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Large-scale sequencing of RNA from individual cells can reveal patterns of gene, isoform and allelic expression across cell types and states 1 . However, current short-read single-cell RNA-sequencing methods have limited ability to count RNAs at allele and isoform resolution, and long-read sequencing techniques lack the depth required for large-scale applications across cells 2 , 3 . Here we introduce Smart-seq3, which combines full-length transcriptome coverage with a 5′ unique molecular identifier RNA counting strategy that enables in silico reconstruction of thousands of RNA molecules per cell. Of the counted and reconstructed molecules, 60% could be directly assigned to allelic origin and 30–50% to specific isoforms, and we identified substantial differences in isoform usage in different mouse strains and human cell types. Smart-seq3 greatly increased sensitivity compared to Smart-seq2, typically detecting thousands more transcripts per cell. We expect that Smart-seq3 will enable large-scale characterization of cell types and states across tissues and organisms. Smart-seq3 enables isoform- and allele-specific reconstruction of RNA molecules.

Mammalian gene expression is inherently stochastic 1 , 2 , and results in discrete bursts of RNA molecules that are synthesized from each allele 3 – 7 . Although transcription is known to be regulated by promoters and enhancers, it is unclear how cis -regulatory sequences encode transcriptional burst kinetics. Characterization of transcriptional bursting, including the burst size and frequency, has mainly relied on live-cell 4 , 6 , 8 or single-molecule RNA fluorescence in situ hybridization 3 , 5 , 8 , 9 recordings of selected loci. Here we determine transcriptome-wide burst frequencies and sizes for endogenous mouse and human genes using allele-sensitive single-cell RNA sequencing. We show that core promoter elements affect burst size and uncover synergistic effects between TATA and initiator elements, which were masked at mean expression levels. Notably, we provide transcriptome-wide evidence that enhancers control burst frequencies, and demonstrate that cell-type-specific gene expression is primarily shaped by changes in burst frequencies. Together, our data show that burst frequency is primarily encoded in enhancers and burst size in core promoters, and that allelic single-cell RNA sequencing is a powerful model for investigating transcriptional kinetics. Allele-specific single-cell RNA sequencing provides insights into transcription kinetics, with data indicating that core promoter sequences affect burst size, whereas enhancers mainly affect burst frequency.

Attention deficit hyperactivity disorder (ADHD) frequently persists into adulthood. Family and twin studies delineate a disorder with strong genetic influences among children and adolescents based on parent- and teacher-reported data but little is known about the genetic and environmental contribution to DSM-IV ADHD symptoms in adulthood. We therefore aimed to investigate the impact of genetic and environmental influences on the inattentive and hyperactive-impulsive symptoms of ADHD in adults. Twin methods were applied to self-reported assessments of ADHD symptoms from a large population-based Swedish twin study that included data from 15 198 Swedish male and female twins aged 20 to 46 years. The broad heritability [i.e., A + D, where A is an additive genetic factor and D (dominance) a non-additive genetic factor] was 37% (A = 11%, D = 26%) for inattention and 38% (A = 18%, D = 20%) for hyperactivity-impulsivity. The results also indicate that 52% of the phenotypic correlation between inattention and hyperactivity-impulsivity (r = 0.43) was explained by genetic influences whereas the remaining part of the covariance was explained by non-shared environmental influences. These results were replicated across age strata. Our findings of moderate broad heritability estimates are consistent with previous literature on self-rated ADHD symptoms in older children, adolescents and adults and retrospective reports of self-rated childhood ADHD by adults but differ from studies of younger children with informant ratings. Future research needs to clarify whether our data indicate a true decrease in the heritability of ADHD in adults compared to children, or whether this relates to the use of self-ratings in contrast to informant data.

Carbon nanotubes (CNTs), hollow cylinders of carbon, hold great promise for advanced technologies, provided their structure remains uniform throughout their length. Their growth takes place at high temperatures across a tube-catalyst interface. Structural defects formed during growth alter CNT properties. These defects are believed to form and heal at the tube-catalyst interface but an understanding of these mechanisms at the atomic-level is lacking. Here we present DeepCNT-22, a machine learning force field (MLFF) to drive molecular dynamics simulations through which we unveil the mechanisms of CNT formation, from nucleation to growth including defect formation and healing. We find the tube-catalyst interface to be highly dynamic, with large fluctuations in the chiral structure of the CNT-edge. This does not support continuous spiral growth as a general mechanism, instead, at these growth conditions, the growing tube edge exhibits significant configurational entropy. We demonstrate that defects form stochastically at the tube-catalyst interface, but under low growth rates and high temperatures, these heal before becoming incorporated in the tube wall, allowing CNTs to grow defect-free to seemingly unlimited lengths. These insights, not readily available through experiments, demonstrate the remarkable power of MLFF-driven simulations and fill long-standing gaps in our understanding of CNT growth mechanisms. There is a lack of atomic level insight on the role of defects on carbon nanotubes' growth. Here, authors present a machine learning force field to drive near-microsecond simulations the entire growth process of this material, unveiling mechanisms of defect formation and healing.

Transcriptional bursts render substantial biological noise in cellular transcriptomes. Here, we investigated the theoretical extent of allelic expression resulting from transcriptional bursting and how it compared to the amount biallelic, monoallelic and allele-biased expression observed in single-cell RNA-sequencing (scRNA-seq) data. We found that transcriptional bursting can explain the allelic expression patterns observed in single cells, including the frequent observations of autosomal monoallelic gene expression. Importantly, we identified that the burst frequency largely determined the fraction of cells with monoallelic expression, whereas the burst size had little effect on monoallelic observations. The high consistency between the bursting model predictions and scRNA-seq observations made it possible to assess the heterogeneity of a group of cells as their deviation in allelic observations from the expected. Finally, both burst frequency and size contributed to allelic imbalance observations and reinforced that studies of allelic imbalance can be confounded from the inherent noise in transcriptional bursting. Altogether, we demonstrate that allele-level transcriptional bursting renders widespread, although predictable, amounts of monoallelic and biallelic expression in single cells and cell populations.

Analyses of transcriptional bursting from single-cell RNA-sequencing data have revealed patterns of variation and regulation in the kinetic parameters that could be inferred. Here we profiled newly transcribed (4-thiouridine-labelled) RNA across 10,000 individual primary mouse fibroblasts to more broadly infer bursting kinetics and coordination. We demonstrate that inference from new RNA profiles could separate the kinetic parameters that together specify the burst size, and that the synthesis rate (and not the transcriptional off rate) controls the burst size. Importantly, transcriptome-wide inference of transcriptional on and off rates provided conclusive evidence that RNA polymerase II transcribes genes in bursts. Recent reports identified examples of transcriptional co-bursting, yet no global analyses have been performed. The deep new RNA profiles we generated with allelic resolution demonstrated that co-bursting rarely appears more frequently than expected by chance, except for certain gene pairs, notably paralogues located in close genomic proximity. Altogether, new RNA single-cell profiling critically improves the inference of transcriptional bursting and provides strong evidence for independent transcriptional bursting of mammalian genes. Ramskold, Hendriks, Larsson et al. use deep single-cell profiling of newly transcribed RNA to uncover the kinetics and dynamics of transcriptional bursting at allelic resolution in primary mouse cells.

Sequencing of newly synthesised RNA can monitor transcriptional dynamics with great sensitivity and high temporal resolution, but is currently restricted to populations of cells. Here, we develop new transcriptome alkylation-dependent single-cell RNA sequencing (NASC-seq), to monitor newly synthesised and pre-existing RNA simultaneously in single cells. We validate the method on pre-labelled RNA, and by demonstrating that more newly synthesised RNA was detected for genes with known high mRNA turnover. Monitoring RNA synthesis during Jurkat T-cell activation with NASC-seq reveals both rapidly up- and down-regulated genes, and that induced genes are almost exclusively detected as newly transcribed. Moreover, the newly synthesised and pre-existing transcriptomes after T-cell activation are distinct, confirming that NASC-seq simultaneously measures gene expression corresponding to two time points in single cells. Altogether, NASC-seq enables precise temporal monitoring of RNA synthesis at single-cell resolution during homoeostasis, perturbation responses and cellular differentiation. Sequencing of newly synthesised RNA can reveal the transcriptional dynamics in a population of cells. Here the authors develop NASC-seq to bring this sensitivity and temporal resolution to single-cell analysis.

Analysis of clonal dynamics in human tissues is enabled by somatic genetic variation. Here, we show that analysis of mitochondrial mutations in single cells is dramatically improved in females when using X chromosome inactivation to select informative clonal mutations. Applying this strategy to human peripheral mononuclear blood cells reveals clonal structures within T cells that otherwise are blurred by non-informative mutations, including the separation of gamma-delta T cells, suggesting this approach can be used to decipher clonal dynamics of cells in human tissues.

Examining direct transcriptional effects of genetic and chemical perturbations is crucial for understanding gene expression mechanisms. Standard RNA-seq experiments often overlook these direct effects, and current methods for profiling nascent RNA are usually time-consuming. Here, we adapted single-cell 4sU-based sequencing into a scalable, automated mini-bulk format to profile new RNA in smaller cell populations. This approach enabled us to map the direct transcriptional effects of epigenetic regulators. Brief exposure to SAHA (an HDAC inhibitor) revealed hundreds of directly responsive genes, many showing altered transcriptional bursting kinetics, with promoter regions enriched in binding sites for factors including bromodomain proteins. Profiling 83 epigenetic compounds uncovered direct transcriptional impacts from inhibitors of bromodomain proteins, histone deacetylases, and histone demethylases. Notably, chemically similar HDAC inhibitors elicited concordant direct responses and intronic expression analyses mirrored the direct effects seen in new RNA. This work highlights powerful approaches for investigating transcriptional mechanisms. Understanding gene expression involves studying the transcriptional effects of perturbations. Here, the authors monitor RNA transcripts after applying 83 epigenetic compounds, identifying responsive genes enriched with chromatin marks and regulators in line with their mechanisms of action.

The existence of trait coordination in roots and leaves has recently been debated, with studies reaching opposing conclusions. Here, we assessed trait coordination across twelve boreal tree species. We show that there is only partial evidence for above-belowground coordination for “fast-slow” economic traits across boreal tree species, i.e., while N content in leaves and roots were positively correlated, as well as dry matter content, root dry matter content and leaf N had no significant relationship. For resource acquisition traits (i.e. related to light capture and nutrient uptake) we did not find strong evidence for trait coordination, as specific root length and specific leaf area were not positively correlated. We further show that site only explained between 0 and 7% of the total trait variation, while within-site variation contributed substantially to the total trait variation for a large number of traits (1.6–96%), and more so for morphological root traits than leaf traits. This likely influences the strength of above-belowground trait coordination found across species in our study. Understanding sources of trait variation and above-belowground trait relationships can contribute to improving global and regional C cycling models. However, fine-scale environmental variability should be accounted for given its importance for driving trait variation.