Plant regeneration is a highly orchestrated process characterised by dynamic cellular reprogramming and lineage plasticity. Although Arabidopsis thaliana demonstrates a remarkable capacity for regeneration following root tip injury, the specific cellular populations and temporal dynamics that govern this response remain poorly defined. This study presents an integrative deep generative framework to identify and characterise regenerative cell states from scRNA-seq data collected across five post-injury time points. Using the resulting representation, we employed a range of complementary strategies, including Latent Dirichlet Allocation (LDA)-based topic modeling, iterative semi-supervised classification with controlled false-discovery rates, entropy-based detection of transitional migratory states, and cluster-level Optimal Transport and Waddington-OT trajectory inference. The topic modeling identified 2,585 high-specificity regeneration-associated cells, which expanded to a total of 3,416 candidates under a constraint of 1% false discovery. Entropy and transport analyses highlighted maximal lineage instability occurring between 4,9 and 14 hours post-injury, followed by subsequent phases characterised by expansion and stabilisation. The integration of orthogonal signals yielded a core of 906 high-confidence regenerative cells, with notable enrichment in the Cortex, Atrichoblast, and Columella LRC lineages. These findings substantiate a temporally structured, multi-phase model of regeneration that is derived directly from single-cell dynamics.

Co-authors: Ahmet Cemal Alıcıoğlu, Bruno Guillotin, Costerwell Khyriem, Ankita Singh, Rasheed Mohammed, Kenneth D. Birnbaum, Rolf Backofen, Omer S. Alkhnbashi