Rapid and Scalable Profiling of Nascent RNA with fastGRO

Abstract

SUMMARY Genome-wide profiling of nascent RNA has become a fundamental tool to study transcription regulation. Unlike steady-state RNA-sequencing (RNA-seq), nascent RNA profiling mirrors real-time activity of RNA polymerases and provides an accurate readout of transcriptome-wide variations. Some species of nuclear RNAs (i.e., large intergenic noncoding RNAs [lincRNAs] and eRNAs) have a short half-life and can only be accurately gauged by nascent RNA techniques. Furthermore, nascent RNA-seq detects post-cleavage RNA at termination sites and promoter-associated antisense RNAs, providing insights into RNA polymerase II (RNAPII) dynamics and processivity. Here, we present a run-on assay with 4-thio ribonucleotide (4-S-UTP) labeling, followed by reversible biotinylation and affinity purification via streptavidin. Our protocol allows streamlined sample preparation within less than 3 days. We named the technique fastGRO (fast Global Run-On). We show that fastGRO is highly reproducible and yields a more complete and extensive coverage of nascent RNA than comparable techniques can. Importantly, we demonstrate that fastGRO is scalable and can be performed with as few as 0.5 × 10 6 cells.

POINT technology illuminates the processing of polymerase-associated intact nascent transcripts

“…This is because a high concentration of strong detergent (3% Empigen) is added during both chromatin isolation and Pol II IP steps, which removes all contaminating RNA species without affecting Pol II IP. These contaminating RNAs, such as mature spliced transcripts are often detected in other nascent RNA labeling approaches (Barbieri et al, 2020). Consequently, POINT technology enables the rapid purification of a truly nascent RNA preparation.…”

HOTTIP-dependent R-loop formation regulates CTCF boundary activity and TAD integrity in leukemia

“…Global Run-On sequencing (GRO-seq) GRO-seq experiment was carried out as previously described (Gardini, 2017, Barbieri et al, 2020. Briefly, 1x10 7 -1x10 8 cells were washed twice with ice-cold PBS, swelled in swelling buffer (10 mM Tris-HCL pH 7.5, 2mM MgCl 2 , 3 mM CaCl 2 , 2U/ml Superasein (Invitrogen)) for 5 min on ice, resuspended in swelling buffer with 10% glycerol, and lysed in lysis buffer (10 mM Tris-HCL pH 7.5, 2 mM MgCl 2 , 3 mM CaCl 2 , 10% glycerol, 1% Igepal (NP-40), 2 U/ml Superase-in) for 5 min on ice.…”

Functional annotation of breast cancer risk loci: current progress and future directions

“…Regulatory factors bounded to the polymerase might be eliminated by the use of sarkosyl to prevent de novo initiation of transcription. fastGRO-seq [ 56 ]: modification of GRO-seq using 4-thio ribonucleotide (4-S-UTP) labelling followed by biotin tagging of the 4-S-UTP residues which are then captured using streptavidin beads. More efficient assay time wise and in terms of cell input (0.5 × 10 6 ) cells required.…”

“…GRO-seq [ 48 ], one of these high- throughput adaptations (Table 3 ), rather than incorporating radionucleotides (as used in the nuclear run- on assays) uses bromodeoxyuridine labelling of nascent RNA transcripts followed by immunoprecipitation using an antibody against bromodeoxyuridine. Subsequent methods (PRO-seq [ 54 ], mNET-seq [ 55 ], fastGRO-seq [ 56 ] and TTchem-seq [ 57 ], Table 3 ) have introduced modifications to this protocol that involve 4-thiouridine labelling, incorporating a biotin tag and/or hydrolysis rather than sonication to fragment the nascent RNAs (Table 3 ). In the context of breast cancer specifically, Franco et al generated GRO-seq data in a series of 13 breast cell lines (11 cancer and two immortalised “normal” breast cell lines), and combined these with RNA-seq and ChIP-seq data to investigate whether subtype-specific gene expression programmes control breast cancer pathogenesis [ 58 ].…”

“…Lenti-MPRA has several advantages that are likely to render these individual assays obsolete, specifically, by generating high- throughput data that capture the “in genome” activity of several thousand CCVs simultaneously in “hard-to-transfect” primary cells. Comparing GRO-seq with other enhancer marks (open chromatin and active histone modifications), Franco et al demonstrated that GRO-seq identifies smaller numbers of high- specificity enhancers [ 58 ] and recent adaptations to the protocol reduce cell numbers, such that it should be possible to generate these data too, in primary cells [ 56 ]. However, without a formal comparison of these data types in the same cell types, and an understanding of ground truth (presumably in the form of extensive well-characterised positive and negative controls), it is not possible to say which methodology performs best in terms of providing a functional readout for bona fide regulatory elements.…”