Immune monitoring technology primer: Single Cell Network Profiling (SCNP).

Abstract

Description of the technology Understanding a patients’ immune status not only from immune cell phenotyping, but also through analysis of functional signaling capacity, enables the generation of a more comprehensive understanding of the complex mechanisms responsible for immunological tolerance in cancer, and generates data that is complementary to other non-functional phenotypic data sets such as immunohistochemical profiling and genomic analyses. Single cell network profiling (SCNP) is a technology that quantifies functional immune signaling capacity and connectivity at a systems biology level. The technology is based on multiparametric flow cytometry that simultaneously quantifies in multiple and rare immune cell subsets, without the need for physical separation, both extracellular surface markers and changes in intracellular signaling proteins in response to extracellular modulators. Quantifying modulated signaling across a panel of modulators (e.g., IFNα, IFNγ, IL-4, IL-10, IL-27, antiCD3 etc.) and intracellular signaling pathways identifies the functional capacity of the signaling network which cannot be assessed by measuring basal (unmodulated) signaling alone. A signaling node is defined as the combination of the extracellular modulator with the intracellular readout. For example TLR4 - > p-Erk defines one signaling node in which TLR4 modulation is quantified through the increase in p-Erk levels as compared to the unmodulated reference. Typically 3 nodes are captured simultaneously per well across multiple immune cell subsets of interest (e.g., TLR4 - > p-Erk, p-S6, IkB). The application of SCNP to clinical decision-making requires the generation of high-content SCNP assays with robust, accurate, quantifiable and reproducible results across time, operators and instruments. Each of the procedural steps associated with an SCNP assay, including pre-analytical sample handling, assay execution and reagents, data acquisition and analysis and the generation of metrics, have been validated [1] (Figs. 1 and 2). Experimental assay setup is performed using proprietary software which enables experimental design/96well plate layouts and data capture to be contiguously linked, ensuring that data from each well is correctly assigned. The laboratory execution can be performed on as many as 30 samples assayed for up to 40 wells (approximately 200–500 SCNP dimensions comprising modulator/ inhibitor/intracellular readout/cell subset combinations) in 2 to 3 days depending on the kinetic time points. A statistical analysis plan (SAP) is drafted for all studies beyond the exploratory phase, based upon clearly stated objectives. For the identification of clinically validated classifiers the time frame for assay development and validation is comparable to that of other technologies (e.g., genomics, IHC) due to the requirements for statistical powering and for verification and validation in independent sample sets.