Social network architecture of human immune cells unveiled by quantitative proteomics

The immune system is unique in its dynamic interplay between numerous cell types. However, a system-wide view of how immune cells communicate to protect against disease has not yet been established. We applied high-resolution mass-spectrometry-based proteomics to characterize 28 primary human hematopoietic cell populations in steady and activated states at a depth of >10,000 proteins in total. Protein copy numbers revealed a specialization of immune cells for ligand and receptor expression, thereby connecting distinct immune functions. By integrating total and secreted proteomes, we discovered fundamental intercellular communication structures and previously unknown connections between cell types. Our publicly accessible (http://www.immprot.org/) proteomic resource provides a framework for the orchestration of cellular interplay and a reference for altered communication associated with pathology

Immune-centric network of cytokines and cells in disease context identified by computational mining of PubMed

Cytokines are signaling molecules secreted and sensed by immune and other cell types, enabling dynamic intercellular communication. Although a vast amount of data on these interactions exists, this information is not compiled, integrated or easily searchable. Here we report immuneXpresso, a text-mining engine that structures and standardizes knowledge of immune intercellular communication. We applied immuneXpresso to PubMed to identify relationships between 340 cell types and 140 cytokines across thousands of diseases. The method is able to distinguish between incoming and outgoing interactions, and it includes the effect of the interaction and the cellular function involved. These factors are assigned a confidence score and linked to the disease. By leveraging the breadth of this network, we predicted and experimentally verified previously unappreciated cell-cytokine interactions. We also built a global immune-centric view of diseases and used it to predict cytokine-disease associations. This standardized knowledgebase (http://www.immunexpresso.org) opens up new directions for interpretation of immune data and model-driven systems immunology