Nuclear Run-On sequencing is a powerful method to measure transcription with high resolution, sensitivity, and directional information, which provides alternative perspective from existing methods such as chromatin immunoprecipitation or mRNA sequencing. Current form of Nuclear Run-On assays such as Precision Run-On sequencing (PRO-seq) involves multiple RNA chemistry steps including RNA end repairs and ligations. These have limited the widespread use of PRO-seq by requiring robust RNA handling skills and multiple days of effort. To solve this, we developed an ultrashort PRO-seq (uPRO) method that requires minimal steps. In uPRO, the requirement of only two reactions -RNA adaptor ligation and template switch reverse transcriptionreduced the procedure into less than a single day. Using uPRO, we generated genome-wide transcription profiles of human haploid cell lines (HAP1) and peripheral blood samples combined with Chromatin Run-On sequencing (pChRO). Blood cell handling procedure is dramatically reduced using pChRO directly on crude chromatin preparations, and enables utilizing archived specimens. As a result, we identified individual differences in the transcriptional profiles of human whole blood from a small volume (~1 ml). We also generated blood cell type specific transcription data, and deconvoluted the nucleated blood cell compositions by modeling to the reference datasets. Overall, uPRO and pChRO provided a powerful platform to identify differentially expressed genes between individuals with minimal sample requirements. Fig 1. Schematics of the uPRO procedure A. Comparison between conventional PRO-seq and uPRO procedures. Adapted from Mahat et al 12

Enhancer RNAs (eRNAs) are non-coding RNAs produced from transcriptional enhancers that are highly correlated with their activities. Using capped nascent RNA sequencing (PRO-cap) dataset in human lymphoblastoid cell lines across individuals, we identified inter-individual variation of expression in over 80 thousand transcribed transcriptional regulatory elements (tTREs), in both enhancers and promoters. Co-expression analysis of eRNAs from tTREs across individuals revealed how enhancers interact with each other and with promoters. Mid-to-long range interactions showed distance-dependent decay, which was modified by TF occupancy. In particular, we found a class of ‘bivalent’ TFs, including Cohesin, which both facilitates and insulates the interaction between enhancers and/or promoters depending on the topology. In short ranges, we observed strand specific interactions between nearby eRNAs in both convergent or divergent orientations. Our finding supports a cooperative convergent eRNA model, which is compatible with eRNA remodeling neighboring enhancers rather than interfering with each other. Therefore, our approach to infer functional interactions from co-expression analyses provided novel insights into the principles of enhancer interactions depending on the distance, orientation, and the binding landscapes of TFs.

Regulation of gene expression takes place at multiple stages of RNA synthesis, processing, and decay. The dynamics of RNA processing is an essential layer of RNA regulation, required for a deeper understanding of gene expression. Here, we present a streamlined analysis combining nascent RNA sequencing and poly(A) tail length sequencing methods, named Stereoscopic Analysis of Transcriptome (STOAT). Using this analysis, we were able to redefine known and unknown transcripts with high precision, and quantitatively assess RNA expression and stability. We also investigated poly(A) tail processing and their linkage to post-transcriptional features in different cell lines and identified the diversity of microRNA networks in RNA stability and opposing effects of HuR-TTP on poly(A) processing among human cell lines. This method can effectively measure promoter activity, RNA synthesis, poly(A) processing, stability, and decay, providing a comprehensive perspective of the dynamic transcriptome as well as discovering diverse transcript isoforms.