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

Genome-wide quantification of RNA flow across subcellular compartments reveals determinants of the mammalian transcript life cycle

Dissecting the regulatory mechanisms controlling mammalian transcripts from production to degradation requires quantitative measurements of mRNA flow across the cell. We developed subcellular TimeLapse-seq to measure the rates at which RNAs are released from chromatin, exported from the nucleus, loaded onto polysomes, and degraded within the nucleus and cytoplasm in human and mouse cells. These rates varied substantially, yet transcripts from genes with related functions or targeted by the same transcription factors and RNA-binding proteins flowed across subcellular compartments with similar kinetics. Verifying these associations uncovered a link between DDX3X and nuclear export. For hundreds of RNA metabolism genes, most transcripts with retained introns were degraded by the nuclear exosome, while the remaining molecules were exported with stable cytoplasmic lifespans. Transcripts residing on chromatin for longer had extended poly(A) tails, whereas the reverse was observed for cytoplasmic mRNAs. Finally, machine learning identified molecular features that predicted the diverse life cycles of mRNAs.

Unraveling the interplay between a small RNA and RNase E in bacteria

Small RNAs (sRNAs) are major regulators of gene expression in bacteria, exerting their regulation primarily via base pairing with their target transcripts and modulating translation. Accumulating evidence suggest that sRNAs can also affect the stability of their target transcripts by altering their accessibility to endoribonucleases. Yet, the effects of sRNAs on transcript stability and the mechanisms underlying them have not been studied in wide scale. Here we employ large-scale RNA-seq-based methodologies in the model bacterium Escherichia coli to quantitatively study the functional interaction between a sRNA and an endoribonuclease in regulating gene expression, using the well-established sRNA, GcvB, and the major endoribonuclease, RNase E. Studying single and double mutants of gcvB and rne and analysing their RNA-seq results by the Double Mutant Cycle approach, we infer distinct modes of the interplay between GcvB and RNase E. Transcriptome-wide mapping of RNase E cleavage sites provides further support to the results of the RNA-seq analysis, identifying cleavage sites in targets in which the functional interaction between GcvB and RNase E is evident. Together, our results indicate that the most dominant mode of GcvB-RNase E functional interaction is GcvB enhancement of RNase E cleavage, which varies in its magnitude between different targets.

Single-cell nascent RNA sequencing unveils coordinated global transcription

Transcription is the primary regulatory step in gene expression. Divergent transcription initiation from promoters and enhancers produces stable RNAs from genes and unstable RNAs from enhancers1,2. Nascent RNA capture and sequencing assays simultaneously measure gene and enhancer activity in cell populations3. However, fundamental questions about the temporal regulation of transcription and enhancer-gene coordination remain unanswered, primarily because of the absence of a single-cell perspective on active transcription. In this study, we present scGRO-seq-a new single-cell nascent RNA sequencing assay that uses click chemistry-and unveil coordinated transcription throughout the genome. We demonstrate the episodic nature of transcription and the co-transcription of functionally related genes. scGRO-seq can estimate burst size and frequency by directly quantifying transcribing RNA polymerases in individual cells and can leverage replication-dependent non-polyadenylated histone gene transcription to elucidate cell cycle dynamics. The single-nucleotide spatial and temporal resolution of scGRO-seq enables the identification of networks of enhancers and genes. Our results suggest that the bursting of transcription at super-enhancers precedes bursting from associated genes. By imparting insights into the dynamic nature of global transcription and the origin and propagation of transcription signals, we demonstrate the ability of scGRO-seq to investigate the mechanisms of transcription regulation and the role of enhancers in gene expression.

Co-transcriptional gene regulation in eukaryotes and prokaryotes

Many steps of RNA processing occur during transcription by RNA polymerases. Co-transcriptional activities are deemed commonplace in prokaryotes, in which the lack of membrane barriers allows mixing of all gene expression steps, from transcription to translation. In the past decade, an extraordinary level of coordination between transcription and RNA processing has emerged in eukaryotes. In this Review, we discuss recent developments in our understanding of co-transcriptional gene regulation in both eukaryotes and prokaryotes, comparing methodologies and mechanisms, and highlight striking parallels in how RNA polymerases interact with the machineries that act on nascent RNA. The development of RNA sequencing and imaging techniques that detect transient transcription and RNA processing intermediates has facilitated discoveries of transcription coordination with splicing, 3'-end cleavage and dynamic RNA folding and revealed physical contacts between processing machineries and RNA polymerases. Such studies indicate that intron retention in a given nascent transcript can prevent 3'-end cleavage and cause transcriptional readthrough, which is a hallmark of eukaryotic cellular stress responses. We also discuss how coordination between nascent RNA biogenesis and transcription drives fundamental aspects of gene expression in both prokaryotes and eukaryotes.

Dynamic changes in mRNA nucleocytoplasmic localization in the nitrate response of Arabidopsis roots

Nitrate is a nutrient and signal that regulates gene expression. The nitrate response has been extensively characterized at the organism, organ, and cell-type-specific levels, but intracellular mRNA dynamics remain unexplored. To characterize nuclear and cytoplasmic transcriptome dynamics in response to nitrate, we performed a time-course expression analysis after nitrate treatment in isolated nuclei, cytoplasm, and whole roots. We identified 402 differentially localized transcripts (DLTs) in response to nitrate treatment. Induced DLT genes showed rapid and transient recruitment of the RNA polymerase II, together with an increase in the mRNA turnover rates. DLTs code for genes involved in metabolic processes, localization, and response to stimulus indicating DLTs include genes with relevant functions for the nitrate response that have not been previously identified. Using single-molecule RNA FISH, we observed early nuclear accumulation of the NITRATE REDUCTASE 1 (NIA1) transcripts in their transcription sites. We found that transcription of NIA1, a gene showing delayed cytoplasmic accumulation, is rapidly and transiently activated; however, its transcripts become unstable when they reach the cytoplasm. Our study reveals the dynamic localization of mRNAs between the nucleus and cytoplasm as an emerging feature in the temporal control of gene expression in response to nitrate treatment in Arabidopsis roots.

Timing is everything: advances in quantifying splicing kinetics

Splicing is a highly regulated process critical for proper pre-mRNA maturation and the maintenance of a healthy cellular environment. Splicing events are impacted by ongoing transcription, neighboring splicing events, and cis and trans regulatory factors on the respective pre-mRNA transcript. Within this complex regulatory environment, splicing kinetics have the potential to influence splicing outcomes but have historically been challenging to study in vivo. In this review, we highlight recent technological advancements that have enabled measurements of global splicing kinetics and of the variability of splicing kinetics at single introns. We demonstrate how identifying features that are correlated with splicing kinetics has increased our ability to form potential models for how splicing kinetics may be regulated in vivo.

DNA lesion bypass and the stochastic dynamics of transcription-coupled repair

DNA base damage is a major source of oncogenic mutations and disruption to gene expression. The stalling of RNA polymerase II (RNAP) at sites of DNA damage and the subsequent triggering of repair processes have major roles in shaping the genome-wide distribution of mutations, clearing barriers to transcription, and minimizing the production of miscoded gene products. Despite its importance for genetic integrity, key mechanistic features of this transcription-coupled repair (TCR) process are controversial or unknown. Here, we exploited a well-powered in vivo mammalian model system to explore the mechanistic properties and parameters of TCR for alkylation damage at fine spatial resolution and with discrimination of the damaged DNA strand. For rigorous interpretation, a generalizable mathematical model of DNA damage and TCR was developed. Fitting experimental data to the model and simulation revealed that RNA polymerases frequently bypass lesions without triggering repair, indicating that small alkylation adducts are unlikely to be an efficient barrier to gene expression. Following a burst of damage, the efficiency of transcription-coupled repair gradually decays through gene bodies with implications for the occurrence and accurate inference of driver mutations in cancer. The reinitation of transcription from the repair site is not a general feature of transcription-coupled repair, and the observed data is consistent with reinitiation never taking place. Collectively, these results reveal how the directional but stochastic activity of TCR shapes the distribution of mutations following DNA damage.

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.

Lifelong persistence of nuclear RNAs in the mouse brain

Genomic DNA that resides in the nuclei of mammalian neurons can be as old as the organism itself. The life span of nuclear RNAs, which are critical for proper chromatin architecture and transcription regulation, has not been determined in adult tissues. In this work, we identified and characterized nuclear RNAs that do not turn over for at least 2 years in a subset of postnatally born cells in the mouse brain. These long-lived RNAs were stably retained in nuclei in a neural cell type-specific manner and were required for the maintenance of heterochromatin. Thus, the life span of neural cells may depend on both the molecular longevity of DNA for the storage of genetic information and also the extreme stability of RNA for the functional organization of chromatin.

Post-transcriptional splicing can occur in a slow-moving zone around the gene

Splicing is the stepwise molecular process by which introns are removed from pre-mRNA and exons are joined together to form mature mRNA sequences. The ordering and spatial distribution of these steps remain controversial, with opposing models suggesting splicing occurs either during or after transcription. We used single-molecule RNA FISH, expansion microscopy, and live-cell imaging to reveal the spatiotemporal distribution of nascent transcripts in mammalian cells. At super-resolution levels, we found that pre-mRNA formed clouds around the transcription site. These clouds indicate the existence of a transcription-site-proximal zone through which RNA move more slowly than in the nucleoplasm. Full-length pre-mRNA undergo continuous splicing as they move through this zone following transcription, suggesting a model in which splicing can occur post-transcriptionally but still within the proximity of the transcription site, thus seeming co-transcriptional by most assays. These results may unify conflicting reports of co-transcriptional versus post-transcriptional splicing.

Prediction of single-cell RNA expression profiles in live cells by Raman microscopy with Raman2RNA

Single-cell RNA sequencing and other profiling assays have helped interrogate cells at unprecedented resolution and scale, but are inherently destructive. Raman microscopy reports on the vibrational energy levels of proteins and metabolites in a label-free and nondestructive manner at subcellular spatial resolution, but it lacks genetic and molecular interpretability. Here we present Raman2RNA (R2R), a method to infer single-cell expression profiles in live cells through label-free hyperspectral Raman microscopy images and domain translation. We predict single-cell RNA sequencing profiles nondestructively from Raman images using either anchor-based integration with single molecule fluorescence in situ hybridization, or anchor-free generation with adversarial autoencoders. R2R outperformed inference from brightfield images (cosine similarities: R2R >0.85 and brightfield <0.15). In reprogramming of mouse fibroblasts into induced pluripotent stem cells, R2R inferred the expression profiles of various cell states. With live-cell tracking of mouse embryonic stem cell differentiation, R2R traced the early emergence of lineage divergence and differentiation trajectories, overcoming discontinuities in expression space. R2R lays a foundation for future exploration of live genomic dynamics.

Dissecting and improving gene regulatory network inference using single-cell transcriptome data

Single-cell transcriptome data has been widely used to reconstruct gene regulatory networks (GRNs) controlling critical biological processes such as development and differentiation. Although a growing list of algorithms has been developed to infer GRNs using such data, achieving an inference accuracy consistently higher than random guessing has remained challenging. To address this, it is essential to delineate how the accuracy of regulatory inference is limited. Here, we systematically characterized factors limiting the accuracy of inferred GRNs and demonstrated that using pre-mRNA information can help improve regulatory inference compared to the typically used information (i.e., mature mRNA). Using kinetic modeling and simulated single-cell data sets, we showed that target genes' mature mRNA levels often fail to accurately report upstream regulatory activities because of gene-level and network-level factors, which can be improved by using pre-mRNA levels. We tested this finding on public single-cell RNA-seq data sets using intronic reads as proxies of pre-mRNA levels and can indeed achieve a higher inference accuracy compared to using exonic reads (corresponding to mature mRNAs). Using experimental data sets, we further validated findings from the simulated data sets and identified factors such as transcription factor activity dynamics influencing the accuracy of pre-mRNA-based inference. This work delineates the fundamental limitations of gene regulatory inference and helps improve GRN inference using single-cell RNA-seq data.

Transcript accumulation rates in the early Caenorhabditis elegans embryo

Dynamic transcriptional changes are widespread in rapidly dividing developing embryos when cell fate decisions are made quickly. The Caenorhabditis elegans embryo overcomes these constraints partly through the rapid production of high levels of transcription factor mRNAs. Transcript accumulation rates for some developmental genes are known at single-cell resolution, but genome-scale measurements are lacking. We estimate zygotic mRNA accumulation rates from single-cell RNA sequencing data calibrated with single-molecule transcript imaging. Rapid transcription is common in the early C. elegans embryo with rates highest soon after zygotic transcription begins. High-rate genes are enriched for recently duplicated cell-fate regulators and share common genomic features. We identify core promoter elements associated with high rate and measure their contributions for two early endomesodermal genes, ceh-51 and sdz-31. Individual motifs modestly affect accumulation rates, suggesting multifactorial control. These results are a step toward estimating absolute transcription kinetics and understanding how transcript dosage drives developmental decisions.

Spatial transcriptomics: recent developments and insights in respiratory research

The respiratory system's complex cellular heterogeneity presents unique challenges to researchers in this field. Although bulk RNA sequencing and single-cell RNA sequencing (scRNA-seq) have provided insights into cell types and heterogeneity in the respiratory system, the relevant specific spatial localization and cellular interactions have not been clearly elucidated. Spatial transcriptomics (ST) has filled this gap and has been widely used in respiratory studies. This review focuses on the latest iterative technology of ST in recent years, summarizing how ST can be applied to the physiological and pathological processes of the respiratory system, with emphasis on the lungs. Finally, the current challenges and potential development directions are proposed, including high-throughput full-length transcriptome, integration of multi-omics, temporal and spatial omics, bioinformatics analysis, etc. These viewpoints are expected to advance the study of systematic mechanisms, including respiratory studies.

Simultaneous measurement of nascent transcriptome and translatome using 4-thiouridine metabolic RNA labeling and translating ribosome affinity purification

Regulation of gene expression in response to various biological processes, including extracellular stimulation and environmental adaptation requires nascent RNA synthesis and translation. Analysis of the coordinated regulation of dynamic RNA synthesis and translation is required to determine functional protein production. However, reliable methods for the simultaneous measurement of nascent RNA synthesis and translation at the gene level are limited. Here, we developed a novel method for the simultaneous assessment of nascent RNA synthesis and translation by combining 4-thiouridine (4sU) metabolic RNA labeling and translating ribosome affinity purification (TRAP) using a monoclonal antibody against evolutionarily conserved ribosomal P-stalk proteins. The P-stalk-mediated TRAP (P-TRAP) technique recovered endogenous translating ribosomes, allowing easy translatome analysis of various eukaryotes. We validated this method in mammalian cells by demonstrating that acute unfolded protein response (UPR) in the endoplasmic reticulum (ER) induces dynamic reprogramming of nascent RNA synthesis and translation. Our nascent P-TRAP (nP-TRAP) method may serve as a simple and powerful tool for analyzing the coordinated regulation of transcription and translation of individual genes in various eukaryotes.

Dynamically regulated transcription factors are encoded by highly unstable mRNAs in the Drosophila larval brain

The level of each RNA species depends on the balance between its rates of production and decay. Although previous studies have measured RNA decay across the genome in tissue culture and single-celled organisms, few experiments have been performed in intact complex tissues and organs. It is therefore unclear whether the determinants of RNA decay found in cultured cells are preserved in an intact tissue, and whether they differ between neighboring cell types and are regulated during development. To address these questions, we measured RNA synthesis and decay rates genome wide via metabolic labeling of whole cultured Drosophila larval brains using 4-thiouridine. Our analysis revealed that decay rates span a range of more than 100-fold, and that RNA stability is linked to gene function, with mRNAs encoding transcription factors being much less stable than mRNAs involved in core metabolic functions. Surprisingly, among transcription factor mRNAs there was a clear demarcation between more widely used transcription factors and those that are expressed only transiently during development. mRNAs encoding transient transcription factors are among the least stable in the brain. These mRNAs are characterized by epigenetic silencing in most cell types, as shown by their enrichment with the histone modification H3K27me3. Our data suggest the presence of an mRNA destabilizing mechanism targeted to these transiently expressed transcription factors to allow their levels to be regulated rapidly with high precision. Our study also demonstrates a general method for measuring mRNA transcription and decay rates in intact organs or tissues, offering insights into the role of mRNA stability in the regulation of complex developmental programs.

bakR: uncovering differential RNA synthesis and degradation kinetics transcriptome-wide with Bayesian hierarchical modeling

Differential expression analysis of RNA sequencing (RNA-seq) data can identify changes in cellular RNA levels, but provides limited information about the kinetic mechanisms underlying such changes. Nucleotide recoding RNA-seq methods (NR-seq; e.g., TimeLapse-seq, SLAM-seq, etc.) address this shortcoming and are widely used approaches to identify changes in RNA synthesis and degradation kinetics. While advanced statistical models implemented in user-friendly software (e.g., DESeq2) have ensured the statistical rigor of differential expression analyses, no such tools that facilitate differential kinetic analysis with NR-seq exist. Here, we report the development of Bayesian analysis of the kinetics of RNA (bakR; https:// github.com/simonlabcode/bakR), an R package to address this need. bakR relies on Bayesian hierarchical modeling of NR-seq data to increase statistical power by sharing information across transcripts. Analyses of simulated data confirmed that bakR implementations of the hierarchical model outperform attempts to analyze differential kinetics with existing models. bakR also uncovers biological signals in real NR-seq data sets and provides improved analyses of existing data sets. This work establishes bakR as an important tool for identifying differential RNA synthesis and degradation kinetics.

grandR: a comprehensive package for nucleotide conversion RNA-seq data analysis

Metabolic labeling of RNA is a powerful technique for studying the temporal dynamics of gene expression. Nucleotide conversion approaches greatly facilitate the generation of data but introduce challenges for their analysis. Here we present grandR, a comprehensive package for quality control, differential gene expression analysis, kinetic modeling, and visualization of such data. We compare several existing methods for inference of RNA synthesis rates and half-lives using progressive labeling time courses. We demonstrate the need for recalibration of effective labeling times and introduce a Bayesian approach to study the temporal dynamics of RNA using snapshot experiments.

LncRNA SLC25A21-AS1 increases the chemosensitivity and inhibits the progression of ovarian cancer by upregulating the expression of KCNK4

Owing to high mortality rate, ovarian cancer seriously threatens women's health. Extensive abdominal metastasis and chemoresistance are the leading causes of ovarian cancer deaths. Through lncRNA sequencing, our previous study identified lncRNA SLC25A21-AS1, which was significantly downregulated in chemoresistant ovarian cancer cells. In this study, we aimed to evaluate the role and mechanism of SLC25A21-AS1 in ovarian cancer. The expression of SLC25A21-AS1 was analyzed by qRT-PCR and online database GEPIA. The biological functions of SLC25A21-AS1 and KCNK4 were analyzed by CCK-8, transwell, and flow cytometry. The specific mechanism was analyzed by RNA-sequencing, RNA binding protein immunoprecipitation, rescue experiments, and bioinformatic analysis. SLC25A21-AS1 was decreased in ovarian cancer tissues and cell lines. Overexpression of SLC25A21-AS1 enhanced the sensitivity of ovarian cancer cells to paclitaxel and cisplatin, and inhibited cell proliferation, invasion, and migration, while SLC25A21-AS1-silencing showed the opposite effect. Potassium channel subfamily K member 4 (KCNK4) was significantly up-regulated upon enforced expression of SLC25A21-AS1. Overexpression of KCNK4 inhibited cell proliferation, invasion, migration ability, and enhanced the sensitivity of ovarian cancer cells to paclitaxel and cisplatin. Meanwhile, KNCK4-overexpression rescued the promotive effect of SLC25A21-AS1-silencing on cell proliferation, invasion and migration. In addition, SLC25A21-AS1 could interact with the transcription factor Enhancer of Zeste Homolog 2 (EZH2), while EZH2 knockdown increased the expression of KCNK4 in some of the ovarian cancer cell lines. SLC25A21-AS1 enhanced the chemosensitivity and inhibited the proliferation, migration, and invasion ability of ovarian cancer cells at least partially by blocking EZH2-mediated silencing of KCNK4.

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.

FTO negatively regulates the cytotoxic activity of natural killer cells

N6 -Methyladenosine (m6 A) is the most abundant epitranscriptomic mark and plays a fundamental role in almost every aspect of mRNA metabolism. Although m6 A writers and readers have been widely studied, the roles of m6 A erasers are not well-understood. Here, we investigate the role of FTO, one of the m6 A erasers, in natural killer (NK) cell immunity. We observe that FTO-deficient NK cells are hyperactivated. Fto knockout (Fto-/- ) mouse NK cells prevent melanoma metastasis in vivo, and FTO-deficient human NK cells enhance the antitumor response against leukemia in vitro. We find that FTO negatively regulates IL-2/15-driven JAK/STAT signaling by increasing the mRNA stability of suppressor of cytokine signaling protein (SOCS) family genes. Our results suggest that FTO is an essential modulator of NK cell immunity, providing a new immunotherapeutic strategy for allogeneic NK cell therapies.

SLAMseq resolves the kinetics of maternal and zygotic gene expression during early zebrafish embryogenesis

The maternal-to-zygotic transition (MZT) is a key developmental process in metazoan embryos that involves the activation of zygotic transcription (ZGA) and degradation of maternal transcripts. We employed metabolic mRNA sequencing (SLAMseq) to deconvolute the compound embryonic transcriptome in zebrafish. While mitochondrial zygotic transcripts prevail prior to MZT, we uncover the spurious transcription of hundreds of short and intron-poor genes as early as the 2-cell stage. Upon ZGA, most zygotic transcripts originate from thousands of maternal-zygotic (MZ) genes that are transcribed at rates comparable to those of hundreds of purely zygotic genes and replenish maternal mRNAs at distinct timescales. Rapid replacement of MZ transcripts involves transcript decay features unrelated to major maternal degradation pathways and promotes de novo synthesis of the core gene expression machinery by increasing poly(A)-tail length and translation efficiency. SLAMseq hence provides insights into the timescales, molecular features, and regulation of MZT during zebrafish embryogenesis.

A selective and sensitive detection system for 4-thiouridine modification in RNA

4-Thiouridine (s4U) is a modified nucleoside, found at positions 8 and 9 in tRNA from eubacteria and archaea. Studies of the biosynthetic pathway and physiological role of s4U in tRNA are ongoing in the tRNA modification field. s4U has also recently been utilized as a biotechnological tool for analysis of RNAs. Therefore, a selective and sensitive system for the detection of s4U is essential for progress in the fields of RNA technologies and tRNA modification. Here, we report the use of biotin-coupled 2-aminoethyl-methanethiosulfonate (MTSEA biotin-XX) for labeling of s4U and demonstrate that the system is sensitive and quantitative. This technique can be used without denaturation; however, addition of a denaturation step improves the limit of detection. Thermus thermophilus tRNAs, which abundantly contain 5-methyl-2-thiouridine, were tested to investigate the selectivity of the MTSEA biotin-XX s4U detection system. The system did not react with 5-methyl-2-thiouridine in tRNAs from a T. thermophilus tRNA 4-thiouridine synthetase (thiI) gene deletion strain. Thus, the most useful advantage of the MTSEA biotin-XX s4U detection system is that MTSEA biotin-XX reacts only with s4U and not with other sulfur-containing modified nucleosides such as s2U derivatives in tRNAs. Furthermore, the MTSEA biotin-XX s4U detection system can analyze multiple samples in a short time span. The MTSEA biotin-XX s4U detection system can also be used for the analysis of s4U formation in tRNA. Finally, we demonstrate that the MTSEA biotin-XX system can be used to visualize newly transcribed tRNAs in S. cerevisiae cells.

Systematically characterizing the roles of E3-ligase family members in inflammatory responses with massively parallel Perturb-seq

E3 ligases regulate key processes, but many of their roles remain unknown. Using Perturb-seq, we interrogated the function of 1,130 E3 ligases, partners and substrates in the inflammatory response in primary dendritic cells (DCs). Dozens impacted the balance of DC1, DC2, migratory DC and macrophage states and a gradient of DC maturation. Family members grouped into co-functional modules that were enriched for physical interactions and impacted specific programs through substrate transcription factors. E3s and their adaptors co-regulated the same processes, but partnered with different substrate recognition adaptors to impact distinct aspects of the DC life cycle. Genetic interactions were more prevalent within than between modules, and a deep learning model, comβVAE, predicts the outcome of new combinations by leveraging modularity. The E3 regulatory network was associated with heritable variation and aberrant gene expression in immune cells in human inflammatory diseases. Our study provides a general approach to dissect gene function.

Rapid nuclear deadenylation of mammalian messenger RNA

Poly(A) tails protect RNAs from degradation and their deadenylation rates determine RNA stability. Although poly(A) tails are generated in the nucleus, deadenylation of tails has mostly been investigated within the cytoplasm. Here, we combined long-read sequencing with metabolic labeling, splicing inhibition and cell fractionation experiments to quantify, separately, the genesis and trimming of nuclear and cytoplasmic tails in vitro and in vivo. We present evidence for genome-wide, nuclear synthesis of tails longer than 200 nt, which are rapidly shortened after transcription. Our data suggests that rapid deadenylation is a nuclear process, and that different classes of transcripts and even transcript isoforms have distinct nuclear tail lengths. For example, many long-noncoding RNAs retain long poly(A) tails. Modeling deadenylation dynamics predicts nuclear deadenylation about 10 times faster than cytoplasmic deadenylation. In summary, our data suggests that nuclear deadenylation might be a key mechanism for regulating mRNA stability, abundance, and subcellular localization.

Kinetics of mRNA nuclear export regulate innate immune response gene expression

The abundance and stimulus-responsiveness of mature mRNA is thought to be determined by nuclear synthesis, processing, and cytoplasmic decay. However, the rate and efficiency of moving mRNA to the cytoplasm almost certainly contributes, but has rarely been measured. Here, we investigated mRNA export rates for innate immune genes. We generated high spatio-temporal resolution RNA-seq data from endotoxin-stimulated macrophages and parameterized a mathematical model to infer kinetic parameters with confidence intervals. We find that the effective chromatin-to-cytoplasm export rate is gene-specific, varying 100-fold: for some genes, less than 5% of synthesized transcripts arrive in the cytoplasm as mature mRNAs, while others show high export efficiency. Interestingly, effective export rates do not determine temporal gene responsiveness, but complement the wide range of mRNA decay rates; this ensures similar abundances of short- and long-lived mRNAs, which form successive innate immune response expression waves.

The genetic and biochemical determinants of mRNA degradation rates in mammals

BACKGROUND

Degradation rate is a fundamental aspect of mRNA metabolism, and the factors governing it remain poorly characterized. Understanding the genetic and biochemical determinants of mRNA half-life would enable more precise identification of variants that perturb gene expression through post-transcriptional gene regulatory mechanisms.

RESULTS

We establish a compendium of 39 human and 27 mouse transcriptome-wide mRNA decay rate datasets. A meta-analysis of these data identified a prevalence of technical noise and measurement bias, induced partially by the underlying experimental strategy. Correcting for these biases allowed us to derive more precise, consensus measurements of half-life which exhibit enhanced consistency between species. We trained substantially improved statistical models based upon genetic and biochemical features to better predict half-life and characterize the factors molding it. Our state-of-the-art model, Saluki, is a hybrid convolutional and recurrent deep neural network which relies only upon an mRNA sequence annotated with coding frame and splice sites to predict half-life (r=0.77). The key novel principle learned by Saluki is that the spatial positioning of splice sites, codons, and RNA-binding motifs within an mRNA is strongly associated with mRNA half-life. Saluki predicts the impact of RNA sequences and genetic mutations therein on mRNA stability, in agreement with functional measurements derived from massively parallel reporter assays.

CONCLUSIONS

Our work produces a more robust ground truth for transcriptome-wide mRNA half-lives in mammalian cells. Using these revised measurements, we trained Saluki, a model that is over 50% more accurate in predicting half-life from sequence than existing models. Saluki succinctly captures many of the known determinants of mRNA half-life and can be rapidly deployed to predict the functional consequences of arbitrary mutations in the transcriptome.

Tissue dissociation for single-cell and single-nuclei RNA sequencing for low amounts of input material

Background

Recent technological advances opened the opportunity to simultaneously study gene expression for thousands of individual cells on a genome-wide scale. The experimental accessibility of such single-cell RNA sequencing (scRNAseq) approaches allowed gaining insights into the cell type composition of heterogeneous tissue samples of animal model systems and emerging models alike. A major prerequisite for a successful application of the method is the dissociation of complex tissues into individual cells, which often requires large amounts of input material and harsh mechanical, chemical and temperature conditions. However, the availability of tissue material may be limited for small animals, specific organs, certain developmental stages or if samples need to be acquired from collected specimens. Therefore, we evaluated different dissociation protocols to obtain single cells from small tissue samples of Drosophila melanogaster eye-antennal imaginal discs.

Results

We show that a combination of mechanical and chemical dissociation resulted in sufficient high-quality cells. As an alternative, we tested protocols for the isolation of single nuclei, which turned out to be highly efficient for fresh and frozen tissue samples. Eventually, we performed scRNAseq and single-nuclei RNA sequencing (snRNAseq) to show that the best protocols for both methods successfully identified relevant cell types. At the same time, snRNAseq resulted in less artificial gene expression that is caused by rather harsh dissociation conditions needed to obtain single cells for scRNAseq. A direct comparison of scRNAseq and snRNAseq data revealed that both datasets share biologically relevant genes among the most variable genes, and we showed differences in the relative contribution of the two approaches to identified cell types.

Conclusion

We present two dissociation protocols that allow isolating single cells and single nuclei, respectively, from low input material. Both protocols resulted in extraction of high-quality RNA for subsequent scRNAseq or snRNAseq applications. If tissue availability is limited, we recommend the snRNAseq procedure of fresh or frozen tissue samples as it is perfectly suited to obtain thorough insights into cellular diversity of complex tissue.

Observation of conformational changes that underlie the catalytic cycle of Xrn2

Nuclear magnetic resonance (NMR) methods that quantitatively probe motions on molecular and atomic levels have propelled the understanding of biomolecular processes for which static structures cannot provide a satisfactory description. In this work, we studied the structure and dynamics of the essential 100-kDa eukaryotic 5′→3′ exoribonuclease Xrn2. A combination of complementary fluorine and methyl-TROSY NMR spectroscopy reveals that the apo enzyme is highly dynamic around the catalytic center. These observed dynamics are in agreement with a transition of the enzyme from the ground state into a catalytically competent state. We show that the conformational equilibrium in Xrn2 shifts substantially toward the active state in the presence of substrate and magnesium. Finally, our data reveal that the dynamics in Xrn2 correlate with the RNA degradation rate, as a mutation that attenuates motions also affects catalytic activity. In that light, our results stress the importance of studies that go beyond static structural information.

Using methyl group and fluorine NMR spectroscopic methods, Overbeck et al revealed that the dynamics of the eukaryotic 5′→3′ exoribonuclease Xrn2 in the region around the active site are correlated with its catalytic activity.

High temporal resolution RNA-seq time course data reveals widespread synchronous activation between mammalian lncRNAs and neighboring protein-coding genes

The advent of massively parallel sequencing revealed extensive transcription beyond protein-coding genes, identifying tens of thousands of long noncoding RNAs (lncRNAs). Selected functional examples raised the possibility that lncRNAs, as a class, may maintain broad regulatory roles. Expression of lncRNAs is strongly linked with adjacent protein-coding gene expression, suggesting potential cis-regulatory functions. A more detailed understanding of these regulatory roles may be obtained through careful examination of the precise timing of lncRNA expression relative to adjacent protein-coding genes. Despite the diversity of reported lncRNA regulatory mechanisms, where causal cis-regulatory relationships exist, lncRNA transcription is expected to precede changes in target gene expression. Using a high temporal resolution RNA-seq time course, we profiled the expression dynamics of several thousand lncRNAs and protein-coding genes in synchronized, transitioning human cells. Our findings reveal that lncRNAs are expressed synchronously with adjacent protein-coding genes. Analysis of lipopolysaccharide-activated mouse dendritic cells revealed the same temporal relationship observed in transitioning human cells. Our findings suggest broad-scale cis-regulatory roles for lncRNAs are not common. The strong association between lncRNAs and adjacent genes may instead indicate an origin as transcriptional by-products from active protein-coding gene promoters and enhancers.

Transcript-specific determinants of pre-mRNA splicing revealed through in vivo kinetic analyses of the 1st and 2nd chemical steps

We generate high-precision measurements of the in vivo rates of both chemical steps of pre-mRNA splicing across the genome-wide complement of substrates in yeast by coupling metabolic labeling, multiplexed primer-extension sequencing, and kinetic modeling. We demonstrate that the rates of intron removal vary widely, splice-site sequences are primary determinants of 1st step but have little apparent impact on 2nd step rates, and the 2nd step is generally faster than the 1st step. Ribosomal protein genes (RPGs) are spliced faster than non-RPGs at each step, and RPGs share evolutionarily conserved properties that may contribute to their faster splicing. A genetic variant defective in the 1st step of the pathway reveals a genome-wide defect in the 1st step but an unexpected, transcript-specific change in the 2nd step. Our work demonstrates that extended co-transcriptional association is an important determinant of splicing rate, a conclusion at odds with recent claims of ultra-fast splicing.

Enhancer RNAs step forward: new insights into enhancer function

Enhancers confer precise spatiotemporal patterns of gene expression in response to developmental and environmental stimuli. Over the last decade, the transcription of enhancer RNAs (eRNAs) – nascent RNAs transcribed from active enhancers – has emerged as a key factor regulating enhancer activity. eRNAs are relatively short-lived RNA species that are transcribed at very high rates but also quickly degraded. Nevertheless, eRNAs are deeply intertwined within enhancer regulatory networks and are implicated in a number of transcriptional control mechanisms. Enhancers show changes in function and sequence over evolutionary time, raising questions about the relationship between enhancer sequences and eRNA function. Moreover, the vast majority of single nucleotide polymorphisms associated with human complex diseases map to the non-coding genome, with causal disease variants enriched within enhancers. In this Primer, we survey the diverse roles played by eRNAs in enhancer-dependent gene expression, evaluating different models for eRNA function. We also explore questions surrounding the genetic conservation of enhancers and how this relates to eRNA function and dysfunction.

Summary: This Primer evaluates the ideas that underpin developing models for eRNA function, exploring cases in which perturbed eRNA function contributes to disease.

Regulation of both transcription and RNA turnover contribute to germline specification

The nuanced mechanisms driving primordial germ cells (PGC) specification remain incompletely understood since genome-wide transcriptional regulation in developing PGCs has previously only been defined indirectly. Here, using SLAMseq analysis, we determined genome-wide transcription rates during the differentiation of embryonic stem cells (ESCs) to form epiblast-like (EpiLC) cells and ultimately PGC-like cells (PGCLCs). This revealed thousands of genes undergoing bursts of transcriptional induction and rapid shut-off not detectable by RNAseq analysis. Our SLAMseq datasets also allowed us to infer RNA turnover rates, which revealed thousands of mRNAs stabilized and destabilized during PGCLC specification. mRNAs tend to be unstable in ESCs and then are progressively stabilized as they differentiate. For some classes of genes, mRNA turnover regulation collaborates with transcriptional regulation, but these processes oppose each other in a surprisingly high frequency of genes. To test whether regulated mRNA turnover has a physiological role in PGC development, we examined three genes that we found were regulated by RNA turnover: Sox2, Klf2 and Ccne1. Circumvention of their regulated RNA turnover severely impaired the ESC-to-EpiLC and EpiLC-to-PGCLC transitions. Our study demonstrates the functional importance of regulated RNA stability in germline development and provides a roadmap of transcriptional and post-transcriptional regulation during germline specification.

A novel mRNA decay inhibitor abolishes pathophysiological cellular transition

In cells, mRNA synthesis and decay are influenced by each other, and their balance is altered by either external or internal cues, resulting in changes in cell dynamics. We previously reported that it is important that an array of mRNAs that shape a phenotype are degraded before cellular transitions, such as cellular reprogramming and differentiation. In adipogenesis, the interaction between DDX6 and 4E-T had a definitive impact on the pathway in the processing body (PB). We screened a library of α-helix analogs with an alkaloid-like backbone to identify compounds that inhibit the binding between DDX6 and 4E-T proteins, which occurs between the α-helix of structured and internally disordered proteins. IAMC-00192 was identified as a lead compound. This compound directly inhibited the interaction between DDX6 and 4E-T. IAMC-00192 inhibited the temporal increase in PB formation that occurs during adipogenesis and epithelial-mesenchymal transition (EMT) and significantly suppressed these cellular transitions. In the EMT model, the half-life of preexisting mRNAs in PBs was extended twofold by the compound. The novel inhibitor of RNA decay not only represents a potentially useful tool to analyze in detail the pathological conditions affected by RNA decay and how it regulates the pathological state. The identification of this inhibitor may lead to the discovery of a first-in-class RNA decay inhibitor drug.

Genomic sequences and RNA binding proteins predict RNA splicing efficiency in various single-cell contexts

MOTIVATION

The RNA splicing efficiency is of high interest for both understanding the regulatory machinery of gene expression and estimating the RNA velocity in single cells. However, its genomic regulation and stochasticity across contexts remain poorly understood.

RESULTS

Here, by leveraging the recent RNA velocity tool, we estimated the relative splicing efficiency across a variety of single-cell RNA-Seq data sets. We further extracted large sets of genomic features and 120 RNA binding protein features and found they are highly predictive to relative RNA splicing efficiency across multiple tissues and organs on human and mouse. This predictive power brings promise to reveal the complexity of RNA processing and to enhance the analysis of single-cell transcription activities.

AVAILABILITY AND IMPLEMENTATION

In order to ensure reproducibility, all preprocessed data sets and scripts used for the prediction and figure generation are publicly available at https://doi.org/10.5281/zenodo.6513669.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Single-nucleus cross-tissue molecular reference maps toward understanding disease gene function

Understanding gene function and regulation in homeostasis and disease requires knowledge of the cellular and tissue contexts in which genes are expressed. Here, we applied four single-nucleus RNA sequencing methods to eight diverse, archived, frozen tissue types from 16 donors and 25 samples, generating a cross-tissue atlas of 209,126 nuclei profiles, which we integrated across tissues, donors, and laboratory methods with a conditional variational autoencoder. Using the resulting cross-tissue atlas, we highlight shared and tissue-specific features of tissue-resident cell populations; identify cell types that might contribute to neuromuscular, metabolic, and immune components of monogenic diseases and the biological processes involved in their pathology; and determine cell types and gene modules that might underlie disease mechanisms for complex traits analyzed by genome-wide association studies.

Estimating RNA dynamics using one time point for one sample in a single-pulse metabolic labeling experiment

Background

Over the past decade, experimental procedures such as metabolic labeling for determining RNA turnover rates at the transcriptome-wide scale have been widely adopted and are now turning to single cell measurements. Several computational methods to estimate RNA synthesis, processing and degradation rates from such experiments have been suggested, but they all require several RNA sequencing samples. Here we present a method that can estimate those three rates from a single sample.

Methods

Our method relies on the analytical solution to the Zeisel model of RNA dynamics. It was validated on metabolic labeling experiments performed on mouse embryonic stem cells. Resulting degradation rates were compared both to previously published rates on the same system and to a state-of-the-art method applied to the same data.

Results

Our method is computationally efficient and outputs rates that correlate well with previously published data sets. Using it on a single sample, we were able to reproduce the observation that dynamic biological processes tend to involve genes with higher metabolic rates, while stable processes involve genes with lower rates. This supports the hypothesis that cells control not only the mRNA steady-state abundance, but also its responsiveness, i.e., how fast steady state is reached. Moreover, degradation rates obtained with our method compare favourably with the other tested method.

Conclusions

In addition to saving experimental work and computational time, estimating rates for a single sample has several advantages. It does not require an error-prone normalization across samples and enables the use of replicates to estimate uncertainty and assess sample quality. Finally the method and theoretical results described here are general enough to be useful in other contexts such as nucleotide conversion methods and single cell metabolic labeling experiments.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04672-4.

Co-transcriptional splicing efficiency is a gene-specific feature that can be regulated by TGFβ

Differential splicing efficiency of specific introns is a mechanism that dramatically increases protein diversity, based on selection of alternative exons for the final mature mRNA. However, it is unclear whether splicing efficiency of introns within the same gene is coordinated and eventually regulated as a mechanism to control mature mRNA levels. Based on nascent chromatin-associated RNA-sequencing data, we now find that co-transcriptional splicing (CTS) efficiency tends to be similar between the different introns of a gene. We establish that two well-differentiated strategies for CTS efficiency exist, at the extremes of a gradient: short genes that produce high levels of pre-mRNA undergo inefficient splicing, while long genes with relatively low levels of pre-mRNA have an efficient splicing. Notably, we observe that genes with efficient CTS display a higher level of mature mRNA relative to their pre-mRNA levels. Further, we show that the TGFβ signal transduction pathway regulates the general CTS efficiency, causing changes in mature mRNA levels. Taken together, our data indicate that CTS efficiency is a gene-specific characteristic that can be regulated to control gene expression.

Co-transcriptional splicing efficiency is a gene-specific characteristic that can be regulated by TGFβ to modulate gene expression.

SM-Omics is an automated platform for high-throughput spatial multi-omics

The spatial organization of cells and molecules plays a key role in tissue function in homeostasis and disease. Spatial transcriptomics has recently emerged as a key technique to capture and positionally barcode RNAs directly in tissues. Here, we advance the application of spatial transcriptomics at scale, by presenting Spatial Multi-Omics (SM-Omics) as a fully automated, high-throughput all-sequencing based platform for combined and spatially resolved transcriptomics and antibody-based protein measurements. SM-Omics uses DNA-barcoded antibodies, immunofluorescence or a combination thereof, to scale and combine spatial transcriptomics and spatial antibody-based multiplex protein detection. SM-Omics allows processing of up to 64 in situ spatial reactions or up to 96 sequencing-ready libraries, of high complexity, in a ~2 days process. We demonstrate SM-Omics in the mouse brain, spleen and colorectal cancer model, showing its broad utility as a high-throughput platform for spatial multi-omics.

In Situ Visualizing Nascent RNA by Exploring DNA-Templated Oxidative Amination of 4-Thiouridine

Tracking and mapping the nascent RNA molecules in cells is essential for deciphering embryonic development and neuronal differentiation. Here, we utilized 4-thiouridine (s4U) as a metabolic tag to label nascent RNA and developed a fluorescence imaging method based on the DNA-templated oxidative amination (DTOA) reaction of s4U. The DTOA reaction occurred between amine-modified DNA and s4U-containing RNA with high sequence specificity and chemical selectivity. Target nascent mRNAs in HeLa cells, including those encoding green fluorescent proteins (GFPs) and endogenous BAG-1, were thus lit up selectively by DTOA-based fluorescence in situ hybridization (DTOA FISH). We believe the DTOA conjugation chemistry shown in this study could be generally applied to investigate the spatial distribution of nascent transcription dynamics in cellular processes.

Splice site proximity influences alternative exon definition

Alternative splicing enables higher eukaryotes to expand mRNA diversity from a finite number of genes through highly combinatorial splice site selection mechanisms that are influenced by the sequence of competing splice sites, cis-regulatory elements binding trans-acting factors, the length of exons and introns harbouring alternative splice sites and RNA secondary structures at putative splice junctions. To test the hypothesis that the intron definition or exon definition modes of splice site recognition direct the selection of alternative splice patterns, we created a database of alternative splice site usage (ALTssDB). When alternative splice sites are embedded within short introns (intron definition), the 5' and 3' splice sites closest to each other across the intron preferentially pair, consistent with previous observations. However, when alternative splice sites are embedded within large flanking introns (exon definition), the 5' and 3' splice sites closest to each other across the exon are preferentially selected. Thus, alternative splicing decisions are influenced by the intron and exon definition modes of splice site recognition. The results demonstrate that the spliceosome pairs splice sites that are closest in proximity within the unit of initial splice site selection.

Interrogating the transcriptome with metabolically incorporated ribonucleosides

RNA is a central player in biological processes, but there remain major gaps in our understanding of transcriptomic processes and the underlying biochemical mechanisms regulating RNA in cells. A powerful strategy to facilitate molecular analysis of cellular RNA is the metabolic incorporation of chemical probes. In this review, we discuss current approaches for RNA metabolic labeling with modified ribonucleosides and their integration with Next-Generation Sequencing, mass spectrometry-based proteomics, and fluorescence microscopy in order to interrogate RNA behavior in its native context.

Addition-Elimination Mechanism-Activated Nucleotide Transition Sequencing for RNA Dynamics Profiling

Dynamic information of intracellular transcripts is essential to understand their functional roles. Routine RNA-sequencing (RNA-seq) methods only measure RNA species at a steady state and do not provide RNA dynamic information. Here, we develop addition-elimination mechanism-activated nucleotide transition sequencing (AENT-seq) for transcriptome-wide profiling of RNA dynamics. In AENT-seq, nascent transcripts are metabolically labeled with 4-thiouridine (4sU). The total RNA is treated with N₂H₄·H₂O under aqueous conditions. N₂H₄·H₂O is demonstrated to convert 4sU to 4-hydrazino cytosine (C*) based on an addition-elimination chemistry. C* is regarded as cytosine (C) during the DNA extension process. This 4sU-to-C transition marks nascent transcripts, so it enables sequencing analysis of RNA dynamics. We apply our AENT-seq to investigate transcript dynamic information of several genes involved in cancer progression and metastasis. This method uses a simple chemical reaction in aqueous solutions and will be rapidly disseminated with extensive applications.

Microfluidic single-cell transcriptomics: moving towards multimodal and spatiotemporal omics

Cells are the basic units of life with vast heterogeneity. Single-cell transcriptomics unveils cell-to-cell gene expression variabilities, discovers novel cell types, and uncovers the critical roles of cellular heterogeneity in biological processes. The recent advances in microfluidic technologies have greatly accelerated the development of single-cell transcriptomics with regard to throughput, sensitivity, cost, and automation. In this article, we review state-of-the-art microfluidic single-cell transcriptomics, with a focus on the methodologies. We first summarize six typical microfluidic platforms for isolation and transcriptomic analysis of single cells. Then the on-going trend of microfluidic transcriptomics towards multimodal omics, which integrates transcriptomics with other omics to provide more comprehensive pictures of gene expression networks, is discussed. We also highlight single-cell spatial transcriptomics and single-cell temporal transcriptomics that provide unprecedented spatiotemporal resolution to reveal transcriptomic dynamics in space and time, respectively. The emerging applications of microfluidic single-cell transcriptomics are also discussed. Finally, we discuss the current challenges to be tackled and provide perspectives on the future development of microfluidic single-cell transcriptomics.

Joint single-cell measurements of nuclear proteins and RNA in vivo

Identifying gene-regulatory targets of nuclear proteins in tissues is a challenge. Here we describe intranuclear cellular indexing of transcriptomes and epitopes (inCITE-seq), a scalable method that measures multiplexed intranuclear protein levels and the transcriptome in parallel across thousands of nuclei, enabling joint analysis of transcription factor (TF) levels and gene expression in vivo. We apply inCITE-seq to characterize cell state-related changes upon pharmacological induction of neuronal activity in the mouse brain. Modeling gene expression as a linear combination of quantitative protein levels revealed genome-wide associations of each TF and recovered known gene targets. TF-associated genes were coexpressed as distinct modules that each reflected positive or negative TF levels, showing that our approach can disentangle relative putative contributions of TFs to gene expression and add interpretability to inferred gene networks. inCITE-seq can illuminate how combinations of nuclear proteins shape gene expression in native tissue contexts, with direct applications to solid or frozen tissues and clinical specimens.

Dynamics of transcriptional and post-transcriptional regulation

Despite gene expression programs being notoriously complex, RNA abundance is usually assumed as a proxy for transcriptional activity. Recently developed approaches, able to disentangle transcriptional and post-transcriptional regulatory processes, have revealed a more complex scenario. It is now possible to work out how synthesis, processing and degradation kinetic rates collectively determine the abundance of each gene's RNA. It has become clear that the same transcriptional output can correspond to different combinations of the kinetic rates. This underscores the fact that markedly different modes of gene expression regulation exist, each with profound effects on a gene's ability to modulate its own expression. This review describes the development of the experimental and computational approaches, including RNA metabolic labeling and mathematical modeling, that have been disclosing the mechanisms underlying complex transcriptional programs. Current limitations and future perspectives in the field are also discussed.

Systematic Analysis of Targets of Pumilio-Mediated mRNA Decay Reveals that PUM1 Repression by DNA Damage Activates Translesion Synthesis

RNA-binding proteins (RBPs) play a pivotal role in gene expression by modulating the stability of transcripts. However, the identification of degradation target mRNAs of RBPs remains difficult. By the combined analysis of transcriptome-wide mRNA stabilities and the binding of mRNAs to human Pumilio 1 (PUM1), we identify 48 mRNAs that both bind to PUM1 and exhibit PUM1-dependent degradation. Analysis of changes in the abundance of PUM1 and its degradation target mRNAs in RNA-seq data indicate that DNA-damaging agents negatively regulate PUM1-mediated mRNA decay. Cells exposed to cisplatin have reduced PUM1 abundance and increased PCNA and UBE2A mRNAs encoding proteins involved in DNA damage tolerance by translesion synthesis (TLS). Cells overexpressing PUM1 exhibit impaired DNA synthesis and TLS and increased sensitivity to the cytotoxic effect of cisplatin. Thus, our method identifies target mRNAs of PUM1-mediated decay and reveals that cells respond to DNA damage by inhibiting PUM1-mediated mRNA decay to activate TLS.

Single-Cell Sequencing Methodologies: From Transcriptome to Multi-Dimensional Measurement

Cells are the basic building blocks of biological systems, with inherent unique molecular features and development trajectories. The study of single cells facilitates in-depth understanding of cellular diversity, disease processes, and organization of multicellular organisms. Single-cell RNA sequencing (scRNA-seq) technologies have become essential tools for the interrogation of gene expression patterns and the dynamics of single cells, allowing cellular heterogeneity to be dissected at unprecedented resolution. Nevertheless, measuring at only transcriptome level or 1D is incomplete; the cellular heterogeneity reflects in multiple dimensions, including the genome, epigenome, transcriptome, spatial, and even temporal dimensions. Hence, integrative single cell analysis is highly desired. In addition, the way to interpret sequencing data by virtue of bioinformatic tools also exerts critical roles in revealing differential gene expression. Here, a comprehensive review that summarizes the cutting-edge single-cell transcriptome sequencing methodologies, including scRNA-seq, spatial and temporal transcriptome profiling, multi-omics sequencing and computational methods developed for scRNA-seq data analysis is provided. Finally, the challenges and perspectives of this field are discussed.