Cell-type resolved prenylomics in mouse liver enabled by a low-input proteomics workflow

Abstract

Protein prenylation is a lipid modification crucial for membrane association and cellular signaling, but its dysregulation is linked to cancer and neurodegenerative diseases. Typical "prenylomic" workflows rely on metabolic labeling with bio-orthogonal isoprenoid probes followed by streptavidin enrichment; however, they generally require milligram-scale protein inputs (15-25 million cells), preventing the study of scarce primary or sorted cell populations. To overcome this limitation, we developed a highly sensitive, low-input prenylomic workflow combining optimized streptavidin enrichment with library-free data-independent acquisition (DIA) mass spectrometry. This method enables deep prenylomic coverage from just 50 ug of total protein input, a 20-fold reduction compared to prior methods. In COS7 cells, this workflow identified 91 prenylated protein groups, outperforming TMT-DDA methods that yielded 81 groups from 2 mg of input, using the same bio-orthogonal probe for enrichment, while coefficient of variation (CV) analysis demonstrated superior quantitative precision and reproducibility. Applied in vivo in mice following subcutaneous probe administration, the low-input methodology overcame sample input limitations to profile cell-type-specific prenylomes from dissociated liver tissue, identifying 29 enriched prenylated proteins in hepatocytes and 18 in macrophages. Pathway enrichment analysis showed that the identified proteins are consistent with the biology of the respective cell-types. This robust methodology significantly lowers the sample threshold for lipid proteomics, expanding the accessibility of prenylomics to limited primary cells and sorted tissue populations to study cell-type-specific prenylation dynamics in disease contexts.