GSE159434
Sequencing-based quantitative mapping of the cellular small RNA landscape
Current next-generation RNA sequencing methods do not provide accurate quantification of small RNAs within a sample due to sequence-dependent biases in capture, ligation, and amplification during library preparation. We present a method – AQRNA-seq – that minimizes biases and provides a direct, linear correlation between sequencing read count and copy number for small RNAs in a sample. The library preparation and data processing steps were optimized and validated using a 963-member microRNA reference library, oligonucleotide standards of varying lengths, and northern blots. Application of AQRNA-seq to a panel of human cancer cells revealed >800 detectable miRNAs that varied as a function of cancer progression, while application to bacterial tRNA pools, a traditionally hard-to-sequence class of RNAs, revealed 80-fold variation in tRNA isoacceptor levels, stress-induced site-specific tRNA fragmentation, quantitative modification maps, and evidence for stress-induced tRNA-driven codon-biased translation. AQRNA-seq thus provides a means to quantitatively map the small RNA landscape in cells. Overall design: 15  human mammary epithelial cell (HMEC)  samples including 3 cell types and 5 replicates each type were analyzed. The three cell types in the HMEC model represent progressive stages of tumorigenesis conferred by engineering the cells with tumor-promoting genes: reactivation of telomerase by expression of telomerase catalytic subunit (hTERT)  immortalizes HMEC 1 cells, additional expression of H-Ras oncoprotein (HRASG12V) further drives aberrant growth in HMEC 2 cells, and further P53 suppression by expression of SV40 large-T and small-t antigens yields HMEC 3 cells that are fully capable of tumor growth in mice.