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Updated: Feb 26, 2026

A Macrophage Reporter Cell Assay to Examine Toll-Like Receptor-Mediated NF-kB/AP-1 Signaling on Adsorbed Protein Layers on Polymeric Surfaces
Published on: January 7, 2020
A Methodology for Comprehensive Analysis of Toll-Like Receptor Signaling in Macrophages
Marijke Koppenol-Raab1, Aleksandra Nita-Lazar2
1Cellular Networks Proteomics Unit, Laboratory of Systems Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, 20892, USA.
Abstract:
A combination of high-throughput, multiplexed, quantitative methods with computational modeling and statistical approaches is required to obtain system-level understanding of biological function. Mass spectrometry (MS)-based proteomics has emerged as a preferred tool for the analysis of changes in protein abundance and their post-translational modification (PTM) levels at a global scale, comparable with genomic experiments and generating data suitable for use in mathematical modeling of signaling pathways. Here we describe a set of parallel bottom-up proteomic approaches to detect and quantify the global protein changes in total intracellular proteins, their phosphorylation, and the proteins released by active and passive secretion or shedding mechanisms (referred to as the secretome as reviewed in Makridakis and Vlahou, J Proteome 73:2291-2305, 2010) in response to the stimulation of Toll-like receptors (TLRs) with specific ligands in cultured macrophages. The method includes protocols for metabolic labeling of cells (SILAC: stable isotope labeling by amino acids in cell culture; Ong et al., Mol Cell Proteomics 1:376-386, 2002), ligand stimulation, cell lysis and media collection, in-gel and in-solution modification and digestion of proteins, phosphopeptide enrichment for phosphoproteomics, and LC-MS/MS analysis. With these methods, we can not only reliably quantify the relative changes in the TLR signaling components (Sjoelund et al., J Proteome Res 13:5185-5197, 2014) but also use the data as constraints for mathematical modeling.

