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DestinyNet: A deep-learning framework for cell-fate analysis from lineage-tracing single-cell RNA sequencing data
Zuozhu Liu1,2,3, Songtao Jiang1,2, Tianxiang Hu1,2
1Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, Zhejiang 310016, China.
Patterns (New York, N.Y.)
|April 20, 2026
Summary
DestinyNet is a new deep-learning framework for cell-fate analysis using lineage-tracing single-cell RNA sequencing data. It accurately clusters, visualizes, and predicts cell development dynamics across various data types.
Area of Science:
- Developmental Biology
- Computational Biology
- Genomics
Background:
- Understanding cell development, lineage commitment, and differentiation is crucial in biology.
- Existing cell-fate analysis tools for lineage-tracing single-cell RNA sequencing (LT-scSeq) data lack comprehensive frameworks for accuracy, robustness, and scalability.
Purpose of the Study:
- To introduce DestinyNet, a novel multi-task deep-learning framework for advanced cell-fate analysis.
- To address key challenges in LT-scSeq data, including fate clustering, fate flow visualization, and early cell-fate bias prediction.
Main Methods:
- DestinyNet employs end-to-end cell representation learning using cell-relation triplets.
- The framework integrates fate and cell-type information for enhanced clustering.
- It visualizes dynamic pseudotime trajectories incorporating fate information for fate flow analysis.
Main Results:
- DestinyNet demonstrates robustness across diverse LT-scSeq data types (static, cumulative, dynamic barcoding; single/multiple time points).
- Experiments on hematopoiesis differentiation and fibroblast reprogramming datasets confirm its effectiveness.
- The framework successfully performs multiple cell-fate analysis tasks, including prediction of early cell-fate biases.
Conclusions:
- DestinyNet provides a comprehensive and effective solution for cell-fate analysis using LT-scSeq data.
- The framework enhances the understanding of cell development dynamics and disease progression.
- DestinyNet offers a scalable and robust approach for analyzing complex biological systems.
