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| Item Type: | Article |
|---|---|
| Title: | C-COMPASS: a user-friendly neural network tool profiles cell compartments at protein and lipid levels |
| Creators Name: | Haas, Daniel T., Weindl, Daniel, Kakimoto, Pamela, Trautmann, Eva-Maria, Schessner, Julia P., Mao, Xia, Gerl, Mathias J., Gerwien, Maximilian, Müller, Timo D., Klose, Christian, Cheng, Xiping, Hasenauer, Jan and Krahmer, Natalie |
| Abstract: | 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 livers 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 localization and tracking of lipid species across different compartments. This provides integrated snapshots of organelle lipid and protein compositions. Overall, C-COMPASS offers an accessible tool for multiomic studies of organelle dynamics without needing advanced computational skills, empowering researchers to explore new questions in lipidomics, proteomics and organelle biology. |
| Source: | Nature Methods |
| ISSN: | 1548-7091 |
| Publisher: | Nature Publishing Group |
| Date: | 4 December 2025 |
| Official Publication: | https://doi.org/10.1038/s41592-025-02880-3 |
| PubMed: | View item in PubMed |
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