DestinyNet: A deep-learning framework for cell-fate analysis from lineage-tracing single-cell RNA sequencing data

Unraveling cell-development dynamics, including lineage commitment, differentiation, and disease progression, is fundamental to biology. Despite advances in single-cell omics and barcoding technologies, comprehensive frameworks for accurate, robust, and scalable cell-fate analysis using lineage-tracing single-cell RNA sequencing (LT-scSeq) data remain limited. We introduce DestinyNet, a multi-task deep-learning framework addressing three key challenges: (1) fate clustering, integrating fate and cell-type information; (2) fate flow, depicting dynamic pseudotime trajectories with fate information; and (3) fate prediction, identifying early-stage cell-fate biases. DestinyNet enables end-to-end cell representation learning through cell-relation triplets and is robust across various LT-scSeq data types, including static, cumulative, and dynamic barcoding with single or multiple time points. Experiments on diverse datasets, including hematopoiesis differentiation and fibroblast reprogramming (in vitro and in vivo), demonstrate DestinyNet's effectiveness in multiple fate-analysis tasks.