Researchers have developed a single-cell multiomic sequencing platform – single-cell DNA–RNA sequencing (SDR-seq) – that enables simultaneous analysis of genomic DNA and RNA in thousands of individual cells. The approach allows precise correlation of both coding and noncoding genetic variants with their impact on gene expression, addressing a major challenge in linking genotype to phenotype at the single-cell level.
Traditional methods for studying endogenous genetic variants are limited by both the accuracy of genome editing and the inability to connect genetic changes directly to transcriptional effects within the same cell. SDR-seq overcomes this by integrating two layers of molecular information: it can profile up to 480 genomic loci and gene expression profiles per cell. This provides zygosity data for variants while capturing the corresponding transcriptomic landscape.
In their study, published in Nature Methods, the authors applied SDR-seq to human induced pluripotent stem cells (iPSCs) and primary B cell lymphoma samples. In iPSCs, the method identified how both coding and noncoding variants influenced distinct gene expression patterns. This capacity to assess noncoding variation is notable, as such regions are often difficult to study yet can play critical regulatory roles.
When applied to samples from patients with B cell lymphoma, SDR-seq revealed a relationship between mutational burden and gene expression signatures associated with tumor behavior. Cells carrying higher numbers of mutations exhibited increased B cell receptor (BCR) signaling and elevated expression of genes linked to tumorigenesis. These findings suggest that SDR-seq could help map the functional consequences of complex mutational landscapes within heterogeneous tumor populations.
For clinical laboratories and translational researchers, SDR-seq represents a potential tool for functional phenotyping – directly linking variant profiles to gene expression outcomes in disease-relevant cell types. While the study’s authors emphasize its use as a research platform, the method could eventually contribute to improved understanding of how variant combinations drive disease mechanisms such as cancer progression or treatment resistance.
By enabling concurrent measurement of genomic and transcriptomic features in the same cells, SDR-seq provides a scalable framework for dissecting regulatory networks that underlie pathology. Its capacity to connect genotype with gene expression at single-cell resolution may facilitate future diagnostic strategies aimed at functionally characterizing variants of uncertain significance.
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