Single-cell Phylogenetic Analysis — Lineage Tree Reconstruction at Single-cell Resolution
Single-cell Phylogenetic and Lineage Tree Reconstruction · Also known as: scPhylogeny, single-cell lineage tracing, clonal phylogenetics, single-cell tree inference
Single-cell phylogenetic analysis reconstructs evolutionary or developmental trees from single-cell sequencing data, tracing how individual cells diverged from a common ancestor. By leveraging somatic mutations, CRISPR-introduced barcodes, or copy-number changes as heritable characters, this method maps clonal relationships within tumors, developing tissues, or immune repertoires with unprecedented cellular resolution.
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When to use it
Use single-cell phylogenetic analysis when the core question concerns the evolutionary or developmental history of individual cells — for example, reconstructing tumor clonal evolution, tracing immune cell lineage during infection, or mapping cell fate decisions in embryogenesis. It requires single-cell data with heritable variant information (somatic mutations, CRISPR barcodes, or copy-number calls); bulk sequencing data or data lacking cell-level resolution are not suitable. Do NOT use this method when cells lack informative heritable markers (e.g., normal tissues with low somatic mutation burden and no lineage barcodes), when the biological question concerns steady-state cell-type composition rather than history, or when the study design has fewer than a few hundred profiled cells, which is insufficient to resolve meaningful tree topologies.
Strengths & limitations
- Resolves clonal architecture at single-cell resolution, revealing subclonal heterogeneity invisible to bulk sequencing.
- Applicable to diverse biological contexts — tumor evolution, immune repertoire, embryonic development, and organoid studies.
- CRISPR-based lineage tracing can be prospectively engineered, enabling controlled experimental designs.
- Integrates seamlessly with scRNA-seq, linking evolutionary history to transcriptional phenotype within the same cells.
- Mature software ecosystem (Cassiopeia, SCITE, SiCloneFit, Monocle) with benchmarked performance on simulated and real data.
- High sequencing dropout introduces missing data in the character matrix, reducing tree accuracy and resolution.
- Somatic mutation-based approaches require deep sequencing coverage, which is expensive at single-cell scale.
- Scalability is limited: exact tree inference algorithms become computationally intractable beyond a few thousand cells.
- Biological interpretation requires complementary annotations (cell-type labels, clinical data) that may not always be available.
- The method infers history from present-day snapshots; transient states or extinct clones leave no observable signal.
Frequently asked
What is the difference between single-cell phylogenetic analysis and pseudotime trajectory inference?
Trajectory inference (pseudotime) orders cells along a continuous axis of transcriptional change to model gradual differentiation; it does not infer genealogical ancestry. Single-cell phylogenetic analysis reconstructs a discrete tree of descent using heritable characters (mutations, barcodes). The two approaches answer different questions: pseudotime asks 'along what transcriptional path does differentiation proceed?' while phylogenetics asks 'which cells share a common ancestor and when did they diverge?'
Can I use somatic SNVs instead of CRISPR barcodes as characters?
Yes. Somatic point mutations accumulating during cell division can serve as natural barcodes. This approach requires deep targeted or whole-genome sequencing at the single-cell level. The main limitation is sparse character matrices — normal cells accumulate very few mutations per division, leaving large amounts of missing data. CRISPR-edited barcodes generate much denser character matrices and are preferred when an experimental intervention is possible.
How many cells do I need for a meaningful single-cell phylogeny?
There is no universal threshold, but trees built from fewer than a few hundred cells typically have very limited resolution. Meaningful clonal architecture studies generally involve hundreds to tens of thousands of cells. The critical factor is the ratio of informative characters per cell: more heritable variants per cell allows fewer cells to yield a well-resolved tree.
Which software should I use?
Cassiopeia is currently the most complete and benchmarked pipeline for CRISPR barcode-based lineage tracing. SCITE and SiCloneFit are well-validated for tumor mutation trees. For organisms where CRISPR engineering is not possible, tools built around copy-number changes (e.g., inferCNV combined with phylogenetic reconstruction) are commonly used. The best choice depends on the character type (barcodes vs. SNVs vs. CNVs) and the computational scale of the dataset.
How do I handle missing data in the character matrix?
Missing data — primarily from sequencing dropout — is a central challenge. Strategies include probabilistic imputation, treating missing entries as a separate character state, or using algorithms specifically designed to marginalise over missing observations. Cassiopeia, for example, implements a missing-data-aware ILP solver. Reporting the fraction of missing data and benchmarking tree robustness under different missing-data assumptions is considered good practice.
Sources
- Jones, M. G., Khodaverdian, A., Quinn, J. J., Chan, M. M., Hussmann, J. A., Wang, R., Xu, C., Weissman, J. S., & Yosef, N. (2020). Inference of single-cell phylogenies from lineage tracing data using Cassiopeia. Genome Biology, 21(1), 92. DOI: 10.1186/s13059-020-02000-8 ↗
- Trapnell, C., Cacchiarelli, D., Grimsby, J., Pokharel, P., Li, S., Morse, M., Lennon, N. J., Livak, K. J., Mikkelsen, T. S., & Rinn, J. L. (2014). The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nature Biotechnology, 32(4), 381-386. DOI: 10.1038/nbt.2859 ↗
How to cite this page
ScholarGate. (2026, June 3). Single-cell Phylogenetic and Lineage Tree Reconstruction. ScholarGate. https://scholargate.app/en/bioinformatics/single-cell-phylogenetic-analysis
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
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- Variant CallingBioinformatics↔ compare