As many factors define cellular phenotype and influence disease beyond mRNA, we develop complementary methods for co-assaying other cellular attributes to enrich our understanding of the drivers of cellular behaviors. Examples including the abundance of additional ‘-omes’, the sequence and amount of important transcripts, cellular history, biophysical properties, spatial position, and functional output. Recently, we have worked to: 1. detect pathogens in cells and potentially actionable associated host factors; 2. query for specific mutations to identify cancer cells; and, 3. extract T cell receptor sequences to examine clonality. We have also formulated computational methods to derive deeper insights from these data (e.g., to examine viral dynamic in infected cells, reproducible features hidden by inter-individual variability, multicellular immune dynamics, intercellular communication, or alteration in cellular ecosystems associated with pathology).

Computational analyses of cancer associated fibroblasts (CAF) and T cells in melanoma revealed likely axes of interaction in cancer response. (Tirosh et al., Science, 2016).

In microwell devices, we can interrogate pairs of interacting cells to understand how they collectively respond to stimuli.

We examine the impact of intercellular interactions on cellular function. We have used coculture, imaging and perturbation strategies, as well as matched computational methods, to reinforce findings from dissociated samples, validate inferred cell-cell communication in vivo (e.g., between sensory neurons and lymph node resident cells), and manipulate multicellular systems (e.g., organoids). We are currently working on building arrayed, synthetically-designed cellular ensembles to examine how ‘tissue’ structure impacts functional response. Our overall goal is to understand cellular co-dependencies that influence niche- and tissue-level response dynamics.