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Haas, D. ; Weindl, D. ; Kakimoto, P. ; Trautmann, E.-M. ; Schessner, J.P.* ; Mao, X.* ; Gerl, M.J.* ; Gerwien, M.* ; Müller, T.D. ; Klose, C.* ; Cheng, X.* ; Hasenauer, J. ; Krahmer, N.

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

Nat. Methods, DOI: 10.1038/s41592-025-02880-3 (2025)
Verlagsversion Forschungsdaten DOI PMC
Open Access Hybrid
Creative Commons Lizenzvertrag
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.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Proteomics; Lipopolysaccharide; Localization; Mitochondria; Pathway; Complex; Liver
ISSN (print) / ISBN 1548-7091
e-ISSN 1548-7105
Zeitschrift Nature Methods
Verlag Nature Publishing Group
Verlagsort New York, NY
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Diabetes and Obesity (IDO)
Institute of Computational Biology (ICB)
Förderungen DZD
European Foundation for the Study of Diabetes (EFSD)
EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 European Research Council (H2020 Excellent Science - European Research Council)
University of Bonn, Schlegel Professorship
Deutsche Forschungsgemeinschaft (German Research Foundation)