Robust transcriptional signatures for low-input RNA samples based on relative expression orderings

BMC Genomics

Research Article

Haidan Yan¹  Xianlong Wang¹  Jun He¹  You Guo¹  Qingzhou Guan¹  Yawei Li¹  Rou Chen¹  Weicheng Zheng¹  Hao Cai¹  Huaping Liu²  Zheng Guo³  Kai Song⁴ 

[1] Department of Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, School of Basic Medical Sciences, Fujian Medical University, 350122, Fuzhou, China;Department of Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, School of Basic Medical Sciences, Fujian Medical University, 350122, Fuzhou, China;Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, 150086, Harbin, China;Department of Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, School of Basic Medical Sciences, Fujian Medical University, 350122, Fuzhou, China;Fujian Key Laboratory of Tumor Microbiology, Fujian Medical University, 350122, Fuzhou, China;Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, 150086, Harbin, China;Key Laboratory of Medical bioinformatics, Fujian Province, China;Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, 150086, Harbin, China;

关键词: Low-input RNA samples - amplification artificial signals - relative expression orderings - transcriptional signatures;   

DOI  :  10.1186/s12864-017-4280-7

 received in 2017-06-02, accepted in 2017-11-03,  发布年份 2017

来源: Springer

【 摘 要 】

BackgroundIt is often difficult to obtain sufficient quantity of RNA molecules for gene expression profiling under many practical situations. Amplification from low-input samples may induce artificial signals.ResultsWe compared the expression measurements of low-input mRNA samples, from 25 pg to 1000 pg mRNA, which were amplified and profiled by Smart-seq, DP-seq and CEL-seq techniques using the Illumina HiSeq 2000 platform, with those of the paired high-input (50 ng) mRNA samples. Even with 1000 pg mRNA input, we found that thousands of genes had at least 2 folds-change of expression levels in the low-input samples compared with the corresponding paired high-input samples. Consequently, a transcriptional signature based on quantitative expression values and determined from high-input RNA samples cannot be applied to low-input samples, and vice versa. In contrast, the within-sample relative expression orderings (REOs) of approximately 90% of all the gene pairs in the high-input samples were maintained in the paired low-input samples with 1000 pg input mRNA molecules. Similar results were observed in the low-input total RNA samples amplified and profiled by the Whole-Genome DASL technique using the Illumina HumanRef-8 v3.0 platform. As a proof of principle, we developed REOs-based signatures from high-input RNA samples for discriminating cancer tissues and showed that they can be robustly applied to low-input RNA samples.ConclusionsREOs-based signatures determined from the high-input RNA samples can be robustly applied to samples profiled with the low-input RNA samples, as low as the 1000 pg and 250 pg input samples but no longer stable in samples with less than 250 pg RNA input to a certain degree.

【 授权许可】

CC BY   
© The Author(s). 2017