Signaling Core

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Summary Single cell mass cytometry has recently led to greatly enhanced high-dimensional, single-cell, quantitative analysis of bioactive molecules on and within cell populations. The antibodies recognize surface markers that delineate cell types or intracellular signaling molecules or transcription factors that demarcate multiple cell functions such as phospho-signaling, apoptosis, DNA damage, metabolism, and cell cycle. By measuring all these parameters simultaneously, the signaling state of an individual cell can be measured at the “network level”. Though measuring single cells at this depth is highly informative, genomic and proteomic profiling of tissue organization in situ remains an important goal. The Core has developed Multiplexed-ion Beam Imaging (MIBI) a technology currently capable of analyzing up to 45 targets simultaneously and which is compatible with standard formalin-fixed, paraffin-embedded (FFPE) tissue specimens, and the most common sample type in clinical repositories worldwide. The MIBI platform has the unique ability to provide extraordinarily sensitive single molecule detection as well as single cell 3D data visualization for solid tissues. The Core has extended this work to the creation of CODEX—a fluorescence based high throughput system capable rendering 50-100 parameters in 3-6 hours (scalable to hundreds of parameters). The CODEX platform converts nearly any fluorescence scope for ~ $10,000 into a high dimensional imaging device – a key utility for this U19 program. Given the capabilities of MIBI and CODEX--and recognizing a growing international biomedical and pharmaceutical interest in imaging applications to immunology, diagnostics, and drug development-- this Core will extend the current features of CODEX and MIBI deep phenotypic profiling of tissues and cells. New algorithms will be developed for feature extraction and correlation to clinical status. The Core will profile, with up to 50 parameters in most cases (and more in others), the immune and tissue cell architectural changes that arise from genetic and pathogen-induced perturbations. Features extracted from this unprecedentedly deep data will be provided for understanding of wholesale and minor tissue alterations that occur—enabling a first ever map of “tissue-omics” at the single cell level. Analysis of these data will be expanded upon in the Bioinformatics Core. The datasets enabled by this work will provide the quantitative robustness that is a foundation of this program. More generally, this work will establish novel platforms for gaining an unprecedented view into immune function and pathogenesis that could be easily adapted in future work to understand immune function at a global level.