biomarkers and to assess the biological and technical variability of EV RNA content in healthy individuals. We sequenced EV small RNA from multiple individuals (biological replicates) and sequenced multiple replicates per individual (technical replicates) using the Illumina Truseq protocol. We observed that the replicates of samples clustered by subject indicating that the biological variability (~95%) was greater than the technical variability (~0.50%). We observed that ~30% of the sequencing reads were miRNAs. We evaluated the technical parameters of sequencing by spiking the EV RNA preparation with a mix of synthetic small RNA and demonstrated a disconnect between input concentration of the spike-in RNA and sequencing read frequencies indicating that bias was introduced during library preparation. To determine whether there are differences between library preparation platforms, we compared the Truseq with the Nextflex protocol that had been designed to reduce bias in library preparation. While both methods were technically robust, the Nextflex protocol reduced the bias and exhibited a linear range across input concentrations of the synthetic spike-ins. Altogether, our results indicate that technical variability is much smaller than biological variability supporting the use of EV small RNAs as potential biomarkers. Our findings also indicate that the choice of library preparation method leads to artificial differences in the datasets generated invalidating the comparability of sequencing data across library preparation platforms.

细胞外囊泡 (EV) 作为临床相关生物标志物的来源具有巨大的潜力，因为它们可以很容易地从生物体液中分离出来，并携带可以反映疾病状态的 microRNA (miRNA)、mRNA 和蛋白质。然而，EV 含量的生物学和技术变异性尚不清楚，因此难以解释健康受试者和患者之间的比较。在这项研究中，我们试图建立一个实验室和生物信息学分析管道来分析患者血清中 EV 中的小 RNA 含量，这些小 RNA 可以作为生物标志物，并评估健康个体中 EV RNA 含量的生物学和技术变异性。我们使用 Illumina Truseq 方案对来自多个个体的 EV 小 RNA（生物重复）进行了测序，并对每个个体的多个重复（技术重复）进行了测序。我们观察到，样本的重复按受试者聚类，表明生物变异性 (~95%) 大于技术变异性 (~0.50%)。我们观察到大约 30% 的测序读数是 miRNA。我们通过在 EV RNA 制剂中掺入合成小 RNA 的混合物来评估测序的技术参数，并证明掺入 RNA 的输入浓度与测序读取频率之间存在脱节，表明在文库制备过程中引入了偏差。为了确定文库制备平台之间是否存在差异，我们将 Truseq 与 Nextflex 协议进行了比较，后者旨在减少文库制备中的偏差。虽然这两种方法在技术上都很可靠，但 Nextflex 方案减少了偏差，并在合成掺入物的输入浓度范围内表现出线性范围。总而言之，我们的结果表明技术变异性远小于生物变异性，支持使用 EV 小 RNA 作为潜在的生物标志物。我们的研究结果还表明，文库制备方法的选择会导致生成的数据集出现人为差异，从而使跨文库制备平台的测序数据的可比性失效。