Single Cell Network Profiling (SCNP) of IFN-α Signaling Pathways In Peripheral Blood Mononuclear Cells From Healthy Donors: Implications for Disease Characterization, Treatment Selection, and Drug Discovery

Abstract

AbstractAbstract 4867 Background:The antiviral and antitumor effects of IFN-α, have been exploited for the treatment of viral infections such as hepatitis C (HCV) as well as for various malignancies, such as hairy cell leukemia and melanoma. However, widespread use of IFN-α for these and other indications is severely hampered by significant side effects which can have a major impact on patient quality of life. Thus, a greater understanding of intracellular signaling pathways regulated by IFN-α may guide in the selection of patients whose disease will have an optimal response with tolerable side effects to this cytokine. Specifically, the Signal Transducer and Activation of Transcription (Stat) transcription factors are known to play a critical role in transducing IFN-α mediated signals. Single cell network profiling (SCNP) is a multiparameter flow-cytometry based approach that can be used to simultaneously measure extracellular surface makers and intracellular signaling proteins in individual cells in response to externally added modulators. Here, we use SCNP to interrogate IFN-α signaling pathways in multiple cell subsets within peripheral blood mononuclear cells (PBMCs) from healthy donors. Objectives:This study was designed to apply SCNP to generate a map of IFN-α-mediated signaling responses, with emphasis on Stat proteins, in PBMCs from healthy donors. The data provides a reference for future studies using PBMCs from patient samples in which IFN-α-mediated signaling is aberrantly regulated. Methods:IFN-α-mediated signaling responses were measured by SCNP in PBMC samples from 12 healthy donors. PBMCs were processed for flow cytometry by fixation and permeabilization followed by incubation with fluorochrome-conjugated antibodies that recognize extracellular lineage markers and intracellular signaling molecules. The levels of several phospho-proteins (p-Stat1, p-Stat3, p-Stat4, p-Stat5, p-Stat6, and p-p38) were measured in multiple cell populations (CD14+ monocytes, CD20+ B cells, CD4+ CD3+ T cells, and CD4- CD3+ T cells) at 15 minutes, 1, 2 and 4 hours post IFN-α exposure. Results:The data revealed distinct phospho-protein activation patterns in different cell subsets within PBMCs in response to IFN-α exposure. For example, activation of p-Stat4 was detected in T cell subsets (both CD4+ and CD4- T cells), but not in monocytes or B cells. Such cell-type specific activation patterns likely play a key role in mediating specific functions within different cell types in response to IFN-α. Differences in the kinetics of activation by IFN-α for different phospho-proteins were also observed. The peak response for activation of p-Stat1, p-Stat3, and p-Stat5 was at 15 minutes in most of the cell types interrogated in this study, whereas for the activation of p-Stat4, p-Stat6, and p-p38 it was at 1 hr in the majority of cell types tested. The relationships between phospho-protein readouts in each cell subset were determined by calculating the Pearson correlation coefficients. For example, the activation of p-Stat1 and p-Stat5 at 15 minutes was positively correlated in both B cells and T cells. Conclusions:In this study, we have measured the activation of intracellular signaling proteins with emphasis on Stat transcription factors in PBMC subsets from healthy donors. We have analyzed the relationships between the activation states of phospho-proteins in the IFN-α signaling network. Characterization of IFN-α signaling pathways in samples from healthy donors has provided a network map that can be used as a reference for identifying alterations in IFN-α signaling that are the consequence of disease and/or therapeutic intervention. Future studies using SCNP to characterize IFN-α signaling pathways in PBMCs from patients with diseases such as viral infections or cancer may enable the optimization of IFN-α dosing and the identification of patient stratification biomarkers as well as the discovery of novel therapeutic agents. Disclosures:Longo: Nodality: Employment, Equity Ownership. Evensen: Nodality: Employment, Equity Ownership. Fantl: Nodality: Equity Ownership. Cesano: Nodality Inc.: Employment, Equity Ownership.