Multiplexed primer extension sequencing: A targeted RNA-seq method that enables high-precision quantitation of mRNA splicing isoforms and rare pre-mRNA splicing intermediates

Machine learning-optimized targeted detection of alternative splicing

RNA-sequencing (RNA-seq) is widely adopted for transcriptome analysis but has inherent biases which hinder the comprehensive detection and quantification of alternative splicing. To address this, we present an efficient targeted RNA-seq method that greatly enriches for splicing-informative junction-spanning reads. Local Splicing Variation sequencing (LSV-seq) utilizes multiplexed reverse transcription from highly scalable pools of primers anchored near splicing events of interest. Primers are designed using Optimal Prime, a novel machine learning algorithm trained on the performance of thousands of primer sequences. In experimental benchmarks, LSV-seq achieves high on-target capture rates and concordance with RNA-seq, while requiring significantly lower sequencing depth. Leveraging deep learning splicing code predictions, we used LSV-seq to target events with low coverage in GTEx RNA-seq data and newly discover hundreds of tissue-specific splicing events. Our results demonstrate the ability of LSV-seq to quantify splicing of events of interest at high-throughput and with exceptional sensitivity.

Timing is everything: advances in quantifying splicing kinetics

Splicing is a highly regulated process critical for proper pre-mRNA maturation and the maintenance of a healthy cellular environment. Splicing events are impacted by ongoing transcription, neighboring splicing events, and cis and trans regulatory factors on the respective pre-mRNA transcript. Within this complex regulatory environment, splicing kinetics have the potential to influence splicing outcomes but have historically been challenging to study in vivo. In this review, we highlight recent technological advancements that have enabled measurements of global splicing kinetics and of the variability of splicing kinetics at single introns. We demonstrate how identifying features that are correlated with splicing kinetics has increased our ability to form potential models for how splicing kinetics may be regulated in vivo.

Alternative splicing outcomes across an RNA-binding protein concentration gradient

Alternative splicing (AS) is a dynamic RNA processing step that produces multiple RNA isoforms from a single pre-mRNA transcript and contributes to the complexity of the cellular transcriptome and proteome. This process is regulated through a network of cis-regulatory sequence elements and trans-acting factors, most-notably RNA binding proteins (RBPs). The muscleblind-like (MBNL) and RNA binding fox-1 homolog (RBFOX) are two well characterized families of RBPs that regulate fetal to adult AS transitions critical for proper muscle, heart, and central nervous system development. To better understand how the concentration of these RBPs influences AS transcriptome wide, we engineered a MBNL1 and RBFOX1 inducible HEK-293 cell line. Modest induction of exogenous RBFOX1 in this cell line modulated MBNL1-dependent AS outcomes in 3 skipped exon events, despite significant levels of endogenous RBFOX1 and RBFOX2. Due to background RBFOX levels, we conducted a focused analysis of dose-dependent MBNL1 skipped exon AS outcomes and generated transcriptome wide dose-response curves. Analysis of this data demonstrates that MBNL1-regulated exclusion events may require higher concentrations of MBNL1 protein to properly regulate AS outcomes compared to inclusion events and that multiple arrangements of YGCY motifs can produce similar splicing outcomes. These results suggest that rather than a simple relationship between the organization of RBP binding sites and a specific splicing outcome, that complex interaction networks govern both AS inclusion and exclusion events across a RBP gradient.

Complexities of mammalian transcriptome revealed by targeted RNA enrichment techniques

Studies using highly sensitive targeted RNA enrichment methods have shown that a large portion of the human transcriptome remains to be discovered and that most of the genome is transcribed in a complex, interleaved fashion characterized by a complex web of transcripts emanating from protein coding and noncoding loci. These results resonate with those from single-cell transcriptome profiling endeavors that reveal the existence of multiple novel, cell type-specific transcripts and clearly demonstrate that our understanding of the complexities of the human transcriptome is far from being complete. Here, we review the current status of the targeted RNA enrichment techniques, their application to the discovery of novel cell type-specific transcripts, and their impact on our understanding of the human genome and transcriptome.

A complementary approach for genetic diagnosis of inborn errors of immunity using proteogenomic analysis

Advances in next-generation sequencing technology have identified many genes responsible for inborn errors of immunity (IEI). However, there is still room for improvement in the efficiency of genetic diagnosis. Recently, RNA sequencing and proteomics using peripheral blood mononuclear cells (PBMCs) have gained attention, but only some studies have integrated these analyses in IEI. Moreover, previous proteomic studies for PBMCs have achieved limited coverage (approximately 3000 proteins). More comprehensive data are needed to gain valuable insights into the molecular mechanisms underlying IEI. Here, we propose a state-of-the-art method for diagnosing IEI using PBMCs proteomics integrated with targeted RNA sequencing (T-RNA-seq), providing unique insights into the pathogenesis of IEI. This study analyzed 70 IEI patients whose genetic etiology had not been identified by genetic analysis. In-depth proteomics identified 6498 proteins, which covered 63% of 527 genes identified in T-RNA-seq, allowing us to examine the molecular cause of IEI and immune cell defects. This integrated analysis identified the disease-causing genes in four cases undiagnosed in previous genetic studies. Three of them could be diagnosed by T-RNA-seq, while the other could only be diagnosed by proteomics. Moreover, this integrated analysis showed high protein-mRNA correlations in B- and T-cell-specific genes, and their expression profiles identified patients with immune cell dysfunction. These results indicate that integrated analysis improves the efficiency of genetic diagnosis and provides a deep understanding of the immune cell dysfunction underlying the etiology of IEI. Our novel approach demonstrates the complementary role of proteogenomic analysis in the genetic diagnosis and characterization of IEI.

The Transition from Cancer "omics" to "epi-omics" through Next- and Third-Generation Sequencing

Deciphering cancer etiopathogenesis has proven to be an especially challenging task since the mechanisms that drive tumor development and progression are far from simple. An astonishing amount of research has revealed a wide spectrum of defects, including genomic abnormalities, epigenomic alterations, disturbance of gene transcription, as well as post-translational protein modifications, which cooperatively promote carcinogenesis. These findings suggest that the adoption of a multidimensional approach can provide a much more precise and comprehensive picture of the tumor landscape, hence serving as a powerful tool in cancer research and precision oncology. The introduction of next- and third-generation sequencing technologies paved the way for the decoding of genetic information and the elucidation of cancer-related cellular compounds and mechanisms. In the present review, we discuss the current and emerging applications of both generations of sequencing technologies, also referred to as massive parallel sequencing (MPS), in the fields of cancer genomics, transcriptomics and proteomics, as well as in the progressing realms of epi-omics. Finally, we provide a brief insight into the expanding scope of sequencing applications in personalized cancer medicine and pharmacogenomics.

Transcript-specific determinants of pre-mRNA splicing revealed through in vivo kinetic analyses of the 1st and 2nd chemical steps

We generate high-precision measurements of the in vivo rates of both chemical steps of pre-mRNA splicing across the genome-wide complement of substrates in yeast by coupling metabolic labeling, multiplexed primer-extension sequencing, and kinetic modeling. We demonstrate that the rates of intron removal vary widely, splice-site sequences are primary determinants of 1st step but have little apparent impact on 2nd step rates, and the 2nd step is generally faster than the 1st step. Ribosomal protein genes (RPGs) are spliced faster than non-RPGs at each step, and RPGs share evolutionarily conserved properties that may contribute to their faster splicing. A genetic variant defective in the 1st step of the pathway reveals a genome-wide defect in the 1st step but an unexpected, transcript-specific change in the 2nd step. Our work demonstrates that extended co-transcriptional association is an important determinant of splicing rate, a conclusion at odds with recent claims of ultra-fast splicing.