Single-Cell RNA-Seq Reveals Lineage and X Chromosome Dynamics in Human Preimplantation Embryos.

Mouse studies have been instrumental in forming our current understanding of early cell-lineage decisions; however, similar insights into the early human development are severely limited. Here, we present a comprehensive transcriptional map of human embryo development, including the sequenced transcriptomes of 1,529 individual cells from 88 human preimplantation embryos. These data show that cells undergo an intermediate state of co-expression of lineage-specific genes, followed by a concurrent establishment of the trophectoderm, epiblast, and primitive endoderm lineages, which coincide with blastocyst formation. Female cells of all three lineages achieve dosage compensation of X chromosome RNA levels prior to implantation. However, in contrast to the mouse, XIST is transcribed from both alleles throughout the progression of this expression dampening, and X chromosome genes maintain biallelic expression while dosage compensation proceeds. We envision broad utility of this transcriptional atlas in future studies on human development as well as in stem cell research.

Global loss of cellular m6A RNA methylation following infection with different SARS-CoV-2 variants.

Insights into host-virus interactions during SARS-CoV-2 infection are needed to understand COVID-19 pathogenesis and may help to guide the design of novel antiviral therapeutics. N 6-Methyladenosine modification (m6A), one of the most abundant cellular RNA modifications, regulates key processes in RNA metabolism during stress response. Gene expression profiles observed postinfection with different SARS-CoV-2 variants show changes in the expression of genes related to RNA catabolism, including m6A readers and erasers. We found that infection with SARS-CoV-2 variants causes a loss of m6A in cellular RNAs, whereas m6A is detected abundantly in viral RNA. METTL3, the m6A methyltransferase, shows an unusual cytoplasmic localization postinfection. The B.1.351 variant has a less-pronounced effect on METTL3 localization and loss of m6A than did the B.1 and B.1.1.7 variants. We also observed a loss of m6A upon SARS-CoV-2 infection in air/liquid interface cultures of human airway epithelia, confirming that m6A loss is characteristic of SARS-CoV-2-infected cells. Further, transcripts with m6A modification are preferentially down-regulated postinfection. Inhibition of the export protein XPO1 results in the restoration of METTL3 localization, recovery of m6A on cellular RNA, and increased mRNA expression. Stress granule formation, which is compromised by SARS-CoV-2 infection, is restored by XPO1 inhibition and accompanied by a reduced viral infection in vitro. Together, our study elucidates how SARS-CoV-2 inhibits the stress response and perturbs cellular gene expression in an m6A-dependent manner.

Voltage-Seq: all-optical postsynaptic connectome-guided single-cell transcriptomics.

Understanding the routing of neuronal information requires the functional characterization of connections. Neuronal projections recruit large postsynaptic ensembles with distinct postsynaptic response types (PRTs). PRT is typically probed by low-throughput whole-cell electrophysiology and is not a selection criterion for single-cell RNA-sequencing (scRNA-seq). To overcome these limitations and target neurons based on specific PRTs for soma harvesting and subsequent scRNA-seq, we created Voltage-Seq. We established all-optical voltage imaging and recorded the PRT of 8,347 neurons in the mouse periaqueductal gray (PAG) evoked by the optogenetic activation of ventromedial hypothalamic (VMH) terminals. PRTs were classified and spatially resolved in the entire VMH-PAG connectome. We built an onsite analysis tool named VoltView to navigate soma harvesting towards target PRTs guided by a classifier that used the VMH-PAG connectome database as a reference. We demonstrated Voltage-seq by locating VMH-driven γ-aminobutyric acid-ergic neurons in the PAG, guided solely by the onsite classification in VoltView.

Genomic encoding of transcriptional burst kinetics.

Mammalian gene expression is inherently stochastic1,2, and results in discrete bursts of RNA molecules that are synthesized from each allele3-7. 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-cell4,6,8 or single-molecule RNA fluorescence in situ hybridization3,5,8,9 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.

Comparative Analysis of Single-Cell RNA Sequencing Methods.

Single-cell RNA sequencing (scRNA-seq) offers new possibilities to address biological and medical questions. However, systematic comparisons of the performance of diverse scRNA-seq protocols are lacking. We generated data from 583 mouse embryonic stem cells to evaluate six prominent scRNA-seq methods: CEL-seq2, Drop-seq, MARS-seq, SCRB-seq, Smart-seq, and Smart-seq2. While Smart-seq2 detected the most genes per cell and across cells, CEL-seq2, Drop-seq, MARS-seq, and SCRB-seq quantified mRNA levels with less amplification noise due to the use of unique molecular identifiers (UMIs). Power simulations at different sequencing depths showed that Drop-seq is more cost-efficient for transcriptome quantification of large numbers of cells, while MARS-seq, SCRB-seq, and Smart-seq2 are more efficient when analyzing fewer cells. Our quantitative comparison offers the basis for an informed choice among six prominent scRNA-seq methods, and it provides a framework for benchmarking further improvements of scRNA-seq protocols.

Transcriptional bursts explain autosomal random monoallelic expression and affect allelic imbalance.

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.

Detection and isolation of airborne SARS-CoV-2 in a hospital setting.

Transmission mechanisms for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are incompletely understood. In particular, aerosol transmission remains unclear, with viral detection in air and demonstration of its infection potential being actively investigated. To this end, we employed a novel electrostatic collector to sample air from rooms occupied by COVID-19 patients in a major Swedish hospital. Electrostatic air sampling in conjunction with extraction-free, reverse-transcriptase polymerase chain reaction (hid-RT-PCR) enabled detection of SARS-CoV-2 in air from patient rooms (9/22; 41%) and adjoining anterooms (10/22; 45%). Detection with hid-RT-PCR was concomitant with viral RNA presence on the surface of exhaust ventilation channels in patients and anterooms more than 2 m from the COVID-19 patient. Importantly, it was possible to detect active SARS-CoV-2 particles from room air, with a total of 496 plaque-forming units (PFUs) being isolated, establishing the presence of infectious, airborne SARS-CoV-2 in rooms occupied by COVID-19 patients. Our results support circulation of SARS-CoV-2 via aerosols and urge the revision of existing infection control frameworks to include airborne transmission.

Random monoallelic expression of autosomal genes: stochastic transcription and allele-level regulation.

Random monoallelic expression (RME) of genes represents a striking example of how stochastic molecular processes can result in cellular heterogeneity. Recent transcriptome-wide studies have revealed both mitotically stable and cell-to-cell dynamic forms of autosomal RME, with the latter presumably resulting from burst-like stochastic transcription. Here, we discuss the distinguishing features of these two forms of RME and revisit literature on their nature, pervasiveness and regulation. Finally, we explore how RME may contribute to phenotypic variation, including the incomplete penetrance and variable expressivity often seen in genetic disease.

Single-cell analyses of X Chromosome inactivation dynamics and pluripotency during differentiation.

Pluripotency, differentiation, and X Chromosome inactivation (XCI) are key aspects of embryonic development. However, the underlying relationship and mechanisms among these processes remain unclear. Here, we systematically dissected these features along developmental progression using mouse embryonic stem cells (mESCs) and single-cell RNA sequencing with allelic resolution. We found that mESCs grown in a ground state 2i condition displayed transcriptomic profiles diffused from preimplantation mouse embryonic cells, whereas EpiStem cells closely resembled the post-implantation epiblast. Sex-related gene expression varied greatly across distinct developmental states. We also identified novel markers that were highly enriched in each developmental state. Moreover, we revealed that several novel pathways, including PluriNetWork and Focal Adhesion, were responsible for the delayed progression of female EpiStem cells. Importantly, we "digitalized" XCI progression using allelic expression of active and inactive X Chromosomes and surprisingly found that XCI states exhibited profound variability in each developmental state, including the 2i condition. XCI progression was not tightly synchronized with loss of pluripotency and increase of differentiation at the single-cell level, although these processes were globally correlated. In addition, highly expressed genes, including core pluripotency factors, were in general biallelically expressed. Taken together, our study sheds light on the dynamics of XCI progression and the asynchronicity between pluripotency, differentiation, and XCI.

Tn5 transposase and tagmentation procedures for massively scaled sequencing projects.

Massively parallel DNA sequencing of thousands of samples in a single machine-run is now possible, but the preparation of the individual sequencing libraries is expensive and time-consuming. Tagmentation-based library construction, using the Tn5 transposase, is efficient for generating sequencing libraries but currently relies on undisclosed reagents, which severely limits development of novel applications and the execution of large-scale projects. Here, we present simple and robust procedures for Tn5 transposase production and optimized reaction conditions for tagmentation-based sequencing library construction. We further show how molecular crowding agents both modulate library lengths and enable efficient tagmentation from subpicogram amounts of cDNA. The comparison of single-cell RNA-sequencing libraries generated using produced and commercial Tn5 demonstrated equal performances in terms of gene detection and library characteristics. Finally, because naked Tn5 can be annealed to any oligonucleotide of choice, for example, molecular barcodes in single-cell assays or methylated oligonucleotides for bisulfite sequencing, custom Tn5 production and tagmentation enable innovation in sequencing-based applications.

scphaser: haplotype inference using single-cell RNA-seq data.

UNLABELLED: Determination of haplotypes is important for modelling the phenotypic consequences of genetic variation in diploid organisms, including cis-regulatory control and compound heterozygosity. We realized that single-cell RNA-seq (scRNA-seq) data are well suited for phasing genetic variants, since both transcriptional bursts and technical bottlenecks cause pronounced allelic fluctuations in individual single cells. Here we present scphaser, an R package that phases alleles at heterozygous variants to reconstruct haplotypes within transcribed regions of the genome using scRNA-seq data. The devised method efficiently and accurately reconstructed the known haplotype for ≥93% of phasable genes in both human and mouse. It also enables phasing of rare and de novo variants and variants far apart within genes, which is hard to attain with population-based computational inference. AVAILABILITY AND IMPLEMENTATION: scphaser is implemented as an R package. Tutorial and code are available at https://github.com/edsgard/scphaser CONTACT: rickard.sandberg@ki.se SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

RNA-binding protein QKI regulates Glial fibrillary acidic protein expression in human astrocytes.

Linkage, association and expression studies previously pointed to the human QKI, KH domain containing, RNA-binding (QKI) as a candidate gene for schizophrenia. Functional studies of the mouse orthologue Qk focused mainly on its role in oligodendrocyte development and myelination, while its function in astroglia remained unexplored. Here, we show that QKI is highly expressed in human primary astrocytes and that its splice forms encode proteins targeting different subcellular localizations. Uncovering the role of QKI in astrocytes is of interest in light of growing evidence implicating astrocyte dysfunction in the pathogenesis of several disorders of the central nervous system. We selectively silenced QKI splice variants in human primary astrocytes and used RNA sequencing to identify differential expression and splice variant composition at the genome-wide level. We found that an mRNA expression of Glial fibrillary acidic protein (GFAP), encoding a major component of astrocyte intermediate filaments, was down-regulated after QKI7 splice variant silencing. Moreover, we identified a potential QKI-binding site within the 3' untranslated region of human GFAP. This sequence was not conserved between mice and humans, raising the possibility that GFAP is a target for QKI in humans but not rodents. Haloperidol treatment of primary astrocytes resulted in coordinated increases in QKI7 and GFAP expression. Taken together, our results provide the first link between QKI and GFAP, two genes with alterations previously observed independently in schizophrenic patients. Our findings for QKI, together with its well-known role in myelination, suggest that QKI is a hub regulator of glia function in humans.

Introducing synthetic thermostable RNase inhibitors to single-cell RNA-seq.

Single-cell RNA-sequencing (scRNAseq) is revolutionizing biomedicine, propelled by advances in methodology, ease of use, and cost reduction of library preparation. Over the past decade, there have been remarkable technical improvements in most aspects of single-cell transcriptomics. Yet, little to no progress has been made in advancing RNase inhibition despite maintained RNA integrity being critical during cell collection, storage, and cDNA library generation. Here, we demonstrate that a synthetic thermostable RNase inhibitor (SEQURNA) yields single-cell libraries of equal or superior quality compared to ubiquitously used protein-based recombinant RNase inhibitors (RRIs). Importantly, the synthetic RNase inhibitor provides additional unique improvements in reproducibility and throughput, enables new experimental workflows including retained RNase inhibition throughout heat cycles, and can reduce the need for dry-ice transports. In summary, replacing RRIs represents a substantial advancement in the field of single-cell transcriptomics.

All-optical voltage imaging-guided postsynaptic single-cell transcriptome profiling with Voltage-Seq.

Neuronal pathways recruit large postsynaptic populations and maintain connections via distinct postsynaptic response types (PRTs). Until recently, PRTs were accessible as a selection criterion for single-cell RNA sequencing only through probing by low-throughput whole-cell electrophysiology. To overcome these limitations and target neurons on the basis of specific PRTs for soma collection and subsequent single-cell RNA sequencing, we developed Voltage-Seq using the genetically encoded voltage indicator Voltron in acute brain slices from mice. We also created an onsite analysis tool, VoltView, to guide soma collection of specific PRTs using a classifier based on a previously acquired database of connectomes from multiple animals. Here we present our procedure for preparing the optical path, the imaging setup and detailing the imaging and analysis steps, as well as a complete procedure for sequencing library preparation. This enables researchers to conduct our high-throughput all-optical synaptic assay and to obtain single-cell transcriptomic data from selected postsynaptic neurons. This also allows researchers to resolve the connectivity ratio of a specific pathway and explore the diversity of PRTs within that connectome. Furthermore, combining high throughput with quick analysis gives unique access to find specific connections within a large postsynaptic connectome. Voltage-Seq also allows the investigation of correlations between connectivity and gene expression changes in a postsynaptic cell-type-specific manner for both excitatory and inhibitory connections. The Voltage-Seq workflow can be completed in ~6 weeks, including 4-5 weeks for viral expression of the Voltron sensor. The technique requires knowledge of basic laboratory techniques, micromanipulator handling skills and experience in molecular biology and bioinformatics.

Soluble and multivalent Jag1 DNA origami nanopatterns activate Notch without pulling force.

The Notch signaling pathway has fundamental roles in embryonic development and in the nervous system. The current model of receptor activation involves initiation via a force-induced conformational change. Here, we define conditions that reveal pulling force-independent Notch activation using soluble multivalent constructs. We treat neuroepithelial stem-like cells with molecularly precise ligand nanopatterns displayed from solution using DNA origami. Notch signaling follows with clusters of Jag1, and with chimeric structures where most Jag1 proteins are replaced by other binders not targeting Notch. Our data rule out several confounding factors and suggest a model where Jag1 activates Notch upon prolonged binding without appearing to need a pulling force. These findings reveal a distinct mode of activation of Notch and lay the foundation for the development of soluble agonists.

Multivalent insulin receptor activation using insulin-DNA origami nanostructures.

Insulin binds the insulin receptor (IR) and regulates anabolic processes in target tissues. Impaired IR signalling is associated with multiple diseases, including diabetes, cancer and neurodegenerative disorders. IRs have been reported to form nanoclusters at the cell membrane in several cell types, even in the absence of insulin binding. Here we exploit the nanoscale spatial organization of the IR to achieve controlled multivalent receptor activation. To control insulin nanoscale spatial organization and valency, we developed rod-like insulin-DNA origami nanostructures carrying different numbers of insulin molecules with defined spacings. Increasing the insulin valency per nanostructure markedly extended the residence time of insulin-DNA origami nanostructures at the receptors. Both insulin valency and spacing affected the levels of IR activation in adipocytes. Moreover, the multivalent insulin design associated with the highest levels of IR activation also induced insulin-mediated transcriptional responses more effectively than the corresponding monovalent insulin nanostructures. In an in vivo zebrafish model of diabetes, treatment with multivalent-but not monovalent-insulin nanostructures elicited a reduction in glucose levels. Our results show that the control of insulin multivalency and spatial organization with nanoscale precision modulates the IR responses, independent of the insulin concentration. Therefore, we propose insulin nanoscale organization as a design parameter in developing new insulin therapies.

Limitations of X:autosome ratio as a measurement of X-chromosome upregulation.

Lentini and Reinius address issues in interpreting non-allelic gene expression measurements of dosage compensation during murine embryonic development.