Authors
Gillman, R., Dukda, S., Sadir, J., Goodnow, C., Luciani, F., Singh, M., Field, M. A.
Abstract
Background: Somatic mutations are increasingly recognised as drivers of diseases beyond cancer, including autoimmune disorders. However, identifying rare, cell type-specific causal mutations remains challenging due to their low frequency within heterogeneous cell populations. Traditional bulk sequencing approaches lack the resolution to detect rare variants, underscoring the need for novel methods specifically designed for single-cell data of heterogeneous cell populations. Methods: We present an integrated single-cell multi-omics computational framework, SCARCE (Single-Cell Analysis of Rare Clonal Events), tailored for single-cell DNA sequencing (scDNA-seq) to statistically prioritise rare somatic mutations within defined cell subpopulations. By comparing variant frequencies across subpopulations, identified through either variant-based clustering or cell type annotation from surface marker expression, we identify variants enriched in specific cell populations. Our method applies multiple user-adjustable filters and statistical enrichment tests to distinguish true somatic variants from technical artifacts. Results: SCARCE successfully identifies rare somatic mutations across three distinct datasets using technologies including MissionBio Tapestri and clonally-amplified whole-genome sequencing of single cells. We demonstrate that SCARCE correctly isolates and identifies true variants in a cell population comprising just 10 of 16,316 cells (0.06% of the total population). Furthermore, in an extensively characterised sample with known causal variants, SCARCE correctly identifies all known pathogenic variants among its top-ranked candidates. Conclusions: SCARCE offers several advantages over existing tools in the field. By integrating genetic and phenotypic information at single-cell resolution, our approach opens new avenues for understanding the clonal origins of diseases driven by somatic mutations in small cell populations.
Preprint server:
bioRxiv
The authors list and abstract were imported from bioRxiv on 01 Nov 2025.
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