Rapid and Scalable Profiling of Nascent RNA with fastGRO

Abstract

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 × 10⁶ cells.

Keywords: 4-S-UTP; RNA Polymerase II dynamics; biotin; global nuclear run-on; nascent RNA; post-termination RNA; promoter-associated antisense RNA; short-lived transcripts.

Figure 1.. fastGRO Generates Global Nascent Transcriptome Data

(A) Schematic of fastGRO procedure. Nuclei are first isolated, and nuclear run-on (NRO) is performed in vitro in the presence of 4-thio-UTP. NRO RNA is isolated and fragmented, biotinylated, recovered using streptavidin-conjugated beads, and processed for library preparation. NG, next-generation. (B and C) Examples of TapeStation run showing mild fragmentation of NRO RNA extracted from LPS-treated and untreated human THP1 cells. (D) Two replicates of control (CTRL; purple) and LPS-treated THP1 (dark pink) fastGRO samples were analyzed by HOMER to identify common and LPS-induced transcripts. (E) Average density profiles of fastGRO signals for CTRL and LPS-induced THP1 at 300 most expressed genes. TSS, Transcription Start Site, TES, Transcription End Site. (F) Average density profiles of fastGRO reads for CTRL and LPS-induced THP1 at 300 most LPS-induced genes. (G) Screenshot of region surrounding the LPS-induced gene SOD2 showing fastGRO reads along gene body, post-TES, and promoter antisense. (H) Average density profiles of sense and antisense fastGRO reads at 79 putative enhancer regions, identified by the level of H3K27ac (see Figure S3). (I) Screenshots of LPS-induced enhancer RNAs. chr7, chromosome 7. (J) Average density profile of sense and antisense fastGRO reads at the transcription start site (TSS) region of 145 LPS-induced genes. (K) Average profile of fastGRO reads from untreated THP1 at 186 long intergenic non-coding RNAs (lincRNAs) and screenshot of the PVT1 lincRNA in untreated THP1 as depicted by fastGRO. (L) Pausing index was calculated from fastGRO reads for 300 highly expressed genes and 300 LPS-induced genes, showing how fastGRO is a useful approach to study RNAPII elongation. Highly expressed and LPS-induced genes were identified from two replicates of CTRL and LPS using HOMER. Replicate 1 was used to generate profiles and screenshots. Correlation between the two replicates of CTRL and LPS is reported in Figure S2. *** p-value <.001; n.s., not significant.

Figure 2.. Profiling RNAPII Kinetics Using fastGRO

(A) Diagram of the experimental design. THP1 cells were treated with 2 μM CDK9 inhibitor flavopiridol for 2 h to block transcription elongation. To release transcription, cells were re-plated in fresh media to wash out flavopiridol, with the addition of LPS to further stimulate inflammatory genes. Samples for fastGRO analysis were collected at 0, 5, 15, and 30 min after washout. (B) Average profiles of fastGRO at 473 highly expressed genes in THP1 cells (>10 kb) reflect the transcriptional front of RNAPII moving progressively past the proximal promoter over the course of 30 min after release of the elongation block. Profiles were normalized to their TSS. (C) Screenshot of the constitutively expressed HNRNPC gene whose expression is fully recovered 30 min after washout of flavopiridol. As a comparison, data of asynchronously growing THP-1 cells (untreated) are provided. FP, Flavopiridol (D) Boxplot analysis of read density at inflammatory (LPS-induced) genes. Normalized read density of fastGRO was calculated over gene quartiles (as well as 5 kb “Pre-TSS” as a control). Time-dependent increases of the 2nd, 3rd, and 4th quartiles indicate a wave of transcription migrating through the gene body after flavopiridol washout. The analysis was performed on 55 LPS-induced genes over 10 kb in length. (E) Screenshot of the early LPS-induced LDLR gene showing the wave of transcription during the first 30 min after LPS stimulation.

Figure 3.. fastGRO Recovers Nascent, Unprocessed, and Short-Lived Transcripts

(A) Splice junction analysis by MAJIQ shows the substantial recovery of processed RNA by rRNA-depleted (gray) and poly(A)-enriched (dark gray) RNA-seq. fastGRO (purple) is significantly enriched for nascent, unspliced RNA. (B) Comparison of fastGRO to other nascent RNA techniques. An advantage of fastGRO is the overall short processing time (2.5 days, using commercially available library prep kits). (C) fastGRO (purple) shows lower enrichment of spliced junctions than comparable nascent RNA-seq techniques such as GRO-seq (blue), PRO-seq (orange), and TT-seq (green) in THP1 cells. (D) Average profiles of fastGRO, GRO-seq, PRO-seq, and TT-seq at 271 highly expressed genes in THP1 cells. fastGRO shows a lower bias toward the 5′ end compared to GRO-seq and recovers more post-termination RNA compared to TT-seq. (E) Screenshot of the CCNL1 gene comparing fastGRO, GRO-seq, PRO-seq, and TT-seq in THP1 cells. (F) Average profiles of fastGRO (purple), GRO-seq (blue), PRO-seq (orange), and mNET-seq (black) at 290 highly expressed genes in HeLa cells. fastGRO shows a homogeneous profile along the whole gene body. (G) Screenshot of the BMP2 gene showing the comparison of fastGRO, GRO-seq, PRO-seq, and mNET-seq tracks in HeLa cells. mNET-seq data are downscaled (right y axis). (H) Average profile of fastGRO, GRO-seq, PRO-seq, and mNET-seq reads at 50 eRNAs in HeLa cells. fastGRO recovers bidirectional short-lived eRNAs. mNET-seq data are downscaled (right y axis). Comparison between techniques were performed using replicate 1 of fastGRO in THP1 and, where possible, the best replicate of experiments deposited in GEO. In HeLa, one replicate each of CTRL and EGF fastGRO was generated and compared to published data deposited in GEO.

Figure 4.. fastGRO Identifies Transient RNA Downstream of the Poly(A) Signal

(A) Boxplot of termination index (TI; calculated as ratio between number of reads post-transcription end site (TES/+3 kb) and number of reads pre-TES (−0.5 kb/TES)) at 271 most expressed genes calculated from fastGRO (purple), GRO-seq (blue), PRO-seq (orange), and TT-seq (green) data. fastGRO and GRO-seq have comparable TIs, while the TIs generated from PRO-seq and TT-seq are lower (lower coverage of post-termination RNA). (B) Average profile of reads around TES of 271 highly expressed genes calculated for fastGRO, GRO-seq, PRO-seq, and TT-seq. fastGRO shows the highest and most homogeneous profile pre- and post-TES compared to other techniques used to study nascent RNA. (C and D) Screenshots of the monocytic gene FUT4 and the constitutively active gene SFPQ showing the high coverage of post-termination RNA retrieved by fastGRO. Comparison between techniques were performed using replicate 1 of fastGRO in THP1 and, where possible, the best replicate of experiments deposited in GEO.

Figure 5.. Low-Input fastGRO

(A and B) Average profiles of fastGRO reads at 200 highly expressed genes (A) and 50 EGF genes (B) in EGF-treated HeLa cells obtained from 20 million (purple) and 5 million (black) cells using standard protocol and biotin-HPDP and from 2.5 million (blue), 1 million (orange), and 0.5 million (green) cells obtained with the fastGRO-LI protocol and biotin-MTS, indicating that fastGRO can be performed with low numbers of cells. (C) Pie charts indicate the percentage of genes with FPKM (fragments per kilobase of exon per million fragments mapped) > 0 in fastGRO obtained with 20 million EGF-induced HeLa cells that have FPKM > 0 in fastGRO obtained from 2.5 million (89%), 1 million (90%), and 0.5 million (80%) EGF-induced HeLa cells. (D) Screenshot of DDIT4 gene showing fastGRO tracks obtained using either standard protocol (20 million cells) or fastGRO-LI protocol (2.5, 1, and 0.5 million cells). (E) Average profiles of fastGRO reads at 305 expressed genes in iPSC-derived neural progenitor cells (NPCs) were obtained from 20 million cells (purple) using standard protocol and biotin-HPDP. Profiles of 5 million cells (black) and 1 million cells (orange) were obtained with the fastGRO-LI protocol and biotin-MTS, showing that fastGRO can be performed with low numbers of primary-like cells. (F) Screenshots of fastGRO tracks for two genes, HES6 (neural specific) and HNRNPH1 (ubiquitously expressed), in NPCs. Scale-down experiments were obtained with 2 replicates of NPC experiments using 1 million and 5 million cells and 1 replicate for all other samples.

Figure 6.. Overview of fastGRO

On day 1, nuclei are isolated, and in vitro run-on is performed in a solution containing 4-thio-UTP that is incorporated in nascent RNA. After isolation using TRIzol and ethanol precipitation, RNA is fragmented and snap-frozen. On day 2, 4-thio-UTP-containing RNA is biotinylated using either biotin-HPDP (standard protocol) or biotin-MTS (fastGRO-LI protocol for low-input sample) and recovered by IP using streptavidin-conjugated beads. Labeled RNA is recovered by elution in dithiothreitol (DTT) solution, purified, and used for NGS library preparation with commercially available kits. NG, next generation.