ParSE-seq: a calibrated multiplexed assay to facilitate the clinical classification of putative splice-altering variants

Interpreting the clinical significance of putative splice-altering variants outside canonical splice sites remains difficult without time-intensive experimental studies. To address this, we introduce Parallel Splice Effect Sequencing (ParSE-seq), a multiplexed assay to quantify variant effects on RNA splicing. We first apply this technique to study hundreds of variants in the arrhythmia-associated gene SCN5A. Variants are studied in 'minigene' plasmids with molecular barcodes to allow pooled variant effect quantification. We perform experiments in two cell types, including disease-relevant induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs). The assay strongly separates known control variants from ClinVar, enabling quantitative calibration of the ParSE-seq assay. Using these evidence strengths and experimental data, we reclassify 29 of 34 variants with conflicting interpretations and 11 of 42 variants of uncertain significance. In addition to intronic variants, we show that many synonymous and missense variants disrupted RNA splicing. Two splice-altering variants in the assay also disrupt splicing and sodium current when introduced into iPSC-CMs by CRISPR-Cas9 editing. ParSE-seq provides high-throughput experimental data for RNA-splicing to support precision medicine efforts and can be readily adopted to study other loss-of-function genotype-phenotype relationships.

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Publication Details

Journal
Nature Communications
Published
2024-09-27
DOI
https://doi.org/10.1038/s41467-024-52474-4
Citations
10
Primary Topic
RNA Research and Splicing
Type
article
Field-Weighted Citation Impact
2.34

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article

ParSE-seq: a calibrated multiplexed assay to facilitate the clinical classification of putative splice-altering variants

Maria Calandranis, Joseph F. Solus, Andrew M. Glazer, M. Lorena Harvey et al.
10 citations
Nature Communications
RNA Research and Splicing
2.34
article

ParSE-seq: a calibrated multiplexed assay to facilitate the clinical classification of putative splice-altering variants

Maria Calandranis, Joseph F. Solus, Andrew M. Glazer, M. Lorena Harvey, Dan M. Roden, Matthew J. O’Neill, Tao Yang, Julie Laudeman
article en
10 citations

Abstract

Interpreting the clinical significance of putative splice-altering variants outside canonical splice sites remains difficult without time-intensive experimental studies. To address this, we introduce Parallel Splice Effect Sequencing (ParSE-seq), a multiplexed assay to quantify variant effects on RNA splicing. We first apply this technique to study hundreds of variants in the arrhythmia-associated gene SCN5A. Variants are studied in 'minigene' plasmids with molecular barcodes to allow pooled variant effect quantification. We perform experiments in two cell types, including disease-relevant induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs). The assay strongly separates known control variants from ClinVar, enabling quantitative calibration of the ParSE-seq assay. Using these evidence strengths and experimental data, we reclassify 29 of 34 variants with conflicting interpretations and 11 of 42 variants of uncertain significance. In addition to intronic variants, we show that many synonymous and missense variants disrupted RNA splicing. Two splice-altering variants in the assay also disrupt splicing and sodium current when introduced into iPSC-CMs by CRISPR-Cas9 editing. ParSE-seq provides high-throughput experimental data for RNA-splicing to support precision medicine efforts and can be readily adopted to study other loss-of-function genotype-phenotype relationships.

Nature CommunicationsVol. 15(1)
Vanderbilt University (US), Vanderbilt University Medical Center (US)
American Heart Association, Vanderbilt University, Servier, National Institutes of Health, Vanderbilt Digestive Diseases Research Center, Vanderbilt University Medical Center, Vanderbilt Institute of Chemical Biology, School of Medicine, Vanderbilt University
Openalex Percentile: Top 11%
RNA Research and Splicing
2.34
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