Single-Cell Alternative Splicing Analysis with Expedition Reveals Splicing Dynamics during Neuron Differentiation

Dynamics of alternative polyadenylation in single root cells of Arabidopsis thaliana

Introduction

Single-cell RNA-seq (scRNA-seq) technologies have been widely used to reveal the diversity and complexity of cells, and pioneering studies on scRNA-seq in plants began to emerge since 2019. However, existing studies on plants utilized scRNA-seq focused only on the gene expression regulation. As an essential post-transcriptional mechanism for regulating gene expression, alternative polyadenylation (APA) generates diverse mRNA isoforms with distinct 3' ends through the selective use of different polyadenylation sites in a gene. APA plays important roles in regulating multiple developmental processes in plants, such as flowering time and stress response.

Methods

In this study, we developed a pipeline to identify and integrate APA sites from different scRNA-seq data and analyze APA dynamics in single cells. First, high-confidence poly(A) sites in single root cells were identified and quantified. Second, three kinds of APA markers were identified for exploring APA dynamics in single cells, including differentially expressed poly(A) sites based on APA site expression, APA markers based on APA usages, and APA switching genes based on 3' UTR (untranslated region) length change. Moreover, cell type annotations of single root cells were refined by integrating both the APA information and the gene expression profile.

Results

We comprehensively compiled a single-cell APA atlas from five scRNA-seq studies, covering over 150,000 cells spanning four major tissue branches, twelve cell types, and three developmental stages. Moreover, we quantified the dynamic APA usages in single cells and identified APA markers across tissues and cell types. Further, we integrated complementary information of gene expression and APA profiles to annotate cell types and reveal subtle differences between cell types.

Discussion

This study reveals that APA provides an additional layer of information for determining cell identity and provides a landscape of APA dynamics during Arabidopsis root development.

NACC2, a molecular effector of miR-132 regulation at the interface between adult neurogenesis and Alzheimer's disease

The generation of new neurons at the hippocampal neurogenic niche, known as adult hippocampal neurogenesis (AHN), and its impairment, have been implicated in Alzheimer's disease (AD). MicroRNA-132 (miR-132), the most consistently downregulated microRNA (miRNA) in AD, was recently identified as a potent regulator of AHN, exerting multilayered proneurogenic effects in adult neural stem cells (NSCs) and their progeny. Supplementing miR-132 in AD mouse brain restores AHN and relevant memory deficits, yet the exact mechanisms involved are still unknown. Here, we identify NACC2 as a novel miR-132 target implicated in both AHN and AD. miR-132 deficiency in mouse hippocampus induces Nacc2 expression and inflammatory signaling in adult NSCs. We show that miR-132-dependent regulation of NACC2 is involved in the initial stages of human NSC differentiation towards astrocytes and neurons. Later, NACC2 function in astrocytic maturation becomes uncoupled from miR-132. We demonstrate that NACC2 is present in reactive astrocytes surrounding amyloid plaques in mouse and human AD hippocampus, and that there is an anticorrelation between miR-132 and NACC2 levels in AD and upon induction of inflammation. Unraveling the molecular mechanisms by which miR-132 regulates neurogenesis and cellular reactivity in AD, will provide valuable insights towards its possible application as a therapeutic target.

RNA Splicing Events in Circulation Distinguish Individuals with and without New-Onset Type 1 Diabetes

CONTEXT

Alterations in RNA splicing may influence protein isoform diversity that contributes to or reflects the pathophysiology of certain diseases. Whereas specific RNA splicing events in pancreatic islets have been investigated in models of inflammation in vitro, how RNA splicing in the circulation correlates with or is reflective of T1D disease pathophysiology in humans remains unexplored.

OBJECTIVE

To use machine learning to investigate if alternative RNA splicing events differ between individuals with and without new-onset type 1 diabetes (T1D) and to determine if these splicing events provide insight into T1D pathophysiology.

METHODS

RNA deep sequencing was performed on whole blood samples from two independent cohorts: a training cohort consisting of 12 individuals with new-onset T1D and 12 age- and sex-matched nondiabetic controls and a validation cohort of the same size and demographics. Machine learning analysis was used to identify specific isoforms that could distinguish individuals with T1D from controls.

RESULTS

Distinct patterns of RNA splicing differentiated participants with T1D from unaffected controls. Notably, certain splicing events, particularly involving retained introns, showed significant association with T1D. Machine learning analysis using these splicing events as features from the training cohort demonstrated high accuracy in distinguishing between T1D subjects and controls in the validation cohort. Gene Ontology pathway enrichment analysis of the retained intron category showed evidence for a systemic viral response in T1D subjects.

CONCLUSIONS

Alternative RNA splicing events in whole blood are significantly enriched in individuals with new-onset T1D and can effectively distinguish these individuals from unaffected controls. Our findings also suggest that RNA splicing profiles offer the potential to provide insights into disease pathogenesis.

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.

Regulation of endocrine cell alternative splicing revealed by single-cell RNA sequencing in type 2 diabetes pathogenesis

The prevalent RNA alternative splicing (AS) contributes to molecular diversity, which has been demonstrated in cellular function regulation and disease pathogenesis. However, the contribution of AS in pancreatic islets during diabetes progression remains unclear. Here, we reanalyze the full-length single-cell RNA sequencing data from the deposited database to investigate AS regulation across human pancreatic endocrine cell types in non-diabetic (ND) and type 2 diabetic (T2D) individuals. Our analysis demonstrates the significant association between transcriptomic AS profiles and cell-type-specificity, which could be applied to distinguish the clustering of major endocrine cell types. Moreover, AS profiles are enabled to clearly define the mature subset of β-cells in healthy controls, which is completely lost in T2D. Further analysis reveals that RNA-binding proteins (RBPs), heterogeneous nuclear ribonucleoproteins (hnRNPs) and FXR1 family proteins are predicted to induce the functional impairment of β-cells through regulating AS profiles. Finally, trajectory analysis of endocrine cells suggests the β-cell identity shift through dedifferentiation and transdifferentiation of β-cells during the progression of T2D. Together, our study provides a mechanism for regulating β-cell functions and suggests the significant contribution of AS program during diabetes pathogenesis.

Single-cell long-read sequencing-based mapping reveals specialized splicing patterns in developing and adult mouse and human brain

RNA isoforms influence cell identity and function. However, a comprehensive brain isoform map was lacking. We analyze single-cell RNA isoforms across brain regions, cell subtypes, developmental time points and species. For 72% of genes, full-length isoform expression varies along one or more axes. Splicing, transcription start and polyadenylation sites vary strongly between cell types, influence protein architecture and associate with disease-linked variation. Additionally, neurotransmitter transport and synapse turnover genes harbor cell-type variability across anatomical regions. Regulation of cell-type-specific splicing is pronounced in the postnatal day 21-to-postnatal day 28 adolescent transition. Developmental isoform regulation is stronger than regional regulation for the same cell type. Cell-type-specific isoform regulation in mice is mostly maintained in the human hippocampus, allowing extrapolation to the human brain. Conversely, the human brain harbors additional cell-type specificity, suggesting gain-of-function isoforms. Together, this detailed single-cell atlas of full-length isoform regulation across development, anatomical regions and species reveals an unappreciated degree of isoform variability across multiple axes.

Developmental isoform diversity in the human neocortex informs neuropsychiatric risk mechanisms

RNA splicing is highly prevalent in the brain and has strong links to neuropsychiatric disorders; yet, the role of cell type-specific splicing and transcript-isoform diversity during human brain development has not been systematically investigated. In this work, we leveraged single-molecule long-read sequencing to deeply profile the full-length transcriptome of the germinal zone and cortical plate regions of the developing human neocortex at tissue and single-cell resolution. We identified 214,516 distinct isoforms, of which 72.6% were novel (not previously annotated in Gencode version 33), and uncovered a substantial contribution of transcript-isoform diversity-regulated by RNA binding proteins-in defining cellular identity in the developing neocortex. We leveraged this comprehensive isoform-centric gene annotation to reprioritize thousands of rare de novo risk variants and elucidate genetic risk mechanisms for neuropsychiatric disorders.

Deep Transcriptomics Reveals Cell-Specific Isoforms of Pan-Neuronal Genes

Profiling gene expression in single neurons using single-cell RNA-Seq is a powerful method for understanding the molecular diversity of the nervous system. Profiling alternative splicing in single neurons using these methods is more challenging, however, due to low capture efficiency and sensitivity. As a result, we know much less about splicing patterns and regulation across neurons than we do about gene expression. Here we leverage unique attributes of the C. elegans nervous system to investigate deep cell-specific transcriptomes complete with biological replicates generated by the CeNGEN consortium, enabling high-confidence assessment of splicing across neuron types even for lowly-expressed genes. Global splicing maps reveal several striking observations, including pan-neuronal genes that harbor cell-specific splice variants, abundant differential intron retention across neuron types, and a single neuron highly enriched for upstream alternative 3' splice sites. We develop an algorithm to identify unique cell-specific expression patterns and use it to discover both cell-specific isoforms and potential regulatory RNA binding proteins that establish these isoforms. Genetic interrogation of these RNA binding proteins in vivo identifies three distinct regulatory factors employed to establish unique splicing patterns in a single neuron. Finally, we develop a user-friendly platform for spatial transcriptomic visualization of these splicing patterns with single-neuron resolution.

Sex-biased gene expression during neural differentiation of human embryonic stem cells

Sex differences in the developing human brain are primarily attributed to hormonal influence. Recently however, genetic differences and their impact on the developing nervous system have attracted increased attention. To understand genetically driven sexual dimorphisms in neurodevelopment, we investigated genome-wide gene expression in an in vitro differentiation model of male and female human embryonic stem cell lines (hESC), independent of the effects of human sex hormones. Four male and four female-derived hESC lines were differentiated into a population of mixed neurons over 37 days. Differential gene expression and gene set enrichment analyses were conducted on bulk RNA sequencing data. While similar differentiation tendencies in all cell lines demonstrated the robustness and reproducibility of our differentiation protocol, we found sex-biased gene expression already in undifferentiated ESCs at day 0, but most profoundly after 37 days of differentiation. Male and female cell lines exhibited sex-biased expression of genes involved in neurodevelopment, suggesting that sex influences the differentiation trajectory. Interestingly, the highest contribution to sex differences was found to arise from the male transcriptome, involving both Y chromosome and autosomal genes. We propose 13 sex-biased candidate genes (10 upregulated in male cell lines and 3 in female lines) that are likely to affect neuronal development. Additionally, we confirmed gene dosage compensation of X/Y homologs escaping X chromosome inactivation through their Y homologs and identified a significant overexpression of the Y-linked demethylase UTY and KDM5D in male hESC during neuron development, confirming previous results in neural stem cells. Our results suggest that genetic sex differences affect neuronal differentiation trajectories, which could ultimately contribute to sex biases during human brain development.

Circulating tumor cell clustering modulates RNA splicing and polyadenylation to facilitate metastasis

Circulating tumor cell (CTC) clusters exhibit significantly higher metastatic potential compared to single CTCs. However, the underlying mechanism behind this phenomenon remains unclear, and the role of posttranscriptional RNA regulation in CTC clusters has not been explored. Here, we conducted a comparative analysis of alternative splicing (AS) and alternative polyadenylation (APA) profiles between single CTCs and CTC clusters. We identified 994 and 836 AS events in single CTCs and CTC clusters, respectively, with ∼20% of AS events showing differential regulation between the two cell types. A key event in this differential splicing was observed in SRSF6, which disrupted AS profiles and contributed to the increased malignancy of CTC clusters. Regarding APA, we found a global lengthening of 3' UTRs in CTC clusters compared to single CTCs. This alteration was primarily governed by 14 core APA factors, particularly PPP1CA. The modified APA profiles facilitated the cell cycle progression of CTC clusters and indicated their reduced susceptibility to oxidative stress. Further investigation revealed that the proportion of H2AFY mRNA with long 3' UTR instead of short 3' UTR was higher in CTC clusters than single CTCs. The AU-rich elements (AREs) within the long 3' UTR of H2AFY mRNA enhance mRNA stability and translation activity, resulting in promoting cell proliferation and invasion, which potentially facilitate the establishment and rapid formation of metastatic tumors mediated by CTC clusters. These findings provide new insights into the mechanisms driving CTC cluster metastasis.

Unique transcriptomes of sensory and non-sensory neurons: insights from Splicing Regulatory States

Alternative Splicing (AS) programs serve as instructive signals of cell type specificity, particularly within the brain, which comprises dozens of molecularly and functionally distinct cell types. Among them, retinal photoreceptors stand out due to their unique transcriptome, making them a particularly well-suited system for studying how AS shapes cell type-specific molecular functions. Here, we use the Splicing Regulatory State (SRS) as a novel framework to discuss the splicing factors governing the unique AS pattern of photoreceptors, and how this pattern may aid in the specification of their highly specialized sensory cilia. In addition, we discuss how other sensory cells with ciliated structures, for which data is much scarcer, also rely on specific SRSs to implement a proteome specialized in the detection of sensory stimuli. By reviewing the general rules of cell type- and tissue-specific AS programs, firstly in the brain and subsequently in specialized sensory neurons, we propose a novel paradigm on how SRSs are established and how they can diversify. Finally, we illustrate how SRSs shape the outcome of mutations in splicing factors to produce cell type-specific phenotypes that can lead to various human diseases.

Quantifying transcriptome diversity: a review

Following the central dogma of molecular biology, gene expression heterogeneity can aid in predicting and explaining the wide variety of protein products, functions and, ultimately, heterogeneity in phenotypes. There is currently overlapping terminology used to describe the types of diversity in gene expression profiles, and overlooking these nuances can misrepresent important biological information. Here, we describe transcriptome diversity as a measure of the heterogeneity in (1) the expression of all genes within a sample or a single gene across samples in a population (gene-level diversity) or (2) the isoform-specific expression of a given gene (isoform-level diversity). We first overview modulators and quantification of transcriptome diversity at the gene level. Then, we discuss the role alternative splicing plays in driving transcript isoform-level diversity and how it can be quantified. Additionally, we overview computational resources for calculating gene-level and isoform-level diversity for high-throughput sequencing data. Finally, we discuss future applications of transcriptome diversity. This review provides a comprehensive overview of how gene expression diversity arises, and how measuring it determines a more complete picture of heterogeneity across proteins, cells, tissues, organisms and species.

Interrogations of single-cell RNA splicing landscapes with SCASL define new cell identities with physiological relevance

RNA splicing shapes the gene regulatory programs that underlie various physiological and disease processes. Here, we present the SCASL (single-cell clustering based on alternative splicing landscapes) method for interrogating the heterogeneity of RNA splicing with single-cell RNA-seq data. SCASL resolves the issue of biased and sparse data coverage on single-cell RNA splicing and provides a new scheme for classifications of cell identities. With previously published datasets as examples, SCASL identifies new cell clusters indicating potentially precancerous and early-tumor stages in triple-negative breast cancer, illustrates cell lineages of embryonic liver development, and provides fine clusters of highly heterogeneous tumor-associated CD4 and CD8 T cells with functional and physiological relevance. Most of these findings are not readily available via conventional cell clustering based on single-cell gene expression data. Our study shows the potential of SCASL in revealing the intrinsic RNA splicing heterogeneity and generating biological insights into the dynamic and functional cell landscapes in complex tissues.

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.

A targeted sequencing extension for transcript genotyping in single-cell transcriptomics

As no existing methods within the single-cell RNA sequencing repertoire combine genotyping of specific genomic loci with high throughput, we evaluated a straightforward, targeted sequencing approach as an extension to high-throughput droplet-based single-cell RNA sequencing. Overlaying standard gene expression data with transcript level genotype information provides a strategy to study the impact of genetic variants. Here, we describe this targeted sequencing extension, explain how to process the data and evaluate how technical parameters such as amount of input cDNA, number of amplification rounds, and sequencing depth influence the number of transcripts detected. Finally, we demonstrate how targeted sequencing can be used in two contexts: (1) simultaneous investigation of the presence of a somatic variant and its potential impact on the transcriptome of affected cells and (2) evaluation of allele-specific expression of a germline variant in ad hoc cell subsets. Through these and other comparable applications, our targeted sequencing extension has the potential to improve our understanding of functional effects caused by genetic variation.

Developmental isoform diversity in the human neocortex informs neuropsychiatric risk mechanisms

RNA splicing is highly prevalent in the brain and has strong links to neuropsychiatric disorders, yet the role of cell-type-specific splicing or transcript-isoform diversity during human brain development has not been systematically investigated. Here, we leveraged single-molecule long-read sequencing to deeply profile the full-length transcriptome of the germinal zone (GZ) and cortical plate (CP) regions of the developing human neocortex at tissue and single-cell resolution. We identified 214,516 unique isoforms, of which 72.6% are novel (unannotated in Gencode-v33), and uncovered a substantial contribution of transcript-isoform diversity, regulated by RNA binding proteins, in defining cellular identity in the developing neocortex. We leveraged this comprehensive isoform-centric gene annotation to re-prioritize thousands of rare de novo risk variants and elucidate genetic risk mechanisms for neuropsychiatric disorders.

One-Sentence Summary

A cell-specific atlas of gene isoform expression helps shape our understanding of brain development and disease.

Structured Abstract

INTRODUCTION: The development of the human brain is regulated by precise molecular and genetic mechanisms driving spatio-temporal and cell-type-specific transcript expression programs. Alternative splicing, a major mechanism increasing transcript diversity, is highly prevalent in the human brain, influences many aspects of brain development, and has strong links to neuropsychiatric disorders. Despite this, the cell-type-specific transcript-isoform diversity of the developing human brain has not been systematically investigated.RATIONALE: Understanding splicing patterns and isoform diversity across the developing neocortex has translational relevance and can elucidate genetic risk mechanisms in neurodevelopmental disorders. However, short-read sequencing, the prevalent technology for transcriptome profiling, is not well suited to capturing alternative splicing and isoform diversity. To address this, we employed third-generation long-read sequencing, which enables capture and sequencing of complete individual RNA molecules, to deeply profile the full-length transcriptome of the germinal zone (GZ) and cortical plate (CP) regions of the developing human neocortex at tissue and single-cell resolution.RESULTS: We profiled microdissected GZ and CP regions of post-conception week (PCW) 15-17 human neocortex in bulk and at single-cell resolution across six subjects using high-fidelity long-read sequencing (PacBio IsoSeq). We identified 214,516 unique isoforms, of which 72.6% were novel (unannotated in Gencode), and >7,000 novel exons, expanding the proteome by 92,422 putative proteoforms. We uncovered thousands of isoform switches during cortical neurogenesis predicted to impact RNA regulatory domains or protein structure and implicating previously uncharacterized RNA-binding proteins in cellular identity and neuropsychiatric disease. At the single-cell level, early-stage excitatory neurons exhibited the greatest isoform diversity, and isoform-centric single-cell clustering led to the identification of previously uncharacterized cell states. We systematically assessed the contribution of transcriptomic features, and localized cell and spatio-temporal transcript expression signatures across neuropsychiatric disorders, revealing predominant enrichments in dynamic isoform expression and utilization patterns and that the number and complexity of isoforms per gene is strongly predictive of disease. Leveraging this resource, we re-prioritized thousands of rare de novo risk variants associated with autism spectrum disorders (ASD), intellectual disability (ID), and neurodevelopmental disorders (NDDs), more broadly, to potentially more severe consequences and revealed a larger proportion of cryptic splice variants with the expanded transcriptome annotation provided in this study.CONCLUSION: Our study offers a comprehensive landscape of isoform diversity in the human neocortex during development. This extensive cataloging of novel isoforms and splicing events sheds light on the underlying mechanisms of neurodevelopmental disorders and presents an opportunity to explore rare genetic variants linked to these conditions. The implications of our findings extend beyond fundamental neuroscience, as they provide crucial insights into the molecular basis of developmental brain disorders and pave the way for targeted therapeutic interventions. To facilitate exploration of this dataset we developed an online portal ( https://sciso.gandallab.org/ ).

Transcriptomic analysis of paired healthy human skeletal muscles to identify modulators of disease severity in DMD

Muscle damage and fibro-fatty replacement of skeletal muscles is a main pathologic feature of Duchenne muscular dystrophy (DMD) with more proximal muscles affected earlier and more distal affected later in the disease course, suggesting that different skeletal muscle groups possess distinctive characteristics that influence their susceptibility to disease. To explore transcriptomic factors driving differential gene expression and modulating DMD skeletal muscle severity, we characterized the transcriptome of vastus lateralis (VL), a more proximal and susceptible muscle, relative to tibialis anterior (TA), a more distal and protected muscle, in 15 healthy individuals using bulk RNA sequencing to identify gene expression differences that may mediate their relative susceptibility to damage with loss of dystrophin. Matching single nuclei RNA sequencing data was generated for 3 of the healthy individuals, to infer cell composition in the bulk RNA sequencing dataset and to improve mapping of differentially expressed genes to their cell source of expression. A total of 3,410 differentially expressed genes were identified and mapped to cell type using single nuclei RNA sequencing of muscle, including long non-coding RNAs and protein coding genes. There was an enrichment of genes involved in calcium release from the sarcoplasmic reticulum, particularly in the myofibers and these myofiber genes were higher in the VL. There was an enrichment of genes in "Collagen-Containing Extracellular Matrix" expressed by fibroblasts, endothelial, smooth muscle and pericytes, with most genes higher in the TA, as well as genes in "Regulation Of Apoptotic Process" expressed across all cell types. Previously reported genetic modifiers were also enriched within the differentially expressed genes. We also identify 6 genes with differential isoform usage between the VL and TA. Lastly, we integrate our findings with DMD RNA sequencing data from the TA, and identify "Collagen-Containing Extracellular Matrix" and "Negative Regulation Of Apoptotic Process" as differentially expressed between DMD compared to healthy. Collectively, these findings propose novel candidate mechanisms that may mediate differential muscle susceptibility in muscular dystrophies and provide new insight into potential therapeutic targets.

From words to complete phrases: insight into single-cell isoforms using short and long reads

The profiling of gene expression patterns to glean biological insights from single cells has become commonplace over the last few years. However, this approach overlooks the transcript contents that can differ between individual cells and cell populations. In this review, we describe early work in the field of single-cell short-read sequencing as well as full-length isoforms from single cells. We then describe recent work in single-cell long-read sequencing wherein some transcript elements have been observed to work in tandem. Based on earlier work in bulk tissue, we motivate the study of combination patterns of other RNA variables. Given that we are still blind to some aspects of isoform biology, we suggest possible future avenues such as CRISPR screens which can further illuminate the function of RNA variables in distinct cell populations.

mRNA isoform balance in neuronal development and disease

Balanced mRNA isoform diversity and abundance are spatially and temporally regulated throughout cellular differentiation. The proportion of expressed isoforms contributes to cell type specification and determines key properties of the differentiated cells. Neurons are unique cell types with intricate developmental programs, characteristic cellular morphologies, and electrophysiological potential. Neuron-specific gene expression programs establish these distinctive cellular characteristics and drive diversity among neuronal subtypes. Genes with neuron-specific alternative processing are enriched in key neuronal functions, including synaptic proteins, adhesion molecules, and scaffold proteins. Despite the similarity of neuronal gene expression programs, each neuronal subclass can be distinguished by unique alternative mRNA processing events. Alternative processing of developmentally important transcripts alters coding and regulatory information, including interaction domains, transcript stability, subcellular localization, and targeting by RNA binding proteins. Fine-tuning of mRNA processing is essential for neuronal activity and maintenance. Thus, the focus of neuronal RNA biology research is to dissect the transcriptomic mechanisms that underlie neuronal homeostasis, and consequently, predispose neuronal subtypes to disease. This article is categorized under: RNA in Disease and Development > RNA in Disease RNA in Disease and Development > RNA in Development.

Single-cell long-read mRNA isoform regulation is pervasive across mammalian brain regions, cell types, and development

RNA isoforms influence cell identity and function. Until recently, technological limitations prevented a genome-wide appraisal of isoform influence on cell identity in various parts of the brain. Using enhanced long-read single-cell isoform sequencing, we comprehensively analyze RNA isoforms in multiple mouse brain regions, cell subtypes, and developmental timepoints from postnatal day 14 (P14) to adult (P56). For 75% of genes, full-length isoform expression varies along one or more axes of phenotypic origin, underscoring the pervasiveness of isoform regulation across multiple scales. As expected, splicing varies strongly between cell types. However, certain gene classes including neurotransmitter release and reuptake as well as synapse turnover, harbor significant variability in the same cell type across anatomical regions, suggesting differences in network activity may influence cell-type identity. Glial brain-region specificity in isoform expression includes strong poly(A)-site regulation, whereas neurons have stronger TSS regulation. Furthermore, developmental patterns of cell-type specific splicing are especially pronounced in the murine adolescent transition from P21 to P28. The same cell type traced across development shows more isoform variability than across adult anatomical regions, indicating a coordinated modulation of functional programs dictating neural development. As most cell-type specific exons in P56 mouse hippocampus behave similarly in newly generated data from human hippocampi, these principles may be extrapolated to human brain. However, human brains have evolved additional cell-type specificity in splicing, suggesting gain-of-function isoforms. Taken together, we present a detailed single-cell atlas of full-length brain isoform regulation across development and anatomical regions, providing a previously unappreciated degree of isoform variability across multiple scales of the brain.

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.

Analysis of RNA processing directly from spatial transcriptomics data reveals previously unknown regulation

Technical advances have led to an explosion in the amount of biological data available in recent years, especially in the field of RNA sequencing. Specifically, spatial transcriptomics (ST) datasets, which allow each RNA molecule to be mapped to the 2D location it originated from within a tissue, have become readily available. Due to computational challenges, ST data has rarely been used to study RNA processing such as splicing or differential UTR usage. We apply the ReadZS and the SpliZ, methods developed to analyze RNA process in scRNA-seq data, to analyze spatial localization of RNA processing directly from ST data for the first time. Using Moran's I metric for spatial autocorrelation, we identify genes with spatially regulated RNA processing in the mouse brain and kidney, re-discovering known spatial regulation in Myl6 and identifying previously-unknown spatial regulation in genes such as Rps24, Gng13, Slc8a1, Gpm6a, Gpx3, ActB, Rps8, and S100A9. The rich set of discoveries made here from commonly used reference datasets provides a small taste of what can be learned by applying this technique more broadly to the large quantity of Visium data currently being created.

Position-dependent effects of RNA-binding proteins in the context of co-transcriptional splicing

Alternative splicing is an important step in eukaryotic mRNA pre-processing which increases the complexity of gene expression programs, but is frequently altered in disease. Previous work on the regulation of alternative splicing has demonstrated that splicing is controlled by RNA-binding proteins (RBPs) and by epigenetic DNA/histone modifications which affect splicing by changing the speed of polymerase-mediated pre-mRNA transcription. The interplay of these different layers of splicing regulation is poorly understood. In this paper, we derived mathematical models describing how splicing decisions in a three-exon gene are made by combinatorial spliceosome binding to splice sites during ongoing transcription. We additionally take into account the effect of a regulatory RBP and find that the RBP binding position within the sequence is a key determinant of how RNA polymerase velocity affects splicing. Based on these results, we explain paradoxical observations in the experimental literature and further derive rules explaining why the same RBP can act as inhibitor or activator of cassette exon inclusion depending on its binding position. Finally, we derive a stochastic description of co-transcriptional splicing regulation at the single-cell level and show that splicing outcomes show little noise and follow a binomial distribution despite complex regulation by a multitude of factors. Taken together, our simulations demonstrate the robustness of splicing outcomes and reveal that quantitative insights into kinetic competition of co-transcriptional events are required to fully understand this important mechanism of gene expression diversity.

IsoCell: An Approach to Enhance Single Cell Clustering by Integrating Isoform-Level Expression Through Orthogonal Projection

Single cell RNA sequencing (scRNA-seq) provides a powerful approach for profiling transcriptomes at single cell resolution. An essential application of scRNA-seq is the discovery of cell types with the aid of clustering analysis. Currently, existing single cell clustering methods are exclusively based on gene-level expression data, without considering alternative splicing information. It has been shown that alternative splicing has an important influence on biological processes such as cell differentiation and cell cycle. We therefore hypothesize that adding information about alternative splicing may help enhance single cell clustering. This motivates us to develop a way to integrate isoform-level expression and gene-level expression. We report an approach to enhance single cell clustering by integrating isoform-level expression through orthogonal projection. First, we construct an orthogonal projection matrix based on gene expression data. Second, isoforms are projected to the gene space to remove the redundant information between them. Third, isoform selection is performed based on the residual of the projected expression and the selected isoforms are combined with gene expression data for subsequent clustering. We applied our method to sixteen scRNA-seq datasets. We find that alternative splicing contains differential information among cell types and can be integrated to enhance single cell clustering. Compared with using only gene-level expression data, the integration of isoform-level expression leads to better clustering performances for most of the datasets. The integration of isoform-level expression also has potential in the detection of novel cell subgroups. Our study shows that integrating isoform and gene-level expression is a promising way to improve single cell clustering. The IsoCell R package is freely available at both Github (https://github.com/genemine/IsoCell) and Zenodo (https://zenodo.org/record/4395707).

Robust Graph Regularized NMF with Dissimilarity and Similarity Constraints for ScRNA-seq Data Clustering

The notable progress in single-cell RNA sequencing (ScRNA-seq) technology is beneficial to accurately discover the heterogeneity and diversity of cells. Clustering is an extremely important step during the ScRNA-seq data analysis. However, it cannot achieve satisfactory performances by directly clustering ScRNA-seq data due to its high dimensionality and noise. To address these issues, we propose a novel ScRNA-seq data representation model, termed Robust Graph regularized Non-Negative Matrix Factorization with Dissimilarity and Similarity constraints (RGNMF-DS), for ScRNA-seq data clustering. To accurately characterize the structure information of the labeled samples and the unlabeled samples, respectively, the proposed RGNMF-DS model adopts a couple of complementary regularizers (i.e., similarity and dissimilar regularizers) to guide matrix decomposition. In addition, we construct a graph regularizer to discover the local geometric structure hidden in ScRNA-seq data. Moreover, we adopt the l_2,1-norm to measure the reconstruction error and thereby effectively improve the robustness of the proposed RGNMF-DS model to the noises. Experimental results on several ScRNA-seq datasets have demonstrated that our proposed RGNMF-DS model outperforms other state-of-the-art competitors in clustering.

Diverse cell-specific patterns of alternative polyadenylation in Drosophila

Most genes in higher eukaryotes express isoforms with distinct 3' untranslated regions (3' UTRs), generated by alternative polyadenylation (APA). Since 3' UTRs are predominant locations of post-transcriptional regulation, APA can render such programs conditional, and can also alter protein sequences via alternative last exon (ALE) isoforms. We previously used 3'-sequencing from diverse Drosophila samples to define multiple tissue-specific APA landscapes. Here, we exploit comprehensive single nucleus RNA-sequencing data (Fly Cell Atlas) to elucidate cell-type expression of 3' UTRs across >250 adult Drosophila cell types. We reveal the cellular bases of multiple tissue-specific APA/ALE programs, such as 3' UTR lengthening in differentiated neurons and 3' UTR shortening in spermatocytes and spermatids. We trace dynamic 3' UTR patterns across cell lineages, including in the male germline, and discover new APA patterns in the intestinal stem cell lineage. Finally, we correlate expression of RNA binding proteins (RBPs), miRNAs and global levels of cleavage and polyadenylation (CPA) factors in several cell types that exhibit characteristic APA landscapes, yielding candidate regulators of transcriptome complexity. These analyses provide a comprehensive foundation for future investigations of mechanisms and biological impacts of alternative 3' isoforms across the major cell types of this widely-studied model organism.

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.

scAPAmod: Profiling Alternative Polyadenylation Modalities in Single Cells from Single-Cell RNA-Seq Data

Alternative polyadenylation (APA) is a key layer of gene expression regulation, and APA choice is finely modulated in cells. Advances in single-cell RNA-seq (scRNA-seq) have provided unprecedented opportunities to study APA in cell populations. However, existing studies that investigated APA in single cells were either confined to a few cells or focused on profiling APA dynamics between cell types or identifying APA sites. The diversity and pattern of APA usages on a genomic scale in single cells remains unappreciated. Here, we proposed an analysis framework based on a Gaussian mixture model, scAPAmod, to identify patterns of APA usage from homogeneous or heterogeneous cell populations at the single-cell level. We systematically evaluated the performance of scAPAmod using simulated data and scRNA-seq data. The results show that scAPAmod can accurately identify different patterns of APA usages at the single-cell level. We analyzed the dynamic changes in the pattern of APA usage using scAPAmod in different cell differentiation and developmental stages during mouse spermatogenesis and found that even the same gene has different patterns of APA usages in different differentiation stages. The preference of patterns of usages of APA sites in different genomic regions was also analyzed. We found that patterns of APA usages of the same gene in 3′ UTRs (3′ untranslated region) and non-3′ UTRs are different. Moreover, we analyzed cell-type-specific APA usage patterns and changes in patterns of APA usages across cell types. Different from the conventional analysis of single-cell heterogeneity based on gene expression profiling, this study profiled the heterogeneous pattern of APA isoforms, which contributes to revealing the heterogeneity of single-cell gene expression with higher resolution.

Quantitative prediction of variant effects on alternative splicing in MAPT using endogenous pre-messenger RNA structure probing

Splicing is highly regulated and is modulated by numerous factors. Quantitative predictions for how a mutation will affect precursor mRNA (pre-mRNA) structure and downstream function are particularly challenging. Here, we use a novel chemical probing strategy to visualize endogenous precursor and mature MAPT mRNA structures in cells. We used these data to estimate Boltzmann suboptimal structural ensembles, which were then analyzed to predict consequences of mutations on pre-mRNA structure. Further analysis of recent cryo-EM structures of the spliceosome at different stages of the splicing cycle revealed that the footprint of the B^act complex with pre-mRNA best predicted alternative splicing outcomes for exon 10 inclusion of the alternatively spliced MAPT gene, achieving 74% accuracy. We further developed a β-regression weighting framework that incorporates splice site strength, RNA structure, and exonic/intronic splicing regulatory elements capable of predicting, with 90% accuracy, the effects of 47 known and 6 newly discovered mutations on inclusion of exon 10 of MAPT. This combined experimental and computational framework represents a path forward for accurate prediction of splicing-related disease-causing variants.

FIRM: Flexible integration of single-cell RNA-sequencing data for large-scale multi-tissue cell atlas datasets

Single-cell RNA-sequencing (scRNA-seq) is being used extensively to measure the mRNA expression of individual cells from deconstructed tissues, organs and even entire organisms to generate cell atlas references, leading to discoveries of novel cell types and deeper insight into biological trajectories. These massive datasets are usually collected from many samples using different scRNA-seq technology platforms, including the popular SMART-Seq2 (SS2) and 10X platforms. Inherent heterogeneities between platforms, tissues and other batch effects make scRNA-seq data difficult to compare and integrate, especially in large-scale cell atlas efforts; yet, accurate integration is essential for gaining deeper insights into cell biology. We present FIRM, a re-scaling algorithm which accounts for the effects of cell type compositions, and achieve accurate integration of scRNA-seq datasets across multiple tissue types, platforms and experimental batches. Compared with existing state-of-the-art integration methods, FIRM provides accurate mixing of shared cell type identities and superior preservation of original structure without overcorrection, generating robust integrated datasets for downstream exploration and analysis. FIRM is also a facile way to transfer cell type labels and annotations from one dataset to another, making it a reliable and versatile tool for scRNA-seq analysis, especially for cell atlas data integration.

acorde unravels functionally interpretable networks of isoform co-usage from single cell data

Alternative splicing (AS) is a highly-regulated post-transcriptional mechanism known to modulate isoform expression within genes and contribute to cell-type identity. However, the extent to which alternative isoforms establish co-expression networks that may be relevant in cellular function has not been explored yet. Here, we present acorde, a pipeline that successfully leverages bulk long reads and single-cell data to confidently detect alternative isoform co-expression relationships. To achieve this, we develop and validate percentile correlations, an innovative approach that overcomes data sparsity and yields accurate co-expression estimates from single-cell data. Next, acorde uses correlations to cluster co-expressed isoforms into a network, unraveling cell type-specific alternative isoform usage patterns. By selecting same-gene isoforms between these clusters, we subsequently detect and characterize genes with co-differential isoform usage (coDIU) across cell types. Finally, we predict functional elements from long read-defined isoforms and provide insight into biological processes, motifs, and domains potentially controlled by the coordination of post-transcriptional regulation. The code for acorde is available at https://github.com/ConesaLab/acorde .

Single-Cell Transcriptomics Reveals Splicing Features of Adult Neural Stem Cells in the Subventricular Zone

The central nervous system has enormously complex cellular diversity with hundreds of distinct cell types, yet alternative splicing features in single cells of important cell types at neurogenic regions are not well understood. By employing in silico analysis, we systematically identified 3,611 alternative splicing events from 1,908 genes in 28 single-cell transcriptomic data of adult mouse ependymal and subependymal regions, and found that single-cell RNA-seq has the advantage in uncovering rare splicing isoforms compared to bulk RNA-seq at the population level. We uncovered that the simultaneous presence of multiple isoforms from the same gene in a single cell is prevalent, and quiescent stem cells, activated stem cells, and neuroblast cells exhibit high heterogeneity of splicing variants. Furthermore, we also demonstrated the existence of novel bicistronic transcripts in quiescent stem cells.

Identification of alternative splicing-derived cancer neoantigens for mRNA vaccine development

Messenger RNA (mRNA) vaccines have shown great potential for anti-tumor therapy due to the advantages in safety, efficacy and industrial production. However, it remains a challenge to identify suitable cancer neoantigens that can be targeted for mRNA vaccines. Abnormal alternative splicing occurs in a variety of tumors, which may result in the translation of abnormal transcripts into tumor-specific proteins. High-throughput technologies make it possible for systematic characterization of alternative splicing as a source of suitable target neoantigens for mRNA vaccine development. Here, we summarized difficulties and challenges for identifying alternative splicing-derived cancer neoantigens from RNA-seq data and proposed a conceptual framework for designing personalized mRNA vaccines based on alternative splicing-derived cancer neoantigens. In addition, several points were presented to spark further discussion toward improving the identification of alternative splicing-derived cancer neoantigens.

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.

Profiling of the full-length transcriptome in abdominal aortic aneurysm using nanopore-based direct RNA sequencing

Abdominal aortic aneurysm (AAA) is a common and serious disease with a high mortality rate, but its genetic determinants have not been fully identified. In this feasibility study, we aimed to elucidate the transcriptome profile of AAA and further reveal its molecular mechanisms through the Oxford Nanopore Technologies (ONT) MinION platform. Overall, 9574 novel transcripts and 781 genes were identified by comparing and analysing the redundant-removed transcripts of all samples with known reference genome annotations. We characterized the alternative splicing, alternative polyadenylation events and simple sequence repeat (SSR) loci information based on full-length transcriptome data, which would help us further understand the genome annotation and gene structure of AAA. Moreover, we proved that ONT methods were suitable for the identification of lncRNAs via identifying the comprehensive expression profile of lncRNAs in AAA. The results of differentially expressed transcript (DET) analysis showed that a total of 7044 transcripts were differentially expressed, of which 4278 were upregulated and 2766 were downregulated among two groups. In the KEGG analysis, 4071 annotated DETs were involved in human diseases, organismal systems and environmental information processing. These pilot findings might provide novel insights into the pathogenesis of AAA and provide new ideas for the optimization of personalized treatment of AAA, which is worthy of further study in subsequent studies.

Single-cell architecture and functional requirement of alternative splicing during hematopoietic stem cell formation

Single-cell transcriptional profiling has rapidly advanced our understanding of the embryonic hematopoiesis; however, whether and what role RNA alternative splicing (AS) plays remains an enigma. This is important for understanding the mechanisms underlying splicing-associated hematopoietic diseases and for the derivation of therapeutic stem cells. Here, we used single-cell full-length transcriptome data to construct an isoform-based transcriptional atlas of the murine endothelial-to-hematopoietic stem cell (HSC) transition, which enables the identification of hemogenic signature isoforms and stage-specific AS events. We showed that the inclusion of these hemogenic-specific AS events was essential for hemogenic function in vitro. Expression data and knockout mouse studies highlighted the critical role of Srsf2: Early Srsf2 deficiency from endothelial cells affected the splicing pattern of several master hematopoietic regulators and significantly impaired HSC generation. These results redefine our understanding of the dynamic HSC developmental transcriptome and demonstrate that elaborately controlled RNA splicing governs cell fate in HSC formation.

OUP accepted manuscript

Alternative polyadenylation increases transcript diversities at the 3’ end, regulating biological processes including cell differentiation, embryonic development and cancer progression. Here, we present a Bayesian method SCAPE, which enables de novo identification and quantification of polyadenylation (pA) sites at single-cell level by utilizing insert size information. We demonstrated its accuracy and robustness and identified 31 558 sites from 36 mouse organs, 43.8% (13 807) of which were novel. We illustrated that APA isoforms were associated with miRNAs binding and regulated in tissue-, cell type-and tumor-specific manners where no difference was found at gene expression level, providing an extra layer of information for cell clustering. Furthermore, we found genome-wide dynamic changes of APA usage during erythropoiesis and induced pluripotent stem cell (iPSC) differentiation, suggesting APA contributes to the functional flexibility and diversity of single cells. We expect SCAPE to aid the analyses of cellular dynamics and diversities in health and disease.

Single-Cell Analysis of the Transcriptome and Epigenome

Epigenome regulation has emerged as an important mechanism for the maintenance of organ function in health and disease. Dissecting epigenomic alterations and resultant gene expression changes in single cells provides unprecedented resolution and insight into cellular diversity, modes of gene regulation, transcription factor dynamics and 3D genome organization. In this chapter, we summarize the transformative single-cell epigenomic technologies that have deepened our understanding of the fundamental principles of gene regulation. We provide a historical perspective of these methods, brief procedural outline with emphasis on the computational tools used to meaningfully dissect information. Our overall goal is to aid scientists using these technologies in their favorite system of interest.

Isoform-level quantification for single-cell RNA sequencing

MOTIVATION

RNA expression at isoform level is biologically more informative than at gene level and can potentially reveal cellular subsets and corresponding biomarkers that are not visible at gene level. However, due to the strong 3' bias sequencing protocol, mRNA quantification for high-throughput single-cell RNA sequencing such as Chromium Single Cell 3' 10× Genomics is currently performed at the gene level.

RESULTS

We have developed an isoform-level quantification method for high-throughput single-cell RNA sequencing by exploiting the concepts of transcription clusters and isoform paralogs. The method, called Scasa, compares well in simulations against competing approaches including Alevin, Cellranger, Kallisto, Salmon, Terminus and STARsolo at both isoform- and gene-level expression. The reanalysis of a CITE-Seq dataset with isoform-based Scasa reveals a subgroup of CD14 monocytes missed by gene-based methods.

AVAILABILITY AND IMPLEMENTATION

Implementation of Scasa including source code, documentation, tutorials and test data supporting this study is available at Github: https://github.com/eudoraleer/scasa and Zenodo: https://doi.org/10.5281/zenodo.5712503.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Comprehensive characterization of single-cell full-length isoforms in human and mouse with long-read sequencing

A modified Chromium 10x droplet-based protocol that subsamples cells for both short-read and long-read (nanopore) sequencing together with a new computational pipeline (FLAMES) is developed to enable isoform discovery, splicing analysis, and mutation detection in single cells. We identify thousands of unannotated isoforms and find conserved functional modules that are enriched for alternative transcript usage in different cell types and species, including ribosome biogenesis and mRNA splicing. Analysis at the transcript level allows data integration with scATAC-seq on individual promoters, improved correlation with protein expression data, and linked mutations known to confer drug resistance to transcriptome heterogeneity.

Alternative RNA splicing in stem cells and cancer stem cells: Importance of transcript-based expression analysis

Alternative ribonucleic acid (RNA) splicing can lead to the assembly of different protein isoforms with distinctive functions. The outcome of alternative splicing (AS) can result in a complete loss of function or the acquisition of new functions. There is a gap in knowledge of abnormal RNA splice variants promoting cancer stem cells (CSCs), and their prospective contribution in cancer progression. AS directly regulates the self-renewal features of stem cells (SCs) and stem-like cancer cells. Notably, octamer-binding transcription factor 4A spliced variant of octamer-binding transcription factor 4 contributes to maintaining stemness properties in both SCs and CSCs. The epithelial to mesenchymal transition pathway regulates the AS events in CSCs to maintain stemness. The alternative spliced variants of CSCs markers, including cluster of differentiation 44, aldehyde dehydrogenase, and doublecortin-like kinase, α6β1 integrin, have pivotal roles in increasing self-renewal properties and maintaining the pluripotency of CSCs. Various splicing analysis tools are considered in this study. LeafCutter software can be considered as the best tool for differential splicing analysis and identification of the type of splicing events. Additionally, LeafCutter can be used for efficient mapping splicing quantitative trait loci. Altogether, the accumulating evidence re-enforces the fact that gene and protein expression need to be investigated in parallel with alternative splice variants.

The Impact of Pro-Inflammatory Cytokines on Alternative Splicing Patterns in Human Islets

Alternative splicing (AS) within the β cell has been proposed as one potential pathway that may exacerbate autoimmunity and unveil novel immunogenic epitopes in type 1 diabetes (T1D). We employed a computational strategy to prioritize pathogenic splicing events in human islets treated with IL-1β + IFN-γ as an ex vivo model of T1D and coupled this analysis with a k-mer based approach to predict RNA binding proteins involved in AS. In total, 969 AS events were identified in cytokine-treated islets, with the majority (44.8%) involving a skipped exon. ExonImpact identified 129 events predicted to impact protein structure. AS occurred with high frequency in MHC Class II-related mRNAs, and targeted qPCR validated reduced inclusion of Exon5 in the MHC Class II gene HLA-DMB. Single molecule RNA FISH confirmed increased HLA-DMB splicing in pancreatic sections from human donors with established T1D and autoantibody positivity. Serine and Arginine Rich Splicing Factor 2 was implicated in 37.2% of potentially pathogenic events, including Exon5 exclusion in HLA-DMB. Together, these data suggest that dynamic control of AS plays a role in the β cell response to inflammatory signals during T1D evolution.

Applications of single-cell genomics and computational strategies to study common disease and population-level variation

The advent and rapid development of single-cell technologies have made it possible to study cellular heterogeneity at an unprecedented resolution and scale. Cellular heterogeneity underlies phenotypic differences among individuals, and studying cellular heterogeneity is an important step toward our understanding of the disease molecular mechanism. Single-cell technologies offer opportunities to characterize cellular heterogeneity from different angles, but how to link cellular heterogeneity with disease phenotypes requires careful computational analysis. In this article, we will review the current applications of single-cell methods in human disease studies and describe what we have learned so far from existing studies about human genetic variation. As single-cell technologies are becoming widely applicable in human disease studies, population-level studies have become a reality. We will describe how we should go about pursuing and designing these studies, particularly how to select study subjects, how to determine the number of cells to sequence per subject, and the needed sequencing depth per cell. We also discuss computational strategies for the analysis of single-cell data and describe how single-cell data can be integrated with bulk tissue data and data generated from genome-wide association studies. Finally, we point out open problems and future research directions.

Isoform cell-type specificity in the mouse primary motor cortex

Full-length SMART-seq1 single-cell RNA sequencing can be used to measure gene expression at isoform resolution, making possible the identification of specific isoform markers for different cell types. Used in conjunction with spatial RNA capture and gene-tagging methods, this enables the inference of spatially resolved isoform expression for different cell types. Here, in a comprehensive analysis of 6,160 mouse primary motor cortex cells assayed with SMART-seq, 280,327 cells assayed with MERFISH2 and 94,162 cells assayed with 10x Genomics sequencing3, we find examples of isoform specificity in cell types-including isoform shifts between cell types that are masked in gene-level analysis-as well as examples of transcriptional regulation. Additionally, we show that isoform specificity helps to refine cell types, and that a multi-platform analysis of single-cell transcriptomic data leveraging multiple measurements provides a comprehensive atlas of transcription in the mouse primary motor cortex that improves on the possibilities offered by any single technology.

RNA splicing programs define tissue compartments and cell types at single-cell resolution

The extent splicing is regulated at single-cell resolution has remained controversial due to both available data and methods to interpret it. We apply the SpliZ, a new statistical approach, to detect cell-type-specific splicing in >110K cells from 12 human tissues. Using 10X Chromium data for discovery, 9.1% of genes with computable SpliZ scores are cell-type-specifically spliced, including ubiquitously expressed genes MYL6 and RPS24. These results are validated with RNA FISH, single-cell PCR, and Smart-seq2. SpliZ analysis reveals 170 genes with regulated splicing during human spermatogenesis, including examples conserved in mouse and mouse lemur. The SpliZ allows model-based identification of subpopulations indistinguishable based on gene expression, illustrated by subpopulation-specific splicing of classical monocytes involving an ultraconserved exon in SAT1. Together, this analysis of differential splicing across multiple organs establishes that splicing is regulated cell-type-specifically.

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

RNA splicing is an important driver of heterogeneity in single cells through the expression of alternative transcripts and as a determinant of transcriptional kinetics. However, the intrinsic coverage limitations of scRNA-seq technologies make it challenging to associate specific splicing events to cell-level phenotypes. BRIE2 is a scalable computational method that resolves these issues by regressing single-cell transcriptomic data against cell-level features. We show that BRIE2 effectively identifies differential disease-associated alternative splicing events and allows a principled selection of genes that capture heterogeneity in transcriptional kinetics and improve RNA velocity analyses, enabling the identification of splicing phenotypes associated with biological changes.

Deconvolution of Expression for Nascent RNA sequencing data (DENR) highlights pre-RNA isoform diversity in human cells

Motivation

Quantification of isoform abundance has been extensively studied at the mature RNA level using RNA-seq but not at the level of precursor RNAs using nascent RNA sequencing.

Results

We address this problem with a new computational method called Deconvolution of Expression for Nascent RNA-sequencing data (DENR), which models nascent RNA-sequencing read-counts as a mixture of user-provided isoforms. The baseline algorithm is enhanced by machine-learning predictions of active transcription start sites and an adjustment for the typical ‘shape profile’ of read-counts along a transcription unit. We show that DENR outperforms simple read-count-based methods for estimating gene and isoform abundances, and that transcription of multiple pre-RNA isoforms per gene is widespread, with frequent differences between cell types. In addition, we provide evidence that a majority of human isoform diversity derives from primary transcription rather than from post-transcriptional processes.

Availability and implementation

DENR and nascentRNASim are freely available at https://github.com/CshlSiepelLab/DENR (version v1.0.0) and https://github.com/CshlSiepelLab/nascentRNASim (version v0.3.0).

Supplementary information

Supplementary data are available at Bioinformatics online.

Progesterone receptor antagonists reverse stem cell expansion and the paracrine effectors of progesterone action in the mouse mammary gland

Background

The ovarian hormones estrogen and progesterone (EP) are implicated in breast cancer causation. A specific consequence of progesterone exposure is the expansion of the mammary stem cell (MSC) and luminal progenitor (LP) compartments. We hypothesized that this effect, and its molecular facilitators, could be abrogated by progesterone receptor (PR) antagonists administered in a mouse model.

Methods

Ovariectomized FVB mice were randomized to 14 days of treatment: sham, EP, EP + telapristone (EP + TPA), EP + mifepristone (EP + MFP). Mice were then sacrificed, mammary glands harvested, and mammary epithelial cell lineages separated by flow cytometry using cell surface markers. RNA from each lineage was sequenced and differential gene expression was analyzed using DESeq. Quantitative PCR was performed to confirm the candidate genes discovered in RNA seq. ANOVA with Tukey post hoc analysis was performed to compare relative expression. Alternative splicing events were examined using the rMATs multivariate analysis tool.

Results

Significant increases in the MSC and luminal mature (LM) cell fractions were observed following EP treatment compared to control (p < 0.01 and p < 0.05, respectively), whereas the LP fraction was significantly reduced (p < 0.05). These hormone-induced effects were reversed upon exposure to TPA and MFP (p < 0.01 for both). Gene Ontology analysis of RNA-sequencing data showed EP-induced enrichment of several pathways, with the largest effect on Wnt signaling in MSC, significantly repressed by PR inhibitors. In LP cells, significant induction of Wnt4 and Rankl, and Wnt pathway intermediates Lrp2 and Axin2 (confirmed by qRTPCR) were reversed by TPA and MFP (p < 0.0001). Downstream signaling intermediates of these pathways (Lrp5, Mmp7) showed similar effects. Expression of markers of epithelial-mesenchymal transition (Cdh1, Cdh3) and the induction of EMT regulators (Zeb1, Zeb2, Gli3, Snai1, and Ptch2) were significantly responsive to progesterone. EP treatment was associated with large-scale alternative splicing events, with an enrichment of motifs associated with Srsf, Esrp, and Rbfox families. Exon skipping was observed in Cdh1, Enah, and Brd4.

Conclusions

PR inhibition reverses known tumorigenic pathways in the mammary gland and suppresses a previously unknown effect of progesterone on RNA splicing events. In total, our results strengthen the case for reconsideration of PR inhibitors for breast cancer prevention.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13058-021-01455-2.