Measure transcript integrity using RNA-seq data

Achieved biospecimens annotated with patient clinical characteristics are unique resources for translational research. However, RNA extracted from the achieved tissues is often degraded. RNA degradation can have a significant impact on the measure of transcript abundance that can lead to an increase rate of erroneous differentially expressed genes. Here, we are presenting the transcript integrity number (TIN) algorithm to measure the RNA degradation at transcript level. When applied to RNA-seq datasets generated from human brain Glioblastome cell line, human peripheral blood mononuclear cells, and metastatic castration resistant prostate cancer (mCRPC) clinical tissues, TIN provided a more reliable and more sensitive measure of RNA degradation than RIN, as demonstrated by much higher concordance with the RNA fragment size estimated from read pairs. More importantly, when comparing 10 mCRPC samples with lower RNA quality to another 10 samples with higher RNA quality, we demonstrated that calibrating gene quantification with TIN scores could mitigate RNA degradation effects and greatly improve gene expression analysis. The detected differentially expressed genes before TIN correction were predominantly ribosomal genes. However, when we adjusted gene quantifications with the corresponding TIN scores, we found differentially expressed genes were highly enriched in prostate cancer specific pathways. When further evaluating the performance of TIN correction using synthetic spike-in transcripts with predetermined abundance in RNA-seq data generated from Sequencing Control Consortium (SEQC), we found TIN adjustment had a better control of false positives and false negatives (sensitivity = 0.89, specificity = 0.91), as compared to gene expression analysis results without TIN correction (sensitivity =0.98, specificity = 0.50). Overall design: RNA sequencing of 20 bone-metastatic castration resistant prostate cancer (mCRPC) using Illumina HiSeq 2500. Out of 20 mCRPC samples, 10 samples have relative low RNA integrity and another 10 samples have relative higher RNA integrity as measured by Agilent RIN score.

经患者临床特征注释的存档生物样本是转化研究的独特资源。然而，从这类存档组织中提取的RNA常发生降解。RNA降解会对转录本丰度的定量产生显著影响，进而提升错误差异表达基因的检出率。本研究提出了转录本完整性指数（transcript integrity number, TIN）算法，用于在转录本层面评估RNA降解程度。当将该算法应用于人脑胶质母细胞瘤细胞系、人外周血单个核细胞以及转移性去势抵抗性前列腺癌（metastatic castration resistant prostate cancer, mCRPC）临床组织的RNA测序数据集时，相较于RNA完整性指数（RIN），TIN能够更可靠、更灵敏地评估RNA降解程度，这一点通过与基于测序读对估算的RNA片段大小具有更高的一致性得到了验证。更为重要的是，在将10例RNA质量较低的mCRPC样本与另外10例RNA质量较高的样本进行对比分析时，本研究证实，利用TIN评分对基因定量结果进行校准，可减轻RNA降解带来的影响，大幅优化基因表达分析效果。TIN校正前检出的差异表达基因以核糖体基因为主；而在采用对应TIN评分校正基因定量后，差异表达基因则显著富集于前列腺癌特异性通路中。此外，本研究利用测序对照联盟（Sequencing Control Consortium, SEQC）生成的RNA测序数据中预先定量的合成掺入转录本，进一步评估了TIN校正的性能，结果显示，相较于未进行TIN校正的基因表达分析结果（灵敏度=0.98，特异度=0.50），TIN校正能够更好地控制假阳性和假阴性结果（灵敏度=0.89，特异度=0.91）。实验整体设计：采用Illumina HiSeq 2500平台对20例骨转移性去势抵抗性前列腺癌（mCRPC）样本进行RNA测序。依据安捷伦RIN评分，20例mCRPC样本中10例RNA完整性相对较低，另外10例RNA完整性相对较高。