Conditional Proximity Labeling in Complex Biological Systems: Design Principles, Emerging Platforms, and Applications
- 1State Key Laboratory of Medicinal Chemical Biology, School of Medicine and Research Centre for Analytical Sciences, College of Chemistry, Nankai University, Tianjin 300071, China
- 2School of Integrative Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
Abstract
Proximity labeling (PL) enables covalent capture of biomolecules near a defined target in living systems, providing a route to map molecular proximity in situ. Conventional PL usually relies on a single targeting cue and may therefore lack the selectivity required to resolve defined molecular events or dynamic processes in complex biological systems. Conditional PL addresses this limitation by coupling labeling activity to additional inputs, including interaction-dependent enzyme reconstitution, localization of catalytic modules at cell–cell interfaces, light-regulated catalysis, and multiple orthogonal labeling reactions. These designs allow labeling to respond to specified spatial, temporal, or molecular conditions. We group conditional PL into four designs—split complementation, interface localization, light control, and multiple labeling—based on what activates labeling and what the labels reveal. We examine their use in studying protein complexes, trafficking, and intercellular communication. We also consider what each strategy encodes, the experimental boundaries of the resulting proximity information, and trade-offs among selectivity, labeling efficiency, temporal control, and compatibility with native biological contexts. Remaining challenges include expanding catalyst and probe diversity, improving in vivo compatibility, and integrating conditional PL with single-cell analysis, spatial omics, and high-resolution imaging.
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BibTeX
@article{yang2026conditional,
title = {{Conditional Proximity Labeling in Complex Biological Systems: Design Principles, Emerging Platforms, and Applications}},
author = {Jia-Ming Yang and Meng-Die Zhu and Shu-Lin Liu and Long Yang and Zhi-Gang Wang},
journal = {International Journal of Molecular Sciences},
year = {2026},
month = oct,
doi = {10.3390/ijms27209284},
url = {https://www.mdpi.com/1422-0067/27/20/9284},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Conditional Proximity Labeling in Complex Biological Systems: Design Principles, Emerging Platforms, and Applications
AU - Jia-Ming Yang
AU - Meng-Die Zhu
AU - Shu-Lin Liu
AU - Long Yang
AU - Zhi-Gang Wang
PY - 2026
DA - 2026/10/01
JO - International Journal of Molecular Sciences
DO - 10.3390/ijms27209284
UR - https://www.mdpi.com/1422-0067/27/20/9284
AB - Proximity labeling (PL) enables covalent capture of biomolecules near a defined target in living systems, providing a route to map molecular proximity in situ. Conventional PL usually relies on a single targeting cue and may therefore lack the selectivity required to resolve defined molecular events or dynamic processes in complex biological systems. Conditional PL addresses this limitation by coupling labeling activity to additional inputs, including interaction-dependent enzyme reconstitution, localization of catalytic modules at cell–cell interfaces, light-regulated catalysis, and multiple orthogonal labeling reactions. These designs allow labeling to respond to specified spatial, temporal, or molecular conditions. We group conditional PL into four designs—split complementation, interface localization, light control, and multiple labeling—based on what activates labeling and what the labels reveal. We examine their use in studying protein complexes, trafficking, and intercellular communication. We also consider what each strategy encodes, the experimental boundaries of the resulting proximity information, and trade-offs among selectivity, labeling efficiency, temporal control, and compatibility with native biological contexts. Remaining challenges include expanding catalyst and probe diversity, improving in vivo compatibility, and integrating conditional PL with single-cell analysis, spatial omics, and high-resolution imaging.
SN - 1422-0067
ER -