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Background RNA velocity analysis of single cells offers the potential to predict temporal dynamics from gene expression. In many systems, RNA velocity has been observed to produce a vector field that qualitatively reflects known features of the system. However, the limitations of RNA velocity estimates are still not well understood. Results We analyze the impact of different steps in the RNA velocity workflow on direction and speed. We consider both high-dimensional velocity estimates and low-dimensional velocity vector fields mapped onto an embedding. We conclude the transition probability method for mapping velocity estimates onto an embedding is effectively interpolating in the embedding space. Our findings reveal a significant dependence of the RNA velocity workflow on smoothing via the k-nearest-neighbors (k-NN) graph of the observed data. This reliance results in considerable estimation errors for both direction and speed in both high- and low-dimensional settings when the k-NN graph fails to accurately represent the true data structure; this is an unknown feature of real data. RNA velocity performs poorly at estimating speed in both low- and high-dimensional spaces, except in very low noise settings. We introduce a novel quality measure that can identify when RNA velocity should not be used. Conclusions Our findings emphasize the importance of choices in the RNA velocity workflow and highlight critical limitations of data analysis. We advise against over-interpreting expression dynamics using RNA velocity, particularly in terms of speed. Finally, we emphasize that the use of RNA velocity in assessing the correctness of a low-dimensional embedding is circular.

A long intergenic noncoding RNA, Firre, is now shown to localize to a domain across its own chromosomal locus and to distinct interacting transchromosomal loci in mouse and human cells. In addition, Firre interacts with nuclear-matrix factor hnRNPU. These results lead to a model in which Firre functions as a nuclear-organization factor modulating the topological organization of multiple chromosomes. RNA, including long noncoding RNA (lncRNA), is known to be an abundant and important structural component of the nuclear matrix. However, the molecular identities, functional roles and localization dynamics of lncRNAs that influence nuclear architecture remain poorly understood. Here, we describe one lncRNA, Firre, that interacts with the nuclear-matrix factor hnRNPU through a 156-bp repeating sequence and localizes across an ~5-Mb domain on the X chromosome. We further observed Firre localization across five distinct trans -chromosomal loci, which reside in spatial proximity to the Firre genomic locus on the X chromosome. Both genetic deletion of the Firre locus and knockdown of hnRNPU resulted in loss of colocalization of these trans -chromosomal interacting loci. Thus, our data suggest a model in which lncRNAs such as Firre can interface with and modulate nuclear architecture across chromosomes.

Vascular endothelial cell (EC) function depends on appropriate organ-specific molecular and cellular specializations. To explore genomic mechanisms that control this specialization, we have analyzed and compared the transcriptome, accessible chromatin, and DNA methylome landscapes from mouse brain, liver, lung, and kidney ECs. Analysis of transcription factor (TF) gene expression and TF motifs at candidate cis-regulatory elements reveals both shared and organ-specific EC regulatory networks. In the embryo, only those ECs that are adjacent to or within the central nervous system (CNS) exhibit canonical Wnt signaling, which correlates precisely with blood-brain barrier (BBB) differentiation and Zic3 expression. In the early postnatal brain, single-cell RNA-seq of purified ECs reveals (1) close relationships between veins and mitotic cells and between arteries and tip cells, (2) a division of capillary ECs into vein-like and artery-like classes, and (3) new endothelial subtype markers, including new validated tip cell markers.

Background The cell cycle is a highly conserved, continuous process which controls faithful replication and division of cells. Single-cell technologies have enabled increasingly precise measurements of the cell cycle both as a biological process of interest and as a possible confounding factor. Despite its importance and conservation, there is no universally applicable approach to infer position in the cell cycle with high-resolution from single-cell RNA-seq data. Results Here, we present tricycle, an R/Bioconductor package, to address this challenge by leveraging key features of the biology of the cell cycle, the mathematical properties of principal component analysis of periodic functions, and the use of transfer learning. We estimate a cell-cycle embedding using a fixed reference dataset and project new data into this reference embedding, an approach that overcomes key limitations of learning a dataset-dependent embedding. Tricycle then predicts a cell-specific position in the cell cycle based on the data projection. The accuracy of tricycle compares favorably to gold-standard experimental assays, which generally require specialized measurements in specifically constructed in vitro systems. Using internal controls which are available for any dataset, we show that tricycle predictions generalize to datasets with multiple cell types, across tissues, species, and even sequencing assays. Conclusions Tricycle generalizes across datasets and is highly scalable and applicable to atlas-level single-cell RNA-seq data.

DNA methylation was first described almost a century ago; however, the rules governing its establishment and maintenance remain elusive. Here we present data demonstrating that active transcription regulates levels of genomic methylation. We identify a novel RNA arising from the CEBPA gene locus that is critical in regulating the local DNA methylation profile. This RNA binds to DNMT1 and prevents CEBPA gene locus methylation. Deep sequencing of transcripts associated with DNMT1 combined with genome-scale methylation and expression profiling extend the generality of this finding to numerous gene loci. Collectively, these results delineate the nature of DNMT1–RNA interactions and suggest strategies for gene-selective demethylation of therapeutic targets in human diseases. RNAs are shown to interact with DNA methyltransferase 1 and prevent DNA methylation of genes at their specific locus, providing evidence that active transcription directly regulates DNA methylation levels. DNA methylation blocked by non-coding RNA DNA methylation is an epigenetic modification associated with silencing of gene expression. Here, Daniel Tenen and colleagues propose that active transcription directly regulates levels of DNA methylation. A non-coding RNA arising from the well-studied methylation-sensitive gene CEBPA interacts with the DNA methyltransferase DNMT1 to prevent methylation at the CEBPA locus, thereby facilitating CEBPA expression. Functional associations between DNMT1 and RNAs seem to occur at numerous gene loci. These findings support the hypothesis that non-coding RNA participates in the regulation of genomic methylation patterns by interacting with DNMT1 and suggest a potential therapeutic strategy for site-specific alteration of aberrant DNA methylation.

Analyzing multi-sample spatial transcriptomics data requires accounting for biological variation. We present multi-sample non-negative spatial factorization (mNSF), an alignment-free framework extending single-sample spatial factorization to multi-sample datasets. mNSF incorporates sample-specific spatial correlation modeling and extracts low-dimensional data representations. Through simulations and real data analysis, we demonstrate mNSF’s efficacy in identifying true factors, shared anatomical regions, and region-specific biological functions. mNSF’s performance is comparable to alignment-based methods when alignment is feasible, while enabling analysis in scenarios where spatial alignment is unfeasible. mNSF shows promise as a robust method for analyzing spatially resolved transcriptomics data across multiple samples.

The prevalence of obesity has led to a surge of interest in understanding the detailed mechanisms underlying adipocyte development. Many protein-coding genes, mRNAs, and microRNAs have been implicated in adipocyte development, but the global expression patterns and functional contributions of long noncoding RNA (lncRNA) during adipogenesis have not been explored. Here we profiled the transcriptome of primary brown and white adipocytes, preadipocytes, and cultured adipocytes and identified 175 lncRNAs that are specifically regulated during adipogenesis. Many lncRNAs are adipose-enriched, strongly induced during adipogenesis, and bound at their promoters by key transcription factors such as peroxisome proliferator-activated receptor γ (PPARγ) and CCAAT/enhancer-binding protein α (CEBPα). RNAi-mediated loss of function screens identified functional lncRNAs with varying impact on adipogenesis. Collectively, we have identified numerous lncRNAs that are functionally required for proper adipogenesis.

The mammalian CNS is capable of tolerating chronic hypoxia, but cell type-specific responses to this stress have not been systematically characterized. In the Norrin KO (NdpKO ) mouse, a model of familial exudative vitreoretinopathy (FEVR), developmental hypo-vascularization of the retina produces chronic hypoxia of inner nuclear-layer (INL) neurons and Muller glia. We used single-cell RNA sequencing, untargeted metabolomics, and metabolite labeling from 13C-glucose to compare WT and NdpKO retinas. In NdpKO retinas, we observe gene expression responses consistent with hypoxia in Muller glia and retinal neurons, and we find a metabolic shift that combines reduced flux through the TCA cycle with increased synthesis of serine, glycine, and glutathione. We also used single-cell RNA sequencing to compare the responses of individual cell types in NdpKO retinas with those in the hypoxic cerebral cortex of mice that were housed for 1 week in a reduced oxygen environment (7.5% oxygen). In the hypoxic cerebral cortex, glial transcriptome responses most closely resemble the response of Muller glia in the NdpKO retina. In both retina and brain, vascular endothelial cells activate a previously dormant tip cell gene expression program, which likely underlies the adaptive neoangiogenic response to chronic hypoxia. These analyses of retina and brain transcriptomes at single-cell resolution reveal both shared and cell type-specific changes in gene expression in response to chronic hypoxia, implying both shared and distinct cell type-specific physiologic responses.

Kabuki syndrome is a Mendelian intellectual disability syndrome caused by mutations in either of two genes (KMT2D and KDM6A) involved in chromatin accessibility. We previously showed that an agent that promotes chromatin opening, the histone deacetylase inhibitor (HDACi) AR-42, ameliorates the deficiency of adult neurogenesis in the granule cell layer of the dentate gyrus and rescues hippocampal memory defects in a mouse model of Kabuki syndrome (Kmt2d +/βGeo). Unlike a drug, a dietary intervention could be quickly transitioned to the clinic. Therefore, we have explored whether treatment with a ketogenic diet could lead to a similar rescue through increased amounts of beta-hydroxybutyrate, an endogenous HDACi. Here, we report that a ketogenic diet in Kmt2d+/βGeo mice modulates H3ac and H3K4me3 in the granule cell layer, with concomitant rescue of both the neurogenesis defect and hippocampal memory abnormalities seen in Kmt2d +/βGeo mice; similar effects on neurogenesis were observed on exogenous administration of beta-hydroxybutyrate. These data suggest that dietary modulation of epigenetic modifications through elevation of beta-hydroxybutyrate may provide a feasible strategy to treat the intellectual disability seen in Kabuki syndrome and related disorders.

Long noncoding RNAs (lncRNAs) have been implicated in numerous cellular processes including brain development. However, the in vivo expression dynamics and molecular pathways regulated by these loci are not well understood. Here, we leveraged a cohort of 13 lncRNA-null mutant mouse models to investigate the spatiotemporal expression of lncRNAs in the developing and adult brain and the transcriptome alterations resulting from the loss of these lncRNA loci. We show that several lncRNAs are differentially expressed both in time and space, with some presenting highly restricted expression in only selected brain regions. We further demonstrate altered regulation of genes for a large variety of cellular pathways and processes upon deletion of the lncRNA loci. Finally, we found that 4 of the 13 lncRNAs significantly affect the expression of several neighboring protein-coding genes in a cis -like manner. By providing insight into the endogenous expression patterns and the transcriptional perturbations caused by deletion of the lncRNA locus in the developing and postnatal mammalian brain, these data provide a resource to facilitate future examination of the specific functional relevance of these genes in neural development, brain function, and disease.