Single-cell RNAseq for the study of isoforms-how is that possible?

Renal Endothelial Single-Cell Transcriptomics Reveals Spatiotemporal Regulation and Divergent Roles of Differential Gene Transcription and Alternative Splicing in Murine Diabetic Nephropathy

Endothelial cell (EC) injury is a crucial contributor to the progression of diabetic kidney disease (DKD), but the specific EC populations and mechanisms involved remain elusive. Kidney ECs (n = 5464) were collected at three timepoints from diabetic BTBRob/ob mice and non-diabetic littermates. Their heterogeneity, transcriptional changes, and alternative splicing during DKD progression were mapped using SmartSeq2 single-cell RNA sequencing (scRNAseq) and elucidated through pathway, network, and gene ontology enrichment analyses. We identified 13 distinct transcriptional EC phenotypes corresponding to different kidney vessel subtypes, confirmed through in situ hybridization and immunofluorescence. EC subtypes along nephrons displayed extensive zonation related to their functions. Differential gene expression analyses in peritubular and glomerular ECs in DKD underlined the regulation of DKD-relevant pathways including EIF2 signaling, oxidative phosphorylation, and IGF1 signaling. Importantly, this revealed the differential alteration of these pathways between the two EC subtypes and changes during disease progression. Furthermore, glomerular and peritubular ECs also displayed aberrant and dynamic alterations in alternative splicing (AS), which is strongly associated with DNA repair. Strikingly, genes displaying differential transcription or alternative splicing participate in divergent biological processes. Our study reveals the spatiotemporal regulation of gene transcription and AS linked to DKD progression, providing insight into pathomechanisms and clues to novel therapeutic targets for DKD treatment.

Comprehensive transcriptome analysis reveals altered mRNA splicing and post-transcriptional changes in the aged mouse brain

A comprehensive understanding of molecular changes during brain aging is essential to mitigate cognitive decline and delay neurodegenerative diseases. The interpretation of mRNA alterations during brain aging is influenced by the health and age of the animal cohorts studied. Here, we carefully consider these factors and provide an in-depth investigation of mRNA splicing and dynamics in the aging mouse brain, combining short- and long-read sequencing technologies with extensive bioinformatic analyses. Our findings encompass a spectrum of age-related changes, including differences in isoform usage, decreased mRNA dynamics and a module showing increased expression of neuronal genes. Notably, our results indicate a reduced abundance of mRNA isoforms leading to nonsense-mediated RNA decay and suggest a regulatory role for RNA-binding proteins, indicating that their regulation may be altered leading to the reshaping of the aged brain transcriptome. Collectively, our study highlights the importance of studying mRNA splicing events during brain aging.

Single-cell and spatial transcriptomics: Bridging current technologies with long-read sequencing

Single-cell technologies have transformed biomedical research over the last decade, opening up new possibilities for understanding cellular heterogeneity, both at the genomic and transcriptomic level. In addition, more recent developments of spatial transcriptomics technologies have made it possible to profile cells in their tissue context. In parallel, there have been substantial advances in sequencing technologies, and the third generation of methods are able to produce reads that are tens of kilobases long, with error rates matching the second generation short reads. Long reads technologies make it possible to better map large genome rearrangements and quantify isoform specific abundances. This further improves our ability to characterize functionally relevant heterogeneity. Here, we show how researchers have begun to combine single-cell, spatial transcriptomics, and long-read technologies, and how this is resulting in powerful new approaches to profiling both the genome and the transcriptome. We discuss the achievements so far, and we highlight remaining challenges and opportunities.

ZEBRA: a hierarchically integrated gene expression atlas of the murine and human brain at single-cell resolution

The molecular causes and mechanisms of neurodegenerative diseases remain poorly understood. A growing number of single-cell studies have implicated various neural, glial, and immune cell subtypes to affect the mammalian central nervous system in many age-related disorders. Integrating this body of transcriptomic evidence into a comprehensive and reproducible framework poses several computational challenges. Here, we introduce ZEBRA, a large single-cell and single-nucleus RNA-seq database. ZEBRA integrates and normalizes gene expression and metadata from 33 studies, encompassing 4.2 million human and mouse brain cells sampled from 39 brain regions. It incorporates samples from patients with neurodegenerative diseases like Alzheimer's disease, Parkinson's disease, and Multiple sclerosis, as well as samples from relevant mouse models. We employed scVI, a deep probabilistic auto-encoder model, to integrate the samples and curated both cell and sample metadata for downstream analysis. ZEBRA allows for cell-type and disease-specific markers to be explored and compared between sample conditions and brain regions, a cell composition analysis, and gene-wise feature mappings. Our comprehensive molecular database facilitates the generation of data-driven hypotheses, enhancing our understanding of mammalian brain function during aging and disease. The data sets, along with an interactive database are freely available at https://www.ccb.uni-saarland.de/zebra.

Current annotation strategies for T cell phenotyping of single-cell RNA-seq data

Single-cell RNA sequencing (scRNA-seq) has become a popular technique for interrogating the diversity and dynamic nature of cellular gene expression and has numerous advantages in immunology. For example, scRNA-seq, in contrast to bulk RNA sequencing, can discern cellular subtypes within a population, which is important for heterogenous populations such as T cells. Moreover, recent advancements in the technology allow the parallel capturing of the highly diverse T-cell receptor (TCR) sequence with the gene expression. However, the field of single-cell RNA sequencing data analysis is still hampered by a lack of gold-standard cell phenotype annotation. This problem is particularly evident in the case of T cells due to the heterogeneity in both their gene expression and their TCR. While current cell phenotype annotation tools can differentiate major cell populations from each other, labelling T-cell subtypes remains problematic. In this review, we identify the common automated strategy for annotating T cells and their subpopulations, and also describe what crucial information is still missing from these tools.

Workability of mRNA Sequencing for Predicting Protein Abundance

Transcriptomics methods (RNA-Seq, PCR) today are more routine and reproducible than proteomics methods, i.e., both mass spectrometry and immunochemical analysis. For this reason, most scientific studies are limited to assessing the level of mRNA content. At the same time, protein content (and its post-translational status) largely determines the cell's state and behavior. Such a forced extrapolation of conclusions from the transcriptome to the proteome often seems unjustified. The ratios of "transcript-protein" pairs can vary by several orders of magnitude for different genes. As a rule, the correlation coefficient between transcriptome-proteome levels for different tissues does not exceed 0.3-0.5. Several characteristics determine the ratio between the content of mRNA and protein: among them, the rate of movement of the ribosome along the mRNA and the number of free ribosomes in the cell, the availability of tRNA, the secondary structure, and the localization of the transcript. The technical features of the experimental methods also significantly influence the levels of the transcript and protein of the corresponding gene on the outcome of the comparison. Given the above biological features and the performance of experimental and bioinformatic approaches, one may develop various models to predict proteomic profiles based on transcriptomic data. This review is devoted to the ability of RNA sequencing methods for protein abundance prediction.

Insulin Receptor Isoforms and Insulin Growth Factor-like Receptors: Implications in Cell Signaling, Carcinogenesis, and Chemoresistance

This comprehensive review thoroughly explores the intricate involvement of insulin receptor (IR) isoforms and insulin-like growth factor receptors (IGFRs) in the context of the insulin and insulin-like growth factor (IGF) signaling (IIS) pathway. This elaborate system encompasses ligands, receptors, and binding proteins, giving rise to a wide array of functions, including aspects such as carcinogenesis and chemoresistance. Detailed genetic analysis of IR and IGFR structures highlights their distinct isoforms, which arise from alternative splicing and exhibit diverse affinities for ligands. Notably, the overexpression of the IR-A isoform is linked to cancer stemness, tumor development, and resistance to targeted therapies. Similarly, elevated IGFR expression accelerates tumor progression and fosters chemoresistance. The review underscores the intricate interplay between IRs and IGFRs, contributing to resistance against anti-IGFR drugs. Consequently, the dual targeting of both receptors could present a more effective strategy for surmounting chemoresistance. To conclude, this review brings to light the pivotal roles played by IRs and IGFRs in cellular signaling, carcinogenesis, and therapy resistance. By precisely modulating these receptors and their complex signaling pathways, the potential emerges for developing enhanced anti-cancer interventions, ultimately leading to improved patient outcomes.

Single-cell multi-gene identification of somatic mutations and gene rearrangements in cancer

In this proof-of-concept study, we developed a single-cell method that provides genotypes of somatic alterations found in coding regions of messenger RNAs and integrates these transcript-based variants with their matching cell transcriptomes. We used nanopore adaptive sampling on single-cell complementary DNA libraries to validate coding variants in target gene transcripts, and short-read sequencing to characterize cell types harboring the mutations. CRISPR edits for 16 targets were identified using a cancer cell line, and known variants in the cell line were validated using a 352-gene panel. Variants in primary cancer samples were validated using target gene panels ranging from 161 to 529 genes. A gene rearrangement was also identified in one patient, with the rearrangement occurring in two distinct tumor sites.

New technologies to study helminth development and host-parasite interactions

How parasites develop and survive, and how they stimulate or modulate host immune responses are important in understanding disease pathology and for the design of new control strategies. Microarray analysis and bulk RNA sequencing have provided a wealth of data on gene expression as parasites develop through different life-cycle stages and on host cell responses to infection. These techniques have enabled gene expression in the whole organism or host tissue to be detailed, but do not take account of the heterogeneity between cells of different types or developmental stages, nor the spatial organisation of these cells. Single-cell RNA-seq (scRNA-seq) adds a new dimension to studying parasite biology and host immunity by enabling gene profiling at the individual cell level. Here we review the application of scRNA-seq to establish gene expression cell atlases for multicellular helminths and to explore the expansion and molecular profile of individual host cell types involved in parasite immunity and tissue repair. Studying host-parasite interactions in vivo is challenging and we conclude this review by briefly discussing the applications of organoids (stem-cell derived mini-tissues) to examine host-parasite interactions at the local level, and as a potential system to study parasite development in vitro. Organoid technology and its applications have developed rapidly, and the elegant studies performed to date support the use of organoids as an alternative in vitro system for research on helminth parasites.

Single-Cell RNA-Seq Identifies Pathways and Genes Contributing to the Hyperandrogenemia Associated with Polycystic Ovary Syndrome

Polycystic ovary syndrome (PCOS) is a common endocrine disorder characterized by hyperandrogenemia of ovarian thecal cell origin, resulting in anovulation/oligo-ovulation and infertility. Our previous studies established that ovarian theca cells isolated and propagated from ovaries of normal ovulatory women and women with PCOS have distinctive molecular and cellular signatures that underlie the increased androgen biosynthesis in PCOS. To evaluate differences between gene expression in single-cells from passaged cultures of theca cells from ovaries of normal ovulatory women and women with PCOS, we performed single-cell RNA sequencing (scRNA-seq). Results from these studies revealed differentially expressed pathways and genes involved in the acquisition of cholesterol, the precursor of steroid hormones, and steroidogenesis. Bulk RNA-seq and microarray studies confirmed the theca cell differential gene expression profiles. The expression profiles appear to be directed largely by increased levels or activity of the transcription factors SREBF1, which regulates genes involved in cholesterol acquisition (LDLR, LIPA, NPC1, CYP11A1, FDX1, and FDXR), and GATA6, which regulates expression of genes encoding steroidogenic enzymes (CYP17A1) in concert with other differentially expressed transcription factors (SP1, NR5A2). This study provides insights into the molecular mechanisms underlying the hyperandrogenemia associated with PCOS and highlights potential targets for molecular diagnosis and therapeutic intervention.

Comprehensive transcript-level analysis reveals transcriptional reprogramming during the progression of Alzheimer's disease

Background

Alzheimer's disease (AD) is a common neurodegenerative disorder that has a multi-step disease progression. Differences between moderate and advanced stages of AD have not yet been fully characterized.

Materials and methods

Herein, we performed a transcript-resolution analysis in 454 AD-related samples, including 145 non-demented control, 140 asymptomatic AD (AsymAD), and 169 AD samples. We comparatively characterized the transcriptome dysregulation in AsymAD and AD samples at transcript level.

Results

We identified 4,056 and 1,200 differentially spliced alternative splicing events (ASEs) that might play roles in the disease progression of AsymAD and AD, respectively. Our further analysis revealed 287 and 222 isoform switching events in AsymAD and AD, respectively. In particular, a total of 163 and 119 transcripts showed increased usage, while 124 and 103 transcripts exhibited decreased usage in AsymAD and AD, respectively. For example, gene APOA2 showed no expression changes between AD and non-demented control samples, but expressed higher proportion of transcript ENST00000367990.3 and lower proportion of transcript ENST00000463812.1 in AD compared to non-demented control samples. Furthermore, we constructed RNA binding protein (RBP)-ASE regulatory networks to reveal potential RBP-mediated isoform switch in AsymAD and AD.

Conclusion

In summary, our study provided transcript-resolution insights into the transcriptome disturbance of AsymAD and AD, which will promote the discovery of early diagnosis biomarkers and the development of new therapeutic strategies for patients with AD.

High-throughput and high-accuracy single-cell RNA isoform analysis using PacBio circular consensus sequencing

Although long-read single-cell RNA isoform sequencing (scISO-Seq) can reveal alternative RNA splicing in individual cells, it suffers from a low read throughput. Here, we introduce HIT-scISOseq, a method that removes most artifact cDNAs and concatenates multiple cDNAs for PacBio circular consensus sequencing (CCS) to achieve high-throughput and high-accuracy single-cell RNA isoform sequencing. HIT-scISOseq can yield >10 million high-accuracy long-reads in a single PacBio Sequel II SMRT Cell 8M. We also report the development of scISA-Tools that demultiplex HIT-scISOseq concatenated reads into single-cell cDNA reads with >99.99% accuracy and specificity. We apply HIT-scISOseq to characterize the transcriptomes of 3375 corneal limbus cells and reveal cell-type-specific isoform expression in them. HIT-scISOseq is a high-throughput, high-accuracy, technically accessible method and it can accelerate the burgeoning field of long-read single-cell transcriptomics.

Identification of cell barcodes from long-read single-cell RNA-seq with BLAZE

Long-read single-cell RNA sequencing (scRNA-seq) enables the quantification of RNA isoforms in individual cells. However, long-read scRNA-seq using the Oxford Nanopore platform has largely relied upon matched short-read data to identify cell barcodes. We introduce BLAZE, which accurately and efficiently identifies 10x cell barcodes using only nanopore long-read scRNA-seq data. BLAZE outperforms the existing tools and provides an accurate representation of the cells present in long-read scRNA-seq when compared to matched short reads. BLAZE simplifies long-read scRNA-seq while improving the results, is compatible with downstream tools accepting a cell barcode file, and is available at https://github.com/shimlab/BLAZE .

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.

Characterization of Ly108-H1 Signaling Reveals Ly108-3 Expression and Additional Strain-Specific Differences in Lupus Prone Mice

Ly108 (SLAMF6) is a homophilic cell surface molecule that binds SLAM-associated protein (SAP), an intracellular adapter protein that modulates humoral immune responses. Furthermore, Ly108 is crucial for the development of natural killer T (NKT) cells and CTL cytotoxicity. Significant attention has been paid towards expression and function of Ly108 since multiple isoforms were identified, i.e., Ly108-1, Ly108-2, Ly108-3, and Ly108-H1, some of which are differentially expressed in several mouse strains. Surprisingly, Ly108-H1 appeared to protect against disease in a congenic mouse model of Lupus. Here, we use cell lines to further define Ly108-H1 function in comparison with other isoforms. We show that Ly108-H1 inhibits IL-2 production while having little effect upon cell death. With a refined method, we could detect phosphorylation of Ly108-H1 and show that SAP binding is retained. We propose that Ly108-H1 may regulate signaling at two levels by retaining the capability to bind its extracellular as well as intracellular ligands, possibly inhibiting downstream pathways. In addition, we detected Ly108-3 in primary cells and show that this isoform is also differentially expressed between mouse strains. The presence of additional binding motifs and a non-synonymous SNP in Ly108-3 further extends the diversity between murine strains. This work highlights the importance of isoform awareness, as inherent homology can present a challenge when interpreting mRNA and protein expression data, especially as alternatively splicing potentially affects function.

Identification of astrocyte regulators by nucleic acid cytometry

Multiple sclerosis is a chronic inflammatory disease of the central nervous system1. Astrocytes are heterogeneous glial cells that are resident in the central nervous system and participate in the pathogenesis of multiple sclerosis and its model experimental autoimmune encephalomyelitis2,3. However, few unique surface markers are available for the isolation of astrocyte subsets, preventing their analysis and the identification of candidate therapeutic targets; these limitations are further amplified by the rarity of pathogenic astrocytes. Here, to address these challenges, we developed focused interrogation of cells by nucleic acid detection and sequencing (FIND-seq), a high-throughput microfluidic cytometry method that combines encapsulation of cells in droplets, PCR-based detection of target nucleic acids and droplet sorting to enable in-depth transcriptomic analyses of cells of interest at single-cell resolution. We applied FIND-seq to study the regulation of astrocytes characterized by the splicing-driven activation of the transcription factor XBP1, which promotes disease pathology in multiple sclerosis and experimental autoimmune encephalomyelitis4. Using FIND-seq in combination with conditional-knockout mice, in vivo CRISPR-Cas9-driven genetic perturbation studies and bulk and single-cell RNA sequencing analyses of samples from mouse experimental autoimmune encephalomyelitis and humans with multiple sclerosis, we identified a new role for the nuclear receptor NR3C2 and its corepressor NCOR2 in limiting XBP1-driven pathogenic astrocyte responses. In summary, we used FIND-seq to identify a therapeutically targetable mechanism that limits XBP1-driven pathogenic astrocyte responses. FIND-seq enables the investigation of previously inaccessible cells, including rare cell subsets defined by unique gene expression signatures or other nucleic acid markers.

Toward a data infrastructure for the Plant Cell Atlas

We review how a data infrastructure for the Plant Cell Atlas might be built using existing infrastructure and platforms. The Human Cell Atlas has developed an extensive infrastructure for human and mouse single cell data, while the European Bioinformatics Institute has developed a Single Cell Expression Atlas, that currently houses several plant data sets. We discuss issues related to appropriate ontologies for describing a plant single cell experiment. We imagine how such an infrastructure will enable biologists and data scientists to glean new insights into plant biology in the coming decades, as long as such data are made accessible to the community in an open manner.

Single-cell and long-read sequencing to enhance modelling of splicing and cell-fate determination

Single-cell sequencing technologies have revolutionised the life sciences and biomedical research. Single-cell sequencing provides high-resolution data on cell heterogeneity, allowing high-fidelity cell type identification, and lineage tracking. Computational algorithms and mathematical models have been developed to make sense of the data, compensate for errors and simulate the biological processes, which has led to breakthroughs in our understanding of cell differentiation, cell-fate determination and tissue cell composition. The development of long-read (a.k.a. third-generation) sequencing technologies has produced powerful tools for investigating alternative splicing, isoform expression (at the RNA level), genome assembly and the detection of complex structural variants (at the DNA level). In this review, we provide an overview of the recent advancements in single-cell and long-read sequencing technologies, with a particular focus on the computational algorithms that help in correcting, analysing, and interpreting the resulting data. Additionally, we review some mathematical models that use single-cell and long-read sequencing data to study cell-fate determination and alternative splicing, respectively. Moreover, we highlight the emerging opportunities in modelling cell-fate determination that result from the combination of single-cell and long-read sequencing technologies.

Systematic analysis of alternative splicing in time course data using Spycone

MOTIVATION

During disease progression or organism development, alternative splicing may lead to isoform switches that demonstrate similar temporal patterns and reflect the alternative splicing co-regulation of such genes. Tools for dynamic process analysis usually neglect alternative splicing.

RESULTS

Here, we propose Spycone, a splicing-aware framework for time course data analysis. Spycone exploits a novel IS detection algorithm and offers downstream analysis such as network and gene set enrichment. We demonstrate the performance of Spycone using simulated and real-world data of SARS-CoV-2 infection.

AVAILABILITY AND IMPLEMENTATION

The Spycone package is available as a PyPI package. The source code of Spycone is available under the GPLv3 license at https://github.com/yollct/spycone and the documentation at https://spycone.readthedocs.io/en/latest/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Broad transcriptomic dysregulation occurs across the cerebral cortex in ASD

Neuropsychiatric disorders classically lack defining brain pathologies, but recent work has demonstrated dysregulation at the molecular level, characterized by transcriptomic and epigenetic alterations1-3. In autism spectrum disorder (ASD), this molecular pathology involves the upregulation of microglial, astrocyte and neural-immune genes, the downregulation of synaptic genes, and attenuation of gene-expression gradients in cortex1,2,4-6. However, whether these changes are limited to cortical association regions or are more widespread remains unknown. To address this issue, we performed RNA-sequencing analysis of 725 brain samples spanning 11 cortical areas from 112 post-mortem samples from individuals with ASD and neurotypical controls. We find widespread transcriptomic changes across the cortex in ASD, exhibiting an anterior-to-posterior gradient, with the greatest differences in primary visual cortex, coincident with an attenuation of the typical transcriptomic differences between cortical regions. Single-nucleus RNA-sequencing and methylation profiling demonstrate that this robust molecular signature reflects changes in cell-type-specific gene expression, particularly affecting excitatory neurons and glia. Both rare and common ASD-associated genetic variation converge within a downregulated co-expression module involving synaptic signalling, and common variation alone is enriched within a module of upregulated protein chaperone genes. These results highlight widespread molecular changes across the cerebral cortex in ASD, extending beyond association cortex to broadly involve primary sensory regions.

Alternative splicing as a source of phenotypic diversity

A major goal of evolutionary genetics is to understand the genetic processes that give rise to phenotypic diversity in multicellular organisms. Alternative splicing generates multiple transcripts from a single gene, enriching the diversity of proteins and phenotypic traits. It is well established that alternative splicing contributes to key innovations over long evolutionary timescales, such as brain development in bilaterians. However, recent developments in long-read sequencing and the generation of high-quality genome assemblies for diverse organisms has facilitated comparisons of splicing profiles between closely related species, providing insights into how alternative splicing evolves over shorter timescales. Although most splicing variants are probably non-functional, alternative splicing is nonetheless emerging as a dynamic, evolutionarily labile process that can facilitate adaptation and contribute to species divergence.

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 .

The SpliZ generalizes 'percent spliced in' to reveal regulated splicing at single-cell resolution

Detecting single-cell-regulated splicing from droplet-based technologies is challenging. Here, we introduce the splicing Z score (SpliZ), an annotation-free statistical method to detect regulated splicing in single-cell RNA sequencing. We applied the SpliZ to human lung cells, discovering hundreds of genes with cell-type-specific splicing patterns including ones with potential implications for basic and translational biology.

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.

Mis-splicing in Huntington's disease: harnessing the power of comparative transcriptomics

A recent paper by Elorza et al. describes an 'intersect-RNA-seq' analysis of Huntington's disease (HD) by parallel RNA sequencing (RNA-seq) profiling of HD brain tissues from humans and mice. This work illustrates a broadly applicable strategy to elucidate splicing alterations in neurological diseases by integrating the transcriptome profiles of human patient tissues and animal models.

Functional Annotation of Custom Transcriptomes

Many eukaryotic genes can give rise to different alternative transcripts depending on stage of development, cell type, and physiological cues. Current transcriptome-wide sequencing technologies highlight the remarkable extent of this regulation in metazoans and allow for RNA isoforms to be profiled in increasingly small biological samples and with a growing confidence. Understanding biological functions of sample-specific transcripts is a major challenge in genomics and RNA processing fields. Here we describe simple bioinformatics workflows that facilitate this task by streamlining reference-guided annotation of novel transcripts. A key part of our protocol is the R package factR that rapidly matches custom-assembled transcripts to their likely host genes, deduces the sequence and domain structure of novel protein products, and predicts sensitivity of newly identified RNA isoforms to nonsense-mediated decay.

BaseScope™ Approach to Visualize Alternative Splice Variants in Tissue

Defining the cell-specific alternative splicing landscape in complex tissues is an important goal to gain functional insights. Deep-sequencing techniques coupled to genetic strategies for cell identification has provided important cues on cell-specific exon usage in complex tissues like the nervous system. BaseScope™ has emerged as a powerful and highly sensitive alternative to in situ hybridization to determine exon composition in tissue with spatial and morphological context. In this protocol, we will review how BaseScope was utilized to detect the e37a-Cacna1b splice variant of the presynaptic calcium channel CaV2.2 or N-type. This splice variant arises from a pair of mutually exclusive exons (e37a and e37b). E37a-Cacna1b is heavily underrepresented relative to e37b-Cacna1b and both exons share 60% of their sequence. By using BaseScope™, we were able to discover that e37a-Cacna1b is expressed in excitatory pyramidal neurons of hippocampus and cortex, as well as motor neurons of the ventral horn of the spinal cord.

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 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.

Chromatin-mediated alternative splicing regulates cocaine-reward behavior

Neuronal alternative splicing is a key gene regulatory mechanism in the brain. However, the spliceosome machinery is insufficient to fully specify splicing complexity. In considering the role of the epigenome in activity-dependent alternative splicing, we and others find the histone modification H3K36me3 to be a putative splicing regulator. In this study, we found that mouse cocaine self-administration caused widespread differential alternative splicing, concomitant with the enrichment of H3K36me3 at differentially spliced junctions. Importantly, only targeted epigenetic editing can distinguish between a direct role of H3K36me3 in splicing and an indirect role via regulation of splice factor expression elsewhere on the genome. We targeted Srsf11, which was both alternatively spliced and H3K36me3 enriched in the brain following cocaine self-administration. Epigenetic editing of H3K36me3 at Srsf11 was sufficient to drive its alternative splicing and enhanced cocaine self-administration, establishing the direct causal relevance of H3K36me3 to alternative splicing of Srsf11 and to reward behavior.

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.

Fermented kimchi rejuvenated precancerous atrophic gastritis via mitigating Helicobacter pylori-associated endoplasmic reticulum and oxidative stress

Dietary intervention to prevent Helicobacter pylori (H. pylori)-gastric cancer might be ideal by long-term intervention, rejuvenating action, and no risk of bacterial resistance. Stimulated with finding that kimchi prevented H. pylori-gastric cancer, we compared the efficacy of cancer preventive kimchi (cpkimchi) and standard recipe kimchi (skimchi) and the efficacy between fermented kimchi and non-fermented kimchi (kimuchi) in H. pylori-initiated gastric cancer model and explored novel mechanisms hinted from RNAseq transcriptome analysis. Animal models assessing gastric pathology on 24 and 36 weeks after H. pylori initiated, salt diet-promoted gastric mutagenesis model showed fermented cpkimchi afforded the best outcome of either rejuvenating atrophic gastritis or inhibiting tumorigenesis compared to skimchi and kimuchi. Highest inhibition of atrophic gastritis was achieved with cpkimchi, while significantly lower in kimuchi. Transcriptomic analysis showed ameliorated-endoplasmic reticulum (ER) stress, -oxidative stress, and -apoptosis as major rejuvenating action of cpkimchi. Homogenates from animal model showed that elevated expressions of p-PERK, IRE, ATF6, p-elf, and XBP1 in control group, while significantly decreased with dietary intake of only cpkimchi. Significantly increased expressions of HO-1 and γ-GCS were only noted with cpkimchi. Conclusively, long-term dietary intervention of fermented cpkimchi can be potential way preventing H. pylori-associated carcinogenesis via rejuvenation of atrophic gastritis.

Whole-genome methylation analysis of testicular germ cells from cryptozoospermic men points to recurrent and functionally relevant DNA methylation changes

BACKGROUND

Several studies have reported an association between male infertility and aberrant sperm DNA methylation patterns, in particular in imprinted genes. In a recent investigation based on whole methylome and deep bisulfite sequencing, we have not found any evidence for such an association, but have demonstrated that somatic DNA contamination and genetic variation confound methylation studies in sperm of severely oligozoospermic men. To find out whether testicular germ cells (TGCs) of such patients might carry aberrant DNA methylation, we compared the TGC methylomes of four men with cryptozoospermia (CZ) and four men with obstructive azoospermia, who had normal spermatogenesis and served as controls (CTR).

RESULTS

There was no difference in DNA methylation at the whole genome level or at imprinted regions between CZ and CTR samples. However, using stringent filters to identify group-specific methylation differences, we detected 271 differentially methylated regions (DMRs), 238 of which were hypermethylated in CZ (binominal test, p < 2.2 × 10-16). The DMRs were enriched for distal regulatory elements (p = 1.0 × 10-6) and associated with 132 genes, 61 of which are differentially expressed at various stages of spermatogenesis. Almost all of the 67 DMRs associated with the 61 genes (94%) are hypermethylated in CZ (63/67, p = 1.107 × 10-14). As judged by single-cell RNA sequencing, 13 DMR-associated genes, which are mainly expressed during meiosis and spermiogenesis, show a significantly different pattern of expression in CZ patients. In four of these genes, the promoter is hypermethylated in CZ men, which correlates with a lower expression level in these patients. In the other nine genes, eight of which downregulated in CZ, germ cell-specific enhancers may be affected.

CONCLUSIONS

We found that impaired spermatogenesis is associated with DNA methylation changes in testicular germ cells at functionally relevant regions of the genome. We hypothesize that the described DNA methylation changes may reflect or contribute to premature abortion of spermatogenesis and therefore not appear in the mature, motile sperm.

Molecular topography of an entire nervous system

We have produced gene expression profiles of all 302 neurons of the C. elegans nervous system that match the single-cell resolution of its anatomy and wiring diagram. Our results suggest that individual neuron classes can be solely identified by combinatorial expression of specific gene families. For example, each neuron class expresses distinct codes of ∼23 neuropeptide genes and ∼36 neuropeptide receptors, delineating a complex and expansive "wireless" signaling network. To demonstrate the utility of this comprehensive gene expression catalog, we used computational approaches to (1) identify cis-regulatory elements for neuron-specific gene expression and (2) reveal adhesion proteins with potential roles in process placement and synaptic specificity. Our expression data are available at https://cengen.org and can be interrogated at the web application CengenApp. We expect that this neuron-specific directory of gene expression will spur investigations of underlying mechanisms that define anatomy, connectivity, and function throughout the C. elegans nervous system.

Splicing and editing of ionotropic glutamate receptors: a comprehensive analysis based on human RNA-Seq data

Ionotropic glutamate receptors (iGluRs) play key roles for signaling in the central nervous system. Alternative splicing and RNA editing are well-known mechanisms to increase iGluR diversity and to provide context-dependent regulation. Earlier work on isoform identification has focused on the analysis of cloned transcripts, mostly from rodents. We here set out to obtain a systematic overview of iGluR splicing and editing in human brain based on RNA-Seq data. Using data from two large-scale transcriptome studies, we established a workflow for the de novo identification and quantification of alternative splice and editing events. We detected all canonical iGluR splice junctions, assessed the abundance of alternative events described in the literature, and identified new splice events in AMPA, kainate, delta, and NMDA receptor subunits. Notable events include an abundant transcript encoding the GluA4 amino-terminal domain, GluA4-ATD, a novel C-terminal GluD1 (delta receptor 1) isoform, GluD1-b, and potentially new GluK4 and GluN2C isoforms. C-terminal GluN1 splicing may be controlled by inclusion of a cassette exon, which shows preference for one of the two acceptor sites in the last exon. Moreover, we identified alternative untranslated regions (UTRs) and species-specific differences in splicing. In contrast, editing in exonic iGluR regions appears to be mostly limited to ten previously described sites, two of which result in silent amino acid changes. Coupling of proximal editing/editing and editing/splice events occurs to variable degree. Overall, this analysis provides the first inventory of alternative splicing and editing in human brain iGluRs and provides the impetus for further transcriptome-based and functional investigations.

RNA Dysregulation: An Expanding Source of Cancer Immunotherapy Targets

Cancer transcriptomes frequently exhibit RNA dysregulation. As the resulting aberrant transcripts may be translated into cancer-specific proteins, there is growing interest in exploiting RNA dysregulation as a source of tumor antigens (TAs) and thus novel immunotherapy targets. Recent advances in high-throughput technologies and rapid accumulation of multiomic cancer profiling data in public repositories have provided opportunities to systematically characterize RNA dysregulation in cancer and identify antigen targets for immunotherapy. However, given the complexity of cancer transcriptomes and proteomes, important conceptual and technological challenges exist. Here, we highlight the expanding repertoire of TAs arising from RNA dysregulation and introduce multiomic and big data strategies for identifying optimal immunotherapy targets. We discuss extant barriers for translating these targets into effective therapies as well as the implications for future research.

NanoSINC-seq dissects the isoform diversity in subcellular compartments of single cells

Alternative mRNA isoforms play a key role in generating diverse protein isoforms. To dissect isoform usage in the subcellular compartments of single cells, we introduced an novel approach, nanopore sequencing coupled with single-cell integrated nuclear and cytoplasmic RNA sequencing, that couples microfluidic fractionation, which separates cytoplasmic RNA from nuclear RNA, with full-length complementary DNA (cDNA) sequencing using a nanopore sequencer. Leveraging full-length cDNA reads, we found that the nuclear transcripts are notably more diverse than cytoplasmic transcripts. Our findings also indicated that transcriptional noise emanating from the nucleus is regulated across the nuclear membrane and then either attenuated or amplified in the cytoplasm depending on the function involved. Overall, our results provide the landscape that shows how the transcriptional noise arising from the nucleus propagates to the cytoplasm.

Computing the Role of Alternative Splicing in Cancer

Most human genes undergo alternative splicing (AS), and dysregulation of alternative splicing contributes to tumor initiation and progression. Computational analysis of genomic and transcriptomic data enables the systematic characterization of alternative splicing and its functional role in cancer. In this review, we summarize the latest computational approaches to studying alternative splicing in cancer and the current limitations of the most popular tools in this field. Finally, we describe some of the current computational challenges in the characterization of the role of alternative splicing in cancer.

Splicing regulation in brain and testis: common themes for highly specialized organs

Expansion of the coding and regulatory capabilities of eukaryotic transcriptomes by alternative splicing represents one of the evolutionary forces underlying the increased structural complexity of metazoans. Brain and testes stand out as the organs that mostly exploit the potential of alternative splicing, thereby expressing the largest repertoire of splice variants. Herein, we will review organ-specific as well as common mechanisms underlying the high transcriptome complexity of these organs and discuss the impact exerted by this widespread alternative splicing regulation on the functionality and differentiation of brain and testicular cells.

A comparison of alternative mRNA splicing in the CD4 and CD8 T cell lineages

T cells can be subdivided into a number of different subsets that are defined by their distinct functions. While the specialization of different T cell subsets is partly achieved by the expression of specific genes, the overall transcriptional profiles of all T cells appear very similar. Alternative mRNA splicing is a mechanism that facilitates greater transcript/protein diversity from a limited number of genes, which may contribute to the functional specialization of distinct T cell subsets. In this study we employ a combination of short-read and long-read sequencing technologies to compare alternative mRNA splicing between the CD4 and CD8 T cell lineages. While long-read technology was effective at assembling full-length alternatively spliced transcripts, the low sequencing depth did not facilitate accurate quantitation. On the other hand, short-read technology was ineffective at assembling full-length transcripts but was highly accurate for quantifying expression. We show that integrating long-read and short-read data together achieves a more complete view of transcriptomic diversity. We found that while the overall usage of transcript isoforms was very similar between the CD4 and CD8 lineages, there were numerous alternative spliced mRNA isoforms that were preferentially used by one lineage over the other. These alternative spliced isoforms included ones with different exon usage, exon exclusion or intron inclusion, all of which are expected to significantly alter the protein sequence.

MicroExonator enables systematic discovery and quantification of microexons across mouse embryonic development

BACKGROUND

Microexons, exons that are ≤ 30 nucleotides, are a highly conserved and dynamically regulated set of cassette exons. They have key roles in nervous system development and function, as evidenced by recent results demonstrating the impact of microexons on behaviour and cognition. However, microexons are often overlooked due to the difficulty of detecting them using standard RNA-seq aligners.

RESULTS

Here, we present MicroExonator, a novel pipeline for reproducible de novo discovery and quantification of microexons. We process 289 RNA-seq datasets from eighteen mouse tissues corresponding to nine embryonic and postnatal stages, providing the most comprehensive survey of microexons available for mice. We detect 2984 microexons, 332 of which are differentially spliced throughout mouse embryonic brain development, including 29 that are not present in mouse transcript annotation databases. Unsupervised clustering of microexons based on their inclusion patterns segregates brain tissues by developmental time, and further analysis suggests a key function for microexons in axon growth and synapse formation. Finally, we analyse single-cell RNA-seq data from the mouse visual cortex, and for the first time, we report differential inclusion between neuronal subpopulations, suggesting that some microexons could be cell type-specific.

CONCLUSIONS

MicroExonator facilitates the investigation of microexons in transcriptome studies, particularly when analysing large volumes of data. As a proof of principle, we use MicroExonator to analyse a large collection of both mouse bulk and single-cell RNA-seq datasets. The analyses enabled the discovery of previously uncharacterized microexons, and our study provides a comprehensive microexon inclusion catalogue during mouse development.

Differential contribution of transcriptomic regulatory layers in the definition of neuronal identity

Previous transcriptomic profiling studies have typically focused on separately analyzing mRNA expression, alternative splicing and alternative polyadenylation differences between cell and tissue types. However, the relative contribution of these three transcriptomic regulatory layers to cell type specification is poorly understood. This question is particularly relevant to neurons, given their extensive heterogeneity associated with brain location, morphology and function. In the present study, we generated profiles for the three regulatory layers from developmentally and regionally distinct subpopulations of neurons from the mouse hippocampus and broader nervous system. Multi-omics factor analyses revealed differing contributions of each transcriptomic layer in the discrimination of neurons based on their stage of development, region, and function. Importantly, profiles of differential alternative splicing and polyadenylation better discriminated specific neuronal subtype populations than gene expression patterns. These results provide evidence for differential relative contributions of coordinated gene regulatory layers in the specification of neuronal subtypes.

Tissue-selective alternate promoters guide NLRP6 expression

The NLRP6 innate immune sensor is regulated by tissue-selective alternate promoters that facilitate translational gene silencing outside of the intestinal epithelium in both humans and mice.

The pryin domain (PYD) domain is involved in protein interactions that lead to assembly of immune-sensing complexes such as inflammasomes. The repertoire of PYD-containing genes expressed by a cell type arms tissues with responses against a range of stimuli. The transcriptional regulation of the PYD gene family however is incompletely understood. Alternative promoter utilization was identified as a mechanism regulating the tissue distribution of human PYD gene family members, including NLRP6 that is translationally silenced outside of intestinal tissue. Results show that alternative transcriptional promoters mediate NLRP6 silencing in mice and humans, despite no upstream genomic synteny. Human NLRP6 contains an internal alternative promoter within exon 2 of the PYD, resulting in a truncated mRNA in nonintestinal tissue. In mice, a proximal promoter was used that expanded the 5′ leader sequence restricting nuclear export and abolishing translational efficiency. Nlrp6 was dispensable in disease models targeting the kidney, which expresses noncanonical isoforms. Thus, alternative promoter use is a critical mechanism not just for isoform modulation but for determining expression profile and function of PYD family members.

Skipped Over: Tuning Natural Killer Cells Toward HIV Through Alternative Splicing

Natural killer (NK) cells provide some of the earliest immune responses to infection, but when viruses manipulate or perturb the immune environment to alter NK cell function, this places the host at a disadvantage. Indeed, others and we observe that in the context of HIV/simian immunodeficiency virus (SIV) infection, although NK cells are not infected, they can become dysfunctional over time. Several studies have characterized protein and transcriptional profiles of NK cells during HIV/SIV infection, but none have examined whether the production of alternative transcripts and corresponding isoforms is modulated. This phenomenon occurs broadly in normal biology and in other disease states, and could provide a novel avenue of investigation that may yield better targets to restore or augment NK cell responses to HIV/SIV. Herein, we briefly summarize published and new data that may provide a perspective on how to target NK cell splice variants.

Single-cell transcriptomic profiling provides insights into retinal endothelial barrier properties

Purpose

To better characterize retinal endothelial barrier properties through analysis of individual transcriptomes of primary bovine retinal microvascular endothelial cells (RMECs).

Methods

Individual RMECs were captured on the Fluidigm C1 system, cDNA libraries were prepared using a Nextera XT kit, and sequencing was performed on a NextSeq system (Illumina). Data analysis was performed using R packages Scater, SC3, and Seurat, and the browser application Automated Single-cell Analysis Pipeline (ASAP). Alternative splicing events in single cells were quantified with Outrigger. Cytoscape was used for network analyses.

Results

Application of a single-cell RNA sequencing (scRNA-seq) analysis workflow showed that RMECs form a relatively homogeneous population in culture, with the main differences related to proliferation status. Expression of markers from along the arteriovenous tree suggested that most cells originated from capillaries. Average gene expression levels across all cells were used to develop an in silico model of the inner blood-retina barrier incorporating junctional proteins not previously reported within the retinal vasculature. Correlation of barrier gene expression among individual cells revealed a subgroup of genes highly correlated with PECAM-1 at the center of the correlation network. Numerous alternative splicing events involving exons within microvascular barrier genes were observed, and in many cases, individual cells expressed one isoform exclusively.

Conclusions

We optimized a workflow for single-cell transcriptomics in primary RMECs. The results provide fundamental insights into the genes involved in formation of the retinal-microvascular barrier.

scAPAtrap: identification and quantification of alternative polyadenylation sites from single-cell RNA-seq data

Alternative polyadenylation (APA) generates diverse mRNA isoforms, which contributes to transcriptome diversity and gene expression regulation by affecting mRNA stability, translation and localization in cells. The rapid development of 3' tag-based single-cell RNA-sequencing (scRNA-seq) technologies, such as CEL-seq and 10x Genomics, has led to the emergence of computational methods for identifying APA sites and profiling APA dynamics at single-cell resolution. However, existing methods fail to detect the precise location of poly(A) sites or sites with low read coverage. Moreover, they rely on priori genome annotation and can only detect poly(A) sites located within or near annotated genes. Here we proposed a tool called scAPAtrap for detecting poly(A) sites at the whole genome level in individual cells from 3' tag-based scRNA-seq data. scAPAtrap incorporates peak identification and poly(A) read anchoring, enabling the identification of the precise location of poly(A) sites, even for sites with low read coverage. Moreover, scAPAtrap can identify poly(A) sites without using priori genome annotation, which helps locate novel poly(A) sites in previously overlooked regions and improve genome annotation. We compared scAPAtrap with two latest methods, scAPA and Sierra, using scRNA-seq data from different experimental technologies and species. Results show that scAPAtrap identified poly(A) sites with higher accuracy and sensitivity than competing methods and could be used to explore APA dynamics among cell types or the heterogeneous APA isoform expression in individual cells. scAPAtrap is available at https://github.com/BMILAB/scAPAtrap.

Reproducibility across single-cell RNA-seq protocols for spatial ordering analysis

As newer single-cell protocols generate increasingly more cells at reduced sequencing depths, the value of a higher read depth may be overlooked. Using data from three different single-cell RNA-seq protocols that lend themselves to having either higher read depth (Smart-seq) or many cells (MARS-seq and 10X), we evaluate their ability to recapitulate biological signals in the context of spatial reconstruction. Overall, we find gene expression profiles after spatial reconstruction analysis are highly reproducible between datasets despite being generated by different protocols and using different computational algorithms. While UMI-based protocols such as 10X and MARS-seq allow for capturing more cells, Smart-seq's higher sensitivity and read-depth allow for analysis of lower expressed genes and isoforms. Additionally, we evaluate trade-offs for each protocol by performing subsampling analyses and find that optimizing the balance between sequencing depth and number of cells within a protocol is necessary for efficient use of resources. Our analysis emphasizes the importance of selecting a protocol based on the biological questions and features of interest.

EEF1A2 and ERN2 could potentially discriminate metastatic status of mediastinal lymph node in lung adenocarcinomas harboring EGFR 19Del/L858R mutations

Background

Early data has indicated that EGFR 19Del mutation and EGFR L585R mutation are two different types of non‐small cell lung cancer (NSCLC). However, how the different molecular mechanisms participate in the process of mediastinal lymph node metastasis (MLNM) in lung adenocarcinoma (LA) harboring EGFR 19Del and EGFR L858R mutation remains unknown. We thus explored the genes responsible for MLNM in LA with EGFR 19Del or L858R mutation.

Methods

We performed transcriptome sequencing and bioinformatics analysis from 10 patients with LA resection specimens of primary tumors. Quantitative reverse transcription‐polymerase chain reaction was used to validate gene expressions.

Results

There were 69 mRNAs upregulated and 100 mRNAs downregulated in five samples with MLNM compared with samples without MLN metastasis. EEF1A2 and ERN2 were observed exhibiting different expression patterns in EGFR 19Del and EGFR L858R samples with MLNM. In samples harboring EGFR 19Del mutation, the expression of EEF1A2 gene in samples with MLNM was significantly lower compared with samples without MLN metastasis, and in samples with EGFR L858R, it was significantly higher in samples with MLNM. The expression pattern of ERN2 was opposite to EEF1A2. In addition, several other genes including SLC6A11, IGHV3‐48, IGHV3‐43, DUSP9, and HOXA9 were also shown to be associated with invasion and metastasis and exhibited an expression pattern similar to EEF1A2 and ERN2 in EGRF 19Del and L858R mutation tumors.

Conclusions

EEF1A2 and ERN2 were for the first time observed exhibiting distinct expression patterns in MLNM in lung adenocarcinomas harboring EGFR 19Del and EGFR L858R mutation by interindividual DEGs analysis.

Key points

Significant findings of the study

In our study, we focused on the mechanisms of metastasis and invasion that different EGFR mutations conferred and identified two critical genes separately involved in this process in EGFR 19Del and L858R mutation tumors.

What this study adds

Our findings not only reinforced theoretical foundations that the EGFR 19Del and L858R mutation tumors should be considered as two kinds of diseases, but also laid the fundamentals for precise determination of the mediastinal lymph node radiation field and improvement of clinical outcome.

VALERIE: Visual-based inspection of alternative splicing events at single-cell resolution

We present VALERIE (Visualising alternative splicing events from single-cell ribonucleic acid-sequencing experiments), an R package for visualising alternative splicing events at single-cell resolution. To explore any given specified genomic region, corresponding to an alternative splicing event, VALERIE generates an ensemble of informative plots to visualise cell-to-cell heterogeneity of alternative splicing profiles across single cells and performs statistical tests to compare percent spliced-in (PSI) values across the user-defined groups of cells. Among the features available, VALERIE displays PSI values, in lieu of read coverage, which is more suitable for representing alternative splicing profiles for a large number of samples typically generated by single-cell RNA-sequencing experiments. VALERIE is available on the Comprehensive R Archive Network (CRAN): https://cran.r-project.org/web/packages/VALERIE/index.html.

RNA-Bloom enables reference-free and reference-guided sequence assembly for single-cell transcriptomes

Despite the rapid advance in single-cell RNA sequencing (scRNA-seq) technologies within the last decade, single-cell transcriptome analysis workflows have primarily used gene expression data while isoform sequence analysis at the single-cell level still remains fairly limited. Detection and discovery of isoforms in single cells is difficult because of the inherent technical shortcomings of scRNA-seq data, and existing transcriptome assembly methods are mainly designed for bulk RNA samples. To address this challenge, we developed RNA-Bloom, an assembly algorithm that leverages the rich information content aggregated from multiple single-cell transcriptomes to reconstruct cell-specific isoforms. Assembly with RNA-Bloom can be either reference-guided or reference-free, thus enabling unbiased discovery of novel isoforms or foreign transcripts. We compared both assembly strategies of RNA-Bloom against five state-of-the-art reference-free and reference-based transcriptome assembly methods. In our benchmarks on a simulated 384-cell data set, reference-free RNA-Bloom reconstructed 37.9%–38.3% more isoforms than the best reference-free assembler, whereas reference-guided RNA-Bloom reconstructed 4.1%–11.6% more isoforms than reference-based assemblers. When applied to a real 3840-cell data set consisting of more than 4 billion reads, RNA-Bloom reconstructed 9.7%–25.0% more isoforms than the best competing reference-based and reference-free approaches evaluated. We expect RNA-Bloom to boost the utility of scRNA-seq data beyond gene expression analysis, expanding what is informatically accessible now.