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 enables 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 scales 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.

Defining direct targets of transcription factors and regulatory pathways is key to understanding their roles in physiology and disease. We combined SLAM-seq [thiol(SH)-linked alkylation for the metabolic sequencing of RNA], a method for direct quantification of newly synthesized messenger RNAs (mRNAs), with pharmacological and chemical-genetic perturbation in order to define regulatory functions of two transcriptional hubs in cancer, BRD4 and MYC, and to interrogate direct responses to BET bromodomain inhibitors (BETis). We found that BRD4 acts as general coactivator of RNA polymerase II-dependent transcription, which is broadly repressed upon high-dose BETi treatment. At doses triggering selective effects in leukemia, BETis 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.

BackgroundLymphocyte receptor repertoires are continually shaped throughout the lifetime of an individual in response to environmental and pathogenic exposure. Thus, they may serve as a fingerprint of an individual’s ongoing immunological status (e.g., healthy, infected, vaccinated), with far-reaching implications for immunodiagnostics applications. The advent of high-throughput immune repertoire sequencing now enables the interrogation of immune repertoire diversity in an unprecedented and quantitative manner. However, steadily increasing sequencing depth has revealed that immune repertoires vary greatly among individuals in their composition; correspondingly, it has been reported that there are few shared sequences indicative of immunological status ('public clones'). Disconcertingly, this means that the wealth of information gained from repertoire sequencing remains largely unused for determining the current status of immune responses, thereby hampering the implementation of immune-repertoire-based diagnostics.MethodsHere, we introduce a bioinformatics repertoire-profiling framework that possesses the advantage of capturing the diversity and distribution of entire immune repertoires, as opposed to singular public clones. The framework relies on Hill-based diversity profiles composed of a continuum of single diversity indices, which enable the quantification of the extent of immunological information contained in immune repertoires.ResultsWe coupled diversity profiles with unsupervised (hierarchical clustering) and supervised (support vector machine and feature selection) machine learning approaches in order to correlate patients’ immunological statuses with their B- and T-cell repertoire data. We could predict with high accuracy (greater than or equal to 80 %) a wide range of immunological statuses such as healthy, transplantation recipient, and lymphoid cancer, suggesting as a proof of principle that diversity profiling can recover a large amount of immunodiagnostic fingerprints from immune repertoire data. Our framework is highly scalable as it easily allowed for the analysis of 1000 simulated immune repertoires; this exceeds the size of published immune repertoire datasets by one to two orders of magnitude.ConclusionsOur framework offers the possibility to advance immune-repertoire-based fingerprinting, which may in the future enable a systems immunogenomics approach for vaccine profiling and the accurate and early detection of disease and infection.Electronic supplementary materialThe online version of this article (doi:10.1186/s13073-015-0169-8) contains supplementary material, which is available to authorized users.