It is currently assumed that 3D chromosomal organisation plays a central role in transcriptional control. However, recent evidence shows that steady-state transcription of only a minority of genes is affected by depletion of architectural proteins such as cohesin and CTCF. Here, we have used Capture Hi-C to interrogate the dynamics of chromosomal contacts of all human gene promoters upon rapid architectural protein degradation. We show that promoter contacts lost in these conditions tend to be long-range, with at least one interaction partner localising in the vicinity of topologically associated domain (TAD) boundaries. In contrast, many shorter-range chromosomal contacts, particularly those that connect active promoters with each other and with active enhancers remain unaffected by cohesin and CTCF depletion. We demonstrate that the effects of cohesin depletion on nascent transcription can be explained by changes in the connectivity of their enhancers. Jointly, these results provide a mechanistic explanation to the limited, but consistent effects of cohesin and CTCF on steady-state transcription and point towards the existence of alternative enhancer-promoter pairing mechanisms that are independent of these proteins.

Features SLAMseq Metabolic RNA Labeling Kit for RNA-Seq and QuantSeq 3’ mRNA-Seq Library Prep Kit FWD for Illumina

Genome-wide profiling of nascent RNA has become a fundamental tool to study transcription regulation. Over the past decade, next-generation sequencing has fostered development of a handful of techniques (i.e. GRO-seq, PRO-seq, TT-seq and NET-seq) that map unprocessed transcripts originating from both the coding and the noncoding portion of the genome. Unlike steady-state RNA sequencing, nascent RNA profiling mirrors the real-time activity of RNA Polymerases and provides an accurate readout of transcriptome-wide variations that occur during short time frames (i.e. response to external stimuli or rapid metabolic changes). Some species of nuclear RNAs, albeit functional, have a short half-life and can only be accurately gauged by nascent RNA techniques (i.e. lincRNAs and eRNAs). Furthermore, these techniques capture uncapped post-cleavage RNA at termination sites or promoter-associated antisense RNAs, providing a unique insight into RNAPII dynamics and processivity.

Here we present a run-on assay with 4s-UTP labelling, 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. Importantly, we demonstrate that fastGRO is scalable and can be performed with as few as 0.5×10^6 cells.

Features SLAMseq Metabolic RNA Labeling Kit for RNA-Seq and SENSE mRNA-Seq Library Prep Kit

WDR5 is a highly-conserved nuclear protein that performs multiple scaffolding functions in the context of chromatin. WDR5 is also a promising target for pharmacological inhibition in cancer, with small molecule inhibitors of an arginine-binding pocket of WDR5 (the ‘WIN’ site) showing efficacy against a range of cancer cell lines in vitro. Efforts to understand WDR5, or establish the mechanism of action of WIN site inhibitors, however, are stymied by its many functions in the nucleus, and a lack of knowledge of the conserved gene networks—if any—that are under its control. Here, we have performed comparative genomic analyses to identify the conserved sites of WDR5 binding to chromatin, and the conserved genes regulated by WDR5, across a diverse panel of cancer cell lines. We show that a specific cohort of protein synthesis genes (PSGs) are invariantly bound by WDR5, demonstrate that the WIN site anchors WDR5 to chromatin at these sites, and establish that PSGs are bona fide, acute, and persistent targets of WIN site blockade. Together, these data reveal that WDR5 plays a predominant transcriptional role in biomass accumulation and provide further evidence that WIN site inhibitors act to repress gene networks linked to protein synthesis homeostasis.

Features SLAMseq Metabolic RNA Labeling Kit for RNA-Seq

The varying rates at which mRNAs decay are tightly coordinated with transcriptional changes to shape gene expression during development and disease. But currently available RNA sequencing approaches lack the temporal information to determine the relative contribution of RNA biogenesis, processing and turnover to the establishment of steady-state gene expression profiles.

Here, we describe a protocol that combines metabolic RNA labeling with chemical nucleoside conversion by thiol-linked alkylation of 4-thiouridine to determine RNA stability in cultured cells (SLAMseq). When coupled to cost-effective mRNA 3′ end sequencing approaches, SLAMseq determines the half-life of polyadenylated transcripts in a global and transcript-specific manner using untargeted or targeted cDNA library preparation protocols.

We provide a step-by-step instruction for time-resolved mRNA 3′ end sequencing, which augments traditional RNA-seq approaches to acquire the temporal resolution necessary to study the molecular principles that control gene expression.

Features SLAMseq Metabolic RNA Labeling Kit for RNA-Seq, QuantSeq 3’ mRNA-Seq Library Prep Kit FWD for Illumina and QuantSeq-Flex Targeted RNA-Seq Library Prep Kit V2 for Illumina

Understanding how gene expression in single cells progress over time is vital for revealing the mechanisms governing cell fate transitions. RNA velocity, which infers immediate changes in gene expression by comparing levels of new (unspliced) versus mature (spliced) transcripts (La Manno et al. 2018), represents an important advance to these efforts. A key question remaining is whether it is possible to predict the most probable cell state backward or forward over arbitrary time-scales. To this end, we introduce an inclusive model (termed Dynamo) capable of predicting cell states over extended time periods, that incorporates promoter state switching, transcription, splicing, translation and RNA/protein degradation by taking advantage of scRNA-seq and the co-assay of transcriptome and proteome. We also implement scSLAM-seq by extending SLAM-seq to plate-based scRNA-seq (Hendriks et al. 2018; Erhard et al. 2019; Cao, Zhou, et al. 2019) and augment the model by explicitly incorporating the metabolic labelling of nascent RNA. We show that through careful design of labelling experiments and an efficient mathematical framework, the entire kinetic behavior of a cell from this model can be robustly and accurately inferred. Aided by the improved framework, we show that it is possible to reconstruct the transcriptomic vector field from sparse and noisy vector samples generated by single cell experiments. The reconstructed vector field further enables global mapping of potential landscapes that reflects the relative stability of a given cell state, and the minimal transition time and most probable paths between any cell states in the state space. This work thus foreshadows the possibility of predicting long-term trajectories of cells during a dynamic process instead of short time velocity estimates. Our methods are implemented as an open source tool, dynamo (https://github.com/aristoteleo/dynamo-release).

Features SLAMseq Metabolic RNA Labeling Kit for RNA-Seq

Analysis of cell-type-specific transcriptomes is vital for understanding the biology of tissues and organs in the context of multicellular organisms. In this Protocol Extension, we combine a previously developed cell-type-specific metabolic RNA labeling method (thiouracil (TU) tagging) and a pipeline to detect the labeled transcripts by a novel RNA sequencing (RNA-seq) method, SLAMseq (thiol (SH)-linked alkylation for the metabolic sequencing of RNA). By injecting a uracil analog, 4-thiouracil, into transgenic mice that express cell-type-specific uracil phosphoribosyltransferase (UPRT), an enzyme required for 4-thiouracil incorporation into newly synthesized RNA, only cells expressing UPRT synthesize thiol-containing RNA. Total RNA isolated from a tissue of interest is then sequenced with SLAMseq, which introduces thymine to cytosine (T>C) conversions at the sites of the incorporated 4-thiouracil. The resulting sequencing reads are then mapped with the T>C-aware alignment software, SLAM-DUNK, which allows mapping of reads containing T>C mismatches. The number of T>C conversions per transcript is further analyzed to identify which transcripts are synthesized in the UPRT-expressing cells. Thus, our method, SLAM-ITseq (SLAMseq in tissue), enables cell-specific transcriptomics without laborious FACS-based cell sorting or biochemical isolation of the labeled transcripts used in TU tagging. In the murine tissues we assessed previously, this method identified ~5,000 genes that are expressed in a cell type of interest from the total RNA pool from the tissue. Any laboratory with access to a high-throughput sequencer and high-power computing can adapt this protocol with ease, and the entire pipeline can be completed in <5 d.

Features SLAMseq Metabolic RNA Labeling Kit for RNA-Seq

The use of high-throughput sequencing (HTS) to identify viruses in biologicals differs from current molecular approaches, since its use enables an unbiased approach to detection without the need to design specific primers to preamplify target sequences. Its broad range of detection and analytical sensitivity make it an important tool to ensure that biologicals are free from adventitious viruses. Similar to other molecular methods, however, identification of viral sequences in cells by HTS does not prove viral infection, since this could reflect carryover of inert viral sequences from reagents or other sources or the presence of transcriptionally inactive cellular sequences. Due to the broad range of detection associated with HTS, the above can potentially be perceived as a drawback for the testing of pharmaceutical biological products using this method. In order to avoid the identification of inert viral sequences, we present a methodology based on metabolic RNA labeling and sequencing, which enables the specific identification of newly synthesized viral RNAs in infected cells, resulting in the ability to unambiguously distinguish active infection by DNA or RNA viruses from inert nucleic acids. In the present study, we report the ability to differentiate Vero cells acutely infected by a single-stranded positive-sense RNA virus (tick-borne encephalitis virus) from cells which have been in contact with nonreplicating virus particles. Additionally, we also found a laboratory contamination by the squirrel monkey retrovirus of our Vero cell line, which was derived from an Old World (African green) monkey, a type of contamination which until now has been identified only in cells derived from primates from the New World.

Features SLAMseq Metabolic RNA Labeling Kit for RNA-Seq

Cell type-specific transcriptome analysis is an essential tool in understanding biological processes in which diverse types of cells are involved. Although cell isolation methods such as fluorescence-activated cell sorting (FACS) in combination with transcriptome analysis have widely been used so far, their time-consuming and harsh procedures limit their applications. Here, we report a novel in vivo metabolic RNA sequencing method, SLAM-ITseq, which metabolically labels RNA with 4-thiouracil in a specific cell type in vivo followed by detection through an RNA-seq-based method that specifically distinguishes the thiolated uridine by base conversion. This method has successfully identified the cell type-specific transcriptome in three different tissues: endothelial cells in brain, epithelial cells in intestine, and adipocytes in white adipose tissue. Since this method does not require isolation of cells or RNA prior to the transcriptomic analysis, SLAM-ITseq provides an easy yet accurate snapshot of the transcriptional state in vivo.

Features SLAMseq Metabolic RNA Labeling Kit for RNA-Seq and QuantSeq 3’ mRNA-Seq Library Prep Kit FWD for Illumina

Defining direct targets of transcription factors and regulatory pathways is key to understanding their roles in physiology and disease. Here we combine SLAM-seq, a method for direct quantification of newly synthesized mRNAs, with pharmacological and chemical-genetic perturbation to define regulatory functions of two transcriptional hubs in cancer, BRD4 and MYC, and to interrogate direct responses to BET bromodomain inhibitors (BETi). We find that BRD4 acts as general co-activator of RNA polymerase II (Pol2)-dependent transcription, which is broadly repressed upon high-dose BETi treatment. At doses triggering selective effects in leukemia, BETi deregulate a small set of hypersensitive targets including MYC. In contrast to BRD4, MYC primarily acts as a selective transcriptional activator controlling metabolic processes such as ribosome biogenesis and de-novo purine synthesis. Our study establishes a simple and scalable strategy to identify direct transcriptional targets of any gene or pathway.

Features SLAMseq Metabolic RNA Labeling Kit for RNA-Seq and QuantSeq 3’ mRNA-Seq Library Prep Kit FWD for Illumina

The combination of metabolic RNA labeling with biochemical nucleoside conversion now adds a broadly applicable temporal dimension to RNA sequencing.

Features SLAMseq Metabolic RNA Labeling Kit for RNA-Seq

Gene expression profiling by high-throughput sequencing reveals qualitative and quantitative changes in RNA species at steady state but obscures the intracellular dynamics of RNA transcription, processing and decay. We developed thiol(SH)-linked alkylation for the metabolic sequencing of RNA (SLAM seq), an orthogonal-chemistry-based RNA sequencing technology that detects 4-thiouridine (s⁴U) incorporation in RNA species at single-nucleotide resolution. In combination with well-established metabolic RNA labeling protocols and coupled to standard, low-input, high-throughput RNA sequencing methods, SLAM seq enabled rapid access to RNA-polymerase-II-dependent gene expression dynamics in the context of total RNA. We validated the method in mouse embryonic stem cells by showing that the RNA-polymerase-II-dependent transcriptional output scaled with Oct4/Sox2/Nanog-defined enhancer activity, and we provide quantitative and mechanistic evidence for transcript-specific RNA turnover mediated by post-transcriptional gene regulatory pathways initiated by microRNAs and N⁶-methyladenosine. SLAM seq facilitates the dissection of fundamental mechanisms that control gene expression in an accessible, cost-effective and scalable manner.

Features QuantSeq 3’ mRNA-Seq Library Prep Kit for Illumina and SLAMseq Metabolic RNA Labeling Kit for RNA-Seq