Article ID: 25129
Multiplex live imaging enables simultaneous visualization of multiple signaling pathways in living cells, offering real-time insights into complex cellular networks. This methodology is essential in research fields such as cancer biology, where signaling activities exhibit heterogeneity, feedback regulation, crosstalk, and dynamic changes during pathological progression and the acquisition of therapeutic resistance. While conventional biochemical assays advanced our understanding of signaling signatures through static or population-level analyses, they lack the temporal resolution required to capture dynamic events at single-cell resolution.
Recent methodological innovations have expanded multiplex live imaging through several strategies. Spectral multiplexing exploits broadened fluorescent protein palettes and optimized biosensor combinations, sometimes coupled with intracellular multiplexing methods that distinguish signals by targeting fluorescence to subcellular compartments. Intercellular multiplexing distributes reporters across cell populations, and temporal multiplexing leverages optical switching to separate signals over time. Additional modalities such as fluorescence anisotropy, fluorescence lifetime, and Raman imaging provide orthogonal readouts. Furthermore, computational approaches reinforce multiplex strategies by improved spectral unmixing, often complemented by deep learning-based algorithms. Collectively, these advances enable simultaneous tracking of multiple signaling pathways within single cells, revealing how diverse inputs are integrated into cellular responses.
Here we review current strategies for multiplex live imaging, especially highlighting its applications to cancer signaling networks. Progress in fluorescent biosensor development, imaging technologies, and computational analysis will further promote the exploration of dynamic cellular regulations in basic research and translational medicine.
Key words: multiplex live imaging, fluorescent biosensors, signal dynamics, image analysis, cancer heterogeneity
