Retrospective single cell lineage tracing with highly mutable homopolymers

Abstract Retrospective reconstruction of cell lineages from somatic mutations holds substantial promise for understanding development, tissue homeostasis, and disease. Yet, current approaches are often constrained by low marker density, technical noise, and incomplete molecular readouts that limit the resolution and accuracy of inferred lineages. Here, we present RETrace2, a single-cell dual-omic method for simultaneous lineage tracing and cell-type identification. By identifying and targeting highly mutable homopolymers, which we show are approximately twice as informative as conventional microsatellite repeats, combined with technical improvements to increase marker coverage and reduce experimental errors of reading and amplifying homopolymers, RETrace2 can trace lineage continuously at an estimated resolution of fewer than five cell divisions in microsatellite-unstable organisms. Simultaneously, sparse methylation profiles were obtained from the same cells for cell-type identification. We validated the method using in vitro ground-truth models and demonstrated its utility in vivo by reconstructing multi-organ lineage trees from the brain, kidney and liver of Msh2 -deficient mice.

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Publication Details

Journal
Nature Communications
Published
2026-11-07
DOI
https://doi.org/10.1038/s41467-026-79104-5
Citations
1
Primary Topic
Single-cell and spatial transcriptomics
Type
article
Field-Weighted Citation Impact
1.46

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article

Retrospective single cell lineage tracing with highly mutable homopolymers

Pin-Chung Cheng, Dmitrii Kamenev, Conor Fitzpatrick, Peter V. Kharchenko et al.
1 citations
Nature Communications
Single-cell and spatial transcriptomics
1.46
article

Retrospective single cell lineage tracing with highly mutable homopolymers

Pin-Chung Cheng, Dmitrii Kamenev, Conor Fitzpatrick, Peter V. Kharchenko, Polina Kameneva, Igor I. Adameyko, Kun Zhang
article en
1 citations

Abstract

Abstract Retrospective reconstruction of cell lineages from somatic mutations holds substantial promise for understanding development, tissue homeostasis, and disease. Yet, current approaches are often constrained by low marker density, technical noise, and incomplete molecular readouts that limit the resolution and accuracy of inferred lineages. Here, we present RETrace2, a single-cell dual-omic method for simultaneous lineage tracing and cell-type identification. By identifying and targeting highly mutable homopolymers, which we show are approximately twice as informative as conventional microsatellite repeats, combined with technical improvements to increase marker coverage and reduce experimental errors of reading and amplifying homopolymers, RETrace2 can trace lineage continuously at an estimated resolution of fewer than five cell divisions in microsatellite-unstable organisms. Simultaneously, sparse methylation profiles were obtained from the same cells for cell-type identification. We validated the method using in vitro ground-truth models and demonstrated its utility in vivo by reconstructing multi-organ lineage trees from the brain, kidney and liver of Msh2 -deficient mice.

Nature Communications
Karolinska Institutet (SE), University of California San Diego (US), Altos Labs (US), St. Anna Children's Cancer Research Institute (AT), Medical University of Vienna (AT)
Alex's Lemonade Stand Foundation for Childhood Cancer, Austrian Science Fund, Knut och Alice Wallenbergs Stiftelse, Vetenskapsrådet
Openalex Percentile: Top 22%
Single-cell and spatial transcriptomics
1.46
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