sc4D: spatio-temporal single-cell transcriptomics analysis through embedded optimal transport identifies joint glial response to Alzheimer's disease pathology
A precise understanding of disease-associated spatio-temporal transcriptional dynamics is critical for nominating therapeutic interventions that drive desired perturbation responses. In disease contexts, pathological processes unfold across diverse cellular states, spatial environments, and timescales; however, current computational approaches have a limited ability to jointly model these complex dynamics or infer cellular trajectories from in silico perturbation experiments.
sc4D treats disease progression as a spatial and temporal modeling problem. The framework combines local-neighborhood analysis around pathology, autoencoder-based embeddings, and optimal transport to infer cell-state and transcriptome trajectories from single-cell transcriptomics data.
In an Alzheimer’s disease mouse-model application, the study reanalyzes longitudinal spatial single-cell data to model glial responses around amyloid-beta plaques and tau pathology. The analysis identifies coordinated microglia and astrocyte activation near plaques and uses in silico perturbations to prioritize candidate regulators and interventions, including CAMTA1 activation and Donepezil, for shifting inflammatory glial states.
