Proximity labeling in neuroscience: decoding molecular landscapes for precision neurology

TL;DR

Proximity labeling helps map molecular interactions in the nervous system, offering new insights into neurological diseases and potential treatments.

Contribution

This review systematically outlines how proximity labeling technologies transform molecular neuroscience and precision neurology.

Findings

Proximity labeling enables high-resolution mapping of proteomes and interactomes in specific neural compartments.

PL technologies are advancing understanding of diseases like Alzheimer’s and Parkinson’s by revealing pathophysiological mechanisms.

Integration of PL with multi-omics and single-cell methods could enhance precision neurology and drug discovery.

Abstract

The intricate cellular architecture and dynamic molecular interplay in the nervous system have long challenged mechanistic studies of neurological diseases. Conventional approaches often miss the transient, low-affinity, or spatially confined interactions that underlie neural homeostasis and pathogenesis. Proximity labeling (PL) technologies overcome this limitation by enabling in situ capture of these elusive molecular events within living systems. Through spatially restricted biotinylation, PL methods, including engineered biotin ligases (e.g., TurboID), peroxidases (e.g., APEX2), and emerging photocatalytic platforms, allow high-resolution mapping of proteomes and interactomes within defined subcellular compartments, cell types, and cell-cell interfaces. In this review, we systematically outline the principles of PL and its transformative applications in constructing molecular atlases of the nervous system. We highlight how these tools are revolutionizing our understanding of brain function by elucidating pathophysiological mechanisms in Alzheimer’s disease, Parkinson’s disease and other neurological disorders. Furthermore, we discuss how PL accelerates the translation of basic research into clinical practice by facilitating the discovery of mechanistic biomarkers and druggable targets. Finally, we address current challenges and future directions, including integration with multi-omics and single-cell methodologies, and conclude that PL can advance precision neurology by bridging molecular neurobiology with therapeutic innovation.

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