BRIE2: computational identification of splicing phenotypes from single-cell transcriptomic experiments

RNA splicing controls organ-wide maturation of postnatal heart in mice

Postnatal cardiac development requires the orchestrated maturation of diverse cellular components for which unifying control mechanisms are still lacking. Using full-length sequencing, we examined the transcriptomic landscape of the maturating mouse heart (E18.5-P28) at single-cell and transcript isoform resolution. We identified dynamically changing intercellular networks as a molecular basis of the maturing heart and alternative splicing (AS) as a common mechanism that distinguished developmental age. Manipulation of RNA-binding proteins (RBPs) remodeled the AS landscape, cardiac cell maturation, and intercellular communication through direct binding of splice targets, which were enriched for functions related to general, as well as cell-type-specific, maturation. Overexpression of an RBP nuclear cap-binding protein subunit 2 (NCBP2) in neonatal hearts repressed cardiac maturation. Together, our data suggest AS regulation by RBPs as an organ-level control mechanism in mammalian postnatal cardiac development and provide insight into the possibility of manipulating RBPs for therapeutic purposes.

Single-cell omics: experimental workflow, data analyses and applications

Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular features. Our exploration of how the genomes orchestrate the formation and maintenance of each cell, and control the cellular phenotypes of various organismsis, is both captivating and intricate. Since the inception of the first single-cell RNA technology, technologies related to single-cell sequencing have experienced rapid advancements in recent years. These technologies have expanded horizontally to include single-cell genome, epigenome, proteome, and metabolome, while vertically, they have progressed to integrate multiple omics data and incorporate additional information such as spatial scRNA-seq and CRISPR screening. Single-cell omics represent a groundbreaking advancement in the biomedical field, offering profound insights into the understanding of complex diseases, including cancers. Here, we comprehensively summarize recent advances in single-cell omics technologies, with a specific focus on the methodology section. This overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.

DifferentialRegulation: a Bayesian hierarchical approach to identify differentially regulated genes

Although transcriptomics data is typically used to analyze mature spliced mRNA, recent attention has focused on jointly investigating spliced and unspliced (or precursor-) mRNA, which can be used to study gene regulation and changes in gene expression production. Nonetheless, most methods for spliced/unspliced inference (such as RNA velocity tools) focus on individual samples, and rarely allow comparisons between groups of samples (e.g. healthy vs. diseased). Furthermore, this kind of inference is challenging, because spliced and unspliced mRNA abundance is characterized by a high degree of quantification uncertainty, due to the prevalence of multi-mapping reads, ie reads compatible with multiple transcripts (or genes), and/or with both their spliced and unspliced versions. Here, we present DifferentialRegulation, a Bayesian hierarchical method to discover changes between experimental conditions with respect to the relative abundance of unspliced mRNA (over the total mRNA). We model the quantification uncertainty via a latent variable approach, where reads are allocated to their gene/transcript of origin, and to the respective splice version. We designed several benchmarks where our approach shows good performance, in terms of sensitivity and error control, vs. state-of-the-art competitors. Importantly, our tool is flexible, and works with both bulk and single-cell RNA-sequencing data. DifferentialRegulation is distributed as a Bioconductor R package.

Mining alternative splicing patterns in scRNA-seq data using scASfind

Single-cell RNA-seq (scRNA-seq) is widely used for transcriptome profiling, but most analyses focus on gene-level events, with less attention devoted to alternative splicing. Here, we present scASfind, a novel computational method to allow for quantitative analysis of cell type-specific splicing events using full-length scRNA-seq data. ScASfind utilizes an efficient data structure to store the percent spliced-in value for each splicing event. This makes it possible to exhaustively search for patterns among all differential splicing events, allowing us to identify marker events, mutually exclusive events, and events involving large blocks of exons that are specific to one or more cell types.

Expression profiling of cerebrospinal fluid identifies dysregulated antiviral mechanisms in multiple sclerosis

Despite the overwhelming evidence that multiple sclerosis is an autoimmune disease, relatively little is known about the precise nature of the immune dysregulation underlying the development of the disease. Reasoning that the CSF from patients might be enriched for cells relevant in pathogenesis, we have completed a high-resolution single-cell analysis of 96 732 CSF cells collected from 33 patients with multiple sclerosis (n = 48 675) and 48 patients with other neurological diseases (n = 48 057). Completing comprehensive cell type annotation, we identified a rare population of CD8+ T cells, characterized by the upregulation of inhibitory receptors, increased in patients with multiple sclerosis. Applying a Multi-Omics Factor Analysis to these single-cell data further revealed that activity in pathways responsible for controlling inflammatory and type 1 interferon responses are altered in multiple sclerosis in both T cells and myeloid cells. We also undertook a systematic search for expression quantitative trait loci in the CSF cells. Of particular interest were two expression quantitative trait loci in CD8+ T cells that were fine mapped to multiple sclerosis susceptibility variants in the viral control genes ZC3HAV1 (rs10271373) and IFITM2 (rs1059091). Further analysis suggests that these associations likely reflect genetic effects on RNA splicing and cell-type specific gene expression respectively. Collectively, our study suggests that alterations in viral control mechanisms might be important in the development of multiple sclerosis.

CamoTSS: analysis of alternative transcription start sites for cellular phenotypes and regulatory patterns from 5' scRNA-seq data

Five-prime single-cell RNA-seq (scRNA-seq) has been widely employed to profile cellular transcriptomes, however, its power of analysing transcription start sites (TSS) has not been fully utilised. Here, we present a computational method suite, CamoTSS, to precisely identify TSS and quantify its expression by leveraging the cDNA on read 1, which enables effective detection of alternative TSS usage. With various experimental data sets, we have demonstrated that CamoTSS can accurately identify TSS and the detected alternative TSS usages showed strong specificity in different biological processes, including cell types across human organs, the development of human thymus, and cancer conditions. As evidenced in nasopharyngeal cancer, alternative TSS usage can also reveal regulatory patterns including systematic TSS dysregulations.

Alternative Transcripts Diversify Genome Function for Phenome Relevance to Health and Diseases

Manipulation using alternative exon splicing (AES), alternative transcription start (ATS), and alternative polyadenylation (APA) sites are key to transcript diversity underlying health and disease. All three are pervasive in organisms, present in at least 50% of human protein-coding genes. In fact, ATS and APA site use has the highest impact on protein identity, with their ability to alter which first and last exons are utilized as well as impacting stability and translation efficiency. These RNA variants have been shown to be highly specific, both in tissue type and stage, with demonstrated importance to cell proliferation, differentiation and the transition from fetal to adult cells. While alternative exon splicing has a limited effect on protein identity, its ubiquity highlights the importance of these minor alterations, which can alter other features such as localization. The three processes are also highly interwoven, with overlapping, complementary, and competing factors, RNA polymerase II and its CTD (C-terminal domain) chief among them. Their role in development means dysregulation leads to a wide variety of disorders and cancers, with some forms of disease disproportionately affected by specific mechanisms (AES, ATS, or APA). Challenges associated with the genome-wide profiling of RNA variants and their potential solutions are also discussed in this review.

Inferring single-cell transcriptomic dynamics with structured latent gene expression dynamics

Gene expression dynamics provide directional information for trajectory inference from single-cell RNA sequencing data. Traditional approaches compute RNA velocity using strict modeling assumptions about transcription and splicing of RNA. This can fail in scenarios where multiple lineages have distinct gene dynamics or where rates of transcription and splicing are time dependent. We present "LatentVelo," an approach to compute a low-dimensional representation of gene dynamics with deep learning. LatentVelo embeds cells into a latent space with a variational autoencoder and models differentiation dynamics on this "dynamics-based" latent space with neural ordinary differential equations. LatentVelo infers a latent regulatory state that controls the dynamics of an individual cell to model multiple lineages. LatentVelo can predict latent trajectories, describing the inferred developmental path for individual cells rather than just local RNA velocity vectors. The dynamics-based embedding batch corrects cell states and velocities, outperforming comparable autoencoder batch correction methods that do not consider gene expression dynamics.

Regulation of pre-mRNA splicing: roles in physiology and disease, and therapeutic prospects

The removal of introns from mRNA precursors and its regulation by alternative splicing are key for eukaryotic gene expression and cellular function, as evidenced by the numerous pathologies induced or modified by splicing alterations. Major recent advances have been made in understanding the structures and functions of the splicing machinery, in the description and classification of physiological and pathological isoforms and in the development of the first therapies for genetic diseases based on modulation of splicing. Here, we review this progress and discuss important remaining challenges, including predicting splice sites from genomic sequences, understanding the variety of molecular mechanisms and logic of splicing regulation, and harnessing this knowledge for probing gene function and disease aetiology and for the design of novel therapeutic approaches.

MARVEL: an integrated alternative splicing analysis platform for single-cell RNA sequencing data

Alternative splicing is an important source of heterogeneity underlying gene expression between individual cells but remains an understudied area due to the paucity of computational tools to analyze splicing dynamics at single-cell resolution. Here, we present MARVEL, a comprehensive R package for single-cell splicing analysis applicable to RNA sequencing generated from the plate- and droplet-based methods. We performed extensive benchmarking of MARVEL against available tools and demonstrated its utility by analyzing multiple publicly available datasets in diverse cell types, including in disease. MARVEL enables systematic and integrated splicing and gene expression analysis of single cells to characterize the splicing landscape and reveal biological insights.

Bioinformatic analysis of single-cell RNA sequencing dataset dissects cellular heterogeneity of triple-negative breast cancer in transcriptional profile, splicing event and crosstalk network

BACKGROUND

Triple-negative breast cancer (TNBC) is a subtype of breast cancer with high tumoral heterogeneity, while the detailed regulatory network is not well known.

METHODS

Via single-cell RNA-sequencing (scRNA-seq) data analysis, we comprehensively investigated the transcriptional profile of different subtypes of TNBC epithelial cells with gene regulatory network (GRN) and alternative splicing (AS) event analysis, as well as the crosstalk between epithelial and non-epithelial cells.

RESULTS

Of note, we found that luminal progenitor subtype exhibited the most complex GRN and splicing events. Besides, hnRNPs negatively regulates AS events in luminal progenitor subtype. In addition, we explored the cellular crosstalk among endothelial cells, stromal cells and immune cells in TNBC and discovered that NOTCH4 was a key receptor and prognostic marker in endothelial cells, which provide potential biomarker and target for TNBC intervention.

CONCLUSIONS

In summary, our study elaborates on the cellular heterogeneity of TNBC, revealing that NOTCH4 in endothelial cells was critical for TNBC intervention. This in-depth understanding of epithelial cell and non-epithelial cell network would provide theoretical basis for the development of new drugs targeting this sophisticated network in TNBC.

Emergence of neuron types

Neuron types are the building blocks of the nervous system, and therefore, of functional circuits. Understanding the origin of neuronal diversity has always been an essential question in neuroscience and developmental biology. While knowledge on the molecular control of their diversification has largely increased during the last decades, it is now possible to reveal the dynamic mechanisms and the actual stepwise molecular changes occurring at single-cell level with the advent of single-cell omics technologies and analysis with high temporal resolution. Here, we focus on recent advances in the field and in technical and analytical tools that enable detailed insights into the emergence of neuron types in the central and peripheral nervous systems.

UniTVelo: temporally unified RNA velocity reinforces single-cell trajectory inference

The recent breakthrough of single-cell RNA velocity methods brings attractive promises to reveal directed trajectory on cell differentiation, states transition and response to perturbations. However, the existing RNA velocity methods are often found to return erroneous results, partly due to model violation or lack of temporal regularization. Here, we present UniTVelo, a statistical framework of RNA velocity that models the dynamics of spliced and unspliced RNAs via flexible transcription activities. Uniquely, it also supports the inference of a unified latent time across the transcriptome. With ten datasets, we demonstrate that UniTVelo returns the expected trajectory in different biological systems, including hematopoietic differentiation and those even with weak kinetics or complex branches.

Schwann cell precursors represent a neural crest-like state with biased multipotency

Schwann cell precursors (SCPs) are nerve-associated progenitors that can generate myelinating and non-myelinating Schwann cells but also are multipotent like the neural crest cells from which they originate. SCPs are omnipresent along outgrowing peripheral nerves throughout the body of vertebrate embryos. By using single-cell transcriptomics to generate a gene expression atlas of the entire neural crest lineage, we show that early SCPs and late migratory crest cells have similar transcriptional profiles characterised by a multipotent "hub" state containing cells biased towards traditional neural crest fates. SCPs keep diverging from the neural crest after being primed towards terminal Schwann cells and other fates, with different subtypes residing in distinct anatomical locations. Functional experiments using CRISPR-Cas9 loss-of-function further show that knockout of the common "hub" gene Sox8 causes defects in neural crest-derived cells along peripheral nerves by facilitating differentiation of SCPs towards sympathoadrenal fates. Finally, specific tumour populations found in melanoma, neurofibroma and neuroblastoma map to different stages of SCP/Schwann cell development. Overall, SCPs resemble migrating neural crest cells that maintain multipotency and become transcriptionally primed towards distinct lineages.

Identifying cell state-associated alternative splicing events and their coregulation

Alternative splicing shapes the transcriptome and contributes to each cell's unique identity, but single-cell RNA sequencing (scRNA-seq) has struggled to capture the impact of alternative splicing. We previously showed that low recovery of mRNAs from single cells led to erroneous conclusions about the cell-to-cell variability of alternative splicing. Here, we present a method, Psix, to confidently identify splicing that changes across a landscape of single cells, using a probabilistic model that is robust against the data limitations of scRNA-seq. Its autocorrelation-inspired approach finds patterns of alternative splicing that correspond to patterns of cell identity, such as cell type or developmental stage, without the need for explicit cell clustering, labeling, or trajectory inference. Applying Psix to data that follow the trajectory of mouse brain development, we identify exons whose alternative splicing patterns cluster into modules of coregulation. We show that the exons in these modules are enriched for binding by distinct neuronal splicing factors and that their changes in splicing correspond to changes in expression of these splicing factors. Thus, Psix reveals cell type-dependent splicing patterns and the wiring of the splicing regulatory networks that control them. Our new method will enable scRNA-seq analysis to go beyond transcription to understand the roles of post-transcriptional regulation in determining cell identity.

Scalable single-cell RNA sequencing from full transcripts with Smart-seq3xpress

Current single-cell RNA sequencing (scRNA-seq) methods with high cellular throughputs sacrifice full-transcript coverage and often sensitivity. Here we describe Smart-seq3xpress, which miniaturizes and streamlines the Smart-seq3 protocol to substantially reduce reagent use and increase cellular throughput. Smart-seq3xpress analysis of peripheral blood mononuclear cells resulted in a granular atlas complete with common and rare cell types. Compared with droplet-based single-cell RNA sequencing that sequences RNA ends, the additional full-transcript coverage revealed cell-type-associated isoform variation.

Serotonin limits generation of chromaffin cells during adrenal organ development

Adrenal glands are the major organs releasing catecholamines and regulating our stress response. The mechanisms balancing generation of adrenergic chromaffin cells and protecting against neuroblastoma tumors are still enigmatic. Here we revealed that serotonin (5HT) controls the numbers of chromaffin cells by acting upon their immediate progenitor "bridge" cells via 5-hydroxytryptamine receptor 3A (HTR3A), and the aggressive HTR3Ahigh human neuroblastoma cell lines reduce proliferation in response to HTR3A-specific agonists. In embryos (in vivo), the physiological increase of 5HT caused a prolongation of the cell cycle in "bridge" progenitors leading to a smaller chromaffin population and changing the balance of hormones and behavioral patterns in adulthood. These behavioral effects and smaller adrenals were mirrored in the progeny of pregnant female mice subjected to experimental stress, suggesting a maternal-fetal link that controls developmental adaptations. Finally, these results corresponded to a size-distribution of adrenals found in wild rodents with different coping strategies.

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.

Robust and annotation-free analysis of alternative splicing across diverse cell types in mice

Although alternative splicing is a fundamental and pervasive aspect of gene expression in higher eukaryotes, it is often omitted from single-cell studies due to quantification challenges inherent to commonly used short-read sequencing technologies. Here, we undertake the analysis of alternative splicing across numerous diverse murine cell types from two large-scale single-cell datasets-the Tabula Muris and BRAIN Initiative Cell Census Network-while accounting for understudied technical artifacts and unannotated events. We find strong and general cell-type-specific alternative splicing, complementary to total gene expression but of similar discriminatory value, and identify a large volume of novel splicing events. We specifically highlight splicing variation across different cell types in primary motor cortex neurons, bone marrow B cells, and various epithelial cells, and we show that the implicated transcripts include many genes which do not display total expression differences. To elucidate the regulation of alternative splicing, we build a custom predictive model based on splicing factor activity, recovering several known interactions while generating new hypotheses, including potential regulatory roles for novel alternative splicing events in critical genes like Khdrbs3 and Rbfox1. We make our results available using public interactive browsers to spur further exploration by the community.

Representation learning of RNA velocity reveals robust cell transitions

RNA velocity is a promising technique for quantifying cellular transitions from single-cell transcriptome experiments and revealing transient cellular dynamics among a heterogeneous cell population. However, the cell transitions estimated from high-dimensional RNA velocity are often unstable or inaccurate, partly due to the high technical noise and less informative projection. Here, we present Velocity Autoencoder (VeloAE), a tailored representation learning method, to learn a low-dimensional representation of RNA velocity on which cellular transitions can be robustly estimated. On various experimental datasets, we show that VeloAE can both accurately identify stimulation dynamics in time-series designs and effectively capture expected cellular differentiation in different biological systems. VeloAE, therefore, enhances the usefulness of RNA velocity for studying a wide range of biological processes.

Dynamic transcriptional reprogramming leads to immunotherapeutic vulnerabilities in myeloma

While there is extensive evidence for genetic variation as a basis for treatment resistance, other sources of variation result from cellular plasticity. Using multiple myeloma as an example of an incurable lymphoid malignancy, we show how cancer cells modulate lineage restriction, adapt their enhancer usage and employ cell-intrinsic diversity for survival and treatment escape. By using single-cell transcriptome and chromatin accessibility profiling, we show that distinct transcriptional states co-exist in individual cancer cells and that differential transcriptional regulon usage and enhancer rewiring underlie these alternative transcriptional states. We demonstrate that exposure to standard treatment further promotes transcriptional reprogramming and differential enhancer recruitment while simultaneously reducing developmental potential. Importantly, treatment generates a distinct complement of actionable immunotherapy targets, such as CXCR4, which can be exploited to overcome treatment resistance. Our studies therefore delineate how to transform the cellular plasticity that underlies drug resistance into immuno-oncologic therapeutic opportunities.