Microenvironment-informed inference of transcriptional progression geometry

Abstract

We present BIOCURRENT, a causal inference framework that reconstructs donor-specific pseudotime geometry in transcriptomic data. By modeling gene expression as a function of baseline characteristics, microenvironmental context, and latent pseudotime, BIOCURRENT enables comparison of compressed or expanded progression intervals across transcriptional state transitions. We introduce $\Delta\Delta T$, a geometry-based estimator that quantifies differences in pseudotime intervals across conditions, enabling evaluation of changes in pseudotime intervals under hypothetical modulation of microenvironmental programs. Applications to thymic T-cell developmental lineages and to COVID-19 immune dysregulation reveal condition- and donor-specific distortions of progression intervals. Counterfactual simulation links microenvironmental context to changes in specific intracellular state transition intervals. By localizing deviations in pseudotime geometry, BIOCURRENT identifies whether shifts in transcriptomic programs emerge early or later along transcriptomic coordinates and reveals upstream programs associated with these distortions. Such localization supports transcriptional stage-aware mechanistic hypotheses and suggests candidate intervention checkpoints in complex biological systems.