Charting cellular plasticity through multi-omics profiling and data integration

Depending on the context and stimuli they receive, immune cells can adopt a plethora of different functional states and have a decisive role in keeping healthy homeostasis or promoting disease development. In our work, we expose primary human macrophages and other immune cells to physiologically relevant stimuli and perform in-depth characterization of different cellular states using transcriptomic, mass spectrometry-based proteomic and phosphoproteomic profiling. We leverage the generated phosphoproteome profiles to infer central regulatory signalling events and highlight individual kinases based on the direct and indirect footprints of their activities. Furthermore, by studying cellular transcriptome profiles, we find instances where the generated in vitro cellular states correspond to cell populations present in the tumours of cancer patients characterized with single cell transcriptomics. Interestingly, differentially regulated genes and proteins in macrophages exposed either to signalling mediators that are abundant in tumour microenvironment or directly to media collected from tumour resections cultured in vitro frequently point to the combined presence of inflammatory and immunosuppressive molecular signatures in the same cell states. In order to integrate the generated multi-omics datasets, we construct cellular networks by mapping representative entities for each omics layer onto knowledge-based protein interactions. We use these maps to identify and describe central regulatory elements, which connect multiple entities altered in the studied phenotypes and we leverage MONET-based network decomposition to highlight functionally connected network modules. The latter approach enables identification of cell state-specific and disease-relevant modules across the plastic phenotypic landscapes. Apart from the here described application to charting regulatory routes in cellular models, we demonstrate that this approach is also of a high value for the analysis of clinical multi-omics datasets.