Functional analysis of structural variants in single cells using Strand-seq.

Area of Science:

• Genomics
• Cancer Biology
• Computational Biology

Background:

• Somatic structural variants (SVs) are common in cancer but their functional impact on disease progression is poorly understood.
• Existing methods lack the ability to directly link SVs to their functional consequences at a single-cell level.

Purpose of the Study:

• To introduce scNOVA, a novel computational method for integrating SV discovery with molecular phenotyping in single cells.
• To characterize the functional consequences of SVs on gene deregulation and signaling pathways in cancer.

Main Methods:

• scNOVA utilizes Strand-seq data for haplotype-aware SV discovery.
• It infers gene expression from nucleosome occupancy to provide a molecular phenotype readout.
• The method was applied to leukemias and cancer cell lines.

Main Results:

• Identified local effects of copy-balanced rearrangements on gene deregulation.
• Revealed consequences of SVs on aberrant signaling pathways in cancer subclones.
• Discovered distinct SV subclones with dysregulated Wnt signaling in chronic lymphocytic leukemia.
• Uncovered consequences of subclonal chromothripsis in T cell acute lymphoblastic leukemia, including c-Myb activation and a primitive cell state.

Conclusions:

• scNOVA enables direct linkage of SVs to their functional effects in single cells.
• The method facilitates systematic single-cell multiomic studies of structural variation in heterogeneous cancer populations.
• Findings inform potential therapeutic strategies, such as targeting subclones with Notch inhibitors.