Uncovering cross-signal interactions in single-cell and spatial omics

Abstract

Individual cells simultaneously encode multiple biological signals in their gene expression profiles, including spatial position, circadian phase, and disease state. These signals can be entangled in complex ways, including circadian patterns that vary along disease progression and cell cycle dynamics that shift along developmental trajectories. While methods to disentangle such signals have been recently developed, characterizing the interaction itself between multicellular signals along with its effects on tissue function, localizing cellular subpopulations which exhibit signal interactions, and identifying potential candidate genes which drive such interactions, all remain major challenges. We present CRISP which characterizes signal interactions in single-cell gene expression data based on the correspondence between the multiple, probabilistic signal-specific projections of the data. Across simulations with tunable couplings and four single-cell and spatial transcriptomic datasets, CRISP recovers diverse biological signal interactions and localizes them to informative cellular subpopulations and groups of associated genes; CRISP distinguishes healthy from fibrotic liver samples and localizes the loss of zonation-position coupling to individual fibrotic hepatocytes, resolves the asymmetric coupling of TGF-{beta} response, EMT, and cell-cycle phase to specific cell subpopulations, and flags transcriptionally delayed hepatocytes in Plasmodium liver infection. Beyond quantifying and localizing signal interactions, CRISP can refine existing labels, uncover continuous cellular transitions and reveal condition-associated genes.