Abstract
Proteins function through complex interaction networks that govern nearly all aspects of cellular physiology. Identifying protein-protein interactions (PPIs) under native conditions remains challenging due to the transient nature of many complexes and technical limitations of conventional approaches. We present TIE-UP-SIN (Targeted Interactome Experiment for Unknown Proteins by Stable Isotope Normalization), a robust and reproducible method for in vivo identification of PPIs. This approach combines metabolic labeling with (15)N isotopes, reversible in vivo formaldehyde crosslinking, affinity purification, and quantitative mass spectrometry. TIE-UP-SIN is specifically designed to preserve transient or weak interactions during purification and to quantify interaction partners using internal light/heavy peptide ratios, reducing experimental variability and increasing reproducibility across biological replicates. The method employs a triple-sample design (WT/WT, Bait/WT, Bait/Bait) to distinguish specific from non-specific interactors. Peptide-level L/H ratios are normalized against sample-specific factors, aggregated at the protein level, and statistically analyzed using moderated testing. This strategy enables reliable detection of differential PPIs across physiological states, even in organisms with limited labeling options. We demonstrate the utility of TIE-UP-SIN by mapping interaction partners of the essential housekeeping sigma factor RpoD (SigA) under control and ethanol stress conditions. Known partners such as RNA polymerase subunits (RpoA, RpoB, RpoC) were robustly enriched, while potential novel candidates, including ClpX and AcpA, were detected at lower abundance. TIE-UP-SIN offers a simple, cost-effective, and modular platform for quantitative interactome analysis and can be adapted to a wide range of bacterial and non-bacterial systems. Compared to established approaches such as label-free IP-MS or proximity-based labeling methods, TIE-UP-SIN is intended as a complementary option. Its combination of specific control, robust quantification, and suitability for low-input material provides an additional tool within the broader proteomics workflow collection.