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Functional phenotyping of genomic variants using joint multiomic single-cell DNA-RNA sequencing

ABSTRACT: Genetic variants (both coding and noncoding) can impact gene function and expression, driving disease mechanisms such as cancer progression. The systematic study of endogenous genetic variants is hindered by inefficient precision editing tools, combined with technical limitations in confidently linking genotypes to gene expression at single-cell resolution. We developed single-cell DNA-RNA sequencing (SDR-seq) to simultaneously profile up to 480 genomic DNA loci and genes in thousands of single cells, enabling accurate determination of coding and noncoding variant zygosity alongside associated gene expression changes. Using SDR-seq, we associate coding and noncoding variants with distinct gene expression in human induced pluripotent stem cells. Furthermore, we demonstrate that in primary B cell lymphoma samples, cells with a higher mutational burden exhibit elevated B cell receptor signaling and tumorigenic gene expression. SDR-seq provides a powerful platform to dissect regulatory mechanisms encoded by genetic variants, advancing our understanding of gene expression regulation and its implications for disease.

ORGANISM(S): Mus musculus Homo sapiens

PROVIDER: GSE268646 | GEO | 2025/06/17

REPOSITORIES: GEO

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Project description:Purpose: mRNA translation into protein is highly regulated, but the role of mRNA isoforms, noncoding RNAs (ncRNAs), and genetic variants has yet to be systematically studied. Using high-throughput sequencing (RNA-seq), we have measured cellular levels of mRNAs and ncRNAs, and their isoforms, in lymphoblast cell lines (LCL) and in polysomal fractions, the latter shown to yield strong correlations of mRNAs with expressed protein levels. Analysis of allelic RNA ratios at heterozygous SNPs served to reveal genetic factors in ribosomal loading. Methods: RNA-seq was performed on cytosolic extracts and polysomal fractions (3 ribosomes or more) from three lymphoblastoid cell lines. As each RNA fraction was amplified (NuGen kit), and relative contributions from various RNA classes differed between cytosol and polysomes, the fraction of any given RNA species loaded onto polysomes was difficult to quantitate. Therefore, we focused on relative recovery of the various RNA classes and rank order of single RNAs compared to total RNA. Results: RNA-seq of coding and non-coding RNAs (including microRNAs) in three LCLs revealed significant differences in polysomal loading of individual RNAs and isoforms, and between RNA classes. Moreover, correlated distribution between protein-coding and non-coding RNAs suggests possible interactions between them. Allele-selective RNA recruitment revealed strong genetic influence on polysomal loading for multiple RNAs. Allelic effects can be attributed to generation of different RNA isoforms before polysomal loading or to differential loading onto polysomes, the latter defining a direct genetic effect on translation. Several variants and genes identified by this approach are also associated with RNA expression and clinical phenotypes in various databases. Conclusions: These results provide a novel approach using complete transcriptome RNA-seq to study polysomal RNA recruitment and regulatory variants affecting protein translation. cells from 3 samples were grown to 5x105 cells/mL density in T75 tissue culture flask and harvested, total RNA and polysome bound RNA was sequenced by Ion Proton

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Functional phenotyping of genomic variants using joint multiomic single-cell DNA-RNA sequencing

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