Cell signaling pathways discovery from multi-modal data

He, C.; Simpson, C.; Cossentino, I.; Zhang, B.; Tkachev, S.; Eddins, D. J.; Kosters, A.; Yang, J.; Sheth, S.; Levy, T.; Possemato, A.; Huang, L.; Tabatsky, E.; Gregoretti, I.; Ariss, M.; Dandekar, D.; Ausekar, A.; Ghosn, E. E. B.; Colonna, M.; Rikova, K.; Nie, Q.; Orlova, D.

doi:10.1101/2025.02.06.636961

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bioRxiv · 10.1101/2025.02.06.636961

Cell signaling pathways discovery from multi-modal data

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Abstract

Deciphering cell signaling pathways is key to understanding biology, disease mechanisms, and developing new therapies. Although advances in multi-omics technologies provide richer insight into signaling, the data remain high-dimensional, heterogeneous, and difficult to interpret, and current computational tools for inferring signaling pathways are limited. To address this, we developed Incytr, a method for efficient discovery of cell signaling pathways through integration of diverse data modalities, including transcriptomics, ATAC-seq, proteomics, phosphoproteomics, and kinomics. We demonstrate its application in COVID-19, Alzheimers disease, and cancer, where it successfully recovers known pathways and generates novel, cell-type-specific hypotheses supported by multiple data types. We further show how integrating Incytr-derived pathways with biomarker and drug databases can support target and drug discovery. Finally, we show that using Incytr-derived signaling pathways as training data for simple natural language processing models can deepen our understanding of cell-cell communication and immune cell dynamics, while helping identify new therapeutic targets.

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He, C., Simpson, C., Cossentino, I., Zhang, B., Tkachev, S., Eddins, D. J., Kosters, A., Yang, J., Sheth, S., Levy, T., Possemato, A., Huang, L., Tabatsky, E., Gregoretti, I., Ariss, M., Dandekar, D., Ausekar, A., Ghosn, E. E. B., Colonna, M., Rikova, K., Nie, Q., Orlova, D.. 2025-02-08. Cell signaling pathways discovery from multi-modal data. https://doi.org/10.1101/2025.02.06.636961

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