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

Springer Science and Business Media LLC - Tập 18 - Trang 1-9 - 2017
Huaping Liu1,2, Yawei Li1, Jun He1, Qingzhou Guan1, Rou Chen1, Haidan Yan1, Weicheng Zheng1, Kai Song2, Hao Cai1, You Guo1, Xianlong Wang1, Zheng Guo1,3,2,4
1Department of Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China
2Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
3Fujian Key Laboratory of Tumor Microbiology, Fujian Medical University, Fuzhou, China
4Key Laboratory of Medical bioinformatics, Fujian Province, China

Tóm tắt

It 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. We 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. REOs-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.

Tài liệu tham khảo

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