Revealing diverse alternative splicing variants of the highly homologous SMN1 and SMN2 genes by targeted long-read sequencing

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.

VIsoQLR: an interactive tool for the detection, quantification and fine-tuning of isoforms in selected genes using long-read sequencing

DNA variants altering the pre-mRNA splicing process represent an underestimated cause of human genetic diseases. Their association with disease traits should be confirmed using functional assays from patient cell lines or alternative models to detect aberrant mRNAs. Long-read sequencing is a suitable technique to identify and quantify mRNA isoforms. Available isoform detection and/or quantification tools are generally designed for the whole transcriptome analysis. However experiments focusing on genes of interest need more precise data fine-tuning and visualization tools.Here we describe VIsoQLR, an interactive analyzer, viewer and editor for the semi-automated identification and quantification of known and novel isoforms using long-read sequencing data. VIsoQLR is tailored to thoroughly analyze mRNA expression in splicing assays of selected genes. Our tool takes sequences aligned to a reference, and for each gene, it defines consensus splice sites and quantifies isoforms. VIsoQLR introduces features to edit the splice sites through dynamic and interactive graphics and tables, allowing accurate manual curation. Known isoforms detected by other methods can also be imported as references for comparison. A benchmark against two other popular transcriptome-based tools shows VIsoQLR accurate performance on both detection and quantification of isoforms. Here, we present VIsoQLR principles and features and its applicability in a case study example using nanopore-based long-read sequencing. VIsoQLR is available at https://github.com/TBLabFJD/VIsoQLR .

Closing the Gap - Detection of 5q-Spinal Muscular Atrophy by Short-Read Next-Generation Sequencing and Unexpected Results in a Diagnostic Patient Cohort

BACKGROUND

The importance of early diagnosis of 5q-Spinal muscular atrophy (5q-SMA) has heightened as early intervention can significantly improve clinical outcomes. In 96% of cases, 5q-SMA is caused by a homozygous deletion of SMN1. Around 4 % of patients carry a SMN1 deletion and a single-nucleotide variant (SNV) on the other allele. Traditionally, diagnosis is based on multiplex ligation probe amplification (MLPA) to detect homozygous or heterozygous exon 7 deletions in SMN1. Due to high homologies within the SMN1/SMN2 locus, sequence analysis to identify SNVs of the SMN1 gene is unreliable by standard Sanger or short-read next-generation sequencing (srNGS) methods.

OBJECTIVE

The objective was to overcome the limitations in high-throughput srNGS with the aim of providing SMA patients with a fast and reliable diagnosis to enable their timely therapy.

METHODS

A bioinformatics workflow to detect homozygous SMN1 deletions and SMN1 SNVs on srNGS analysis was applied to diagnostic whole exome and panel testing for suggested neuromuscular disorders (1684 patients) and to fetal samples in prenatal diagnostics (260 patients). SNVs were detected by aligning sequencing reads from SMN1 and SMN2 to an SMN1 reference sequence. Homozygous SMN1 deletions were identified by filtering sequence reads for the ,, gene-determining variant" (GDV).

RESULTS

10 patients were diagnosed with 5q-SMA based on (i) SMN1 deletion and hemizygous SNV (2 patients), (ii) homozygous SMN1 deletion (6 patients), and (iii) compound heterozygous SNVs in SMN1 (2 patients).

CONCLUSIONS

Applying our workflow in srNGS-based panel and whole exome sequencing (WES) is crucial in a clinical laboratory, as otherwise patients with an atypical clinical presentation initially not suspected to suffer from SMA remain undiagnosed.