Machine Learning Models for Predicting Cellular Differentiation in Regenerative Medicine (Biotechnology and Regenerative Sciences E: 3117-6445 P: 3117-6453, 2024)
Cellular differentiation -- the process by which pluripotent stem cells commit to specific cell lineages -- is a critical determinant of regenerative medicine therapeutic outcomes, yet remains incompletely understood due to the complexity of the transcriptional regulatory networks governing lineage commitment decisions. Accurate computational prediction of cellular differentiation trajectories and terminal cell fates from molecular profiles would substantially accelerate the development of cell therapy protocols, reduce experimental iteration costs, and improve the reproducibility of directed differentiation protocols. This paper proposes the Cellular Differentiation Prediction (CDP) framework, a machine learning methodology for predicting differentiation trajectories and terminal cell fates from single-cell RNA sequencing (scRNA-seq) and epigenomic data. CDP integrates four ML components: a graph neural network trajectory predictor (GNN-TP) that models transcriptional state transitions as directed graphs; a multi- modal epigenomic integrator (MEI) that incorporates ATAC-seq chromatin accessibility with transcriptomic data; a fate probability estimator (FPE) that quantifies the probability of each cell committing to each available lineage; and a differentiation protocol optimiser (DPO) that recommends growth factor combinations and timing predicted to maximise directed differentiation efficiency. CDP is evaluated on five publicly available scRNA-seq datasets spanning haematopoiesis, neurogenesis, and cardiac differentiation, and validated against three experimental directed differentiation protocols. CDP achieves trajectory prediction accuracy of 84.6% (SD = 3.8%) versus baseline pseudotime methods (68.4%, SD = 4.6%) and fate assignment accuracy of 91.2% (SD = 2.8%) versus Monocle3 baseline (76.8%, SD = 3.4%). The DPO protocol recommendations achieve 24.8% higher directed differentiation efficiency in three experimental validation assays. The study contributes the CDP specification, a Differentiation Prediction Score (DPS), and open-source software for ML-accelerated regenerative medicine research.