Uncovering cross-signal interactions in single-cell and spatial omics
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…
The study titled "Uncovering cross-signal interactions in single-cell and spatial omics" delves into the complex interactions between multiple biological signals encoded within individual cells. These signals, such as spatial position, circadian phase, and disease state, can interact in intricate ways, like disease progression affecting circadian patterns or cell cycle dynamics shifting during development.
Despite recent advancements in separating these signals, understanding their interactions, their impact on tissue function, pinpointing cellular subpopulations displaying these interactions, and identifying genes that drive such phenomena remain significant challenges. The researchers introduce CRISP, a tool designed to analyze signal interactions in single-cell gene expression data.
CRISP achieves this by examining the correspondence between multiple probabilistic signal-specific projections of the data. When tested against simulations featuring adjustable couplings and four single-cell and spatial transcriptomic datasets, CRISP excels in revealing diverse biological signal interactions. It accurately localizes these interactions to meaningful cellular subpopulations and groups of associated genes.
Notably, CRISP distinguishes between healthy and fibrotic liver samples, identifies the loss of zonation-position coupling in fibrotic hepatocytes, deciphers the asymmetric coupling among TGF-β response, epithelial-mesenchymal transition (EMT), and cell-cycle phase to certain cell subpopulations, and highlights transcriptionally delayed hepatocytes in Plasmodium liver infection.
Furthermore, CRISP can enhance existing labels, uncover continuous cellular transitions, and uncover genes associated with specific conditions.
Written by urgent.news from bioRxiv's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
