Cardiovascular Research Center, Massachusetts General Hospital, Harvard University, 185 Cambridge Street, Boston, MA, 02114, USA.
Neurogenomics Division, Translational Genomics Research Institute, 445 N. 5th St, Phoenix, AZ, 85004, USA.
BMC Genomics. 2018 May 5;19(1):331. doi: 10.1186/s12864-018-4726-6.
Evolving interest in comprehensively profiling the full range of small RNAs present in small tissue biopsies and in circulating biofluids, and how the profile differs with disease, has launched small RNA sequencing (RNASeq) into more frequent use. However, known biases associated with small RNASeq, compounded by low RNA inputs, have been both a significant concern and a hurdle to widespread adoption. As RNASeq is becoming a viable choice for the discovery of small RNAs in low input samples and more labs are employing it, there should be benchmark datasets to test and evaluate the performance of new sequencing protocols and operators. In a recent publication from the National Institute of Standards and Technology, Pine et al., 2018, the investigators used a commercially available set of three tissues and tested performance across labs and platforms.
In this paper, we further tested the performance of low RNA input in three commonly used and commercially available RNASeq library preparation kits; NEB Next, NEXTFlex, and TruSeq small RNA library preparation. We evaluated the performance of the kits at two different sites, using three different tissues (brain, liver, and placenta) with high (1 μg) and low RNA (10 ng) input from tissue samples, or 5.0, 3.0, 2.0, 1.0, 0.5, and 0.2 ml starting volumes of plasma. As there has been a lack of robust validation platforms for differentially expressed miRNAs, we also compared low input RNASeq data with their expression profiles on three different platforms (Abcam Fireplex, HTG EdgeSeq, and Qiagen miRNome).
The concordance of RNASeq results on these three platforms was dependent on the RNA expression level; the higher the expression, the better the reproducibility. The results provide an extensive analysis of small RNASeq kit performance using low RNA input, and replication of these data on three downstream technologies.
人们对全面分析小组织活检和循环生物体液中存在的小 RNA 全谱，以及这些谱如何因疾病而不同的兴趣不断增加，这促使小 RNA 测序（RNASeq）得到更频繁的应用。然而，与 RNASeq 相关的已知偏倚，加上低 RNA 输入，一直是广泛采用的一个重要关注点和障碍。随着 RNASeq 成为低输入样本中小 RNA 发现的可行选择，越来越多的实验室正在使用它，应该有基准数据集来测试和评估新测序方案和操作人员的性能。在最近由国家标准与技术研究院发表的一篇文章中，Pine 等人，2018 年，研究人员使用了一组市售的三种组织，并在不同实验室和平台上测试了性能。
在本文中，我们进一步测试了三种常用市售 RNASeq 文库制备试剂盒（NEB Next、NEXTFlex 和 TruSeq small RNA library preparation）在低 RNA 输入时的性能。我们在两个不同的地点使用三种不同的组织（脑、肝和胎盘）进行了评估，这些组织的 RNA 输入量分别为高（1 μg）和低（10 ng），或起始体积为 5.0、3.0、2.0、1.0、0.5 和 0.2 ml 的血浆。由于缺乏用于差异表达 miRNA 的稳健验证平台，我们还将低输入 RNASeq 数据与三种不同平台（Abcam Fireplex、HTG EdgeSeq 和 Qiagen miRNome）上的表达谱进行了比较。
这三个平台上的 RNASeq 结果的一致性取决于 RNA 表达水平；表达水平越高，重现性越好。这些结果提供了使用低 RNA 输入对小 RNA 测序试剂盒性能的广泛分析，并在三种下游技术上复制了这些数据。
