Spatial Metabolomics by Desthiobiotin Ligase (DESTNI) in Live Cells

Abstract

Proximity labeling has transformed spatial proteomics by enabling compartment-resolved mapping of protein environments in living cells, yet its extension to small-molecule metabolites has not been demonstrated, probably due to limitations in labeling chemistry and labeled metabolites identification. Here, we introduce DESTNI, an engineered desthiobiotin (DTB) ligase derived from TurboID through directed evolution, and establish a platform for spatially resolved profiling of amine-containing metabolites. A directed evolution strategy based on a yeast display system yielded DESTNI with efficient DTB-dependent reactivity, enabling robust and compartment-specific proximity labeling across diverse subcellular environments. To identify the DTB-modified amino metabolome, we developed an integrated analytical framework combining DTB-modified amino metabolite standards, in vitro DESTNI profiling, and in silico MS/MS prediction, enabling systematic annotation of DTB-modified amino metabolites. To extend this chemistry to metabolites, we combined synthetic DTB-conjugated metabolite reference standards, in vitro DESTNI-reactive metabolite discovery, and machine-learning prediction of DTB-derivatized metabolites and oligopeptides. Organelle-targeted DESTNI recovered reproducible compartment-enriched amino metabolite signatures, including mitochondrial matrix-enriched glycine, 5-aminolevulinic acid, ornithine and spermidine adducts, as well as nuclear-enriched {gamma}-aminobutyric acid and 5-aminovaleric acid adducts. Together, this work establishes DESTNI as a proximity labeling platform that bridges spatial proteomics and metabolomics and provides a general strategy for mapping subcellular biochemical environments in living cells.