satuRn: Scalable analysis of differential transcript usage for bulk and single-cell RNA-sequencing applications

PRPF40A induces inclusion of exons in GC-rich regions important for human myeloid cell differentiation

We characterized the regulatory mechanisms and role in human myeloid cell survival and differentiation of PRPF40A, a splicing factor lacking a canonical RNA Binding Domain. Upon PRPF40A knockdown, HL-60 cells displayed increased cell death, decreased proliferation and slight differentiation phenotype with upregulation of immune activation genes. Suggestive of both redundant and specific functions, cell death but not proliferation was rescued by overexpression of its paralog PRPF40B. Transcriptomic analysis revealed the predominant role of PRPF40A as an activator of cassette exon inclusion of functionally relevant splicing events. Mechanistically, the exons exclusively upregulated by PRPF40A are flanked by short and GC-rich introns which tend to localize to nuclear speckles in the nucleus center. These PRPF40A regulatory features are shared with other splicing regulators such as SRRM2, SON, PCBP1/2, and to a lesser extent TRA2B and SRSF2, as a part of a functional network that regulates splicing partly via co-localization in the nucleus.

Long-read RNA sequencing identifies region- and sex-specific C57BL/6J mouse brain mRNA isoform expression and usage

Alternative splicing (AS) contributes to the biological heterogeneity between species, sexes, tissues, and cell types. Many diseases are either caused by alterations in AS or by alterations to AS. Therefore, measuring AS accurately and efficiently is critical for assessing molecular phenotypes, including those associated with disease. Long-read sequencing enables more accurate quantification of differentially spliced isoform expression than short-read sequencing approaches, and third-generation platforms facilitate high-throughput experiments. To assess differences in AS across the cerebellum, cortex, hippocampus, and striatum by sex, we generated and analyzed Oxford Nanopore Technologies (ONT) long-read RNA sequencing (lrRNA-Seq) C57BL/6J mouse brain cDNA libraries. From > 85 million reads that passed quality control metrics, we calculated differential gene expression (DGE), differential transcript expression (DTE), and differential transcript usage (DTU) across brain regions and by sex. We found significant DGE, DTE, and DTU across brain regions and that the cerebellum had the most differences compared to the other three regions. Additionally, we found region-specific differential splicing between sexes, with the most sex differences in DTU in the cortex and no DTU in the hippocampus. We also report on two distinct patterns of sex DTU we observed, sex-divergent and sex-specific, that could potentially help explain sex differences in the prevalence and prognosis of various neurological and psychiatric disorders in future studies. Finally, we built a Shiny web application for researchers to explore the data further. Our study provides a resource for the community; it underscores the importance of AS in biological heterogeneity and the utility of long-read sequencing to better understand AS in the brain.

Challenges and best practices in omics benchmarking

Technological advances enabling massively parallel measurement of biological features - such as microarrays, high-throughput sequencing and mass spectrometry - have ushered in the omics era, now in its third decade. The resulting complex landscape of analytical methods has naturally fostered the growth of an omics benchmarking industry. Benchmarking refers to the process of objectively comparing and evaluating the performance of different computational or analytical techniques when processing and analysing large-scale biological data sets, such as transcriptomics, proteomics and metabolomics. With thousands of omics benchmarking studies published over the past 25 years, the field has matured to the point where the foundations of benchmarking have been established and well described. However, generating meaningful benchmarking data and properly evaluating performance in this complex domain remains challenging. In this Review, we highlight some common oversights and pitfalls in omics benchmarking. We also establish a methodology to bring the issues that can be addressed into focus and to be transparent about those that cannot: this takes the form of a spreadsheet template of guidelines for comprehensive reporting, intended to accompany publications. In addition, a survey of recent developments in benchmarking is provided as well as specific guidance for commonly encountered difficulties.

Cytokine profiling and transcriptomics in mononuclear cells define immune variants in Meniere Disease

Meniere Disease (MD) is a chronic inner ear disorder characterized by vertigo attacks, sensorineural hearing loss, tinnitus, and aural fullness. Extensive evidence supporting the inflammatory etiology of MD has been found, therefore, by using transcriptome analysis, we aim to describe the inflammatory variants of MD. We performed Bulk RNAseq on 45 patients with definite MD and 15 healthy controls. MD patients were classified according to their basal levels of IL-1β into 2 groups: high and low. Differentially expression analysis was performed using the ExpHunter Suite, and cell type proportion was evaluated using the estimation algorithms xCell, ABIS, and CIBERSORTx. MD patients showed 15 differentially expressed genes (DEG) compared to controls. The top DEGs include IGHG1 (p = 1.64  ×  10-6) and IGLV3-21 (p = 6.28  ×  10-3), supporting a role in the adaptative immune response. Cytokine profiling defines a subgroup of patients with high levels of IL-1β with up-regulation of IL6 (p = 7.65  ×  10-8) and INHBA (p = 3.39  ×  10-7) genes. Transcriptomic data from peripheral blood mononuclear cells support a proinflammatory subgroup of MD patients with high levels of IL6 and an increase in naïve B-cells, and memory CD8+ T cells.

Detecting differential transcript usage in complex diseases with SPIT

Differential transcript usage (DTU) plays a crucial role in determining how gene expression differs among cells, tissues, and developmental stages, contributing to the complexity and diversity of biological systems. In abnormal cells, it can also lead to deficiencies in protein function and underpin disease pathogenesis. Analyzing DTU via RNA sequencing (RNA-seq) data is vital, but the genetic heterogeneity in populations with complex diseases presents an intricate challenge due to diverse causal events and undetermined subtypes. Although the majority of common diseases in humans are categorized as complex, state-of-the-art DTU analysis methods often overlook this heterogeneity in their models. We therefore developed SPIT, a statistical tool that identifies predominant subgroups in transcript usage within a population along with their distinctive sets of DTU events. This study provides comprehensive assessments of SPIT's methodology and applies it to analyze brain samples from individuals with schizophrenia, revealing previously unreported DTU events in six candidate genes.

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.

Comprehensive benchmark of differential transcript usage analysis for static and dynamic conditions

RNA sequencing offers unique insights into transcriptome diversity, and a plethora of tools have been developed to analyze alternative splicing. One important task is to detect changes in the relative transcript abundance in differential transcript usage (DTU) analysis. The choice of the right analysis tool is non-trivial and depends on experimental factors such as the availability of single- or paired-end and bulk or single-cell data. To help users select the most promising tool for their task, we performed a comprehensive benchmark of DTU detection tools. We cover a wide array of experimental settings, using simulated bulk and single-cell RNA-seq data as well as real transcriptomics datasets, including time-series data. Our results suggest that DEXSeq, edgeR, and LimmaDS are better choices for paired-end data, while DSGseq and DEXSeq can be used for single-end data. In single-cell simulation settings, we showed that satuRn performs better than DTUrtle. In addition, we showed that Spycone is optimal for time series DTU/IS analysis based on the evidence provided using GO terms enrichment analysis.

Long-read RNA-seq atlas of novel microglia isoforms elucidates disease-associated genetic regulation of splicing

Microglia, the innate immune cells of the central nervous system, have been genetically implicated in multiple neurodegenerative diseases. We previously mapped the genetic regulation of gene expression and mRNA splicing in human microglia, identifying several loci where common genetic variants in microglia-specific regulatory elements explain disease risk loci identified by GWAS. However, identifying genetic effects on splicing has been challenging due to the use of short sequencing reads to identify causal isoforms. Here we present the isoform-centric microglia genomic atlas (isoMiGA) which leverages the power of long-read RNA-seq to identify 35,879 novel microglia isoforms. We show that the novel microglia isoforms are involved in stimulation response and brain region specificity. We then quantified the expression of both known and novel isoforms in a multi-ethnic meta-analysis of 555 human microglia short-read RNA-seq samples from 391 donors, the largest to date, and found associations with genetic risk loci in Alzheimer's disease and Parkinson's disease. We nominate several loci that may act through complex changes in isoform and splice site usage.

Benchmarking long-read RNA-sequencing analysis tools using in silico mixtures

The lack of benchmark data sets with inbuilt ground-truth makes it challenging to compare the performance of existing long-read isoform detection and differential expression analysis workflows. Here, we present a benchmark experiment using two human lung adenocarcinoma cell lines that were each profiled in triplicate together with synthetic, spliced, spike-in RNAs (sequins). Samples were deeply sequenced on both Illumina short-read and Oxford Nanopore Technologies long-read platforms. Alongside the ground-truth available via the sequins, we created in silico mixture samples to allow performance assessment in the absence of true positives or true negatives. Our results show that StringTie2 and bambu outperformed other tools from the six isoform detection tools tested, DESeq2, edgeR and limma-voom were best among the five differential transcript expression tools tested and there was no clear front-runner for performing differential transcript usage analysis between the five tools compared, which suggests further methods development is needed for this application.

Expression and splicing mediate distinct biological signals

BACKGROUND

Through alternative splicing, most human genes produce multiple isoforms in a cell-, tissue-, and disease-specific manner. Numerous studies show that alternative splicing is essential for development, diseases, and their treatments. Despite these important examples, the extent and biological relevance of splicing are currently unknown.

RESULTS

To solve this problem, we developed pairedGSEA and used it to profile transcriptional changes in 100 representative RNA-seq datasets. Our systematic analysis demonstrates that changes in splicing, on average, contribute to 48.1% of the biological signal in expression analyses. Gene-set enrichment analysis furthermore indicates that expression and splicing both convey shared and distinct biological signals.

CONCLUSIONS

These findings establish alternative splicing as a major regulator of the human condition and suggest that most contemporary RNA-seq studies likely miss out on critical biological insights. We anticipate our results will contribute to the transition from a gene-centric to an isoform-centric research paradigm.

Quantitative Analysis of Isoform Switching in Cancer

Over the past 8 years, multiple studies examined the phenomenon of isoform switching in human cancers and discovered that isoform switching is widespread, with hundreds to thousands of such events per cancer type. Although all of these studies used slightly different definitions of isoform switching, which in part led to a rather poor overlap of their results, they all leveraged transcript usage, a proportion of the transcript's expression in the total expression level of the parent gene, to detect isoform switching. However, how changes in transcript usage correlate with changes in transcript expression is not sufficiently explored. In this article, we adopt the most common definition of isoform switching and use a state-of-the-art tool for the analysis of differential transcript usage, SatuRn, to detect isoform switching events in 12 cancer types. We analyze the detected events in terms of changes in transcript usage and the relationship between transcript usage and transcript expression on a global scale. The results of our analysis suggest that the relationship between changes in transcript usage and changes in transcript expression is far from straightforward, and that such quantitative information can be effectively used for prioritizing isoform switching events for downstream analyses.

Variants in SART3 cause a spliceosomopathy characterised by failure of testis development and neuronal defects

Squamous cell carcinoma antigen recognized by T cells 3 (SART3) is an RNA-binding protein with numerous biological functions including recycling small nuclear RNAs to the spliceosome. Here, we identify recessive variants in SART3 in nine individuals presenting with intellectual disability, global developmental delay and a subset of brain anomalies, together with gonadal dysgenesis in 46,XY individuals. Knockdown of the Drosophila orthologue of SART3 reveals a conserved role in testicular and neuronal development. Human induced pluripotent stem cells carrying patient variants in SART3 show disruption to multiple signalling pathways, upregulation of spliceosome components and demonstrate aberrant gonadal and neuronal differentiation in vitro. Collectively, these findings suggest that bi-allelic SART3 variants underlie a spliceosomopathy which we tentatively propose be termed INDYGON syndrome (Intellectual disability, Neurodevelopmental defects and Developmental delay with 46,XY GONadal dysgenesis). Our findings will enable additional diagnoses and improved outcomes for individuals born with this condition.

Full-Length Spatial Transcriptomics Reveals the Unexplored Isoform Diversity of the Myocardium Post-MI

We introduce Single-cell Nanopore Spatial Transcriptomics (scNaST), a software suite to facilitate the analysis of spatial gene expression from second- and third-generation sequencing, allowing to generate a full-length near-single-cell transcriptional landscape of the tissue microenvironment. Taking advantage of the Visium Spatial platform, we adapted a strategy recently developed to assign barcodes to long-read single-cell sequencing data for spatial capture technology. Here, we demonstrate our workflow using four short axis sections of the mouse heart following myocardial infarction. We constructed a de novo transcriptome using long-read data, and successfully assigned 19,794 transcript isoforms in total, including clinically-relevant, but yet uncharacterized modes of transcription, such as intron retention or antisense overlapping transcription. We showed a higher transcriptome complexity in the healthy regions, and identified intron retention as a mode of transcription associated with the infarct area. Our data revealed a clear regional isoform switching among differentially used transcripts for genes involved in cardiac muscle contraction and tissue morphogenesis. Molecular signatures involved in cardiac remodeling integrated with morphological context may support the development of new therapeutics towards the treatment of heart failure and the reduction of cardiac complications.

Transcriptomic analysis of frontotemporal lobar degeneration with TDP-43 pathology reveals cellular alterations across multiple brain regions

Frontotemporal lobar degeneration (FTLD) is a group of heterogeneous neurodegenerative disorders affecting the frontal and temporal lobes of the brain. Nuclear loss and cytoplasmic aggregation of the RNA-binding protein TDP-43 represents the major FTLD pathology, known as FTLD-TDP. To date, there is no effective treatment for FTLD-TDP due to an incomplete understanding of the molecular mechanisms underlying disease development. Here we compared postmortem tissue RNA-seq transcriptomes from the frontal cortex, temporal cortex, and cerebellum between 28 controls and 30 FTLD-TDP patients to profile changes in cell-type composition, gene expression and transcript usage. We observed downregulation of neuronal markers in all three regions of the brain, accompanied by upregulation of microglia, astrocytes, and oligodendrocytes, as well as endothelial cells and pericytes, suggesting shifts in both immune activation and within the vasculature. We validate our estimates of neuronal loss using neuropathological atrophy scores and show that neuronal loss in the cortex can be mainly attributed to excitatory neurons, and that increases in microglial and endothelial cell expression are highly correlated with neuronal loss. All our analyses identified a strong involvement of the cerebellum in the neurodegenerative process of FTLD-TDP. Altogether, our data provides a detailed landscape of gene expression alterations to help unravel relevant disease mechanisms in FTLD.

Transcriptomic analysis of frontotemporal lobar degeneration with TDP-43 pathology reveals cellular alterations across multiple brain regions

Frontotemporal lobar degeneration (FTLD) is a group of heterogeneous neurodegenerative disorders affecting the frontal and temporal lobes of the brain. Nuclear loss and cytoplasmic aggregation of the RNA-binding protein TDP-43 represents the major FTLD pathology, known as FTLD-TDP. To date, there is no effective treatment for FTLD-TDP due to an incomplete understanding of the molecular mechanisms underlying disease development. Here we compared postmortem tissue RNA-seq transcriptomes from the frontal cortex, temporal cortex, and cerebellum between 28 controls and 30 FTLD-TDP patients to profile changes in cell-type composition, gene expression and transcript usage. We observed downregulation of neuronal markers in all three regions of the brain, accompanied by upregulation of microglia, astrocytes, and oligodendrocytes, as well as endothelial cells and pericytes, suggesting shifts in both immune activation and within the vasculature. We validate our estimates of neuronal loss using neuropathological atrophy scores and show that neuronal loss in the cortex can be mainly attributed to excitatory neurons, and that increases in microglial and endothelial cell expression are highly correlated with neuronal loss. All our analyses identified a strong involvement of the cerebellum in the neurodegenerative process of FTLD-TDP. Altogether, our data provides a detailed landscape of gene expression alterations to help unravel relevant disease mechanisms in FTLD.

A molecular map of long non-coding RNA expression, isoform switching and alternative splicing in osteoarthritis

Osteoarthritis is a prevalent joint disease and a major cause of disability worldwide with no curative therapy. Development of disease-modifying therapies requires a better understanding of the molecular mechanisms underpinning disease. A hallmark of osteoarthritis is cartilage degradation. To define molecular events characterizing osteoarthritis at the whole transcriptome level, we performed deep RNA sequencing in paired samples of low- and high-osteoarthritis grade knee cartilage derived from 124 patients undergoing total joint replacement. We detected differential expression between low- and high-osteoarthritis grade articular cartilage for 365 genes and identified a 38-gene signature in osteoarthritis cartilage by replicating our findings in an independent dataset. We also found differential expression for 25 novel long non-coding RNA genes (lncRNAs) and identified potential lncRNA interactions with RNA-binding proteins in osteoarthritis. We assessed alterations in the relative usage of individual gene transcripts and identified differential transcript usage for 82 genes, including ABI3BP, coding for an extracellular matrix protein, AKT1S1, a negative regulator of the mTOR pathway and TPRM4, coding for a transient receptor potential channel. We further assessed genome-wide differential splicing, for the first time in osteoarthritis, and detected differential splicing for 209 genes, which were enriched for extracellular matrix, proteoglycans and integrin surface interactions terms. In the largest study of its kind in osteoarthritis, we find that isoform and splicing changes, in addition to extensive differences in both coding and non-coding sequence expression, are associated with disease and demonstrate a novel layer of genomic complexity to osteoarthritis pathogenesis.

Probing Isoform Switching Events in Various Cancer Types: Lessons From Pan-Cancer Studies

Alternative splicing is an essential regulatory mechanism for gene expression in mammalian cells contributing to protein, cellular, and species diversity. In cancer, alternative splicing is frequently disturbed, leading to changes in the expression of alternatively spliced protein isoforms. Advances in sequencing technologies and analysis methods led to new insights into the extent and functional impact of disturbed alternative splicing events. In this review, we give a brief overview of the molecular mechanisms driving alternative splicing, highlight the function of alternative splicing in healthy tissues and describe how alternative splicing is disrupted in cancer. We summarize current available computational tools for analyzing differential transcript usage, isoform switching events, and the pathogenic impact of cancer-specific splicing events. Finally, the strategies of three recent pan-cancer studies on isoform switching events are compared. Their methodological similarities and discrepancies are highlighted and lessons learned from the comparison are listed. We hope that our assessment will lead to new and more robust methods for cancer-specific transcript detection and help to produce more accurate functional impact predictions of isoform switching events.