SummaryTo build comprehensive models of cellular states and interactions in normal and diseased tissue, genetic and proteomic information must be extracted with single-cell and spatial resolution. Here, we extended imaging mass cytometry to enable multiplexed detection of mRNA and proteins in tissues. Three mRNA target species were detected by RNAscope-based metal in situ hybridization with simultaneous antibody detection of 16 proteins. Analysis of 70 breast cancer samples showed that HER2 and CK19 mRNA and protein levels are moderately correlated on the single-cell level, but that only HER2, and not CK19, has strong mRNA-to-protein correlation on the cell population level. The chemoattractant CXCL10 was expressed in stromal cell clusters, and the frequency of CXCL10-expressing cells correlated with T cell presence. Our flexible and expandable method will allow an increase in the information content retrieved from patient samples for biomedical purposes, enable detailed studies of tumor biology, and serve as a tool to bridge comprehensive genomic and proteomic tissue analysis.

Acinar cells make up the majority of all cells in the pancreas, yet the source of new acinar cells during homeostasis remains unknown. Using multicolor lineage-tracing and organoid-formation assays, we identified the presence of a progenitor-like acinar cell subpopulation. These cells have long-term self-renewal capacity, albeit in a unipotent fashion. We further demonstrate that binuclear acinar cells are terminally differentiated acinar cells. Transcriptome analysis of single acinar cells revealed the existence of a minor population of cells expressing progenitor markers. Interestingly, a gain of the identified markers accompanied by a transient gain of proliferation was observed following chemically induced pancreatitis. Altogether, our study identifies a functionally and molecularly distinct acinar subpopulation and thus transforms our understanding of the acinar cell compartment as a pool of equipotent secretory cells.

Signaling networks are key regulators of cellular function. Although the concentrations of signaling proteins are perturbed in disease states, such as cancer, and are modulated by drug therapies, our understanding of how such changes shape the properties of signaling networks is limited. Here we couple mass cytometry-based single-cell analysis with overexpression of tagged signaling proteins to study the dependence of signaling relationships and dynamics on protein node abundance. Focusing on the epidermal growth factor receptor (EGFR) signaling network in HEK293T cells, we analyze 20 signaling proteins during a one hour EGF stimulation time course using a panel of 35 antibodies. Data analysis with BP-R2, a measure that quantifies complex signaling relationships, reveals abundance-dependent network states and identifies novel signaling relationships. Further, we show that upstream signaling proteins have abundance-dependent effects on downstream signaling dynamics. Our approach elucidates the influence of node abundance on signal transduction networks and will further our understanding of signaling in health and disease.