As the amount of data we have relating to cells, properties, surroundings, and interactions increases exponentially, we are motivated to develop pan-system measurements and analyses to paint comprehensive pictures of immune response in health and disease. Relying on massive transcriptomic datasets generated from complex tissues, like melanoma tumors, inflamed human gut, M. tuberculosis (MTB)-induced granulomas, and healthy or SHIV-infected monkey tissues, we have begun to construct social networks of integrated responses to physiological perturbations. The technologies outlined above uniquely enable us to generate foundational datasets (e.g., transcriptomes from interacting cell pairs) for deconvolving and interpreting the potential drivers of observed ensemble behaviors, as well as for identifying which properties we cannot explain, and thus need to study. To date, our lab has generated over 2 million single-cell transcriptomes across multiple tissues, individuals, and species; we are utilizing this data, paired with metadata and additional characteristics, to look for common cellular network motifs, such as division of labor, quorum sensing, persistence, or bet-hedging.