简化的工作流程从同一样本生成small RNA和RNA-seq文库，以更深入地洞察肿瘤与正常配对组织

Small noncoding RNAs (sncRNAs) play fundamental roles in many biological processes including cancer development and drug resistance. Increasing evidence shows that sncRNA expression signatures can be used to discriminate between normal and cancer tissues, highlighting their potential as diagnostic and prognostic biomarkers of the disease. Differentially expressed sncRNAs can be investigated using small RNA next generation sequencing methods and can be used to infer downstream effects on the transcriptome. However, performing RNA-seq from the same samples is necessary to confidently determine expression of any given affected transcript and understand the overall transcription profile. Making both small RNA and standard RNA-seq libraries can be challenging when sample material is limiting. Workflows that enable multiple data types to be generated from a single sample are needed for comprehensive understanding of disease. Here we demonstrate the use of the NEB Monarch RNA size fractionation workflow to allow a single sample to be used for both small RNA and ribosomal depleted RNA-seq library preps. The Monarch RNA cleanup columns were used to enrich small RNA (< 200 nucleotides) and recover large transcripts (> 200 nucleotides) from 6 different human tissues (brain, testis, placenta, bladder, ovary, and esophagus) as well as 5 sets of matched normal and tumor samples (breast, stomach, rectum, colon, and liver). Small RNA libraries were prepared using either total RNA or enriched small RNA samples (from the Monarch RNA column) with the NEBNext® Low-bias Small RNA Library Prep Kit. Ribosomal depleted RNA-seq libraries were prepared from the large transcript fractions using the NEBNext UltraExpress® RNA Library Prep Kit downstream of NEBNext rRNA Depletion Kit v2. We correlated expression of individual sncRNAs between total RNA and enriched small RNA libraries across all six normal tissues. In matched tumor and normal samples, we evaluated the relationships between miRNAs and their targets within the differentially expressed transcripts from both small RNA and transcriptome datasets. Overall, generation of small RNA and RNA-seq libraries from the same starting material permits a better understanding of the biological regulation of transcriptomes in individual samples.