A collaborative team from Helmholtz Munich, the German Center for Diabetes Research, and the University of Bonn has introduced C-COMPASS, a new software tool designed to make spatial proteomics and lipidomics accessible to researchers without coding skills. The platform enables scientists to profile where proteins and lipids are found within cells and observe how these distributions change due to disease or other influences.
Traditional tools in spatial proteomics often face limitations, such as difficulty predicting multiple protein localizations or quantifying across cellular compartments. Many also require programming knowledge and lack user-friendly interfaces, which can restrict their use among researchers who do not have coding experience. Spatial lipidomics remains challenging largely because there are few reliable markers for determining where lipids reside in cells.
C-COMPASS addresses these issues by using neural networks that can predict several subcellular locations for proteins and by integrating total proteome data to monitor changes in both protein distribution and organelle abundance. The software features a graphical user interface with standardized steps aimed at supporting reproducible analysis.
"With C-COMPASS, we wanted to create a tool that makes spatial proteomics more accessible and easier to reproduce," said Daniel Haas, one of the developers.
The research group demonstrated C-COMPASS by analyzing spatial protein patterns in humanized liver tissue under different metabolic conditions. They expanded the workflow by merging proteomic with lipidomic data, enabling spatial lipidomics analyses for the first time. Lipids were mapped onto reference maps based on proteomics results; this was applied to humanized mouse liver samples and uncovered shifts in lipid localization linked to metabolic disturbances.
The team intends to use C-COMPASS on additional datasets to further explore metabolism-related changes in protein location. They are also working on enhancements such as support for other spatial omics techniques like spatial transcriptomics.
