C-COMPASS: A user-friendly neural network tool profiles cell compartments at protein and lipid levels

Systematic proteomic organelle profiling methods including Protein Correlation Profiling and LOPIT have advanced our understanding of cellular compartmentalization. To manage the complexity of organelle profiling data, we introduce C-COMPASS, a user-friendly open-source software that employs a neural network-based regression model to predict the spatial cellular distribution of proteins. C-COMPASS handles complex multilocalization patterns and integrates protein abundance to model organelle composition changes across conditions. We apply C-COMPASS to mice with humanized liver to elucidate organelle remodeling during metabolic perturbations. Additionally, by training neural networks with co-generated marker protein profiles, C-COMPASS extends spatial profiling to lipids, overcoming the lack of organelle-specific lipid markers, allowing for determination of the localization, and tracking of lipid species across different compartments. This provides, for the first time, integrated snapshots of organelle lipid and protein composition. C-COMPASS offers an accessible tool for multi-omics studies of organelle dynamics without needing advanced computational skills, empowering researchers to explore new questions in lipidomics, proteomics, and organelle biology.