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Deciphering Cell Fate and Clonal Dynamics via Integrative Single-Cell Lineage Modeling
Nancy Zhang1, Yuntian Fu2,3,4, Divij Mathew5
1Graduate Program in Genomics and Computational Biology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
Research Square
|October 3, 2025
Summary
Clonotrace integrates gene expression and lineage barcodes to improve cell fate inference. This computational framework enhances understanding of cell differentiation dynamics, particularly for T cells in clinical settings.
Area of Science:
- Single-cell biology
- Computational biology
- Immunology
Background:
- Single-cell technologies enable joint measurement of molecular states and clonal identities.
- Current computational methods for cell differentiation often overlook lineage information from barcodes.
- T cell analysis, crucial in clinical settings, can benefit from integrating transcriptional and clonal data.
Purpose of the Study:
- To develop a computational framework, Clonotrace, that jointly models gene expression and clonotype information.
- To improve the fidelity of inferring cell state transitions and fate biases.
- To provide a broadly applicable tool for lineage-barcoded single-cell datasets.
Main Methods:
- Developed Clonotrace, a computational framework integrating gene expression and clonotype data.
- Applied Clonotrace to diverse systems including T cells, hematopoietic differentiation, and cancer models.
- Validated the framework's ability to reveal differentiation hierarchies and identify fate-determining genes.
Main Results:
- Clonotrace infers cell state transitions and fate biases with higher fidelity than methods relying solely on transcriptional similarity.
- The framework successfully revealed differentiation hierarchies in various biological systems.
- Identified candidate fate-determining genes driving lineage commitment across different cell types.
Conclusions:
- Clonotrace offers a powerful approach to leverage lineage barcode information for enhanced cell fate inference.
- The framework is broadly applicable to various lineage-barcoded single-cell datasets, including T cells in immunotherapy and tumor microenvironments.
- This method advances the study of cell dynamics, differentiation, and lineage commitment.
