IsoVis - a webserver for visualization and annotation of alternative RNA isoforms.

Genes commonly express multiple RNA products (RNA isoforms), which differ in exonic content and can have different functions. Making sense of the plethora of known and novel RNA isoforms being identified by transcriptomic approaches requires a user-friendly way to visualize gene isoforms and how they differ in exonic content, expression levels and potential functions. Here we introduce IsoVis, a freely available webserver that accepts user-supplied transcriptomic data and visualizes the expressed isoforms in a clear, intuitive manner. IsoVis contains numerous features, including the ability to visualize all RNA isoforms of a gene and their expression levels; the annotation of known isoforms from external databases; mapping of protein domains and features to exons, allowing changes to protein sequence and function between isoforms to be established; and extensive species compatibility. Datasets visualised on IsoVis remain private to the user, allowing analysis of sensitive data. IsoVis visualisations can be downloaded to create publication-ready figures. The IsoVis webserver enables researchers to perform isoform analyses without requiring programming skills, is free to use, and available at https://isomix.org/isovis/.

Control of Neuronal Survival and Development Using Conductive Diamond.

This study demonstrates the control of neuronal survival and development using nitrogen-doped ultrananocrystalline diamond (N-UNCD). We highlight the role of N-UNCD in regulating neuronal activity via near-infrared illumination, demonstrating the generation of stable photocurrents that enhance neuronal survival and neurite outgrowth and foster a more active, synchronized neuronal network. Whole transcriptome RNA sequencing reveals that diamond substrates improve cellular-substrate interaction by upregulating extracellular matrix and gap junction-related genes. Our findings underscore the potential of conductive diamond as a robust and biocompatible platform for noninvasive and effective neural tissue engineering.

Increased paternal corticosterone exposure influences offspring behaviour and expression of urinary pheromones.

BACKGROUND: Studies have shown that paternal stress prior to conception can influence the innate behaviours of their offspring. The evolutionary impacts of such intergenerational effects are therefore of considerable interest. Our group previously showed in a model of daily stress that glucocorticoid treatment of adult male mouse breeders prior to conception leads to increased anxiety-related behaviours in male offspring. Here, we aimed to understand the transgenerational effects of paternal stress exposure on the social behaviour of progeny and its potential influence on reproductive success. RESULTS: We assessed social parameters including social reward, male attractiveness and social dominance, in the offspring (F1) and grand-offspring (F2). We report that paternal corticosterone treatment was associated with increased display of subordination towards other male mice. Those mice were unexpectedly more attractive to female mice while expressing reduced levels of the key rodent pheromone Darcin, contrary to its conventional role in driving female attraction. We investigated the epigenetic regulation of major urinary protein (Mup) expression by performing the first Oxford Nanopore direct methylation of sperm DNA in a mouse model of stress, but found no differences in Mup genes that could be attributed to corticosterone-treatment. Furthermore, no overt differences of the prefrontal cortex transcriptome were found in F1 offspring, implying that peripheral mechanisms are likely contributing to the phenotypic differences. Interestingly, no phenotypic differences were observed in the F2 grand-offspring. CONCLUSIONS: Overall, our findings highlight the potential of moderate paternal stress to affect intergenerational (mal)adaptive responses, informing future studies of adaptiveness in rodents, humans and other species.

Pervasive effects of RNA degradation on Nanopore direct RNA sequencing.

Oxford Nanopore direct RNA sequencing (DRS) is capable of sequencing complete RNA molecules and accurately measuring gene and isoform expression. However, as DRS is designed to profile intact RNA, expression quantification may be more heavily dependent upon RNA integrity than alternative RNA sequencing methodologies. It is currently unclear how RNA degradation impacts DRS or whether it can be corrected for. To assess the impact of RNA integrity on DRS, we performed a degradation time series using SH-SY5Y neuroblastoma cells. Our results demonstrate that degradation is a significant and pervasive factor that can bias DRS measurements, including a reduction in library complexity resulting in an overrepresentation of short genes and isoforms. Degradation also biases differential expression analyses; however, we find that explicit correction can almost fully recover meaningful biological signal. In addition, DRS provided less biased profiling of partially degraded samples than Nanopore PCR-cDNA sequencing. Overall, we find that samples with RNA integrity number (RIN) > 9.5 can be treated as undegraded and samples with RIN > 7 can be utilized for DRS with appropriate correction. These results establish the suitability of DRS for a wide range of samples, including partially degraded in vivo clinical and post-mortem samples, while limiting the confounding effect of degradation on expression quantification.

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 .

Examining the Geospatial Distribution of Health and Support Services for Transgender, Gender Nonbinary, and Other Gender Diverse People in New York City.

A geospatial analysis of services that support transgender and gender diverse ("trans") people in New York City (NYC) was conducted to investigate associations with neighborhood-level sociodemographic characteristics. In June 2019, there were 5.3 services for every 100,000 of the general NYC population; controlling for other covariates, they were more commonly located in neighborhoods with larger populations of non-Hispanic Black (rate ratio [RR]=1.02, 95% confidence interval [CI]: 1.00-1.04), Hispanic/Latino (RR=1.03, 95% CI: 1.00-1.06), and gay/lesbian people (RR=1.53, 95% CI: 1.03-2.34). These findings suggest that the distribution of trans-focused services in NYC is proximal to communities that are most in need, but research should examine proximity to trans people specifically and distribution in nonurban areas.

NanoSplicer: accurate identification of splice junctions using Oxford Nanopore sequencing.

MOTIVATION: Long-read sequencing methods have considerable advantages for characterizing RNA isoforms. Oxford Nanopore sequencing records changes in electrical current when nucleic acid traverses through a pore. However, basecalling of this raw signal (known as a squiggle) is error prone, making it challenging to accurately identify splice junctions. Existing strategies include utilizing matched short-read data and/or annotated splice junctions to correct nanopore reads but add expense or limit junctions to known (incomplete) annotations. Therefore, a method that could accurately identify splice junctions solely from nanopore data would have numerous advantages. RESULTS: We developed 'NanoSplicer' to identify splice junctions using raw nanopore signal (squiggles). For each splice junction, the observed squiggle is compared to candidate squiggles representing potential junctions to identify the correct candidate. Measuring squiggle similarity enables us to compute the probability of each candidate junction and find the most likely one. We tested our method using (i) synthetic mRNAs with known splice junctions and (ii) biological mRNAs from a lung-cancer cell-line. The results from both datasets demonstrate NanoSplicer improves splice junction identification, especially when the basecalling error rate near the splice junction is elevated. AVAILABILITY AND IMPLEMENTATION: NanoSplicer is available at https://github.com/shimlab/NanoSplicer and archived at https://doi.org/10.5281/zenodo.6403849. Data is available from ENA: ERS7273757 and ERS7273453. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Long-Read RNA Sequencing Identifies Polyadenylation Elongation and Differential Transcript Usage of Host Transcripts During SARS-CoV-2 In Vitro Infection.

Better methods to interrogate host-pathogen interactions during Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infections are imperative to help understand and prevent this disease. Here we implemented RNA-sequencing (RNA-seq) using Oxford Nanopore Technologies (ONT) long-reads to measure differential host gene expression, transcript polyadenylation and isoform usage within various epithelial cell lines permissive and non-permissive for SARS-CoV-2 infection. SARS-CoV-2-infected and mock-infected Vero (African green monkey kidney epithelial cells), Calu-3 (human lung adenocarcinoma epithelial cells), Caco-2 (human colorectal adenocarcinoma epithelial cells) and A549 (human lung carcinoma epithelial cells) were analyzed over time (0, 2, 24, 48 hours). Differential polyadenylation was found to occur in both infected Calu-3 and Vero cells during a late time point (48 hpi), with Gene Ontology (GO) terms such as viral transcription and translation shown to be significantly enriched in Calu-3 data. Poly(A) tails showed increased lengths in the majority of the differentially polyadenylated transcripts in Calu-3 and Vero cell lines (up to ~101 nt in mean poly(A) length, padj = 0.029). Of these genes, ribosomal protein genes such as RPS4X and RPS6 also showed downregulation in expression levels, suggesting the importance of ribosomal protein genes during infection. Furthermore, differential transcript usage was identified in Caco-2, Calu-3 and Vero cells, including transcripts of genes such as GSDMB and KPNA2, which have previously been implicated in SARS-CoV-2 infections. Overall, these results highlight the potential role of differential polyadenylation and transcript usage in host immune response or viral manipulation of host mechanisms during infection, and therefore, showcase the value of long-read sequencing in identifying less-explored host responses to disease.

Accurate expression quantification from nanopore direct RNA sequencing with NanoCount.

Accurately quantifying gene and isoform expression changes is essential to understanding cell functions, differentiation and disease. Sequencing full-length native RNAs using long-read direct RNA sequencing (DRS) has the potential to overcome many limitations of short and long-read sequencing methods that require RNA fragmentation, cDNA synthesis or PCR. However, there are a lack of tools specifically designed for DRS and its ability to identify differential expression in complex organisms is poorly characterised. We developed NanoCount for fast, accurate transcript isoform quantification in DRS and demonstrate it outperforms similar methods. Using synthetic controls and human SH-SY5Y cell differentiation into neuron-like cells, we show that DRS accurately quantifies RNA expression and identifies differential expression of genes and isoforms. Differential expression of 231 genes, 333 isoforms, plus 27 isoform switches were detected between undifferentiated and differentiated SH-SY5Y cells and samples clustered by differentiation state at the gene and isoform level. Genes upregulated in neuron-like cells were associated with neurogenesis. NanoCount quantification of thousands of novel isoforms discovered with DRS likewise enabled identification of their differential expression. Our results demonstrate enhanced DRS isoform quantification with NanoCount and establish the ability of DRS to identify biologically relevant differential expression of genes and isoforms.

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.

Corrigendum: Isoform Age - Splice Isoform Profiling Using Long-Read Technologies.

[This corrects the article DOI: 10.3389/fmolb.2021.711733.].

Isoform Age - Splice Isoform Profiling Using Long-Read Technologies.

Alternative splicing (AS) of RNA is a key mechanism that results in the expression of multiple transcript isoforms from single genes and leads to an increase in the complexity of both the transcriptome and proteome. Regulation of AS is critical for the correct functioning of many biological pathways, while disruption of AS can be directly pathogenic in diseases such as cancer or cause risk for complex disorders. Current short-read sequencing technologies achieve high read depth but are limited in their ability to resolve complex isoforms. In this review we examine how long-read sequencing (LRS) technologies can address this challenge by covering the entire RNA sequence in a single read and thereby distinguish isoform changes that could impact RNA regulation or protein function. Coupling LRS with technologies such as single cell sequencing, targeted sequencing and spatial transcriptomics is producing a rapidly expanding suite of technological approaches to profile alternative splicing at the isoform level with unprecedented detail. In addition, integrating LRS with genotype now allows the impact of genetic variation on isoform expression to be determined. Recent results demonstrate the potential of these techniques to elucidate the landscape of splicing, including in tissues such as the brain where AS is particularly prevalent. Finally, we also discuss how AS can impact protein function, potentially leading to novel therapeutic targets for a range of diseases.

The long and the short of it: unlocking nanopore long-read RNA sequencing data with short-read differential expression analysis tools.

Application of Oxford Nanopore Technologies' long-read sequencing platform to transcriptomic analysis is increasing in popularity. However, such analysis can be challenging due to the high sequence error and small library sizes, which decreases quantification accuracy and reduces power for statistical testing. Here, we report the analysis of two nanopore RNA-seq datasets with the goal of obtaining gene- and isoform-level differential expression information. A dataset of synthetic, spliced, spike-in RNAs ('sequins') as well as a mouse neural stem cell dataset from samples with a null mutation of the epigenetic regulator Smchd1 was analysed using a mix of long-read specific tools for preprocessing together with established short-read RNA-seq methods for downstream analysis. We used limma-voom to perform differential gene expression analysis, and the novel FLAMES pipeline to perform isoform identification and quantification, followed by DRIMSeq and limma-diffSplice (with stageR) to perform differential transcript usage analysis. We compared results from the sequins dataset to the ground truth, and results of the mouse dataset to a previous short-read study on equivalent samples. Overall, our work shows that transcriptomic analysis of long-read nanopore data using long-read specific preprocessing methods together with short-read differential expression methods and software that are already in wide use can yield meaningful results.

Transcriptional and epi-transcriptional dynamics of SARS-CoV-2 during cellular infection.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) uses subgenomic RNA (sgRNA) to produce viral proteins for replication and immune evasion. We apply long-read RNA and cDNA sequencing to in vitro human and primate infection models to study transcriptional dynamics. Transcription-regulating sequence (TRS)-dependent sgRNA upregulates earlier in infection than TRS-independent sgRNA. An abundant class of TRS-independent sgRNA consisting of a portion of open reading frame 1ab (ORF1ab) containing nsp1 joins to ORF10, and the 3' untranslated region (UTR) upregulates at 48 h post-infection in human cell lines. We identify double-junction sgRNA containing both TRS-dependent and -independent junctions. We find multiple sites at which the SARS-CoV-2 genome is consistently more modified than sgRNA and that sgRNA modifications are stable across transcript clusters, host cells, and time since infection. Our work highlights the dynamic nature of the SARS-CoV-2 transcriptome during its replication cycle.

Targeted single-cell RNA sequencing of transcription factors enhances the identification of cell types and trajectories.

Single-cell RNA sequencing (scRNA-seq) is a widely used method for identifying cell types and trajectories in biologically heterogeneous samples, but it is limited in its detection and quantification of lowly expressed genes. This results in missing important biological signals, such as the expression of key transcription factors (TFs) driving cellular differentiation. We show that targeted sequencing of ∼1000 TFs (scCapture-seq) in iPSC-derived neuronal cultures greatly improves the biological information garnered from scRNA-seq. Increased TF resolution enhanced cell type identification, developmental trajectories, and gene regulatory networks. This allowed us to resolve differences among neuronal populations, which were generated in two different laboratories using the same differentiation protocol. ScCapture-seq improved TF-gene regulatory network inference and thus identified divergent patterns of neurogenesis into either excitatory cortical neurons or inhibitory interneurons. Furthermore, scCapture-seq revealed a role for of retinoic acid signaling in the developmental divergence between these different neuronal populations. Our results show that TF targeting improves the characterization of human cellular models and allows identification of the essential differences between cellular populations, which would otherwise be missed in traditional scRNA-seq. scCapture-seq TF targeting represents a cost-effective enhancement of scRNA-seq, which could be broadly applied to improve scRNA-seq resolution.

CARMN Loss Regulates Smooth Muscle Cells and Accelerates Atherosclerosis in Mice.

[Figure: see text].

Open-label phase 3 study of intravenous golimumab in patients with polyarticular juvenile idiopathic arthritis.

OBJECTIVES: To assess efficacy, pharmacokinetics (PK) and safety of intravenous (i.v.) golimumab in patients with polyarticular-course JIA (pc-JIA). METHODS: Children aged 2 to <18 years with active pc-JIA despite MTX therapy for ≥2 months received 80 mg/m2 golimumab at weeks 0, 4, then every 8 weeks through week 52 plus MTX weekly through week 28. The primary and major secondary endpoints were PK exposure and model-predicted steady-state area under the curve (AUCss) over an 8-week dosing interval at weeks 28 and 52, respectively. JIA ACR response and safety were also assessed. RESULTS: In total, 127 children were treated with i.v. golimumab. JIA ACR 30, 50, 70, and 90 response rates were 84%, 80%, 70% and 47%, respectively, at week 28 and were maintained through week 52. Golimumab serum concentrations and AUCss were 0.40 µg/ml and 399 µg ⋅ day/ml at week 28. PK exposure was maintained at week 52. Steady-state trough golimumab concentrations and AUCss were consistent across age categories and comparable to i.v. golimumab dosed 2 mg/kg in adults with rheumatoid arthritis. Golimumab antibodies and neutralizing antibodies were detected via a highly sensitive drug-tolerant assay in 31% (39/125) and 19% (24/125) of patients, respectively. Median trough golimumab concentration was lower in antibody-positive vs antibody-negative patients. Serious infections were reported in 6% of patients, including one death due to septic shock. CONCLUSION: Body surface area-based dosing of i.v. golimumab was well tolerated and provided adequate PK exposure for clinical efficacy in paediatric patients with active pc-JIA.ClinicalTrials.gov number NCT02277444.

Multiomic analysis elucidates Complex I deficiency caused by a deep intronic variant in NDUFB10.

The diagnosis of Mendelian disorders following uninformative exome and genome sequencing remains a challenging and often unmet need. Following uninformative exome and genome sequencing of a family quartet including two siblings with suspected mitochondrial disorder, RNA sequencing (RNAseq) was pursued in one sibling. Long-read amplicon sequencing was used to determine and quantify transcript structure. Immunoblotting studies and quantitative proteomics were performed to demonstrate functional impact. Differential expression analysis of RNAseq data identified significantly decreased expression of the mitochondrial OXPHOS Complex I subunit NDUFB10 associated with a cryptic exon in intron 1 of NDUFB10, that included an in-frame stop codon. The cryptic exon contained a rare intronic variant that was homozygous in both affected siblings. Immunoblot and quantitative proteomic analysis of fibroblasts revealed decreased abundance of Complex I subunits, providing evidence of isolated Complex I deficiency. Through multiomic analysis we present data implicating a deep intronic variant in NDUFB10 as the cause of mitochondrial disease in two individuals, providing further support of the gene-disease association. This study highlights the importance of transcriptomic and proteomic analyses as complementary diagnostic tools in patients undergoing genome-wide diagnostic evaluation.

Targeted RNA sequencing enhances gene expression profiling of ultra-low input samples.

RNA-seq is the standard method for profiling gene expression in many biological systems. Due to the wide dynamic range and complex nature of the transcriptome, RNA-seq provides an incomplete characterization, especially of lowly expressed genes and transcripts. Targeted RNA sequencing (RNA CaptureSeq) focuses sequencing on genes of interest, providing exquisite sensitivity for transcript detection and quantification. However, uses of CaptureSeq have focused on bulk samples and its performance on very small populations of cells is unknown. Here we show CaptureSeq greatly enhances transcriptomic profiling of target genes in ultra-low-input samples and provides equivalent performance to that on bulk samples. We validate the performance of CaptureSeq using multiple probe sets on samples of iPSC-derived cortical neurons. We demonstrate up to 275-fold enrichment for target genes, the detection of 10% additional genes and a greater than 5-fold increase in identified gene isoforms. Analysis of spike-in controls demonstrated CaptureSeq improved both detection sensitivity and expression quantification. Comparison to the CORTECON database of cerebral cortex development revealed CaptureSeq enhanced the identification of sample differentiation stage. CaptureSeq provides sensitive, reliable and quantitative expression measurements on hundreds-to-thousands of target genes from ultra-low-input samples and has the potential to greatly enhance transcriptomic profiling when samples are limiting.

Long-read sequencing reveals the complex splicing profile of the psychiatric risk gene CACNA1C in human brain.

RNA splicing is a key mechanism linking genetic variation with psychiatric disorders. Splicing profiles are particularly diverse in brain and difficult to accurately identify and quantify. We developed a new approach to address this challenge, combining long-range PCR and nanopore sequencing with a novel bioinformatics pipeline. We identify the full-length coding transcripts of CACNA1C in human brain. CACNA1C is a psychiatric risk gene that encodes the voltage-gated calcium channel CaV1.2. We show that CACNA1C's transcript profile is substantially more complex than appreciated, identifying 38 novel exons and 241 novel transcripts. Importantly, many of the novel variants are abundant, and predicted to encode channels with altered function. The splicing profile varies between brain regions, especially in cerebellum. We demonstrate that human transcript diversity (and thereby protein isoform diversity) remains under-characterised, and provide a feasible and cost-effective methodology to address this. A detailed understanding of isoform diversity will be essential for the translation of psychiatric genomic findings into pathophysiological insights and novel psychopharmacological targets.