Cell-type-specific mRNA transcription and degradation kinetics in zebrafish embryogenesis from metabolically labeled single-cell RNA-seq.

During embryonic development, pluripotent cells assume specialized identities by adopting particular gene expression profiles. However, systematically dissecting the relative contributions of mRNA transcription and degradation to shaping those profiles remains challenging, especially within embryos with diverse cellular identities. Here, we combine single-cell RNA-Seq and metabolic labeling to capture temporal cellular transcriptomes of zebrafish embryos where newly-transcribed (zygotic) and pre-existing (maternal) mRNA can be distinguished. We introduce kinetic models to quantify mRNA transcription and degradation rates within individual cell types during their specification. These models reveal highly varied regulatory rates across thousands of genes, coordinated transcription and destruction rates for many transcripts, and link differences in degradation to specific sequence elements. They also identify cell-type-specific differences in degradation, namely selective retention of maternal transcripts within primordial germ cells and enveloping layer cells, two of the earliest specified cell types. Our study provides a quantitative approach to study mRNA regulation during a dynamic spatio-temporal response.

Calculating RNA degradation rates using large-scale normalization in mouse embryonic stem cells.

Data normalization is critical to the process of estimating RNA degradation by analyzing RNA levels when transcription is blocked. Here, we present a protocol for measuring mRNA degradation rates, optimized for mouse embryonic stem cells, using α-amanitin inhibitor. We describe steps for a time course α-amanitin treatment, RNA-seq, and alignment; we then detail procedures for analyzing data and sequence enrichment. Our method relies on large-scale normalization of stable transcripts in genomic RNA-seq measurements, providing reliable readouts. For complete details on the use and execution of this protocol, please refer to Viegas et al.1.

Single-cell temporal dynamics reveals the relative contributions of transcription and degradation to cell-type specific gene expression in zebrafish embryos.

During embryonic development, pluripotent cells assume specialized identities by adopting particular gene expression profiles. However, systematically dissecting the underlying regulation of mRNA transcription and degradation remains a challenge, especially within whole embryos with diverse cellular identities. Here, we collect temporal cellular transcriptomes of zebrafish embryos, and decompose them into their newly-transcribed (zygotic) and pre-existing (maternal) mRNA components by combining single-cell RNA-Seq and metabolic labeling. We introduce kinetic models capable of quantifying regulatory rates of mRNA transcription and degradation within individual cell types during their specification. These reveal different regulatory rates between thousands of genes, and sometimes between cell types, that shape spatio-temporal expression patterns. Transcription drives most cell-type restricted gene expression. However, selective retention of maternal transcripts helps to define the gene expression profiles of germ cells and enveloping layer cells, two of the earliest specified cell-types. Coordination between transcription and degradation restricts expression of maternal-zygotic genes to specific cell types or times, and allows the emergence of spatio-temporal patterns when overall mRNA levels are held relatively constant. Sequence-based analysis links differences in degradation to specific sequence motifs. Our study reveals mRNA transcription and degradation events that control embryonic gene expression, and provides a quantitative approach to study mRNA regulation during a dynamic spatio-temporal response.

RNA degradation eliminates developmental transcripts during murine embryonic stem cell differentiation via CAPRIN1-XRN2.

Embryonic stem cells (ESCs) are self-renewing and pluripotent. In recent years, factors that control pluripotency, mostly nuclear, have been identified. To identify non-nuclear regulators of ESCs, we screened an endogenously labeled fluorescent fusion-protein library in mouse ESCs. One of the more compelling hits was the cell-cycle-associated protein 1 (CAPRIN1). CAPRIN1 knockout had little effect in ESCs, but it significantly altered differentiation and gene expression programs. Using RIP-seq and SLAM-seq, we found that CAPRIN1 associates with, and promotes the degradation of, thousands of RNA transcripts. CAPRIN1 interactome identified XRN2 as the likely ribonuclease. Upon early ESC differentiation, XRN2 is located in the nucleus and colocalizes with CAPRIN1 in small RNA granules in a CAPRIN1-dependent manner. We propose that CAPRIN1 regulates an RNA degradation pathway operating during early ESC differentiation, thus eliminating undesired spuriously transcribed transcripts in ESCs.

Massively Parallel Analysis of Regulatory RNA Sequences.

The stability of RNA transcripts is regulated by signals within their sequences, but the identity of those signals still remain elusive in many biological systems. Recently introduced massively parallel tools for the analysis of regulatory RNA sequences provide the ability to detect functional cis-regulatory sequences of post-transcriptional RNA regulation at a much larger scale and resolution than before. Their application formulates the underlying sequence-based rules and predicts the impact of genetic variations. Here, we describe the application of UTR-Seq, as a strategy to uncover cis-regulatory signals of RNA stability during early zebrafish embryogenesis. The method combines massively parallel reporter assays (MPRA) with computational regression models. It surveys the effect of tens of thousands of regulatory sequences on RNA stability and analyzes the results via regression models to identify sequence signals that impact RNA stability and to predict the in vivo effect of sequence variations.

A Massively Parallel Reporter Assay of 3' UTR Sequences Identifies In Vivo Rules for mRNA Degradation.

The stability of mRNAs is regulated by signals within their sequences, but a systematic and predictive understanding of the underlying sequence rules remains elusive. Here we introduce UTR-seq, a combination of massively parallel reporter assays and regression models, to survey the dynamics of tens of thousands of 3' UTR sequences during early zebrafish embryogenesis. UTR-seq revealed two temporal degradation programs: a maternally encoded early-onset program and a late-onset program that accelerated degradation after zygotic genome activation. Three signals regulated early-onset rates: stabilizing poly-U and UUAG sequences and destabilizing GC-rich signals. Three signals explained late-onset degradation: miR-430 seeds, AU-rich sequences, and Pumilio recognition sites. Sequence-based regression models translated 3' UTRs into their unique decay patterns and predicted the in vivo effect of sequence signals on mRNA stability. Their application led to the successful design of artificial 3' UTRs that conferred specific mRNA dynamics. UTR-seq provides a general strategy to uncover the rules of RNA cis regulation.

Simultaneous measurement of genome-wide transcription elongation speeds and rates of RNA polymerase II transition into active elongation with 4sUDRB-seq.

4sUDRB-seq separately measures, on a genomic scale, the distinct contributions of transcription elongation speed and rate of RNA polymerase II (Pol II) transition into active elongation (TAE) to the overall mRNA production rate. It uses reversible inhibition of transcription elongation with 5,6-dichloro-1-ß-D-ribofuranosylbenzimidazole (DRB), combined with a pulse of 4-thiouridine (4sU), to tag newly transcribed RNA. After DRB removal, cells are collected at several time points, and tagged RNA is biotinylated, captured on streptavidin beads and sequenced. 4sUDRB-seq enables the comparison of elongation speeds between different developmental stages or different cell types, and it allows the impact of specific transcription factors on transcription elongation speed versus TAE to be studied. RNA preparation takes ∼4 d to complete, with deep sequencing requiring an additional ∼4-11 d plus 1-3 d for bioinformatics analysis. The experimental protocol requires basic molecular biology skills, whereas data analysis requires knowledge in bioinformatics, particularly MATLAB and the Linux environment.

Immunogenetics. Dynamic profiling of the protein life cycle in response to pathogens.

Protein expression is regulated by the production and degradation of messenger RNAs (mRNAs) and proteins, but their specific relationships remain unknown. We combine measurements of protein production and degradation and mRNA dynamics so as to build a quantitative genomic model of the differential regulation of gene expression in lipopolysaccharide-stimulated mouse dendritic cells. Changes in mRNA abundance play a dominant role in determining most dynamic fold changes in protein levels. Conversely, the preexisting proteome of proteins performing basic cellular functions is remodeled primarily through changes in protein production or degradation, accounting for more than half of the absolute change in protein molecules in the cell. Thus, the proteome is regulated by transcriptional induction for newly activated cellular functions and by protein life-cycle changes for remodeling of preexisting functions.

High-resolution sequencing and modeling identifies distinct dynamic RNA regulatory strategies.

Cells control dynamic transitions in transcript levels by regulating transcription, processing, and/or degradation through an integrated regulatory strategy. Here, we combine RNA metabolic labeling, rRNA-depleted RNA-seq, and DRiLL, a novel computational framework, to quantify the level; editing sites; and transcription, processing, and degradation rates of each transcript at a splice junction resolution during the LPS response of mouse dendritic cells. Four key regulatory strategies, dominated by RNA transcription changes, generate most temporal gene expression patterns. Noncanonical strategies that also employ dynamic posttranscriptional regulation control only a minority of genes, but provide unique signal processing features. We validate Tristetraprolin (TTP) as a major regulator of RNA degradation in one noncanonical strategy. Applying DRiLL to the regulation of noncoding RNAs and to zebrafish embryogenesis demonstrates its broad utility. Our study provides a new quantitative approach to discover transcriptional and posttranscriptional events that control dynamic changes in transcript levels using RNA sequencing data.

Metabolic labeling of RNA uncovers principles of RNA production and degradation dynamics in mammalian cells.

Cellular RNA levels are determined by the interplay of RNA production, processing and degradation. However, because most studies of RNA regulation do not distinguish the separate contributions of these processes, little is known about how they are temporally integrated. Here we combine metabolic labeling of RNA at high temporal resolution with advanced RNA quantification and computational modeling to estimate RNA transcription and degradation rates during the response of mouse dendritic cells to lipopolysaccharide. We find that changes in transcription rates determine the majority of temporal changes in RNA levels, but that changes in degradation rates are important for shaping sharp 'peaked' responses. We used sequencing of the newly transcribed RNA population to estimate temporally constant RNA processing and degradation rates genome wide. Degradation rates vary significantly between genes and contribute to the observed differences in the dynamic response. Certain transcripts, including those encoding cytokines and transcription factors, mature faster. Our study provides a quantitative approach to study the integrative process of RNA regulation.

Computational prediction of RNA structural motifs involved in post-transcriptional regulatory processes.

mRNA molecules are tightly regulated, mostly through interactions with proteins and other RNAs, but the mechanisms that confer the specificity of such interactions are poorly understood. It is clear, however, that this specificity is determined by both the nucleotide sequence and secondary structure of the mRNA. We developed RNApromo, an efficient computational tool for identifying structural elements within mRNAs that are involved in specifying post-transcriptional regulations. Using RNApromo, we predicted putative motifs in sets of mRNAs with substantial experimental evidence for common post-transcriptional regulation, including mRNAs with similar decay rates, mRNAs that are bound by the same RNA binding protein, and mRNAs with a common cellular localization. Our new RNA motif discovery tool reveals unexplored layers of post-transcriptional regulations in groups of RNAs, and is therefore an important step toward a better understanding of the regulatory information conveyed within RNA molecules.

A large intergenic noncoding RNA induced by p53 mediates global gene repression in the p53 response.

Recently, more than 1000 large intergenic noncoding RNAs (lincRNAs) have been reported. These RNAs are evolutionarily conserved in mammalian genomes and thus presumably function in diverse biological processes. Here, we report the identification of lincRNAs that are regulated by p53. One of these lincRNAs (lincRNA-p21) serves as a repressor in p53-dependent transcriptional responses. Inhibition of lincRNA-p21 affects the expression of hundreds of gene targets enriched for genes normally repressed by p53. The observed transcriptional repression by lincRNA-p21 is mediated through the physical association with hnRNP-K. This interaction is required for proper genomic localization of hnRNP-K at repressed genes and regulation of p53 mediates apoptosis. We propose a model whereby transcription factors activate lincRNAs that serve as key repressors by physically associating with repressive complexes and modulate their localization to sets of previously active genes.

Feed-forward regulation of a cell fate determinant by an RNA-binding protein generates asymmetry in yeast.

Saccharomyces cerevisiae can divide asymmetrically so that the mother and daughter cells have different fates. We show that the RNA-binding protein Khd1 regulates asymmetric expression of FLO11 to determine daughter cell fate during filamentous growth. Khd1 represses transcription of FLO11 indirectly through its regulation of ASH1 mRNA. Khd1 also represses FLO11 through a post-transcriptional mechanism independent of ASH1. Cross-linking immunoprecipitation (CLIP) coupled with high-throughput sequencing shows that Khd1 directly binds repetitive sequences in FLO11 mRNA. Khd1 inhibits translation through this interaction, establishing feed-forward repression of FLO11. This regulation enables changes in FLO11 expression between mother and daughter cells, which establishes the asymmetry required for the developmental transition between yeast form and filamentous growth.

Computational prediction of RNA structural motifs involved in posttranscriptional regulatory processes.

Messenger RNA molecules are tightly regulated, mostly through interactions with proteins and other RNAs, but the mechanisms that confer the specificity of such interactions are poorly understood. It is clear, however, that this specificity is determined by both the nucleotide sequence and secondary structure of the mRNA. Here, we develop RNApromo, an efficient computational tool for identifying structural elements within mRNAs that are involved in specifying posttranscriptional regulations. By analyzing experimental data on mRNA decay rates, we identify common structural elements in fast-decaying and slow-decaying mRNAs and link them with binding preferences of several RNA binding proteins. We also predict structural elements in sets of mRNAs with common subcellular localization in mouse neurons and fly embryos. Finally, by analyzing pre-microRNA stem-loops, we identify structural differences between pre-microRNAs of animals and plants, which provide insights into the mechanism of microRNA biogenesis. Together, our results reveal unexplored layers of posttranscriptional regulations in groups of RNAs and are therefore an important step toward a better understanding of the regulatory information conveyed within RNA molecules. Our new RNA motif discovery tool is available online.