Detection of genome structural variation in normal cells and tissues by single molecule sequencing

Detecting somatic mutations in normal cells and tissues is notoriously challenging due to their low abundance, orders of magnitude below the sequencing error rate. While several techniques, such as single-cell and single-molecule sequencing, have been developed to identify somatic mutations, they are insufficient for detecting genomic structural variants (SVs), which have a significantly greater impact than single-nucleotide variants (SNVs). We introduce Single-Molecule Mutation Sequencing for Structural Variants (SMM-SV-seq), a novel method combining Tn5-mediated, chimera-free library preparation with the precision of error-corrected next-generation sequencing (ecNGS). This approach enhances SV detection accuracy without relying on independent supporting sequencing reads. Our validation studies on human primary fibroblasts treated with varying concentrations of the clastogen bleomycin demonstrated a significant, up to tenfold and dose-dependent, increase in deletions and translocations 24 hours post-treatment. Evaluating SMM-SV-seq’s performance against established computational tools for SV detection, such as Manta and DELLY, using a well-characterized human cell line, SMM-SV-seq showed precision and recall rates of 61.9% and 85.8%, respectively, significantly outperforming Manta (10% precision, 23% recall) and DELLY (15% precision, 32% recall). Using SMM-SV-seq, we documented clear, direct evidence of negative selection against structural variants over time. After a single 2 Gy dose of ionizing radiation, SVs in normal human primary fibroblasts peaked at 24 hours post-intervention and then declined to nearly background levels by day six, highlighting the cellular mechanisms that selectively disadvantage cells harboring these mutations. Additionally, SMM-SV-seq revealed that BRCA1-deficient human breast epithelial cells are more susceptible to the mutagenic effects of ionizing radiation compared to BRCA1-proficient isogenic control cells, suggesting a potential molecular mechanism for increased breast cancer risk in BRCA1 mutation carriers. SMM-SV-seq represents a significant advancement in genomic analysis, enabling the accurate detection of somatic structural variants in normal cells and tissues for the first time. This method complements our previously published Single-Molecule Mutation sequencing (SMM-seq), effective for detecting single-nucleotide variants (SNVs) and small insertions and deletions (INDELs). By addressing challenges such as self-ligation in library preparation and leveraging a powerful ecNGS strategy, SMM-SV-seq enhances the robustness of our genomic analysis toolkit. This breakthrough paves the way for new research into genetic variability and mutation processes, offering deeper insights that could advance our understanding of aging, cancer, and other human diseases.