Single-cell analysis of structural variations and complex rearrangements with tri-channel processing

Area of Science:

• Genomics
• Cancer Biology
• Computational Biology

Background:

• Structural variations (SVs) are key drivers of genetic variability and cancer pathway dysregulation.
• Detecting somatic SVs, especially copy-number-neutral and complex types, in single cells remains a significant challenge.

Purpose of the Study:

• To develop a computational framework for comprehensive SV discovery in single cells.
• To analyze SV landscapes in various cell types, including cancer cells.
• To investigate SV mutational processes and their role in clonal evolution.

Main Methods:

• Developed single-cell tri-channel processing (scTRIP), a computational framework.
• Integrated read depth, template strand, and haplotype phase for SV detection.
• Applied scTRIP to survey SV landscapes in 565 single cells, including transformed epithelial cells and leukemic samples.

Main Results:

• scTRIP comprehensively discovers abundant SV classes, including inversions, translocations, and complex rearrangements.
• Analysis of leukemic samples revealed four times more somatic SVs than traditional cytogenetic karyotyping.
• Identified submicroscopic copy-number alterations, oncogenic copy-neutral rearrangements, and a subclonal chromothripsis event.

Conclusions:

• scTRIP advances current methods for SV detection in single cells.
• The framework enables direct measurement of SV mutational processes, such as breakage-fusion-bridge cycles.
• Facilitates studies on clonal evolution, genetic mosaicism, and SV formation, potentially improving precision medicine and disease classification.