C-COMPASS: A Neural Network Tool for Multi-Omic Classification of Cell Compartments

Abstract

Functional compartmentalization in eukaryotic cells is essential for maintaining physiological processes. The development of systematic organelle proteomic techniques, such as Protein Correlation Profiling (PCP) and Localization of Organelle Proteins by Isotope Tagging (LOPIT), has enhanced our understanding of organelle dynamics and protein localization. However, the complexity of the data and the need for advanced computational skills limit their accessibility. To address this, we introduce C-COMPASS, an open-source software featuring a user-friendly interface that utilizes a neural network-based regression model to predict the spatial distribution of proteins across cellular compartments. C-COMPASS manages complex multilocalization patterns and integrates protein abundance information to model organelle composition changes under various biological conditions. Using C-COMPASS, we mapped the organelle proteomic landscapes of humanized liver mice in different metabolic states and modeled changes in organelle composition to provide insights into cellular adaptations. Additionally, we extended cellular maps to the lipid level by co-generating protein and lipid profiles. C-COMPASS trains neural networks with marker protein profiles to predict lipid localizations, enabling parallel mapping of lipid and protein localization. This approach overcomes the lack of knowledge of organelle-specific lipid markers and identifies previously unknown organelle-specific lipid species. C-COMPASS offers a comprehensive solution for studying organelle dynamics at the multi-omics level, designed to be accessible without requiring extensive computational expertise or specialized high-performance computing equipment.