MiTo: tracing the phenotypic evolution of somatic cell lineages via mitochondrial single-cell multi-omics

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Abstract Mitochondrial single-cell lineage tracing (MT-scLT) has recently emerged as a scalable and non-invasive tool to trace somatic cell lineages. However, the reliability and resolution of MT-scLT remains highly debated. Here, we present MiTo , the first end-to-end framework for robust MT-scLT data analysis. Thanks to highly-optimized algorithms and user-friendly interfaces, this modular toolkit offers unprecedented control across the entire MT-scLT workflow. Benchmarked against novel real-world datasets (375–2,757 cells; 8–216 lentiviral clones), MiTo outperformed state-of-the-art methods and baselines in MT-scLT data pre-processing and clonal inference. Applied to a time-resolved dataset of breast cancer evolution (>2,500 cells), MiTo accurately inferred ground-truth cell lineages (ARI=0.94) and cell state transitions, detected clonal fitness markers, and quantified heritability of gene regulatory networks. Comparing alternative lineage markers, MiTo quantified the resolution limit of existing MT-scLT systems, which currently enable reliable inference of coarse-grained cellular ancestries, but not high-resolution phylogenetic inference. In conclusion, this work provides robust tools and practical guidelines to dissect somatic evolution with single-cell multi-omics.

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